A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease

BMC Medical Informatics and Decision Making

Research Article

Georgia Malamut¹  Sherine Khater¹  Christophe Cellier¹  Anne-Sophie Jannot²  Anita Burgun²  Bastien Rance²  Jean-Baptiste Escudié³ 

[1] Georges Pompidou European Hospital (HEGP), AP-HP, Paris, France;Georges Pompidou European Hospital (HEGP), AP-HP, Paris, France;INSERM UMRS 1138, Paris Descartes University, Paris, France;Georges Pompidou European Hospital (HEGP), AP-HP, Paris, France;INSERM UMRS 1138, Paris Descartes University, Paris, France;Pôle Informatique Médicale et Santé Publique, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75015, Paris, France;

关键词: Autoimmune diseases;    Celiac disease;    Electronic health records;    Icd 10;    Phenotype;    Prevalence study;    Diabetes mellitus, type 1;    Dermatitis herpetiformis;    Thyroiditis, autoimmune;    Arthritis, rheumatoid;    Lupus erythematosus, systemic;    Multiple sclerosis;    Sjogren’s syndrome;    Addison disease;    Arthritis, juvenile;    Hepatitis, autoimmune;    Graves’ disease;    Myasthenia gravis;    Polyendocrinopathies, autoimmune;    Antiphospholipid syndrome;   

DOI  :  10.1186/s12911-017-0537-y

 received in 2016-11-18, accepted in 2017-09-12,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundData collected in EHRs have been widely used to identifying specific conditions; however there is still a need for methods to define comorbidities and sources to identify comorbidities burden. We propose an approach to assess comorbidities burden for a specific disease using the literature and EHR data sources in the case of autoimmune diseases in celiac disease (CD).MethodsWe generated a restricted set of comorbidities using the literature (via the MeSH® co-occurrence file). We extracted the 15 most co-occurring autoimmune diseases of the CD. We used mappings of the comorbidities to EHR terminologies: ICD-10 (billing codes), ATC (drugs) and UMLS (clinical reports). Finally, we extracted the concepts from the different data sources. We evaluated our approach using the correlation between prevalence estimates in our cohort and co-occurrence ranking in the literature.ResultsWe retrieved the comorbidities for 741 patients with CD. 18.1% of patients had at least one of the 15 studied autoimmune disorders. Overall, 79.3% of the mapped concepts were detected only in text, 5.3% only in ICD codes and/or drugs prescriptions, and 15.4% could be found in both sources. Prevalence in our cohort were correlated with literature (Spearman’s coefficient 0.789, p = 0.0005). The three most prevalent comorbidities were thyroiditis 12.6% (95% CI 10.1–14.9), type 1 diabetes 2.3% (95% CI 1.2–3.4) and dermatitis herpetiformis 2.0% (95% CI 1.0–3.0).ConclusionWe introduced a process that leveraged the MeSH terminology to identify relevant autoimmune comorbidities of the CD and several data sources from EHRs to phenotype a large population of CD patients. We achieved prevalence estimates comparable to the literature.

【 授权许可】

CC BY   
© The Author(s). 2017