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

ABSTRACT:

Background

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.

Methods

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 m

SUBMITTER: Huang Z 

PROVIDER: S-EPMC12597335 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature