Nomogram based on pan-immune-inflammation value to predict short-term prognosis in spontaneous intracerebral...

The aim of this study was to investigate the impact of the Pan-Immune-Inflammation Value (PIV) on the prognosis of spontaneous intracerebral hemorrhage (ICH) and to develop and validate a nomogram for identifying patients with a poor prognosis following ICH.

We retrospectively collected the clinical data of 742 patients with ICH admitted to the Affiliated Hospital of Xuzhou Medical University from September 2018 to March 2024. A modified Rankin Scale score > 3 at 90 days after discharge was defined as a poor short-term prognosis. The enrolled patients were randomly assigned to a training cohort and a validation cohort in a 7:3 ratio. In the training cohort, risk factors associated with poor short-term prognosis were identified through univariate and multivariate logistic regression analyses. Based on these risk factors, a nomogram was developed and validated.

Of the 742 ICH patients included in this study, 519 were assigned to the training cohort and 223 to the validation cohort. Multivariate logistic regression analysis identified several risk factors for poor prognosis of ICH: brainstem hemorrhage (OR = 3.17, 95% CI = 1.80-5.59, p < 0.01), reduced activated partial thromboplastin time (APTT) (OR = 0.94, 95% CI = 0.89-0.99, p = 0.047), large bleeding volume (OR = 1.06, 95% CI = 1.04-1.09, p < 0.01), low Glasgow Coma Scale (GCS) score (OR = 0.76, 95% CI = 0.70-0.82, p < 0.01), and high PIV level (OR = 1.01, 95% CI = 1.01-1.01, p < 0.01). A nomogram was constructed based on these factors. The area under the receiver operating characteristic curve was 0.86, indicating good discrimination ability. The Hosmer-Lemeshow goodness-of-fit test for the validation cohort demonstrated that the model had satisfactory calibration. Decision curve analysis revealed that the nomogram had clinical utility across a wide range of threshold probabilities.

A high PIV level, large bleeding volume, and low GCS score are significant risk factors for poor prognosis in patients with ICH. The nomogram based on these factors demonstrates robust predictive performance.