Evidence map›Paper›PMID 22985033›Full record

ArticleHealth services research2012

Hospital-level variation in the use of intensive care.

Christopher W Seymour, Theodore J Iwashyna, William J Ehlenbach, Hannah Wunsch, Colin R Cooke

Erratum issuedAbstract read
In one paragraph

Article in Health services research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 76 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 1 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

76 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Methicillin-resistantAntimicrobial stewardship & healthcare epidemiology : ASHE · 2025
    Article
  8. Article
  9. Intensive care unit admission criteria: a scoping review.Journal of the Intensive Care Society · 2024
    Article
  10. Article
  11. Article
  12. Article
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  14. Article
  15. Hospital Capacity Strain as a Window into the Value of ICU Admission: Some Answers, More Questions.American journal of respiratory and critical care medicine · 2022
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

16 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

5 authors.

Christopher W SeymourDepartments of Critical Care and Emergency Medicine, University of Pittsburgh School of Medicine, Core Faculty, Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, 639 Scaife Hall 3550 Terrace Street, Pittsburgh, PA 15261, USA. seymourcw@upmc.edu
Theodore J Iwashyna
William J Ehlenbach
Hannah Wunsch
Colin R Cooke

Funding

Institute for Translational Health Science (KL2)KL2RR025015 · NCRR · UNIVERSITY OF WASHINGTON · PI DISIS, MARY L. · 2007 to 2011
$9.3M
Cognitive outcomes and neuropathology in older adults following critical illnessK23AG038352 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI EHLENBACH, WILLIAM JOSEPH · 2011 to 2015
$759k
The structure and outcomes of critical care transfer networkK08HL091249 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI IWASHYNA, THEODORE J · 2008 to 2012
$661k
Use of intensive care services for elderly patients undergoing high risk surgicalK08AG038477 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WUNSCH, HANNAH · 2010 to 2014
$628k
NCRR NIH HHS KL2 RR025015NHLBI NIH HHS K08 HL091249NHLBI NIH HHS K08HL091249-01NIA NIH HHS K08 AG038477NIA NIH HHS K08AG038477NIA NIH HHS K23 AG038352
6 · The paper itself

Abstract

objectiveTo determine the extent to which hospitals vary in the use of intensive care, and the proportion of variation attributable to differences in hospital practice that is independent of known patient and hospital factors. DATA SOURCE: Hospital discharge data in the State Inpatient Database for Maryland and Washington States in 2006. STUDY

designCross-sectional analysis of 90 short-term, acute care hospitals with critical care capabilities. DATA COLLECTION/

methodsWe quantified the proportion of variation in intensive care use attributable to hospitals using intraclass correlation coefficients derived from mixed-effects logistic regression models after successive adjustment for known patient and hospital factors. PRINCIPAL

findingsThe proportion of hospitalized patients admitted to an intensive care unit (ICU) across hospitals ranged from 3 to 55 percent (median 12 percent; IQR: 9, 17 percent). After adjustment for patient factors, 19.7 percent (95 percent CI: 15.1, 24.4) of total variation in ICU use across hospitals was attributable to hospitals. When observed hospital characteristics were added, the proportion of total variation in intensive care use attributable to unmeasured hospital factors decreased by 26-14.6 percent (95 percent CI: 11, 18.3 percent).

conclusionsWide variability exists in the use of intensive care across hospitals, not attributable to known patient or hospital factors, and may be a target to improve efficiency and quality of critical care.

Indexed as

AdultAgedCritical CareCross-Sectional StudiesFemaleHospitalizationHospitals, SpecialHumansIntensive Care UnitsLogistic ModelsMaleMarylandMiddle AgedPatient AdmissionWashington

Identifiers

PMID22985033
PMCPMC3513618

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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.