Public health insurance, frailty, and lack of home support predict rehab discharge following elective anterior cervical discectomy and fusion

• Published in: The spine journal Vol. 23; no. 12; pp. 1830 - 1837
• Main Authors: Powers, Andrew Y, Chang, David C, Stippler, Martina, Papavassiliou, Efstathios, Moses, Ziev B
• Format: Journal Article
• Language: English
• Published: United States 01-12-2023
• Subjects: Aged; Cervical Vertebrae - surgery; Cohort Studies; Diskectomy - methods; Frailty; Humans; Medicare; Patient Discharge; Postoperative Complications; Retrospective Studies; Spinal Fusion - methods; Treatment Outcome; United States; United States; Insurance; Machine learning; Frailty; Patient discharge; Intervertebral disc herniation; Neurosurgery; Rehabilitation; Cervical spine; Spinal fusion
• ISSN: 1529-9430; 1878-1632
• DOI: 10.1016/j.spinee.2023.08.018

Anterior cervical discectomy and fusion (ACDF) is a commonly-performed and generally well-tolerated procedure used to treat cervical disc herniation. Rarely, patients require discharge to inpatient rehab, leading to inconvenience for the patient and increased healthcare expenditure for the medical system.
The objective of this study was to create an accurate and practical predictive model for, as well as delineate associated factors with, rehab discharge following elective ACDF.
This was a retrospective, single-center, cohort study.
Patients who underwent ACDF between 2012 and 2022 were included. Those with confounding diagnoses or who underwent concurrent, staged, or nonelective procedures were excluded.
Primary outcomes for this study included measurements of accuracy for predicting rehab discharge. Secondary outcomes included associations of variables with rehab discharge.
Current Procedural Terminology codes identified patients. Charts were reviewed to obtain additional demographic and clinical characteristics on which an initial univariate analysis was performed. Two logistic regression and two machine learning models were trained and evaluated on the data using cross-validation. A multimodel logistic regression was implemented to analyze independent variable associations with rehab discharge.
A total of 466 patients were included in the study. The logistic regression model with minimum corrected Akaike information criterion score performed best overall, with the highest values for area under the receiver operating characteristic curve (0.83), Youden's J statistic (0.71), balanced accuracy (85.7%), sensitivity (90.3%), and positive predictive value (38.5%). Rehab discharge was associated with a modified frailty index of 2 (p=.007), lack of home support (p=.002), and having Medicare or Medicaid insurance (p=.007) after correction for multiple hypotheses.
Nonmedical social determinants of health, such as having public insurance or a lack of support at home, may play a role in rehab discharge following elective ACDF. In combination with the modified frailty index and other variables, these factors can be used to predict rehab discharge with high accuracy, improving the patient experience and reducing healthcare costs.