Haodi Cai1, Yunfei Han1, Wen Sun2, Mingming Zha1, Xuan Shi3, Kangmo Huang3, Qingwen Yang1, Xiaoke Wang3, Rui Liu1, Xinfeng Liu1. 1. Department of Neurology, 12579Medical School of Southeast University, Jinling Hospital, China. 2. Stroke Center and Department of Neurology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, 12652University of Science and Technology of China, China. 3. Department of Neurology, 12581Medical School of Nanjing University, Jinling Hospital, China.
Abstract
OBJECTIVES: This study aims at exploring the 3-month outcome predicting ability of delayed neurological improvement and the cause of delayed neurological improvement. MATERIALS AND METHODS: Early neurological improvement and delayed neurological improvement were calculated to represent the neurological improvements. Good functional outcome was defined as a 90-day modified Rankin Scale score 0-2. We used multivariant logistic regression to explore the influential factors of good functional outcome as well as delayed neurological improvement. We applied net reclassification improvement and integrated discrimination improvement to assess the quantitative improvement of the predictive model. RESULTS: Early neurological improvement was observed in 50 (23%) patients and delayed neurological improvement exhibited in 67 (30%) patients. Early neurological improvement and delayed neurological improvement were both independent predictive factors to good functional outcome. In the basic model (adjusted for age, admission glucose level, baseline National Institute of Health Stroke Scale, and complications and number of retrieval attempts), early neurological improvement and delayed neurological improvement statistically improved the predictive ability (early neurological improvement: net reclassification improvement = 0.34, 95% confidence interval, 95% confidential interval (0.06, 0.69); integrated discrimination improvement = 0.05, p < 0.001; delayed neurological improvement: net reclassification improvement = 0.79, 95% confidential interval (0.47, 1.12); integrated discrimination improvement = 0.14, p < 0.001) delayed neurological improvement could predict clinical outcomes more accurately than early neurological improvement (early neurological improvement vs. delayed neurological improvement: integrated discrimination improvement = 0.09, p < 0.001). Moreover, delayed neurological improvement was affected by hypertension (odds ratio = 0.40, 95% CI (0.18, 0.88), p = 0.02), early neurological improvement (odds ratio = 20.10, 95% confidential interval (8.24, 19.02), p < 0.001), number of retrieval attempts (odds ratio = 0.39, 95% confidential interval (0.24, 0.66), p < 0.001), and complication (odds ratio = 0.25, 95% confidential interval (0.12, 0.54), p < 0.001). CONCLUSIONS: Delayed neurological improvement could predict clinical outcomes more accurately than early neurological improvement. Hypertension, early neurological improvement, numbers of retrieval attempts, and complications were all predicting factors to delayed neurological improvement.
OBJECTIVES: This study aims at exploring the 3-month outcome predicting ability of delayed neurological improvement and the cause of delayed neurological improvement. MATERIALS AND METHODS: Early neurological improvement and delayed neurological improvement were calculated to represent the neurological improvements. Good functional outcome was defined as a 90-day modified Rankin Scale score 0-2. We used multivariant logistic regression to explore the influential factors of good functional outcome as well as delayed neurological improvement. We applied net reclassification improvement and integrated discrimination improvement to assess the quantitative improvement of the predictive model. RESULTS: Early neurological improvement was observed in 50 (23%) patients and delayed neurological improvement exhibited in 67 (30%) patients. Early neurological improvement and delayed neurological improvement were both independent predictive factors to good functional outcome. In the basic model (adjusted for age, admission glucose level, baseline National Institute of Health Stroke Scale, and complications and number of retrieval attempts), early neurological improvement and delayed neurological improvement statistically improved the predictive ability (early neurological improvement: net reclassification improvement = 0.34, 95% confidence interval, 95% confidential interval (0.06, 0.69); integrated discrimination improvement = 0.05, p < 0.001; delayed neurological improvement: net reclassification improvement = 0.79, 95% confidential interval (0.47, 1.12); integrated discrimination improvement = 0.14, p < 0.001) delayed neurological improvement could predict clinical outcomes more accurately than early neurological improvement (early neurological improvement vs. delayed neurological improvement: integrated discrimination improvement = 0.09, p < 0.001). Moreover, delayed neurological improvement was affected by hypertension (odds ratio = 0.40, 95% CI (0.18, 0.88), p = 0.02), early neurological improvement (odds ratio = 20.10, 95% confidential interval (8.24, 19.02), p < 0.001), number of retrieval attempts (odds ratio = 0.39, 95% confidential interval (0.24, 0.66), p < 0.001), and complication (odds ratio = 0.25, 95% confidential interval (0.12, 0.54), p < 0.001). CONCLUSIONS: Delayed neurological improvement could predict clinical outcomes more accurately than early neurological improvement. Hypertension, early neurological improvement, numbers of retrieval attempts, and complications were all predicting factors to delayed neurological improvement.
Entities:
Keywords:
Acute ischemic stroke; National Institute of Health Stroke Scale; delayed neurological improvement; early neurological improvement; mechanical thrombectomy
Authors: Álvaro García-Tornel; Manuel Requena; Marta Rubiera; Marian Muchada; Jorge Pagola; David Rodriguez-Luna; Matias Deck; Jesus Juega; Noelia Rodríguez-Villatoro; Sandra Boned; Marta Olivé-Gadea; Alejandro Tomasello; David Hernández; Carlos A Molina; Marc Ribo Journal: Stroke Date: 2019-06-10 Impact factor: 7.914
Authors: M Ichijo; E Iwasawa; Y Numasawa; K Miki; S Ishibashi; M Tomita; H Tomimitsu; T Kamata; H Fujigasaki; S Shintani; H Mizusawa Journal: AJNR Am J Neuroradiol Date: 2015-07-23 Impact factor: 3.825
Authors: Mirja M Wirtz; Philipp Hendrix; Oded Goren; Lisa A Beckett; Heather R Dicristina; Clemens M Schirmer; Shamsher Dalal; Gregory Weiner; Paul M Foreman; Ramin Zand; Christoph J Griessenauer Journal: J Neurosurg Date: 2019-12-20 Impact factor: 5.115