TY - JOUR
T1 - Performance of a novel risk model for deep sternal wound infection after coronary artery bypass grafting
AU - Orlandi, Bianca Maria Maglia
AU - Mejia, Omar Asdrúbal Vilca
AU - Sorio, Jennifer Loría
AU - de Barros e Silva, Pedro
AU - Oliveira, Marco Antonio Praça
AU - Nakazone, Marcelo Arruda
AU - Tiveron, Marcos Gradim
AU - Campagnucci, Valquíria Pelliser
AU - Lisboa, Luiz Augusto Ferreira
AU - Zubelli, Jorge
AU - Normand, Sharon Lise
AU - Jatene, Fabio Biscegli
N1 - Funding Information:
To the REPLICCAR Study Group: We thank the Harvard Medical School coordinating team (Haley Abing); InCor/HCFMUSP (Evelinda Trindade, MD, and grant students: Daniella de L. Pes, Gabrielle Barbosa Borgomoni and Débora Maziero). To partner hospitals and their collaborators: Hospital Samaritano Paulista (Valter Furlan, MD; Nilza Lastra, RN and Mariana Okada); Hospital de Base de São José de Rio Preto (Mariana Pastor, MD); Beneficência Portuguesa (Flavia Cortez, MD, Gilmara Silveira da Silva, MD); Hospital Albert Einstein; Santa Casa de Marília; Santa Casa de São Paulo (Gabriel Mitsumoto, MD). To the Commission and Healthcare-Related Infection Control (Tania MV Strabelli, MD). To the Ministry of Health, FAPESP and PPSUS.
Funding Information:
This study was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP).
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85137495924&partnerID=8YFLogxK
U2 - 10.1038/s41598-022-19473-1
DO - 10.1038/s41598-022-19473-1
M3 - Article
C2 - 36071086
AN - SCOPUS:85137495924
SN - 2045-2322
VL - 12
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 15177
ER -