Evidence map›Paper›PMID 34344671›Full record

ArticleBMJ open2021

Published models that predict hospital readmission: a critical appraisal.

Lisa Grossman Liu, James R Rogers, Rollin Reeder, Colin G Walsh, Devan Kansagara, David K Vawdrey, Hojjat Salmasian

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Article
  3. Risk of Hospital Readmission among People with CKD: A Population-Based Cohort Study.Clinical journal of the American Society of Nephrology : CJASN · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. The risk of death or unplanned readmission after discharge from a COVID-19 hospitalization in Alberta and Ontario.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2022
    Article
  9. Trust in AI: why we should be designing for APPROPRIATE reliance.Journal of the American Medical Informatics Association : JAMIA · 2021
    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

7 authors.

Lisa Grossman LiuDepartment of Biomedical Informatics, Columbia University, New York, New York, USA lvg2104@cumc.columbia.edu.ORCID 0000-0002-1153-1590
James R RogersDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Rollin ReederDepartment of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.
Colin G WalshDepartment of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.
Devan KansagaraDepartment of Medicine, Oregon Health and Science University and VA Portland Health Care System, Portland, Oregon, USA.
David K VawdreyDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Hojjat SalmasianDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.ORCID 0000-0002-9004-7149

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe number of readmission risk prediction models available has increased rapidly, and these models are used extensively for health decision-making. Unfortunately, readmission models can be subject to flaws in their development and validation, as well as limitations in their clinical usefulness.

objectiveTo critically appraise readmission models in the published literature using Delphi-based recommendations for their development and validation.

methodsWe used the modified Delphi process to create

resultsWe found that published models (n=81) followed 6.8 recommendations (45%) on average. Many models had weaknesses in their development, including failure to internally validate (12%), failure to account for readmission at other institutions (93%), failure to account for missing data (68%), failure to discuss data preprocessing (67%) and failure to state the model's eligibility criteria (33%).

conclusionsThe high prevalence of weaknesses in model development identified in the published literature is concerning, as these weaknesses are known to compromise predictive validity. CAMPR may support researchers, clinicians and administrators to identify and prevent future weaknesses in model development.

Indexed as

Patient ReadmissionHumansRisk Factorshealth informaticsinformation technologystatistics & research methods

Identifiers

PMID34344671
PMCPMC8336235

What OpenQuestion holds

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LicenceCC BY-NC
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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.