Development of a core predictor set of weaning in critically ill patients: a Delphi-based study protocol

Prolonged mechanical ventilation in intensive care units (ICUs) leads to increased morbidity, higher mortality rates, and elevated healthcare costs. Predicting successful weaning from mechanical ventilation with accuracy is essential for optimizing resource use and improving patient outcomes. The International Classification of Functioning, Disability and Health (ICF) framework offers a holistic perspective on health conditions and can be adapted to identify key predictors of weaning readiness. This study aims to develop a Delphi-based core predictor set for weaning in critically ill patients, utilizing the ICF model.

The core predictor set development comprises three steps: (1) Literature review and expert consultation to gather weaning predictors, (2) Predictor alignment with ICF categories per established rules, and (3) Three-round Delphi survey with a multidisciplinary team. A systematic review across major databases will be conducted to identify predictors related to weaning predictors in critically ill adults from cohort studies, trials, and reviews. Predictors will then be categorized within ICF domains. A multidisciplinary expert panel will evaluate the relevance of each predictor using a 9-point Likert scale to achieve consensus.

This study will contribute to the development of a standardized, evidence-based predictor set for weaning readiness in critically ill patients. Using the ICF framework, this study aims to encompass the complex factors that influence weaning, thereby enabling personalized care plans and improving weaning outcomes. The Delphi methodology guarantees a thorough, iterative process for building consensus by integrating diverse clinical perspectives.

The proposed Delphi-based study protocol aims to establish a core set of predictors for weaning in the ICU setting, guided by the ICF model. Successful implementation of this predictor set could enhance decision-making around weaning trials, reduce unnecessary ventilation days, and ultimately improve patient outcomes and healthcare efficiency. Future validation and implementation studies will be essential to confirm the utility and generalizability of this predictor set in clinical practice.