A nomogram model for predicting postoperative prognosis in patients with aneurysmal subarachnoid hemorrhage using preoperative biochemical indices.

OBJECTIVE

The nutritional status and inflammatory responses of patients with aneurysmal subarachnoid hemorrhage (aSAH) play a vital prognostic role. We investigated the relationship between preoperative prognostic nutritional index (PNI)、neutrophil/albumin ratio (NAR)、platelet/albumin ratio (PAR) and other factors and the clinical prognosis of patients who underwent clipping for aSAH and its predictive model.

METHODS

The clinical data of 212 patients with aSAH who underwent neurosurgery at Nanyang Central Hospital between 2018 and 2023 were retrospectively analyzed. Based on the Glasgow Outcome Scale (GOS) score at 6 months postoperatively, the patients were categorized into two groups: poor (GOSI-III) and good (GOSIV-V) prognosis groups. Multivariate logistic regression analysis was performed to determine the predictive value of preoperative PNI、NAR、PAR、hyperlipidemia and Glasgow Coma Scale (GCS) for prognosis. Furthermore, nomograms and prognostic prediction models were constructed. Receiver operating characteristic curves and area under the curve (AUC) were utilized to determine the predictive values.

RESULTS

Multivariate logistic regression analysis revealed that PNI (OR = 1.250, 95%CI 1.060 ~ 1.475, P = 0.008), NAR (OR = 0.000, 95%CI 0.000 ~ 0.004, P = 0.000), PAR(OR = 0.515, 95%CI 0.283 ~ 0.937, P = 0.030), hyperlipidemia (OR = 4.627, 95%CI 1.166 ~ 18.367, P = 0.029), and GCS(OR = 1.446, 95%CI 1.041 ~ 2.008, P = 0.028) are independent risk factors for poor postoperative prognosis. The total score of the nomogram was 200, and the AUC value was 0.972.

CONCLUSIONS

PNI and NAR can reflect the nutritional status and inflammatory responses of patients.They are significantly associated with the postoperative prognosis of patients with aSAH. Comprehensively analyzing PNI and NAR combined with other clinical indicators can more effectively guide treatment and help predict prognosis.