Prediction models for incident stroke in the community: a systematic review and meta-analysis of predictive performance
Many models have been derived for stroke prediction, however, they are rarely externally validated, and studies are limited by a high risk of bias, poor reporting of calibration and a lack of clinical utility analysis or prospective validation. Thus, the evidence base is insufficient to translate these models to clinical practice.
Authors
- Harriet Larvin (ORCID: https://orcid.org/0000-0001-7263-4182)
- Tanina Younsi
- Elizabeth Romer
- Jianhua Wu
- Chris Wilkinson
- Mohammad Haris
- A Cameron
- G F Romiti
- C P Gale
- Gregory Y H Lip
Institutions
- University of Leeds (GB)
- Medical University of Białystok (PL)
- University of Liverpool (GB)
- Queen Mary University of London (GB)
- Leeds Teaching Hospitals NHS Trust (GB)
- James Cook University Hospital (GB)
- South Tees Hospitals NHS Foundation Trust (GB)
- Airedale NHS Foundation Trust (GB)
- Liverpool Heart and Chest Hospital (GB)
- Leeds Dental Hospital (GB)
- University of York (GB)
- Liverpool John Moores University (GB)
- Hull York Medical School (GB)
- University of Glasgow (GB)
- Sapienza University of Rome (IT)
- Aalborg University (DK)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2025-12-11
- DOI
- https://doi.org/10.1093/ehjdh/ztaf147
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00