Development and Validation of a Predictive Model for Intracranial Haemorrhage in Patients on Direct Oral Anticoagulants
| Autor: | Yuanyuan Liu MD, Linjie Li MD, Jingge Li MD, Hangkuan Liu MD, A Geru MD, Yulong Wang MD, Yongle Li MD, PhD, Ching-Hui Sia MBBS, MRCP, Gregory Y. H. Lip MD, Qing Yang MD, PhD, Xin Zhou MD, PhD |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Clinical and Applied Thrombosis/Hemostasis, Vol 30 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1938-2723 10760296 |
| DOI: | 10.1177/10760296241271338 |
| Popis: | 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 |
| Databáze: | Directory of Open Access Journals |
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