Evidence map›Paper›PMID 31927706›Full record

ArticleJournal of medical systems2020

Web-Based Dashboard for the Interactive Visualization and Analysis of National Risk-Standardized Mortality Rates of Sepsis in the US.

Meng-Tse Lee, Fong-Ci Lin, Szu-Ta Chen, Wan-Ting Hsu, Samuel Lin, Tzer-Shyong Chen, Feipei Lai, Chien-Chang Lee

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Article in Journal of medical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

8 authors.

Meng-Tse LeeDepartment of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Fong-Ci LinGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Szu-Ta ChenDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Wan-Ting HsuDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Samuel LinDepartment of Data Sciences, University of California, Berkeley, CA, USA.
Tzer-Shyong ChenDepartment of Information Management, Tunghai University, Taichung, Taiwan.
Feipei LaiDepartment of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Chien-Chang LeeGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. cclee100@gmail.com.

Funding

Taiwan National Ministry of Science and Technology Grants MOST 104-2314-B-002 -039 -MY3
6 · The paper itself

Abstract

Sepsis mortality is heavily influenced by the quality of care in hospitals. Comparing risk-standardized mortality rate (RSMR) of sepsis patients in different states in the United States has potentially important clinical and policy implications. In the current study, we aimed to compare national sepsis RSMR using an interactive web-based dashboard. We analyzed sepsis mortality using the National Inpatient Sample Database of the US. The RSMR was calculated by the hierarchical logistic regression model. We wrote the interactive web-based dashboard using the Shiny framework, an R package that integrates R-based statistics computation and graphics generation. Visual summarizations (e.g., heat map, and time series chart), and interactive tools (e.g., year selection, automatic year play, map zoom, copy or print data, ranking data by name or value, and data search) were implemented to enhance user experience. The web-based dashboard (https://sepsismap.shinyapps.io/index2/) is cross-platform and publicly available to anyone with interest in sepsis outcomes, health inequality, and administration of state/federal healthcare. After extrapolation to the national level, approximately 35 million hospitalizations were analyzed for sepsis mortality each year. Eight years of sepsis mortality data were summarized into four easy to understand dimensions: Sepsis Identification Criteria; Sepsis Mortality Predictors; RSMR Map; RSMR Trend. Substantial variation in RSMR was observed for different states in the US. This web-based dashboard allows anyone to visualize the substantial variation in RSMR across the whole US. Our work has the potential to support healthcare transparency, information diffusion, health decision-making, and the formulation of new public policies.

Indexed as

Hospital MortalityData DisplayElectronic Health RecordsFemaleHealth Status DisparitiesHumansInformation Storage and RetrievalLogistic ModelsMaleOutcome and Process Assessment, Health CareRisk AssessmentSepsisUnited StatesAnd visualizationDashboardRisk standardized mortality rateSepsis

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.