High, serial NSE values are strong predictors of poor outcome after OHCA. Targeted temperature management at 33°C or 36°C does not significantly affect NSE levels. (Target Temperature Management After Cardiac Arrest [TTM]; NCT01020916).

Master protocols have received a growing interest during the last years. By assigning patients to specific substudies, they aim at targeting and accelerating clinical development. Given their complexity, basket, umbrella, and platform designs have raised challenging regulatory and statistical questions, especially the control of multiplicity in confirmatory trials. In basket trials, regulatory assessment of the benefit/risk in pooled populations and choice of the treatment indication is challenging. We provide here our perspectives on these topics. In master protocols, as long as the statistical hypotheses tested between the different substudies are independent, no supplementary adjustment for multiplicity over the different substudies should be required. Moreover, sharing a control arm within an umbrella or a platform trial investigating different drugs would not require a correction for the type I error rate, whereas the chance of multiple false positive regulatory decisions should be recognized. In basket trials, pooling across substudies requires a rationale supporting the intended indication and should be preplanned. Assessment of the benefit/risk in pooled target populations can be complicated by differences in design or in efficacy/safety signals between the substudies. While trials governed by a master protocol can offer logistic and financial advantages, more experience is needed to gain a deeper insight into this novel framework.

BackgroundPrediction of clinical outcome after acute myocardial infarction (AMI) is challenging and would benefit from new biomarkers. We investigated the prognostic value of 4 circulating microRNAs (miRNAs) after AMI.MethodsWe enrolled 150 patients after AMI. Blood samples were obtained at discharge for determination of N-terminal pro-brain natriuretic peptide (Nt-proBNP) and levels of miR-16, miR-27a, miR-101 and miR-150. Patients were assessed by echocardiography at 6 months follow-up and the wall motion index score (WMIS) was used as an indicator of left ventricular (LV) contractility. We assessed the added predictive value of miRNAs against a multi-parameter clinical model including Nt-proBNP.ResultsPatients with anterior AMI and elevated Nt-proBNP levels at discharge from the hospital were at high risk of subsequent impaired LV contractility (follow-up WMIS>1.2, n = 71). A combination of the 4 miRNAs (miR-16/27a/101/150) improved the prediction of LV contractility based on clinical variables (P = 0.005). Patients with low levels of miR-150 (odds ratio [95% confidence interval] 0.08 [0.01–0.48]) or miR-101 (0.19 [0.04–0.97]) and elevated levels of miR-16 (15.9 [2.63–95.91]) or miR-27a (4.18 [1.36–12.83]) were at high risk of impaired LV contractility. The 4 miRNA panel reclassified a significant proportion of patients with a net reclassification improvement of 66% (P = 0.00005) and an integrated discrimination improvement of 0.08 (P = 0.001).ConclusionOur results indicate that panels of miRNAs may aid in prognostication of outcome after AMI.

BackgroundWe aimed to investigate the diagnostic performance of S100 as an outcome predictor after out-of-hospital cardiac arrest (OHCA) and the potential influence of two target temperatures (33 °C and 36 °C) on serum levels of S100.MethodsThis is a substudy of the Target Temperature Management after Out-of-Hospital Cardiac Arrest (TTM) trial. Serum levels of S100 were measured a posteriori in a core laboratory in samples collected at 24, 48, and 72 h after OHCA. Outcome at 6 months was assessed using the Cerebral Performance Categories Scale (CPC 1–2 = good outcome, CPC 3–5 = poor outcome).ResultsWe included 687 patients from 29 sites in Europe. Median S100 values were higher in patients with a poor outcome at 24, 48, and 72 h: 0.19 (IQR 0.10–0.49) versus 0.08 (IQR 0.06–0.11) μg/ml, 0.16 (IQR 0.10–0.44) versus 0.07 (IQR 0.06–0.11) μg/L, and 0.13 (IQR 0.08–0.26) versus 0.06 (IQR 0.05–0.09) μg/L (p < 0.001), respectively. The ability to predict outcome was best at 24 h with an AUC of 0.80 (95% CI 0.77–0.83). S100 values were higher at 24 and 72 h in the 33 °C group than in the 36 °C group (0.12 [0.07–0.22] versus 0.10 [0.07–0.21] μg/L and 0.09 [0.06–0.17] versus 0.08 [0.05–0.10], respectively) (p < 0.02). In multivariable analyses including baseline variables and the allocated target temperature, the addition of S100 improved the AUC from 0.80 to 0.84 (95% CI 0.81–0.87) (p < 0.001), but S100 was not an independent outcome predictor. Adding S100 to the same model including neuron-specific enolase (NSE) did not further improve the AUC.ConclusionsThe allocated target temperature did not affect S100 to a clinically relevant degree. High S100 values are predictive of poor outcome but do not add value to present prognostication models with or without NSE. S100 measured at 24 h and afterward is of limited value in clinical outcome prediction after OHCA.Trial registrationClinicalTrials.gov identifier: NCT01020916. Registered on 25 November 2009.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-017-1729-7) contains supplementary material, which is available to authorized users.

clinicaltrials.gov Identifier: NCT01020916.

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For the development of coronavirus disease 2019 (COVID-19) drugs during the ongoing pandemic, speed is of essence whereas quality of evidence is of paramount importance. Although thousands of COVID-19 trials were rapidly started, many are unlikely to provide robust statistical evidence and meet regulatory standards (e.g., because of lack of randomization or insufficient power). This has led to an inefficient use of time and resources. With more coordination, the sheer number of patients in these trials might have generated convincing data for several investigational treatments. Collaborative platform trials, comparing several drugs to a shared control arm, are an attractive solution. Those trials can utilize a variety of adaptive design features in order to accelerate the finding of life-saving treatments. In this paper, we discuss several possible designs, illustrate them via simulations, and also discuss challenges, such as the heterogeneity of the target population, time-varying standard of care, and the potentially high number of false hypothesis rejections in phase II and phase III trials. We provide corresponding regulatory perspectives on approval and reimbursement, and note that the optimal design of a platform trial will differ with our societal objective and by stakeholder. Hasty approvals may delay the development of better alternatives, whereas searching relentlessly for the single most efficacious treatment may indirectly diminish the number of lives saved as time is lost. We point out the need for incentivizing developers to participate in collaborative evidence-generation initiatives when a positive return on investment is not met.