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Validation of ICD-10 Codes for Severe Maternal Morbidity at Delivery in a Public Hospital

Sheree L. Boulet, Kaitlyn K. Stanhope, Arielle N. Valdez‐Sinon, Danielle M. Vuncannon, Jessica Preslar, Hannah Bergbower, Brendan Gray, Asmita Gathoo, Nora Hansen, Kerri E. Andre, Sabrine Bensouda, Cally Braun, Marissa Platner

2024Epidemiology14 citationsDOI

Abstract

BACKGROUND: Severe maternal morbidity is a composite measure of serious obstetric complications that is often identified in administrative data using the International Classification of Diseases (ICD) diagnosis and procedure codes for a set of 21 indicators. Prior studies of screen-positive cases have demonstrated low predictive value for ICD codes relative to the medical record. To our knowledge, the validity of ICD-10 codes for identifying severe maternal morbidity has not been fully described. METHODS: We estimated the sensitivity, specificity, positive predictive value, and negative predictive value of ICD-10 codes for severe maternal morbidity occurring at delivery, compared with medical record abstraction (gold standard), for 1,000 deliveries that took place during 2016-2018 at a large, public hospital. RESULTS: We identified a total of 67 cases of severe maternal morbidity using the ICD-10 definition and 74 cases in the medical record. The sensitivity was 26% (95% confidence interval [CI] = 16%, 37%), the positive predictive value was 28% (95% CI = 18%, 41%), the specificity was 95% (95% CI = 93%, 96%), and the negative predictive value was 94% (95% CI = 92%, 96%). CONCLUSIONS: The validity of ICD-10 codes for severe maternal morbidity in our high-burden population was poor, suggesting considerable potential for bias.

Topics & Concepts

MedicineMedical recordGold standard (test)Confidence intervalPredictive valuePositive predicative valuePredictive value of testsDiagnosis codeICD-10PopulationPediatricsEmergency medicineObstetricsSurgeryInternal medicineEnvironmental healthPsychiatryMaternal and fetal healthcareMedical Coding and Health InformationGestational Diabetes Research and Management
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