Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients
Abstract Objective The objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy. Methods We applied the LASSO regression model to identify the independent risk factors associated with POD. Subsequently, we utilized...
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BMC
2025-01-01
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| Series: | BMC Surgery |
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| Online Access: | https://doi.org/10.1186/s12893-025-02759-2 |
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| author | Yu Zhu Renrui Liang Ying Wang Jian-Jun Yang Ning Zhou Cheng-Mao Zhou |
| author_facet | Yu Zhu Renrui Liang Ying Wang Jian-Jun Yang Ning Zhou Cheng-Mao Zhou |
| author_sort | Yu Zhu |
| collection | DOAJ |
| description | Abstract Objective The objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy. Methods We applied the LASSO regression model to identify the independent risk factors associated with POD. Subsequently, we utilized R software to develop and validate a nomogram model capable of accurately predicting the incidence of POD. Results The final variables selected by the LASSO method were: Ramelteon, Age, Sex, Alcohol, Viral status, Cardiovascular disease, ASA class, Total bilirubin, Prothrombin time, Laparoscopic approach, and Blood transfusion. The performance of the nomogram was measured using ROC curve analysis, with an AUC of 0.854 (95% CI: 0.794–0.914) for the model. At the optimal cutoff value, the model demonstrated a sensitivity of 91.9% and a specificity of 68.8%. Model validation was performed using internal bootstrap validation to further verify the regression analysis. The ROC curve was generated by repeating the bootstrapping process 500 times, resulting in an AUC of 0.848 (95% CI: 0.786–0.904) for the model. The DCA curve representing the net benefit demonstrated the strong clinical validity of the model in predicting postoperative delirium. Conclusion Our results demonstrated that LASSO-based regression effectively constructed a nomogram model for predicting post-hepatectomy delirium. |
| format | Article |
| id | doaj-art-a1481800122d4ce69b4abe6ca543576b |
| institution | Kabale University |
| issn | 1471-2482 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Surgery |
| spelling | doaj-art-a1481800122d4ce69b4abe6ca543576b2025-01-19T12:08:00ZengBMCBMC Surgery1471-24822025-01-012511910.1186/s12893-025-02759-2Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patientsYu Zhu0Renrui Liang1Ying Wang2Jian-Jun Yang3Ning Zhou4Cheng-Mao Zhou5Department of Anaesthesiology, Central People’s Hospital of ZhanjiangDepartment of Nursing, Central People’s Hospital of ZhanjiangDepartment of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou UniversityDepartment of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou UniversityDepartment of Emergency, Central People’s Hospital of ZhanjiangDepartment of Anaesthesiology, Central People’s Hospital of ZhanjiangAbstract Objective The objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy. Methods We applied the LASSO regression model to identify the independent risk factors associated with POD. Subsequently, we utilized R software to develop and validate a nomogram model capable of accurately predicting the incidence of POD. Results The final variables selected by the LASSO method were: Ramelteon, Age, Sex, Alcohol, Viral status, Cardiovascular disease, ASA class, Total bilirubin, Prothrombin time, Laparoscopic approach, and Blood transfusion. The performance of the nomogram was measured using ROC curve analysis, with an AUC of 0.854 (95% CI: 0.794–0.914) for the model. At the optimal cutoff value, the model demonstrated a sensitivity of 91.9% and a specificity of 68.8%. Model validation was performed using internal bootstrap validation to further verify the regression analysis. The ROC curve was generated by repeating the bootstrapping process 500 times, resulting in an AUC of 0.848 (95% CI: 0.786–0.904) for the model. The DCA curve representing the net benefit demonstrated the strong clinical validity of the model in predicting postoperative delirium. Conclusion Our results demonstrated that LASSO-based regression effectively constructed a nomogram model for predicting post-hepatectomy delirium.https://doi.org/10.1186/s12893-025-02759-2NomogramPODHepatectomyLASSOPredicting |
| spellingShingle | Yu Zhu Renrui Liang Ying Wang Jian-Jun Yang Ning Zhou Cheng-Mao Zhou Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients BMC Surgery Nomogram POD Hepatectomy LASSO Predicting |
| title | Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients |
| title_full | Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients |
| title_fullStr | Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients |
| title_full_unstemmed | Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients |
| title_short | Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients |
| title_sort | development of a lasso machine learning algorithm based model for postoperative delirium prediction in hepatectomy patients |
| topic | Nomogram POD Hepatectomy LASSO Predicting |
| url | https://doi.org/10.1186/s12893-025-02759-2 |
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