Predicting weaning failure from invasive mechanical ventilation: The promise and pitfalls of clinical prediction scores.

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.