Article Abstract

Development and validation of risk-adjustment models for elective, single-level posterior lumbar spinal fusions

Authors: David N. Bernstein, Aakash Keswani, Debbie Chi, James E. Dowdell, Samuel C. Overley, Saad B. Chaudhary, Addisu Mesfin


Background: There is a paucity of literature examining the development and subsequent validation of risk-adjustment models that inform the trade-off between adequate risk-adjustment and data collection burden. We aimed to evaluate patient risk stratification by surgeons with the development and validation of risk-adjustment models for elective, single-level, posterior lumbar spinal fusions (PLSFs).
Methods: Patients undergoing PLSF from 2011–2014 were identified in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). The derivation cohort included patients from 2011–2013, while the validation cohort included patients from 2014. Outcomes of interest were severe adverse events (SAEs) and unplanned readmission. Bivariate analysis of risk factors followed by a stepwise logistic regression model was used. Limited risk-adjustment models were created and analyzed by sequentially adding variables until the full model was reached.
Results: A total of 7,192 and 4,182 patients were included in our derivation and validation cohorts, respectively. Full model performance was similar for the derivation and validation cohorts in both 30-day SAEs (C-statistic =0.66 vs. 0.69) and 30-day unplanned readmission (C-statistic =0.62 vs. 0.65). All models demonstrated good calibration and fit (P≥0.58). Intraoperative variables, laboratory values, and comorbid conditions explained >75% of the variation in 30-day SAEs; ASA class, laboratory values, and comorbid conditions accounted for >80% of model risk prediction for 30-day unplanned readmission. Four variables for the 30-day SAE models (age, gender, ASA ≥3, operative time) and 3 variables for the 30-day unplanned readmission models (age, ASA ≥3, operative time) were sufficient to achieve a C-statistic within four percentage points of the full model.
Conclusions: Risk-adjustment models for PLSF demonstrated acceptable calibration and discrimination using variables commonly found in health records and demonstrated only a limited set of variables were required to achieve an appropriate level of risk prediction.

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