ArticleBMJ open2016
Automatic identification of type 2 diabetes, hypertension, ischaemic heart disease, heart failure and their levels of severity from Italian General Practitioners' electronic medical records: a validation study.
Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 34 citations in OpenAlex.
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- Diabetes Duration, Cholesterol Levels, and Risk of Cardiovascular Diseases in Individuals With Type 2 Diabetes.The Journal of clinical endocrinology and metabolism · 2024Article
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- A nationwide cohort study on diabetes severity and risk of Parkinson disease.NPJ Parkinson's disease · 2023Article
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- Assessing the severity of cardiovascular disease in 213 088 patients with coronary heart disease: a retrospective cohort study.Open heart · 2021Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Development and validation of the DIabetes Severity SCOre (DISSCO) in 139 626 individuals with type 2 diabetes: a retrospective cohort study.BMJ open diabetes research & care · 2020Article
- Validity of Acute Cardiovascular Outcome Diagnoses Recorded in European Electronic Health Records: A Systematic Review.Clinical epidemiology · 2020Review
- Correlation of miR-21 and BNP with pregnancy-induced hypertension complicated with heart failure and the diagnostic value.Experimental and therapeutic medicine · 2019Article
- Patterns and trends of utilization of incretin-based medicines between 2008 and 2014 in three Italian geographic areas.BMC endocrine disorders · 2019Article
- Measurement error and misclassification in electronic medical records: methods to mitigate bias.Current epidemiology reports · 2018Article
- Diabetes management in the primary care setting: a comparison of physicians' performance by gender.Primary health care research & development · 2018Article
- Epidemiology of acromegaly in Italy: analysis from a large longitudinal primary care database.Endocrine · 2018Article
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- Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe.Yearbook of medical informatics · 2017Article
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Authors and funding
16 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe Italian project MATRICE aimed to assess how well cases of type 2 diabetes (T2DM), hypertension, ischaemic heart disease (IHD) and heart failure (HF) and their levels of severity can be automatically extracted from the Health Search/CSD Longitudinal Patient Database (HSD). From the medical records of the general practitioners (GP) who volunteered to participate, cases were extracted by algorithms based on diagnosis codes, keywords, drug prescriptions and results of diagnostic tests. A random sample of identified cases was validated by interviewing their GPs.
settingHSD is a database of primary care medical records. A panel of 12 GPs participated in this validation study.
participants300 patients were sampled for each disease, except for HF, where 243 patients were assessed. OUTCOME MEASURES: The positive predictive value (PPV) was assessed for the presence/absence of each condition against the GP's response to the questionnaire, and Cohen's κ was calculated for agreement on the severity level.
resultsThe PPV was 100% (99% to 100%) for T2DM and hypertension, 98% (96% to 100%) for IHD and 55% (49% to 61%) for HF. Cohen's kappa for agreement on the severity level was 0.70 for T2DM and 0.69 for hypertension and IHD.
conclusionsThis study shows that individuals with T2DM, hypertension or IHD can be validly identified in HSD by automated identification algorithms. Automatic queries for levels of severity of the same diseases compare well with the corresponding clinical definitions, but some misclassification occurs. For HF, further research is needed to refine the current algorithm.
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