Evidence map›Paper›PMID 26650336›Full record

ArticleAnnals of the American Thoracic Society2016

Validation of a Method to Identify Immunocompromised Patients with Severe Sepsis in Administrative Databases.

Jared A Greenberg, Samuel F Hohmann, Jesse B Hall, John P Kress, Michael Z David

Abstract readValidation Study
In one paragraph

Article in Annals of the American Thoracic Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 3 pooled it
–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

36 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Guideline
  3. Pooled it
  4. Article
  5. Large language models accurately identify immunosuppression in intensive care unit patients.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  6. A New Emerging Threat in Critical Care:Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025
    Article
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  10. Review
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  16. Article
  17. Immunostimulatory Activity ofMicroorganisms · 2022
    Article
  18. Review
  19. Review
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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

5 authors.

Jared A Greenberg1 Division of Pulmonary and Critical Care Medicine, Department of Medicine, Rush University Medical Center, Chicago, Illinois.
Samuel F Hohmann2 University HealthSystem Consortium, Oak Brook, Illinois.
Jesse B Hall3 Section of Pulmonary and Critical Care Medicine, and.
John P Kress3 Section of Pulmonary and Critical Care Medicine, and.
Michael Z David4 Section of Infectious Disease and Global Health, Department of Medicine, University of Chicago, Chicago, Illinois.

Funding

RESEARCH TRAINING IN RESPIRATORY BIOLOGYT32HL007605 · NHLBI · UNIVERSITY OF CHICAGO · PI Yun Fang, Gokhan M. Mutlu · 1985 to 2026
$18.1M
NHLBI NIH HHS 2 T32 HL007605-28NHLBI NIH HHS T32 HL007605
6 · The paper itself

Abstract

rationaleImmunocompromised patients are at high risk for developing severe sepsis. Currently, there are no validated strategies for identifying this group of patients in large administrative databases.

objectivesWe set out to define and validate a method that could be used to identify immunocompromised patients with severe sepsis in administrative databases.

methodsPatients were categorized as immunocompromised based on the presence of International Classification of Diseases, 9th revision discharge diagnosis codes and medication data. We validated this strategy by comparing the discriminatory ability of the search algorithm to that of manual chart review. MEASUREMENTS AND MAIN

resultsWe identified 4,438 patients at a single academic center with severe sepsis using a definition applied to administrative data described by Angus and colleagues. There were 1,185 (26.7%) who were categorized as immunocompromised based on our novel administrative data search strategy. Compared with identification by medical record review, the new administrative data search strategy had positive and negative predictive values of 94.4% (95% confidence interval [CI], 88.8-97.7%) and 94.3% (95% CI, 91.0-96.6%). The sensitivity and specificity were 87.4% (95% CI, 80.6-92.5%) and 97.6% (95% CI, 95.0-99.9%).

conclusionsPatients who are immunosuppressed are a large subgroup of those with severe sepsis. Following its validation as a search strategy using other large databases, and its adaptation for International Classification of Diseases, 10th revision, this novel method may allow researchers to account for a patient's immune state when examining outcomes.

Indexed as

AlgorithmsImmunocompromised HostDatabases, FactualFemaleHematologic NeoplasmsHIV InfectionsHumansImmunologic Deficiency SyndromesImmunosuppressive AgentsInternational Classification of DiseasesMaleMiddle AgedNeoplasmsOrgan TransplantationRheumatic DiseasesSepsisImmunosuppressive Agentsadministrative databaseimmunosuppressionsevere sepsis

Identifiers

PMID26650336
PMCPMC5461953

What OpenQuestion holds

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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.