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Abstract Background Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. Methods In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell’s C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. Results Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. Conclusions Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure.

Abstract Background Distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach. Methods We employ DataSHIELD as the technology to carry out distributed analyses, and we use a newly developed algorithm to validate the prediction score by conducting distributed and privacy-preserving ROC analysis. Calibration curves are constructed from mean values over sites. The determination of ROC and its AUC is based on a generalized linear model (GLM) approximation of the true ROC curve, the ROC-GLM, as well as on ideas of differential privacy (DP). DP adds noise (quantified by the $$\ell _2$$ ℓ 2 sensitivity $$\Delta _2(\hat{f})$$ Δ 2 ( f ^ ) ) to the data and enables a global handling of placement numbers. The impact of DP parameters was studied by simulations. Results In our simulation scenario, the true and distributed AUC measures differ by $$\Delta \text {AUC} < 0.01$$ Δ AUC < 0.01 depending heavily on the choice of the differential privacy parameters. It is recommended to check the accuracy of the distributed AUC estimator in specific simulation scenarios along with a reasonable choice of DP parameters. Here, the accuracy of the distributed AUC estimator may be impaired by too much artificial noise added from DP. Conclusions The applicability of our algorithms depends on the $$\ell _2$$ ℓ 2 sensitivity $$\Delta _2(\hat{f})$$ Δ 2 ( f ^ ) of the underlying statistical/predictive model. The simulations carried out have shown that the approximation error is acceptable for the majority of simulated cases. For models with high $$\Delta _2(\hat{f})$$ Δ 2 ( f ^ ) , the privacy parameters must be set accordingly higher to ensure sufficient privacy protection, which affects the approximation error. This work shows that complex measures, as the AUC, are applicable for validation in distributed setups while preserving an individual’s privacy.

Abstract As a chronic inflammatory disease of the central nervous system, multiple sclerosis (MS) is of great individual health and socio-economic significance. To date, there is no prognostic model that is used in routine clinical care to predict the very heterogeneous course of the disease. Despite several research groups working on different prognostic models using traditional statistics, machine learning and/or artificial intelligence approaches, the use of published models in clinical decision making is limited because of poor model performance, lack of transferability and/or lack of validated models. To provide a systematic overview, we conducted a “Cochrane review” that assessed 75 published prediction models using relevant checklists (CHARMS, PROBAST, TRIPOD). We have summarized the relevant points from this analysis here so that the use of prognostic models for therapy decisions in clinical routine can be successful in the future.

Abstract Background Individualizing and optimizing treatment of relapsing-remitting multiple sclerosis patients is a challenging problem, which would benefit from a clinically valid decision support. Stühler et al. presented black box models for this aim which were developed and internally evaluated in a German registry but lacked external validation. Methods In patients from the French OFSEP registry, we independently built and validated models predicting being free of relapse and free of confirmed disability progression (CDP), following the methodological roadmap and predictors reported by Stühler. Hierarchical Bayesian models were fit to predict the outcomes under 6 disease-modifying treatments given the individual disease course up to the moment of treatment change. Data was temporally split on 2017, and models were developed in patients treated earlier (n = 5517). Calibration curves, discrimination, mean squared error (MSE) and relative percentage of root MSE (RMSE%) were assessed by external validation of models in more-recent patients (n = 3768). Non-Bayesian fixed-effects GLMs were also applied and their outcomes were compared to these of the Bayesian ones. For both, we modelled the number of on-therapy relapses with a negative binomial distribution, and CDP occurrence with a binomial distribution. Results The performance of our temporally-validated relapse model (MSE: 0.326, C-Index: 0.639) is potentially superior to that of Stühler’s (MSE: 0.784, C-index: 0.608). Calibration plots revealed miscalibration. Our CDP model (MSE: 0.072, C-Index: 0.777) was also better than its counterpart (MSE: 0.131, C-index: 0.554). Results from non-Bayesian fixed-effects GLM models were similar to the Bayesian ones. Conclusions The relapse and CDP models rebuilt and externally validated in independent data could compare and strengthen the credibility of the Stühler models. Their model-building strategy was replicable.

Background: The PRIDE trial (NOA-28; ARO-2024-01; AG-NRO-06; NCT05871021) is designed to determine whether a dose escalation with 75.0 Gy in 30 fractions can enhance the median overall survival (OS) in patients with methylguanine methyltransferase (MGMT) promotor unmethylated glioblastoma compared to historical median OS rates, while being isotoxic to historical cohorts through the addition of concurrent bevacizumab (BEV). To ensure protocol-compliant irradiation planning with all study centers, a dummy run was planned and the plan quality was evaluated. Methods: A suitable patient case was selected and the computed tomography (CT), magnetic resonance imaging (MRI) and O-(2-[18F]fluoroethyl)-L-tyrosine (FET) positron emission tomography (PET) contours were made available. Participants at the various intended study sites performed radiation planning according to the PRIDE clinical trial protocol. The treatment plans and dose grids were uploaded as Digital Imaging and Communications in Medicine (DICOM) files to a cloud-based platform. Plan quality and protocol adherence were analyzed using a standardized checklist, scorecards and indices such as Dice Score (DSC) and Hausdorff Distance (HD). Results: Median DSC was 0.89, 0.90, 0.88 for PTV60, PTV60ex (planning target volume receiving 60.0 Gy for the standard and the experimental plan, respectively) and PTV75 (PTV receiving 75.0 Gy in the experimental plan), respectively. Median HD values were 17.0 mm, 13.9 mm and 12.1 mm, respectively. These differences were also evident in the volumes: The PTV60 had a volume range of 219.1–391.3 cc (median: 261.9 cc) for the standard plans, while the PTV75 volumes for the experimental plans ranged from 71.5–142.7 cc (median: 92.3 cc). The structures with the largest deviations in Dice score were the pituitary gland (median 0.37, range 0.00–0.69) and the right lacrimal gland (median 0.59, range 0.42–0.78). Conclusions: The deviations revealed the necessity of systematic trainings with appropriate feedback before the start of clinical trials in radiation oncology and the constant monitoring of protocol compliance throw-out the study. Trial registration: NCT05871021

Abstract Introduction In Multiple Sclerosis (MS), patients´ characteristics and (bio)markers that reliably predict the individual disease prognosis at disease onset are lacking. Cohort studies allow a close follow-up of MS histories and a thorough phenotyping of patients. Therefore, a multicenter cohort study was initiated to implement a wide spectrum of data and (bio)markers in newly diagnosed patients. Methods ProVal-MS (Prospective study to validate a multidimensional decision score that predicts treatment outcome at 24 months in untreated patients with clinically isolated syndrome or early Relapsing–Remitting-MS) is a prospective cohort study in patients with clinically isolated syndrome (CIS) or Relapsing–Remitting (RR)-MS (McDonald 2017 criteria), diagnosed within the last two years, conducted at five academic centers in Southern Germany. The collection of clinical, laboratory, imaging, and paraclinical data as well as biosamples is harmonized across centers. The primary goal is to validate (discrimination and calibration) the previously published DIFUTURE MS-Treatment Decision score (MS-TDS). The score supports clinical decision-making regarding the options of early (within 6 months after study baseline) platform medication (Interferon beta, glatiramer acetate, dimethyl/diroximel fumarate, teriflunomide), or no immediate treatment (> 6 months after baseline) of patients with early RR-MS and CIS by predicting the probability of new or enlarging lesions in cerebral magnetic resonance images (MRIs) between 6 and 24 months. Further objectives are refining the MS-TDS score and providing data to identify new markers reflecting disease course and severity. The project also provides a technical evaluation of the ProVal-MS cohort within the IT-infrastructure of the DIFUTURE consortium (Data Integration for Future Medicine) and assesses the efficacy of the data sharing techniques developed. Perspective Clinical cohorts provide the infrastructure to discover and to validate relevant disease-specific findings. A successful validation of the MS-TDS will add a new clinical decision tool to the armamentarium of practicing MS neurologists from which newly diagnosed MS patients may take advantage. Trial registration ProVal-MS has been registered in the German Clinical Trials Register, `Deutsches Register Klinischer Studien` (DRKS)—ID: DRKS00014034, date of registration: 21 December 2018; https://drks.de/search/en/trial/DRKS00014034

Although AI algorithms and applications become more and popular in the healthcare sector, only few institutions have an operational AI strategy. Identifying the best suited processes for ML algorithm implementation and adoption is a big challenge. Also, raising human confidence in AI systems is elementary to building trustworthy, socially beneficial and responsible AI. A commonly agreed AI auditing framework that provides best practices and tools could help speeding up the adoption process. In this paper, we first highlight important concepts in the field of AI auditing and then restructure and subsume them into an ML auditing core criteria catalog. We conducted a scoping study where we analyzed sources being associated with the term “Auditable AI” in a qualitative way. We utilized best practices from Mayring (2000), Miles and Huberman (1994), and Bortz and Döring (2006). Based on referrals, additional relevant white papers and sources in the field of AI auditing were also included. The literature base was compared using inductively constructed categories. Afterwards, the findings were reflected on and synthesized into a resulting ML auditing core criteria catalog. The catalog is grouped into the categories: Conceptual Basics, Data & Algorithm Design and Assessment Metrics. As a practical guide, it consists of 30 questions developed to cover the mentioned categories and to guide ML implementation teams. Our consensus-based ML auditing criteria catalog is intended as a starting point for the development of evaluation strategies by specific stakeholders. We believe it will be beneficial to healthcare organizations that have been or will start implementing ML algorithms. Not only to help them being prepared for any upcoming legally required audit activities, but also to create better, well-perceived and accepted products. Potential limitations could be overcome by utilizing the proposed catalog in practice on real use cases to expose gaps and to further improve the catalog. Thus, this paper is seen as a starting point towards the development of a framework, where essential technical components can be specified.

Objective This study aims to examine the effects of the individually tailored complex intervention Participation Enabling Care in Nursing (PECAN) on activities and participation of residents with joint contractures.Design Multicentre pragmatic cluster-randomised controlled trial.Setting 35 nursing homes in Germany (August 2018–February 2020).Participants 562 nursing home residents aged ≥65 years with ≥1 major joint contracture (303 intervention group, 259 control group).Interventions Nursing homes were randomised to PECAN (18 clusters) or optimised standard care (17 clusters) with researcher-concealed cluster allocation by facsimile. The intervention targeted impairments in activities and participation. Implementation included training and support for selected staff. Control group clusters received brief information.Primary and secondary outcome measures The primary endpoint PaArticular Scales combined residents’ activities and participation at 12 months. The secondary outcome comprised quality of life. Safety measures were falls, fall-related consequences and physical restraints. Residents, staff and researchers were unblinded. Data collection, data entry and statistical analysis were blinded. Primary analyses were intention-to-treat at cluster level and individual level using a generalised mixed-effect regression model and imputation of missing data.Results Primary outcome analyses included 301 intervention group residents and 259 control group residents. The mean change on the Activities Scale was −1.47 points (SD 12.2) in the intervention group and 0.196 points (SD 12.5) in the control group and −3.87 points (SD 19.7) vs −3.18 points (SD 20.8) on the Participation Scale. The mean differences of changes between the groups were not statistically significant: Activities Scale: −1.72 (97.5% CI −6.05 to 2.61); Participation Scale: −1.24 (97.5% CI −7.02 to 4.45). We found no significant difference in the secondary outcome and no effects on safety measures.Conclusion The complex intervention did not improve the activities and participation of nursing home residents on the PaArticular Scales at 12 months. Current nursing conditions in Germany may hamper implementation.Trial registration number DRKS00015185.

BackgroundBetahistine was registered in Europe in the 1970s and approved in more than 80 countries as a first-line treatment for Menière's disease. It has been administered to more than 150 million patients. However, according to a Cochrane systematic review of betahistine and recent meta-analyses, there is insufficient evidence to say whether betahistine has any effect in the currently approved dosages of up to 48 mg/d. A combination with the monoamine oxidase B (MAO-B) inhibitor, selegiline, may increase the bioavailability of betahistine to levels similar to the well-established combination of L-DOPA with carbidopa or benserazide in the treatment of Parkinson's disease. We investigated the effect of selegiline on betahistine pharmacokinetics and the safety of the combination in humans.MethodsIn an investigator-initiated prospective, non-randomized, single-sequence, two-period titration, open label single-center phase 1 study, 15 healthy volunteers received three single oral dosages of betahistine (24, 48, and 96 mg in this sequence with at least 2 days' washout period) without and with selegiline (5 mg/d with a loading period of 7 days). Betahistine serum concentrations were measured over a period of 240 min at eight time points (area under the curve, AUC0-240 min). This trial is registered with EudraCT (2019-002610-39) and ClinicalTrials.gov.FindingsIn all three single betahistine dosages, selegiline increased the betahistine bioavailability about 80- to 100-fold. For instance, the mean (±SD) of the area under curve for betahistine 48 mg alone was 0.64 (+/-0.47) h*ng/mL and for betahistine plus selegiline 53.28 (+/-37.49) h*ng/mL. The half-life time of around 30 min was largely unaffected, except for the 24 mg betahistine dosage. In total, 14 mild adverse events were documented.InterpretationThis phase 1 trial shows that the MAO-B inhibitor selegiline increases betahistine bioavailability by a factor of about 80 to 100. No safety concerns were detected. Whether the increased bioavailability has an impact on the preventive treatment of Menière's disease, acute vestibular syndrome, or post-BPPV residual dizziness has to be evaluated in placebo-controlled trials.Clinical trial registrationhttps://clinicaltrials.gov/study/NCT05938517?intr=betahistine%20and%20selegiline&rank=1, identifier: NCT05938517.

Abstract Background Although of high individual and socioeconomic relevance, a reliable prediction model for the prognosis of juvenile stroke (18–55 years) is missing. Therefore, the study presented in this protocol aims to prospectively validate the discriminatory power of a prediction score for the 3 months functional outcome after juvenile stroke or transient ischemic attack (TIA) that has been derived from an independent retrospective study using standard clinical workup data. Methods PREDICT-Juvenile-Stroke is a multi-centre (n = 4) prospective observational cohort study collecting standard clinical workup data and data on treatment success at 3 months after acute ischemic stroke or TIA that aims to validate a new prediction score for juvenile stroke. The prediction score has been developed upon single center retrospective analysis of 340 juvenile stroke patients. The score determines the patient’s individual probability for treatment success defined by a modified Rankin Scale (mRS) 0–2 or return to pre-stroke baseline mRS 3 months after stroke or TIA. This probability will be compared to the observed clinical outcome at 3 months using the area under the receiver operating characteristic curve. The primary endpoint is to validate the clinical potential of the new prediction score for a favourable outcome 3 months after juvenile stroke or TIA. Secondary outcomes are to determine to what extent predictive factors in juvenile stroke or TIA patients differ from those in older patients and to determine the predictive accuracy of the juvenile stroke prediction score on other clinical and paraclinical endpoints. A minimum of 430 juvenile patients (

Abstract Background In the last few years, multi-omics data, that is, datasets containing different types of high-dimensional molecular variables for the same samples, have become increasingly available. To date, several comparison studies focused on feature selection methods for omics data, but to our knowledge, none compared these methods for the special case of multi-omics data. Given that these data have specific structures that differentiate them from single-omics data, it is unclear whether different feature selection strategies may be optimal for such data. In this paper, using 15 cancer multi-omics datasets we compared four filter methods, two embedded methods, and two wrapper methods with respect to their performance in the prediction of a binary outcome in several situations that may affect the prediction results. As classifiers, we used support vector machines and random forests. The methods were compared using repeated fivefold cross-validation. The accuracy, the AUC, and the Brier score served as performance metrics. Results The results suggested that, first, the chosen number of selected features affects the predictive performance for many feature selection methods but not all. Second, whether the features were selected by data type or from all data types concurrently did not considerably affect the predictive performance, but for some methods, concurrent selection took more time. Third, regardless of which performance measure was considered, the feature selection methods mRMR, the permutation importance of random forests, and the Lasso tended to outperform the other considered methods. Here, mRMR and the permutation importance of random forests already delivered strong predictive performance when considering only a few selected features. Finally, the wrapper methods were computationally much more expensive than the filter and embedded methods. Conclusions We recommend the permutation importance of random forests and the filter method mRMR for feature selection using multi-omics data, where, however, mRMR is considerably more computationally costly.

Abstract Biological products, whether they are innovator products or biosimilars, can incite an immunogenic response ensuing in the development of anti-drug antibodies (ADA). The presence of ADA’s often affects the drug clearance, resulting in an increase in the variability of pharmacokinetic (PK) analysis and challenges in the design and analysis of PK similarity studies. Immunogenic response is a complex process which may be manifested by product and non-product-related factors. Potential imbalances in non-product-related factors between treatment groups may lead to differences in antibodies formation and thus in PK outcome. The current standard statistical approaches dismiss any associations between immunogenicity and PK outcomes. However, we consider PK and immunogenicity as the two correlated outcomes of the study treatment. In this research, we propose a factorization model for the simultaneous analysis of PK parameters (normal variable after taking log-transformation) and immunogenic response subgroup (binary variable). The central principle of the factorization model is to describe the likelihood function as the product of the marginal distribution of one outcome and the conditional distribution of the second outcome given the previous one. Factorization model captures the additional information contained in the correlation between the outcomes, it is more efficient than models that ignore potential dependencies between the outcomes. In our context, factorization model accounts for variability in PK data by considering the influence of immunogenicity. Based on our simulation studies, the factorization model provides more accurate and efficient estimates of the treatment effect in the PK data by taking into account the impact of immunogenicity. These findings are supported by two PK similarity clinical studies with a highly immunogenic biologic.

Abstract Background Analysis of circulating free DNA (cfDNA) is a promising tool for personalized management of colorectal cancer (CRC) patients. Untargeted cfDNA analysis using whole-genome sequencing (WGS) does not need a priori knowledge of the patient´s mutation profile. Methods Here we established LIquid biopsy Fragmentation, Epigenetic signature and Copy Number Alteration analysis (LIFE-CNA) using WGS with ~ 6× coverage for detection of circulating tumor DNA (ctDNA) in CRC patients as a marker for CRC detection and monitoring. Results We describe the analytical validity and a clinical proof-of-concept of LIFE-CNA using a total of 259 plasma samples collected from 50 patients with stage I-IV CRC and 61 healthy controls. To reliably distinguish CRC patients from healthy controls, we determined cutoffs for the detection of ctDNA based on global and regional cfDNA fragmentation patterns, transcriptionally active chromatin sites, and somatic copy number alterations. We further combined global and regional fragmentation pattern into a machine learning (ML) classifier to accurately predict ctDNA for cancer detection. By following individual patients throughout their course of disease, we show that LIFE-CNA enables the reliable prediction of response or resistance to treatment up to 3.5 months before commonly used CEA. Conclusion In summary, we developed and validated a sensitive and cost-effective method for untargeted ctDNA detection at diagnosis as well as for treatment monitoring of all CRC patients based on genetic as well as non-genetic tumor-specific cfDNA features. Thus, once sensitivity and specificity have been externally validated, LIFE-CNA has the potential to be implemented into clinical practice. To the best of our knowledge, this is the first study to consider multiple genetic and non-genetic cfDNA features in combination with ML classifiers and to evaluate their potential in both cancer detection and treatment monitoring. Trial registration DRKS00012890.

Abstract Background Endothelin-1 (ET-1) and adrenomedullin (ADM) are commonly known as vasoactive peptides that regulate vascular homeostasis. Less recognised is the fact that both peptides could affect glucose metabolism. Here, we investigated whether ET-1 and ADM, measured as C-terminal-proET-1 (CT-proET-1) and mid-regional-proADM (MR-proADM), respectively, were associated with incident type 2 diabetes. Methods Based on the population-based Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) Consortium data, we performed a prospective cohort study to examine associations of CT-proET-1 and MR-proADM with incident type 2 diabetes in 12,006 participants. During a median follow-up time of 13.8 years, 862 participants developed type 2 diabetes. The associations were examined in Cox proportional hazard models. Additionally, we performed two-sample Mendelian randomisation analyses using published data. Results CT-proET-1 and MR-proADM were positively associated with incident type 2 diabetes. The multivariable hazard ratios (HRs) [95% confidence intervals (CI)] were 1.10 [1.03; 1.18], P = 0.008 per 1-SD increase of CT-proET-1 and 1.11 [1.02; 1.21], P = 0.016 per 1-SD increase of log MR-proADM, respectively. We observed a stronger association of MR-proADM with incident type 2 diabetes in obese than in non-obese individuals (P-interaction with BMI

Abstract Background Frailty is expressed by a reduction in physical capacity, mobility, muscle strength, and endurance. (Pre-)frailty is present in up to 42% of the older surgical population, with an increased risk for peri- and postoperative complications. Consequently, these patients often suffer from a delayed or limited recovery, loss of autonomy and quality of life, and a decrease in functional and cognitive capacities. Since frailty is modifiable, prehabilitation may improve the physiological reserves of patients and reduce the care dependency 12 months after surgery. Methods Patients ≥ 70 years old scheduled for elective surgery or intervention will be recruited in this multicenter, randomized controlled study, with a target of 1400 participants with an allocation ratio of 1:1. The intervention consists of (1) a shared decision-making process with the patient, relatives, and an interdisciplinary and interprofessional team and (2) a 3-week multimodal, individualized prehabilitation program including exercise therapy, nutritional intervention, mobility or balance training, and psychosocial interventions and medical assessment. The frequency of the supervised prehabilitation is 5 times/week for 3 weeks. The primary endpoint is defined as the level of care dependency 12 months after surgery or intervention. Discussion Prehabilitation has been proven to be effective for different populations, including colorectal, transplant, and cardiac surgery patients. In contrast, evidence for prehabilitation in older, frail patients has not been clearly established. To the best of our knowledge, this is currently the largest prehabilitation study on older people with frailty undergoing general elective surgery. Trial registration ClinicalTrials.gov NCT04418271 . Registered on 5 June 2020. Universal Trial Number (UTN): U1111-1253-4820

Abstract Background Obesity in pregnancy and related early-life factors place the offspring at the highest risk of being overweight. Despite convincing evidence on these associations, there is an unmet public health need to identify “high-risk” offspring by predicting very early deviations in weight gain patterns as a subclinical stage towards overweight. However, data and methods for individual risk prediction are lacking. We aimed to identify those infants exposed to obesity in pregnancy at ages 3 months, 1 year, and 2 years who likely will follow a higher-than-normal body mass index (BMI) growth trajectory towards manifest overweight by developing an early-risk quantification system. Methods This study uses data from the prospective mother-child cohort study Programming of Enhanced Adiposity Risk in CHildhood–Early Screening (PEACHES) comprising 1671 mothers with pre-conception obesity and without (controls) and their offspring. Exposures were pre- and postnatal risks documented in patient-held maternal and child health records. The main outcome was a “higher-than-normal BMI growth pattern” preceding overweight, defined as BMI z-score >1 SD (i.e., World Health Organization [WHO] cut-off “at risk of overweight”) at least twice during consecutive offspring growth periods between age 6 months and 5 years. The independent cohort PErinatal Prevention of Obesity (PEPO) comprising 11,730 mother-child pairs recruited close to school entry (around age 6 years) was available for data validation. Cluster analysis and sequential prediction modelling were performed. Results Data of 1557 PEACHES mother-child pairs and the validation cohort were analyzed comprising more than 50,000 offspring BMI measurements. More than 1-in-5 offspring exposed to obesity in pregnancy belonged to an upper BMI z-score cluster as a distinct pattern of BMI development (above the cut-off of 1 SD) from the first months of life onwards resulting in preschool overweight/obesity (age 5 years: odds ratio [OR] 16.13; 95% confidence interval [CI] 9.98–26.05). Contributing early-life factors including excessive weight gain (OR 2.08; 95% CI 1.25–3.45) and smoking (OR 1.94; 95% CI 1.27–2.95) in pregnancy were instrumental in predicting a “higher-than-normal BMI growth pattern” at age 3 months and re-evaluating the risk at ages 1 year and 2 years (area under the receiver operating characteristic [AUROC] 0.69–0.79, sensitivity 70.7–76.0%, specificity 64.7–78.1%). External validation of prediction models demonstrated adequate predictive performances. Conclusions We devised a novel sequential strategy of individual prediction and re-evaluation of a higher-than-normal weight gain in “high-risk” infants well before developing overweight to guide decision-making. The strategy holds promise to elaborate interventions in an early preventive manner for integration in systems of well-child care.

Background: Multiple sclerosis (MS) is a chronic neuroinflammatory disease affecting about 2.8 million people worldwide. Disease course after the most common diagnoses of relapsing-remitting multiple sclerosis (RRMS) and clinically isolated syndrome (CIS) is highly variable and cannot be reliably predicted. This impairs early personalized treatment decisions. Objectives: The main objective of this study was to algorithmically support clinical decision-making regarding the options of early platform medication or no immediate treatment of patients with early RRMS and CIS. Design: Retrospective monocentric cohort study within the Data Integration for Future Medicine (DIFUTURE) Consortium. Methods: Multiple data sources of routine clinical, imaging and laboratory data derived from a large and deeply characterized cohort of patients with MS were integrated to conduct a retrospective study to create and internally validate a treatment decision score [Multiple Sclerosis Treatment Decision Score (MS-TDS)] through model-based random forests (RFs). The MS-TDS predicts the probability of no new or enlarging lesions in cerebral magnetic resonance images (cMRIs) between 6 and 24 months after the first cMRI. Results: Data from 65 predictors collected for 475 patients between 2008 and 2017 were included. No medication and platform medication were administered to 277 (58.3%) and 198 (41.7%) patients. The MS-TDS predicted individual outcomes with a cross-validated area under the receiver operating characteristics curve (AUROC) of 0.624. The respective RF prediction model provides patient-specific MS-TDS and probabilities of treatment success. The latter may increase by 5–20% for half of the patients if the treatment considered superior by the MS-TDS is used. Conclusion: Routine clinical data from multiple sources can be successfully integrated to build prediction models to support treatment decision-making. In this study, the resulting MS-TDS estimates individualized treatment success probabilities that can identify patients who benefit from early platform medication. External validation of the MS-TDS is required, and a prospective study is currently being conducted. In addition, the clinical relevance of the MS-TDS needs to be established.

Abstract Background Asymptomatic carotid artery stenosis (ACS) can be treated with carotid endarterectomy (CEA), carotid artery stenting (CAS), or best medical treatment (BMT) only. For all treatment options, optimization of vascular risk factors such as arterial hypertension, hyperlipidemia, smoking, obesity, and insufficient physical activity is essential. Data on adherence to BMT and lifestyle modification in patients with ACS are sparse. The subject of this investigation is the implementation and quality of risk factor adjustment in the context of a randomized controlled trial. Methods A total of 513 patients in the prematurely terminated, randomized, controlled, multicenter SPACE-2 trial (ISRCTN 78592017) were analyzed within one year after randomization into 3 groups (CEA, CAS, and BMT only) for implementation of prespecified BMT recommendations and lifestyle modifications. Measurement time points were the screening visit and visits after one month (D30), 6 months (M6), and one year (A1). Differences between groups and follow-up visits (FUVs) relative to the screening visit were investigated. Findings For all FUVs, a significant increase in statin medication (91% at A1; p 25) patients achieved sufficient weight reduction after one year without significant changes at all FUVs (median BMI 27 at A1; p = 0.1201). The BMT group showed significantly (p = 0.024) higher rates of adequate physical activity than the intervention groups. Furthermore, after one year, the BMT group showed a comparatively significantly better implementation of risk factor modification (77%; p = 0.027) according to the treating physician. Interpretation SPACE-2 demonstrated sustained improvement in the noninterventional management of vascular risk factors in patients treated in a clinical trial by general practitioners, internists and neurologists. The best implemented treatment targets were a reduction in cholesterol and HbA1c levels. In this context, a significant increase in statin use was demonstrated. Blood pressure control missed its target but was significantly reduced by intensification of antihypertensive medication. Patients on BMT only had better adjusted lipid parameters and were more physically active. However, all groups failed to achieve sufficient weight reduction. Due to insufficient patient recruitment, the results must be interpreted cautiously. Trial registration: ISRCTN Registry, ISRCTN78592017, Registered 16 June 2007, https://www.isrctn.com/search?q=78592017 .

Abstract Background The development of secondary resistance (SR) in metastatic colorectal cancer (mCRC) treated with anti-epidermal growth factor receptor (anti-EGFR) antibodies is not fully understood at the molecular level. Here we tested in vivo selection of anti-EGFR SR tumors in CRC patient-derived xenograft (PDX) models as a strategy for a molecular dissection of SR mechanisms. Methods We analyzed 21 KRAS, NRAS, BRAF, and PI3K wildtype CRC patient-derived xenograft (PDX) models for their anti-EGFR sensitivity. Furthermore, 31 anti-EGFR SR tumors were generated via chronic in vivo treatment with cetuximab. A multi-omics approach was employed to address molecular primary and secondary resistance mechanisms. Gene set enrichment analyses were used to uncover SR pathways. Targeted therapy of SR PDX models was applied to validate selected SR pathways. Results In vivo anti-EGFR SR could be established with high efficiency. Chronic anti-EGFR treatment of CRC PDX tumors induced parallel evolution of multiple resistant lesions with independent molecular SR mechanisms. Mutations in driver genes explained SR development in a subgroup of CRC PDX models, only. Transcriptional reprogramming inducing anti-EGFR SR was discovered as a common mechanism in CRC PDX models frequently leading to RAS signaling pathway activation. We identified cAMP and STAT3 signaling activation, as well as paracrine and autocrine signaling via growth factors as novel anti-EGFR secondary resistance mechanisms. Secondary resistant xenograft tumors could successfully be treated by addressing identified transcriptional changes by tailored targeted therapies. Conclusions Our study demonstrates that SR PDX tumors provide a unique platform to study molecular SR mechanisms and allow testing of multiple treatments for efficient targeting of SR mechanisms, not possible in the patient. Importantly, it suggests that the development of anti-EGFR tolerant cells via transcriptional reprogramming as a cause of anti-EGFR SR in CRC is likely more prevalent than previously anticipated. It emphasizes the need for analyses of SR tumor tissues at a multi-omics level for a comprehensive molecular understanding of anti-EGFR SR in CRC.

BackgroundArea deprivation has been shown to be associated with various adverse health outcomes including communicable as well as non-communicable diseases. Our objective was to assess potential associations between area deprivation and COVID-19 standardized incidence and mortality ratios in Bavaria over a period of nearly 2 years. Bavaria is the federal state with the highest infection dynamics in Germany and demographically comparable to several other European countries.MethodsIn this retrospective, observational ecological study, we estimated the strength of associations between area deprivation and standardized COVID-19 incidence and mortality ratios (SIR and SMR) in Bavaria, Germany. We used official SARS-CoV-2 reporting data aggregated in monthly periods between March 1, 2020 and December 31, 2021. Area deprivation was assessed using the quintiles of the 2015 version of the Bavarian Index of Multiple Deprivation (BIMD 2015) at district level, analyzing the overall index as well as its single domains.ResultsDeprived districts showed higher SIR and SMR than less deprived districts. Aggregated over the whole period, the SIR increased by 1.04 (95% confidence interval (95% CI): 1.01 to 1.07, p = 0.002), and the SMR by 1.11 (95% CI: 1.07 to 1.16, p < 0.001) per BIMD quintile. This represents a maximum difference of 41% between districts in the most and least deprived quintiles in the SIR and 110% in the SMR. Looking at individual months revealed clear linear association between the BIMD quintiles and the SIR and SMR in the first, second and last quarter of 2021. In the summers of 2020 and 2021, infection activity was low.ConclusionsIn more deprived areas in Bavaria, Germany, higher incidence and mortality ratios were observed during the COVID-19 pandemic with particularly strong associations during infection waves 3 and 4 in 2020/2021. Only high infection levels reveal the effect of risk factors and socioeconomic inequalities. There may be confounding between the highly deprived areas and border regions in the north and east of Bavaria, making the relationship between area deprivation and infection burden more complex. Vaccination appeared to balance incidence and mortality rates between the most and least deprived districts. Vaccination makes an important contribution to health equality.

Abstract In 2007 the German government passed smoke-free legislation, leaving the details of implementation to the individual federal states. In January 2008 Bavaria implemented one of the strictest laws in Germany. We investigated its impact on pregnancy outcomes and applied an interrupted time series (ITS) study design to assess any changes in preterm birth, small for gestational age (primary outcomes), and low birth weight, stillbirth and very preterm birth. We included 1,236,992 singleton births, comprising 83,691 preterm births and 112,143 small for gestational age newborns. For most outcomes we observed unclear effects. For very preterm births, we found an immediate drop of 10.4% (95%CI − 15.8, − 4.6%; p = 0.0006) and a gradual decrease of 0.5% (95%CI − 0.7, − 0.2%, p = 0.0010) after implementation of the legislation. The majority of subgroup and sensitivity analyses confirm these results. Although we found no statistically significant effect of the Bavarian smoke-free legislation on most pregnancy outcomes, a substantial decrease in very preterm births was observed. We cannot rule out that despite our rigorous methods and robustness checks, design-inherent limitations of the ITS study as well as country-specific factors, such as the ambivalent German policy context have influenced our estimation of the effects of the legislation.

Abstract Background High N-terminal pro-brain-type natriuretic peptide levels have been associated with a lower risk of type 2 diabetes mellitus (T2D). However, less is known about other cardiac stress biomarkers in this context. Here we evaluated the association of mid-regional pro-atrial natriuretic peptide (MR-proANP), C-terminal pro-arginine vasopressin (copeptin), C-terminal pro-endothelin-1 (CT-proET-1) and mid-regional pro-adrenomedullin (MR-proADM) with incident T2D and changes in glucose metabolism. Methods We performed a prospective cohort study using data from the population-based KORA F4/FF4 study. 1773 participants (52.3% women) with MR-proANP measurements and 960 (52.7% women) with copeptin, CT-proET-1 and MR-proADM measurements were included. We examined associations of circulating plasma levels of MR-proANP, copeptin, CT-proET-1 and MR-proADM with incident T2D, the combined endpoint of incident prediabetes/T2D and with fasting and 2 h-glucose, fasting insulin, HOMA-IR, HOMA-B and HbA1c at follow-up. Logistic and linear regression models adjusted for age, sex, waist circumference, height, hypertension, total/HDL cholesterol ratio, triglycerides, smoking, physical activity and parental history of diabetes were used to compute effect estimates. Results During a median follow-up time of 6.4 years (25th and 75th percentiles: 6.0 and 6.6, respectively), 119 out of the 1773 participants and 72 out of the 960 participants developed T2D. MR-proANP was inversely associated with incident T2D (odds ratio [95% confidence interval]: 0.75 [0.58; 0.96] per 1-SD increase of log MR-proANP). Copeptin was positively associated with incident prediabetes/T2D (1.29 [1.02; 1.63] per 1-SD increase of log copeptin). Elevated levels of CT-proET-1 were associated with increased HOMA-B at follow-up, while elevated MR-proADM levels were associated with increased fasting insulin, HOMA-IR and HOMA-B at follow-up. These associations were independent of previously described diabetes risk factors. Conclusions High plasma concentrations of MR-proANP contributed to a lower risk of incident T2D, whereas high plasma concentrations of copeptin were associated with an increased risk of incident prediabetes/T2D. Furthermore, high plasma concentrations of CT-proET-1 and MR-proADM were associated with increased insulin resistance. Our study provides evidence that biomarkers implicated in cardiac stress are associated with incident T2D and changes in glucose metabolism.

Summary: Background: Cardiac autonomic dysfunction after myocardial infarction identifies patients at high risk despite only moderately reduced left ventricular ejection fraction. We aimed to show that telemedical monitoring with implantable cardiac monitors in these patients can improve early detection of subclinical but prognostically relevant arrhythmic events. Methods: We did a prospective investigator-initiated, randomised, multicentre, open-label, diagnostic trial at 33 centres in Germany and Austria. Survivors of acute myocardial infarction with left ventricular ejection fraction of 36–50% had biosignal analysis for assessment of cardiac autonomic function. Patients with abnormal periodic repolarisation dynamics (≥5·75 deg2) or abnormal deceleration capacity (≤2·5 ms) were randomly assigned (1:1) to telemedical monitoring with implantable cardiac monitors or conventional follow-up. Primary endpoint was time to detection of serious arrhythmic events defined by atrial fibrillation 6 min or longer, atrioventricular block class IIb or higher and fast non-sustained (>187 beats per min; ≥40 beats) or sustained ventricular tachycardia or fibrillation. This study is registered with ClinicalTrials.gov, NCT02594488. Findings: Between May 12, 2016, and July 20, 2020, 1305 individuals were screened and 400 patients at high risk were randomly assigned (median age 64 years [IQR 57–73]); left ventricular ejection fraction 45% [40–48]) to telemedical monitoring with implantable cardiac monitors (implantable cardiac monitor group; n=201) or conventional follow-up (control group; n=199). During median follow-up of 21 months, serious arrhythmic events were detected in 60 (30%) patients of the implantable cardiac monitor group and 12 (6%) patients of the control group (hazard ratio 6·33 [IQR 3·40–11·78]; p

ObjectivesTo assess the usability of German hospital administrative claims data (GHACD) to determine inpatient management patterns, healthcare resource utilization, and quality-of-care in patients with multiple myeloma (PwMM).MethodsBased on German tertiary hospital's claims data (2015-2017), PwMM aged >18 years were included if they had an International Classification of Diseases, Tenth Revision, code of C90.0 or received anti-MM therapy. Subgroup analysis was performed on stem cell transplantation (SCT) patients.ResultsOf 230 PwMM, 59.1% were men; 56.1% were aged ≥65 years. Hypertension and infections were present in 50% and 67.0%, respectively. Seventy percent of PwMM received combination therapy. Innovative drugs such as bortezomib and lenalidomide were given to 36.1% and 10.9% of the patients, respectively. Mean number of admissions and mean hospitalization length/patient were 3.69 (standard deviation (SD) 2.71 (1-16)) and 12.52 (SD 9.55 (1-68.5)) days, respectively. In-hospital mortality was recorded in 12.2%. Seventy-two percent of SCT patients (n = 88) were aged ≤65 years, 22.7% required second transplantation, and 89.8% received platelet transfusion at a mean of 1.42(SD 0.63 (1-3)).ConclusionGHACD provided relevant information essential for healthcare studies about PwMM from routine care settings. Data fundamental for quality-of-care assessment were also captured.

Background: Kawasaki disease (KD) is the leading cause of acquired heart disease in children. However, distinguishing KD from febrile infections early in the disease course remains difficult. Our goal was to estimate the immune cell composition in KD patients and febrile controls (FC), and to develop a tool for KD diagnosis.Methods: We used a machine-learning algorithm, CIBERSORT, to estimate the proportions of 22 immune cell types based on blood samples from children with KD and FC. Using these immune cell compositions, a diagnostic score for predicting KD was then constructed based on LASSO regression for binary outcomes.Results: In the training set (n = 496), a model was fit which consisted of eight types of immune cells. The area under the curve (AUC) values for diagnosing KD in a held-out test set (n = 212) and an external validation set (n = 36) were 0.80 and 0.77, respectively. The most common cell types in KD blood samples were monocytes, neutrophils, CD4+-naïve and CD8+ T cells, and M0 macrophages. The diagnostic score was highly correlated to genes that had been previously reported as associated with KD, such as interleukins and chemokine receptors, and enriched in reported pathways, such as IL-6/JAK/STAT3 and TNFα signaling pathways.Conclusion: Altogether, the diagnostic score for predicting KD could potentially serve as a biomarker. Prospective studies could evaluate how incorporating the diagnostic score into a clinical algorithm would improve diagnostic accuracy further.

Abstract Background Colorectal cancer (CRC) is the second most common malignant disease and the second most common cause of cancer death in Germany. Official CRC screening starts at age 50. As there is evidence that individuals with a family history of CRC have an increased risk of developing CRC before age 50, there are recommendations to start screening for this group earlier. This study aims to evaluate the clinical and economic effects of a risk-adapted screening program for CRC in individuals between 25 and 50 years of age with potentially increased familial CRC risk. Methods FARKOR (Familiäres Risiko für das Kolorektale Karzinom) is a population-based prospective intervention study. All members of cooperating statutory health insurance companies between 25 and 50 years of age living in a model region in Germany (federal state of Bavaria, 3.5 million inhabitants in this age group) can participate in the program between October 2018 and March 2020. Recruitment takes place through physicians and through a public campaign. Additionally, insurances contact recently diagnosed CRC patients in order to encourage their relatives to participate in the program. Physicians assess a participant’s familial history of CRC using a short questionnaire. All participants with a family history of CRC are invited to a shared decision making process to decide on further screening options consisting of either undergoing an immunological test for fecal occult blood or colonoscopy. Comprehensive data collection based on self-reported lifestyle information, medical documentation and health administrative databases accompanies the screening program. Longterm benefits, harms and the cost-effectiveness of the risk-adapted CRC screening program will be assessed by decision analytic modeling. Discussion The data collected in this study will add important pieces of information that are still missing in the evaluation of the effects and the cost-effectiveness of a risk-adapted CRC screening strategy for individuals under 50 years of age. Trial registration German Clinical Trials Register, DRKS-IDDRKS00015097 .

Abstract Background Statistical Process Monitoring (SPM) is not typically used in traditional quality assurance of inpatient care. While SPM allows a rapid detection of performance deficits, SPM results strongly depend on characteristics of the evaluated process. When using SPM to monitor inpatient care, in particular the hospital risk profile, hospital volume and properties of each monitored performance indicator (e.g. baseline failure probability) influence the results and must be taken into account to ensure a fair process evaluation. Here we study the use of CUSUM charts constructed for a predefined false alarm probability within a single process, i.e. a given hospital and performance indicator. We furthermore assess different monitoring schemes based on the resulting CUSUM chart and their dependence on the process characteristics. Methods We conduct simulation studies in order to investigate alarm characteristics of the Bernoulli log-likelihood CUSUM chart for crude and risk-adjusted performance indicators, and illustrate CUSUM charts on performance data from the external quality assurance of hospitals in Bavaria, Germany. Results Simulating CUSUM control limits for a false alarm probability allows to control the number of false alarms across different conditions and monitoring schemes. We gained better understanding of the effect of different factors on the alarm rates of CUSUM charts. We propose using simulations to assess the performance of implemented CUSUM charts. Conclusions The presented results and example demonstrate the application of CUSUM charts for fair performance evaluation of inpatient care. We propose the simulation of CUSUM control limits while taking into account hospital and process characteristics.

Introduction An abscopal effect is a clinical observation whereby a local treatment is associated with regression of metastatic cancer at a site distant from the primary location of treatment. Here, we describe the clinical systemic effect induced by regional hyperthermia combined with low-dose chemotherapy and provide immunologic correlates. Case presentation A 15-year-old patient had been diagnosed with alveolar rhabdomyosarcoma (ARMS). All previous treatment options failed in the patient including haploidentical stem cell transplantation and donor lymphocyte infusion. The patient presented with local and metastatic disease, and upon admission, underwent regional hyperthermia combined with low-dose chemotherapy. Immediately following therapy severe skin reactions were observed. Skin biopsies revealed an intraepithelial lymphocytic infiltration dominated by CD3+/CD8+ T cells with a regular network of dendritic cells. Clinical images compared before and during sequential treatment cycles showed complete metabolic response of the local tumor for more than 10 months of therapy. In addition, metastases completely regressed although they were not direct targets of regional hyperthermia. The systemic effect was associated with enhanced frequency of NK cells and T cells expressing the lectin-like natural-killer group 2 D activating receptor (NKG2D), an increase of the CD56bright subset of NK cells, as well as an increase of effector/memory and effector CD8+ and CD4+ T cells in the blood while the percentage of CD25+FOXP3+ regulatory T cells declined. Conclusions Regional hyperthermia combined with low-dose chemotherapy had the potential to create a systemic effect which was associated with activation of NK cells and T cells.

Abstract Background Vestibular migraine (VM) is the most frequent cause of recurrent spontaneous attacks of vertigo causally related to migraine. The objective of the Prophylactic treatment of vestibular migraine with metoprolol (PROVEMIG) trial was to demonstrate that metoprolol succinate is superior to placebo in the prevention of episodic vertigo- and migraine-related symptoms in patients with VM. Methods This phase III, two-arm, parallel-group, double-blind, randomized placebo-controlled trial was designed to be conducted at tertiary referral centres at neurology and ear, nose and throat departments of eight German university hospitals. The planned sample size was a total of 266 patients to be allocated. Adults aged 18 years or above diagnosed with probable or definitive VM according to the Neuhauser criteria 2001 were randomly assigned 1:1 to 6 months blinded metoprolol (maintenance dosage of 95 mg daily) or placebo. The primary efficacy outcome was the self-reported number of vertiginous attacks per 30 days documented by means of a paper-based daily symptom diary. The pre-specified time period of primary interest was defined as months 4 to 6. Secondary outcomes included the patient-reported number of migraine days and vertigo days, the Dizziness Handicap Inventory, and clinical assessments. Adverse events were reported throughout the whole 9-month study period. Results At the time of trial termination, no evidence for a difference in the incidence of vertiginous attacks between groups was detected. For the full analysis set, the incidence rate ratio was 0.983 (95% confidence interval (CI) 0.902–1.071) for metoprolol versus placebo. In both groups, there was a significant decline over time in the overall monthly vertigo attacks by a factor of 0.830 (95% CI 0.776–0.887). Results were consistent for all subjective and objective key measures of efficacy. The treatment was well tolerated with no unexpected safety findings. Conclusions After randomizing 130 patients PROVEMIG had to be discontinued because of poor participant accrual not related to the tolerability of the study medication or safety concerns; no treatment benefit of metoprolol over placebo could be established. Additional preparatory work is much needed in the development, psychometric evaluation and interpretation of clinically meaningful end points in trials on episodic syndromes like VM taking into consideration the complexity of this disease entity comprising two domains (vertigo- and headache-related disability). Trial registration EudraCT, 2009-013701-34. Prospectively registered on 8 April 2011.

Florian Naudet and co-authors discuss strengthening requirements for sharing clinical trial data.

Background The Munich Breathlessness Service has adapted novel support services to the German context, to reduce burden in patients and carers from breathlessness in advanced disease. It has been evaluated in a pragmatic fast-track randomised controlled trial (BreathEase; NCT02622412) with embedded qualitative interviews and postal survey. The aim of this article is to describe the intervention model and study design, analyse recruitment to the trial and compare sample characteristics with other studies in the field. Methods Analysis of recruitment pathways and enrolment, sociodemographic and clinical characteristics of participants and carers. Results Out of 439 people screened, 253 (58%) were offered enrolment and 183 (42%) participated. n=97 (70%) carers participated. 186 (42%) people did not qualify for inclusion, mostly because breathlessness could not be attributed to an underlying disease. All participants were self-referring; 60% through media sources. Eligibility and willingness to participate were associated to social networks and illness-related activities as recruitment routes. Mean age of participants was 71 years (51% women), with COPD (63%), chronic heart failure (8%), interstitial lung disease (9%), pulmonary hypertension (6%) and cancer (7%) as underlying conditions. Postal survey response rate was 89%. Qualitative interviews were conducted with 16 patients and nine carers. Conclusion The BreathEase study has a larger and more heterogeneous sample compared to other trials. The self-referral-based and prolonged recruitment drawing on media sources approximates real-world conditions of early palliative care. Integrating qualitative and quantitative components will allow a better understanding and interpretation of the results of the main effectiveness study.

Background: A regimen of dual (DAT) vs. triple (TAT) antithrombotic therapy reduces bleeding in patients with atrial fibrillation (AF) undergoing percutaneous coronary intervention (PCI). However, recent evidence suggests that DAT may be associated with an increased ischemic risk. This raises the question whether DAT rather than TAT should be recommended to AF patients that undergo PCI for acute coronary syndrome (ACS), carrying a particularly high risk of both bleeding and ischemic events, studied only as subgroups of previous trials. Methods and design: The APPROACH-ACS-AF-(DZHK-7) trial is a multicenter prospective, randomized, open-label, blinded endpoint (PROBE) trial which will include patients presenting with an ACS managed by PCI and requiring oral anticoagulation (OAC) due to AF. The trial will test, whether a DAT-regimen comprising clopidogrel plus the non-Vitamin-K-antagonist oral anticoagulant (NOAC) apixaban is superior to a TAT-regimen of vitamin-K-antagonist (VKA) plus dual anti-platelet therapy (APT) with respect to bleeding. A total of 400 patients will be randomized 1:1 to a control-arm with guideline-recommended TAT with VKA plus clopidogrel and acetylsalicylic-acid and a study arm receiving DAT comprising apixaban plus clopidogrel. Patients will be followed-up for 6 months. The primary endpoint of the study is the cumulative incidence of BARC type ≥2 bleeding, secondary endpoints include a composite clinical ischemic outcome and net clinical outcome. Conclusions: APPROACH-ACS-AF is the first trial dedicated to ACS patients, testing whether in terms of bleeding a DAT with NOAC is superior to a TAT regimen with VKA in high-risk ACS patients with AF.

Identification of fusion genes in clinical routine is mostly based on cytogenetics and targeted molecular genetics, such as metaphase karyotyping, fluorescence in situ hybridization and reverse-transcriptase polymerase chain reaction. However, sequencing technologies are becoming more important in clinical routine as processing time and costs per sample decrease. To evaluate the performance of fusion gene detection by RNAsequencing compared to standard diagnostic techniques, we analyzed 806 RNA-sequencing samples from patients with acute myeloid leukemia using two state-of-the-art software tools, namely Arriba and FusionCatcher. RNA-sequencing detected 90% of fusion events that were reported by routine with high evidence, while samples in which RNA-sequencing failed to detect fusion genes had overall lower and inhomogeneous sequence coverage. Based on properties of known and unknown fusion events, we developed a workflow with integrated filtering strategies for the identification of robust fusion gene candidates by RNA-sequencing. Thereby, we detected known recurrent fusion events in 26 cases that were not reported by routine and found discrepancies in evidence for known fusion events between routine and RNA-sequencing in three cases. Moreover, we identified 157 fusion genes as novel robust candidates and comparison to entries from ChimerDB or Mitelman Database showed novel recurrence of fusion genes in 14 cases. Finally, we detected the novel recurrent fusion gene NRIP1- MIR99AHG resulting from inv(21)(q11.2;q21.1) in nine patients (1.1%) and LTN1-MX1 resulting from inv(21)(q21.3;q22.3) in two patients (0.25%). We demonstrated that NRIP1-MIR99AHG results in overexpression of the 3' region of MIR99AHG and the disruption of the tricistronic miRNA cluster miR-99a/let-7c/miR-125b-2. Interestingly, upregulation of MIR99AHG and deregulation of the miRNA cluster, residing in the MIR99AHG locus, are known mechanisms of leukemogenesis in acute megakaryoblastic leukemia. Our findings demonstrate that RNA-sequencing has a strong potential to improve the systematic detection of fusion genes in clinical applications and provides a valuable tool for fusion discovery.

Abstract Background Fluorescence optical imaging (FOI) enables visualization of inflammation in the hands in rheumatic joint diseases with currently a lack of long-term follow-up studies. Objective To investigate FOI for treatment monitoring in a homogenous cohort of patients with early (disease duration 3.2) RA over a period of 12 months. Methods Thirty-five RA patients (24 (68.6%) females, mean age 53.3 years (SD 13.6)) were investigated clinically by DAS28, tender joint count (TJC) and swollen joint count (SJC) and by FOI in phases 1–3 and PrimaVistaMode (PVM) before therapy change and after 12 months. The FOI activity score (FOIAS) was calculated based on individual joint scores from 0 to 3 in 30 joints per patient, adding up to a sum score (0–90). Results We found a statistically significant reduction of FOIAS in phase 1 from baseline (median 5.0, IQR 24.96) to follow-up (median 1.0, IQR 4.0) in all patients (p = 0.0045), both in responders and non-responders according to EULAR response criteria by DAS28. Statistically significant reductions over 12 months were found for median DAS28(ESR) 5.61 to 3.31, TJC 7.0 to 1.0, and SJC 5.0 to 1.0 (each p

Abstract Background Nursing home residents are frequently affected by joint contractures, which impacts their participation and daily activities. A complex intervention, the Participation Enabling Care in Nursing (PECAN), was previously developed and pilot tested to address their needs. Its effectiveness and safety will be evaluated in the present study. Methods/design This multicentre cluster-randomised controlled trial will be conducted in 32 nursing homes spread over two regions of Germany. A total of 578 residents over 65 years old with joint contractures will be included. To compare the effect of the PECAN intervention with optimised standard care (usual care and an information session), randomisation will take place at a cluster level. The individually tailored intervention was designed using the biopsychosocial model in the International Classification of Functioning, Disability and Health (ICF) to reduce activity limitations and participation restrictions resulting from existing joint contractures by addressing barriers and by strengthening supportive factors on an individual level and an organisational level. The implementation strategy comprises a facilitators’ workshop, a peer mentoring approach including a peer mentor visit and telephone peer counselling, an in-house information event, an information session for the nursing team and a training session on collegial consultation for the facilitators. The in-house information event will also take place in the nursing homes of the control group. The primary outcome is the residents’ participation and activities after 12 months of follow-up as assessed using the PaArticular Scales. The secondary outcome is the residents’ quality of life. A cost-effectiveness analysis (costs per additional resident who experienced a decrease of ten points in the participation or activities subscale of the PaArticular Scales) and a cost–utility analysis (costs per additional quality adjusted life year) will be conducted. We will investigate barriers and facilitators in a comprehensive process evaluation. Discussion We expect a clinically relevant improvement of participation and activities in residents with joint contractures. Our findings will provide important insights regarding participation in the situation of the affected individuals. Trial registration DRKS, DRKS00015185. Registered on 1 August 2018. Universal Trial Number U1111–1218-1555. Registered on 26 July 2018.

BackgroundIt is of interest to explore the variability in how the COVID-19 pandemic evolved geographically during the first twelve months. To this end, we apply inequality indices over regions to incidences, infection related mortality, and infection fatality rates. If avoiding of inequality in health is an important political goal, a metric must be implemented to track geographical inequality over time.MethodsThe relative and absolute Gini index as well as the Theil index are used to quantify inequality. Data are taken from international data bases. Absolute counts are transformed to rates adjusted for population size.ResultsComparing continents, the absolute Gini index shows an unfavorable development in four continents since February 2020. In contrast, the relative Gini as well as the Theil index support the interpretation of less inequality between European countries compared to other continents. Infection fatality rates within the EU as well as within the U.S. express comparable improvement towards more equality (as measured by both Gini indices).ConclusionsThe use of inequality indices to monitor changes in geographic inequality over time for key health indicators is a valuable tool to inform public health policies. The absolute and relative Gini index behave complementary and should be reported simultaneously in order to gain a meta-perspective on very complex dynamics.

This report contains the updated consensus recommendations for optimal hemophilia care produced in 2019 by three Working Groups (WG) on behalf of the European Directorate for Quality of Medicines and Healthcare in the frame of the Kreuth V Initiative. WG1 recommended access to prophylaxis for all patients, the achievement of plasma factor trough levels of at least 3-5% when extended half-life factor VIII (FVIII) and FIX products are used, a personalized treatment regimen, and a choice of chromogenic assays for treatment monitoring. It was also emphasized that innovative therapies should be supervised by hemophilia comprehensive care centers. WG2 recommended mandatory collection of postmarketing data to assure the long-term safety and efficacy of new hemophilia therapies, the establishment of national patient registries including the core data recommended by the European Medicines Agency and the International Society on Thrombosis and Haemostasis, with adequate support under public control, and greater collaboration to facilitate a comprehensive data evaluation throughout Europe. WG3 discussed methodological aspects of hemophilia care in the context of access decisions, particularly for innovative therapies, and recommended that clinical studies should be designed to provide the quality of evidence needed by regulatory authorities, HTA bodies and healthcare providers. The dialogue between all stakeholders in hemophilia care and patient organizations should be fostered to implement these recommendations.

ObjectivesTo assess biostatistical quality of study protocols submitted to German medical ethics committees according to personal appraisal of their statistical members.DesignWe conducted a web-based survey among biostatisticians who have been active as members in German medical ethics committees during the past 3 years.SettingThe study population was identified by a comprehensive web search on websites of German medical ethics committees.ParticipantsThe final list comprised 86 eligible persons. In total, 57 (66%) completed the survey.QuestionnaireThe first item checked whether the inclusion criterion was met. The last item assessed satisfaction with the survey. Four items aimed to characterise the medical ethics committee in terms of type and location, one item asked for the urgency of biostatistical training addressed to the medical investigators. The main 2×12 items reported an individual assessment of the quality of biostatistical aspects in the submitted study protocols, while distinguishing studies according to the German Medicines Act (AMG)/German Act on Medical Devices (MPG) and studies non-regulated by these laws.Primary and secondary outcome measuresThe individual assessment of the quality of biostatistical aspects corresponds to the primary objective. Thus, participants were asked to complete the sentence ‘In x% of the submitted study protocols, the following problem occurs’, where 12 different statistical problems were formulated. All other items assess secondary endpoints.ResultsFor all biostatistical aspects, 45 of 49 (91.8%) participants judged the quality of AMG/MPG study protocols much better than that of ‘non-regulated’ studies. The latter are in median affected 20%–60% more often by statistical problems. The highest need for training was reported for sample size calculation, missing values and multiple comparison procedures.ConclusionsBiostatisticians being active in German medical ethics committees classify the biostatistical quality of study protocols as low for ‘non-regulated’ studies, whereas quality is much better for AMG/MPG studies.

Abstract Background Traumatic events like critical illness and intensive care are threats to life and bodily integrity and pose a risk factor for posttraumatic stress disorder (PTSD). PTSD affects the quality of life and morbidity and may increase health-care costs. Limited access to specialist care results in PTSD patients being treated in primary care settings. Narrative exposure therapy (NET) is based on the principles of cognitive behavioral therapy and has shown positive effects when delivered by health-care professionals other than psychologists. The primary aims of the PICTURE trial (from “PTSD after ICU survival”) are to investigate the effectiveness and applicability of NET adapted for primary care with case management in adults diagnosed with PTSD after intensive care. Methods/design This is an investigator-initiated, multi-center, primary care-based, randomized controlled two-arm parallel group, observer-blinded superiority trial conducted throughout Germany. In total, 340 adult patients with a total score of at least 20 points on the posttraumatic diagnostic scale (PDS-5) 3 months after receiving intensive care treatment will be equally randomized to two groups: NET combined with case management and improved treatment as usual (iTAU). All primary care physicians (PCPs) involved will be instructed in the diagnosis and treatment of PTSD according to current German guidelines. PCPs in the iTAU group will deliver usual care during three consultations. In the experimental group, PCPs will additionally be trained to deliver an adapted version of NET (three sessions) supported by phone-based case management by a medical assistant. At 6 and 12 months after randomization, structured blinded telephone interviews will assess patient-reported outcomes. The primary composite endpoint is the absolute change from baseline at month 6 in PTSD symptom severity measured by the PDS-5 total score, which also incorporates the death of any study patients. Secondary outcomes cover the domains depression, anxiety, disability, health-related quality-of-life, and cost-effectiveness. The principal analysis is by intention to treat. Discussion If the superiority of the experimental intervention over usual care can be demonstrated, the combination of brief NET and case management could be a treatment option to relieve PTSD-related symptoms and to improve primary care after intensive care. Trial registration ClinicalTrials.gov, NCT03315390. Registered on 10 October 2017. German Clinical Trials Register, DRKS00012589. Registered on 17 October 2017.

Abstract Background People aged 40–60 years with a family history (FH) of colorectal cancer (CRC) in 1st degree relatives (FDRs) have a 2- to 4-fold increased risk of CRC compared to the average risk population. Therefore, experts recommend starting CRC screening earlier for this high-risk group. However, information on prevalence of relevant colonoscopic findings in this group is sparse, and no risk adapted screening offers are implemented in the German health care system. For example, screening colonoscopy is uniformly offered from age 55 on, regardless of family history. Thus, we initiated a multicenter epidemiological study - the RaPS study (Risk adapted prevention strategies for colorectal cancer) – with the following aims: to determine the prevalence of having a FH of CRC in FDR in the German population aged 40–54 years; to investigate the prevalence of colorectal neoplasms among people with a FDR; and to develop risk-adapted prevention strategies for this high-risk group based on the collected information. Methods/Design A random sample of 160.000 persons from the general population aged 40–54 years from the catchment areas of three study centers in Germany (Dresden, Munich and Stuttgart) are contacted to assess FH of CRC by an online-questionnaire. Those with a FH of CRC in FDRs are invited to the study centers for individual consultation regarding CRC prevention. Participants are asked to donate blood and stool samples and medical records of colonoscopies will be obtained. Prevalence of CRC and its precursors will be evaluated. Furthermore, genetic, epigenetic and proteomic biomarkers in blood and microbiomic biomarkers in stool will be investigated. Risk markers and their eligibility for risk adapted screening offers will be examined. Discussion This study will provide data on the prevalence of colorectal neoplasms among persons with a FH of CRC in the age group 40–54 years, which will enable us to derive evidence based screening strategies for this high-risk group. Trial registration This trial was registered retrospectively in the German Clinical Trials Register (DRKS) on 29th of December 2016: German Clinical Trials Register DRKS-ID: DRKS00007842.

Lung adenocarcinoma (LUAD) is a common and very lethal cancer. Accurate staging is a prerequisite for its effective diagnosis and treatment. Therefore, improving the accuracy of the stage prediction of LUAD patients is of great clinical relevance. Previous works have mainly focused on single genomic data information or a small number of different omics data types concurrently for generating predictive models. A few of them have considered multi-omics data from genome to proteome. We used a publicly available dataset to illustrate the potential of multi-omics data for stage prediction in LUAD. In particular, we investigated the roles of the specific omics data types in the prediction process. We used a self-developed method, Omics-MKL, for stage prediction that combines an existing feature ranking technique Minimum Redundancy and Maximum Relevance (mRMR), which avoids redundancy among the selected features, and multiple kernel learning (MKL), applying different kernels for different omics data types. Each of the considered omics data types individually provided useful prediction results. Moreover, using multi-omics data delivered notably better results than using single-omics data. Gene expression and methylation information seem to play vital roles in the staging of LUAD. The Omics-MKL method retained 70 features after the selection process. Of these, 21 (30%) were methylation features and 34 (48.57%) were gene expression features. Moreover, 18 (25.71%) of the selected features are known to be related to LUAD, and 29 (41.43%) to lung cancer in general. Using multi-omics data from genome to proteome for predicting the stage of LUAD seems promising because each omics data type may improve the accuracy of the predictions. Here, methylation and gene expression data may play particularly important roles.

The data presented relates to the publication “Enhancing Requirements Engineering for Patient Registry Software Systems with Evidence-based Components” (Lindoerfer and Mansmann, 2017) [1], which describes the strategy behind the development of the CIPROS checklist. This manuscript also compares CIPROS with general requirements specification templates, and standards. The data is shortly described in Section 2.4 and presented in Appendix A. The examples represent the material extracted from the literature used in qualitative analysis. The explanations summarize the example contents from which the CIPROS checklist was created. Patient registries are a crucial part of medical research. High quality registries use efficient information systems software selected from a wide variety of existing software solutions. An efficient selection process requires focused selection criteria. The evidence-based CIPROS checklist [2] accelerates this requirements engineering process. CIPROS was developed in a multistep procedure: (1) A systematic literature review provided an exhaustive collection of relevant publications (64 articles), (2) a catalogue of relevant criteria was derived by a qualitative content analysis, and (3) the checklist containing 72 items was composed which provides a minimal appraisal standard. The data presented per checklist item provide the relevant textual information (examples) and a first qualitative summary (explanation). The examples and explanations provide the background information on CIPROS. They elucidate how to implement the checklist items in other projects. The literature list and the selected texts serve as a reference for scientists and system developers.

Background: Postpartum hemorrhage (PPH) is a leading cause of maternal mortality and morbidity. The WOMAN trial showed that tranexamic acid (TXA) reduces death due to bleeding in women with PPH. To determine whether TXA has pro-thrombotic effects in women with PPH, we measured endogenous thrombin potential (ETP), coagulation factors V, VIII, von Willebrand (vW), fibrinogen, D-Dimers and platelet function. Methods: We conducted a sub-study within the WOMAN trial, an international randomized, parallel-group, double blind, placebo-controlled trial. Women with primary PPH were randomly allocated to receive 1 gram of tranexamic acid or matching placebo. Baseline blood samples were collected just prior to the first dose and a follow up sample was collected 30±15 minutes afterwards. We compared before and after changes in coagulation parameters between treatment groups using repeated measurement ANOVA. Change in ETP was the primary outcome. We did an intention-to-treat analysis using ANCOVA with adjustment for baseline and the time interval between the blood samples. Findings: A total of 187 patients were randomized to receive TXA (n=93) or matching placebo (n=94). Six patients were excluded due to incomplete data. The reduction in ETP from baseline to follow up was 43.2 nM*min (95%CI, -16.6 to 103.1) in the TXA group and 4.6 nM*min (95%CI, -51.4 to 60.6) in the placebo group. The difference was not statistically significant (95%CI, -42.9 to 120). There were no significant effects of TXA treatment on any other parameters (ADPtest, TRAPtest, coagulation factors activity, fibrinogen levels, D-Dimer level). Conclusion: We found no evidence that tranexamic acid treatment for PPH has substantial pro-coagulant effects. However, larger studies are needed to confirm or refute more modest effects. Trial registration: ISRCTN76912190 (initially registered 10/12/2008, WOMAN-ETAPlat included on 28/10/2013) and NCT00872469 (initially registered 31/03/2009, WOMAN-ETAPlat included on 28/10/2013).

BACKGROUND:Maternal pre-conception obesity is a strong risk factor for childhood overweight. However, prenatal mechanisms and their effects in susceptible gestational periods that contribute to this risk are not well understood. We aimed to assess the impact of late-pregnancy dysglycemia in obese pregnancies with negative testing for gestational diabetes mellitus (GDM) on long-term mother-child outcomes. METHODS AND FINDINGS:The prospective cohort study Programming of Enhanced Adiposity Risk in Childhood-Early Screening (PEACHES) (n = 1,671) enrolled obese and normal weight mothers from August 2010 to December 2015 with trimester-specific data on glucose metabolism including GDM status at the end of the second trimester and maternal glycated hemoglobin (HbA1c) at delivery as a marker for late-pregnancy dysglycemia (HbA1c ≥ 5.7% [39 mmol/mol]). We assessed offspring short- and long-term outcomes up to 4 years, and maternal glucose metabolism 3.5 years postpartum. Multivariable linear and log-binomial regression with effects presented as mean increments (Δ) or relative risks (RRs) with 95% confidence intervals (CIs) were used to examine the association between late-pregnancy dysglycemia and outcomes. Linear mixed-effects models were used to study the longitudinal development of offspring body mass index (BMI) z-scores. The contribution of late-pregnancy dysglycemia to the association between maternal pre-conception obesity and offspring BMI was estimated using mediation analysis. In all, 898 mother-child pairs were included in this unplanned interim analysis. Among obese mothers with negative testing for GDM (n = 448), those with late-pregnancy dysglycemia (n = 135, 30.1%) had higher proportions of excessive total gestational weight gain (GWG), excessive third-trimester GWG, and offspring with large-for-gestational-age birth weight than those without. Besides higher birth weight (Δ 192 g, 95% CI 100-284) and cord-blood C-peptide concentration (Δ 0.10 ng/ml, 95% CI 0.02-0.17), offspring of these women had greater weight gain during early childhood (Δ BMI z-score per year 0.18, 95% CI 0.06-0.30, n = 262) and higher BMI z-score at 4 years (Δ 0.58, 95% CI 0.18-0.99, n = 43) than offspring of the obese, GDM-negative mothers with normal HbA1c values at delivery. Late-pregnancy dysglycemia in GDM-negative mothers accounted for about one-quarter of the association of maternal obesity with offspring BMI at age 4 years (n = 151). In contrast, childhood BMI z-scores were not affected by a diagnosis of GDM in obese pregnancies (GDM-positive: 0.58, 95% CI 0.36-0.79, versus GDM-negative: 0.62, 95% CI 0.44-0.79). One mechanism triggering late-pregnancy dysglycemia in obese, GDM-negative mothers was related to excessive third-trimester weight gain (RR 1.72, 95% CI 1.12-2.65). Furthermore, in the maternal population, we found a 4-fold (RR 4.01, 95% CI 1.97-8.17) increased risk of future prediabetes or diabetes if obese, GDM-negative women had a high versus normal HbA1c at delivery (absolute risk: 43.2% versus 10.5%). There is a potential for misclassification bias as the predominantly used GDM test procedure changed over the enrollment period. Further studies are required to validate the findings and elucidate the possible third-trimester factors contributing to future mother-child health status. CONCLUSIONS:Findings from this interim analysis suggest that offspring of obese mothers treated because of a diagnosis of GDM appeared to have a better BMI outcome in childhood than those of obese mothers who-following negative GDM testing-remained untreated in the last trimester and developed dysglycemia. Late-pregnancy dysglycemia related to uncontrolled weight gain may contribute to the development of child overweight and maternal diabetes. Our data suggest that negative GDM testing in obese pregnancies is not an "all-clear signal" and should not lead to reduced attention and risk awareness of physicians and obese women. Effective strategies are needed to maintain third-trimester glycemic and weight gain control among otherwise healthy obese pregnant women.

Background: Postpartum haemorrhage (PPH) is a leading cause of maternal death. The WOMAN trial showed that tranexamic acid (TXA) reduces death due to bleeding in women with PPH. We evaluated the effect of TXA on fibrinolysis and coagulation in a sample of WOMAN trial participants. Methods: Adult women with a clinical diagnosis of PPH were randomised to receive 1 g TXA or matching placebo in the WOMAN trial. Participants in the WOMAN trial at University College Hospital (Ibadan, Nigeria) also had venous blood taken just before administration of the first dose of trial treatment and again 30 (±15) min after the first dose (the ETAC study). We aimed to determine the effects of TXA on fibrinolysis (D-dimer and rotational thromboelastometry maximum clot lysis (ML)) and coagulation (international normalized ratio and clot amplitude at 5 min). We compared outcomes in women receiving TXA and placebo using linear regression, adjusting for baseline measurements. Results: Women (n=167) were randomised to receive TXA (n=83) or matching placebo (n=84). Due to missing data, seven women were excluded from analysis. The mean (SD) D-dimer concentration was 7.1 (7.0) mg/l in TXA-treated women and 9.6 (8.6) mg/l in placebo-treated women (p=0.09). After adjusting for baseline, the D-dimer concentration was 2.16 mg/l lower in TXA-treated women (-2.16, 95% CI -4.31 to 0.00, p=0.05). There was no significant difference in ML between TXA- and placebo-treated women (12.3% (18.4) and 10.7% (12.6), respectively; p=0.52) and no significant difference after adjusting for baseline ML (1.02, 95% CI -3.72 to 5.77, p=0.67). There were no significant effects of TXA on any other parameters. Conclusion: TXA treatment was associated with reduced D-dimer levels but had no apparent effects on thromboelastometry parameters or coagulation tests. Registration: ISRCTN76912190 (initially registered 10/12/2008, WOMAN-ETAC included on 22/03/2012) and NCT00872469 (initially registered 31/03/2009, WOMAN-ETAC included on 22/03/2012).

Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856) to develop a predictor of therapy resistance, which was validated in an independent cohort analyzed by RNA sequencing (n=250). In addition to gene expression markers, standard clinical and laboratory variables as well as the mutation status of 68 genes were considered during construction of the model. The final predictor (PS29MRC) consisted of 29 gene expression markers and a cytogenetic risk classification. A continuous predictor is calculated as a weighted linear sum of the individual variables. In addition, a cut off was defined to divide patients into a high-risk and a low-risk group for resistant disease. PS29MRC was highly significant in the validation set, both as a continuous score (OR=2.39, P=8.63·10−9, AUC=0.76) and as a dichotomous classifier (OR=8.03, P=4.29·10−9); accuracy was 77%. In multivariable models, only TP53 mutation, age and PS29MRC (continuous: OR=1.75, P=0.0011; dichotomous: OR=4.44, P=0.00021) were left as significant variables. PS29MRC dominated all models when compared with currently used predictors, and also predicted overall survival independently of established markers. When integrated into the European LeukemiaNet (ELN) 2017 genetic risk stratification, four groups (median survival of 8, 18, 41 months, and not reached) could be defined (P=4.01·10−10). PS29MRC will make it possible to design trials which stratify induction treatment according to the probability of response, and refines the ELN 2017 classification.

A growing framework of legal and ethical requirements limit scientific and commercial evaluation of personal data. Typically, pseudonymization, encryption, or methods of distributed computing try to protect individual privacy. However, computational infrastructures still depend on human system administrators. This introduces severe security risks and has strong impact on privacy: system administrators have unlimited access to the computers that they manage including encryption keys and pseudonymization-tables. Distributed computing and data obfuscation technologies reduce but do not eliminate the risk of privacy leakage by administrators. They produce higher implementation effort and possible data quality degradation. This paper proposes the Trusted Server as an alternative approach that provides a sealed and inaccessible computational environment in a cryptographically strict sense. During operation or by direct physical access to storage media, data stored and processed inside the Trusted Server can by no means be read, manipulated or leaked, other than by brute-force. Thus, secure and privacy-compliant data processing or evaluation of plain person-related data becomes possible even from multiple sources, which want their data kept mutually secret.