Evidence map›Paper›PMID 34169434›Full record

ReviewDigestive diseases and sciences2022

Regression and Random Forest Machine Learning Have Limited Performance in Predicting Bowel Preparation in Veteran Population.

Jacob E Kurlander, Akbar K Waljee, Stacy B Menees, Rachel Lipson, Alex N Kokaly, Andrew J Read, Karmel S Shehadeh, Amy Cohn, Sameer D Saini

Abstract readReview
PubMed Publisher
In one paragraph

Review in Digestive diseases and sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Article
  3. Observational
  4. Review
  5. Article
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

9 authors.

Jacob E KurlanderDepartment of Internal Medicine, University of Michigan, 3912 Taubman Center, 1500 E. Medical Center Dr., SPC 5362, Ann Arbor, MI, 48109-5362, USA. jkurland@umich.edu.ORCID 0000-0003-1798-060X
Akbar K WaljeeDepartment of Internal Medicine, University of Michigan, 3912 Taubman Center, 1500 E. Medical Center Dr., SPC 5362, Ann Arbor, MI, 48109-5362, USA.
Stacy B MeneesDepartment of Internal Medicine, University of Michigan, 3912 Taubman Center, 1500 E. Medical Center Dr., SPC 5362, Ann Arbor, MI, 48109-5362, USA.
Rachel LipsonVeterans Affairs Ann Arbor Center for Clinical Management Research, Ann Arbor, MI, USA.
Alex N KokalyDepartment of Medicine, UCLA Health, 200 UCLA Medical Plaza, Suite 420, Los Angeles, 90095-1685, CA, USA.
Andrew J ReadDepartment of Internal Medicine, University of Michigan, 3912 Taubman Center, 1500 E. Medical Center Dr., SPC 5362, Ann Arbor, MI, 48109-5362, USA.
Karmel S ShehadehDepartment of Industrial and Systems Engineering, Lehigh University, 200 West Packer Ave, Bethlehem, PA, 18015, USA.
Amy CohnDepartment of Industrial and Operations Engineering, University of Michigan, 2015 Beal Ave, Ann Arbor, MI, 4819-2117, USA.
Sameer D SainiDepartment of Internal Medicine, University of Michigan, 3912 Taubman Center, 1500 E. Medical Center Dr., SPC 5362, Ann Arbor, MI, 48109-5362, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInadequate bowel preparation undermines the quality of colonoscopy, but patients likely to be affected are difficult to identify beforehand.

aimsThis study aimed to develop, validate, and compare prediction models for bowel preparation inadequacy using conventional logistic regression (LR) and random forest machine learning (RFML).

methodsWe created a retrospective cohort of patients who underwent outpatient colonoscopy at a single VA medical center between January 2012 and October 2015. Candidate predictor variables were chosen after a literature review. We extracted all available predictor variables from the electronic medical record, and bowel preparation from the endoscopy database. The data were split into 70% training and 30% validation sets. Multivariable LR and RFML were used to predict preparation inadequacy as a dichotomous outcome.

resultsThe cohort included 6,885 Veterans, of whom 964 (14%) had inadequate preparation. Using LR, the area under the receiver operating characteristic curve (AUC) for the validation cohort was 0.66 (95% CI 0.62, 0.69) and the Brier score, in which a lower score indicates better performance, was 0.11. Using RFML, the AUC for the validation cohort was 0.61 (95% CI 0.58, 0.65) and the Brier score was 0.12.

conclusionsLR and RFML had similar performance in predicting bowel preparation, which was modest and likely insufficient for use in practice. Future research is needed to identify additional predictor variables and to test other machine learning algorithms. At present, endoscopy units should focus on universal strategies to enhance preparation adequacy.

Indexed as

VeteransHumansLogistic ModelsMachine LearningRetrospective StudiesRisk AssessmentBowel preparationColonoscopyHealthcare qualityPrediction modelsRandom forest machine learningVeterans health

Identifiers

PMID34169434

What OpenQuestion holds

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Registered trials

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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.