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Missing risks in opportunistic screening for type 2 diabetes - CroDiabGP study.

Marija Vrca Botica1, Linda Carcaxhiu2, Josipa Kern3

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PubMed
Summary
This summary is machine-generated.

Electronic medical records and family medicine assessments struggle to capture all risk factors for undetected type 2 diabetes. Overweight is the strongest predictor, but data gaps persist in patient history and medical records.

Keywords:
classificationelectronic recordspreconsultation phaseprimary care

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Area of Science:

  • Medical Informatics
  • Public Health
  • Endocrinology

Background:

  • Undetected type 2 diabetes (T2D) poses a significant public health challenge.
  • Accurate risk factor identification is crucial for early diagnosis and intervention.
  • Existing methods for risk extraction from electronic medical records (EMR) and family medicine (FM) assessments require evaluation.

Purpose of the Study:

  • To compare the effectiveness of EMR-derived and FM assessment-based risk factor extraction for identifying undetected type 2 diabetes (T2D).
  • To assess data completeness and identify predictors of T2D using two distinct data extraction methods.

Main Methods:

  • A prospective study involving 1883 patients aged 45-70 with identified risks.
  • Risk factors were extracted from EMR (continuity variables) and FM assessments (disease episodes, personal information).
  • Patients were categorized based on diagnostic tests: normoglycaemia, impaired fasting glycaemia, and undetected T2D.

Main Results:

  • The overall prevalence of diabetes was 10.9%, with a 1.4% incidence of new cases.
  • EMR data showed significant missingness for hypolipemic treatment (32.5%) and FM data for gestational diabetes mellitus (97.2%) and family history (53.1%).
  • Overweight was the strongest predictor for undiagnosed T2D (OR: 2.11, CI: 1.41-3.15, p<.001).

Conclusions:

  • Both EMR and FM assessment methods demonstrated limitations in capturing complete data, particularly for disease episodes and personal history.
  • Significant data gaps were observed in family history and overweight assessment by FM.
  • Improving coded and structured data within EMR systems is recommended to enhance T2D risk detection.