Evidence map›Paper›PMID 38567014›Full record

ArticleJournal of spine surgery (Hong Kong)2024

Development and validation of a point-of-care clinical risk score to predict surgical site complication-associated readmissions following open spine surgery.

Kyle B Mueller, Yuefeng Hou, Karen Beach, Leah P Griffin

Abstract read
In one paragraph

Article in Journal of spine surgery (Hong Kong), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Observational
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Kyle B MuellerDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID https://orcid.org/0000-0003-2967-460X
Yuefeng Hou3M Healthcare, St. Paul, MN, USA.ORCID https://orcid.org/0000-0002-4986-8922
Karen Beach3M Healthcare, St. Paul, MN, USA.ORCID https://orcid.org/0009-0002-2171-0303
Leah P Griffin3M Healthcare, St. Paul, MN, USA.ORCID https://orcid.org/0009-0009-9219-2356

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Surgical site complications (SSCs) contribute to increased healthcare costs. Predictive analytics can aid in identifying high-risk patients and implementing optimization strategies. This study aimed to develop and validate a risk-assessment score for SSC-associated readmissions (SSC-ARs) in patients undergoing open spine surgery. Methods: The Premier Healthcare Database (PHD) of adult patients (n=157,664; 3,182 SSC-ARs) between January 2019 and September 2020 was used for retrospective data analysis to create an SSC risk score using mixed effects logistic regression modeling. Full and reduced models were developed using patient-, facility-, or procedure-related predictors. The full model used 37 predictors and the reduced used 19. Results: The reduced model exhibited fair discriminatory capability (C-statistic =74.12%) and demonstrated better model fit [Pearson chi-square/degrees of freedom (DF) =0.93] compared to the full model (C-statistic =74.56%; Pearson chi-square/DF =0.92). The risk scoring system, based on the reduced model, comprised the following factors: female (1 point), blood disorder [2], congestive heart failure [2], dementia [3], chronic pulmonary disease [2], rheumatic disease [3], hypertension [2], obesity [2], severe comorbidity [2], nicotine dependence [1], liver disease [2], paraplegia and hemiplegia [3], peripheral vascular disease [2], renal disease [2], cancer [1], diabetes [2], revision surgery [2], operative hours ≥5 [4], emergency/urgent surgery [2]. A final risk score (sum of the points for each surgery; range, 0-40) was validated using a 1,000-surgery random hold-out sample (C-statistic =85.16%). Conclusions: The resulting SSC-AR risk score, composed of readily obtainable clinical information, could serve as a robust predictive tool for unplanned readmissions related to wound complications in the preoperative setting of open spine surgery.

Indexed as

incisional managementinpatientPredictive analyticsrisk mitigationspinal fusion

Identifiers

PMID38567014
PMCPMC10982919

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LicenceCC BY-NC-ND
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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.