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Background: Frailty in newly-diagnosed multiple myeloma (NDMM) patients is associated with treatment-related toxicity, which negatively affects health-related quality of life (HRQoL). Currently, data on changes in HRQoL of frail and intermediate-fit MM patients during active treatment and post-treatment follow-up are absent. Methods: The HOVON123 study (NTR4244) was a phase II trial in which NDMM patients ≥ 75 years were treated with nine dose-adjusted cycles of Melphalan-Prednisone-Bortezomib (MPV). Two HRQoL instruments (EORTC QLQ-C30 and -MY20) were obtained before start of treatment, after 3 and 9 months of treatment and 6 and 12 months after treatment for patients who did not yet start second-line treatment. HRQoL changes and/or differences in frail and intermediate-fit patients (IMWG frailty score) were reported only when both statistically significant (p < 0.005) and clinically relevant (>MID). Results: 137 frail and 71 intermediate-fit patients were included in the analysis. Compliance was high and comparable in both groups. At baseline, frail patients reported lower global health status, lower physical functioning scores and more fatigue and pain compared to intermediate-fit patients. Both groups improved in global health status and future perspective; polyneuropathy complaints worsened over time. Frail patients improved over time in physical functioning, fatigue and pain. Improvement in global health status occurred earlier than in intermediate-fit patients. Conclusion: HRQoL improved during anti-myeloma treatment in both intermediate-fit and frail MM patients. In frail patients, improvement occurred faster and, in more domains, which was retained during follow-up. This implies that physicians should not withhold safe and effective therapies from frail patients in fear of HRQoL deterioration.

01 februari 2023, Item does not contain fulltext, Suboptimal guideline adherence in chronic myeloid leukemia (CML) care is associated with worse treatment outcomes. Current study focused on adherence to seven quality indicators (QIs) based on the European Leukemia Network guideline (one diagnostic, one therapeutic, and five monitoring indicators). Data were obtained from population-based registries in the Netherlands of 405 newly diagnosed chronic phase CML patients between January 2008 and April 2013. Compliance rates regarding diagnostic and therapeutic indicator were 83% and 78%, respectively. Monitoring indicators rates were lower: 21-27% for indicators concerning the first year and 58% and 62% for the second and third year, respectively. Noncompliance occurred mostly due to non-timely monitoring. Twenty cases did not comply with any indicator, 6% complied with all indicators. After adjustment for age, overall survival rates did not differ significantly between the groups. Adherence to guideline-based QIs was suboptimal. This demonstrates the evidence-practice gap, shows room for improvement and underscores the need for real-world data.

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Contains fulltext : 299845.pdf (Publisher’s version ) (Closed access), OBJECTIVES: Acquired missense mutations in the BCR::ABL1 kinase domain (KD) may cause tyrosine kinase inhibitor (TKI) treatment failure. Based on mutation-specific in vitro derived IC50-values, alternative TKI may be selected. We assessed clinical practice of BCR::ABL1 KD mutation testing, clinical response in relation to IC50-values, and clinical outcome of tested patients. METHODS: Patients from six Dutch CML reference centers and a national registry were included once a mutational analysis was performed. Reasons for testing were categorized as suboptimal TKI response, and primary or secondary TKI resistance. RESULTS: Four hundred twenty analyses were performed in 275 patients. Sixty-nine patients harbored at least one mutation. Most analyses were performed because of suboptimal TKI response but with low mutation incidence (4%), while most mutations were found in primary and secondary resistant patients (21% and 51%, respectively). Harboring a BCR::ABL1 mutation was associated with inferior overall survival (HR 3.2 [95% CI, 1.7-6.1; p < .001]). Clinically observed responses to TKI usually corresponded with the predicted TKI sensitivity based on the IC50-values, but a high IC50-value did not preclude a good clinical response per se. CONCLUSIONS: We recommend BCR::ABL1 KD mutation testing in particular in the context of primary or secondary resistance. IC50-values can direct the TKI choice for CML patients, but clinical efficacy can be seen despite adverse in vitro resistance., 01 december 2023

Contains fulltext : 299611.pdf (Publisher’s version ) (Closed access), BACKGROUND: Many older patients with acute myeloid leukaemia die or cannot undergo allogeneic haematopoietic stem-cell transplantation (HSCT) due to toxicity caused by intensive chemotherapy. We hypothesised that replacing intensive chemotherapy with decitabine monotherapy could improve outcomes. METHODS: This open-label, randomised, controlled, phase 3 trial was conducted at 54 hospitals in nine European countries. Patients aged 60 years and older who were newly diagnosed with acute myeloid leukaemia and had not yet been treated were enrolled if they had an Eastern Cooperative Oncology Group performance status of 2 or less and were eligible for intensive chemotherapy. Patients were randomly assigned (1:1) to receive decitabine or standard chemotherapy (known as 3 + 7). For the decitabine group, decitabine (20 mg/m(2)) was administered for the first 10 days in the first 28-day cycle, followed by 28-day cycles consisting of 5 days or 10 days of decitabine. For the 3 + 7 group, daunorubicin (60 mg/m(2)) was administered over the first 3 days and cytarabine (200 mg/m(2)) over the first 7 days, followed by 1-3 additional chemotherapy cycles. Allogeneic HSCT was strongly encouraged. Overall survival in the intention-to-treat population was the primary endpoint. Safety was assessed in all patients who received the allocated treatment. This trial is registered at ClinicalTrials.gov, NCT02172872, and is closed to new participants. FINDINGS: Between Dec 1, 2014, and Aug 20, 2019, 606 patients were randomly assigned to the decitabine (n=303) or 3 + 7 (n=303) group. Following an interim analysis which showed futility, the IDMC recommended on May 22, 2019, that the study continued as planned considering the risks and benefits for the patients participating in the study. The cutoff date for the final analysis presented here was June 30, 2021. At a median follow-up of 4·0 years (IQR 2·9-4·8), 4-year overall survival was 26% (95% CI 21-32) in the decitabine group versus 30% (24-35, 01 november 2023

Encryption on the internet with the shift to HTTPS has been an important step to improve the privacy of internet users. However, there is an increasing body of work about extracting information from encrypted internet traffic without having to decrypt it. Such attacks bypass security guarantees assumed to be given by HTTPS and thus need to be understood. Prior works showed that the variable bitrates of video streams are sufficient to identify which video someone is watching. These works generally have to make trade-offs in aspects such as accuracy, scalability, robustness, etc. These trade-offs complicate the practical use of these attacks. To that end, we propose a deep metric learning framework based on the triplet loss method. Through this framework, we achieve robust, generalisable, scalable and transferable encrypted video stream detection. First, the triplet loss is better able to deal with video streams not seen during training. Second, our approach can accurately classify videos not seen during training. Third, we show that our method scales well to a dataset of over 1000 videos. Finally, we show that a model trained on video streams over Chrome can also classify streams over Firefox. Our results suggest that this side-channel attack is more broadly applicable than originally thought. We provide our code alongside a diverse and up-to-date dataset for future research.

A major problem in cybersecurity research is the correct labeling of up-to-date datasets. It relies on the availability of human experts, and is as such very cumbersome. Motivated by this, two techniques have been proposed for efficient labeling: Active Learning (AL) and Semi-Supervised Learning (SeSL). In this paper, we introduce Plusmine: an intrusion detection method that combines the benefits of AL and SeSL to efficiently automate classification. We develop new techniques for both components. Moreover, we empirically show that Plusmine obtains good and more robust results than benchmark methods.

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One of the reasons that the deployment of network intrusion detection methods falls short is the lack of realistic labeled datasets, which makes it challenging to develop and compare techniques. It is caused by the large amounts of effort that it takes for a cyber expert to classify network connections. This has raised the need for methods that learn from both labeled and unlabeled data which observations are best to present to the human expert. Hence, Active Learning (AL) methods are of interest. In this paper, we propose a new hybrid AL method called Jasmine. Firstly, it uses the uncertainty score and anomaly score to determine how suitable each observation is for querying, i.e., how likely it is to enhance classification. Secondly, Jasmine introduces dynamic updating. This allows the model to adjust the balance between querying uncertain, anomalous and randomly selected observations. To this end, Jasmine is able to learn the best query strategy during the labeling process. This is in contrast to the other AL methods in cybersecurity that all have static, predetermined query functions. We show that dynamic updating, and therefore Jasmine, is able to consistently obtain good and more robust results than querying only uncertainties, only anomalies or a fixed combination of the two.

Contains fulltext : 243950.pdf (Publisher’s version ) (Open Access), INTRODUCTION: Efficacy of lenalidomide plus rituximab (R-LEN) compared to rituximab monotherapy (R-mono) for patients with previously treated follicular lymphoma (FL) was investigated in AUGMENT (NCT01938001). Our aim was to evaluate the cost-effectiveness of R-LEN versus R-mono in this setting from a Dutch perspective. AREAS COVERED: Cost-effectiveness was assessed through a partitioned survival model from three perspectives (i.e. societal, healthcare, and societal, including future non-medical costs). Patient-level data from AUGMENT informed effectiveness parameters (i.e. long-term survival) and health state utilities. Resource use and prices were based on AUGMENT and the literature. Clinical experts validated efficacy input parameters and results. Uncertainty was explored through sensitivity and scenario analyses. EXPERT OPINION: R-LEN resulted in 1.7 incremental discounted quality-adjusted life years (QALYs). Total incremental discounted costs were 67,161 EUR from a societal perspective. In conclusion, R-LEN was cost-effective at a willingness-to-pay (WTP) threshold of 50,000 EUR/QALY in the base-case analyses(incremental cost-effectiveness ratio = 40,493 EUR/QALY). Scenario and sensitivity analyses indicated some level of uncertainty regarding this conclusion, depending on the chosen WTP-threshold and perspective.

Contains fulltext : 244701.pdf (Publisher’s version ) (Open Access), OBJECTIVES: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients. DESIGN: Multicenter retrospective cohort study. SETTING: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020. PATIENTS: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded. MEASUREMENTS AND MAIN RESULTS: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio(2), set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao(2)/Fio(2) ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant (p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves. CONCLUSIONS: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists.

Contains fulltext : 244124.pdf (Publisher’s version ) (Open Access), BACKGROUND: There has been a cultural shift toward patient engagement in health, with a growing demand from patients to access their results. OBJECTIVE: The Lymphoma Intervention (LIVE) trial is conducted to examine the impact of return of individual patient-reported outcome (PRO) results and a web-based self-management intervention on psychological distress, self-management, satisfaction with information, and health care use in a population-based setting. METHODS: Return of PRO results included comparison with age- and sex-matched peers and was built into the Patient-Reported Outcomes Following Initial Treatment and Long-Term Evaluation of Survivorship registry. The self-management intervention is an adaptation of a fully automated evidence-based intervention for breast cancer survivors. Patients with lymphoma who completed the web-based questionnaire were equally randomized to care as usual, return of PRO results, and return of PRO results plus self-management intervention. Patients completed questionnaires 9 to 18 months after diagnosis (T0; n=227), 4 months (T1; n=190), 12 months (T2; n=170), and 24 months (T3; n=98). RESULTS: Of all invited patients, 51.1% (456/892) responded and web-based participants (n=227) were randomly assigned to care as usual (n=76), return of PRO results (n=74), or return of PRO results and access to Living with lymphoma (n=77). Return of PRO results was viewed by 76.7% (115/150) of those with access. No statistically significant differences were observed for psychological distress, self-management, satisfaction with information provision, and health care use between patients who received PRO results and those who did not (P>.05). Use of the self-management intervention was low (2/76, 3%), and an effect could therefore not be determined. CONCLUSIONS: Return of individual PRO results seems to meet patients' wishes but had no beneficial effects on patient outcome. No negative effects were found when individual PRO results were disclosed

Contains fulltext : 238831.pdf (Publisher’s version ) (Open Access), BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. METHODS: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. RESULTS: Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each pa

The goals of this study were to examine whether machine-learning algorithms outper-form multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to in-vestigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Fi-nally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly

Treatment results of AML in elderly patients are unsatisfactory. We hypothesized that addition of tosedostat, an aminopeptidase inhibitor, to intensive chemotherapy may improve outcome in this population. After establishing a safe dose in a run-in phase of the study in 22 patients, 231 eligible patients with AML above 65 years of age (median 70, range 66–81) were randomly assigned in this open label randomized Phase II study to receive standard chemotherapy (3+7) with or without tosedostat at the selected daily dose of 120 mg (n = 116), days 1–21. In the second cycle, patients received cytarabine 1000 mg/m2 twice daily on days 1-6 with or without tosedostat. CR/CRi rates in the 2 arms were not significantly different (69% (95% C.I. 60–77%) vs 64% (55–73%), respectively). At 24 months, event-free survival (EFS) was 20% for the standard arm versus 12% for the tosedostat arm (Cox-p = 0.01) and overall survival (OS) 33% vs 18% respectively (p = 0.006). Infectious complications accounted for an increased early death rate in the tosedostat arm. Atrial fibrillation wa

Contains fulltext : 244677.pdf (Publisher’s version ) (Open Access), INTRODUCTION: Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. METHODS: We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. RESULTS: A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale a