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

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).

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.

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.

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.