Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of “modifiable risk factors”, measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details.
Control of Confounding and Reporting of Results in Causal Inference Studies. Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals
The diaphragm is the main inspiratory muscle and is required to be highly active throughout the life span. The diaphragm muscle must be able to produce and sustain various behaviors that range from ventilatory to nonventilatory such as those required for airway maintenance and clearance. Throughout the life span various circumstances and conditions may affect the ability of the diaphragm muscle to generate requisite forces, and in turn the diaphragm muscle may undergo significant weakness and dysfunction. For example, hypoxic stress, critical illness, cancer cachexia, chronic obstructive pulmonary disorder, and age-related sarcopenia all represent conditions in which significant diaphragm muscle dysfunction exits. This perspective review article presents several interesting topics involving diaphragm plasticity in aging and disease that were presented at the International Union of Physiological Sciences Conference in 2017. This review seeks to maximize the broad and collective research impact on diaphragm muscle dysfunction in the search for transformative treatment approaches to improve the diaphragm muscle health during aging and disease.