ArticleDigital health
Risk-standardized sepsis mortality map of the United States.
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.
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Who cites it
7 citing papers in PubMed.
- Mitigating Bias in Machine Learning Models with Ethics-Based Initiatives: The Case of Sepsis.The American journal of bioethics : AJOB · 2026Article
- Machine learning for predicting acute myocardial infarction in patients with sepsis.Scientific reports · 2024Article
- Deep reinforcement learning extracts the optimal sepsis treatment policy from treatment records.Communications medicine · 2024Article
- Personalized, disease-stage specific, rapid identification of immunosuppression in sepsis.Frontiers in immunology · 2024Article
- Article
- Prevalence, risk factors, and outcomes associated with delayed second doses of antibiotics in sepsis at a large academic medical center.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023Article
- Development and validation of risk-adjusted quality indicators for the long-term outcome of acute sepsis care in German hospitals based on health claims data.Frontiers in medicine · 2022Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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