A novel model for predicting the outcome of intracerebral hemorrhage: Based on 1186 Patients

Abstract

Objective: To establish a model for predicting the outcome according to the clinical and computed tomography(CT) image data of patients with intracerebral hemorrhage(ICH). Methods: The clinical and CT image data of the patients with ICH in Qinghai Provincial People's Hospital and Xuzhou Central Hospital were collected. The risk factors related to the poor outcome of the patients were determined by univariate and multivariate logistic regression analysis. To determine the effect of factors related to poor outcome, the nomogram model was made by software of R 3.5.2 and the support vector machine operation was completed by software of SPSS Modelor. Results: A total of 8265 patients were collected and 1186 patients met the criteria of the study. Age, hospitalization days, blend sign, intraventricular extension, subarachnoid hemorrhage, midline shift, diabetes and baseline hematoma volume were independent predictors of poor outcome. Among these factors, baseline hematoma volume20ml (odds ratio:13.706, 95% confidence interval:9.070-20.709, p < 0.001) was the most significant factor for poor outcome, followed by the volume among 10ml-20ml (odds ratio:11.834, 95% confidence interval:7.909-17.707, p < 0.001). It was concluded that the highest percentage of weight in outcome was baseline hematoma volume (25.0%), followed by intraventricular hemorrhage (23.0%). Conclusion: This predictive model might accurately predict the outcome of patients with ICH. It might have a wide range of application prospects in clinical.

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

“…Similar to many previous studies, we found that age ( Inoue et al, 2018 ; He et al, 2020 ), GCS score ( Cui et al, 2020 ), and bleeding volume ( Al-Shahi Salman et al, 2018 ; Du et al, 2020 ; He et al, 2020 ; Kang et al, 2021 ; Masotti et al, 2021 ; Pasi et al, 2021 ) were independent risk factors for the poor prognosis of ICH patients. It is generally believed that the main causes of ICH are hypertension and CAA ( Schrag and Kirshner, 2020 ).…”

“…These data indicate that the constructed model can reduce the number of variables and its predictive performance is not worse than that of commonly used scoring systems. Similar to many previous studies, we found that age (Inoue et al, 2018;He et al, 2020), GCS score (Cui et al, 2020), and bleeding volume (Al-Shahi Salman et al, 2018;Du et al, 2020;FIGURE 3 Thirty-day mortality nomogram for intracerebral hemorrhage. Nomogram included age, GCS, creatinine, WBC, temperature, glucose, urine output, and bleeding volume for predicting 30-day mortality after an acute intracerebral hemorrhage.…”

“…Based on this value, the risk of 30-day morality could be predicted. He et al, 2020;Kang et al, 2021;Masotti et al, 2021;Pasi et al, 2021) were independent risk factors for the poor prognosis of ICH patients. It is generally believed that the main causes of ICH are hypertension and CAA (Schrag and Kirshner, 2020).…”

“…A nomogram is a commonly used tool to evaluate oncology and medical prognosis and can graphically represent complex mathematical formulas ( Balachandran et al, 2015 ). We reviewed several studies of nomogram prediction models with ICH patient outcomes as endpoints and found they included data on adult ICH patients (patient numbers ranging from 200 to 1000) at one or several medical centers ( Cui et al, 2020 ; Du et al, 2020 ; He et al, 2020 ; Kang et al, 2021 ). All studies excluded patients with secondary ICH.…”

Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage

“…Baseline hematoma volume (> 20 ml), IVH, age (> 53 years), and diabetes had the highest percentage of influence weight in a ML model developed by He and colleagues [ 18 ] to estimate poor outcome in supratentorial spontaneous ICH treated with conservative treatment. Another ML-based radiomics-clinical model constructed to predict IVH growth after spontaneous ICH found as independent predictors of IVH growth: hypercholesterolemia, baseline Graeb score, time to initial CT, international normalized ratio, and Rad score [ 43 ].…”