Development and Validation of a Multivariate Prediction Model of Perioperative Mortality in Neurosurgery: The New Zealand Neurosurgical Risk Tool (NZRISK-NEURO). Issue 3 (16th May 2020) - Clark, Stephanie; Boyle, Luke; Matthews, Phoebe; Schweder, Patrick; Deng, Carolyn; Campbell, Doug
Abstract: BACKGROUND: Multivariate risk prediction models individualize prediction of adverse outcomes, assisting perioperative decision-making. There are currently no models specifically designed for the neurosurgical population. OBJECTIVE: To develop and validate a neurosurgical risk prediction model, with 30-d, 1-yr, and 2-yr mortality endpoints. METHODS: We accessed information on all adults in New Zealand who underwent neurosurgery or spinal surgery between July 1, 2011, and June 30, 2016, from an administrative database. Our dataset comprised of 18 375 participants, split randomly into derivation (75%) and validation (25%) datasets. Previously established covariates tested included American Society of Anesthesiologists physical status grade (ASA-PS), surgical acuity, operative severity, cancer status, and age. Exploratory covariates included anatomical site, gender, diabetes, trauma, ethnicity, and socioeconomic status. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct 30-d, 1-yr, and 2-yr mortality models. RESULTS: Our final models included 8 covariates: age, ASA-PS grade, surgical acuity, cancer status, anatomical site, diabetes, ethnicity, and trauma. The area under the receiver operating curve for the 30-d, 1-yr, and 2-yr mortality models was 0.90, 0.91, and 0.91 indicating excellent discrimination, respectively. Calibration also showed excellent performance with McFadden's pseudo R 2 statistics of 0.28, 0.37, and 0.41 and calibration plot slopes of 0.93, 0.95, and 0.94, respectively. The strongest predictors of mortality were ASA-PS 4 and 5 (30 d) and cancer (1 and 2 yr). CONCLUSION: NZRISK-NEURO is a robust multivariate calculator created specifically for neurosurgery, enabling physicians to generate data-driven individualized risk estimates, assisting shared decision-making and perioperative planning. Graphical Abstract: