Evidence map›Paper›PMID 35772983›Full record

ArticleInternational journal of spine surgery2022

Predicting Length of Stay After Thoracolumbar Trauma: A Single-Center, Retrospective Analysis.

Justin E Kung, Jael E Camacho, Jacob Bruckner, Ivan B Ye, Alexandra E Thomson, Daniel Cavanaugh, Eugene Y Koh, Daniel E Gelb, Charles Sansur, Steven C Ludwig

Abstract read
In one paragraph

Article in International journal of spine surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Justin E KungDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Jael E CamachoDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Jacob BrucknerDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Ivan B YeDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Alexandra E ThomsonDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Daniel CavanaughDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Eugene Y KohDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Daniel E GelbDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Charles SansurDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Steven C LudwigDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA sludwig@som.umaryland.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLength of stay (LOS) is a meaningful outcome measure for more efficient and effective quality of care. However, algorithms to predict LOS have yet to be created for patients who undergo surgical management for traumatic spinal fractures.

objectivesThe objectives of this study were to (1) identify preoperative, perioperative, and postoperative factors associated with increased LOS and (2) create predictive formulas to estimate LOS in thoracolumbar trauma patients who undergo surgical correction.

methodsThis is a retrospective case series of 196 patients operated for thoracolumbar spine trauma from January 2012 to December 2017 at a level 1 trauma and academic institution. Bivariate analysis between LOS and various preoperative, perioperative, and postoperative factors was conducted to identify significant associations. Multivariate analysis was conducted to create models capable of predicting LOS.

resultsLOS was significantly associated with various preoperative (eg, Charlson Comorbidity Index, Glasgow Coma Scale [GCS], injury severity score), operative (eg, length of surgery, number of instrumented segments, surgical technique), and postoperative variables (eg, complications, discharge location). Multivariate analysis of preoperative variables identified 5 significant independent predictors that could predict LOS with strong correlation with observed LOS (

conclusionsVarious preoperative, perioperative, and postoperative factors are significantly associated with LOS in traumatic thoracolumbar spine patients. We developed models with good predictive capacity for LOS. If validated, these models should help in risk stratifying patients for increased LOS and consequently improve perioperative patient counseling. CLINICAL RELEVANCE: This article contributes to identifying and predicting patients who are high risk for extended LOS after traumatic thoracolumbar injuries. LEVEL OF EVIDENCE: 4:

Indexed as

formulalength of staypredictorsspine traumathoracolumbartrauma

Identifiers

PMID35772983
PMCPMC9650191

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.