Identifying Key Factors Influencing Hospital Stay After Spine Surgery: A Comprehensive Predictive Model

Study DesignRetrospective Cohort Study.ObjectivesTo develop and validate a multivariable predictive model for length of hospital stay (LOS) following spine surgery, incorporating sociodemographic characteristics, medical data, and self-reported patient outcomes.MethodsA retrospective analysis of 4583 patients from a spine surgery registry was conduct-ed. Predictors included age, sex, BMI, ASA score, surgical complexity, and patient-reported outcomes. Binary logistic regression was used to model LOS (<3 days vs ≥3 days).ResultsLower age, active work status, lower ASA scores, and specific surgical procedures were associated with shorter LOS. The model demonstrated good accuracy and dis-criminative ability.ConclusionsSociodemographic, medical, and patient-reported outcomes are valuable predictors of LOS. These findings can help improve preoperative planning and resource allocation in spine surgery.

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Publication Details

Journal
Global Spine Journal
Published
2025-04-01
DOI
https://doi.org/10.1177/21925682251331451
Citations
1
Primary Topic
Spine and Intervertebral Disc Pathology
Type
article
Field-Weighted Citation Impact
1.47
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article

Identifying Key Factors Influencing Hospital Stay After Spine Surgery: A Comprehensive Predictive Model

Francesco Langella, Domenico Compagnone, Daniele Vanni, Federico Fusini et al.
1 citations
Global Spine Journal
Spine and Intervertebral Disc Pathology
1.47
article

Identifying Key Factors Influencing Hospital Stay After Spine Surgery: A Comprehensive Predictive Model

Francesco Langella, Domenico Compagnone, Daniele Vanni, Federico Fusini, Pablo Bellosta‐López, Pedro Berjano, Marco Damilano, Francesca Barile
article en
1 citations

Abstract

Study DesignRetrospective Cohort Study.ObjectivesTo develop and validate a multivariable predictive model for length of hospital stay (LOS) following spine surgery, incorporating sociodemographic characteristics, medical data, and self-reported patient outcomes.MethodsA retrospective analysis of 4583 patients from a spine surgery registry was conduct-ed. Predictors included age, sex, BMI, ASA score, surgical complexity, and patient-reported outcomes. Binary logistic regression was used to model LOS (<3 days vs ≥3 days).ResultsLower age, active work status, lower ASA scores, and specific surgical procedures were associated with shorter LOS. The model demonstrated good accuracy and dis-criminative ability.ConclusionsSociodemographic, medical, and patient-reported outcomes are valuable predictors of LOS. These findings can help improve preoperative planning and resource allocation in spine surgery.

Global Spine JournalVol. 15(8)
CTO Hospital (IT), Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT), Istituto Clinico Sant'Ambrogio (IT), Universidad San Jorge (ES), University of Turin (IT)
Decent work and economic growth
Openalex Percentile: Top 21%
Spine and Intervertebral Disc Pathology
1.47
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