Wanliang Du1, Xingquan Zhao1,2, Yilong Wang1,3, Guitao Zhang4, Jiming Fang5, Yuesong Pan4, Liping Liu1, Kehui Dong1, Gaifen Liu1,4, Yongjun Wang1,2,3,4,6. 1. Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China. 2. Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing 100070, China. 3. Center of Stroke, Beijing Institute for Brain Disorders, Beijing 100070, China. 4. China National Clinical Research Center for Neurological Diseases, Beijing 100070, China. 5. Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. 6. Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100070, China.
Abstract
BACKGROUND: For patients hospitalized after acute ischemic stroke (AIS), the preadmission comorbidities, level of consciousness (LOC), age and neurologic deficit (PLAN) score can help to identify those who may have a poor outcome. Implementing the PLAN score in other types of stroke may also have predictive value. Our study aimed to evaluate the PLAN score's prognostic accuracy in predicting 1-year mortality and severe disability after intracerebral hemorrhage (ICH). METHODS: We analyzed data found in the China National Stroke Registry (CNSR) of 2,453 hospitalized patients in 132 urban Chinese hospitals, diagnosed with ICH from September 2007 to August 2008. The outcomes analysis included 30-day mortality, modified Rankin Scale score (mRS) of 5-6 at discharge, and 1-year mortality. Univariate and multivariate analysis was performed, and we calculated consistency statistics (C statistic). We evaluated the PLAN score performance using area under the curve (AUC) calculations. RESULTS: We found that the 30-day mortality was 12.6%, the frequency of a mRS 5-6 at discharge was 20.6%, and 1-year mortality was 21.9%. The PLAN score had good predictive value in 30-day mortality (C statistic, 0.82), death or severe dependence at discharge (0.84), and 1-year mortality (0.82). CONCLUSIONS: In patients hospitalized for ICH, the 30-day mortality, death or severe dependence at discharge and 1-year mortality can be predicted by the PLAN score. Similarly to patients hospitalized after AIS, the PLAN score can help to identify patients likely to have poor outcomes following hospitalization for ICH. 2020 Annals of Translational Medicine. All rights reserved.
BACKGROUND: For patients hospitalized after acute ischemic stroke (AIS), the preadmission comorbidities, level of consciousness (LOC), age and neurologic deficit (PLAN) score can help to identify those who may have a poor outcome. Implementing the PLAN score in other types of stroke may also have predictive value. Our study aimed to evaluate the PLAN score's prognostic accuracy in predicting 1-year mortality and severe disability after intracerebral hemorrhage (ICH). METHODS: We analyzed data found in the China National Stroke Registry (CNSR) of 2,453 hospitalized patients in 132 urban Chinese hospitals, diagnosed with ICH from September 2007 to August 2008. The outcomes analysis included 30-day mortality, modified Rankin Scale score (mRS) of 5-6 at discharge, and 1-year mortality. Univariate and multivariate analysis was performed, and we calculated consistency statistics (C statistic). We evaluated the PLAN score performance using area under the curve (AUC) calculations. RESULTS: We found that the 30-day mortality was 12.6%, the frequency of a mRS 5-6 at discharge was 20.6%, and 1-year mortality was 21.9%. The PLAN score had good predictive value in 30-day mortality (C statistic, 0.82), death or severe dependence at discharge (0.84), and 1-year mortality (0.82). CONCLUSIONS: In patients hospitalized for ICH, the 30-day mortality, death or severe dependence at discharge and 1-year mortality can be predicted by the PLAN score. Similarly to patients hospitalized after AIS, the PLAN score can help to identify patients likely to have poor outcomes following hospitalization for ICH. 2020 Annals of Translational Medicine. All rights reserved.
Authors: Eric E Smith; Nandavar Shobha; David Dai; Daiwai M Olson; Mathew J Reeves; Jeffrey L Saver; Adrian F Hernandez; Eric D Peterson; Gregg C Fonarow; Lee H Schwamm Journal: Circulation Date: 2010-09-27 Impact factor: 29.690
Authors: José L Ruiz-Sandoval; Erwin Chiquete; Samuel Romero-Vargas; Juan J Padilla-Martínez; Salvador González-Cornejo Journal: Stroke Date: 2007-03-22 Impact factor: 7.914
Authors: Phyo Kyaw Myint; Allan B Clark; Chun Shing Kwok; John Davis; Ramesh Durairaj; Anand K Dixit; Anil K Sharma; Gary A Ford; John F Potter Journal: Int J Stroke Date: 2013-07-09 Impact factor: 5.266
Authors: John Michael Reid; Dingwei Dai; Susanna Delmonte; Carl Counsell; Stephen J Phillips; Mary Joan MacLeod Journal: Age Ageing Date: 2017-05-01 Impact factor: 10.668
Authors: Gustavo Saposnik; Moira K Kapral; Ying Liu; Ruth Hall; Martin O'Donnell; Stavroula Raptis; Jack V Tu; Muhammad Mamdani; Peter C Austin Journal: Circulation Date: 2011-02-07 Impact factor: 29.690
Authors: Natalia S Rost; Eric E Smith; Yuchiao Chang; Ryan W Snider; Rishi Chanderraj; Kristin Schwab; Emily FitzMaurice; Lauren Wendell; Joshua N Goldstein; Steven M Greenberg; Jonathan Rosand Journal: Stroke Date: 2008-06-12 Impact factor: 7.914