Evidence map›Paper›PMID 40272220›Full record

ArticleCritical care medicine2025

Geographic Clusters in Sepsis Hospital Mortality and the Role of Targeted Regionalization.

Nicholas M Mohr, Yiqi Tang, David F Gaieski, David G Buckler, Brendan Carr, Alexis Zebrowski

Abstract read
In one paragraph

Article in Critical care medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Nicholas M MohrDepartments of Emergency Medicine, Anesthesia Critical Care, and Epidemiology, University of Iowa Carver College of Medicine, Iowa City, IA.ORCID 0000-0003-0497-5828
Yiqi TangDepartment of Statistics, Colby College, Waterville, ME.
David F GaieskiDepartment of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA.
David G BucklerDepartments of Emergency Medicine and Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY.
Brendan CarrDepartments of Emergency Medicine and Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY.
Alexis ZebrowskiDepartments of Emergency Medicine and Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY.

Funding

Conduits: Mount Sinai Health System Translational Science HubUL1TR004419 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rosalind J Wright · 2022 to 2026
$46.4M
A POPULATION BASED APPROACH TO IMPROVE OUTCOMES AFTER OUT-OF-HOSPITAL CARDIAC ARRESTR01HL141841 · NHLBI · THOMAS JEFFERSON UNIVERSITY · PI CARR, BRENDAN G · 2018 to 2022
$3.8M
COVID and Translational Science supercomputer (CATS)S10OD030463 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2021 to 2021
$2.0M
Big Omics Data Engine 2 SupercomputerS10OD026880 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2019 to 2019
$2.0M
AHRQ HHS K08 HS025753AHRQ HHS R01 HS023614NCATS NIH HHS UL1 TR004419NHLBI NIH HHS R01 HL141841NIH HHS S10 OD026880NIH HHS S10 OD030463
6 · The paper itself

Abstract

objectivesSepsis is a severe condition associated with high mortality, and hospital performance is variable. The objective of this study was to develop geospatial sepsis clusters, identify sources of variation between clusters, and test the hypothesis that redistributing sepsis patients from low-performing hospitals to higher-performing hospitals within a cluster will improve sepsis outcomes. DESIGN, SETTING, AND PATIENTS: We conducted a cohort study of age-qualifying Medicare beneficiaries using administrative claims data from 2013 to 2015. We calculated risk-standardized mortality for hospitals then used a clustering algorithm to define geospatial cluster boundaries based on care-seeking and interhospital transfer patterns. Finally, we used simulation to model the effect of reallocating sepsis patients to higher-performing hospitals within the same cluster.

interventionsNone. MEASUREMENTS AND MAIN

resultsWe included 1,125,308 patients, and they were grouped into 222 regional clusters. High-performing clusters were located largely in the Midwest, and they tended to be in less urban regions with smaller hospitals. In our simulation, the most impactful strategy was reassigning cases from the lowest-performing hospital in a cluster to the highest-performing hospital in the cluster, which was predicted to prevent 1705 deaths per year in the United States. This aggregate benefit was lower than the 5702 deaths predicted from reducing mortality by 1% absolute in hospitals in the lower half of the performance distribution.

conclusionsGeospatial clusters provide insight into regional approaches to system-based acute care. In a simulation study, targeted sepsis regionalization appears less effective than local performance improvement in reducing preventable sepsis deaths.

Indexed as

Hospital MortalitySepsisAgedAged, 80 and overCluster AnalysisCohort StudiesFemaleHospitalsHumansMaleMedicareUnited Statescomputer simulationquality of healthcareregional medical programssepsis

Identifiers

PMID40272220
PMCPMC12213171

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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.