Performance of a novel risk model for deep sternal wound infection after coronary artery bypass grafting

Bianca Maria Maglia Orlandi, Omar Asdrúbal Vilca Mejia, Jennifer Loría Sorio, Pedro de Barros e Silva, Marco Antonio Praça Oliveira, Marcelo Arruda Nakazone, Marcos Gradim Tiveron, Valquíria Pelliser Campagnucci, Luiz Augusto Ferreira Lisboa, Jorge Zubelli, Sharon Lise Normand, Fabio Biscegli Jatene

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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. The external validation of REPINF across multiple centers is an important quality improvement tool to generalize the model and to guide healthcare professionals in the prevention of DSWI after CABG surgery.

Original languageBritish English
Article number15177
JournalScientific Reports
Issue number1
StatePublished - Dec 2022


Dive into the research topics of 'Performance of a novel risk model for deep sternal wound infection after coronary artery bypass grafting'. Together they form a unique fingerprint.

Cite this