Evidence map›Paper›PMID 27057783›Full record

ArticleAnnals of the American Thoracic Society2016

The Effect of Intensive Care Unit Admission Patterns on Mortality-based Critical Care Performance Measures.

Ian J Barbash, Tri Q Le, Francis Pike, Amber E Barnato, Derek C Angus, Jeremy M Kahn

Abstract readMulticenter Study
In one paragraph

Article in Annals of the American Thoracic Society, 2016. 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. Article
  2. 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.

Ian J Barbash1 Division of Pulmonary, Allergy, and Critical Care Medicine.
Tri Q Le2 CRISMA Center, Department of Critical Care Medicine, and.
Francis Pike2 CRISMA Center, Department of Critical Care Medicine, and.
Amber E Barnato2 CRISMA Center, Department of Critical Care Medicine, and.
Derek C Angus2 CRISMA Center, Department of Critical Care Medicine, and.
Jeremy M Kahn1 Division of Pulmonary, Allergy, and Critical Care Medicine.

Funding

Translational Training Program in Pulmonary Biology and MedicineT32HL007563 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Melanie Koenigshoff · 1985 to 2026
$12.3M
Novel approaches to profiling hospitals on critical illness mortalityR01HL126694 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KAHN, JEREMY M · 2015 to 2017
$1.1M
Developing a Robust Measure of Hospital End-of-Life IntensityR01AG035112 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BARNATO, AMBER E · 2009 to 2011
$454k
NHLBI NIH HHS L30 HL129329NHLBI NIH HHS R01 HL126694NHLBI NIH HHS T32 HL007563NIA NIH HHS R01 AG035112
6 · The paper itself

Abstract

rationaleCurrent mortality-based critical care performance measurement focuses on intensive care unit (ICU) admissions as a single group, conflating low-severity and high-severity ICU patients for whom performance may differ and neglecting severely ill patients treated solely on hospital wards.

objectivesTo assess the relationship between hospital performance as measured by risk-standardized mortality for severely ill ICU patients, less severely ill ICU patients, and severely ill patients outside the ICU.

methodsUsing a statewide, all-payer dataset from the Pennsylvania Healthcare Cost Containment Council, we analyzed discharge data for patients with nine clinical conditions with frequent ICU use. Using a validated severity-of-illness measure, we categorized hospitalized patients as either high severity (predicted probability of in-hospital death in top quartile) or low severity (all others). We then created three mutually exclusive groups: high-severity ICU admissions, low-severity ICU admissions, and high-severity ward patients. We used hierarchical logistic regression to generate hospital-specific 30-day risk-standardized mortality rates for each group and then compared hospital performance across groups using Spearman's rank correlation. MEASUREMENTS AND MAIN

resultsWe analyzed 87 hospitals with 22,734 low-severity ICU admissions (mean per hospital, 261 ± 187), 10,991 high-severity ICU admissions (mean per hospital, 126 ± 105), and 6,636 high-severity ward patients (mean per hospital, 76 ± 48). We found little correlation between hospital performance for high-severity ICU patients versus low-severity ICU patients (ρ = 0.15; P = 0.17). There were 29 hospitals (33%) that moved up or down at least two quartiles of performance across the ICU groups. There was weak correlation between hospital performance for high-severity ICU patients versus high-severity ward patients (ρ = 0.25; P = 0.02). There were 24 hospitals (28%) that moved up or down at least two quartiles of performance across the high-severity groups.

conclusionsHospitals that perform well in caring for high-severity ICU patients do not necessarily also perform well in caring for low-severity ICU patients or high-severity ward patients, indicating that risk-standardized mortality rates for ICU admissions as a whole offer only a narrow window on a hospital's overall performance for critically ill patients.

Indexed as

Hospital MortalityAgedAged, 80 and overCritical CareCritical IllnessCross-Sectional StudiesFemaleHumansIntensive Care UnitsLogistic ModelsMaleMiddle AgedPatient AdmissionPatient Outcome AssessmentPennsylvaniaProbabilitycritical carehealth care quality assessmenthealth servicesintensive carepatient outcome assessment

Identifiers

PMID27057783
PMCPMC5018925

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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.