BACKGROUND: The prevalence of diabetes is increasing with growing levels of obesity and an aging population. New practical guidelines for diabetes provide an applicable classification. Inconsistent coding of diabetes hampers the use of computerised disease registers for quality improvement, and limits the monitoring of disease trends. OBJECTIVE: To develop a consensus set of codes that should be used when recording diabetes diagnostic data. METHODS: The consensus approach was hierarchical, with a preference for diagnostic/disorder codes, to define each type of diabetes and non-diabetic hyperglycaemia, which were listed as being completely, partially or not readily mapped to available codes. The practical classification divides diabetes into type 1 (T1DM), type 2 (T2DM), genetic, other, unclassified and non-diabetic fasting hyperglycaemia. We mapped the classification to Read version 2, Clinical Terms version 3 and SNOMED CT. RESULTS: T1DM and T2DM were completely mapped to appropriate codes. However, in other areas only partial mapping is possible. Genetics is a fastmoving field and there were considerable gaps in the available labels for genetic conditions; what the classification calls 'other' the coding system labels 'secondary' diabetes. The biggest gap was the lack of a code for diabetes where the type of diabetes was uncertain. Notwithstanding these limitations we were able to develop a consensus list. CONCLUSIONS: It is a challenge to develop codes that readily map to contemporary clinical concepts. However, clinicians should adopt the standard recommended codes; and audit the quality of their existing records.
BACKGROUND: The prevalence of diabetes is increasing with growing levels of obesity and an aging population. New practical guidelines for diabetes provide an applicable classification. Inconsistent coding of diabetes hampers the use of computerised disease registers for quality improvement, and limits the monitoring of disease trends. OBJECTIVE: To develop a consensus set of codes that should be used when recording diabetes diagnostic data. METHODS: The consensus approach was hierarchical, with a preference for diagnostic/disorder codes, to define each type of diabetes and non-diabetic hyperglycaemia, which were listed as being completely, partially or not readily mapped to available codes. The practical classification divides diabetes into type 1 (T1DM), type 2 (T2DM), genetic, other, unclassified and non-diabetic fasting hyperglycaemia. We mapped the classification to Read version 2, Clinical Terms version 3 and SNOMED CT. RESULTS: T1DM and T2DM were completely mapped to appropriate codes. However, in other areas only partial mapping is possible. Genetics is a fastmoving field and there were considerable gaps in the available labels for genetic conditions; what the classification calls 'other' the coding system labels 'secondary' diabetes. The biggest gap was the lack of a code for diabetes where the type of diabetes was uncertain. Notwithstanding these limitations we were able to develop a consensus list. CONCLUSIONS: It is a challenge to develop codes that readily map to contemporary clinical concepts. However, clinicians should adopt the standard recommended codes; and audit the quality of their existing records.
Authors: Ebenezer S Adjah Owusu; Mayukh Samanta; Jonathan E Shaw; Azeem Majeed; Kamlesh Khunti; Sanjoy K Paul Journal: Nutr Diabetes Date: 2018-06-01 Impact factor: 5.097
Authors: Agnieszka Lemanska; Rachel C Byford; Clare Cruickshank; David P Dearnaley; Filipa Ferreira; Clare Griffin; Emma Hall; William Hinton; Simon de Lusignan; Julian Sherlock; Sara Faithfull Journal: BMC Med Res Methodol Date: 2020-07-25 Impact factor: 4.615