Early Warning and Stratified Diagnosis of Postoperative Respiratory Failure Based on Ventilator Waveform Image Features

I. Study Background Postoperative respiratory failure (PRF) is a common and serious complication following major surgery, significantly increasing the rates of ICU admission and mortality. Traditional early warning methods primarily rely on blood gas analysis and vital sign monitoring, which are often delayed and may fail to identify early pathological changes in a timely manner. In recent years, ventilator waveforms, as dynamic information that directly reflects respiratory mechanics and airway conditions, have gradually attracted increasing attention in the optimization of respiratory support. International studies have suggested that analysis of ventilator waveform features may help identify patient-ventilator asynchrony, excessive spontaneous respiratory effort, ventilation-perfusion mismatch, and ventilator-related complications. In China, although a limited number of studies have explored this area, most have focused on individual parameters and lack systematic and prospective clinical validation. Therefore, this study aims to establish a large prospective cohort and integrate image feature extraction with stratified diagnostic modeling to achieve early warning and risk stratification of postoperative respiratory failure. This study is expected not only to address the current gap in this field in China but also to provide evidence-based support for precision respiratory management and improved clinical outcomes.