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A LASSO-derived model for the prediction of coagulation disorders after coronary artery bypass grafting
Honglei Zhao1, Xiaonan Li1, Haiyang Li1
1Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Insights
A new model predicts coagulation disorders after coronary artery bypass grafting (CABG) in elderly patients. This tool helps identify high-risk individuals for enhanced postoperative monitoring and care.
Area of Science:
- Cardiovascular Surgery
- Hematology
- Medical Informatics
Background:
- Postoperative coagulation dysfunction is a frequent complication following coronary artery bypass grafting (CABG), particularly in elderly populations.
- Effective identification of high-risk patients is crucial for optimizing postoperative management and preventing adverse outcomes.
Purpose of the Study:
- To develop and validate a predictive model for coagulation disorders in elderly patients undergoing CABG.
- To identify key risk factors contributing to postoperative coagulation dysfunction.
- To facilitate early detection and targeted monitoring of at-risk individuals.
Main Methods:
- Retrospective analysis of patients who underwent CABG between February 2019 and December 2020.
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression for variable selection and model building.
- Defined coagulation disorder by a disseminated intravascular coagulation (DIC) score of ≥2.
- Evaluated model performance using confusion matrix and Receiver Operating Characteristic (ROC) curves, including Area Under the Curve (AUC), sensitivity, and specificity.
Main Results:
- LASSO regression identified significant risk factors including patient weight, preoperative estimated glomerular filtration rate (eGFR), B-type natriuretic peptide (BNP), platelet count (PLT), and use of heparin and angiotensin receptor-neprilysin inhibitor (ARNI).
- Intraoperative factors such as epinephrine, norepinephrine, dopamine, cephalosporins, cardiopulmonary bypass (CPB), intra-aortic balloon pump (IABP), extracorporeal membrane oxygenation (ECMO), operation duration, and fluid input were also significant.
- The model demonstrated strong predictive performance with AUCs of 0.818 in the derivation set and 0.827 in the validation set, alongside high sensitivity and specificity.
Conclusions:
- The developed LASSO regression model effectively predicts coagulation disorders post-CABG in elderly patients.
- The model's robust performance in both derivation and validation sets supports its utility for early risk identification.
- This tool can aid clinicians in strengthening postoperative monitoring for patients at high risk of coagulation disorders.
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.
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