Validation of ICD-10 Codes for Gestational and Pregestational Diabetes During Pregnancy in a Large, Public Hospital.


Journal

Epidemiology (Cambridge, Mass.)
ISSN: 1531-5487
Titre abrégé: Epidemiology
Pays: United States
ID NLM: 9009644

Informations de publication

Date de publication:
01 03 2021
Historique:
pubmed: 1 12 2020
medline: 1 6 2021
entrez: 30 11 2020
Statut: ppublish

Résumé

The use of billing codes (ICD-10) to identify and track cases of gestational and pregestational diabetes during pregnancy is common in clinical quality improvement, research, and surveillance. However, specific diagnoses may be misclassified using ICD-10 codes, potentially biasing estimates. The goal of this study is to provide estimates of validation parameters (sensitivity, specificity, positive predictive value, and negative predictive value) for pregestational and gestational diabetes diagnosis using ICD-10 diagnosis codes compared with medical record abstraction at a large public hospital in Atlanta, Georgia. This study includes 3,654 deliveries to Emory physicians at Grady Memorial Hospital in Atlanta, Georgia, between 2016 and 2018. We linked information abstracted from the medical record to ICD-10 diagnosis codes for gestational and pregestational diabetes during the delivery hospitalization. Using the medical record as the gold standard, we calculated sensitivity, specificity, positive predictive value, and negative predictive value for each. For both pregestational and gestational diabetes, ICD-10 codes had a high-negative predictive value (>99%, Table 3) and specificity (>99%). For pregestational diabetes, the sensitivity was 85.9% (95% CI = 78.8, 93.0) and positive predictive value 90.8% (95% CI = 85, 97). For gestational diabetes, the sensitivity was 95% (95% CI = 92, 98) and positive predictive value 86% (95% CI = 81, 90). In a large public hospital, ICD-10 codes accurately identified cases of pregestational and gestational diabetes with low numbers of false positives.

Sections du résumé

BACKGROUND
The use of billing codes (ICD-10) to identify and track cases of gestational and pregestational diabetes during pregnancy is common in clinical quality improvement, research, and surveillance. However, specific diagnoses may be misclassified using ICD-10 codes, potentially biasing estimates. The goal of this study is to provide estimates of validation parameters (sensitivity, specificity, positive predictive value, and negative predictive value) for pregestational and gestational diabetes diagnosis using ICD-10 diagnosis codes compared with medical record abstraction at a large public hospital in Atlanta, Georgia.
METHODS
This study includes 3,654 deliveries to Emory physicians at Grady Memorial Hospital in Atlanta, Georgia, between 2016 and 2018. We linked information abstracted from the medical record to ICD-10 diagnosis codes for gestational and pregestational diabetes during the delivery hospitalization. Using the medical record as the gold standard, we calculated sensitivity, specificity, positive predictive value, and negative predictive value for each.
RESULTS
For both pregestational and gestational diabetes, ICD-10 codes had a high-negative predictive value (>99%, Table 3) and specificity (>99%). For pregestational diabetes, the sensitivity was 85.9% (95% CI = 78.8, 93.0) and positive predictive value 90.8% (95% CI = 85, 97). For gestational diabetes, the sensitivity was 95% (95% CI = 92, 98) and positive predictive value 86% (95% CI = 81, 90).
CONCLUSIONS
In a large public hospital, ICD-10 codes accurately identified cases of pregestational and gestational diabetes with low numbers of false positives.

Identifiants

pubmed: 33252439
pii: 00001648-202103000-00015
doi: 10.1097/EDE.0000000000001311
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

277-281

Informations de copyright

Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Déclaration de conflit d'intérêts

The authors report no funding and conflicts of interest.

Références

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Auteurs

Kaitlyn K Stanhope (KK)

From the Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA.

Naima T Joseph (NT)

From the Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA.

Marissa Platner (M)

From the Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA.

Ciara Hutchison (C)

Emory University School of Medicine, Atlanta, GA.

Shawn Wen (S)

Emory University School of Medicine, Atlanta, GA.

Adrienne Laboe (A)

Emory University School of Medicine, Atlanta, GA.

Katie Labgold (K)

Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA.

Denise J Jamieson (DJ)

From the Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA.

Sheree L Boulet (SL)

From the Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA.

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