Predictors of prolonged mechanical ventilation weaning in critically ill patients: a review

INTRODUCTION: The increasing number of patients requiring prolonged mechanical ventilation (MV) is associated with a higher risk of complications, including ventilator-associated pneumonia and diaphragm dysfunction. Timely assessment of a patient's readiness for weaning from respiratory support helps minimize the risks associated with prolonged MV; however, existing predictive criteria require comprehensive evaluation due to their variable diagnostic significance. OBJECTIVE: Analysis of the clinical efficacy of predictive indicators for discontinuing MV. MATERIALS AND METHODS: A literature search was performed in PubMed and eLibrary databases in Russian and English. Inclusion criteria: randomized controlled trials, meta-analyses, clinical guidelines; studies with MV duration of less than 24 hours were excluded. RESULTS: The analysis showed that the Rapid Shallow Breathing Index (RSBI) remains the most extensively studied predictor of successful weaning from MV. However, its predictive value varies significantly depending on the etiology of respiratory failure and the methodology of the spontaneous breathing trial. Parameters such as P0.1 (airway occlusion pressure) and NIF (negative inspiratory force), which reflect respiratory effort, demonstrated a strong correlation with successful weaning but require careful interpretation considering the underlying disease and current ventilator settings. Promising approaches include ultrasound assessment of diaphragm function and metabolic marker analysis. Clinical studies confirm the importance of a comprehensive patient evaluation, including fluid balance and echocardiography data, to predict potential hemodynamic complications. Special attention should be paid to the cough reflex, which serves as a significant additional criterion for weaning readiness. CONCLUSIONS: Optimizing the weaning process from MV requires a comprehensive assessment of respiratory, hemodynamic, and metabolic parameters. The development of standardized algorithms with a personalized approach is a priority for future research.