Mingming FAN, Qian HAN, Zhengyao ZUO, Dongpo SU, Qing WANG, Mei Lİ, Tong CHEN

Identification and Validation of a Novel Nomogram Predicting 7-day Death in Patients with Intracerebral Hemorrhage

Background: Intracerebral hemorrhage (ICH) is a serious brain condition with high mortality and disability rates. In recent decades, several risk factors related to death risk have been identified, with several models predicting mortality, but rarely used and accepted in daily clinical practice. Aims: To establish and validate a predictive nomogram of spontaneous ICH death that can be used to predict patient death within 7 days. Study Design: Cohort study. Methods: A cohort of 449 patients with ICH, diagnosed clinically from January 2015 to December 2017, were identified as the model training cohort. Univariate analysis and least absolute contraction and selection operator (Lasso) regression were used to determine the most powerful predictors of patients with ICH. Discrimination, calibration, and clinical applicability were used to assess the function of the new nomogram. In external validation, we also evaluated the nomogram in another 148 subjects (validation cohort) examined between January and December 2018. Results: We observed no significant differences in patient baseline characteristics in the training and validation cohorts, including sex, age, Glasgow coma scale (GCS) score, and one-week mortality rates. The model included three predictive variables from univariate and multivariate analysis, including GCS scores, hematoma volume, and brainstem hemorrhage (BSH). Internal validation revealed that the nomogram had a good discrimination, the area under the receiver operating characteristic curve (AUC) was 0.935, and calibration was good (U = -0.004, P = 0.801). Similarly, this nomogram also showed good differentiation ability (AUC = 0.925) and good accuracy (U = -0.007, P = 0.241) in the validation cohort data. Decision curve analysis indicated that the new prediction model was helpful. Conclusion: At the early stages of the condition, our prediction model accurately predicts the death of patients with ICH.

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