Development of a Nomogram Model Based on Lactate-To-Albumin Ratio for Prognostic Prediction in Hospitalized Patients with Intracerebral Hemorrhage

This study aims to develop a nomogram model incorporating lactate-to-albumin ratio (LAR) to predict the prognosis of hospitalized patients with intracerebral hemorrhage (ICH) and demonstrate its excellent predictive performance. A total of 226 patients with ICH from the MIMIC Ⅲ database were randomly split into 8:2 ratio training and experimental groups, and 38 patients from the eICU-CRD for external validation. Univariate and multivariate Cox proportional hazards regression analysis was performed to identify independent factors associated with ICH, and multivariate Cox regression was used to construct nomograms for 7-day and 14-day overall survival (OS). The performance of nomogram was verified by the calibration curves, decision curves and receiver operating characteristic (ROC) curves. Our study identified LAR, glucose, mean blood pressure, sodium and ethnicity as independent factors influencing in-hospital prognosis. The predictive performance of our nomogram model for predicting 7-day and 14 -day OS (AUCs: 0.845 and 0.830 respectively) are both superior to OASIS, SAPS II, and SIRS (AUCs: 0.617, 0.620 and 0.591 and AUCs: 0.709, 0.715 and 0.640, respectively) in internal validation, and also demonstrate favorable predictive performance in external validation (AUCs: 0.778 and 0.778 respectively). LAR as a novel biomarker is closely associated with an increased risk of in-hospital mortality of patients with ICH. The nomogram model incorporating LAR along with glucose, mean blood pressure, sodium and ethnicity demonstrate excellent predictive performance for predicting the prognosis of 7- and 14-day OS of hospitalized patients with ICH.