Abstract
Although patients with peripheral arterial disease (PAD) are at a high risk of major bleeding owing to their comorbidity and risk profile, no validated tools exist to predict bleeding risk. To make matters worse, several randomized and controlled trials have excluded patients who are at a high risk of bleeding. Using routine health insurance claims data and machine learning methods, a pragmatic prediction model was developed and internally validated. The OAC(3)-PAD risk score identified eight variables that can predict major bleeding events within 1 year of inpatient treatment for PAD. This risk score can help to carry out a tailored patient-centered risk-benefit assessment in order to obtain the maximum potential from available antithrombotic treatment strategies.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Medizin |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
ISSN: | 0948-7034 |
Sprache: | Englisch |
Dokumenten ID: | 111841 |
Datum der Veröffentlichung auf Open Access LMU: | 02. Apr. 2024, 07:30 |
Letzte Änderungen: | 02. Apr. 2024, 07:30 |