Abstract
BACKGROUND CONTEXT
The number of elective spinal fusion procedures performed each year continues to grow,
making risk factors for post-operative complications following this procedure increasingly
clinically relevant. Nonhome discharge (NHD) is of particular interest due to its
associations with increased costs of care and rates of complications. Notably, increased
age has been found to influence rates of NHD.
PURPOSE
To identify aged-adjusted risk factors for nonhome discharge following elective lumbar
fusion through the utilization of Machine Learning-generated predictions within stratified
age groupings.
STUDY DESIGN
Retrospective Database Study.
PATIENT SAMPLE
The American College of Surgeons National Quality Improvement Program (ACS-NSQIP)
database years 2008 to 2018.
OUTCOME MEASURES
Postoperative discharge destination.
METHODS
ACS-NSQIP was queried to identify adult patients undergoing elective lumbar spinal
fusion from 2008 to 2018. Patients were then stratified into the following age ranges:
30 to 44 years, 45 to 64 years, and ≥65 years. These groups were then analyzed by
eight ML algorithms, each tasked with predicting post-operative discharge destination.
RESULTS
Prediction of NHD was performed with average AUCs of 0.591, 0.681, and 0.693 for those
aged 30 to 44, 45 to 64, and ≥65 years respectively. In patients aged 30 to 44, operative
time (p<.001), African American/Black race (p=.003), female sex (p=.002), ASA class
three designation (p=.002), and preoperative hematocrit (p=.002) were predictive of
NHD. In ages 45 to 64, predictive variables included operative time, age, preoperative
hematocrit, ASA class two or class three designation, insulin-dependent diabetes,
female sex, BMI, and African American/Black race all with p<.001. In patients ≥65
years, operative time, adult spinal deformity, BMI, insulin-dependent diabetes, female
sex, ASA class four designation, inpatient status, age, African American/Black race,
and preoperative hematocrit were predictive of NHD with p<.001. Several variables
were distinguished as predictive for only one age group including ASA Class two designation
in ages 45 to 64 and adult spinal deformity, ASA class four designation, and inpatient
status for patients ≥65 years.
CONCLUSIONS
Application of ML algorithms to the ACS-NSQIP dataset identified a number of highly
predictive and age-adjusted variables for NHD. As age is a risk factor for NHD following
spinal fusion, our findings may be useful in both guiding perioperative decision-making
and recognizing unique predictors of NHD among specific age groups.
Keywords
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Article info
Publication history
Published online: April 04, 2023
Accepted:
March 26,
2023
Received in revised form:
March 1,
2023
Received:
December 19,
2022
Publication stage
In Press Journal Pre-ProofFootnotes
FDA device/drug status: Not applicable.
Author disclosures: AC: Nothing to disclose. AB: Nothing to disclose. MN: Nothing to disclose. JR: Nothing to disclose. OR: Nothing to disclose. CMB: Other: NASA, TSJ Editor in Chief, Stipend; Royalties: Wolters Kluwer, royalties for Books Elsevier, royalties for Books; Consulting: United Health Care. WC: Consulting: Orthofix (E); Medtronic (D); Speaking and/or Teaching Arrangements: Radius (D); Grants: DePuy (E). OD: Royalties: Globus Medical (F); Consulting: Stryker Spine (2021 B), Stryker Spine (2020 B); Speaking and/or Teaching Arrangements: SpineArt, No longer current but in 2019 (C); Trips/Travel: American Board of Orthopedic Surgery (ABOS)- Serves as oral examiner (B), Musculoskeletal Transplant Foundation (MTF)- travel and expenses to Annual Meeting- as Medical board member (B); Grant: NuVasive-(clinical trial) Decade Plate Study (C), Musculoskeletal Transplant Foundation (MTF) (B).
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