Evidence map›Paper›PMID 33411792›Full record

ArticlePloS one2021

Validating International Classification of Disease 10th Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations.

Mackenzie A Hamilton, Andrew Calzavara, Scott D Emerson, Mohamed Djebli, Maria E Sundaram, Adrienne K Chan, Rafal Kustra, Stefan D Baral, Sharmistha Mishra, Jeffrey C Kwong

Abstract readValidation Study
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 1 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

55 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

10 authors.

Mackenzie A HamiltonICES, Toronto, Ontario, Canada.ORCID 0000-0003-2423-3629
Andrew CalzavaraICES, Toronto, Ontario, Canada.
Scott D EmersonICES, Toronto, Ontario, Canada.
Mohamed DjebliICES, Toronto, Ontario, Canada.
Maria E SundaramICES, Toronto, Ontario, Canada.
Adrienne K ChanDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Rafal KustraDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Stefan D BaralDepartment of Epidemiology, John Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Sharmistha MishraInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-8492-5470
Jeffrey C KwongICES, Toronto, Ontario, Canada.

Funding

CIHR PJT 159516CIHR VR5 172683
6 · The paper itself

Abstract

objectiveRoutinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10th revision (ICD -10) algorithms that identify laboratory-confirmed influenza or laboratory-confirmed respiratory syncytial virus (RSV) hospitalizations using population-based health administrative data from Ontario, Canada. STUDY DESIGN AND

settingInfluenza and RSV laboratory data from the 2014-15, 2015-16, 2016-17 and 2017-18 respiratory virus seasons were obtained from the Ontario Laboratories Information System (OLIS) and were linked to hospital discharge abstract data to generate influenza and RSV reference cohorts. These reference cohorts were used to assess the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the ICD-10 algorithms. To minimize misclassification in future studies, we prioritized specificity and PPV in selecting top-performing algorithms.

results83,638 and 61,117 hospitalized patients were included in the influenza and RSV reference cohorts, respectively. The best influenza algorithm had a sensitivity of 73% (95% CI 72% to 74%), specificity of 99% (95% CI 99% to 99%), PPV of 94% (95% CI 94% to 95%), and NPV of 94% (95% CI 94% to 95%). The best RSV algorithm had a sensitivity of 69% (95% CI 68% to 70%), specificity of 99% (95% CI 99% to 99%), PPV of 91% (95% CI 90% to 91%) and NPV of 97% (95% CI 97% to 97%).

conclusionWe identified two highly specific algorithms that best ascertain patients hospitalized with influenza or RSV. These algorithms may be applied to hospitalized patients if data on laboratory tests are not available, and will thereby improve the power of future epidemiologic studies of influenza, RSV, and potentially other severe acute respiratory infections.

Indexed as

HospitalizationAdolescentAdultAgedAged, 80 and overAlgorithmsChildChild, PreschoolFemaleHumansInfantInfant, NewbornInfluenza, HumanInternational Classification of DiseasesMaleMiddle Aged

Identifiers

PMID33411792
PMCPMC7790248

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.