# A nomogram based on systemic inflammation response index and clinical risk factors for prediction of short-term prognosis of very elderly patients with hypertensive intracerebral hemorrhage

## TL;DR

This study creates a prediction tool using inflammation and clinical factors to forecast outcomes in elderly patients with brain hemorrhage due to high blood pressure.

## Contribution

A novel nomogram combining systemic inflammation response index and clinical factors for predicting prognosis in elderly hypertensive intracerebral hemorrhage patients.

## Key findings

- The nomogram achieved high predictive accuracy (AUC of 0.940 in training and 0.884 in validation).
- GCS score, hematoma expansion, COPD, and SIRI were identified as independent predictors of poor prognosis.
- The model showed good calibration and clinical applicability based on calibration curves and decision curve analysis.

## Abstract

To develop and validate a nomogram based on systemic inflammation response index (SIRI) and clinical risk factors to predict short-term prognosis in very elderly patients with hypertensive intracerebral hemorrhage (HICH).

A total of 324 very elderly HICH patients from January 2017 to June 2024 were retrospectively enrolled and randomly divided into two cohorts for training (n = 227) and validation (n = 97) according to the ratio of 7:3. Independent predictors of poor prognosis were analyzed using univariate and multivariate logistic regression analyses. Furthermore, a nomogram prediction model was built. The area under the receiver operating characteristic curves (AUC), calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the nomogram in predicting the prognosis of very elderly HICH.

By univariate and stepwise multivariate logistic regression analyses, GCS score (p < 0.001), hematoma expansion (p = 0.049), chronic obstructive pulmonary disease (p = 0.010), and SIRI (p = 0.005) were independent predictors for the prognosis in very elderly patients with HICH. The nomogram showed the highest predictive efficiency in the training cohort (AUC = 0.940, 95% CI: 0.909 to 0.971) and the validation cohort (AUC = 0.884, 95% CI: 0.813 to 0.954). The calibration curve indicated that the nomogram had good calibration. DCA showed that the nomogram had high applicability in clinical practice.

The nomogram incorporated with the SIRI and clinical risk factors has good potential in predicting the short-term prognosis of very elderly HICH.