Integration of Chronological Age Does Not Improve the Performance of a Mixed-Effect Model Using Comorbidity Burden and Frailty to Predict 90-Day Readmission After Surgery for Degenerative Scoliosis

Shane Shahrestani; Lawrance K Chung; Nolan J Brown; Sofia Reese; Ryan C Liu; Apurva A Prasad; R Kiran Alluri; Raymond Hah; John C Liu; Michael M Safaee

doi:10.1016/j.wneu.2024.04.129

Integration of Chronological Age Does Not Improve the Performance of a Mixed-Effect Model Using Comorbidity Burden and Frailty to Predict 90-Day Readmission After Surgery for Degenerative Scoliosis

World Neurosurg. 2024 Jul:187:e560-e567. doi: 10.1016/j.wneu.2024.04.129. Epub 2024 Apr 26.

Authors

Affiliations

¹ Department of Neurological Surgery, University of Southern California, Los Angeles, CA, USA; Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA; Department of Neurological Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
² Department of Neurological Surgery, University of Southern California, Los Angeles, CA, USA.
³ School of Medicine, University of California, Irvine, Orange, CA, USA.
⁴ Department of Orthopedic Surgery, University of Southern California, Los Angeles, CA, USA.
⁵ Department of Neurological Surgery, University of Southern California, Los Angeles, CA, USA. Electronic address: michael.safaee@med.usc.edu.

PMID: 38679382
DOI: 10.1016/j.wneu.2024.04.129

Abstract

Objective: We evaluated the contributions of chronological age, comorbidity burden, and/or frailty in predicting 90-day readmission in patients undergoing degenerative scoliosis surgery.

Methods: Patients were identified through the Healthcare Cost and Utilization Project Nationwide Readmissions Database. Frailty was assessed using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. Comorbidity was assessed using the Elixhauser Comorbidity Index (ECI). Generalized linear mixed-effects models were created to predict readmission using age, frailty, and/or ECI. Area under the curve (AUC) was compared using DeLong's test.

Results: A total of 8104 patients were identified. Readmission rate was 9.8%, with infection representing the most common cause (3.5%). Our first model utilized chronological age, ECI, and/or frailty as primary predictors. The combination of ECI + frailty + age performed best, but the inclusion of chronological age did not significantly improve performance compared to ECI + frailty alone (AUC 0.603 vs. 0.599, P = 0.290). A second model using only chronological age and frailty as primary predictors performed better, however the inclusion of chronological age worsened performance when compared to frailty alone (AUC 0.747 vs. 0.743, P = 0.043).

Conclusions: These data support frailty as a predictor of 90-day readmission within a nationally representative sample. Frailty alone performed better than combinations of ECI and age. Interestingly, the integration of chronological age did not dramatically improve the model's performance. Limitations include the use of a national registry and a single frailty index. This provides impetus to explore biological age, rather than chronological age, as a potential tool for surgical risk assessment.

Keywords: Age; Comorbidity; Frailty; Readmission; Spinal deformity; Spine surgery.

MeSH terms

Age Factors
Aged
Aged, 80 and over
Comorbidity*
Female
Frailty* / epidemiology
Humans
Male
Middle Aged
Patient Readmission* / statistics & numerical data
Postoperative Complications / epidemiology
Scoliosis* / surgery