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Validation of an international classification of disease, tenth revision, clinical modification (ICD-10-CM) algorithm in identifying severe hypoglycaemia events for real-world studies.
Her, Qoua L; Dejene, Sara Z; Ismail, Sherin; Wang, Tiansheng; Jonsson-Funk, Michele; Pate, Virigina; Min, Jea Young; Flory, James.
Afiliação
  • Her QL; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Dejene SZ; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Ismail S; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Wang T; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Jonsson-Funk M; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Pate V; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Min JY; Department of Population Health Sciences, Weill Cornell Medical College, New York, New York, USA.
  • Flory J; Endocrinology Service, Department of Subspecialty Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Diabetes Obes Metab ; 26(4): 1282-1290, 2024 Apr.
Article em En | MEDLINE | ID: mdl-38204417
ABSTRACT

AIM:

The transition to the ICD-10-CM coding system has reduced the utility of hypoglycaemia algorithms based on ICD-9-CM diagnosis codes in real-world studies of antidiabetic drugs. We mapped a validated ICD-9-CM hypoglycaemia algorithm to ICD-10-CM codes to create an ICD-10-CM hypoglycaemia algorithm and assessed its performance in identifying severe hypoglycaemia. MATERIALS AND

METHODS:

We assembled a cohort of Medicare patients with DM and linked electronic health record (EHR) data to the University of North Carolina Health System and identified candidate severe hypoglycaemia events from their Medicare claims using the ICD-10-CM hypoglycaemia algorithm. We confirmed severe hypoglycaemia by EHR review and computed a positive predictive value (PPV) of the algorithm to assess its performance. We refined the algorithm by removing poor performing codes (PPV ≤0.5) and computed a Cohen's κ statistic to evaluate the agreement of the EHR reviews.

RESULTS:

The algorithm identified 642 candidate severe hypoglycaemia events, and we confirmed 455 as true severe hypoglycaemia events, PPV of 0.709 (95% confidence interval 0.672, 0.744). When we refined the algorithm, the PPV increased to 0.893 (0.862, 0.918) and missed <2.42% (<11) true severe hypoglycaemia events. Agreement between reviewers was high, κ = 0.93 (0.89, 0.97).

CONCLUSIONS:

We translated an ICD-9-CM hypoglycaemia algorithm to an ICD-10-CM version and found its performance was modest. The performance of the algorithm improved by removing poor performing codes at the trade-off of missing very few severe hypoglycaemia events. The algorithm has the potential to be used to identify severe hypoglycaemia in real-world studies of antidiabetic drugs.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Classificação Internacional de Doenças / Hipoglicemia Tipo de estudo: Prognostic_studies Limite: Aged / Humans País como assunto: America do norte Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Classificação Internacional de Doenças / Hipoglicemia Tipo de estudo: Prognostic_studies Limite: Aged / Humans País como assunto: America do norte Idioma: En Ano de publicação: 2024 Tipo de documento: Article