World neurosurgery · 2020
Predictive Factors and Nomogram to Evaluate the Risk of Symptomatic Intracerebral Hemorrhage for Stroke Patients Receiving Thrombectomy.
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Qian Yu, Qian Zheng-Ting, Huang Chun-Hong, Wang Hong-Ye, Lu Xuan, Cao Kan, Sun Jin-Yu, Li Qiao-Yu
Аннотация
BACKGROUND: Symptomatic intracerebral hemorrhage (sICH) is a severe complication of mechanical thrombectomy (MT). This study is to identify predictive factors and create a nomogram to evaluate the risk of sICH after MT treatment.
METHODS: We conducted a retrospective analysis on 127 consecutive stroke patients treated by MT therapy. We evaluated multiple predictive factors for the incidence of sICH using univariate and multivariate logistic regressions. Based on the identified and other possible factors, a nomogram was constructed to predict the risk of sICH.
RESULTS: We identified several predictive factors for sICH in the univariate analysis, including thrombectomy maneuvers >3 (odds ratio [OR], 4.42; 95% confidence interval [CI], 1.25-15.6; P = 0.0211), admission blood glucose (OR, 1.29; 95% CI, 1.13-1.48; P = 0.0002), diabetes mellitus (OR, 4.44; 95% CI, 1.64-12.0; P = 0.0033), and admission National Institutes of Health Stroke Scale (NIHSS) score (OR, 1.05; 95% CI, 1.01-1.10; P = 0.0263). The multivariate analysis showed that admission NIHSS score and blood glucose significantly affected the prognosis. Moreover, the proposed nomogram showed reliable identification ability with an area under the curve of 0.82 (95% CI, 0.71-0.93), specificity of 0.745, sensitivity of 0.762, accuracy of 0.748, and negative predictive value of 0.941.
CONCLUSIONS: Our study identified the admission NIHSS score and admission blood glucose level as predictive factors for sICH. Moreover, the proposed nomogram based on possible factors showed reliable predictive performance in evaluating the risk of sICH.
METHODS: We conducted a retrospective analysis on 127 consecutive stroke patients treated by MT therapy. We evaluated multiple predictive factors for the incidence of sICH using univariate and multivariate logistic regressions. Based on the identified and other possible factors, a nomogram was constructed to predict the risk of sICH.
RESULTS: We identified several predictive factors for sICH in the univariate analysis, including thrombectomy maneuvers >3 (odds ratio [OR], 4.42; 95% confidence interval [CI], 1.25-15.6; P = 0.0211), admission blood glucose (OR, 1.29; 95% CI, 1.13-1.48; P = 0.0002), diabetes mellitus (OR, 4.44; 95% CI, 1.64-12.0; P = 0.0033), and admission National Institutes of Health Stroke Scale (NIHSS) score (OR, 1.05; 95% CI, 1.01-1.10; P = 0.0263). The multivariate analysis showed that admission NIHSS score and blood glucose significantly affected the prognosis. Moreover, the proposed nomogram showed reliable identification ability with an area under the curve of 0.82 (95% CI, 0.71-0.93), specificity of 0.745, sensitivity of 0.762, accuracy of 0.748, and negative predictive value of 0.941.
CONCLUSIONS: Our study identified the admission NIHSS score and admission blood glucose level as predictive factors for sICH. Moreover, the proposed nomogram based on possible factors showed reliable predictive performance in evaluating the risk of sICH.