Predicting prolonged length of stay in patients undergoing transforaminal lumbar interbody fusion

Cathleen C Kuo; Ryan M Hess; Mohamed A R Soliman; Asham Khan; John Pollina; Jeffrey P Mullin

doi:10.1007/s00701-022-05334-3

Predicting prolonged length of stay in patients undergoing transforaminal lumbar interbody fusion

Acta Neurochir (Wien). 2022 Oct;164(10):2655-2665. doi: 10.1007/s00701-022-05334-3. Epub 2022 Aug 4.

Authors

Cathleen C Kuo¹, Ryan M Hess^{2

3}, Mohamed A R Soliman^{2

4}, Asham Khan^{2

3}, John Pollina^{2

3}, Jeffrey P Mullin^{5

6}

Affiliations

¹ Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
² Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 100 High Street, Suite B4, Buffalo, NY, 14203, USA.
³ Department of Neurosurgery, Buffalo General Medical Center, Kaleida Health, Buffalo, NY, USA.
⁴ Department of Neurosurgery, Faculty of Medicine, Cairo University, Cairo, Egypt.
⁵ Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 100 High Street, Suite B4, Buffalo, NY, 14203, USA. jmullin@ubns.com.
⁶ Department of Neurosurgery, Buffalo General Medical Center, Kaleida Health, Buffalo, NY, USA. jmullin@ubns.com.

PMID: 35927508
DOI: 10.1007/s00701-022-05334-3

Abstract

Background: With growing emphasis on high-value care, many institutions have been working on improving surgical efficiency, quality, and complication reduction. Unfortunately, data are limited regarding perioperative factors that may influence length of stay (LOS) following transforaminal lumbar interbody fusion (TLIF). We sought to design a predictive algorithm that determined patients at risk of prolonged LOS after TLIF. The goal was to identify patients who would benefit from preoperative intervention aimed to reduce LOS.

Methods: We conducted a review of perioperative data for patients who underwent TLIF between 2014 and 2019. Univariate and multivariate stepwise regression models were used to analyze risk factor effects on postoperative LOS.

Results: Two hundred and sixty-nine patients were identified (57.2% women). Mean age at surgery was 61.7 ± 12.3 years. Mean postoperative LOS was 3.08 ± 1.54 days. In multivariate analysis, American Society of Anesthesiologists class (odds ratio [OR] = 1.441, 95% confidence interval [CI] 1.321-1.571), preoperative functional status (OR = 1.237, 95% CI 1.122-1.364), Oswestry Disability Index (OR = 1.010, 95% CI 1.004-1.016), and estimated blood loss (OR = 1.050, 95% CI 1.003-1.101) were independent risk factors for postoperative LOS ≥ 5 days. The final model had an area under the curve of 0.948 with good discrimination and was implemented in the form of an online calculator ( https://spine.shinyapps.io/TLIF_LOS/ ).

Conclusion: The prediction tool derived can be useful for assessing likelihood of prolonged LOS in patients undergoing TLIF. With external validation, this calculator may ultimately assist healthcare providers in identifying patients at risk for prolonged hospitalization so preoperative interventions can be undertaken to reduce LOS, thus reducing resource utilization.

Keywords: Fusion; Length of stay; Risk factor; Spine surgery; Transforaminal lumbar interbody fusion.

Publication types

Review

MeSH terms

Aged
Female
Humans
Length of Stay
Lumbar Vertebrae / surgery
Male
Middle Aged
Minimally Invasive Surgical Procedures
Postoperative Period
Spinal Fusion* / adverse effects
Treatment Outcome