Evidence map›Paper›PMID 35096409›Full record

ArticleDigital health

Risk-standardized sepsis mortality map of the United States.

Jiun-Ruey Hu, Chia-Hung Yo, Hsin-Ying Lee, Chin-Hua Su, Ming-Yang Su, Amy Huaishiuan Huang, Ye Liu, Wan-Ting Hsu, Matthew Lee, Yee-Chun Chen and 1 more

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

11 authors.

Jiun-Ruey HuDepartment of Internal Medicine, Yale School of Medicine, USA.
Chia-Hung YoDepartment of Emergency Medicine, Far Eastern Memorial Hospital, Taiwan.
Hsin-Ying LeeDepartment of Medicine, College of Medicine, National Taiwan University, Taiwan.
Chin-Hua SuDepartment of Emergency Medicine, National Taiwan University Hospital, Taiwan.
Ming-Yang SuDepartment of Surgery, Chang Gung Memorial Hospital, Taiwan.
Amy Huaishiuan HuangDepartment of Emergency Medicine, National Taiwan University Hospital, Taiwan.
Ye LiuDepartment of Health Care Organization and Policy, University of Alabama at Birmingham, School of Public Health, USA.
Wan-Ting HsuDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, USA.
Matthew LeeMedical Wizdom, LLC, USA.
Yee-Chun ChenDepartment of Medicine, College of Medicine, National Taiwan University, Taiwan.
Chien-Chang LeeDepartment of Emergency Medicine, National Taiwan University Hospital, Taiwan.ORCID https://orcid.org/0000-0002-1243-2463

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveSepsis is the leading cause of in-hospital mortality in the United States (US). Quality improvement initiatives for improving sepsis care depend on accurate estimates of sepsis mortality. While hospital 30-day risk-standardized mortality rates have been published for patients hospitalized with acute myocardial infarction, heart failure, and pneumonia, risk-standardized mortality rates for sepsis have not been well characterized. We aimed to construct a sepsis risk-standardized mortality rate map for the United States, to illustrate disparities in sepsis care across the country.

methodsThis cross-sectional study included adults from the US Nationwide Inpatient Sample who were hospitalized with sepsis between 1 January 2010 and 30 December 2011. Hospital-level risk-standardized mortality rates were calculated using hierarchical logistic modelling, and were risk-adjusted with predicted mortality derived from (1) the Sepsis Risk Prediction Score, a logistic regression model, and (2) gradient-boosted decision trees, a supervised machine learning (ML) algorithm.

resultsAmong 1,739,033 adults hospitalized with sepsis, 50% were female, and the median age was 71 years (interquartile range: 58-81). The national median risk-standardized mortality rate for sepsis was 18.4% (interquartile range: 17.0, 21.0) by the boosted tree model, which had better discrimination than the Sepsis Risk Prediction Score model (C-statistic 0.87 and 0.78, respectively). The highest risk-standardized mortality rates were found in Wyoming, North Dakota, and Mississippi, while the lowest were found in Arizona, Colorado, and Michigan.

conclusionsWide variation exists in sepsis risk-standardized mortality rates across states, representing opportunities for improvement in sepsis care. This represents the first map of state-level variation of risk-standardized mortality rates in sepsis.

Indexed as

healthcare qualitymachine learningmortalityrisk standardized mortality ratesepsis

Identifiers

PMID35096409
PMCPMC8796072

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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.