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Abstract Randomized controlled trials (RCTs) are the gold standard to establish the benefit-risk ratio of novel drugs. However, the evaluation of mature results often takes many years. We hypothesized that the addition of Bayesian inference methods at interim analysis time points might accelerate and enforce the knowledge that such trials may generate. In order to test that hypothesis, we retrospectively applied a Bayesian approach to the HOVON 132 trial, in which 800 newly diagnosed AML patients aged 18 to 65 years were randomly assigned to a “7 + 3” induction with or without lenalidomide. Five years after the first patient was recruited, the trial was negative for its primary endpoint with no difference in event-free survival (EFS) between experimental and control groups (hazard ratio [HR] 0.99, p = 0.96) in the final conventional analysis. We retrospectively simulated interim analyses after the inclusion of 150, 300, 450, and 600 patients using a Bayesian methodology to detect early lack of efficacy signals. The HR for EFS comparing the lenalidomide arm with the control treatment arm was 1.21 (95% CI 0.81–1.69), 1.05 (95% CI 0.86–1.30), 1.00 (95% CI 0.84–1.19), and 1.02 (95% CI 0.87–1.19) at interim analysis 1, 2, 3 and 4, respectively. Complete remission rates were lower in the lenalidomide arm, and early deaths more frequent. A Bayesian approach identified that the probability of a clinically relevant benefit for EFS (HR

Abstract Background We recently reported results of the prospective, open‐label HOVON‐100 trial in 334 adult patients with acute lymphoblastic leukemia (ALL) randomized to first‐line treatment with or without clofarabine (CLO). No improvement of event‐free survival (EFS) was observed, while a higher proportion of patients receiving CLO obtained minimal residual disease (MRD) negativity. Aim In order to investigate the effects of CLO in more depth, two multi‐state models were developed to identify why CLO did not show a long‐term survival benefit despite more MRD‐negativity. Methods The first model evaluated the effect of CLO on going off‐protocol (not due to refractory disease/relapse, completion or death) as a proxy of severe treatment‐related toxicity, while the second model evaluated the effect of CLO on obtaining MRD negativity. The subsequent impact of these intermediate events on death or relapsed/refractory disease was assessed in both models. Results Overall, patients receiving CLO went off‐protocol more frequently than control patients (35/168 [21%] vs. 18/166 [11%], p = 0.019; HR 2.00 [1.13–3.52], p = 0.02), especially during maintenance (13/44 [30%] vs. 6/56 [11%]; HR 2.85 [95%CI 1.08–7.50], p = 0.035). Going off‐protocol was, however, not associated with more relapse or death. Patients in the CLO arm showed a trend towards an increased rate of MRD‐negativity compared with control patients (HR MRD‐negativity: 1.35 [0.95–1.91], p = 0.10), which did not translate into a significant survival benefit. Conclusion We conclude that the intermediate states, i.e., going off‐protocol and MRD‐negativity, were affected by adding CLO, but these transitions were not associated with subsequent survival estimates, suggesting relatively modest antileukemic activity in ALL.

Abstract Background Prognostic models developed in general cohorts with a mixture of heart failure (HF) phenotypes, though more widely applicable, are also likely to yield larger prediction errors in settings where the HF phenotypes have substantially different baseline mortality rates or different predictor-outcome associations. This study sought to use individual participant data meta-analysis to develop an HF phenotype stratified model for predicting 1-year mortality in patients admitted with acute HF. Methods Four prospective European cohorts were used to develop an HF phenotype stratified model. Cox model with two rounds of backward elimination was used to derive the prognostic index. Weibull model was used to obtain the baseline hazard functions. The internal-external cross-validation (IECV) approach was used to evaluate the generalizability of the developed model in terms of discrimination and calibration. Results 3577 acute HF patients were included, of which 2368 were classified as having HF with reduced ejection fraction (EF) (HFrEF; EF

Abstract Background Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. Methods Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). Results The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. Conclusions The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.

Abstract Aims Episodes of acute heart failure (AHF) may lead to end‐organ dysfunction. In this post hoc analysis of the Relaxin in Acute Heart Failure trial, we used the MELD‐XI (Model of End‐Stage Liver Dysfunction) score to examine hepatorenal dysfunction in patients with AHF. Methods and results On admission, the MELD‐XI score was elevated (abnormal) in 918 (82%) patients, with 638 (57%) having isolated renal dysfunction (creatinine > 1 mg/dL), 73 (6.5%) isolated liver dysfunction (bilirubin > 1 mg/dL), and 207 (18.5%) coexisting dysfunction of the kidneys and the liver (both creatinine and bilirubin > 1 mg/dL). The percentage of patients with elevated MELD‐XI score remained constant through a 60 day follow‐up, as we observed a gradual decrease of liver dysfunction prevalence, counterbalanced by an increase in renal dysfunction. Serelaxin treatment was associated with a lower MELD‐XI score on Day 2 and Day 5 (both P

Objective: The aim of this study was to compare the importance that patients with type 2 diabetes mellitus from the Netherlands and Turkey attach to certain drug effects of oral anti-diabetic drugs.Methods: Data were collected through a cross-sectional survey containing demographic questions and a discrete choice experiment assessing preferences for oral anti-diabetic drugs. Adults from the Netherlands and Turkey were included if they had type 2 diabetes mellitus and had received a prescription of an oral anti-diabetic drug in the last 4 months. The oral anti-diabetic drugs in the discrete choice experiment were described in terms of six attributes: effects on HbA1c, cardiovascular diseases, weight change, gastrointestinal adverse drug events hypoglycemic events, and bladder cancer. Multinomial logit models with country as an interaction factor were fitted.Results: In total, 381 patients were included, 199 from the Netherlands and 182 from Turkey. Patients’ preferences toward drug effects varied between the countries. Turkish patients attached the highest importance to reducing the risk of cardiovascular diseases (relative weight: 0.51, 95% CI 0.45–0.55), followed by reducing hypoglycemic events (relative weight: 0.16, 95% CI 0.11–0.22), and reducing gastrointestinal adverse drug events (relative weight: 0.11, 95% CI 0.07–0.18). Patients from the Netherlands attached the highest importance to gastrointestinal ADEs (relative weight: 0.22, 95% CI 0.14–0.39), followed by reducing hypoglycemic events (relative weight: 0.22, 95% CI 0.16–0.25), and reducing the risk of cardiovascular diseases (relative weight: 0.20, 95% CI 0.13–0.23).Conclusion: Patient preferences may differ across countries. Such differences should be acknowledged in regulatory decisions and clinical practice.

Abstract Background Translating prognostic and diagnostic biomarker candidates into clinical applications takes time, is very costly, and many candidates fail. It is therefore crucial to be able to select those biomarker candidates that have the highest chance of successfully being adopted in the clinic. This requires an early estimate of the potential clinical impact and commercial value. In this paper, we aim to demonstratively evaluate a set of novel biomarkers in terms of clinical impact and commercial value, using occurrence of cardiovascular disease (CVD) in type-2 diabetes (DM2) patients as a case study. Methods We defined a clinical application for the novel biomarkers, and subsequently used data from a large cohort study in The Netherlands in a modeling exercise to assess the potential clinical impact and headroom for the biomarkers. Results The most likely application of the biomarkers would be to identify DM2 patients with a low CVD risk and subsequently withhold statin treatment. As a result, one additional CVD event in every 75 patients may be expected. The expected downstream savings resulted in a headroom for a point-of-care device ranging from €119.09 at a willingness to accept of €0 for one additional CVD event, to €0 at a willingness to accept of €15,614 or more. Conclusion It is feasible to evaluate novel biomarkers on outcomes directly relevant to technological development and clinical adoption. Importantly, this may be attained at the same point in time and using the same data as used for the evaluation of association with disease and predictive power.

BackgroundSeveral clinical studies have evaluated the association between galectin‐3 levels and outcome in patients with heart failure (HF). However, little is known about the predictive value of repeated galectin‐3 measurements. This study evaluates the prognostic value of repeated time‐dependent galectin‐3 measurements in acute HF patients. Methods and ResultsIn the TRIUMPH (Translational Initiative on Unique and Novel Strategies for Management of Patients with Heart Failure) clinical cohort study, 496 acute HF patients were enrolled in 14 hospitals in The Netherlands, between 2009 and 2014. Repeated blood samples (7) were drawn during 1‐year follow‐up. Associations between repeated biomarker measurements and the primary end point were assessed using a joint model. Median age was 74 years and 37% were women. The primary end point, composite of all‐cause mortality and HF rehospitalization, was reached in 188 patients (40%), during a median follow‐up of 325 days (interquartile range 85–401). The median baseline galectin‐3 level was 24 ng/mL (interquartile range 18–34). The mean number of galectin‐3 measurements available per patient was 4.3. After adjustment for clinical factors and N‐terminal pro‐brain natriuretic peptide, there was a weak association between baseline galectin‐3 and risk of the primary end point. When repeated measurements were taken into account, the adjusted hazard ratio per 1 SD increase of the galectin‐3 level (on the log2 scale) at any time point increased to 1.67 (95% confidence interval, 1.24–2.23, P