Stroke risk prediction models: A systematic review and meta-analysis

被引:1
作者
Asowata, Osahon Jeffery [1 ]
Okekunle, Akinkunmi Paul [1 ,2 ,3 ,10 ]
Olaiya, Muideen Tunbosun [4 ]
Akinyemi, Joshua [1 ]
Owolabi, Mayowa [2 ,5 ,6 ]
Akpa, Onoja M. [1 ,7 ,8 ,9 ]
机构
[1] Univ Ibadan, Dept Epidemiol & Med Stat, Ibadan 200284, Nigeria
[2] Univ Ibadan, Coll Med, Dept Med, Ibadan 200284, Nigeria
[3] Seoul Natl Univ, Res Inst Human Ecol, Seoul 08826, South Korea
[4] Monash Univ, Sch Clin Sci Monash Hlth, Stroke & Ageing Res, Clayton, Vic 3168, Australia
[5] Lebanese Amer Univ, Beirut 11022801, Lebanon
[6] Univ Ibadan, Coll Med, Ctr Genom & Precis Med, Ibadan 200284, Nigeria
[7] Univ Ibadan, Inst Cardiovasc Dis, Coll Med, Prevent Cardiol Res Unit, Ibadan 200284, Nigeria
[8] Univ Memphis, Sch Publ Hlth, Div Epidemiol Biostat & Environm Hlth, Memphis, TN USA
[9] Univ Ibadan, Coll Med, Dept Epidemiol & Med Stat, Ibadan 200284, Nigeria
[10] Seoul Natl Univ, Seoul 08826, South Korea
基金
新加坡国家研究基金会;
关键词
Stroke; Prediction model; Machine learning; Risk score; Brain Health; Data Science; Precision Medicine; HEALTH-CARE PROFESSIONALS; ISCHEMIC-STROKE; CARDIOVASCULAR-DISEASE; EXTERNAL VALIDATION; LOGISTIC-REGRESSION; POPULATION; PREVENTION; EUROPE; TOOL; APPLICABILITY;
D O I
10.1016/j.jns.2024.122997
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Background: Prediction algorithms/models are viable methods for identifying individuals at high risk of stroke across diverse populations for timely intervention. However, evidence summarizing the performance of these models is limited. This study examined the performance and weaknesses of existing stroke risk -score -prediction models (SRSMs) and whether performance varied by population and region. Methods: PubMed, EMBASE, and Web of Science were searched for articles on SRSMs from the earliest records until February 2022. The Prediction Model Risk of Bias Assessment Tool was used to assess the quality of eligible articles. The performance of the SRSMs was assessed by meta -analyzing C -statistics (0 and 1) estimates from identified studies to determine the overall pooled C -statistics by fitting a linear restricted maximum likelihood in a random effect model. Results: Overall, 17 articles (cohort study = 15, nested case -control study = 2) comprising 739,134 stroke cases from 6,396,594 participants from diverse populations/regions (Asia; n = 8, United States; n = 3, and Europe and the United Kingdom; n = 6) were eligible for inclusion. The overall pooled c -statistics of SRSMs was 0.78 (95%CI: 0.75, 0.80; I 2 = 99.9%), with most SRSMs developed using cohort studies; 0.78 (95%CI: 0.75, 0.80; I 2 = 99.9%). The subgroup analyses by geographical region: Asia [0.81 (95%CI: 0.79, 0.83; I 2 = 99.8%)], Europe and the United Kingdom [0.76 (95%CI: 0.69, 0.83; I 2 = 99.9%)] and the United States only [0.75 (95%CI: 0.72, 0.78; I 2 = 73.5%)] revealed relatively indifferent performances of SRSMs. Conclusion: SRSM performance varied widely, and the pooled c -statistics of SRSMs suggested a fair predictive performance, with very few SRSMs validated in independent population group(s) from diverse world regions.
引用
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页数:15
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