Key result
The REPINF risk model showed better discrimination for 30-day deep sternal wound infection after CABG than the STS PredDeep model, with a c-index of 0.83 vs 0.72 in external validation.
Why the study?
Existing clinical prediction models for deep sternal wound infections after CABG surgery have shown poor impact in external validation studies.
Does the REPINF model improve the prediction of 30-day deep sternal wound infection after CABG compared to the STS PredDeep model in adults undergoing isolated CABG?
Cohort (n=4,583)
Yes
Does the REPINF model improve the prediction of 30-day deep sternal wound infection after CABG compared to the STS PredDeep model in adults undergoing isolated CABG?
Effect estimate: c-index 0.83 (95% CI 0.72-0.95)
Absolute Event Rate: 0.83% vs 0.72%
The novel REPINF model demonstrated superior discrimination compared to the STS PredDeep model for predicting 30-day deep sternal wound infection after CABG in a Brazilian cohort, highlighting the need for population-specific risk scores.
Supports tailored DSWI risk prediction in Brazilian CABG patients; leaves open generalizability and clinical utility pending broader validation.
Clinical prediction models for deep sternal wound infections (DSWI) after coronary artery bypass graft (CABG) surgery exist, although they have a poor impact in external validation studies. We developed and validated a new predictive model for 30-day DSWI after CABG (REPINF) and compared it with the Society of Thoracic Surgeons model (STS). The REPINF model was created through a multicenter cohort of adults undergoing CABG surgery (REPLICCAR II Study) database, using least absolute shrinkage and selection operator (LASSO) logistic regression, internally and externally validated comparing discrimination, calibration in-the-large (CL), net reclassification improvement (NRI) and integrated discrimination improvement (IDI), trained between the new model and the STS PredDeep, a validated model for DSWI after cardiac surgery. In the validation data, c-index = 0.83 (95% CI 0.72–0.95). Compared to the STS PredDeep, predictions improved by 6.5% (IDI). However, both STS and REPINF had limited calibration. Different populations require independent scoring systems to achieve the best predictive effect. As the STS, the REPINF external validation across multiple centers it’s important to guide healthcare professionals as a quality improvement tool in the prevention of DSWI after CABG surgery.
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Orlandi et al. (2021) conducted a cohort in Deep sternal wound infection after Coronary Artery Bypass Grafting (n=4,583). REPINF risk model vs. STS PredDeep model was evaluated on Discrimination (c-index) for 30-day deep sternal wound infection in external validation (c-index 0.83, 95% CI 0.72-0.95). The REPINF risk model showed better discrimination for 30-day deep sternal wound infection after CABG than the STS PredDeep model, with a c-index of 0.83 vs 0.72 in external validation.
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