Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction modelResearch in context
Summary: Background: Current models for predicting intraoperative hemorrhage in cesarean scar ectopic pregnancy (CSEP) are constrained by known risk factors and conventional statistical methods. Our objective is to develop an interpretable prediction model using machine learning (ML) techniques to...
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Elsevier
2024-12-01
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| Series: | EClinicalMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589537024005480 |
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| author | Xinli Chen Huan Zhang Dongxia Guo Siyuan Yang Bao Liu Yiping Hao Qingqing Liu Teng Zhang Fanrong Meng Longyun Sun Xinlin Jiao Wenjing Zhang Yanli Ban Yugang Chi Guowei Tao Baoxia Cui |
| author_facet | Xinli Chen Huan Zhang Dongxia Guo Siyuan Yang Bao Liu Yiping Hao Qingqing Liu Teng Zhang Fanrong Meng Longyun Sun Xinlin Jiao Wenjing Zhang Yanli Ban Yugang Chi Guowei Tao Baoxia Cui |
| author_sort | Xinli Chen |
| collection | DOAJ |
| description | Summary: Background: Current models for predicting intraoperative hemorrhage in cesarean scar ectopic pregnancy (CSEP) are constrained by known risk factors and conventional statistical methods. Our objective is to develop an interpretable prediction model using machine learning (ML) techniques to assess the risk of intraoperative hemorrhage during CSEP in women, followed by external validation and clinical application. Methods: This multicenter retrospective study utilized electronic medical record (EMR) data from four tertiary medical institutions. The model was developed using data from 1680 patients with CSEP diagnosed and treated at Qilu Hospital of Shandong University, Chongqing Health Center for Women and Children, and Dezhou Maternal and Child Health Care Hospital between January 1, 2008, and December 31, 2023. External validation data were obtained from Liao Cheng Dong Chang Fu District Maternal and Child Health Care Hospital between January 1, 2021, and December 31, 2023. Random forest (RF), Lasso, Boruta, and Extreme Gradient Boosting (XGBoost) were employed to identify the most influential variables in the model development data set; the best variables were selected based on reaching the λmin value. Model development involved eight machine learning methods with ten-fold cross-validation. Accuracy and decision curve analysis (DCA) were used to assess model performance for selection of the optimal model. Internal validation of the model utilized area under the receiver operating characteristic curve (AUC), sensitivity, specificity, Matthews correlation coefficient, and F1 score. These same indicators were also applied to evaluate external validation performance of the model. Finally, visualization techniques were used to present the optimal model which was then deployed for clinical application via network applications. Findings: Setting λmin at the value of 0.003, the optimal variable combination containing 9 variables was selected for model development. The optimal prediction model (Bayes) had an accuracy of 0.879 (95% CI: 0.857–0.901) an AUC of 0.882 (95% CI: 0.860–0.904), a DCA curve maximum threshold probability of 0.41, and a maximum return of 7.86%. The internal validation accuracy was 0.869 (95% CI: 0.847–0.891), an AUC of 0.822 (95% CI: 0.801–0.843), a sensitivity of 0.938, a specificity of 0.422, a Matthews correlation coefficient of 0.392, and an F1 score of 0.925. In the external validation, the accuracy was 0.936 (95% CI: 0.913–0.959), an AUC of 0.853 (95% CI: 0.832–0.874), a sensitivity of 0.954, a specificity of 0.5, a Matthews correlation coefficient of 0.365, and an F1 score of 0.966. This indicates that the prediction model performed well in both internal and external validation. Interpretation: The developed prediction model, deployed in the network application, is capable of forecasting the risk of intraoperative hemorrhage during CSEP. This tool can facilitate targeted preoperative assessment and clinical decision-making for clinicians. Prospective data should be utilized in future studies to further validate the extended applicability of the model. Funding: Natural Science Foundation of Shandong Province; Qilu Hospital of Shandong University. |
| format | Article |
| id | doaj-art-761e65774ab744f28f4c06ddecfdd83d |
| institution | Kabale University |
| issn | 2589-5370 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EClinicalMedicine |
| spelling | doaj-art-761e65774ab744f28f4c06ddecfdd83d2024-11-30T07:13:50ZengElsevierEClinicalMedicine2589-53702024-12-0178102969Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction modelResearch in contextXinli Chen0Huan Zhang1Dongxia Guo2Siyuan Yang3Bao Liu4Yiping Hao5Qingqing Liu6Teng Zhang7Fanrong Meng8Longyun Sun9Xinlin Jiao10Wenjing Zhang11Yanli Ban12Yugang Chi13Guowei Tao14Baoxia Cui15Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, ChinaDezhou Maternal and Child Health Care Hospital, Dezhou, ChinaLiao Cheng Dong Chang Fu District Maternal and Child Health Care Hospital, Liaocheng, ChinaDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, ChinaChongqing Health Center for Women and Children, Chongqing, China; Women and Children’s Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, ChinaDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, ChinaDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, ChinaLiao Cheng Dong Chang Fu District Maternal and Child Health Care Hospital, Liaocheng, ChinaDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, ChinaDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, ChinaDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, ChinaDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, ChinaChongqing Health Center for Women and Children, Chongqing, China; Women and Children’s Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Ultrasound, Qilu Hospital of Shandong University, Jinan, China; Corresponding author. No. 107, West Culture Road, Jinan, Shandong Province, China.Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, China; Corresponding author. No. 107, West Culture Road, Jinan, Shandong Province, China.Summary: Background: Current models for predicting intraoperative hemorrhage in cesarean scar ectopic pregnancy (CSEP) are constrained by known risk factors and conventional statistical methods. Our objective is to develop an interpretable prediction model using machine learning (ML) techniques to assess the risk of intraoperative hemorrhage during CSEP in women, followed by external validation and clinical application. Methods: This multicenter retrospective study utilized electronic medical record (EMR) data from four tertiary medical institutions. The model was developed using data from 1680 patients with CSEP diagnosed and treated at Qilu Hospital of Shandong University, Chongqing Health Center for Women and Children, and Dezhou Maternal and Child Health Care Hospital between January 1, 2008, and December 31, 2023. External validation data were obtained from Liao Cheng Dong Chang Fu District Maternal and Child Health Care Hospital between January 1, 2021, and December 31, 2023. Random forest (RF), Lasso, Boruta, and Extreme Gradient Boosting (XGBoost) were employed to identify the most influential variables in the model development data set; the best variables were selected based on reaching the λmin value. Model development involved eight machine learning methods with ten-fold cross-validation. Accuracy and decision curve analysis (DCA) were used to assess model performance for selection of the optimal model. Internal validation of the model utilized area under the receiver operating characteristic curve (AUC), sensitivity, specificity, Matthews correlation coefficient, and F1 score. These same indicators were also applied to evaluate external validation performance of the model. Finally, visualization techniques were used to present the optimal model which was then deployed for clinical application via network applications. Findings: Setting λmin at the value of 0.003, the optimal variable combination containing 9 variables was selected for model development. The optimal prediction model (Bayes) had an accuracy of 0.879 (95% CI: 0.857–0.901) an AUC of 0.882 (95% CI: 0.860–0.904), a DCA curve maximum threshold probability of 0.41, and a maximum return of 7.86%. The internal validation accuracy was 0.869 (95% CI: 0.847–0.891), an AUC of 0.822 (95% CI: 0.801–0.843), a sensitivity of 0.938, a specificity of 0.422, a Matthews correlation coefficient of 0.392, and an F1 score of 0.925. In the external validation, the accuracy was 0.936 (95% CI: 0.913–0.959), an AUC of 0.853 (95% CI: 0.832–0.874), a sensitivity of 0.954, a specificity of 0.5, a Matthews correlation coefficient of 0.365, and an F1 score of 0.966. This indicates that the prediction model performed well in both internal and external validation. Interpretation: The developed prediction model, deployed in the network application, is capable of forecasting the risk of intraoperative hemorrhage during CSEP. This tool can facilitate targeted preoperative assessment and clinical decision-making for clinicians. Prospective data should be utilized in future studies to further validate the extended applicability of the model. Funding: Natural Science Foundation of Shandong Province; Qilu Hospital of Shandong University.http://www.sciencedirect.com/science/article/pii/S2589537024005480Cesarean scar ectopic pregnancyIntraoperative hemorrhageInterpretable machine learningPrediction modelVisualization |
| spellingShingle | Xinli Chen Huan Zhang Dongxia Guo Siyuan Yang Bao Liu Yiping Hao Qingqing Liu Teng Zhang Fanrong Meng Longyun Sun Xinlin Jiao Wenjing Zhang Yanli Ban Yugang Chi Guowei Tao Baoxia Cui Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction modelResearch in context EClinicalMedicine Cesarean scar ectopic pregnancy Intraoperative hemorrhage Interpretable machine learning Prediction model Visualization |
| title | Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction modelResearch in context |
| title_full | Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction modelResearch in context |
| title_fullStr | Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction modelResearch in context |
| title_full_unstemmed | Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction modelResearch in context |
| title_short | Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction modelResearch in context |
| title_sort | risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery development and validation of an interpretable machine learning prediction modelresearch in context |
| topic | Cesarean scar ectopic pregnancy Intraoperative hemorrhage Interpretable machine learning Prediction model Visualization |
| url | http://www.sciencedirect.com/science/article/pii/S2589537024005480 |
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