Effects of Compliance With the Early Management Bundle (SEP-1) on Mortality Changes Among Medicare Beneficiaries With Sepsis
Open accessAbstract
Background US hospitals have reported compliance with the SEP-1 quality measure to Medicare since 2015. Finding an association between compliance and outcomes is essential to gauge measure effectiveness. Research question What is the association between compliance with SEP-1 and 30-day mortality among Medicare beneficiaries? Study design and methods Studying patient-level data reported to Medicare by 3,241 hospitals from October 1, 2015, to March 31, 2017, we used propensity score matching and a hierarchical general linear model (HGLM) to estimate the treatment effects associated with compliance with SEP-1. Compliance was defined as completion of all qualifying SEP-1 elements including lactate measurements, blood culture collection, broad-spectrum antibiotic administration, 30 mL/kg crystalloid fluid administration, application of vasopressors, and patient reassessment. The primary outcome was a change in 30-day mortality. Secondary outcomes included changes in length of stay. Results We completed two matches to evaluate population-level treatment effects. In standard match, 122,870 patients whose care was compliant were matched with the same number whose care was noncompliant. Compliance was associated with a reduction in 30-day mortality (21.81% vs 27.48%, respectively), yielding an absolute risk reduction (ARR) of 5.67% (95% CI, 5.33-6.00; P Interpretation Compliance with SEP-1 was associated with lower 30-day mortality. Rendering SEP-1 compliant care may reduce the incide…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
SEP-1's complexity creates a substantial abstraction burden.
“SEP-1 was designed to measure time-sensitive care, but its complexity creates a substantial abstraction burden.”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [19]
Strict SEP-1 abstraction may not fully capture clinical judgment in complex cases, particularly when fluid dosing or antibiotic timing decisions need to be individualized.
“while acknowledging that strict SEP-1 abstraction may not fully capture clinical judgment in complex cases, particularly when fluid dosing or antibiotic timing decisions need to be individualized [8,19].”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [19]
In a large propensity score matched cohort of Medicare beneficiaries, SEP-1 compliant care was associated with lower 30-day mortality compared with noncompliant care.
“In a large propensity score matched cohort of Medicare beneficiaries, SEP-1 compliant care was associated with lower 30-day mortality compared with noncompliant care, supporting the plausibility that timely, coordinated processes matter [19].”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [19]
While some studies associate compliance with improved outcomes, measurement variability and limited evidence for certain mandated elements remain central concerns.
“While some studies associate compliance with improved outcomes, measurement variability and limited evidence for certain mandated elements remain central concerns [7,8,15,19].”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [19]
National SEP-1 performance remains modest and variably measured.
“National SEP-1 performance remains modest and variably measured.”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [19]
Cites
Austin, P. C. (2009) · Statistics in Medicine · doi:10.1002/sim.3697 · Cited by 6,758 · see citing works
Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation
Rubin, D. B. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020363010465 · Cited by 2,228 · see citing works
Time to treatment and mortality during mandated emergency care for sepsis.
Seymour, C. W., Gesten, F., Prescott, H. C., Friedrich, M. E., Iwashyna, T. J., Phillips, G. S., Lemeshow, S., Osborn, T., Terry, K. M., & Levy, M. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1703058 · Cited by 2,225 · see citing works
Self-citationLederer, D. J., Bell, S. C., Branson, R. D., Chalmers, J. D., Marshall, R., Maslove, D. M., Ost, D. E., Punjabi, N. M., Schatz, M., Smyth, A. R., et al. (2019) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201808-564ps · Cited by 715 · see citing works
The Intervention Selection Bias: An Underrecognized Confound in Intervention Research.
Larzelere, R. E., Kuhn, B. R., & Johnson, B. (2004) · Psychological Bulletin · doi:10.1037/0033-2909.130.2.289 · Cited by 144 · see citing works
Rhee, C., Brown, S. R., Jones, T. M., O’Brien, C., Pande, A., Hamad, Y., Bulger, A. L., Tobin, K. A., Massaro, A. F., Anderson, D. J., et al. (2018) · Infection Control & Hospital Epidemiology · doi:10.1017/ice.2018.134 · Cited by 51 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Rhee, C., Yu, T., Wang, R., Kadri, S. S., Fram, D., Chen, H.-C., Klompas, M., & Program, C. P. E. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.38596 · Cited by 81 · see citing works
Rhee, C., Strich, J. R., Chiotos, K., Classen, D. C., Cosgrove, S. E., Greeno, R., Heil, E. L., Kadri, S. S., Kalil, A. C., Gilbert, D. N., et al. (2024) · Clinical Infectious Diseases · doi:10.1093/cid/ciad447 · Cited by 40 · see citing works
Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis
Nguyen, H. B., Krishtopaytis, E., Lopez, E., Farnoudi, N., Van, T., Kharalampova, V., & Coz Yataco, A. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15093477
“Each element is tied to a strict time window [7,19].”
InfluentialSelf-citation
- Authors
- Townsend, S. R., Phillips, G. S., Duseja, R., Tefera, L., Cruikshank, D., Dickerson, R., Nguyen, H. B., Schorr, C. A., Levy, M. M., Dellinger, R. P., Conway, W. A., Browner, W. S., & Rivers, E. P.
- Affiliations
- University of California, San Francisco (US)
- The Ohio State University (US)
- Centers for Medicare and Medicaid Services (US)
- Alameda Health System (US)
- General Dynamics (United States) (US)
- Mathematica Policy Research (US)
- Loma Linda University (US)
- Brown University (US)
- Henry Ford Health (US)
- Wayne State University (US)
- Corresponding author
- Townsend, Sean R.
- Venue
- CHEST
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 161
- Issue
- 2
- Pages
- 392-406
- ISSN
- 0012-3692 (ISSN-L)
- OpenAlex
- W3189244054
- Cited by
- 69
- References
- 6
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 23 · 2023: 15 · 2022: 9 · 2021: 1
- FWCI
- 8.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHEST Journal (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Gordon and Betty Moore Foundation
- Centers for Medicare and Medicaid Services
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Health Professions › Health Information Management › Healthcare Quality and Management
- Health Sciences › Medicine › Infectious Diseases › Infection Control in Healthcare
- Keywords
- sepsis, 30-day mortality, quality of care, lactate measurement, blood culture, broad-spectrum antibiotics, crystalloid fluid resuscitation, vasopressors, length of hospital stay, Medicare beneficiaries, hospital quality reporting, risk-adjusted mortality, treatment effects, absolute risk reduction, sepsis mortality
- MeSH terms
- Aged, Female, Medicare, Humans, Length of Stay, Male, United States, Sepsis, Guideline Adherence, Propensity Score, Patient Care Bundles
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review