Evidence map›Paper›PMID 27526267›Full record

ArticleCritical care medicine2017

Hospital Contributions to Variability in the Use of ICUs Among Elderly Medicare Recipients.

Andrew J Admon, Hannah Wunsch, Theodore J Iwashyna, Colin R Cooke

Open access · greenAbstract read
In one paragraph

Article in Critical care medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.7field-weighted citation impact, top 8% of its field
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

15 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
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  4. Methicillin-resistantAntimicrobial stewardship & healthcare epidemiology : ASHE · 2025
    Article
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  9. Review
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  12. Article
  13. Intensivists in U.S. Acute Care Hospitals.Critical care medicine · 2019
    Article
  14. Observational
  15. Observational
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

4 authors at 2 institutions in 2 countries.

Andrew J Admon1Department of Internal Medicine, University of Michigan, Ann Arbor, MI. 2Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. 3Department of Anesthesia, University of Toronto, Toronto, ON, Canada. 4Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI. 5Center for Clinical Management Research, VA Ann Arbor Health System, Ann Arbor, MI. 6Center for Healthcare Outcomes and Policy, Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI.
Hannah Wunsch
Theodore J Iwashyna
Colin R Cooke
University of Michigan–Ann Arbor · USUniversity of Toronto · CA

Funding

Understanding Variation in the Use of Critical Care ServicesK08HS020672 · AHRQ · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI COOKE, COLIN · 2012 to 2016
$768k
Use of intensive care services for elderly patients undergoing high risk surgicalK08AG038477 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WUNSCH, HANNAH · 2010 to 2014
$628k
AHRQ HHS K08 HS020672NIA NIH HHS K08 AG038477
6 · The paper itself

Abstract

objectiveHospitals vary widely in ICU admission rates across numerous medical diagnoses. The extent to which variability in ICU use is specific to individual diagnoses or is a function of the hospital, regardless of disease, is unknown.

designRetrospective cohort study.

settingA total of 1,120 acute care hospitals with ICU capabilities. PATIENTS: Medicare beneficiaries 65 years old or older admitted for five medical diagnoses (acute myocardial infarction, congestive heart failure, stroke, pneumonia, and chronic obstructive pulmonary disease) and a surgical diagnosis (hip fracture treated with arthroplasty) in 2010.

interventionsNone. MEASUREMENTS AND MAIN

resultsWe used multilevel models to calculate risk- and reliability-adjusted ICU admission rates, examined the correlation in ICU admission rates across diagnosis and calculated intraclass correlation coefficients and median odds ratios to quantify the variability in ICU admission rate that was attributable to hospitals. We also examined the ability of a high ICU-use hospital for one condition to predict high ICU use for other conditions. We identified 348,462 patients with one of the eligible conditions. ICU admission rates were positively correlated within hospitals for included medical diagnoses (r range, 0.38-0.59; p < 0.01). The top hospital quartile of ICU use for congestive heart failure had a sensitivity of 50-60% and specificity of 79-81% for detecting top quartile hospitals for each other conditions. After adjustment for patient and hospital characteristics, hospitals accounted for 17.6% (95% CI, 16.2-19.1%) of variability in ICU admission, corresponding to a median odds ratio of 2.3, compared to 25.8% (95% CI, 24.5-27.1%) and median odds ratio 2.8 for diagnosis. This suggests a patient with median baseline risk of ICU admission would more than double his/her odds of ICU admission if moving to a higher utilizing hospital.

conclusionsHospitals account for a significant proportion of variation independent of measured patient and hospital characteristics, suggesting the need for further work to evaluate the causes of variation at the hospital level and potential consequences of variation across hospitals.

Indexed as

AgedAged, 80 and overCohort StudiesFemaleHeart FailureHip FracturesHospitalsHumansIntensive Care UnitsMaleMedicareMyocardial InfarctionPatient AdmissionPneumoniaPulmonary Disease, Chronic ObstructiveRetrospective Studies

Identifiers

PMID27526267
PMCPMC5669057
OpenAlexW2507580993

What OpenQuestion holds

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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.