Impact Of C-Reactive Protein/Albumin Ratio on Intra- Hospital Mortality Among Patients with Spontaneous Intracerebral Hemorrhage

Abstract

Background: Spontaneous intracerebral hemorrhage (ICH) is a severe neurological emergency associated with high morbidity and mortality. Intra-hospital mortality rates remain substantial, and accurate prediction of outcomes is critical for effective management. The C-reactive protein (CRP)/Albumin ratio has been suggested as a prognostic marker in various diseases, but its role in predicting intra-hospital mortality in ICH patients remains underexplored.

Objective: This study aimed to investigate the impact of the CRP/Albumin ratio upon admission on intra-hospital mortality in patients with spontaneous ICH.

Methods: A prospective observational study was conducted over one year at Government Theni Medical College and Hospital. A total of 120 patients diagnosed with spontaneous ICH were included, with 80 survivors and 40 non-survivors. Baseline clinical data, including CRP and albumin levels, comorbidities, and treatment regimens, were collected. Statistical analyses were performed to assess the association between the CRP/Albumin ratio and in-hospital mortality.

Results: The CRP/Albumin ratio was significantly higher in non-survivors compared to survivors (P < 0.001). Elevated CRP levels (P < 0.001) and lower albumin levels (P < 0.001) were also significantly associated with increased mortality. Other factors such as systolic blood pressure (P < 0.001) and premedication (P = 0.005) were also found to influence survival outcomes, while comorbidities like diabetes and smoking status did not show significant effects on mortality.

Conclusion: The CRP/Albumin ratio is a reliable and significant predictor of intra-hospital mortality in patients with spontaneous intracerebral hemorrhage. Monitoring this ratio could aid clinicians in early risk stratification and improve the management of ICH patients. Future studies with larger sample sizes are warranted to further validate these findings and explore additional biomarkers for outcome prediction.