Evidence map›Paper›PMID 28032554›Full record

ArticleThe Journal of surgical research2017

Detection of clinically important colorectal surgical site infection using Bayesian network.

Sunghwan Sohn, David W Larson, Elizabeth B Habermann, James M Naessens, Jasim Y Alabbad, Hongfang Liu

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PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of surgical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 4 pooled it
10.7field-weighted citation impact, top 2% of its field
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

31 citing papers in PubMed, 4 syntheses or guidelines pooled it, 71 citations in OpenAlex.

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  19. Prediction of Incident Dementia Using Patient Temporal Health Status.Studies in health technology and informatics · 2022
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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

6 authors at 2 institutions in 1 country.

Sunghwan SohnDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.
David W LarsonDivision of Colorectal Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota.
Elizabeth B HabermannDivision of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.
James M NaessensDivision of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.
Jasim Y AlabbadDivision of Colorectal Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota.
Hongfang LiuDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota. Electronic address: liu.hongfang@mayo.edu.
Mayo Clinic in Florida · USMayo Clinic · US

Funding

Secondary use of EMRs for surgical complication surveillanceR01EB019403 · NIBIB · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, STORLIE, CURTIS · 2015 to 2022
$3.8M
Natural language processing for clinical and translational researchR01GM102282 · NIGMS · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, PAKHOMOV, SERGUEI VS · 2013 to 2016
$2.5M
NIBIB NIH HHS R01 EB019403NIGMS NIH HHS R01 GM102282
6 · The paper itself

Abstract

backgroundDespite extensive efforts to monitor and prevent surgical site infections (SSIs), real-time surveillance of clinical practice has been sparse and expensive or nonexistent. However, natural language processing (NLP) and machine learning (i.e., Bayesian network analysis) may provide the methodology necessary to approach this issue in a new way. We investigated the ability to identify SSIs after colorectal surgery (CRS) through an automated detection system using a Bayesian network. MATERIALS AND

methodsPatients who underwent CRS from 2010 to 2012 and were captured in our institutional American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) comprised our cohort. A Bayesian network was applied to detect SSIs using risk factors from ACS-NSQIP data and keywords extracted from clinical notes by NLP. Two surgeons provided expertise informing the Bayesian network to identify clinically meaningful SSIs (CM-SSIs) occurring within 30 d after surgery.

resultsWe used data from 751 CRS cases experiencing 67 (8.9%) SSIs and 78 (10.4%) CM-SSIs. Our Bayesian network detected ACS-NSQIP-captured SSIs with a receiver operating characteristic area under the curve of 0.827, but this value increased to 0.892 when using surgeon-identified CM-SSIs.

conclusionsA Bayesian network coupled with NLP has the potential to be used in real-time SSI surveillance. Moreover, surgeons identified CM-SSI not captured under current NSQIP definitions. Future efforts to expand CM-SSI identification may lead to improved and potentially automated approaches to survey for postoperative SSI in clinical practice.

Indexed as

Bayes TheoremColorectal SurgeryNatural Language ProcessingSurgical Wound InfectionHumansMedical AuditQuality ImprovementRetrospective StudiesBayesian networkColorectal surgeryNatural language processingSurgical site infection

Identifiers

PMID28032554
PMCPMC5391146
OpenAlexW2529488695

What OpenQuestion holds

Textmetadata
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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.