Evidence map›Paper›PMID 27261637›Full record

ArticleMedical care2016

Hospital Phenotypes in the Management of Patients Admitted for Acute Myocardial Infarction.

Xiao Xu, Shu-Xia Li, Haiqun Lin, Sharon-Lise T Normand, Tara Lagu, Nihar Desai, Michael Duan, Eugene A Kroch, Harlan M Krumholz

Abstract read
In one paragraph

Article in Medical care, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2019
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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

9 authors.

Xiao Xu*Department of Obstetrics, Gynecology and Reproductive Sciences, Yale School of Medicine †Center for Outcomes Research and Evaluation, Yale-New Haven Hospital ‡Department of Biostatistics, Yale School of Public Health, New Haven, CT §Department of Health Care Policy, Harvard Medical School ∥Department of Biostatistics, Harvard T.H. Chan School of Public Health ¶Division of General Medicine, Tufts University School of Medicine, Boston #Baystate Medical Center, Springfield, MA **Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT ††Premier Inc., Charlotte, NC ‡‡Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA §§Booz Allen Hamilton Inc., McLean, VA ∥∥Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale School of Medicine ¶¶Department of Health Policy and Management, Yale School of Public Health, New Haven, CT.
Shu-Xia Li
Haiqun Lin
Sharon-Lise T Normand
Tara Lagu
Nihar Desai
Michael Duan
Eugene A Kroch
Harlan M Krumholz

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Center for Cardiovascular Outcomes Research at Yale UniversityU01HL105270 · NHLBI · YALE UNIVERSITY · PI CURTIS, JEPTHA PELHAM, KRUMHOLZ, HARLAN MARC · 2010 to 2013
$5.8M
Defining Optimal Clinical Strategies for Critically Ill Heart Failure PatientsK01HL114745 · NHLBI · BAYSTATE MEDICAL CENTER, INC. · PI LAGU, TARA C · 2013 to 2017
$721k
NCATS NIH HHS UL1 TR001863NHLBI NIH HHS K01 HL114745NHLBI NIH HHS U01 HL105270
6 · The paper itself

Abstract

objectivesTo characterize hospital phenotypes by their combined utilization pattern of percutaneous coronary interventions (PCI), coronary artery bypass grafting (CABG) procedures, and intensive care unit (ICU) admissions for patients hospitalized for acute myocardial infarction (AMI). RESEARCH

designUsing the Premier Analytical Database, we identified 129,138 hospitalizations for AMI from 246 hospitals with the capacity for performing open-heart surgery during 2010-2013. We calculated year-specific, risk-standardized estimates of PCI procedure rates, CABG procedure rates, and ICU admission rates for each hospital, adjusting for patient clinical characteristics and within-hospital correlation of patients. We used a mixture modeling approach to identify groups of hospitals (ie, hospital phenotypes) that exhibit distinct longitudinal patterns of risk-standardized PCI, CABG, and ICU admission rates.

resultsWe identified 3 distinct phenotypes among the 246 hospitals: (1) high PCI-low CABG-high ICU admission (39.2% of the hospitals), (2) high PCI-low CABG-low ICU admission (30.5%), and (3) low PCI-high CABG-moderate ICU admission (30.4%). Hospitals in the high PCI-low CABG-high ICU admission phenotype had significantly higher risk-standardized in-hospital costs and 30-day risk-standardized payment yet similar risk-standardized mortality and readmission rates compared with hospitals in the low PCI-high CABG-moderate ICU admission phenotype. Hospitals in these phenotypes differed by geographic region.

conclusionsHospitals differ in how they manage patients hospitalized for AMI. Their distinctive practice patterns suggest that some hospital phenotypes may be more successful in producing good outcomes at lower cost.

Indexed as

Acute DiseaseAgedCoronary Artery BypassHospital CostsHospitalizationHospitalsHumansIntensive Care UnitsMyocardial InfarctionPercutaneous Coronary Intervention

Identifiers

PMID27261637
PMCPMC5305177

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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.