Evidence map›Paper›PMID 29406420›Full record

Observational studyCritical care medicine2018

Reporting of Sepsis Cases for Performance Measurement Versus for Reimbursement in New York State.

Hallie C Prescott, Tara M Cope, Foster C Gesten, Tatiana A Ledneva, Marcus E Friedrich, Theodore J Iwashyna, Tiffany M Osborn, Christopher W Seymour, Mitchell M Levy

Abstract readComparative StudyObservational Study
In one paragraph

Observational study in Critical care medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Review
  3. Observational
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  5. Article
  6. Article
  7. Observational
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Observational
  14. Observational
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  16. Observational
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  19. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Hallie C PrescottDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI.
Tara M CopeFormer affiliation: New York State Department of Health, Albany, NY.
Foster C GestenFormer affiliation: New York State Department of Health, Albany, NY.
Tatiana A LednevaFormer affiliation: New York State Department of Health, Albany, NY.
Marcus E FriedrichFormer affiliation: New York State Department of Health, Albany, NY.
Theodore J IwashynaDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI.
Tiffany M OsbornDepartments of Surgery and Emergency Medicine, Washington University, St. Louis, MO.
Christopher W SeymourDepartments of Critical Care and Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA.
Mitchell M LevyDepartment of Medicine, Brown University, Providence, RI.

Funding

Sepsis subtypes: biology, treatment, and bedside applicationR35GM119519 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Christopher W. Seymour · 2016 to 2026
$4.9M
General pathways and personalized risk of morbidity after sepsisK08GM115859 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PRESCOTT, HALLIE CHRISTINE · 2015 to 2018
$776k
NIGMS NIH HHS K08 GM115859NIGMS NIH HHS R35 GM119519
6 · The paper itself

Abstract

objectivesUnder "Rory's Regulations," New York State Article 28 acute care hospitals were mandated to implement sepsis protocols and report patient-level data. This study sought to determine how well cases reported under state mandate align with discharge records in a statewide administrative database.

designObservational cohort study.

settingFirst 27 months of mandated sepsis reporting (April 1, 2014, to June 30, 2016). PATIENTS: Hospitalizations with sepsis at New York State Article 28 acute care hospitals.

interventionSepsis regulations with mandated reporting. MEASUREMENTS AND MAIN

resultsWe compared cases reported to the New York State Department of Health Sepsis Clinical Database with discharge records in the Statewide Planning and Research Cooperative System database. We classified discharges as 1) "coded sepsis discharges"-a diagnosis code for severe sepsis or septic shock and 2) "possible sepsis discharges," using Dombrovskiy and Angus criteria. Of 111,816 sepsis cases reported to the New York State Department of Health Sepsis Clinical Database, 105,722 (94.5%) were matched to discharge records in Statewide Planning and Research Cooperative System. The percentage of coded sepsis discharges reported increased from 67.5% in the first quarter to 81.3% in the final quarter of the study period (mean, 77.7%). Accounting for unmatched cases, as many as 82.7% of coded sepsis discharges were potentially reported, whereas at least 17.3% were unreported. Compared with unreported discharges, reported discharges had higher rates of acute organ dysfunction (e.g., cardiovascular dysfunction 63.0% vs 51.8%; p < 0.001) and higher in-hospital mortality (30.2% vs 26.1%; p < 0.001). Hospital characteristics (e.g., number of beds, teaching status, volume of sepsis cases) were similar between hospitals with a higher versus lower percent of discharges reported, p values greater than 0.05 for all. Hospitals' percent of discharges reported was not correlated with risk-adjusted mortality of their submitted cases (Pearson correlation coefficient 0.11; p = 0.17).

conclusionsApproximately four of five discharges with a diagnosis code of severe sepsis or septic shock in the Statewide Planning and Research Cooperative System data were reported in the New York State Department of Health Sepsis Clinical Database. Incomplete reporting appears to be driven more by underrecognition than attempts to game the system, with minimal bias to risk-adjusted hospital performance measurement.

Indexed as

Reimbursement MechanismsGovernment RegulationHospitalsHumansMandatory ReportingNew YorkPatient DischargeQuality Indicators, Health CareSepsis

Identifiers

PMID29406420
PMCPMC5899037

What OpenQuestion holds

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Registered trials

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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.