Evidence map›Paper›PMID 29921683›Full record

ArticleBMJ open2018

Validation of discharge diagnosis codes to identify serious infections among middle age and older adults.

Andrew D Wiese, Marie R Griffin, C Michael Stein, William Schaffner, Robert A Greevy, Edward F Mitchel, Carlos G Grijalva

Open access · goldAbstract readValidation Study
In one paragraph

Article in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
8.4field-weighted citation impact, top 2% of its field
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

44 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Andrew D WieseDepartment of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.ORCID 0000-0002-0699-4224
Marie R GriffinDepartment of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
C Michael SteinDepartment of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
William SchaffnerDepartment of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Robert A GreevyDepartment of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Edward F MitchelDepartment of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Carlos G GrijalvaDepartment of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Vanderbilt University · US

Funding

Opioid selection and the risk of serious infections in older adultsR01AG043471 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GRIJALVA, CARLOS G · 2013 to 2017
$2.3M
The Vanderbilt Institute for Clinical and Translational Research (VICTR)TL1TR000447 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BERNARD, GORDON RAPHAEL · 2012 to 2016
$1.2M
Opiod Analgesics and the Risk of Serious Infections in SeniorsR03AG042981 · NIA · VANDERBILT UNIVERSITY · PI GRIJALVA, CARLOS G · 2012 to 2013
$152k
NCATS NIH HHS TL1 TR000447NIA NIH HHS R01 AG043471NIA NIH HHS R03 AG042981
6 · The paper itself

Abstract

objectivesHospitalisations for serious infections are common among middle age and older adults and frequently used as study outcomes. Yet, few studies have evaluated the performance of diagnosis codes to identify serious infections in this population. We sought to determine the positive predictive value (PPV) of diagnosis codes for identifying hospitalisations due to serious infections among middle age and older adults. SETTING AND

participantsWe identified hospitalisations for possible infection among adults >=50 years enrolled in the Tennessee Medicaid healthcare programme (2008-2012) using International Classifications of Diseases, Ninth Revision diagnosis codes for pneumonia, meningitis/encephalitis, bacteraemia/sepsis, cellulitis/soft-tissue infections, endocarditis, pyelonephritis and septic arthritis/osteomyelitis.

designMedical records were systematically obtained from hospitals randomly selected from a stratified sampling framework based on geographical region and hospital discharge volume. MEASURES: Two trained clinical reviewers used a standardised extraction form to abstract information from medical records. Predefined algorithms served as reference to adjudicate confirmed infection-specific hospitalisations. We calculated the PPV of diagnosis codes using confirmed hospitalisations as reference. Sensitivity analyses determined the robustness of the PPV to definitions that required radiological or microbiological confirmation. We also determined inter-rater reliability between reviewers.

resultsThe PPV of diagnosis codes for hospitalisations for infection (n=716) was 90.2% (95% CI 87.8% to 92.2%). The PPV was highest for pneumonia (96.5% (95% CI 93.9% to 98.0%)) and cellulitis (91.1% (95% CI 84.7% to 94.9%)), and lowest for meningitis/encephalitis (50.0% (95% CI 23.7% to 76.3%)). The adjudication reliability was excellent (92.7% agreement; first agreement coefficient: 0.91). The overall PPV was lower when requiring microbiological confirmation (45%) and when requiring radiological confirmation for pneumonia (79%).

conclusionsDischarge diagnosis codes have a high PPV for identifying hospitalisations for common, serious infections among middle age and older adults. PPV estimates for rare infections were imprecise.

Indexed as

International Classification of DiseasesPatient DischargeAgedAged, 80 and overAlgorithmsClinical CodingFemaleHumansInfectionsMaleMedicaidMedical RecordsMiddle AgedPredictive Value of TestsReproducibility of ResultsRetrospective Studiescoding algorithmsmedicaidolder adultsserious infections

Identifiers

PMID29921683
PMCPMC6009457
OpenAlexW2809024468

What OpenQuestion holds

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LicenceCC BY-NC
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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.