Constructing a nomogram for short-term prognosis in postoperative patients with aneurysmal subarachnoid hemorrhage: a...

Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition with high morbidity and mortality. Early prediction of prognosis remains challenging. This study aimed to develop a nomogram incorporating clinical and inflammatory biomarkers to predict short-term outcomes in postoperative aSAH patients. Thus optimizing the intervention strategy and improve patient quality of life. Logistic regression analysis was used to determine the single predictor of aneurysmal subarachnoid hemorrhage. Based on these independent predictors, a nomogram was created in R studio. The results showed that the aneurysmal site (3.35[95% CI, 1.05-10.66], P = 0.041), affected side (3.77[95% CI, 1.17-12.11], P = 0.026), hydrocephalus (0.03[95% CI, 0.01-0.12], P < 0.001), Hunt-Hess grade (4.13[95% CI, 1.17-14.49], P = 0.027), GCS score (4.08[95% CI, 1.02-16.25], P = 0.046), hypertension history (0.18[95% CI, 0.06-0.55], P = 0.003), WBC (3.49[95% CI, 1.06-11.56], P = 0.04), MLR (0.33[95% CI, 0.12-0.92], P = 0.035) were independent predictors. The nomogram demonstrated superior predictive accuracy compared to existing models, with lower calibration errors (training group: 0.018; validation group: 0.052) and high AUC values (0.95 and 0.901, respectively). Given the class imbalance (84.3% of patients had favorable outcomes), sensitivity analyses were performed to verify the consistency and reliability of the findings. The nomogram constructed based on aneurysm location, affected side, presence of hydrocephalus, Hunt-Hess grade, GCS score, hypertension, WBC and MLR can enables clinicians to identify high-risk aSAH patients early, facilitating targeted interventions such as anti-inflammatory therapy or hydrocephalus management. This tool may improve resource allocation and reduce disability rates in critical care settings. Thus optimizing the intervention strategy and improve patient quality of life.