Development and validation of a gonadotropin dose selection model for optimized ovarian stimulation in IVF/ICSI: an individual participant data meta-analysis.

Background: The ovarian response to gonadotropin stimulation varies widely among women, and could impact the probability of live birth as well as treatment risks. Many studies have evaluated the impact of different gonadotropin starting doses, mainly based on predictive variables like ovarian reserve tests (ORT) including anti-Müllerian hormone (AMH), antral follicle count (AFC), and basal follicle-stimulating hormone (bFSH). A Cochrane systematic review revealed that individualizing the gonadotropin starting dose does not affect efficacy in terms of ongoing pregnancy/live birth rates, but may reduce treatment risks such as the development of ovarian hyperstimulation syndrome (OHSS).

Did aetiology matter in illness duration and complications in patients presenting in primary care with acute respiratory tract infections early in the COVID-19 pandemic: An observational study in nine countries.

Background: Despite considerable research into COVID-19 sequelae, little is known about differences in illness duration and complications in patients presenting in primary care with symptoms of acute respiratory tract infections (RTI) that are and are not attributed to SARS-CoV-2 infection.

Objective: To explore whether aetiology impacted course of illness and prediction of complications in patients presenting in primary care with symptoms of RTI early in the COVID-19 pandemic.

Methods: Between April 2020-March 2021 general practitioners from nine European countries recruited consecutively contacting patients with RTI symptoms.

Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart.

Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries.

Assessment of risk of ALS conferred by the GGGGCC hexanucleotide repeat expansion in among first-degree relatives of patients with ALS carrying the repeat expansion.

Objectives: We aimed to estimate the age-related risk of ALS in first-degree relatives of patients with ALS carrying the repeat expansion.

Methods: We included all patients with ALS carrying a repeat expansion in The Netherlands. Using structured questionnaires, we determined the number of first-degree relatives, their age at death due to ALS or another cause, or age at time of questionnaire.

Identifying multivariate disease trajectories and potential phenotypes of early knee osteoarthritis in the CHECK cohort.

Objective: To gain better understanding of osteoarthritis (OA) heterogeneity and its predictors for distinguishing OA phenotypes. This could provide the opportunity to tailor prevention and treatment strategies and thus improve care.

Design: Ten year follow-up data from CHECK (1002 early-OA subjects with first general practitioner visit for complaints ≤6 months before inclusion) was used.

Endometrial scratching in women undergoing IVF/ICSI: an individual participant data meta-analysis.

Background: In IVF/ICSI treatment, the process of embryo implantation is the success rate-limiting step. Endometrial scratching has been suggested to improve this process, but it is unclear if this procedure increases the chance of implantation and live birth (LB) and, if so, for whom, and how the scratch should be performed.

Objective And Rationale: This individual participant data meta-analysis (IPD-MA) aims to answer the question of whether endometrial scratching in women undergoing IVF/ICSI influences the chance of a LB, and whether this effect is different in specific subgroups of women.

Sex differences in the generalizability of randomized clinical trials in heart failure with reduced ejection fraction.

Aims: In order to understand how sex differences impact the generalizability of randomized clinical trials (RCTs) in patients with heart failure (HF) and reduced ejection fraction (HFrEF), we sought to compare clinical characteristics and clinical outcomes between RCTs and HF observational registries stratified by sex.

Methods And Results: Data from two HF registries and five HFrEF RCTs were used to create three subpopulations: one RCT population (n = 16 917; 21.7% females), registry patients eligible for RCT inclusion (n = 26 104; 31.

Development and Evaluation of a Simulation-Based Algorithm to Optimize the Planning of Interim Analyses for Clinical Trials in ALS.

Background And Objectives: Late-phase clinical trials for neurodegenerative diseases have a low probability of success. In this study, we introduce an algorithm that optimizes the planning of interim analyses for clinical trials in amyotrophic lateral sclerosis (ALS) to better use the time and resources available and minimize the exposure of patients to ineffective or harmful drugs.

Methods: A simulation-based algorithm was developed to determine the optimal interim analysis scheme by integrating prior knowledge about the success rate of ALS clinical trials with drug-specific information obtained in early-phase studies.

Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation.

Background: The predictive capability of time-lapse monitoring (TLM) selection algorithms is influenced by patient characteristics, type and quality of data included in the analysis and the used statistical methods. Previous studies excluded DET cycles of which only one embryo implanted, introducing bias into the data. Therefore, we wanted to develop a TLM prediction model that is able to predict pregnancy chances after both single- and double embryo transfer (SET and DET).

Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care.

Predicting pregnancy chances leading to term live birth in oligo/anovulatory women diagnosed with PCOS.

Research Question: Which patient features predict the time to pregnancy (TTP) leading to term live birth in infertile women diagnosed with polycystic ovary syndrome (PCOS)?

Design: Prospective cohort follow-up study was completed, in which initial standardized phenotyping was conducted at two Dutch university medical centres from January 2004 to January 2014. Data were linked to the Netherlands Perinatal Registry to obtain pregnancy outcomes for each participant. All women underwent treatment according to a standardized protocol, starting with ovulation induction as first-line treatment.

Long-term effectiveness of dual CFTR modulator treatment of cystic fibrosis.

Background: Although short-term efficacy of lumacaftor/ivacaftor and tezacaftor/ivacaftor is clearly established in clinical trials, data on long-term effectiveness is limited. This registry-based cohort study assessed real-world longitudinal outcomes of F508del-homozygous people with cystic fibrosis (pwCF) ≥12 years, up to 3 years after the introduction of dual cystic fibrosis transmembrane conductance regulator (CFTR) modulators.

Methods: Annual data (2010-2019) were retrieved from the Dutch Cystic Fibrosis Registry.

Blood pressure and the risk of rebleeding and delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage.

Introduction And Objective: Blood pressure is presumably related to rebleeding and delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (aSAH) and could serve as a target to improve outcome. We assessed the associations between blood pressure and rebleeding or DCI in aSAH-patients.

Materials And Methods: In this observational study in 1167 aSAH-patients admitted to the intensive care unit (ICU), adjusted hazard ratio's (aHR) were calculated for the time-dependent association of blood pressure and rebleeding or DCI.

Individual treatment effect estimation in the presence of unobserved confounding using proxies: a cohort study in stage III non-small cell lung cancer.

Randomized Controlled Trials (RCT) are the gold standard for estimating treatment effects but some important situations in cancer care require treatment effect estimates from observational data. We developed "Proxy based individual treatment effect modeling in cancer" (PROTECT) to estimate treatment effects from observational data when there are unobserved confounders, but proxy measurements of these confounders exist. We identified an unobserved confounder in observational cancer research: overall fitness.

Forskolin-induced organoid swelling is associated with long-term cystic fibrosis disease progression.

Rationale: Cystic fibrosis (CF) is a monogenic life-shortening disease associated with highly variable individual disease progression which is difficult to predict. Here we assessed the association of forskolin-induced swelling (FIS) of patient-derived organoids with long-term CF disease progression in multiple organs and compared FIS with the golden standard biomarker sweat chloride concentration (SCC).

Methods: We retrieved 9-year longitudinal clinical data from the Dutch CF Registry of 173 people with mutations in the cystic fibrosis transmembrane conductance regulator () gene.

Comparison of statistical methods for the analysis of recurrent adverse events in the presence of non-proportional hazards and unobserved heterogeneity: a simulation study.

Background: In preventive drug trials such as intermittent preventive treatment for malaria prevention during pregnancy (IPTp), where there is repeated treatment administration, recurrence of adverse events (AEs) is expected. Challenges in modelling the risk of the AEs include accounting for time-to-AE and within-patient-correlation, beyond the conventional methods. The correlation comes from two sources; (a) individual patient unobserved heterogeneity (i.

Generalizability of randomized controlled trials in heart failure with reduced ejection fraction.

Background: Heart failure (HF) trials have stringent inclusion and exclusion criteria, but limited data exist regarding generalizability of trials. We compared patient characteristics and outcomes between patients with HF and reduced ejection fraction (HFrEF) in trials and observational registries.

Methods And Results: Individual patient data for 16 922 patients from five randomized clinical trials and 46 914 patients from two HF registries were included.

Prediction of Behavioral Improvement Through Resting-State Electroencephalography and Clinical Severity in a Randomized Controlled Trial Testing Bumetanide in Autism Spectrum Disorder.

Background: Mechanism-based treatments such as bumetanide are being repurposed for autism spectrum disorder. We recently reported beneficial effects on repetitive behavioral symptoms that might be related to regulating excitation-inhibition (E/I) balance in the brain. Here, we tested the neurophysiological effects of bumetanide and the relationship to clinical outcome variability and investigated the potential for machine learning-based predictions of meaningful clinical improvement.

Innovating Clinical Trials for Amyotrophic Lateral Sclerosis: Challenging the Established Order.

Development of effective treatments for amyotrophic lateral sclerosis (ALS) has been hampered by disease heterogeneity, a limited understanding of underlying pathophysiology, and methodologic design challenges. We have evaluated 2 major themes in the design of pivotal, phase 3 clinical trials for ALS-(1) patient selection and (2) analytical strategy-and discussed potential solutions with the European Medicines Agency. Several design considerations were assessed using data from 5 placebo-controlled clinical trials (n = 988), 4 population-based cohorts (n = 5,100), and 2,436 placebo-allocated patients from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database.

Developing more generalizable prediction models from pooled studies and large clustered data sets.

Prediction models often yield inaccurate predictions for new individuals. Large data sets from pooled studies or electronic healthcare records may alleviate this with an increased sample size and variability in sample characteristics. However, existing strategies for prediction model development generally do not account for heterogeneity in predictor-outcome associations between different settings and populations.

An old friend who has overstayed their welcome: the ALSFRS-R total score as primary endpoint for ALS clinical trials.

The ALSFRS-R is limited by multidimensionality, which originates from the summation of various subscales. This prevents a direct comparison between patients with identical total scores. We aim to evaluate how multidimensionality affects the performance of the ALSFRS-R in clinical trials.

Developing excellence in biostatistics leadership, training and science in Africa: How the Sub-Saharan Africa Consortium for Advanced Biostatistics (SSACAB) training unites expertise to deliver excellence.

The increase in health research in sub-Saharan Africa (SSA) has led to a high demand for biostatisticians to develop study designs, contribute and apply statistical methods in data analyses. Initiatives exist to address the dearth in statistical capacity and lack of local biostatisticians in SSA health projects. The Sub-Saharan African Consortium for Advanced Biostatistics (SSACAB) led by African institutions was initiated to improve biostatistical capacity according to the needs identified by African institutions, through collaborative masters and doctoral training in biostatistics.

Effect of disease related biases on the subjective assessment of social functioning in Alzheimer's disease and schizophrenia patients.

Background: Questionnaires are the current hallmark for quantifying social functioning in human clinical research. In this study, we compared self- and proxy-rated (caregiver and researcher) assessments of social functioning in Schizophrenia (SZ) and Alzheimer's disease (AD) patients and evaluated if the discrepancy between the two assessments is mediated by disease-related factors such as symptom severity.

Methods: We selected five items from the WHO Disability Assessment Schedule 2.

A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data.

The use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated preprocessing framework for deriving behavioral phenotypes from smartphone-based location data is currently lacking. Here, we present a preprocessing framework consisting of methods that are validated in the context of geospatial data.