Age-based prediction of non-routine discharge after anterior cervical discectomy and fusion using machine learning
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Updated: Sep 10, 2025
Anterior Cervical Discectomy and Fusion in the Ovine Model
Published on: October 5, 2009
Paul G Mastrokostas1,2, Leonidas E Mastrokostas3,4, Aaron B Lavi3
1SUNY Downstate Health Sciences University, Brooklyn, USA. Pmastrokostas06@gmail.com.
Purpose:
To examine factors influencing non-routine discharge in ACDF patients stratified by age utilizing machine learning.
Methods:
A cohort of 219,380 weighted ACDF cases from the National Inpatient Sample (NIS) database spanning 2016-2020 was divided into three age groups: 50-64, 65-79, and 80 + years. Eight supervised machine learning models predicted non-routine discharge based on patient characteristics, including age, length of stay (LOS), and comorbidities. Chi-square and t-tests compared outcomes. After Bonferroni correction, significance was set at P < 0.004.
Results:
Across all age groups, several patient-specific factors were associated with non-routine discharge. In the 50-64 group, deficiency anemias (1.1% vs. 0.6%, P < 0.001), paralysis (1.2% vs. 0.1%, P < 0.001), and race (Black: 15.4% vs. 10.0%, P < 0.001) were significant predictors. For 65-79, heart failure (1.2% vs. 0.5%, P < 0.001) and dementia (0.5% vs. 0.1%, P < 0.001) increased risk. In the 80 + group, racial disparities persisted. Machine learning models-especially AdaBoost and Gradient Boosting-demonstrated strong predictive performance, with AUCs exceeding 80% for the 65-79 and 80 + cohorts. Prolonged LOS was also significantly associated with non-routine discharge across all age groups, with patients staying over twice as long on average (all P < 0.001).
Conclusion:
Non-routine discharge after ACDF is influenced by patient-specific factors. Strategies targeting older patients with complex comorbidities could help reduce adverse outcomes.