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. 2016 Dec 9;6(12):e012413.
doi: 10.1136/bmjopen-2016-012413.

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

Rosa Gini  1   2 , Martijn J Schuemie  3   4 , Giampiero Mazzaglia  5 , Francesco Lapi  5 , Paolo Francesconi  1 , Alessandro Pasqua  5 , Elisa Bianchini  5 , Carmelo Montalbano  6 , Giuseppe Roberto  1 , Valentina Barletta  1 , Iacopo Cricelli  6 , Claudio Cricelli  7 , Giulia Dal Co  8 , Mariadonata Bellentani  8 , Miriam Sturkenboom  2 , Niek Klazinga  9
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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

Rosa Gini et al. BMJ Open. .

Abstract

Objectives: The 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.

Setting: HSD is a database of primary care medical records. A panel of 12 GPs participated in this validation study.

Participants: 300 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.

Results: The 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.

Conclusions: This 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.

Keywords: DIABETES & ENDOCRINOLOGY; PRIMARY CARE; Validation study; electronic medical records.

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Conflict of interest statement

MJS is an employee of a pharmaceutical company. Genomedics is a private company owned by IC. RG, FL, GR and MS have conducted pharmacoepidemiological studies funded by pharmaceutical companies.

Figures

Figure 1
Figure 1
Screenshot of the data collection plugin. The GP participating in the study was asked to fill in the four tables, one per disease. In the table, ‘S.P.’ meant ‘without the disease’ and ‘N.S.’ meant that the patient had the disease but the GP was not able to assess the level of severity. The clinical definition of the condition and of each level of severity could be browsed in the upper part of the screen, while, whenever the pointer stopped on a cell in the table, a tooltip suggested the label of the corresponding level. The patients listed in the figure are not real. GP, general practitioner.
See this image and copyright information in PMC

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References

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