Emicizumab prophylaxis in infants with hemophilia A (HAVEN 7): primary analysis of a phase 3b open-label trial.

ABSTRACT: Subcutaneous emicizumab enables prophylaxis for people with hemophilia A (HA) from birth, potentially reducing risk of bleeding and intracranial hemorrhage (ICH). HAVEN 7 (NCT04431726) is the first clinical trial of emicizumab dedicated to infants, designed to investigate the efficacy, safety, pharmacokinetics, and pharmacodynamics of emicizumab in those aged ≤12 months with severe HA without factor VIII (FVIII) inhibitors. Participants in this phase 3b trial received emicizumab 3 mg/kg maintenance dose every 2 weeks for 52 weeks and are continuing emicizumab during the 7-year long-term follow-up. Efficacy end points included annualized bleed rate (ABR): treated, all, treated spontaneous, and treated joint bleeds. Safety end points included adverse events (AEs), thromboembolic events (TEs), thrombotic microangiopathies (TMAs), and immunogenicity (anti-emicizumab antibodies [ADAs] and FVIII inhibitors). At primary analysis, 55 male participants had received emicizumab (median treatment duration: 100.3; range, 52-118 weeks). Median age at informed consent was 4.0 months (range, 9 days to 11 months 30 days). Model-based ABR for treated bleeds was 0.4 (95% confidence interval, 0.30-0.63), with 54.5% of participants (n = 30) having zero treated bleeds. No ICH occurred. All 42 treated bleeds in 25 participants (45.5%) were traumatic. Nine participants (16.4%) had ≥1 emicizumab-related AE (all grade 1 injection-site reactions). No AE led to treatment changes. No deaths, TEs, or TMAs occurred. No participant tested positive for ADAs. Two participants were confirmed positive for FVIII inhibitors. This primary analysis of HAVEN 7 indicates that emicizumab is efficacious and well tolerated in infants with severe HA without FVIII inhibitors.

Crovalimab treatment in patients with paroxysmal nocturnal haemoglobinuria: Long-term results from the phase I/II COMPOSER trial.

OBJECTIVES: This study reports long-term outcomes from the open-label extension (OLE) period of the Phase I/II COMPOSER trial (NCT03157635) that evaluated crovalimab in patients with paroxysmal nocturnal haemoglobinuria, who were treatment-naive or switched from eculizumab at enrolment. METHODS: COMPOSER consists of four sequential parts followed by the OLE. The primary OLE objective was to assess long-term crovalimab safety, with a secondary objective to assess crovalimab pharmacokinetics and pharmacodynamics. Exploratory efficacy endpoints included change in lactate dehydrogenase (LDH), transfusion avoidance, haemoglobin stabilisation and breakthrough haemolysis (BTH). RESULTS: A total 43 of 44 patients entered the OLE after completing the primary treatment period. Overall, 14 of 44 (32%) experienced treatment-related adverse events. Steady state exposure levels of crovalimab and terminal complement inhibition were maintained over the OLE. During the OLE, mean normalised LDH was generally maintained at ≤1.5× upper limit of normal, transfusion avoidance was achieved in 83%-92% of patients and haemoglobin stabilisation was reached in 79%-88% of patients across each 24-week interval. Five BTH events occurred with none leading to withdrawal. CONCLUSIONS: Over a 3-year median treatment duration, crovalimab was well tolerated and sustained C5 inhibition was achieved. Intravascular haemolysis control, haemoglobin stabilisation and transfusion avoidance were maintained, signifying long-term crovalimab efficacy.

Unbiased Recursive Partitioning Enables Robust and Reliable Outcome Prediction in Acute Spinal Cord Injury.

Neurological disorders usually present very heterogeneous recovery patterns. Nonetheless, accurate prediction of future clinical end-points and robust definition of homogeneous cohorts are necessary for scientific investigation and targeted care. For this, unbiased recursive partitioning with conditional inference trees (URP-CTREE) have received increasing attention in medical research, especially, but not limited to traumatic spinal cord injuries (SCIs). URP-CTREE was introduced to SCI as a clinical guidance tool to explore and define homogeneous outcome groups by clinical means, while providing high accuracy in predicting future clinical outcomes. The validity and predictive value of URP-CTREE to provide improvements compared with other more common approaches applied by clinicians has recently come under critical scrutiny. Therefore, a comprehensive simulation study based on traumatic, cervical complete spinal cord injuries provides a framework to investigate and quantify the issues raised. First, we assessed the replicability and robustness of URP-CTREE to identify homogeneous subgroups. Second, we implemented a prediction performance comparison of URP-CTREE with traditional statistical techniques, such as linear or logistic regression, and a novel machine learning method. URP-CTREE's ability to identify homogeneous subgroups proved to be replicable and robust. In terms of prediction, URP-CTREE yielded a high prognostic performance comparable to a machine learning algorithm. The simulation study provides strong evidence for the robustness of URP-CTREE, which is achieved without compromising prediction accuracy. The slightly lower prediction performance is offset by URP-CTREE's straightforward interpretation and application in clinical settings based on simple, data-driven decision rules.

Model-based random forests for ordinal regression.

We study and compare several variants of random forests tailored to prognostic models for ordinal outcomes. Models of the conditional odds function are employed to understand the various random forest flavours. Existing random forest variants for ordinal outcomes, such as Ordinal Forests and Conditional Inference Forests, are evaluated in the presence of a non-proportional odds impact of prognostic variables. We propose two novel random forest variants in the model-based transformation forest family, only one of which explicitly assumes proportional odds. These two novel transformation forests differ in the specification of the split procedures for the underlying ordinal trees. One of these split criteria is able to detect changes in non-proportional odds situations and the other one focuses on finding proportional-odds signals. We empirically evaluate the performance of the existing and proposed methods using a simulation study and illustrate the practical aspects of the procedures by a re-analysis of the respiratory sub-item in functional rating scales of patients suffering from Amyotrophic Lateral Sclerosis (ALS).

Correction to: Baseline-adjusted proportional odds models for the quantification of treatment effects in trials with ordinal sum score outcomes.

An amendment to this paper has been published and can be accessed via the original article.

Baseline-adjusted proportional odds models for the quantification of treatment effects in trials with ordinal sum score outcomes.

BACKGROUND: Sum scores of ordinal outcomes are common in randomized clinical trials. The approaches routinely employed for assessing treatment effects, such as t-tests or Wilcoxon tests, are not particularly powerful in detecting changes in relevant parameters or lack the ability to incorporate baseline information. Hence, tailored statistical methods are needed for the analysis of ordinal outcomes in clinical research. METHODS: We propose baseline-adjusted proportional odds logistic regression models to overcome previous limitations in the analysis of ordinal outcomes in randomized clinical trials. For the validation of our method, we focus on common ordinal sum score outcomes of neurological clinical trials such as the upper extremity motor score, the spinal cord independence measure, and the self-care subscore of the latter. We compare the statistical power of our models to other conventional approaches in a large simulation study of two-arm randomized clinical trials based on data from the European Multicenter Study about Spinal Cord Injury (EMSCI, ClinicalTrials.gov Identifier: NCT01571531). We also use the new method as an alternative analysis of the historical Sygen®clinical trial. RESULTS: The simulation study of all postulated trial settings demonstrated that the statistical power of the novel method was greater than that of conventional methods. Baseline adjustments were more suited for the analysis of the upper extremity motor score compared to the spinal cord independence measure and its self-care subscore. CONCLUSIONS: The proposed baseline-adjusted proportional odds models allow the global treatment effect to be directly interpreted. This clear interpretation, the superior statistical power compared to the conventional analysis approaches, and the availability of open-source software support the application of this novel method for the analysis of ordinal outcomes of future clinical trials.