Xifeng Zheng1, Fang Fang2, Weidong Nong3, Dehui Feng4, Yu Yang4. 1. Department of Geriatrics, Affiliated Hospital of Guangdong Medical University, No.57, South of Renming Road, Zhanjiang, Guangdong, 524001, People's Republic of China. zhengxf@gdmu.edu.cn. 2. Department of General Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, Guangdong, China. 3. Department of Neurology, Affiliated Minzu Hospital of Guangxi Medical University, Nanning, Guangxi, China. 4. Department of Geriatrics, Affiliated Hospital of Guangdong Medical University, No.57, South of Renming Road, Zhanjiang, Guangdong, 524001, People's Republic of China.
Abstract
OBJECTIVES: This study aimed to construct and validate a prediction model of acute ischemic stroke in geriatric patients with primary hypertension. METHODS: This retrospective file review collected information on 1367 geriatric patients diagnosed with primary hypertension and with and without acute ischemic stroke between October 2018 and May 2020. The study cohort was randomly divided into a training set and a testing set at a ratio of 70 to 30%. A total of 15 clinical indicators were assessed using the chi-square test and then multivariable logistic regression analysis to develop the prediction model. We employed the area under the curve (AUC) and calibration curves to assess the performance of the model and a nomogram for visualization. Internal verification by bootstrap resampling (1000 times) and external verification with the independent testing set determined the accuracy of the model. Finally, this model was compared with four machine learning algorithms to identify the most effective method for predicting the risk of stroke. RESULTS: The prediction model identified six variables (smoking, alcohol abuse, blood pressure management, stroke history, diabetes, and carotid artery stenosis). The AUC was 0.736 in the training set and 0.730 and 0.725 after resampling and in the external verification, respectively. The calibration curve illustrated a close overlap between the predicted and actual diagnosis of stroke in both the training set and testing validation. The multivariable logistic regression analysis and support vector machine with radial basis function kernel were the best models with an AUC of 0.710. CONCLUSION: The prediction model using multiple logistic regression analysis has considerable accuracy and can be visualized in a nomogram, which is convenient for its clinical application.
OBJECTIVES: This study aimed to construct and validate a prediction model of acute ischemic stroke in geriatric patients with primary hypertension. METHODS: This retrospective file review collected information on 1367 geriatric patients diagnosed with primary hypertension and with and without acute ischemic stroke between October 2018 and May 2020. The study cohort was randomly divided into a training set and a testing set at a ratio of 70 to 30%. A total of 15 clinical indicators were assessed using the chi-square test and then multivariable logistic regression analysis to develop the prediction model. We employed the area under the curve (AUC) and calibration curves to assess the performance of the model and a nomogram for visualization. Internal verification by bootstrap resampling (1000 times) and external verification with the independent testing set determined the accuracy of the model. Finally, this model was compared with four machine learning algorithms to identify the most effective method for predicting the risk of stroke. RESULTS: The prediction model identified six variables (smoking, alcohol abuse, blood pressure management, stroke history, diabetes, and carotid artery stenosis). The AUC was 0.736 in the training set and 0.730 and 0.725 after resampling and in the external verification, respectively. The calibration curve illustrated a close overlap between the predicted and actual diagnosis of stroke in both the training set and testing validation. The multivariable logistic regression analysis and support vector machine with radial basis function kernel were the best models with an AUC of 0.710. CONCLUSION: The prediction model using multiple logistic regression analysis has considerable accuracy and can be visualized in a nomogram, which is convenient for its clinical application.
Authors: Bryan Williams; Giuseppe Mancia; Wilko Spiering; Enrico Agabiti Rosei; Michel Azizi; Michel Burnier; Denis L Clement; Antonio Coca; Giovanni de Simone; Anna Dominiczak; Thomas Kahan; Felix Mahfoud; Josep Redon; Luis Ruilope; Alberto Zanchetti; Mary Kerins; Sverre E Kjeldsen; Reinhold Kreutz; Stephane Laurent; Gregory Y H Lip; Richard McManus; Krzysztof Narkiewicz; Frank Ruschitzka; Roland E Schmieder; Evgeny Shlyakhto; Costas Tsioufis; Victor Aboyans; Ileana Desormais Journal: Eur Heart J Date: 2018-09-01 Impact factor: 29.983
Authors: William J Powers; Alejandro A Rabinstein; Teri Ackerson; Opeolu M Adeoye; Nicholas C Bambakidis; Kyra Becker; José Biller; Michael Brown; Bart M Demaerschalk; Brian Hoh; Edward C Jauch; Chelsea S Kidwell; Thabele M Leslie-Mazwi; Bruce Ovbiagele; Phillip A Scott; Kevin N Sheth; Andrew M Southerland; Deborah V Summers; David L Tirschwell Journal: Stroke Date: 2018-01-24 Impact factor: 7.914
Authors: Salim S Virani; Alvaro Alonso; Hugo J Aparicio; Emelia J Benjamin; Marcio S Bittencourt; Clifton W Callaway; April P Carson; Alanna M Chamberlain; Susan Cheng; Francesca N Delling; Mitchell S V Elkind; Kelly R Evenson; Jane F Ferguson; Deepak K Gupta; Sadiya S Khan; Brett M Kissela; Kristen L Knutson; Chong D Lee; Tené T Lewis; Junxiu Liu; Matthew Shane Loop; Pamela L Lutsey; Jun Ma; Jason Mackey; Seth S Martin; David B Matchar; Michael E Mussolino; Sankar D Navaneethan; Amanda Marma Perak; Gregory A Roth; Zainab Samad; Gary M Satou; Emily B Schroeder; Svati H Shah; Christina M Shay; Andrew Stokes; Lisa B VanWagner; Nae-Yuh Wang; Connie W Tsao Journal: Circulation Date: 2021-01-27 Impact factor: 29.690