Evidence map›Paper›PMID 39427152›Full record

ArticleBMC nephrology2024

Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning.

John W Larkin, Suman Lama, Sheetal Chaudhuri, Joanna Willetts, Anke C Winter, Yue Jiao, Manuela Stauss-Grabo, Len A Usvyat, Jeffrey L Hymes, Franklin W Maddux and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Small Bowel Lesions and Bleeding Risk in Hemodialysis Patients: A Narrative Review.Hemodialysis international. International Symposium on Home Hemodialysis · 2026
    Review
  2. Article
  3. Review
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

14 authors.

John W LarkinFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA. John.Larkin@freseniusmedicalcare.com.
Suman LamaFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Sheetal ChaudhuriFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Joanna WillettsFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Anke C WinterFresenius Medical Care, Global Medical Office, Bad Homburg, Germany.
Yue JiaoFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Manuela Stauss-GraboFresenius Medical Care, Global Medical Office, Bad Homburg, Germany.
Len A UsvyatFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Jeffrey L HymesFresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.
Franklin W MadduxFresenius Medical Care AG, Global Medical Office, Bad Homburg, Germany.
David C WheelerUniversity College London, London, UK.
Peter StenvinkelDept of Renal Medicine, Karolinska University Hospital, Stockholm, Sweden.
Jürgen FloegeDivisions of Nephrology and Cardiology, University Hospital RWTH Aachen, Aachen, Germany.
INSPIRE Core Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGastrointestinal bleeding (GIB) is a clinical challenge in kidney failure. INSPIRE group assessed if machine learning could determine a hemodialysis (HD) patient's 180-day GIB hospitalization risk.

methodsAn eXtreme Gradient Boosting (XGBoost) and logistic regression model were developed using an HD dataset in United States (2017-2020). Patient data was randomly split (50% training, 30% validation, and 20% testing). HD treatments ≤ 180 days before GIB hospitalization were classified as positive observations; others were negative. Models considered 1,303 exposures/covariates. Performance was measured using unseen testing data.

resultsIncidence of 180-day GIB hospitalization was 1.18% in HD population (n = 451,579), and 1.12% in testing dataset (n = 38,853). XGBoost showed area under the receiver operating curve (AUROC) = 0.74 (95% confidence interval (CI) 0.72, 0.76) versus logistic regression showed AUROC = 0.68 (95% CI 0.66, 0.71). Sensitivity and specificity were 65.3% (60.9, 69.7) and 68.0% (67.6, 68.5) for XGBoost versus 68.9% (64.7, 73.0) and 57.0% (56.5, 57.5) for logistic regression, respectively. Associations in exposures were consistent for many factors. Both models showed GIB hospitalization risk was associated with older age, disturbances in anemia/iron indices, recent all-cause hospitalizations, and bone mineral metabolism markers. XGBoost showed high importance on outcome prediction for serum 25 hydroxy (25OH) vitamin D levels, while logistic regression showed high importance for parathyroid hormone (PTH) levels.

conclusionsMachine learning can be considered for early detection of GIB event risk in HD. XGBoost outperforms logistic regression, yet both appear suitable. External and prospective validation of these models is needed. Association between bone mineral metabolism markers and GIB events was unexpected and warrants investigation.

trial registrationThis retrospective analysis of real-world data was not a prospective clinical trial and registration is not applicable.

Indexed as

Gastrointestinal HemorrhageHospitalizationMachine LearningRenal DialysisAgedFemaleHumansIncidenceKidney Failure, ChronicLogistic ModelsMaleMiddle AgedRisk AssessmentRisk FactorsBleedingGastrointestinalHospitalizationKidney FailurePredictive Modeling

Identifiers

PMID39427152
PMCPMC11490046

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.