Evidence map›Paper›PMID 31627721›Full record

ArticleBMC medical research methodology2019

Sensitivity and specificity of an algorithm based on medico-administrative data to identify hospitalized patients with major bleeding presenting to an emergency department.

Emmanuel Oger, Marie-Anne Botrel, Catherine Juchault, Jacques Bouget

Abstract read
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Article in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

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20citing papers in PubMed
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1 · What the graph read from it

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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

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Emmanuel OgerUniv Rennes, EA 7449 REPERES [Pharmacoepidemiology and Health Services Research], Rennes, France. emmanuel.oger@univ-rennes1.fr.ORCID 0000-0001-9837-2977
Marie-Anne BotrelUniv Rennes, EA 7449 REPERES [Pharmacoepidemiology and Health Services Research], Rennes, France.
Catherine JuchaultDepartment of Medical Information, CHU Rennes, Rennes, France.
Jacques BougetUniv Rennes, EA 7449 REPERES [Pharmacoepidemiology and Health Services Research], Rennes, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundValidation studies on an ICD-10-based algorithm to identify major bleeding events are scarce, and mostly focused on positive predictive values.

objectiveTo evaluate the sensitivity and specificity of an ICD-10-based algorithm in adult patients referred to hospital.

methodsThis was a cross-sectional, retrospective analysis. Among all hospital stays of adult patients referred to Rennes University Hospital, France, through the emergency ward in 2014, we identified major bleeding events according to an index test based on a list of ICD-10 diagnoses. As a reference, a two-step process was applied: firstly, a computerized request for electronic health records from the emergency ward, using several hemorrhage-related diagnostic codes and specific emergency therapies so as to discard stays with a very low probability of bleeding; secondly, a chart review of selected records was conducted by a medical expert blinded to the index test results and each hospital stay was classified into one of two exclusive categories: major bleeding or no major bleeding, according to pre-specified criteria.

resultsOut of 16,012 hospital stays, the reference identified 736 major bleeding events and left 15,276 stays considered as without the target condition. The index test identified 637 bleeding events: 293 intracranial hemorrhages, 197 gastrointestinal hemorrhages and 147 other bleeding events. Overall, sensitivity was 65% (95%CI, 62 to 69), and specificity was 99.0%. We observed differential sensitivity and specificity across bleeding types, with the highest values for intracranial hemorrhage. Positive predictive values ranged from 59% for "other" bleeding events, to 71% (95%CI, 65 to 78) for gastrointestinal hemorrhage, and 96% for intracranial hemorrhage.

conclusionsLow sensitivity and differential measures of accuracy across bleeding types support the need for specific data collection and medical validation rather than using an ICD-10-based algorithm for assessing the incidence of major bleeding.

Indexed as

AlgorithmsInternational Classification of DiseasesCross-Sectional StudiesDiagnosis-Related GroupsEmergency Service, HospitalFalse Positive ReactionsFemaleFranceHemorrhageHospitals, UniversityHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityAlgorithmICD-10Major bleedingValidation study

Identifiers

PMID31627721
PMCPMC6798331

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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.