Yuanyuan Liu, Linjie Li, Jingge Li, Hangkuan Liu, A Geru, Yulong Wang, Yongle Li, Ching-Hui Sia, Gregory Y H Lip, Qing Yang, Xin Zhou
{"title":"直接口服抗凝剂患者颅内出血预测模型的开发与验证。","authors":"Yuanyuan Liu, Linjie Li, Jingge Li, Hangkuan Liu, A Geru, Yulong Wang, Yongle Li, Ching-Hui Sia, Gregory Y H Lip, Qing Yang, Xin Zhou","doi":"10.1177/10760296241271338","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Intracranial haemorrhage (ICH) poses a significant threat to patients on Direct Oral Anticoagulants (DOACs), with existing risk scores inadequately predicting ICH risk in these patients. We aim to develop and validate a predictive model for ICH risk in DOAC-treated patients.</p><p><strong>Methods: </strong>24,794 patients treated with a DOAC were identified in a province-wide electronic medical and health data platform in Tianjin, China. The cohort was randomly split into a 4:1 ratio for model development and validation. We utilized forward stepwise selection, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost) to select predictors. Model performance was compared using the area under the curve (AUC) and net reclassification index (NRI). The optimal model was stratified and compared with the DOAC model.</p><p><strong>Results: </strong>The median age is 68.0 years, and 50.4% of participants are male. The XGBoost model, incorporating six independent factors (history of hemorrhagic stroke, peripheral artery disease, venous thromboembolism, hypertension, age, low-density lipoprotein cholesterol levels), demonstrated superior performance in the development dateset. It showed moderate discrimination (AUC: 0.68, 95% CI: 0.64-0.73), outperforming existing DOAC scores (ΔAUC = 0.063, <i>P </i>= 0.003; NRI = 0.374, <i>P </i>< 0.001). Risk categories significantly stratified ICH risk (low risk: 0.26%, moderate risk: 0.74%, high risk: 5.51%). Finally, the model demonstrated consistent predictive performance in the internal validation.</p><p><strong>Conclusion: </strong>In a real-world Chinese population using DOAC therapy, this study presents a reliable predictive model for ICH risk. The XGBoost model, integrating six key risk factors, offers a valuable tool for individualized risk assessment in the context of oral anticoagulation therapy.</p>","PeriodicalId":10335,"journal":{"name":"Clinical and Applied Thrombosis/Hemostasis","volume":"30 ","pages":"10760296241271338"},"PeriodicalIF":2.3000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11325470/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development and Validation of a Predictive Model for Intracranial Haemorrhage in Patients on Direct Oral Anticoagulants.\",\"authors\":\"Yuanyuan Liu, Linjie Li, Jingge Li, Hangkuan Liu, A Geru, Yulong Wang, Yongle Li, Ching-Hui Sia, Gregory Y H Lip, Qing Yang, Xin Zhou\",\"doi\":\"10.1177/10760296241271338\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Intracranial haemorrhage (ICH) poses a significant threat to patients on Direct Oral Anticoagulants (DOACs), with existing risk scores inadequately predicting ICH risk in these patients. We aim to develop and validate a predictive model for ICH risk in DOAC-treated patients.</p><p><strong>Methods: </strong>24,794 patients treated with a DOAC were identified in a province-wide electronic medical and health data platform in Tianjin, China. The cohort was randomly split into a 4:1 ratio for model development and validation. We utilized forward stepwise selection, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost) to select predictors. Model performance was compared using the area under the curve (AUC) and net reclassification index (NRI). The optimal model was stratified and compared with the DOAC model.</p><p><strong>Results: </strong>The median age is 68.0 years, and 50.4% of participants are male. The XGBoost model, incorporating six independent factors (history of hemorrhagic stroke, peripheral artery disease, venous thromboembolism, hypertension, age, low-density lipoprotein cholesterol levels), demonstrated superior performance in the development dateset. It showed moderate discrimination (AUC: 0.68, 95% CI: 0.64-0.73), outperforming existing DOAC scores (ΔAUC = 0.063, <i>P </i>= 0.003; NRI = 0.374, <i>P </i>< 0.001). Risk categories significantly stratified ICH risk (low risk: 0.26%, moderate risk: 0.74%, high risk: 5.51%). Finally, the model demonstrated consistent predictive performance in the internal validation.</p><p><strong>Conclusion: </strong>In a real-world Chinese population using DOAC therapy, this study presents a reliable predictive model for ICH risk. The XGBoost model, integrating six key risk factors, offers a valuable tool for individualized risk assessment in the context of oral anticoagulation therapy.</p>\",\"PeriodicalId\":10335,\"journal\":{\"name\":\"Clinical and Applied Thrombosis/Hemostasis\",\"volume\":\"30 \",\"pages\":\"10760296241271338\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11325470/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical and Applied Thrombosis/Hemostasis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/10760296241271338\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEMATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical and Applied Thrombosis/Hemostasis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/10760296241271338","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEMATOLOGY","Score":null,"Total":0}
Development and Validation of a Predictive Model for Intracranial Haemorrhage in Patients on Direct Oral Anticoagulants.
Background: Intracranial haemorrhage (ICH) poses a significant threat to patients on Direct Oral Anticoagulants (DOACs), with existing risk scores inadequately predicting ICH risk in these patients. We aim to develop and validate a predictive model for ICH risk in DOAC-treated patients.
Methods: 24,794 patients treated with a DOAC were identified in a province-wide electronic medical and health data platform in Tianjin, China. The cohort was randomly split into a 4:1 ratio for model development and validation. We utilized forward stepwise selection, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost) to select predictors. Model performance was compared using the area under the curve (AUC) and net reclassification index (NRI). The optimal model was stratified and compared with the DOAC model.
Results: The median age is 68.0 years, and 50.4% of participants are male. The XGBoost model, incorporating six independent factors (history of hemorrhagic stroke, peripheral artery disease, venous thromboembolism, hypertension, age, low-density lipoprotein cholesterol levels), demonstrated superior performance in the development dateset. It showed moderate discrimination (AUC: 0.68, 95% CI: 0.64-0.73), outperforming existing DOAC scores (ΔAUC = 0.063, P = 0.003; NRI = 0.374, P < 0.001). Risk categories significantly stratified ICH risk (low risk: 0.26%, moderate risk: 0.74%, high risk: 5.51%). Finally, the model demonstrated consistent predictive performance in the internal validation.
Conclusion: In a real-world Chinese population using DOAC therapy, this study presents a reliable predictive model for ICH risk. The XGBoost model, integrating six key risk factors, offers a valuable tool for individualized risk assessment in the context of oral anticoagulation therapy.
期刊介绍:
CATH is a peer-reviewed bi-monthly journal that addresses the practical clinical and laboratory issues involved in managing bleeding and clotting disorders, especially those related to thrombosis, hemostasis, and vascular disorders. CATH covers clinical trials, studies on etiology, pathophysiology, diagnosis and treatment of thrombohemorrhagic disorders.