Dynamic heterogeneity in COVID-19: Insights from a mathematical model.

Critical illness, such as severe COVID-19, is heterogenous in presentation and treatment response. However, it remains possible that clinical course may be influenced by dynamic and/or random events such that similar patients subject to similar injuries may yet follow different trajectories. We deployed a mechanistic mathematical model of COVID-19 to determine the range of possible clinical courses after SARS-CoV-2 infection, which may follow from specific changes in viral properties, immune properties, treatment modality and random external factors such as initial viral load. We find that treatment efficacy and baseline patient or viral features are not the sole determinant of outcome. We found patients with enhanced innate or adaptive immune responses can experience poor viral control, resolution of infection or non-infectious inflammatory injury depending on treatment efficacy and initial viral load. Hypoxemia may result from poor viral control or ongoing inflammation despite effective viral control. Adaptive immune responses may be inhibited by very early effective therapy, resulting in viral load rebound after cessation of therapy. Our model suggests individual disease course may be influenced by the interaction between external and patient-intrinsic factors. These data have implications for the reproducibility of clinical trial cohorts and timing of optimal treatment.

In silico clinical studies for optimal COVID-19 vaccination schedules in patients with cancer.

This study introduces a tailored COVID-19 model for patients with cancer, incorporating viral variants and immune-response dynamics. The model aims to optimize vaccination strategies, contributing to personalized healthcare for vulnerable groups.

How Treatment Effect Heterogeneity Works.

How Treatment Effect Heterogeneity WorksThis Stats, STAT! animated video explores the concept of treatment effect heterogeneity. Differences in the effectiveness of treatments across participants in a clinical trial is important to understand when deciding how to apply clinical trial results to clinical practice.

How Censoring Works.

How Censoring WorksA common challenge in clinical research is determining the time to occurrence of a given event. This animated video explores the concept of censoring in survival analysis and how investigators deal with ambiguity in the time of an event's occurrence.

Good Intentions to Treat.

Good Intentions to Treat This Stats, STAT! animated video explores common approaches to analyzing data from randomized controlled trials, including intention-to-treat, per-protocol, and as-treated analyses.

How Statistical Power Works.

How Statistical Power WorksThis Stats, STAT! animated video explores the concept of statistical power and explains how clinical investigators determine how many participants to enroll in a randomized trial.

Bayesian Way.

Bayesian WayThis animated video explores two possible approaches to analyzing data in a randomized controlled trial: "Frequentist" versus "Bayesian."

Large Language Models.

Large Language ModelsIn the latest edition of Stats, STAT!, Fralick and colleagues explain the statistics behind large language models - used in chat bots like ChatGPT and Bard. While these new tools may seem remarkably intelligent, at their core they just assemble sentences based on statistics from large amounts of text.

NEJM Evidence - A New Journal in the NEJM Group Family.

NEJM Evidence - A New Journal in the NEJM Group Family In January 2022, the NEJM Group will be publishing a new journal, NEJM Evidence. This monthly, peer-reviewed, online-only, general medical journal will publish original research, along the full spectrum of clinical investigation, that takes ideas and turns them into reality.

Fossil-Fuel Pollution and Climate Change - A New NEJM Group Series.

Fossil-Fuel Pollution and Climate ChangeThe editors announce a new NEJM Group series on climate change and the increasingly urgent health and care delivery challenges we face. Articles will appear in the New England Journal of Medicine, in NEJM Evidence, and in NEJM Catalyst Innovations in Care Delivery.

Interface - A New Series from NEJM Evidence.

Interface - A New Series from NEJM Evidence A major motivation for the launch of NEJM Evidence was a belief that understanding the nuances of study design and execution is key to assessing how the results of a study can, or cannot, influence clinical practice. A corollary is that maximizing clinical utility should be the major focus of study design. It is now widely appreciated that these goals are not optimally achieved solely by relying on binary interpretation of P values in traditional randomized controlled trials.1.