Evidence map›Paper›PMID 31451920›Full record

ArticleSurgical endoscopy2020

Can we better predict readmission for dehydration following creation of a diverting loop ileostomy: development and validation of a prediction model and web-based risk calculator.

Mohammed Alqahtani, Richard Garfinkle, Kaiqiong Zhao, Carol-Ann Vasilevsky, Nancy Morin, Gabriela Ghitulescu, Julio Faria, Marylise Boutros

Abstract readMulticenter StudyValidation Study
PubMed Publisher
In one paragraph

Article in Surgical endoscopy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Predicting Colorectal Surgery Readmission Risk: a Surgery-Specific Predictive Model.IISE transactions on healthcare systems engineering · 2023
    Article
  9. Article
  10. Development and Validation of Machine Learning Models to Predict Readmission After Colorectal Surgery.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2022
    Article
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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

8 authors.

Mohammed Alqahtani *Division of Colon and Rectal Surgery, Jewish General Hospital, Montreal, QC, Canada.
Richard Garfinkle *Division of Colon and Rectal Surgery, Jewish General Hospital, Montreal, QC, Canada.
Kaiqiong ZhaoDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
Carol-Ann VasilevskyDivision of Colon and Rectal Surgery, Jewish General Hospital, Montreal, QC, Canada.
Nancy MorinDivision of Colon and Rectal Surgery, Jewish General Hospital, Montreal, QC, Canada.
Gabriela GhitulescuDivision of Colon and Rectal Surgery, Jewish General Hospital, Montreal, QC, Canada.
Julio FariaDivision of Colon and Rectal Surgery, Jewish General Hospital, Montreal, QC, Canada.
Marylise BoutrosDivision of Colon and Rectal Surgery, Jewish General Hospital, Montreal, QC, Canada. mboutros@jgh.mcgill.ca.ORCID http://orcid.org/0000-0001-8006-321X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDehydration is the most common morbidity following creation of a diverting loop ileostomy (DLI). We aimed to develop and validate a prediction model and web-based risk calculator for readmission for dehydration following DLI creation.

methodsAfter institutional review board approval, we retrospectively reviewed the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database between 2012 and 2017. Adult patients (> 18 years) who underwent DLI with a resection for colorectal cancer, inflammatory bowel disease, or diverticulitis were identified. Patient demographics, operative and postoperative data were collected. The final prediction model, developed in 60% of the cohort (training set) and which modeled the 30-day cumulative incidence of readmission for dehydration, was selected using highest area under the receiver operating curve (AUC) criterion. Model calibration was assessed with the Hosmer-Lemeshow goodness-of-fit test. The model was then assessed in validation and test sets, using 20% of the cohort for each.

resultsOf 25,638 patients in the ACS-NSQIP database who met inclusion criteria, 15,222 patients were randomly selected for the training set. The incidence of readmission for dehydration in this cohort was 2.1%. The final model with the highest AUC retained 12 candidate variables: age, sex, smoking status, diabetes, hypertension, American Society of Anesthesiologists score, type of admission, underlying diagnosis, procedure performed, operative time, index admission length of stay, and major morbidity. The model demonstrated good discrimination (AUC 0.76, 95% CI 0.74-0.79) and the Hosmer-Lemeshow goodness-of-fit test confirmed good calibration (p = 0.50). Five-thousand and seventy-three patients were available for the validation and test sets, respectively, and the model remained strong in both the validation and test sets (AUCs of 0.73 and 0.73, respectively). The prediction model was then converted into a web-based risk calculator.

conclusionsA prediction model and web-based risk calculator for readmission for dehydration after DLI creation was developed and validated, demonstrating good predictive capabilities.

Indexed as

AgedArea Under CurveDatabases, FactualDehydrationDiagnosis, Computer-AssistedFemaleHumansIleostomyIncidenceInternetMaleMiddle AgedPatient ReadmissionPostoperative ComplicationsQuality ImprovementRetrospective StudiesAcute renal failureDehydrationIleostomyMorbidityReadmission

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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.