A nomogram for predicting the risk of poor prognosis in patients with poor-grade aneurysmal subarachnoid hemorrhage following microsurgical clipping

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.

A nomogram for the prediction of short-term mortality in patients with aneurysmal subarachnoid hemorrhage requiring mechanical ventilation: a post-hoc analysis

Background

Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating stroke subtype with high morbidity and mortality. Although several studies have developed a prediction model in aSAH to predict individual outcomes, few have addressed short-term mortality in patients requiring mechanical ventilation. The study aimed to construct a user-friendly nomogram to provide a simple, precise, and personalized prediction of 30-day mortality in patients with aSAH requiring mechanical ventilation.

Methods

We conducted a post-hoc analysis based on a retrospective study in a French university hospital intensive care unit (ICU). All patients with aSAH requiring mechanical ventilation from January 2010 to December 2015 were included. Demographic and clinical variables were collected to develop a nomogram for predicting 30-day mortality. The least absolute shrinkage and selection operator (LASSO) regression method was performed to identify predictors, and multivariate logistic regression was used to establish a nomogram. The discriminative ability, calibration, and clinical practicability of the nomogram to predict short-term mortality were tested using the area under the curve (AUC), calibration plot, and decision curve analysis (DCA).

Results

Admission GCS, SAPS II, rebleeding, early brain injury (EBI), and external ventricular drain (EVD) were significantly associated with 30-day mortality in patients with aSAH requiring mechanical ventilation. Model A incorporated four clinical factors available in the early stages of the aSAH: GCS, SAPS II, rebleeding, and EBI. Then, the prediction model B with the five predictors was developed and presented in a nomogram. The predictive nomogram yielded an AUC of 0.795 [95% CI, 0.731-0.858], and in the internal validation with bootstrapping, the AUC was 0.780. The predictive model was well-calibrated, and decision curve analysis further confirmed the clinical usefulness of the nomogram.

Conclusion

We have developed two models and constructed a nomogram that included five clinical characteristics to predict 30-day mortality in patients with aSAH requiring mechanical ventilation, which may aid clinical decision-making.