Prediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: Development and validation of a risk score model.
<h4>Background</h4>There is currently no validated risk prediction model for recurrent events among patients with acute ischemic stroke (AIS) and atrial fibrillation (AF). Considering that the application of conventional risk scores has contextual limitations, new strategies are needed t...
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oai:doaj.org-article:e6451e4ff4974b3187bc494fecd0a1942021-12-02T20:17:07ZPrediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: Development and validation of a risk score model.1932-620310.1371/journal.pone.0258377https://doaj.org/article/e6451e4ff4974b3187bc494fecd0a1942021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258377https://doaj.org/toc/1932-6203<h4>Background</h4>There is currently no validated risk prediction model for recurrent events among patients with acute ischemic stroke (AIS) and atrial fibrillation (AF). Considering that the application of conventional risk scores has contextual limitations, new strategies are needed to develop such a model. Here, we set out to develop and validate a comprehensive risk prediction model for stroke recurrence in AIS patients with AF.<h4>Methods</h4>AIS patients with AF were collected from multicenter registries in South Korea and Japan. A developmental dataset was constructed with 5648 registered cases from both countries for the period 2011‒2014. An external validation dataset was also created, consisting of Korean AIS subjects with AF registered between 2015 and 2018. Event outcomes were collected during 1 year after the index stroke. A multivariable prediction model was developed using the Fine-Gray subdistribution hazard model with non-stroke mortality as a competing risk. The model incorporated 21 clinical variables and was further validated, calibrated, and revised using the external validation dataset.<h4>Results</h4>The developmental dataset consisted of 4483 Korean and 1165 Japanese patients (mean age, 74.3 ± 10.2 years; male 53%); 338 patients (6%) had recurrent stroke and 903 (16%) died. The clinical profiles of the external validation set (n = 3668) were comparable to those of the developmental dataset. The c-statistics of the final model was 0.68 (95% confidence interval, 0.66 ‒0.71). The developed prediction model did not show better discriminative ability for predicting stroke recurrence than the conventional risk prediction tools (CHADS2, CHA2DS2-VASc, and ATRIA).<h4>Conclusions</h4>Neither conventional risk stratification tools nor our newly developed comprehensive prediction model using available clinical factors seemed to be suitable for identifying patients at high risk of recurrent ischemic stroke among AIS patients with AF in this modern direct oral anticoagulant era. Detailed individual information, including imaging, may be warranted to build a more robust and precise risk prediction model for stroke survivors with AF.Beom Joon KimKeon-Joo LeeEun Lyeong ParkKanta TanakaMasatoshi KogaSohei YoshimuraRyo ItabashiJae-Kwan ChaByung-Chul LeeHisanao AkiyamaYoshinari NagakaneJuneyoung LeeKazunori ToyodaSAMURAI Study InvestigatorsHee-Joon BaeCRCS-K InvestigatorsPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258377 (2021) |
| institution |
DOAJ |
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DOAJ |
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Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Beom Joon Kim Keon-Joo Lee Eun Lyeong Park Kanta Tanaka Masatoshi Koga Sohei Yoshimura Ryo Itabashi Jae-Kwan Cha Byung-Chul Lee Hisanao Akiyama Yoshinari Nagakane Juneyoung Lee Kazunori Toyoda SAMURAI Study Investigators Hee-Joon Bae CRCS-K Investigators Prediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: Development and validation of a risk score model. |
| description |
<h4>Background</h4>There is currently no validated risk prediction model for recurrent events among patients with acute ischemic stroke (AIS) and atrial fibrillation (AF). Considering that the application of conventional risk scores has contextual limitations, new strategies are needed to develop such a model. Here, we set out to develop and validate a comprehensive risk prediction model for stroke recurrence in AIS patients with AF.<h4>Methods</h4>AIS patients with AF were collected from multicenter registries in South Korea and Japan. A developmental dataset was constructed with 5648 registered cases from both countries for the period 2011‒2014. An external validation dataset was also created, consisting of Korean AIS subjects with AF registered between 2015 and 2018. Event outcomes were collected during 1 year after the index stroke. A multivariable prediction model was developed using the Fine-Gray subdistribution hazard model with non-stroke mortality as a competing risk. The model incorporated 21 clinical variables and was further validated, calibrated, and revised using the external validation dataset.<h4>Results</h4>The developmental dataset consisted of 4483 Korean and 1165 Japanese patients (mean age, 74.3 ± 10.2 years; male 53%); 338 patients (6%) had recurrent stroke and 903 (16%) died. The clinical profiles of the external validation set (n = 3668) were comparable to those of the developmental dataset. The c-statistics of the final model was 0.68 (95% confidence interval, 0.66 ‒0.71). The developed prediction model did not show better discriminative ability for predicting stroke recurrence than the conventional risk prediction tools (CHADS2, CHA2DS2-VASc, and ATRIA).<h4>Conclusions</h4>Neither conventional risk stratification tools nor our newly developed comprehensive prediction model using available clinical factors seemed to be suitable for identifying patients at high risk of recurrent ischemic stroke among AIS patients with AF in this modern direct oral anticoagulant era. Detailed individual information, including imaging, may be warranted to build a more robust and precise risk prediction model for stroke survivors with AF. |
| format |
article |
| author |
Beom Joon Kim Keon-Joo Lee Eun Lyeong Park Kanta Tanaka Masatoshi Koga Sohei Yoshimura Ryo Itabashi Jae-Kwan Cha Byung-Chul Lee Hisanao Akiyama Yoshinari Nagakane Juneyoung Lee Kazunori Toyoda SAMURAI Study Investigators Hee-Joon Bae CRCS-K Investigators |
| author_facet |
Beom Joon Kim Keon-Joo Lee Eun Lyeong Park Kanta Tanaka Masatoshi Koga Sohei Yoshimura Ryo Itabashi Jae-Kwan Cha Byung-Chul Lee Hisanao Akiyama Yoshinari Nagakane Juneyoung Lee Kazunori Toyoda SAMURAI Study Investigators Hee-Joon Bae CRCS-K Investigators |
| author_sort |
Beom Joon Kim |
| title |
Prediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: Development and validation of a risk score model. |
| title_short |
Prediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: Development and validation of a risk score model. |
| title_full |
Prediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: Development and validation of a risk score model. |
| title_fullStr |
Prediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: Development and validation of a risk score model. |
| title_full_unstemmed |
Prediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: Development and validation of a risk score model. |
| title_sort |
prediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: development and validation of a risk score model. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/e6451e4ff4974b3187bc494fecd0a194 |
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