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Validation of an International Statistical Classification of Diseases and Related Health Problems 10th Revision coding algorithm for hospital encounters with hypoglycemia

Hodge MC, Dixon S, Garg AX, Clemens KK. Can J Diabetes. 2017; 41(3):322-8. Epub 2017 Mar 4.


Objectives — To determine the positive predictive value and sensitivity of an International Statistical Classification of Diseases and Related Health Problems, 10th Revision, coding algorithm for hospital encounters concerning hypoglycemia.

Methods — We carried out 2 retrospective studies in Ontario, Canada. We examined medical records from 2002 through 2014, in which older adults (mean age, 76) were assigned at least 1 code for hypoglycemia (E15, E160, E161, E162, E1063, E1163, E1363, E1463). The positive predictive value of the algorithm was calculated using a gold-standard definition (blood glucose value <4 mmol/L or physician diagnosis of hypoglycemia). To determine the algorithm's sensitivity, we used linked healthcare databases to identify older adults (mean age, 77) with laboratory plasma glucose values <4 mmol/L during a hospital encounter that took place between 2003 and 2011. We assessed how frequently a code for hypoglycemia was present. We also examined the algorithm's performance in differing clinical settings (e.g. inpatient vs. emergency department, by hypoglycemia severity).

Results — The positive predictive value of the algorithm was 94.0% (95% confidence interval 89.3% to 97.0%), and its sensitivity was 12.7% (95% confidence interval 11.9% to 13.5%). It performed better in the emergency department and in cases of more severe hypoglycemia (plasma glucose values <3.5 mmol/L compared with ≥3.5 mmol/L).

Conclusions — Our hypoglycemia algorithm has a high positive predictive value but is limited in sensitivity. Although we can be confident that older adults who are assigned 1 of these codes truly had a hypoglycemia event, many episodes will not be captured by studies using administrative databases.

Keywords: Diabetes Research and statistical methods Data validation

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