Kyung Hyun Min, Woorim Kim, Jun Hyeob Kim, Jin Yeon Gil, Kyung Hee Choi, Ji Min Han, Kyung Eun Lee
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Of these, patients with the last two identical warfarin prescriptions within 6 months before the bleeding events were defined as the case group, while patients with no bleeding events within 6 months after HVS and two consecutive identical warfarin prescriptions were defined as the control group. Three machine learning models-logistic regression, support vector machine, and random forest-were trained and scored by fivefold validation to validate our feature selection processes. We developed a risk scoring system using adjusted odds ratios from multivariate logistic regression.</p><p><strong>Results: </strong>Of 1 137 861 subjects, 1093 patients were eligible for the study cohort; 173 and 298 were selected as the case and control groups, respectively. After a series of machine learning processes, eight features were identified as significant risk factors for bleeding events.</p><p><strong>Conclusion: </strong>Our finding suggests that furosemide, spironolactone, lacrimal system disorders, ursodeoxycholic acid, captopril, chronic kidney disease, zolpidem, and valsartan are the most important features for predicting bleeding events in patients taking a stable warfarin dose after HVS.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 9","pages":"e70209"},"PeriodicalIF":2.4000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12437997/pdf/","citationCount":"0","resultStr":"{\"title\":\"One-Year Risk of Bleeding in Patients on a Stable Warfarin Dose After Prosthetic Heart Valve Surgery.\",\"authors\":\"Kyung Hyun Min, Woorim Kim, Jun Hyeob Kim, Jin Yeon Gil, Kyung Hee Choi, Ji Min Han, Kyung Eun Lee\",\"doi\":\"10.1002/pds.70209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Anticoagulation therapy is required to prevent thromboembolic complications in patients with heart valve surgery (HVS). However, caution must be taken due to the risk of bleeding. This study aimed to identify bleeding risk factors in patients with stable warfarin therapy and develop a predictive tool for high-risk patients.</p><p><strong>Methods: </strong>This study is a nested case-control design using the Korean National Health Insurance Service-National Sample Cohort Data. We identified patients who underwent HVS and were prescribed warfarin within 1 week after the procedure. Of these, patients with the last two identical warfarin prescriptions within 6 months before the bleeding events were defined as the case group, while patients with no bleeding events within 6 months after HVS and two consecutive identical warfarin prescriptions were defined as the control group. Three machine learning models-logistic regression, support vector machine, and random forest-were trained and scored by fivefold validation to validate our feature selection processes. We developed a risk scoring system using adjusted odds ratios from multivariate logistic regression.</p><p><strong>Results: </strong>Of 1 137 861 subjects, 1093 patients were eligible for the study cohort; 173 and 298 were selected as the case and control groups, respectively. After a series of machine learning processes, eight features were identified as significant risk factors for bleeding events.</p><p><strong>Conclusion: </strong>Our finding suggests that furosemide, spironolactone, lacrimal system disorders, ursodeoxycholic acid, captopril, chronic kidney disease, zolpidem, and valsartan are the most important features for predicting bleeding events in patients taking a stable warfarin dose after HVS.</p>\",\"PeriodicalId\":19782,\"journal\":{\"name\":\"Pharmacoepidemiology and Drug Safety\",\"volume\":\"34 9\",\"pages\":\"e70209\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12437997/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pharmacoepidemiology and Drug Safety\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/pds.70209\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmacoepidemiology and Drug Safety","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pds.70209","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
One-Year Risk of Bleeding in Patients on a Stable Warfarin Dose After Prosthetic Heart Valve Surgery.
Purpose: Anticoagulation therapy is required to prevent thromboembolic complications in patients with heart valve surgery (HVS). However, caution must be taken due to the risk of bleeding. This study aimed to identify bleeding risk factors in patients with stable warfarin therapy and develop a predictive tool for high-risk patients.
Methods: This study is a nested case-control design using the Korean National Health Insurance Service-National Sample Cohort Data. We identified patients who underwent HVS and were prescribed warfarin within 1 week after the procedure. Of these, patients with the last two identical warfarin prescriptions within 6 months before the bleeding events were defined as the case group, while patients with no bleeding events within 6 months after HVS and two consecutive identical warfarin prescriptions were defined as the control group. Three machine learning models-logistic regression, support vector machine, and random forest-were trained and scored by fivefold validation to validate our feature selection processes. We developed a risk scoring system using adjusted odds ratios from multivariate logistic regression.
Results: Of 1 137 861 subjects, 1093 patients were eligible for the study cohort; 173 and 298 were selected as the case and control groups, respectively. After a series of machine learning processes, eight features were identified as significant risk factors for bleeding events.
Conclusion: Our finding suggests that furosemide, spironolactone, lacrimal system disorders, ursodeoxycholic acid, captopril, chronic kidney disease, zolpidem, and valsartan are the most important features for predicting bleeding events in patients taking a stable warfarin dose after HVS.
期刊介绍:
The aim of Pharmacoepidemiology and Drug Safety is to provide an international forum for the communication and evaluation of data, methods and opinion in the discipline of pharmacoepidemiology. The Journal publishes peer-reviewed reports of original research, invited reviews and a variety of guest editorials and commentaries embracing scientific, medical, statistical, legal and economic aspects of pharmacoepidemiology and post-marketing surveillance of drug safety. Appropriate material in these categories may also be considered for publication as a Brief Report.
Particular areas of interest include:
design, analysis, results, and interpretation of studies looking at the benefit or safety of specific pharmaceuticals, biologics, or medical devices, including studies in pharmacovigilance, postmarketing surveillance, pharmacoeconomics, patient safety, molecular pharmacoepidemiology, or any other study within the broad field of pharmacoepidemiology;
comparative effectiveness research relating to pharmaceuticals, biologics, and medical devices. Comparative effectiveness research is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition, as these methods are truly used in the real world;
methodologic contributions of relevance to pharmacoepidemiology, whether original contributions, reviews of existing methods, or tutorials for how to apply the methods of pharmacoepidemiology;
assessments of harm versus benefit in drug therapy;
patterns of drug utilization;
relationships between pharmacoepidemiology and the formulation and interpretation of regulatory guidelines;
evaluations of risk management plans and programmes relating to pharmaceuticals, biologics and medical devices.