Prediction models for postoperative pulmonary complications: a systematic review and meta-analysis

Postoperative pulmonary complications (PPCs) increase mortality, hospital stays, and healthcare costs. Multivariable prediction models can guide patient care by identifying high-risk patients. The discriminative ability and potential for clinical impact of PPC prediction models remains unclear.

We systematically searched Cochrane, Embase, and PubMed (up to June 2024) for studies developing or validating prediction models for PPCs that reported c-statistic. The primary outcome was the c-statistic of prediction models for composite PPCs, and the secondary outcome was the c-statistic for individual PPCs, including pneumonia, respiratory failure, reintubation, and others. Data were extracted using the CHARMS checklist, and bias was assessed with PROBAST. For models with data from three or more cohorts, discrimination was synthesised by pooling c-statistic using Bayesian meta-analysis, with heterogeneity assessed through prediction intervals.

A total of 123 studies were included, covering 116 prediction models for PPCs, with 14 models (1 004 029 patients) eligible for meta-analysis. The c-statistic of all models ranged from 0.614 to 0.996 (median 0.80), with 50% of models self-reporting good (c-statistic >0.8) discrimination. In meta-analysis, the ARISCAT PPC score (summary c-statistic 0.76, 95% CI 0.67-0.86), Xue's model (0.82, 0.75-0.89), and CARDOT score (0.73, 0.61-0.85) demonstrated moderate (c-statistic 0.7-0.8) to good discrimination for composite PPCs; the DAGDA score (0.81, 0.74-0.88), Wang's model (0.78, 0.70-0.86), and Jin's model (0.75, 0.68-0.82) for postoperative pneumonia; and Yoon's model (0.90, 0.84-0.96) and Nizamuddin's model (0.85, 0.78-0.92) for postoperative respiratory failure. The reliability of these models, however, is currently limited by the lack of external validation cohorts. Overall, 90.2% of models were assessed as having a high risk of bias.

Many prediction models postoperative pulmonary complications have been developed, but the clinical utility of the vast majority remains uncertain.

PROSPERO database (CRD42024580216).