Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In The Journal of the American Academy of Orthopaedic Surgeons

INTRODUCTION : The movement toward reducing healthcare expenditures has led to an increased volume of outpatient anterior cervical diskectomy and fusions (ACDFs). Appropriateness for outpatient surgery can be gauged based on the duration of recovery each patient will likely need.

METHODS : Patients undergoing 1- or 2-level ACDFs were retrospectively identified at a single Level I spine surgery referral institution. Length of stay (LOS) was categorized binarily as either less than two midnights or two or more midnights. The data were split into training (80%) and test (20%) sets. Two multivariate regressions and three machine learning models were developed to predict a probability of LOS ≥ 2 based on preoperative patient characteristics. Using each model, coefficients were computed for each risk factor based on the training data set and used to create a calculatable ACDF Predictive Scoring System (APSS). Performance of each APSS was then evaluated on a subsample of the data set withheld from training. Decision curve analysis was done to evaluate benefit across probability thresholds for the best performing model.

RESULTS : In the final analysis, 1,516 patients had a LOS <2 and 643 had a LOS ≥2. Patient characteristics used for predictive modeling were American Society of Anesthesiologists score, age, body mass index, sex, procedure type, history of chronic pulmonary disease, depression, diabetes, hypertension, and hypothyroidism. The best performing APSS was modeled after a lasso regression. When applied to the withheld test data set, the APSS-lasso had an area under the curve from the receiver operating characteristic curve of 0.68, with a specificity of 0.78 and a sensitivity of 0.49. The calculated APSS scores ranged between 0 and 45 and corresponded to a probability of LOS ≥2 between 4% and 97%.

CONCLUSION : Using classic statistics and machine learning, this scoring system provides a platform for stratifying patients undergoing ACDF into an inpatient or outpatient surgical setting.

Russo Glenn S, Canseco Jose A, Chang Michael, Levy Hannah A, Nicholson Kristen, Karamian Brian A, Mangan John, Fang Taolin, Vaccaro Alexander R, Kepler Christopher K