The prediction of malignant middle cerebral artery infarction: a predicting approach using random forest
Ru Chen1, Zelin Deng2, Zhi Song1
1Neurological Department, The Third Xiangya Hospital of Central South University, Hunan, China.
Background:
Malignant middle cerebral artery infarction (MMI) is always associated with high mortality rates. Early decompressive craniectomy is crucial to its treatment. The purpose of this study was to establish a reliable model for an early prediction of MMI.
Methods:
Using a retrospective survey, we have collected the data of 132 patients with middle cerebral artery infarction. According to a prognosis, the patients are divided into the MMI group (n = 36) and the non-MMI group (n = 96). All the patients are represented by their clinical, biochemical, and imaging features. Then a random forest (RF) prediction model is established on the clinical data. Meanwhile, 3 traditional prediction models, including univariate linear discriminant analysis (LDA) model, multivariate LDA model, and binary logistic regression analysis (BLRA), are built to compare with the RF model. The prediction performance of different models is assessed by the area under the receiver operating characteristic curves (AUCs).
Results:
Four parameters, Glasgow Coma Scale, midline shifting, area, and volume of focus, selected as predictors in all models. As independent predictors, their AUCs are .72-.80, and when the sensitivities are high (.91-.95), the specificities are low (.32-.53). The AUC of RF model is .96, 95% confidence interval (CI) is (.93-.99), sensitivity is 1, and specificity is .85. The AUC of the multivariate LDA model is .87 and 95% CI is (.80-.93). The AUC of the BLRA model is .86 and 95% CI is (.80-.93).
Conclusions:
The RF performs very well in the given clinical data set, which indicates that the RF is applicable to the early prediction of the MMI.
Insights
A new random forest model accurately predicts malignant middle cerebral artery infarction (MMI) early. This approach offers high sensitivity and specificity, improving patient outcomes for this critical condition.
Area of Science:
- Neurology
- Medical Informatics
Background:
- Malignant middle cerebral artery infarction (MMI) presents a significant mortality risk.
- Early decompressive craniectomy is a vital treatment for MMI.
Purpose of the Study:
- To develop a reliable predictive model for early identification of MMI.
- To compare the performance of a random forest (RF) model against traditional methods.
Main Methods:
- Retrospective analysis of 132 patients with middle cerebral artery infarction.
- Development of a random forest (RF) prediction model using clinical data.
- Comparison with univariate and multivariate linear discriminant analysis (LDA) and binary logistic regression analysis (BLRA) models.
Main Results:
- Four key predictors identified: Glasgow Coma Scale, midline shifting, lesion area, and volume.
- The RF model achieved an AUC of 0.96 (95% CI: 0.93-0.99), with 100% sensitivity and 85% specificity.
- Traditional models showed lower performance (AUCs ranging from 0.86 to 0.87).
Conclusions:
- The random forest model demonstrates high applicability and performance for early MMI prediction.
- This model can aid clinicians in timely decision-making for MMI patients.
