Evidence map›Paper›PMID 36382338›Full record

ReviewCritical care explorations2022

Accuracy of

Bonnie Liu, Milena Hadzi-Tosev, Yang Liu, Kayla J Lucier, Anchit Garg, Sophie Li, Nancy M Heddle, Bram Rochwerg, Shuoyan Ning

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Critical care explorations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

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  15. Impact of data sources and ascertainment methods on reporting paediatric genetic condition prevalence: A scoping review.Health information management : journal of the Health Information Management Association of Australia · 2026
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  17. Changes in the Prevalence of Non-AIDS Conditions Among Hospitalized Persons With HIV in the United States and Canada, 2008-2018.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025
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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

9 authors.

Bonnie LiuDepartment of Medicine, McMaster Centre for Transfusion Research, McMaster University, Hamilton, ON, Canada.
Milena Hadzi-TosevDepartment of Medicine, McMaster Centre for Transfusion Research, McMaster University, Hamilton, ON, Canada.
Yang LiuDepartment of Medicine, McMaster Centre for Transfusion Research, McMaster University, Hamilton, ON, Canada.
Kayla J LucierDepartment of Medicine, McMaster Centre for Transfusion Research, McMaster University, Hamilton, ON, Canada.
Anchit GargDepartment of Medicine, McMaster Centre for Transfusion Research, McMaster University, Hamilton, ON, Canada.
Sophie LiDepartment of Medicine, McMaster Centre for Transfusion Research, McMaster University, Hamilton, ON, Canada.
Nancy M HeddleDepartment of Medicine, McMaster Centre for Transfusion Research, McMaster University, Hamilton, ON, Canada.
Bram RochwergDepartment of Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
Shuoyan NingDepartment of Medicine, McMaster Centre for Transfusion Research, McMaster University, Hamilton, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Administrative databases are increasingly used in research studies to capture clinical outcomes such as sepsis. This systematic review and meta-analysis examines the accuracy of DATA SOURCES: We searched MEDLINE, EMBASE, Web of Science, CENTRAL, Epistemonikos, and McMaster Superfilters from inception to September 7, 2021. STUDY SELECTION: We included studies that validated the accuracy of sepsis ICD-10 codes against any reference standard. DATA EXTRACTION: Three authors, working in duplicate, independently extracted data. We conducted meta-analysis using a random effects model to pool sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated individual study risk of bias using the Quality Assessment of Diagnostic Accuracy Studies tool and assessed certainty in pooled diagnostic effect measures using the Grading of Recommendations Assessment, Development, and Evaluation framework. DATA SYNTHESIS: Thirteen eligible studies were included in the qualitative synthesis and the meta-analysis. Eleven studies used manual chart review as the reference standard, and four studies used registry databases. Only one study evaluated pediatric patients exclusively. Compared with the reference standard of detailed chart review and/or registry databases, the pooled sensitivity for sepsis ICD-10 codes was 35% (95% CI, 22-48, low certainty), whereas the pooled specificity was 98% (95% CI: 98-99, low certainty). The PPV for ICD-10 codes ranged from 9.8% to 100% (median, 72.0%; interquartile range [IQR], 50.0-84.7%). NPV ranged from 54.7% to 99.1% (median, 95.9%; interquartile range, 85.5-98.3%).

conclusionsSepsis is undercoded in administrative databases. Future research is needed to explore if greater consistency in ICD-10 code definitions and enhanced quality measures for ICD-10 coders can improve the coding accuracy of sepsis in large databases.

Indexed as

10th revision codesadministrativeInternational Classification of Diseasesmedical recordssepsissystematic reviewvalidation

Identifiers

PMID36382338
PMCPMC9649267

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.