Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

Jianyu Zou; Huihuang Chen; Cuiqing Liu; Zhenbin Cai; Jie Yang; Yunlong Zhang; Shaojin Li; Hongsheng Lin; Minghui Tan

doi:10.3389/fnins.2022.942100

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

Front Neurosci. 2022 Aug 10:16:942100. doi: 10.3389/fnins.2022.942100. eCollection 2022.

Authors

Jianyu Zou¹, Huihuang Chen², Cuiqing Liu³, Zhenbin Cai¹, Jie Yang¹, Yunlong Zhang¹, Shaojin Li¹, Hongsheng Lin¹, Minghui Tan¹

Affiliations

¹ Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, China.
² Department of Rehabilitation, The First Affiliated Hospital of Jinan University, Guangzhou, China.
³ Department of Nursing, The First Affiliated Hospital of Jinan University, Guangzhou, China.

Abstract

Background: Intracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.

Methods: ICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results: A total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.

Conclusion: This study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

Keywords: MIMIC III database; intracerebral hemorrhage; mortality; nomogram; prognosis.