Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective
<i>Background and Objectives</i>: Postoperative delirium (POD) is a frequent and severe complication following cardiac surgery, particularly in high-risk patients undergoing coronary artery bypass grafting (CABG) and aortic valve replacement (AVR). Despite extensive research, predicting...
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MDPI AG
2025-05-01
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| author | Marija Stošić Velimir Perić Dragan Milić Milan Lazarević Jelena Živadinović Vladimir Stojiljković Aleksandar Kamenov Aleksandar Nikolić Mlađan Golubović |
| author_facet | Marija Stošić Velimir Perić Dragan Milić Milan Lazarević Jelena Živadinović Vladimir Stojiljković Aleksandar Kamenov Aleksandar Nikolić Mlađan Golubović |
| author_sort | Marija Stošić |
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| description | <i>Background and Objectives</i>: Postoperative delirium (POD) is a frequent and severe complication following cardiac surgery, particularly in high-risk patients undergoing coronary artery bypass grafting (CABG) and aortic valve replacement (AVR). Despite extensive research, predicting POD remains challenging due to the multifactorial and often non-linear nature of its risk factors. This study aimed to improve POD prediction using an interpretable machine learning approach and to explore the combined effects of clinical, biochemical, and perioperative variables. <i>Materials and Methods</i>: This study included 131 patients who underwent CABG or AVR. POD occurrence was assessed using standard diagnostic criteria. Clinical, biochemical, and perioperative variables were collected, including patient age, sedation type, and mechanical ventilation status. Machine learning analysis was performed using an XGBoost classifier, with model interpretation achieved through SHapley Additive exPlanations (SHAP). Univariate logistic regression was applied to identify significant predictors, while SHAP analysis revealed variable interactions. <i>Results</i>: POD occurred in 34.3% of patients (n = 45). Patients who developed POD were significantly older (67.7 ± 6.5 vs. 64.5 ± 8.7 years, <i>p</i> = 0.020). Sedation with mechanical ventilation and the type of sedative used were strongly associated with POD (both <i>p</i> < 0.001). Sedation during mechanical ventilation showed the strongest association (OR = 2520.0; 95% CI: 80.9–78,506.7; <i>p</i> < 0.00001). XGBoost classifier achieved excellent performance (AUC = 0.998, accuracy = 97.6%, F1 score = 0.976). SHAP analysis identified sedation, mechanical ventilation, and their interactions with fibrinogen, troponin I, leukocyte parameters, and lung infection as key predictors. <i>Conclusions</i>: This study demonstrates that an interpretable machine learning approach can enhance POD prediction, providing insights into the combined impact of multiple clinical, biochemical, and perioperative factors. Integration of such models into perioperative workflows may enable early identification of high-risk patients and support individualized preventive strategies. |
| format | Article |
| id | doaj-art-3baa7ee9a1b54fea819eb247071f2f26 |
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| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-3baa7ee9a1b54fea819eb247071f2f262025-08-20T01:56:32ZengMDPI AGMedicina1010-660X1648-91442025-05-0161588310.3390/medicina61050883Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning PerspectiveMarija Stošić0Velimir Perić1Dragan Milić2Milan Lazarević3Jelena Živadinović4Vladimir Stojiljković5Aleksandar Kamenov6Aleksandar Nikolić7Mlađan Golubović8Clinic for Cardiac Surgery, University Clinical Center Nis, 18000 Nis, SerbiaClinic for Cardiac Surgery, University Clinical Center Nis, 18000 Nis, SerbiaClinic for Cardiac Surgery, University Clinical Center Nis, 18000 Nis, SerbiaFaculty of Medicine, University of Nis, 18000 Nis, SerbiaFaculty of Medicine, University of Nis, 18000 Nis, SerbiaClinic for Cardiac Surgery, University Clinical Center Nis, 18000 Nis, SerbiaClinic for Cardiac Surgery, University Clinical Center Nis, 18000 Nis, SerbiaFaculty of Medicine, University of Nis, 18000 Nis, SerbiaClinic for Cardiac Surgery, University Clinical Center Nis, 18000 Nis, Serbia<i>Background and Objectives</i>: Postoperative delirium (POD) is a frequent and severe complication following cardiac surgery, particularly in high-risk patients undergoing coronary artery bypass grafting (CABG) and aortic valve replacement (AVR). Despite extensive research, predicting POD remains challenging due to the multifactorial and often non-linear nature of its risk factors. This study aimed to improve POD prediction using an interpretable machine learning approach and to explore the combined effects of clinical, biochemical, and perioperative variables. <i>Materials and Methods</i>: This study included 131 patients who underwent CABG or AVR. POD occurrence was assessed using standard diagnostic criteria. Clinical, biochemical, and perioperative variables were collected, including patient age, sedation type, and mechanical ventilation status. Machine learning analysis was performed using an XGBoost classifier, with model interpretation achieved through SHapley Additive exPlanations (SHAP). Univariate logistic regression was applied to identify significant predictors, while SHAP analysis revealed variable interactions. <i>Results</i>: POD occurred in 34.3% of patients (n = 45). Patients who developed POD were significantly older (67.7 ± 6.5 vs. 64.5 ± 8.7 years, <i>p</i> = 0.020). Sedation with mechanical ventilation and the type of sedative used were strongly associated with POD (both <i>p</i> < 0.001). Sedation during mechanical ventilation showed the strongest association (OR = 2520.0; 95% CI: 80.9–78,506.7; <i>p</i> < 0.00001). XGBoost classifier achieved excellent performance (AUC = 0.998, accuracy = 97.6%, F1 score = 0.976). SHAP analysis identified sedation, mechanical ventilation, and their interactions with fibrinogen, troponin I, leukocyte parameters, and lung infection as key predictors. <i>Conclusions</i>: This study demonstrates that an interpretable machine learning approach can enhance POD prediction, providing insights into the combined impact of multiple clinical, biochemical, and perioperative factors. Integration of such models into perioperative workflows may enable early identification of high-risk patients and support individualized preventive strategies.https://www.mdpi.com/1648-9144/61/5/883postoperative deliriumcoronary artery bypassaortic valve replacementpredictive modelingmachine learning |
| spellingShingle | Marija Stošić Velimir Perić Dragan Milić Milan Lazarević Jelena Živadinović Vladimir Stojiljković Aleksandar Kamenov Aleksandar Nikolić Mlađan Golubović Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective Medicina postoperative delirium coronary artery bypass aortic valve replacement predictive modeling machine learning |
| title | Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective |
| title_full | Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective |
| title_fullStr | Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective |
| title_full_unstemmed | Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective |
| title_short | Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective |
| title_sort | analyzing key predictors of postoperative delirium following coronary artery bypass grafting and aortic valve replacement a machine learning perspective |
| topic | postoperative delirium coronary artery bypass aortic valve replacement predictive modeling machine learning |
| url | https://www.mdpi.com/1648-9144/61/5/883 |
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