Establishment of a risk prediction model for cerebrogenic multiple organ dysfunction syndrome in patients with acute cerebral hemorrhage
10.3760/cma.j.cn121430-20240202-00109
- VernacularTitle:急性脑出血患者并发脑源性多器官功能障碍综合征风险预测模型的建立
- Author:
Huaibiao WU
1
;
Hao ZHANG
;
Chengjun GUO
Author Information
1. 皖南康复医院急诊与重症医学科，安徽芜湖 241000
- Keywords:
Acute cerebral hemorrhage;
Cerebrogenic multiple organ dysfunction syndrome;
Influencing factor;
Risk prediction model
- From:
Chinese Critical Care Medicine
2024;36(9):966-971
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct and validate a predictive model for the risk of cerebrogenic multiple organ dysfunction syndrome (CMODS) in patients with acute cerebral hemorrhage.Methods:Clinical data of 93 patients with acute cerebral hemorrhage admitted to Wannan Rehabilitation Hospital from January 2019 to June 2023 were retrospectively analyzed. Data included baseline information, disease severity score, laboratory examination indicators, cerebral hemorrhage status, treatment status, etc. Patients were divided into CMODS group and non-CMODS group according to whether CMODS occurred during hospitalization. The clinical data of the two groups were compared. Multivariate Logistic regression was used to analyze the risk factors of CMODS in patients with acute cerebral hemorrhage. A nomogram model was constructed to predict the risk of CMODS in patients with acute cerebral hemorrhage, and the model was validated. Receiver operator characteristic curve (ROC curve) was used to evaluate the predictive efficiency of nomogram model for CMODS in patients with acute cerebral hemorrhage.Results:A total of 93 patients with acute cerebral hemorrhage were enrolled, including 26 patients in CMODS group and 67 patients in non-CMODS group. Compared with the non-CMODS group, the ratio of diabetes, acute physiological and chronic health evaluationⅡ(APACHEⅡ)≥35 score, cerebral hemorrhage volume ≥30 mL, endotoxemia, and national institutes of health stroke scale (NIHSS) and intracranial pressure of patients in the CMODS group were significantly higher, while the Glasgow coma score (GCS) was significantly lower and the length of hospital stay was significantly longer, with statistically significant differences (all P < 0.05). Multivariate Logistic regression analysis showed that diabetes mellitus [odds ratio ( OR) = 3.615, 95% confidence interval (95% CI) was 1.487-8.785, P = 0.000], APACHEⅡscore ( OR = 4.697, 95% CI was 1.933-11.416, P = 0.000), endotoxemia ( OR = 4.577, 95% CI was 1.883-11.123, P = 0.000), and cerebral hemorrhage volume ( OR = 4.039, 95% CI was 1.662-9.816, P = 0.000) were the risk factors for CMODS in patients with acute cerebral hemorrhage. Taking the above risk factors as predictive variables, a nomogram prediction model was established. The verification results of the nomogram model showed that the C index was 0.804 (95% CI was 0.768-0.841), and the calibration curve was close to the ideal curve with good fit ( P > 0.05). ROC curve results showed that the sensitivity and specificity of the nomogram model in predicting CMODS in patients with acute cerebral hemorrhage were 76.92%, 86.57%, respectively, and the area under the ROC curve (AUC) was 0.855 (95% CI was 0.776-0.935). Conclusions:Diabetes mellitus, APACHEⅡscore, endotoxemia and intracerebral hemorrhage are risk factors for CMODS in patients with acute cerebral hemorrhage. The risk prediction model based on these risk factors is effective in evaluating the risk of CMODS in patients with acute cerebral hemorrhage.
