A nomogram for predicting prognostic risk factors in individuals with poor grade aneurysmal subarachnoid hemorrhage: a retrospective study

Poor-grade aneurysmal subarachnoid hemorrhage (PGASAH) is associated with high mortality and morbidity despite advanced treatments. Accurate prediction of prognosis remains a clinical challenge. This study aimed to identify independent risk factors and develop a predictive nomogram for unfavorable outcomes in PGASAH patients. This retrospective study included 348 PGASAH patients treated between 2003 and 2016. Univariate and multivariate logistic regression analyses were performed to evaluate the association of premorbid conditions, admission aSAH clinical parameters, admission laboratory parameters, and clinical complications with unfavorable outcomes, defined as mRS > 2 at 6 months. Independent predictors were identified, and a nomogram was constructed to assess predictive accuracy and clinical utility. The median age of PGASAH patients was 55.1 years, 64.9% were females. Independent predictors of unfavorable outcomes (mRS > 2) at 6 months included age > 55 years (aOR = 2.44), cardiac valve disease (aOR = 6.50), dilated pupils at admission (aOR = 2.64), early infarction (aOR = 5.56), and delayed cerebral ischemia (aOR = 5.09). The nomogram demonstrated good predictive performance with an AUC of 0.807. Decision curve analysis affirmed its clinical applicability. The intuitive nomogram helps physicians predict PGASAH prognosis at discharge, improving long-term management, optimizing resource allocation, and enhancing patient and family awareness and psychological preparedness. External validation is required for broader clinical use.