Validating International Classification of Disease 10th Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2021
2021
Historique:
received:
08
08
2020
accepted:
15
12
2020
entrez:
7
1
2021
pubmed:
8
1
2021
medline:
11
5
2021
Statut:
epublish
Résumé
Routinely 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. Influenza 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. 83,638 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%). We 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.
Identifiants
pubmed: 33411792
doi: 10.1371/journal.pone.0244746
pii: PONE-D-20-24822
pmc: PMC7790248
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0244746Subventions
Organisme : CIHR
ID : PJT 159516
Pays : Canada
Organisme : CIHR
ID : VR5 172683
Pays : Canada
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Indian J Ophthalmol. 2008 Jan-Feb;56(1):45-50
pubmed: 18158403
Emerg Infect Dis. 2006 Oct;12(10):1603-4
pubmed: 17176584
Open Forum Infect Dis. 2017 Feb 03;4(1):ofx006
pubmed: 28480279
J Pediatric Infect Dis Soc. 2013 Mar;2(1):63-6
pubmed: 26619444
Influenza Other Respir Viruses. 2020 Nov;14(6):630-637
pubmed: 31206246
Vaccine. 2019 Jul 18;37(31):4392-4400
pubmed: 31221563
Med Princ Pract. 2014;23(6):568-73
pubmed: 25059566
AMIA Annu Symp Proc. 2020 Mar 04;2019:804-811
pubmed: 32308876
Emerg Infect Dis. 2007 Feb;13(2):207-16
pubmed: 17479881
PLoS One. 2016 Mar 09;11(3):e0150416
pubmed: 26958849
Am J Epidemiol. 1993 Dec 1;138(11):1007-15
pubmed: 8256775
BMC Public Health. 2017 Jun 30;17(1):612
pubmed: 28666433
J Pediatric Infect Dis Soc. 2014 Sep;3(3):255-60
pubmed: 26625389