P Gong1, X Zhang2, Y Gong3, Y Liu1, S Wang1, Z Li1, W Chen4, F Zhou1, J Zhou1, T Jiang1, Y Zhang1. 1. Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China. 2. Department of Neurology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China. 3. Department of Gerontology, Nantong Third People's Hospital, Nantong University, Nantong, Jiangsu, China. 4. Department of Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Abstract
BACKGROUND AND PURPOSE: Acute ischaemic stroke (AIS) is a vital cause of mortality and morbidity in China. Many AIS patients develop early neurological deterioration (END). This study aimed to construct a nomogram to predict END in AIS patients. METHODS: Acute ischaemic stroke patients in Nanjing First Hospital were recruited as the training cohort. Additional patients in Nantong Third People's Hospital were enrolled as the validation cohort. Multivariate logistic regression was utilized to establish the nomogram. Discrimination and calibration performance of the nomogram were tested by concordance index and calibration plots. Decision curve analysis was employed to assess the utility of the nomogram. RESULTS: In all, 1889 and 818 patients were recruited in the training and validation cohorts, respectively. Age [odds ratio (OR) 1.075; 95% confidence interval (CI) 1.059-1.091], diabetes mellitus (OR 1.673; 95% CI 1.181-2.370), atrial fibrillation (OR 3.297; 95% CI 2.005-5.421), previous antiplatelet medication (OR 0.473; 95% CI 0.301-0.744), hyper-sensitive C-reactive protein (OR 1.049; 95% CI 1.036-1.063) and baseline National Institutes of Health Stroke Scale (OR 1.071; 95% CI 1.045-1.098) were associated with END and incorporated in the nomogram. The concordance index was 0.826 (95% CI 0.785-0.885) and 0.798 (95% CI 0.749-0.847) in the training and validation cohorts. By decision curve analysis, the model was relevant between thresholds of 0.06 and 0.90 in the training cohort and 0.08 and 0.77 in the validation cohort. CONCLUSIONS: The nomogram composed of hyper-sensitive C-reactive protein, age, diabetes mellitus, atrial fibrillation, previous antiplatelet medication and baseline National Institutes of Health Stroke Scale may predict the risk of END in AIS patients.
BACKGROUND AND PURPOSE: Acute ischaemic stroke (AIS) is a vital cause of mortality and morbidity in China. Many AISpatients develop early neurological deterioration (END). This study aimed to construct a nomogram to predict END in AISpatients. METHODS: Acute ischaemic strokepatients in Nanjing First Hospital were recruited as the training cohort. Additional patients in Nantong Third People's Hospital were enrolled as the validation cohort. Multivariate logistic regression was utilized to establish the nomogram. Discrimination and calibration performance of the nomogram were tested by concordance index and calibration plots. Decision curve analysis was employed to assess the utility of the nomogram. RESULTS: In all, 1889 and 818 patients were recruited in the training and validation cohorts, respectively. Age [odds ratio (OR) 1.075; 95% confidence interval (CI) 1.059-1.091], diabetes mellitus (OR 1.673; 95% CI 1.181-2.370), atrial fibrillation (OR 3.297; 95% CI 2.005-5.421), previous antiplatelet medication (OR 0.473; 95% CI 0.301-0.744), hyper-sensitive C-reactive protein (OR 1.049; 95% CI 1.036-1.063) and baseline National Institutes of Health Stroke Scale (OR 1.071; 95% CI 1.045-1.098) were associated with END and incorporated in the nomogram. The concordance index was 0.826 (95% CI 0.785-0.885) and 0.798 (95% CI 0.749-0.847) in the training and validation cohorts. By decision curve analysis, the model was relevant between thresholds of 0.06 and 0.90 in the training cohort and 0.08 and 0.77 in the validation cohort. CONCLUSIONS: The nomogram composed of hyper-sensitive C-reactive protein, age, diabetes mellitus, atrial fibrillation, previous antiplatelet medication and baseline National Institutes of Health Stroke Scale may predict the risk of END in AISpatients.