Your browser doesn't support javascript.
loading
Application of a data continuity prediction algorithm to an electronic health record-based pharmacoepidemiology study.
Flory, James H; Zhang, Yongkang; Banerjee, Samprit; Wang, Fei; Min, Jea Y; I Mushlin, Alvin.
Afiliación
  • Flory JH; Endocrinology Service, Department of Subspecialty Medicine, Memorial Sloan Kettering Cancer Center, New York City, New York, USA.
  • Zhang Y; Department of Population Health Sciences, Weill Cornell Medical College, New York City, New York, USA.
  • Banerjee S; Department of Population Health Sciences, Weill Cornell Medical College, New York City, New York, USA.
  • Wang F; Department of Population Health Sciences, Weill Cornell Medical College, New York City, New York, USA.
  • Min JY; Department of Population Health Sciences, Weill Cornell Medical College, New York City, New York, USA.
  • I Mushlin A; Department of Population Health Sciences, Weill Cornell Medical College, New York City, New York, USA.
J Eval Clin Pract ; 30(4): 716-725, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38696462
ABSTRACT
BACKGROUND AND

OBJECTIVES:

Use of algorithms to identify patients with high data-continuity in electronic health records (EHRs) may increase study validity. Practical experience with this approach remains limited.

METHODS:

We developed and validated four algorithms to identify patients with high data continuity in an EHR-based data source. Selected algorithms were then applied to a pharmacoepidemiologic study comparing rates of COVID-19 hospitalization in patients exposed to insulin versus noninsulin antidiabetic drugs.

RESULTS:

A model using a short list of five EHR-derived variables performed as well as more complex models to distinguish high- from low-data continuity patients. Higher data continuity was associated with more accurate ascertainment of key variables. In the pharmacoepidemiologic study, patients with higher data continuity had higher observed rates of the COVID-19 outcome and a large unadjusted association between insulin use and the outcome, but no association after propensity score adjustment.

DISCUSSION:

We found that a simple, portable algorithm to predict data continuity gave comparable performance to more complex methods. Use of the algorithm significantly impacted the results of an empirical study, with evidence of more valid results at higher levels of data continuity.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Farmacoepidemiología / Registros Electrónicos de Salud / Hipoglucemiantes Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Eval Clin Pract Asunto de la revista: PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Farmacoepidemiología / Registros Electrónicos de Salud / Hipoglucemiantes Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Eval Clin Pract Asunto de la revista: PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido