Risk Analysis Index Predicts Nonhome Discharge Following Resection of Cavernous Malformations

Pemla Jagtiani1, Kranti Rumalla2, Joanna M Roy3

  • 1School of Medicine, SUNY Downstate Health Sciences University, New York, NY, USA; Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Sandy, UT, USA.

World Neurosurgery
|July 3, 2024
PubMed
Abstract

Insights

Frailty, measured by the Risk Analysis Index (RAI), predicts adverse outcomes like nonhome discharge after intracranial cavernous malformation resection (CMR). This tool helps optimize patient selection and resource allocation for better surgical results.

Area of Science:

  • Neurosurgery
  • Vascular Neurology
  • Geriatrics

Background:

  • Intracranial cavernous malformations (CMs) are vascular lesions linked to hemorrhage and neurological deficits.
  • Patient age is a less effective predictor of neurosurgical outcomes than frailty.
  • The Risk Analysis Index (RAI) is a validated measure of frailty.

Purpose of the Study:

  • To utilize the RAI to predict adverse outcomes following cavernous malformation resection (CMR).
  • To assess the discriminatory accuracy of the RAI for predicting nonhome discharge (NHD) after CMR.

Main Methods:

  • Retrospective analysis of the Nationwide Inpatient Sample (2019-2020) for patients undergoing craniotomy for CMR.
  • Multivariate analysis to evaluate the association between RAI-defined frailty and outcomes (NHD, extended length of stay [eLOS], postoperative adverse events).
  • Receiver operating characteristic (ROC) curve analysis to determine the predictive accuracy of RAI for NHD.

Main Results:

  • 1200 CMR patients were identified, with a mean age of 38 years.
  • Increasing frailty tiers (based on RAI) were significantly associated with eLOS and higher rates of NHD (P<0.05).
  • The RAI demonstrated strong discriminatory accuracy (C-statistic=0.722) for predicting NHD.

Conclusions:

  • Preoperative frailty, assessed by RAI, independently predicts adverse outcomes (eLOS, NHD) in patients undergoing cranial CM resection.
  • Integrating RAI into preoperative assessments can optimize risk stratification and patient selection.
  • Improved risk assessment may lead to better allocation of perioperative resources and enhanced patient outcomes.

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