Gaddam M, Gullapalli D, Adrish ZA, Reddy AY, Adrish M. Predicting weaning failure from invasive mechanical ventilation: The promise and pitfalls of clinical prediction scores. World J Crit Care Med 2025; 14(3): 108272 [DOI: 10.5492/wjccm.v14.i3.108272]
Corresponding Author of This Article
Muhammad Adrish, Associate Professor, Section of Pulmonary and Critical Care Medicine, Ben Taub Hospital/Baylor College of Medicine, 1504 Taub Loop, Houston, TX 77030, United States. aadrish@gmail.com
Research Domain of This Article
Critical Care Medicine
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Crit Care Med. Sep 9, 2025; 14(3): 108272 Published online Sep 9, 2025. doi: 10.5492/wjccm.v14.i3.108272
Predicting weaning failure from invasive mechanical ventilation: The promise and pitfalls of clinical prediction scores
Maneesh Gaddam, Dedeepya Gullapalli, Zayaan A Adrish, Arnav Y Reddy, Muhammad Adrish
Maneesh Gaddam, Department of Pulmonary, Critical Care and Sleep Medicine, Appalachian Regional Healthcare, Hazard, KY 41701, United States
Dedeepya Gullapalli, Department of Internal Medicine, Appalachian Regional Healthcare, Harlan, KY 40831, United States
Zayaan A Adrish, Arnav Y Reddy, Lawrence E Elkins High School, Missouri City, TX 77479, United States
Muhammad Adrish, Section of Pulmonary and Critical Care Medicine, Ben Taub Hospital/Baylor College of Medicine, Houston, TX 77030, United States
Author contributions: Gaddam M and Adrish M contributed to the study’s conceptualization and methodology; All co-authors contributed to data acquisition; The original draft was prepared by Gaddam M, Gullapalli D, Adrish ZA and Reddy AY, and additional changes were made by Adrish M; Adrish M has supervised and final edited the manuscript; All co-authors provided intellectual contributions and made critical revisions to this paper; All authors approved the final version of the manuscript.
Conflict-of-interest statement: Authors report no financial conflicts relevant to this paper.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Muhammad Adrish, Associate Professor, Section of Pulmonary and Critical Care Medicine, Ben Taub Hospital/Baylor College of Medicine, 1504 Taub Loop, Houston, TX 77030, United States. aadrish@gmail.com
Received: April 9, 2025 Revised: April 29, 2025 Accepted: June 3, 2025 Published online: September 9, 2025 Processing time: 100 Days and 9.9 Hours
Abstract
Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice. Several prediction scores have been developed to guide success during spontaneous breathing trials to help with weaning decisions. These scores aim to provide a structured framework to support clinical judgment. However, their effectiveness varies across patient populations, and their predictive accuracy remains inconsistent. In this review, we aim to identify the strengths and limitations of commonly used clinical prediction tools in assessing readiness for ventilator liberation. While scores such as the Rapid Shallow Breathing Index and the Integrative Weaning Index are widely adopted, their sensitivity and specificity often fall short in complex clinical settings. Factors such as underlying disease pathophysiology, patient characteristics, and clinician subjectivity impact score performance and reliability. Moreover, disparities in validation across diverse populations limit generalizability. With growing interest in artificial intelligence (AI) and machine learning, there is potential for enhanced prediction models that integrate multidimensional data and adapt to individual patient profiles. However, current AI approaches face challenges related to interpretability, bias, and ethical implementation. This paper underscores the need for more robust, individualized, and transparent prediction systems and advocates for careful integration of emerging technologies into clinical workflows to optimize weaning success and patient outcomes.
Core Tip: This paper aims to review the roles and limitations of clinical prediction scores in guiding ventilator weaning decisions. It highlights the variability in score performance, the need for population-specific validation, and explores the emerging potential of artificial intelligence-driven models to enhance accuracy, personalization, and safety in invasive mechanical ventilation liberation.
