Study finds, Novel Scoring System to Predict Length of Stay
The movement toward reducing healthcare expenditures has led to an increased volume of outpatient anterior cervical diskectomy and fusions (ACDFs).

Patients undergoing 1- or 2-level ACDFs were retrospectively identified at a single Level I spine surgery. Length of stay (LOS) was categorized binarily as either less than two midnights or two or more midnights. Two multivariate regressions and three machine learning models were developed to predict a probability of LOS more 2 based on preoperative patient characteristics.

--1,516 patients had a LOS less than 2 and 643 had a LOS more than 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.

--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 more than 2 between 4% and 97%.

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