Comparative Evaluation of Risk Scores for Predicting Postoperative Pulmonary Complications

Postoperative pulmonary complications (PPCs) are a major cause of morbidity and mortality in surgical patients, particularly among the critically ill. Several risk prediction models have been developed to stratify the risk of PPCs. However, comparative evidence on their performance in critically ill populations remains scarce.

In the present retrospective cohort study, 495 critically ill surgical subjects who were admitted to a tertiary hospital ICU in China were assessed for PPCs using 3 established risk models: Local Assessment of Ventilatory Management During General Anesthesia for Surgery (LAS VEGAS), Assess Respiratory risk in Surgical Patients in Catalonia (ARISCAT), and Chinese Brief Predictive Risk Index (CHI-BPRI). Inclusion required intra-operative mechanical ventilation and postoperative ICU care. Then, predictive performance was evaluated by receiver operating characteristic analysis, with discrimination quantified using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and odds ratio.

In the cohort, 20.4% developed PPCs. The LAS VEGAS score had the highest AUC (0.63, 95% CI 0.57-0.68), with 74% sensitivity and 47% specificity. However, the ARISCAT and CHI-BPRI scores had lower AUCs of 0.60 and 0.55, respectively. Despite the numerical differences, none of the scores achieved statistically superior discrimination (P = .44, .058, .16), and all AUCs fell below 0.70, indicating suboptimal predictive accuracy. The LAS VEGAS score had the strongest association with PPCs (OR 2.26, 95% CI 1.42-3.60).

In critically ill Chinese surgical subjects, the LAS VEGAS, ARISCAT, and CHI-BPRI scores had limited predictive performance for PPCs, and none of the scores achieved strong discriminative power. Among these, the LAS VEGAS score performed best and may offer modest utility for early risk identification. These findings underscore the need for improved, population-specific prediction tools tailored to the unique risk profiles of critically ill surgical patients.