Risk prediction models for prolonged mechanical ventilation following coronary artery bypass grafting surgery: a systematic review and meta-analysis.

Abstract

Objective: Prolonged mechanical ventilation (PMV) results in significant morbidity, mortality, and associated hospital costs. Models predicting PMV following Coronary artery bypass grafting (CABG) surgery were growing. However, the reliability, validity and clinical applicability of these models remain unclear. This systematic review and meta-analysis aim to provide a comprehensive quality assessment of PMV-risk prediction models for patients after CABG.

Methods: Nine relevant domestic and international databases were systematically searched from inception until November 4, 2024 using PICOTS format. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate the risk of bias and applicability of each study. A meta-analysis of the area under the curve (AUC) values from model external validations was conducted using R software.

Results: Fifteen studies detailing 12 PMV-risk prediction models were included, with AUC values ranging from 0.561 to 0. 875. In the meta-analysis, the pooled AUC was 0.696 (95% CI: 0.553, 0.839, I-squared = 90.4%) for externally validated studies of three Society of Thoracic Surgeons (STS) models. The most frequently used predictors in the models were grouped into demographics, medical history, examination, and supportive therapy.

Conclusions: Although studies were judged as high overall risk of bias according to PROBAST guidelines evidence from our review indicates that risk factors of PMV in Post CABG Patients include age, BMI, history of cardiac surgery, history of cardiovascular disease, COPD, EF/LVEF, IABP, and cardiopulmonary bypass.

Systematic review registration: PROSPERO CRD42024608639.