A LASSO-derived model for the prediction of coagulation disorders after coronary artery bypass grafting.

BACKGROUND

Postoperative coagulation dysfunction is one of the common complications after coronary artery bypass grafting (CABG), especially in elderly patients. The aim of this study was to establish a risk prediction model for coagulation disorders in elderly patients after CABG, effectively identify high-risk patients who are prone to coagulation disorders, and strengthen postoperative treatment monitoring for these patients.

METHODS

Patients who underwent CABG were retrospectively included between February 2019 and December 2020, and were randomly divided into a derivation set and a validation set at a ratio of 7:3. The disseminated intravascular coagulation (DIC) score of ≥2 was defined as coagulation disorder. The least absolute shrinkage and selection operator (LASSO) regression was used for variable selection and the establishment of a regression model. The confusion matrix and receiver operating characteristic (ROC) curve were used to evaluate the model prediction effect.

RESULTS

The risk factors associated with postoperative coagulation dysfunction, selected by LASSO regression, including patient weight, preoperative baseline estimated glomerular filtration rate (eGFR), B-type natriuretic peptide (BNP), platelet count (PLT), preoperative use of heparin and angiotensin receptor-neprilysin inhibitor (ARNI), as well as intraoperative utilization of epinephrine, norepinephrine, dopamine, cephalosporins, cardiopulmonary bypass (CPB), intra-aortic balloon pump (IABP), extracorporeal membrane oxygenation (ECMO), operation duration, and total intraoperative fluid input. The area under curve (AUC) of the derivation set was 0.818 [95% confidence interval (CI): 0.775-0.862], while the AUC of the validation set was 0.827 (95% CI: 0.755-0.898). The sensitivity and specificity of the model in the derivation set were 80.0% and 70.0%. In the validation set, the sensitivity was 76.6% and the specificity was 81.7%, indicating that the model has good predictive performance.

CONCLUSIONS

The LASSO regression model for predicting coagulation disorders after CABG showed a good predictive performance in both the derivation set and the validation set, which is helpful for early identification of high-risk patients with coagulation disorders after CABG.