Vrca Botica Marija, Carcaxhiu Linda, Kern Josipa, Kuehlein Thomas, Botica Iva, Gavran Larisa, Zelić Ines, Iliev Darko, Haralović Dijana, Vrca Anđelko
Department of Family Medicine, School of Medicine, University of Zagreb, Zagreb, Croatia.
Department of Family Medicine, University of Pristina, Pristina, Kosovo.
Med Glas (Zenica). 2017 Feb 1;14(1):55-60. doi: 10.17392/874-16.
Aim To examine two methods of extracting risks for undetected type 2 diabetes (T2D): derived from electronic medical record(EMR) and family medicine (FM) assessment during pre-consultation phase. All risks were structured in three lists of patients' data using Wonca International Classification Committee(WICC). Missing data were detected in each list. Methods A prospective study included a group of 1883 patients(aged 45-70) identified with risks. Risks were assessed based on EMR for continuity variables and FM's assessment for episodes of disease and personal related information. Patients were categorized with final diagnostic test in normoglycaemia, impaired fasting glycaemia and undetected T2D. Results Total prevalence of diabetes was 10.9% (new 1.4%), of which 59.3% were females; mean age was 57.4. The EMR risks were hypertension in 1274 patients (yes 67.6%, no 27.9%, missing 4.4%), hypolipemic treatment in 690 (yes 36.6%, no 30.9%, miss 32.5%). In the episodes of disease: gestational diabetes mellitus in 31 women (yes 2.8%, missing 97.2%). Personal information: family history of diabetes in 649 (yes 34.5%, no 12.4%, missing 53.1%), overweight in 1412 (yes 75.0%, no 8.4%, missing 16.6%), giving birth to babies >4000g in 11 women (yes 0.9%, missing 99.1%). Overweight alone was the best predictor for undiagnosed type 2 diabetes, OR: 2.11 (CI: 1.41-3.15) (p<.001). Conclusion Two methods of extraction could not detect data for episodes of the disease. In the list of personal information, FMs could not assess overweight for one in six patients and family history for every other patient. The study can stimulate improving coded and structured data in EMR.
研究两种提取未检测出的2型糖尿病(T2D)风险的方法,一种源自电子病历(EMR),另一种源自会诊前阶段的家庭医学(FM)评估。使用世界家庭医生组织国际分类委员会(WICC)将所有风险整理成三份患者数据清单。在每份清单中检测缺失数据。方法:一项前瞻性研究纳入了一组1883名(年龄45 - 70岁)有风险的患者。基于EMR评估连续性变量的风险,基于FM评估疾病发作情况和个人相关信息的风险。通过最终诊断测试将患者分类为血糖正常、空腹血糖受损和未检测出T2D。结果:糖尿病总患病率为10.9%(新发病例1.4%),其中59.3%为女性;平均年龄为57.4岁。EMR风险方面,1274名患者患有高血压(是67.6%,否27.9%,缺失4.4%),690名患者接受降脂治疗(是36.6%,否30.9%,缺失32.5%)。在疾病发作情况方面:31名女性有妊娠期糖尿病(是2.8%,缺失97.2%)。个人信息方面:649名患者有糖尿病家族史(是34.5%,否12.4%,缺失53.1%),1412名患者超重(是75.0%,否8.4%,缺失16.6%),11名女性分娩过体重>4000g的婴儿(是0.9%,缺失99.1%)。仅超重是未诊断出的2型糖尿病的最佳预测因素,比值比(OR):2.11(置信区间:1.41 - 3.15)(p <.001)。结论:两种提取方法均无法检测出疾病发作情况的数据。在个人信息清单中,FM无法评估六分之一患者的超重情况以及每两名患者中一名患者的家族史。该研究可促使改善EMR中的编码和结构化数据。