The rate-pressure product combined model within 24 h on admission predicts the 30-day mortality rate in conservatively treated patients with intracerebral hemorrhage

TL;DR

A new model using four risk factors, including rate-pressure product, helps predict 30-day mortality in patients with brain hemorrhage within 24 hours of admission.

Contribution

A novel predictive model combining rate-pressure product and other factors for early mortality risk assessment in intracerebral hemorrhage patients.

Findings

The model includes four predictors: Glasgow Coma Scale, hematoma volume, rate-pressure product, and c-reactive protein.

The model's C-index was 0.933 in training and 0.954 in validation, showing strong predictive ability.

The model performed as well as the classic ICH score in predicting mortality risk.

Abstract

Recently, some literature has proposed new indicators such as rate-pressure product, platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, etc. However, there has been no literature that has utilized these new indicators to establish a predictive model for assessing the risk of mortality in patients within 24 h on admission. Therefore, this study aims to build a predictive model that can rapidly assess the likelihood of mortality in patients within 24 h of admission.

The datasets used in this study are available from the corresponding author upon reasonable request. Patients were randomly assigned to the training or validation cohort based on a ratio of 7:3, which was implemented as internal validations for the final predictive models. In the training set, least absolute shrinkage and selection operator (LASSO) regression was employed to select predictive factors, followed by both univariate and subsequent multivariate analysis. The predictive ability was assessed by the area under the receiver operating characteristic (ROC) curve.

A total of 428 patients were included in our research. The final model included 4 independent predictors (Glasgow Coma Scale, hematoma volume, rate-pressure product, c-reactive protein) and was developed as a simple-to-use nomogram. The training set and internal validation set model’s C-index are 0.933 and 0.954, demonstrating moderate predictive ability with regard to risks of mortality. Compared to ICH score (AUC: 0.910 and 0.925), the net reclassification index (NRI) is 0.298 (CI = −0.105 to 0.701, p: 0.147) and integrated discrimination improvement (IDI) is 0.089 (CI = −0.049 to 0.228, p: 0.209). Our model is equally excellent as the classic ICH score model.

We developed a model with four independent risk factors to predict the mortality of ICH patients. Our predictive model is effective in assessing the risk of mortality in patients within 24 h on admission, which might be worth considering in clinical settings after further external validation.

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