Evidence map›Paper›PMID 32617377›Full record

ArticleOpen forum infectious diseases2020

Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons.

Chanu Rhee, Zhonghe Li, Rui Wang, Yue Song, Sameer S Kadri, Edward J Septimus, Huai-Chun Chen, David Fram, Robert Jin, Russell Poland and 2 more

Abstract read
In one paragraph

Article in Open forum infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Inclusion of social determinants of health improves sepsis readmission prediction models.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  2. Adjusting Client-Level Risks Impacts on Home Care Organization Ranking.International journal of environmental research and public health · 2021
    Article
  3. Article
  4. 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

12 authors.

Chanu RheeDepartment of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Zhonghe LiDepartment of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Rui WangDepartment of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Yue SongDepartment of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Sameer S KadriCritical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, Massachusetts, USA.
Edward J SeptimusDepartment of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Huai-Chun ChenCommonwealth Informatics, Waltham, Massachusetts, USA.
David FramCommonwealth Informatics, Waltham, Massachusetts, USA.
Robert JinDepartment of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Russell PolandDepartment of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Kenneth SandsDepartment of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Michael KlompasDepartment of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.

Funding

Big Data - Epidemiology of Critical Illness and SepsisZIACL090045 · CLC · CLINICAL CENTER · PI KADRI, SAMEER · 2017 to 2025
$0k
6 · The paper itself

Abstract

backgroundA reliable risk-adjusted sepsis outcome measure could complement current national process metrics by identifying outlier hospitals and catalyzing additional improvements in care. However, it is unclear whether integrating clinical data into risk adjustment models identifies similar high- and low-performing hospitals compared with administrative data alone, which are simpler to acquire and analyze.

methodsWe ranked 200 US hospitals by their Centers for Disease Control and Prevention Adult Sepsis Event (ASE) mortality rates and assessed how rankings changed after applying (1) an administrative risk adjustment model incorporating demographics, comorbidities, and codes for severe illness and (2) an integrated clinical and administrative model replacing severity-of-illness codes with laboratory results, vasopressors, and mechanical ventilation. We assessed agreement between hospitals' risk-adjusted ASE mortality rates when ranked into quartiles using weighted kappa statistics (к).

resultsThe cohort included 4 009 631 hospitalizations, of which 245 808 met ASE criteria. Risk-adjustment had a large effect on rankings: 22/50 hospitals (44%) in the worst quartile using crude mortality rates shifted into better quartiles after administrative risk adjustment, and a further 21/50 (42%) of hospitals in the worst quartile using administrative risk adjustment shifted to better quartiles after incorporating clinical data. Conversely, 14/50 (28%) hospitals in the best quartile using administrative risk adjustment shifted to worse quartiles with clinical data. Overall agreement between hospital quartile rankings when risk-adjusted using administrative vs clinical data was moderate (к = 0.55).

conclusionsIncorporating clinical data into risk adjustment substantially changes rankings of hospitals' sepsis mortality rates compared with using administrative data alone. Comprehensive risk adjustment using both administrative and clinical data is necessary before comparing hospitals by sepsis mortality rates.

Indexed as

Adult Sepsis Eventhospital comparisonsoutcome measurerisk adjustmentsepsis

Identifiers

PMID32617377
PMCPMC7320830

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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.