Evidence map›Paper›PMID 30709587›Full record

ArticleSurgery2019

Simplified risk prediction indices do not accurately predict 30-day death or readmission after discharge following colorectal surgery.

David G Brauer, Sarah A Lyons, Matthew R Keller, Matthew G Mutch, Graham A Colditz, Sean C Glasgow

Abstract read
In one paragraph

Article in Surgery, 2019. 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. Review
  2. Predicting Colorectal Surgery Readmission Risk: a Surgery-Specific Predictive Model.IISE transactions on healthcare systems engineering · 2023
    Article
  3. 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

6 authors.

David G BrauerDepartment of Surgery, Washington University School of Medicine, St. Louis, MO.
Sarah A LyonsDepartment of Surgery, Washington University School of Medicine, St. Louis, MO.
Matthew R KellerDepartment of Medicine, Washington University School of Medicine.
Matthew G MutchDepartment of Surgery, Washington University School of Medicine, St. Louis, MO.
Graham A ColditzDepartment of Surgery, Washington University School of Medicine, St. Louis, MO.
Sean C GlasgowDepartment of Surgery, Washington University School of Medicine, St. Louis, MO; John Cochran Veteran's Administration Medical Center, St. Louis, MO. Electronic address: glasgows@wustl.edu.

Funding

Washington University Center for Cellular ImagingP30CA091842 · NCI · WASHINGTON UNIVERSITY · PI TIMOTHY J. EBERLEIN · 2001 to 2026
$128.0M
WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
SURGICAL ONCOLOGY TRAINING GRANTT32CA009621 · NCI · WASHINGTON UNIVERSITY · PI TIMOTHY J. EBERLEIN, William E. Gillanders · 1988 to 2026
$12.9M
WASHINGTON UNIVERSITY CER MENTORED CAREER DEVELOPMENT AWARD KM1KM1CA156708 · NCI · WASHINGTON UNIVERSITY · PI FRASER, VICTORIA J. · 2010 to 2010
$2.5M
WASHINGTON UNIVERSITY COMPARATIVE EFFECTIVENESS ADMINISTRATIVE DATA REPOSITORYR24HS019455 · AHRQ · WASHINGTON UNIVERSITY · PI FRASER, VICTORIA J. · 2010 to 2010
$969k
AHRQ HHS R24 HS019455NCATS NIH HHS UL1 TR000448NCATS NIH HHS UL1 TR002345NCI NIH HHS KM1 CA156708NCI NIH HHS P30 CA091842NCI NIH HHS T32 CA009621
6 · The paper itself

Abstract

backgroundRisk-prediction indices are one category of the many tools implemented to guide efforts to decrease readmissions. However, using fied models to predict a complex process can prove challenging. In addition, no risk-prediction index has been developed for patients undergoing colorectal surgery. Therefore, we evaluated the performance of a widely utilized simplified index developed at the hospital level - LACE (length of stay, acute admission, Charlson comorbidity index score, and emergency department visits) and developed and evaluated a novel index in predicting readmissions in this patient population.

methodsUsing a retrospective split-sample cohort, patients discharged after colorectal surgery were identified within the inpatient databases of the Healthcare Cost and Utilization Project for the states of New York, California, and Florida (2006-2014). The primary outcome was death or readmission within 30 days after discharge. Multivariable logistic regression models incorporated patient comorbidities, postoperative complications, and hospitalization details, and were evaluated using the C statistic.

resultsA total of 440,742 patients met eligibility criteria. The rate of death or readmission within 30 days after discharge was 14.0% (n = 61,757). When applied to surgical patients, the LACE index demonstrated a poor model fit (C = 0.631). The model fit improved significantly-but remained poor (C = 0.654; P < .001)-with the addition of the following variables, which are known to be associated with readmission after colorectal surgery: age, indication for surgery, and creation of a new ostomy. A novel, simplified model also yielded a poor model fit (C = 0.660).

conclusionPostdischarge death or readmission after colorectal surgery is not accurately modeled using existing, modified, or novel simplified risk prediction models. Payers and providers must ensure that quality improvement efforts applying simplified models to complex processes, such as readmissions following colorectal surgery, may not be appropriate, and that models reflect the relevant patient population.

Indexed as

AgedColonColonic DiseasesDigestive System Surgical ProceduresFemaleHumansLength of StayLogistic ModelsMaleMiddle AgedPatient ReadmissionPostoperative ComplicationsPrognosisQuality ImprovementRectal DiseasesRectum

Identifiers

PMID30709587
PMCPMC6502644

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.