Evidence map›Paper›PMID 26606167›Full record

Trial reportWound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society

Rapid identification of slow healing wounds.

Kenneth Jung, Scott Covington, Chandan K Sen, Michael Januszyk, Robert S Kirsner, Geoffrey C Gurtner, Nigam H Shah

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 4 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

47 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  13. eIF6 modulates skin wound healing by upregulating keratin 6B.Stem cells translational medicine · 2024
    Article
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  15. Approach to the Atypical Wound.American journal of clinical dermatology · 2024
    Review
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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

7 authors.

Kenneth JungProgram in Biomedical Informatics, Stanford University, Stanford, California.
Scott CovingtonHealogics Inc., Jacksonville, Florida.
Chandan K SenDepartments of Surgery, Comprehensive Wound Center, Davis Heart and Lung Research Institute, Centers for Regenerative Medicine and Cell-based Therapies, The Ohio State University Medical Center, Columbus, Ohio.
Michael JanuszykDepartment of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, California.
Robert S KirsnerDepartment of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida.
Geoffrey C GurtnerDepartment of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, California.
Nigam H ShahCenter for Biomedical Informatics Research, Stanford University, Stanford, California.

Funding

STANFORD COMPOSITE(RMI)U54HG004028 · NHGRI · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2005 to 2014
$36.2M
Reversal of Progenitor Cell Deficits in DiabetesR01DK074095 · NIDDK · UNIVERSITY OF ARIZONA · PI GURTNER, GEOFFREY C · 2005 to 2025
$6.4M
Methods for generalized ontology terms enrichment analysisR01LM011369 · NLM · STANFORD UNIVERSITY · PI SHAH, NIGAM H · 2013 to 2020
$5.0M
Mining health data for drug safety profilesR01GM101430 · NIGMS · STANFORD UNIVERSITY · PI SHAH, NIGAM H · 2013 to 2017
$2.9M
Progenitor Cell Dysfunction and Impaired Neovascularization in DiabetesR56DK074095 · NIDDK · STANFORD UNIVERSITY · PI BROWNLEE, MICHAEL A, GURTNER, GEOFFREY C · 2009 to 2009
$500k
NHGRI NIH HHS U54 HG004028NIDDK NIH HHS DK-074095NIDDK NIH HHS R01 DK074095NIDDK NIH HHS R56 DK074095NIGMS NIH HHS R01 GM101430NLM NIH HHS R01 LM011369
6 · The paper itself

Abstract

Chronic nonhealing wounds have a prevalence of 2% in the United States, and cost an estimated $50 billion annually. Accurate stratification of wounds for risk of slow healing may help guide treatment and referral decisions. We have applied modern machine learning methods and feature engineering to develop a predictive model for delayed wound healing that uses information collected during routine care in outpatient wound care centers. Patient and wound data was collected at 68 outpatient wound care centers operated by Healogics Inc. in 26 states between 2009 and 2013. The dataset included basic demographic information on 59,953 patients, as well as both quantitative and categorical information on 180,696 wounds. Wounds were split into training and test sets by randomly assigning patients to training and test sets. Wounds were considered delayed with respect to healing time if they took more than 15 weeks to heal after presentation at a wound care center. Eleven percent of wounds in this dataset met this criterion. Prognostic models were developed on training data available in the first week of care to predict delayed healing wounds. A held out subset of the training set was used for model selection, and the final model was evaluated on the test set to evaluate discriminative power and calibration. The model achieved an area under the curve of 0.842 (95% confidence interval 0.834-0.847) for the delayed healing outcome and a Brier reliability score of 0.00018. Early, accurate prediction of delayed healing wounds can improve patient care by allowing clinicians to increase the aggressiveness of intervention in patients most at risk.

Indexed as

Machine LearningWound HealingAdolescentAdultAgedAged, 80 and overChildChild, PreschoolChronic DiseaseDisease ManagementEarly DiagnosisEarly Medical InterventionFemaleHumansInfantInfant, Newborndata miningpredictive modelprognostic modelstatisticswound healing

Identifiers

PMID26606167
PMCPMC4820011

What OpenQuestion holds

Textmetadata
LicenceTDM
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.