{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T03:54:04Z","timestamp":1793418844744,"version":"4.5.0"},"reference-count":54,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"5","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Stroke"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:sec>\n            <jats:title>BACKGROUND:<\/jats:title>\n            <jats:p>Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors\u2019 engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>METHODS:<\/jats:title>\n            <jats:p>We analyzed clinical and socioeconomic data from a prospectively collected public health care\u2013based data set of 41\u2005975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014\u20132020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and &gt;365 days, respectively. C statistics and area under the receiver operating characteristic curve were used to assess the accuracy of the models.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS:<\/jats:title>\n            <jats:p>\n              Overall, 16.21% (5932 of 36\u2005114) of patients had stroke recurrence during a median follow-up of 2.69 years. The most powerful predictors of stroke recurrence were time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex, which were used to create a simplified model with similar performance, together with modifiable vascular risk factors (glycemia, body mass index, high blood pressure, cholesterol, tobacco dependence, and alcohol abuse). The areas under the receiver operating characteristic curve were 0.76 (95% CI, 0.74\u20130.77), 0.60 (95% CI, 0.58\u20130.61), and 0.71 (95% CI, 0.69\u20130.72) for early, late, and long-term recurrence risk, respectively. The areas under the receiver operating characteristic curve of the Cox risk class probability were 0.73 (95% CI, 0.72\u20130.75), 0.59 (95% CI, 0.57\u20130.61), and 0.67 (95% CI, 0.66\u20130.70); machine learning approaches (random forest and AdaBoost) showed statistically significant improvement (\n              <jats:italic toggle=\"yes\">P<\/jats:italic>\n              &lt;0.05) over the Cox model for the 3 recurrence time periods. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors.\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>CONCLUSIONS:<\/jats:title>\n            <jats:p>PRERISK is a novel approach that provides a personalized and fairly accurate risk prediction of stroke recurrence over time. The model has the potential to incorporate dynamic control of risk factors.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/strokeaha.123.043691","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T09:00:56Z","timestamp":1711616456000},"page":"1200-1209","update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":24,"title":["PRERISK: A Personalized, Artificial Intelligence\u2013Based and Statistically\u2013Based Stroke Recurrence Predictor for Recurrent Stroke"],"prefix":"10.1161","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2454-9076","authenticated-orcid":true,"given":"Giorgio","family":"Colangelo","sequence":"first","affiliation":[{"name":"Vall d\u2019Hebron Research Institute, Passeig de la Vall d\u2019Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., \u00c1.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)."},{"name":"Nora Health, Passeig de la Vall d\u2019Hebron, Barcelona, Spain (G.C., E.M.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9242-043X","authenticated-orcid":true,"given":"Marc","family":"Ribo","sequence":"additional","affiliation":[{"name":"Vall d\u2019Hebron Research Institute, Passeig de la Vall d\u2019Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., \u00c1.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)."},{"name":"Hospital Universitari Vall d\u2019Hebron, Stroke Unit, Neurology Department, Passeig de la Vall d\u2019Hebron, Barcelona, Spain (M. Ribo, M.O.-G., M.M., \u00c1.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Estefan\u00eda","family":"Montiel","sequence":"additional","affiliation":[{"name":"Nora Health, Passeig de la Vall d\u2019Hebron, Barcelona, Spain (G.C., E.M.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0258-6194","authenticated-orcid":true,"given":"Didier","family":"Dominguez","sequence":"additional","affiliation":[{"name":"Programa d\u2019Anal\u00edtica de Dades per a la Recerca i la Innovaci\u00f3 en Salut, Ag\u00e8ncia de Qualitat i Avaluaci\u00f3 Sanit\u00e0ries de Catalunya, Departament de Salut, Generalitat de Catalunya, Carrer de Roc Boronat, Barcelona, Spain (D.D.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Oliv\u00e9-Gadea","sequence":"additional","affiliation":[{"name":"Vall d\u2019Hebron Research Institute, Passeig de la Vall d\u2019Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., \u00c1.G.-T., M. 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