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Abstract Background In clinical trials, the determination of an adequate sample size is a challenging task, mainly due to the uncertainty about the value of the effect size and nuisance parameters. One method to deal with this uncertainty is a sample size recalculation. Thereby, an interim analysis is performed based on which the sample size for the remaining trial is adapted. With few exceptions, previous literature has only examined the potential of recalculation in two-stage trials. Methods In our research, we address sample size recalculation in three-stage trials, i.e. trials with two pre-planned interim analyses. We show how recalculation rules from two-stage trials can be modified to be applicable to three-stage trials. We also illustrate how a performance measure, recently suggested for two-stage trial recalculation (the conditional performance score) can be applied to evaluate recalculation rules in three-stage trials, and we describe performance evaluation in those trials from the global point of view. To assess the potential of recalculation in three-stage trials, we compare, in a simulation study, two-stage group sequential designs with three-stage group sequential designs as well as multiple three-stage designs with recalculation. Results While we observe a notable favorable effect in terms of power and expected sample size by using three-stage designs compared to two-stage designs, the benefits of recalculation rules appear less clear and are dependent on the performance measures applied. Conclusions Sample size recalculation is also applicable in three-stage designs. However, the extent to which recalculation brings benefits depends on which trial characteristics are most important to the applicants.

Abstract Background Group sequential designs incorporating the option to stop for futility at the time point of an interim analysis can save time and resources. Thereby, the choice of the futility boundary importantly impacts the design’s resulting performance characteristics, including the power and probability to correctly or wrongly stop for futility. Several authors contributed to the topic of selecting good futility boundaries. For binary endpoints, Simon’s designs (Control Clin Trials 10:1–10, 1989) are commonly used two-stage designs for single-arm phase II studies incorporating futility stopping. However, Simon’s optimal design frequently yields an undesirably high probability of falsely declaring futility after the first stage, and in Simon’s minimax design often a high proportion of the planned sample size is already evaluated at the interim analysis leaving only limited benefit in case of an early stop. Methods This work focuses on the optimality criteria introduced by Schüler et al. (BMC Med Res Methodol 17:119, 2017) and extends their approach to binary endpoints in single-arm phase II studies. An algorithm for deriving optimized futility boundaries is introduced, and the performance of study designs implementing this concept of optimal futility boundaries is compared to the common Simon’s minimax and optimal designs, as well as modified versions of these designs by Kim et al. (Oncotarget 10:4255–61, 2019). Results The introduced optimized futility boundaries aim to maximize the probability of correctly stopping for futility in case of small or opposite effects while also setting constraints on the time point of the interim analysis, the power loss, and the probability of stopping the study wrongly, i.e. stopping the study even though the treatment effect shows promise. Overall, the operating characteristics, such as maximum sample size and expected sample size, are comparable to those of the classical and modified Simon’s designs and sometimes better. Unlike Simon’s designs, which have binding stopping rules, the optimized futility boundaries proposed here are not adjusted to exhaust the full targeted nominal significance level and are thus still valid for non-binding applications. Conclusions The choice of the futility boundary and the time point of the interim analysis have a major impact on the properties of the study design. Therefore, they should be thoroughly investigated at the planning stage. The introduced method of selecting optimal futility boundaries provides a more flexible alternative to Simon’s designs with non-binding stopping rules. The probability of wrongly stopping for futility is minimized and the optimized futility boundaries don’t exhibit the unfavorable properties of an undesirably high probability of falsely declaring futility or a high proportion of the planned sample evaluated at the interim time point.

Abstract Background Sample size calculation is a central aspect in planning of clinical trials. The sample size is calculated based on parameter assumptions, like the treatment effect and the endpoint’s variance. A fundamental problem of this approach is that the true distribution parameters are not known before the trial. Hence, sample size calculation always contains a certain degree of uncertainty, leading to the risk of underpowering or oversizing a trial. One way to cope with this uncertainty are adaptive designs. Adaptive designs allow to adjust the sample size during an interim analysis. There is a large number of such recalculation rules to choose from. To guide the choice of a suitable adaptive design with sample size recalculation, previous literature suggests a conditional performance score for studies with a normally distributed endpoint. However, binary endpoints are also frequently applied in clinical trials and the application of the conditional performance score to binary endpoints is not yet investigated. Methods We extend the theory of the conditional performance score to binary endpoints by suggesting a related one-dimensional score parametrization. We moreover perform a simulation study to evaluate the operational characteristics and to illustrate application. Results We find that the score definition can be extended without modification to the case of binary endpoints. We represent the score results by a single distribution parameter, and therefore derive a single effect measure, which contains the difference in proportions $$p_{I}-p_{C}$$ p I - p C between the intervention and the control group, as well as the endpoint proportion $$p_{C}$$ p C in the control group. Conclusions This research extends the theory of the conditional performance score to binary endpoints and demonstrates its application in practice.

Introduction The Berlin Long-term Observation of Vascular Events is a prospective cohort study that aims to improve prediction and disease-overarching mechanistic understanding of cardiovascular (CV) disease progression by comprehensively investigating a high-risk patient population with different organ manifestations.Methods and analysis A total of 8000 adult patients will be recruited who have either suffered an acute CV event (CVE) requiring hospitalisation or who have not experienced a recent acute CVE but are at high CV risk. An initial study examination is performed during the acute treatment phase of the index CVE or after inclusion into the chronic high risk arm. Deep phenotyping is then performed after ~90 days and includes assessments of the patient’s medical history, health status and behaviour, cardiovascular, nutritional, metabolic, and anthropometric parameters, and patient-related outcome measures. Biospecimens are collected for analyses including ‘OMICs’ technologies (e.g., genomics, metabolomics, proteomics). Subcohorts undergo MRI of the brain, heart, lung and kidney, as well as more comprehensive metabolic, neurological and CV examinations. All participants are followed up for up to 10 years to assess clinical outcomes, primarily major adverse CVEs and patient-reported (value-based) outcomes. State-of-the-art clinical research methods, as well as emerging techniques from systems medicine and artificial intelligence, will be used to identify associations between patient characteristics, longitudinal changes and outcomes.Ethics and dissemination The study was approved by the Charité—Universitätsmedizin Berlin ethics committee (EA1/066/17). The results of the study will be disseminated through international peer-reviewed publications and congress presentations.Study registration First study phase: Approved WHO primary register: German Clinical Trials Register: https://drks.de/search/de/trial/DRKS00016852; WHO International Clinical Registry Platform: http://apps.who.int/trialsearch/Trial2.aspx?TrialID=DRKS00016852. Recruitment started on July 18, 2017.Second study phase: Approved WHO primary register: German Clinical Trials Register DRKS00023323, date of registration: November 4, 2020, URL: http://www.drks.de/ DRKS00023323. Recruitment started on January 1, 2021.

Abstract Aims Telemedical interventions in heart failure patients intend to avoid unfavourable, indication‐related events by an early, individualized care, which reacts to the current patients need. However, telemedical support is an expensive intervention, and usually only patients with high risk for unfavourable follow‐up events will be able to profit from it. Möckel et al. therefore adapted a new design which we call ‘prognostic‐efficacy‐combination design’. This design allows to define a biomarker cut‐off and to perform a randomized controlled trial (RCT) in a biomarker‐selected population within a single study. However, so far, it has not been evaluated if this double use of the control group for biomarker cut‐off definition and efficacy assessment within the RCT leads to a bias in treatment effect estimation. In this methodological research work, we therefore want to evaluate whether the ‘prognostic‐efficacy‐combination design’ leads to biased treatment effect estimates and also compare it to alternative designs. If there is a bias, we further want to analyse its magnitude under different parameter settings. Methods We perform a systematic Monte Carlo simulation study to investigate among others potential bias, root mean square error and sensitivity, and specificity as well as the total treatment effect estimate in various realistic trial scenarios that mimic and vary the true data characteristics of the published TIM‐HF2 Trial. In particular, we vary the event proportion, the sample size, the biomarker distribution, and the lower bound for the sensitivity. Results The results show that indeed the proposed design leads to some bias in the effect estimators, indicating an overestimation of the effect. However, this bias is relatively small in most scenarios. Conclusions The ‘prognostic‐efficacy‐combination design’ can generally be recommended for clinical applications due to its efficiency compared to two separate trials. We recommend a sufficiently large sample size depending on the trial scenario. Our simulation code can be adapted to explore suitable sample sizes for other settings.

Background Cut-offs on self-report depression screening tools are designed to identify many more people than those who meet criteria for major depressive disorder. In a recent analysis of the European Health Interview Survey (EHIS), the percentage of participants with Patient Health Questionnaire-8 (PHQ-8) scores ≥10 was reported as major depression prevalence.Objective We used a Bayesian framework to re-analyse EHIS PHQ-8 data, accounting for the imperfect diagnostic accuracy of the PHQ-8.Methods The EHIS is a cross-sectional, population-based survey in 27 countries across Europe with 258 888 participants from the general population. We incorporated evidence from a comprehensive individual participant data meta-analysis on the accuracy of the PHQ-8 cut-off of ≥10. We evaluated the joint posterior distribution to estimate the major depression prevalence, prevalence differences between countries and compared with previous EHIS results.Findings Overall, major depression prevalence was 2.1% (95% credible interval (CrI) 1.0% to 3.8%). Mean posterior prevalence estimates ranged from 0.6% (0.0% to 1.9%) in the Czech Republic to 4.2% (0.2% to 11.3%) in Iceland. Accounting for the imperfect diagnostic accuracy resulted in insufficient power to establish prevalence differences. 76.4% (38.0% to 96.0%) of observed positive tests were estimated to be false positives. Prevalence was lower than the 6.4% (95% CI 6.2% to 6.5%) estimated previously.Conclusions Prevalence estimation needs to account for imperfect diagnostic accuracy.Clinical implications Major depression prevalence in European countries is likely lower than previously reported on the basis of the EHIS survey.

Abstract Background Additive/adjuvant chemotherapy as concept after local treatment of colorectal metastases has not been proven to be successful by phase III trials. Accordingly, a standard of care to improve relapse rates and long-term survival is not established and adjuvant chemotherapy cannot be recommended as a standard therapy due to limited evidence in literature. The PORT trial aims to generate evidence that post-resection/ablation/radiation chemotherapy improves the survival in patients with metastatic colorectal cancer. Methods Patients to be included into this trial must have synchronous or metachronous metastases of colorectal cancer—either resected (R0 or R1) and/or effectively treated by ablation or radiation within 3–10 weeks before randomization—and have the primary tumor resected, without radiographic evidence of active metastatic disease at study entry. The primary endpoint of the trial is progression-free survival after 24 months, secondary endpoints include overall survival, safety, quality of life, treatments (including efficacy) beyond study participation, translational endpoints, and others. One arm of the study comprising 2/3 of the population will be treated for 6 months with modified FOLFOXIRI or modified FOLFOX6 (investigator´s choice, depending on the performance status of the patients but determined before randomization), while the other arm (1/3 of the population) will be observed and undergo scheduled follow-up computed tomography scans according to the interventional arm. Discussion Optimal oncological management after removal of colorectal metastases is unclear. The PORT trial aims to generate evidence that additive/adjuvant chemotherapy after definitive treatment of colorectal metastases improves progression free and overall survival in patients with colorectal cancer. Trial registration This study is registered with clinicaltrials.gov ( NCT05008809 ) and EudraCT (2020–006,144-18).

Abstract Aims In acute heart failure (AHF), changes of venous haemoglobin (Hb) concentrations, haematocrit (Hct), and estimated plasma volume (ePV) have been proposed as surrogates of decongestion. These estimates are based on the theoretical assumptions that changes of Hb concentrations and Hct are driven by the intravascular volume status and that the intravascular Hb pool remains stable. The objective of this study was to assess the relationship of changes of measured plasma volume (mPV) with changes of Hb, Hct, and ePV in AHF. Methods and results We studied 36 AHF patients, who received two sequential assessments of mPV, measured red cell volume (mRCV) and measured total blood volume (mTBV) (48 h apart), during the course of diuretic therapy using a novel visible fluorescent injectate (VFI) technique based on the indicator dilution principle. Changes of ePV were calculated based on the Kaplan–Hakim or Strauss formula. AHF patients receiving diuretics (median intravenous furosemide equivalent 160 mg/48 h) displayed a wide range of changes of mPV (−25.4% to +37.0%). Changes in mPV were not significantly correlated with changes of Hb concentration [Pearson's r (r) = −0.241, P = 0.157], Hct (r = −0.307, P = 0.069), ePVKaplan–Hakim (r = 0.228, P = 0.182), or ePVStrauss (r = 0.237, P = 0.163). In contrast to theoretical assumptions, changes of mTBV were poorly correlated with changes of Hb concentrations and some patients displayed unanticipated variability of mRCV, suggesting an unstable intravascular red cell pool. Conclusions Changes of Hb or Hct were not reflective of directly measured changes of intravascular volume status in AHF patients. Basing clinical assessment of decongestion on changes of Hb or Hct may misguide clinical decision‐making on an individual patient level.

Abstract Background In clinical trials the study interest often lies in the comparison of a treatment to a control regarding a time to event endpoint. A composite endpoint allows to consider several time to event endpoints at once. Usually, only the time to the first occurring event for a patient is thereby analyzed. However, an individual may experience more than one non-fatal event. Including all observed events in the analysis can increase the power and provides a more complete picture of the disease. Thus, analytical methods for recurrent events are required. A challenge is that the different event types belonging to the composite often are of different clinical relevance. In this case, weighting the event types according to their clinical relevance is an option. Different weight-based methods for composite time to event endpoints were proposed. So far, there exists no systematic comparison of these methods. Methods Within this work we provide a systematic comparison of three methods proposed for weighted composite endpoints in a recurrent event setting combining non-fatal and fatal events of different clinical relevance. We consider an extension of an approach proposed by Wei and Lachin, an approach by Rauch et al., and an approach by Bakal et al.. Comparison is done based on a simulation study and based on a clinical study example. Results For all three approaches closed formula test statistics are available. The Wei-Lachin approach and the approach by Rauch et al. show similar results in mean squared error. For the approach by Wei and Lachin confidence intervals are provided. The approach by Bakal et al. is not related to a quantifiable estimand. The relevance weights of the different approaches work on different level, i.e. either on cause-specific hazard ratios or on event count. Conclusion The provided comparison and simulations can help to guide applied researchers to choose an adequate method for the analysis of composite endpoints combining (recurrent) events of different clinical relevance. The approach by Wei and Lachin and Rauch et al. can be recommended in scenarios where the composite effect is time-independent. The approach by Bakal et al. should be applied carefully.

Abstract Background Statistical model building requires selection of variables for a model depending on the model’s aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed “background knowledge” truly is. In fact, “known” predictors might be findings from preceding studies which may also have employed inappropriate model building strategies. Methods We conducted a simulation study assessing the influence of treating variables as “known predictors” in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a “known” predictor if a predefined number of preceding studies identified it as relevant. Results Even if several preceding studies identified a variable as a “true” predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection. Conclusions The source of “background knowledge” should be evaluated with care. Knowledge generated on preceding studies can cause misspecification.

Abstract Aims We aimed to test whether the endogenous filtration markers serum creatinine or cystatin C and equation‐based estimates of glomerular filtration rate (GFR) based on these markers appropriately reflect changes of measured GFR in patients with acute heart failure. Methods In this prospective cohort study of 50 hospitalized acute heart failure patients undergoing decongestive therapy, we applied an intravenous visible fluorescent injectate (VFI), consisting of a low molecular weight component to measure GFR and a high molecular weight component to correct for measured plasma volume. Thirty‐eight patients had two sequential GFR measurements 48 h apart. The co‐primary endpoints of the study were safety of VFI and plasma stability of the high molecular weight component. A key secondary endpoint was to compare changes in measured GFR (mGFR) to changes of serum creatinine, cystatin C and estimated GFR. Results VFI‐based GFR measurements were safe and consistent with plasma stability of the high molecular weight component and glomerular filtration of the low molecular weight component. Filtration marker‐based point estimates of GFR, when compared with mGFR, provided only moderate correlation (Pearson's r, range 0.80–0.88, depending on equation used), precision (r2, range 0.65–0.78) and accuracy (56%–74% of estimates scored within 30% of mGFR). Correlations of 48‐h changes GFR estimates and changes of mGFR were significant (P 15% decrease in mGFR. Conclusions In patients hospitalized for acute heart failure, serum creatinine‐ and cystatin C‐based predictions performed poorly in detecting actual changes of GFR. These data challenge current clinical strategies to evaluate dynamics of kidney function in acute heart failure.

IntroductionChemotherapy-induced polyneuropathy (CIPN) and post-chemotherapy cognitive impairment (PCCI) are frequent side effects of paclitaxel treatment. CIPN/PCCI are potentially irreversible, reduce quality of life and often lead to treatment limitations, which affect patients’ outcome. We previously demonstrated that paclitaxel enhances an interaction of the Neuronal calcium sensor-1 protein (NCS-1) with the Inositol-1,4,5-trisphosphate receptor (InsP3R), which disrupts calcium homeostasis and triggers neuronal cell death via the calcium-dependent protease calpain in dorsal root ganglia neurons and neuronal precursor cells. Prophylactic treatment of rodents with lithium inhibits the NCS1-InsP3R interaction and ameliorates paclitaxel-induced polyneuropathy and cognitive impairment, which is in part supported by limited retrospective clinical data in patients treated with lithium carbonate at the time of chemotherapy. Currently no data are available from a prospective clinical trial to demonstrate its efficacy.Methods and analysisThe PREPARE study will be conducted as a multicenter, randomized, double-blind, placebo-controlled phase-2 trial with parallel group design. N = 84 patients with breast cancer will be randomized 1:1 to either lithium carbonate treatment (targeted serum concentration 0.5–0.8 mmol/l) or placebo with sham dose adjustments as add-on to (nab-) paclitaxel. The primary endpoint is the validated Total Neuropathy Score reduced (TNSr) at 2 weeks after the last (nab-) paclitaxel infusion. The aim is to show that the lithium carbonate group is superior to the placebo group, meaning that the mean TNSr after (nab-) paclitaxel is lower in the lithium carbonate group than in the placebo group. Secondary endpoints include: (1) severity of CIPN, (2) amount and dose of pain medication, (3) cumulative dose of (nab-) paclitaxel, (4) patient-reported symptoms of CIPN, quality of life and symptoms of anxiety and depression, (5) severity of cognitive impairment, (6) hippocampal volume and changes in structural/functional connectivity and (7) serum Neurofilament light chain protein concentrations.Ethics and disseminationThe study protocol was approved by the Berlin ethics committee (reference: 21/232 – IV E 10) and the respective federal agency (Bundesinstitut für Arzneimittel und Medizinprodukte, reference: 61-3910-4044771). The results of the study will be published in peer-reviewed medical journals as well as presented at relevant (inter)national conferences.Clinical trial registration[https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00027165], identifier [DRKS00027165].

Introduction: Acute kidney injury (AKI) is an important complication in COVID-19, but its precise etiology has not fully been elucidated. Insights into AKI mechanisms may be provided by analyzing the temporal associations of clinical parameters reflecting disease processes and AKI development. Methods: We performed an observational cohort study of 223 consecutive COVID-19 patients treated at 3 sites of a tertiary care referral center to describe the evolvement of severe AKI (Kidney Disease: Improving Global Outcomes stage 3) and identify conditions promoting its development. Descriptive statistics and explanatory multivariable Cox regression modeling with clinical parameters as time-varying covariates were used to identify risk factors of severe AKI. Results: Severe AKI developed in 70 of 223 patients (31%) with COVID-19, of which 95.7% required kidney replacement therapy. Patients with severe AKI were older, predominantly male, had more comorbidities, and displayed excess mortality. Severe AKI occurred exclusively in intensive care unit patients, and 97.3% of the patients developing severe AKI had respiratory failure. Mechanical ventilation, vasopressor therapy, and inflammatory markers (serum procalcitonin levels and leucocyte count) were independent time-varying risk factors of severe AKI. Increasing inflammatory markers displayed a close temporal association with the development of severe AKI. Sensitivity analysis on risk factors of AKI stage 2 and 3 combined confirmed these findings. Conclusion: Severe AKI in COVID-19 was tightly coupled with critical illness and systemic inflammation and was not observed in milder disease courses. These findings suggest that traditional systemic AKI mechanisms rather than kidney-specific processes contribute to severe AKI in COVID-19.

Abstract Background Arterial hypertension is a major risk factor for cardiovascular disease and leads to target organ damage including stroke, heart failure, and kidney disease. About 1.5 billion people worldwide have hypertension, and it is estimated that it causes about 8 million deaths each year. Although there are several drugs available to lower blood pressure (BP), a great proportion of treated patients does not reach recommended treatment targets. Typical antihypertensive drugs target the vessels, the kidneys, and the heart. However, our gut microbiota also influences cardiovascular health, and gut dysbiosis is associated with hypertension. In this study protocol, we investigate the potential BP-lowering effect of a probiotic in patients with grade 1 hypertension. Methods This study is an exploratory, randomized, double-blind, placebo-controlled, parallel-group study. One hundred ten patients with grade 1 hypertension (treated or untreated) will be randomized to either the probiotic Vivomixx® or placebo. The primary endpoint is the nocturnal systolic BP measured by ambulatory blood pressure monitoring after 8 weeks adjusted for the baseline value. The secondary endpoints are changes from baseline in nocturnal diastolic BP, antihypertensive medication, fecal microbiome composition, fecal and serum metabolome, immune cell phenotypes, glucose variability after three standardized breakfasts, and health-related quality of life (PROMIS-29). We also assess the safety profile of the intervention. Discussion We postulate that various administrated bacteria (Lactobacilli, Bifidobacteria, and Streptococcus thermophilus) convert dietary components into active metabolites that positively affect immune cell function. A reduction of pro-inflammatory immune cell function could promote a BP-lowering effect. Trial registration ClinicalTrials.gov NCT03906578 . Registered on 08 April 2019

Abstract Background In clinical trials with fixed study designs, statistical inference is only made when the trial is completed. In contrast, group sequential designs allow an early stopping of the trial at interim, either for efficacy when the treatment effect is significant or for futility when the treatment effect seems too small to justify a continuation of the trial. Efficacy stopping boundaries based on alpha spending functions have been widely discussed in the statistical literature, and there is also solid work on the choice of adequate futility stopping boundaries. Still, futility boundaries are often chosen with little or completely without theoretical justification, in particular in investigator initiated trails. Some authors contributed to fill this gap. In here, we rely on an idea of Schüler et al. (2017) who discuss optimality criteria for futility boundaries for the special case of trials with (multiple) time-to-event endpoints. Their concept can be adopted to define “optimal” futility boundaries (with respect to given performance indicators) for continuous endpoints. Methods We extend Schülers’ definition for “optimal” futility boundaries to the most common study situation of a single continuous primary endpoint compared between two groups. First, we introduce the analytic algorithm to derive these futility boundaries. Second, the new concept is applied to a real clinical trial example. Finally, the performance of a study design with an “optimal” futility boundary is compared to designs with arbitrarily chosen futility boundaries. Results The presented concept of deriving futility boundaries allows to control the probability of wrongly stopping for futility, that means stopping for futility even if the treatment effect is promizing. At the same time, the loss in power is also controlled by this approach. Moreover, “optimal” futility boundaries improve the probability of correctly stopping for futility under the null hypothesis of no difference between two groups. Conclusions The choice of futility boundaries should be thoroughly investigated at the planning stage. The sometimes met, arbitrary choice of futility boundaries can lead to a substantial negative impact on performance. Applying futility boundaries with predefined optimization criteria increases efficiency of group sequential designs. Other optimization criteria than proposed in here might be incorporated.

Abstract Background Risk diseases and risk factors for stroke include atrial fibrillation, hypertension, diabetes mellitus, smoking, and elevated LDL-cholesterol. Due to modern treatment options, the impact of these risk diseases on subsequent cardiovascular events or death after a first stroke is less clear and needs to be elucidated. We therefore aimed to get insights into the persistence of adverse prognostic effects of these risk diseases and risk factors on subsequent stroke or death events 1 year after the first stroke by using the new weighted all-cause hazard ratio. Methods This study evaluates the 1 year follow-up of 470 first ever stroke cases identified in the area of Ludwigshafen, Germany, with 23 deaths and 34 subsequent stroke events. For this purpose, the recently introduced “weighted all-cause hazard ratio” was used, which allows a weighting of the competing endpoints within a composite endpoint. Moreover, we extended this approach to allow an adjustment for covariates. Results None of these risk factors and risk diseases, most probably being treated after the first stroke, remained to be associated with a subsequent death or stroke [weighted hazard ratios (95% confidence interval) for diabetes mellitus, atrial fibrillation, high cholesterol, hypertension, and smoking are 0.4 (0.2–0.9), 0.8 (0.4–2.2), 1.3 (0.5–2.5), 1.2 (0.3–2.7), 1.6 (0.8–3.6), respectively]. However, when analyzed separately in terms of death and stroke, the risk factors and risk diseases under investigation affect the subsequent event rate to a variable degree. Conclusions Using the new weighted hazard ratio, established risk factors and risk diseases for the occurrence of a first stroke do not remain to be significant predictors for subsequent events like death or recurrent stroke. It has been demonstrated that the new weighted hazard ratio can be used for a more adequate analysis of cardiovascular risk and disease progress. The results have to be confirmed within a larger study with more events.

Abstract The quality of medical research importantly depends, among other aspects, on a valid statistical planning of the study, analysis of the data, and reporting of the results, which is usually guaranteed by a biostatistician. However, there are several related professions next to the biostatistician, for example epidemiologists, medical informaticians and bioinformaticians. For medical experts, it is often not clear what the differences between these professions are and how the specific role of a biostatistician can be described. For physicians involved in medical research, this is problematic because false expectations often lead to frustration on both sides. Therefore, the aim of this article is to outline the tasks and responsibilities of biostatisticians in clinical trials as well as in other fields of application in medical research.

Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses.

Even though adaptive two-stage designs with unblinded interim analyses are becoming increasingly popular in clinical trial designs, there is a lack of statistical software to make their application more straightforward. The package adoptr fills this gap for the common case of two-stage one- or two-arm trials with (approximately) normally distributed outcomes. In contrast to previous approaches, adoptr optimizes the entire design upfront which allows maximal efficiency. To facilitate experimentation with different objective functions, adoptr supports a flexible way of specifying both (composite) objective scores and (conditional) constraints by the user. Special emphasis was put on providing measures to aid practitioners with the validation process of the package.

Abstract Background Patient Reported Outcomes (PRO) are gaining more and more importance in the context of clinical trials. The assessment of PRO is frequently performed by questionnaires where the multiple items of a questionnaire are usually pooled within summarizing scores. These scores are used as variables to measure subjective aspects of treatments and diseases. In clinical research, the calculation of these scores is mostly kept very simple, e.g. by a simple summation of item values. In the medical literature, there is hardly any guidance for performing a refinements of questionnaires and for deducing adequate scores. In contrast, in psychometric literature, there are plenty of more sophisticated methods, which overcome typical assumptions made in traditional (sum) scores, however to the prize of more complicated algorithms, which might be difficult to communicate. When faced with the practical task to refine an existing questionnaire, there exist a clear gap of guidance for applied medical researchers. By this article we try to fill this important gap between psychometric theory and medical application by illustrating our methodological choices on the example of a clinical PRO questionnaire. Methods Based on our experiences with the refinement of the BCTOS, a PRO questionnaire to assess aesthetic and function after breast conserving therapy in breast cancer patients, we present the following general steps that we performed by refining the BCTOS questionnaire and its scores: 1. Refinement of the length of the questionnaire and the (item-factor) structure. 2. Selection of the factor score estimation method. 3. Validation of the refined questionnaire and scores with respect to validity, reliability and structure based on a validation cohort. Results Our step-step-step procedure helped us to shorten the current form of the BCTOS and to redefine the factor structure. By this, the compliance of patients can be increased and the interpretation of the results becomes more coherent. Conclusions We present a step-by-step procedure to refine an existing medical questionnaire along with its scores illustrated and discussed by the refinement of the BCTOS. Trial registration Due to the character of the study (no intervention study), no registration was performed.

Abstract Background The rationale for the use of composite time-to-event endpoints is to increase the number of expected events and thereby the power by combining several event types of clinical interest. The all-cause hazard ratio is the standard effect measure for composite endpoints where the all-cause hazard function is given as the sum of the event-specific hazards. However, the effect of the individual components might differ, in magnitude or even in direction, which leads to interpretation difficulties. Moreover, the individual event types often are of different clinical relevance which further complicates interpretation. Our working group recently proposed a new weighted effect measure for composite endpoints called the ‘weighted all-cause hazard ratio’. By imposing relevance weights for the components, the interpretation of the composite effect becomes more ‘natural’. Although the weighted all-cause hazard ratio seems an elegant solution to overcome interpretation problems, the originally published approach has several shortcomings: First, the proposed point estimator requires pre-specification of a parametric survival model. Second, no closed formula for a corresponding test statistic was provided. Instead, a permutation test was proposed. Third, no clear guidance for the choice of the relevance weights was provided. In this work, we will overcome these problems. Methods Within this work a new non-parametric estimator and a related closed formula test statistic are presented. Performance of the new estimator and test is compared to the original ones by a Monte-Carlo simulation study. Results The original parametric estimator is sensible to miss-specifications of the survival model. The new non-parametric estimator turns out to be very robust even if the required assumptions are not met. The new test shows considerably better power properties than the permutation test, is computationally much less expensive but might not preserve type one error in all situations. A scheme for choosing the relevance weights in the planning stage is provided. Conclusion We recommend to use the non-parametric estimator along with the new test to assess the weighted all-cause hazard ratio. Concrete guidance for the choice of the relevance weights is now available. Thus, applying the weighted all-cause hazard ratio in clinical applications is both - feasible and recommended.

There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.

BackgroundThe COVID-19 pandemic led to the implementation of drastic shutdown measures worldwide. While quarantine, self-isolation and shutdown laws helped to effectively contain and control the spread of SARS-CoV-2, the impact of COVID-19 shutdowns on trauma care in emergency departments (EDs) remains elusive.MethodsAll ED patient records from the 35-day COVID-19 shutdown (SHUTDOWN) period were retrospectively compared to a calendar-matched control period in 2019 (CTRL) as well as to a pre (PRE)- and post (POST)-shutdown period in an academic Level I Trauma Center in Berlin, Germany. Total patient and orthopedic trauma cases and contacts as well as trauma causes and injury patterns were evaluated during respective periods regarding absolute numbers, incidence rate ratios (IRRs) and risk ratios (RRs).FindingsDaily total patient cases (SHUTDOWN vs. CTRL, 106.94 vs. 167.54) and orthopedic trauma cases (SHUTDOWN vs. CTRL, 30.91 vs. 52.06) decreased during the SHUTDOWN compared to the CTRL period with IRRs of 0.64 and 0.59. While absolute numbers decreased for most trauma causes during the SHUTDOWN period, we observed increased incidence proportions of household injuries and bicycle accidents with RRs of 1.31 and 1.68 respectively. An RR of 2.41 was observed for injuries due to domestic violence. We further recorded increased incidence proportions of acute and regular substance abuse during the SHUTDOWN period with RRs of 1.63 and 3.22, respectively.ConclusionsWhile we observed a relevant decrease in total patient cases, relative proportions of specific trauma causes and injury patterns increased during the COVID-19 shutdown in Berlin, Germany. As government programs offered prompt financial aid during the pandemic to individuals and businesses, additional social support may be considered for vulnerable domestic environments.

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.

In the last decades, statistical methodology has developed rapidly, in particular in the field of regression modeling. Multivariable regression models are applied in almost all medical research projects. Therefore, the potential impact of statistical misconceptions within this field can be enormous Indeed, the current theoretical statistical knowledge is not always adequately transferred to the current practice in medical statistics. Some medical journals have identified this problem and published isolated statistical articles and even whole series thereof. In this systematic review, we aim to assess the current level of education on regression modeling that is provided to medical researchers via series of statistical articles published in medical journals. The present manuscript is a protocol for a systematic review that aims to assess which aspects of regression modeling are covered by statistical series published in medical journals that intend to train and guide applied medical researchers with limited statistical knowledge. Statistical paper series cannot easily be summarized and identified by common keywords in an electronic search engine like Scopus. We therefore identified series by a systematic request to statistical experts who are part or related to the STRATOS Initiative (STRengthening Analytical Thinking for Observational Studies). Within each identified article, two raters will independently check the content of the articles with respect to a predefined list of key aspects related to regression modeling. The content analysis of the topic-relevant articles will be performed using a predefined report form to assess the content as objectively as possible. Any disputes will be resolved by a third reviewer. Summary analyses will identify potential methodological gaps and misconceptions that may have an important impact on the quality of analyses in medical research. This review will thus provide a basis for future guidance papers and tutorials in the field of regression modeling which will enable medical researchers 1) to interpret publications in a correct way, 2) to perform basic statistical analyses in a correct way and 3) to identify situations when the help of a statistical expert is required.

Abstract Background Neoadjuvant chemotherapy (NACT) is a standard approach of the multidisciplinary treatment of breast cancer. Depending on the biological subtype a pathological complete response in the breast (bpCR) can be achieved in up to 60% of the patients. However, only limited accuracy can be reached when using imaging for prediction of bpCR prior to surgery. Due to this diagnostic uncertainty, surgery after NACT is considered to be obligatory for all patients in order to either completely remove residual disease or to diagnose a bpCR histologically. The purpose of this trial is to evaluate the accuracy of a vacuum-assisted biopsy (VAB) to diagnose a bpCR after NACT prior to surgery. Methods This study is a multicenter, confirmative, one-armed, intra-individually-controlled, open, diagnostic trial. The study will take place at 21 trial sites in Germany. Six hundred female patients with breast cancer after completed NACT showing at least a partial response to NACT treatment will be enrolled. A vacuum-assisted biopsy (VAB) guided either by ultrasound or mammography will be performed followed by histopathological evaluation of the VAB specimen before standard, guideline-adherent breast surgery. The study is designed to prove that the false negative rate of the VAB is below 10%. Discussion As a bpCR is becoming a more frequent result after NACT, the question arises whether breast surgery is therapeutically necessary in such cases. To study this subject further, it will be crucial to develop a reliable test to diagnose a bpCR without surgery. During the study we anticipate possible problems in patient recruitment as the VAB intervention does not provide participating patients with any personal benefit. Hence, a proficient informed consent discussion with the patient and a detailed explanation of the study aim will be crucial for patient recruitment. Another critical issue is the histopathological VAB evaluation of a non-tumorous specimen as this may have been taken either from the former tumor region (bpCR) or outside of the (former) tumor region (non-representative VAB, sampling error). Trial registration The trial has been registered at clinicaltrials.gov with the identifier NCT02948764 on October 28, 2016 and at the German Clinical Trials Register (DRKS00011761) on February 20, 2017. The date of enrolment of the first participant to the trial was on March 8, 2017.

Abstract Background Many clinical trials focus on the comparison of the treatment effect between two or more groups concerning a rarely occurring event. In this situation, showing a relevant effect with an acceptable power requires the observation of a large number of patients over a long period of time. For feasibility issues, it is therefore often considered to include several event types of interest, non-fatal or fatal, and to combine them within a composite endpoint. Commonly, a composite endpoint is analyzed with standard survival analysis techniques by assessing the time to the first occurring event. This approach neglects that an individual may experience more than one event which leads to a loss of information. As an alternative, composite endpoints could be analyzed by models for recurrent events. There exists a number of such models, e.g. regression models based on count data or Cox-based models such as the approaches of Andersen and Gill, Prentice, Williams and Peterson or, Wei, Lin and Weissfeld. Although some of the methods were already compared within the literature there exists no systematic investigation for the special requirements regarding composite endpoints. Methods Within this work a simulation-based comparison of recurrent event models applied to composite endpoints is provided for different realistic clinical trial scenarios. Results We demonstrate that the Andersen-Gill model and the Prentice- Williams-Petersen models show similar results under various data scenarios whereas the Wei-Lin-Weissfeld model delivers effect estimators which can considerably deviate under commonly met data scenarios. Conclusion Based on the conducted simulation study, this paper helps to understand the pros and cons of the investigated methods in the context of composite endpoints and provides therefore recommendations for an adequate statistical analysis strategy and a meaningful interpretation of results.

In clinical phase II studies, the efficacy of a promising therapy is tested in patients for the first time. Based on the results, it is decided whether the development programme should be stopped or whether the benefit-risk profile is promising enough to justify the initiation of large phase III studies. In oncology, phase II trials are commonly conducted as single-arm trials with planned interim analyses to allow for an early stopping for futility. The specification of an adequate study design that guarantees control of the type I and II error rates is a key task in the planning stage of such a trial. A variety of statistical methods exists which can be used to optimise the planning and analysis of such studies. However, there are currently neither commercial nor non-commercial software tools available that support the practical application of these methods comprehensively. The R package OneArmPhaseTwoStudy was implemented to fill this gap. The package allows determining an adequate study design for the particular situation at hand as well as monitoring the progress of the study and evaluating the results with valid and efficient analyses methods. This article describes the features of the R package and its application.

Abstract Background In clinical trials, the opportunity for an early stop during an interim analysis (either for efficacy or for futility) may relevantly save time and financial resources. This is especially important, if the planning assumptions required for power calculation are based on a low level of evidence. For example, when including two primary endpoints in the confirmatory analysis, the power of the trial depends on the effects of both endpoints and on their correlation. Assessing the feasibility of such a trial is therefore difficult, as the number of parameter assumptions to be correctly specified is large. For this reason, so-called ‘group sequential designs’ are of particular importance in this setting. Whereas the choice of adequate boundaries to stop a trial early for efficacy has been broadly discussed in the literature, the choice of optimal futility boundaries has not been investigated so far, although this may have serious consequences with respect to performance characteristics. Methods In this work, we propose a general method to construct ‘optimal’ futility boundaries according to predefined criteria. Further, we present three different group sequential designs for two endpoints applying these futility boundaries. Our methods are illustrated by a real clinical trial example and by Monte-Carlo simulations. Results By construction, the provided method of choosing futility boundaries maximizes the probability to correctly stop in case of small or opposite effects while limiting the power loss and the probability of stopping the study ‘wrongly’. Our results clearly demonstrate the benefit of using such ‘optimal’ futility boundaries, especially compared to futility boundaries commonly applied in practice. Conclusions As the properties of futility boundaries are often not considered in practice and unfavorably chosen futility boundaries may imply bad properties of the study design, we recommend assessing the performance of these boundaries according to the criteria proposed in here.

Background and Objective: In multiple sclerosis (MS) patients, Double Inversion Recovery (DIR) magnetic resonance imaging (MRI) can be used to detect cortical lesions (CL). While the quantity and distribution of CLs seems to be associated with patients' disease course, literature lacks frequent assessments of CL volumes (CL-V) in this context. We investigated the reliability of DIR for the longitudinal assessment of CL-V development with frequent follow-up MRIs and examined the course of CL-V progressions in relation to white-matter lesions (WML), contrast enhancing lesions (CEL) and clinical parameters in patients with Relapsing-Remitting Multiple Sclerosis (RRMS).Methods: In this post-hoc analysis, image- and clinical data of a subset of 24 subjects that were part of a phase IIa clinical trial on the “Safety, Tolerability and Mechanisms of Action of Boswellic Acids in Multiple Sclerosis (SABA)” (ClinicalTrials.gov, NCT01450124) were included. The study was divided in three phases (screening, treatment, study-end). All patients received 12 MRI follow-up-examinations (including DIR) during a 16-months period. CL-Vs were assessed for each patient on each follow-up MRI separately by two experienced neuroradiologists. Results of neurological screening tests, as well as other MRI parameters (WML number and volume and CELs) were included from the SABA investigation data.Results: Inter-rater agreement regarding CL-V assessment over time was good-to-excellent (κ = 0.89). Mean intraobserver variability was 1.1%. In all patients, a total number of 218 CLs was found. Total CL-Vs of all patients increased during the 4 months of baseline screening followed by a continuous and significant decrease from month 5 until study-end (p < 0.001, Kendall'W = 0.413). A positive association between WML volumes and CL-Vs was observed during baseline screening. Decreased CL-V were associated with lower EDSS and also with improvements of SDMT- and SCRIPPS scores.Conclusion: DIR MRI seems to be a reliable tool for the frequent assessment of CL-Vs. Overall CL-Vs decreased during the follow-up period and were associated with improvements of cognitive and disability status scores. Our results suggest the presence of short-term CL-V dynamics in RRMS patients and we presume that the laborious evaluation of lesion volumes may be worthwhile for future investigations.Clinical Trial Numbers:www.ClinicalTrials.gov, “The SABA trial”; number: NCT01450124

Renal cell carcinoma (RCC) is characterized by a profound disruption of proapoptotic signaling networks leading to chemo- and radioresistance. A key mediator of DNA damage-induced apoptosis is the BH3-only protein PUMA. Given its central role in proapoptotic signaling, we analyzed a series of more than 600 precision-annotated primary RCC specimens for PUMA protein expression. We found a reduced expression of PUMA in 22.6% of RCCs analyzed. Unexpectedly, however, PUMA deficiency was not associated with more aggressive tumor characteristic as expected. Instead, a reduced PUMA expression was associated with a lower TNM stage, lower histopathologic grade, and more favorable cancer-specific patient survival. A direct correlation in a separate patient cohort revealed a profound disconnection between PUMA expression and apoptosis as exemplified by the fact that the tumor with the highest level of apoptotic cells was PUMA deficient. In a series of in vitro studies, we corroborated these results and discovered the highest propensity to undergo apoptosis in an RCC cell line with virtually undetectable PUMA expression. At the same time, PUMA expression was not necessarily associated with stronger apoptosis induction, which underscores the striking functional heterogeneity of PUMA expression and apoptosis in RCC. Collectively, our findings suggest that PUMA-independent mechanisms of cell death exist and may play an important role in suppressing malignant progression. They underscore the functional heterogeneity of RCCs and suggest that PUMA expression alone may not be a suitable predictive biomarker. A better understanding of alternative proapoptotic pathways, however, may help to design novel therapeutic strategies for patients with advanced RCC.

Background: Evaluation of the potential efficacy and safety of combination therapies for advanced soft tissue sarcomas (STS) has increased substantially after approval of trabectedin and pazopanib. Trabectedin's introduction in Europe in 2007 depended mainly on its activity in so-called L-sarcomas (liposarcoma and leiomyosarcoma); combination of trabectedin with other chemotherapies used in STS seems of particular interest. Methods: We initiated within the German Interdisciplinary Sarcoma Group (GISG) a phase I dose escalating trial evaluating the combination of trabectedin and gemcitabine in patients with advanced and/or metastatic L-sarcomas (GISG-02; ClinicalTrials.gov NCT01426633). Patients were treated with increasing doses of trabectedin and gemcitabine. The primary endpoint was to determine the maximum tolerated dose. Results: Five patients were included in the study. Two patients were treated on dose level 1 comprising trabectedin 0.9 mg/m2 on day 1 and gemcitabine 700 mg/m2 on days 1 + 8, every 3 weeks. Due to dose-limiting toxicity (DLT) in both patients (elevated transaminases and thrombocytopenia), an additional three patients were treated on dose level −1 with trabectedin 0.7 mg/m2 plus gemcitabine 700 mg/m2. Of these three patients, two demonstrated another DLT; therefore, the trial was stopped and none of the dose levels could be recommended for phase II testing. Conclusion: The GISG-02 phase I study was stopped with the conclusion that the combination of gemcitabine and trabectedin is generally not recommended for the treatment of patients with advanced and/or metastatic leiomyosarcoma or liposarcoma. Also, this phase I study strongly supports the necessity for careful evaluation of combination therapies.

In this prospective study, we examined postoperative follow-up and preoperative IFN-γ T cell responses against 14 non-small cell lung cancer (NSCLC)-associated antigens in the blood of 51 patients with NSCLC, 7 patients with benign pulmonary tumors, and 10 tumor-free patients by enzyme-linked immunospot assay. The phenotype and function of T cells specific for tumor-associated antigens (TAAs) in the blood or tumor tissue of 9 NSCLC patients were characterized in detail using TNF-α, IL-2, and IFN-γ cytokine capture assays. We found that circulating TAA-specific T cells were significantly enriched in NSCLC compared with tumor-free patients. The most frequently recognized TAAs were Aurora kinase A, HER2/neu, NY-ESO-1, and p53. TNF-α was the most abundant cytokine secreted by TAA-specific T cells in the blood as well as by in situ-activated tumor-infiltrating lymphocytes, most of which were effector memory cells. The absence of TAA-reactive T cells identified patients at higher risk of tumor recurrence, irrespective of tumor stage (OR = 8.76, 95% CI: 1.57–34.79, p = 0.008). We conclude that pre-existing TAA-reactive circulating T cells are a strong independent prognostic factor for recurrence-free survival. These data may help discriminating high-risk from low-risk patients, improving prognostication, and redirecting adjuvant therapy. Our findings suggest the therapeutic relevance of Aurora kinase A, HER2/neu, NY-ESO-1, and p53 as targets for immunotherapy. This study is registered on Clinicaltrials.gov with trial identification number: NCT02515760.

The application of a cervical collar is a standard procedure in trauma patients in emergency medicine. It is often observed that cervical collars are applied incorrectly, resulting in reduced immobilization of the cervical spine. The objective of this study was to analyze the practical skills of trained professional rescue personnel concerning the application of cervical collars.Within emergency medical conferences, n = 104 voluntary test subjects were asked to apply a cervical collar to a training doll, wherein each step that was performed received an evaluation. Furthermore, personal and occupational data of all study participants were collected using a questionnaire.The test subjects included professional rescue personnel (80.8%) and emergency physicians (12.5%). The average occupational experience of all study participants in pre-clinical emergency care was 11.1±8.9 years. Most study participants had already attended a certified training on trauma care (61%) and felt "very confident" in handling a cervical collar (84%). 11% applied the cervical collar to the training doll without errors. The most common error consisted of incorrect adjustment of the size of the cervical collar (66%). No association was found between the correct application of the cervical collar and the occupational group of the test subjects (trained rescue personnel vs. emergency physicians) or the participation in certified trauma courses.Despite pronounced subjective confidence regarding the application of cervical collars, this study allows the conclusion that there are general deficits in practical skills when cervical collars are applied. A critical assessment of the current training contents on the subject of trauma care must, therefore, be demanded.