Evidence map›Paper›PMID 27755392›Full record

ArticleMedical care2017

The HOSPITAL Score Predicts Potentially Preventable 30-Day Readmissions in Conditions Targeted by the Hospital Readmissions Reduction Program.

Robert E Burke, Jeffrey L Schnipper, Mark V Williams, Edmondo J Robinson, Eduard E Vasilevskis, Sunil Kripalani, Joshua P Metlay, Grant S Fletcher, Andrew D Auerbach, Jacques D Donzé

Abstract readMulticenter Study
In one paragraph

Article in Medical care, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 3 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

26 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  12. Predicting Colorectal Surgery Readmission Risk: a Surgery-Specific Predictive Model.IISE transactions on healthcare systems engineering · 2023
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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

10 authors.

Robert E Burke*Hospital Medicine and Research Sections, Eastern Colorado Health Care System, DE †Department of Medicine, Division of General Internal Medicine, University of Colorado School of Medicine, Aurora, CO ‡BWH Hospitalist Service, Division of General Medicine, Brigham and Women's Hospital §Harvard Medical School, Boston, MA ∥Center for Health Services Research, University of Kentucky College of Medicine, Lexington, KY ¶Value Institute, Christiana Care Health System, Wilmington, DE #Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Vanderbilt University **VA Tennessee Valley, Geriatric Research, Education and Clinical Center (GRECC) ††Center for Clinical Quality and Implementation Research, Vanderbilt University, Nashville, TN ‡‡Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA §§Department of Medicine, Harborview Medical Center, University of Washington, Seattle, WA ∥∥Division of Hospital Medicine, University of California, San Francisco, CA ¶¶Division of General Internal Medicine, Bern University Hospital, Bern, Switzerland.
Jeffrey L Schnipper
Mark V Williams
Edmondo J Robinson
Eduard E Vasilevskis
Sunil Kripalani
Joshua P Metlay
Grant S Fletcher
Andrew D Auerbach
Jacques D Donzé

Funding

Predicting Acute Brain Dysfunction in Older Patients: A Quality Improvement ToolK23AG040157 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI VASILEVSKIS, EDUARD E · 2011 to 2015
$609k
Improving the outcomes of older adults discharged to post-acute care facilities after hospitalizationR03AG050885 · NIA · DENVER RESEARCH INSTITUTE · PI BURKE, ROBERT EDWARD · 2015 to 2016
$171k
HSRD VA IK2 HX001796NIA NIH HHS K23 AG040157NIA NIH HHS R03 AG050885
6 · The paper itself

Abstract

BACKGROUND/

objectivesNew tools to accurately identify potentially preventable 30-day readmissions are needed. The HOSPITAL score has been internationally validated for medical inpatients, but its performance in select conditions targeted by the Hospital Readmission Reduction Program (HRRP) is unknown.

designRetrospective cohort study.

settingSix geographically diverse medical centers. PARTICIPANTS/EXPOSURES: All consecutive adult medical patients discharged alive in 2011 with 1 of the 4 medical conditions targeted by the HRRP (acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and heart failure) were included. Potentially preventable 30-day readmissions were identified using the SQLape algorithm. The HOSPITAL score was calculated for all patients. MEASUREMENTS: A multivariable logistic regression model accounting for hospital effects was used to evaluate the accuracy (Brier score), discrimination (c-statistic), and calibration (Pearson goodness-of-fit) of the HOSPITAL score for each 4 medical conditions.

resultsAmong the 9181 patients included, the overall 30-day potentially preventable readmission rate was 13.6%. Across all 4 diagnoses, the HOSPITAL score had very good accuracy (Brier score of 0.11), good discrimination (c-statistic of 0.68), and excellent calibration (Hosmer-Lemeshow goodness-of-fit test, P=0.77). Within each diagnosis, performance was similar. In sensitivity analyses, performance was similar for all readmissions (not just potentially preventable) and when restricted to patients age 65 and above.

conclusionsThe HOSPITAL score identifies a high-risk cohort for potentially preventable readmissions in a variety of practice settings, including conditions targeted by the HRRP. It may be a valuable tool when included in interventions to reduce readmissions within or across these conditions.

Indexed as

AdultAgedAged, 80 and overFemaleHeart FailureHospital AdministrationHumansLogistic ModelsMaleMiddle AgedModels, TheoreticalMyocardial InfarctionPatient ReadmissionPneumoniaPulmonary Disease, Chronic ObstructiveRetrospective Studies

Identifiers

PMID27755392
PMCPMC5309170

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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.