Risk stratification of patients undergoing outpatient lumbar decompression surgery.

A total of 1,656 patients were included in our cohort with 1,078 dischared on day of surgery and 578 patients spending ≥ 1 midnight in the hospital. Our model determined older patients (OR=1.06, p<.001) with a higher BMI (OR=1.04, p<0.001), higher back pain (OR=1.06, p=.019), increasing American Society of Anesthesiologists (ASA) score (OR=1.39, p=.012), and patients with more levels decompressed (OR=3.66, p<0.001) all had increased risks of staying overnight. Patients who were female (OR=0.59, p=.009) and those with private insurance (OR=0.64, p=.023) were less likely to be admitted overnight. Further, weighted scores based on training data were then created and patients with a cumulative score over 118 points had a 82.9% likelihood of overnight. Analysis of the 331 patients in the test data demonstrated using a cut-off of 118 points accurately predicted 64.8% of patients meeting inpatient criteria compared to 23.0% meeting outpatient criteria (p<0.001). Area under the curve analysis showed a score greater than 118 predicted admission 81.4% of the time. The algorithm was incorporated into an open access digital application available here: https://rothmanstatisticscalculators.shinyapps.io/Inpatient_Calculator/?_ga=2.171493472.1789252330.1671633274-469992803.1671633274 CONCLUSIONS: Utilizing machine-learning algorithms we created a highly reliable predictive calculator to determine if patients undergoing outpatient lumbar decompression would require admission. Patients who were younger, had lower BMI, lower preoperative back pain, lower ASA score, less levels decompressed, private insurance, lived with someone at home, and with minimal comorbidities were ideal candidates for outpatient surgery.