Evidence map›Paper›PMID 37658409›Full record

ArticleAntimicrobial resistance and infection control2023

Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study.

Guosong Wu, Cheligeer Cheligeer, Danielle A Southern, Elliot A Martin, Yuan Xu, Jenine Leal, Jennifer Ellison, Kathryn Bush, Tyler Williamson, Hude Quan and 1 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Antimicrobial resistance and infection control, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

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

11 authors.

Guosong Wu *Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. guosong.wu@ucalgary.ca.
Cheligeer Cheligeer *The Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Danielle A SouthernDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Elliot A MartinThe Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Yuan XuDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Jenine LealDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Jennifer EllisonInfection Prevention and Control Surveillance and Standards, Alberta Health Services, Calgary, AB, Canada.
Kathryn BushInfection Prevention and Control Surveillance and Standards, Alberta Health Services, Calgary, AB, Canada.
Tyler WilliamsonDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Hude QuanDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Cathy A EastwoodDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPopulation based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection.

methodsThis retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision-recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated.

resultsThere were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835-0.978), PR AUC of 0.637 (95% CI 0.528-0.746), and F1 score of 0.79 (0.67-0.90).

conclusionsOur findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.

Indexed as

Arthroplasty, Replacement, KneeAdolescentAdultAlbertaHumansMachine LearningRetrospective StudiesSurgical Wound InfectionMachine learningSurgical site infectionsTotal hip arthroplastyTotal knee arthroplasty

Identifiers

PMID37658409
PMCPMC10474760

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.