Accuracy of hemoglobin A1c imputation using fasting plasma glucose in diabetes research using electronic health records data

Abstract

In studies that use electronic health record data, imputation of important data elements such as Glycated hemoglobin (A1c) has become common. However, few studies have systematically examined the validity of various imputation strategies for missing A1c values. We derived a complete dataset using an incident diabetes population that has no missing values in A1c, fasting and random plasma glucose (FPG and RPG), age, and gender. We then created missing A1c values under two assumptions: missing completely at random (MCAR) and missing at random (MAR). We then imputed A1c values, compared the imputed values to the true A1c values, and used these data to assess the impact of A1c on initiation of antihyperglycemic therapy. Under MCAR, imputation of A1c based on FPG 1) estimated a continuous A1c within ± 1.88% of the true A1c 68.3% of the time; 2) estimated a categorical A1c within ± one category from the true A1c about 50% of the time. Including RPG in imputation slightly improved the precision but did not improve the accuracy. Under MAR, including gender and age in addition to FPG improved the accuracy of imputed continuous A1c but not categorical A1c. Moreover, imputation of up to 33% of missing A1c values did not change the accuracy and precision and did not alter the impact of A1c on initiation of antihyperglycemic therapy. When using A1c values as a predictor variable, a simple imputation algorithm based only on age, sex, and fasting plasma glucose gave acceptable results.

Preventable Major Cardiovascular Events Associated With Uncontrolled Glucose, Blood Pressure, and Lipids and Active Smoking in Adults With Diabetes With and Without Cardiovascular Disease: A Contemporary Analysis

“…Baseline status for comorbid conditions was assigned based on two or more outpatient diagnosis codes or one or more inpatient diagnosis codes on or before the cohort entry date for chronic kidney disease (CKD; ICD-9 code 585.xx), CVD (ICD-9 codes 410–414.xx and 429.2), heart failure (HF; ICD-9 codes 428–428.9), hemorrhagic stroke (ICD-9 codes 430–432.9), ischemic stroke (ICD-9 codes 433–434.91), and transient ischemic attack (ICD-9 code 435.xx). Multiple imputation was used for missing data on A1C, LDL-C, and HDL-C following previous work using the SUPREME-DM cohort (12). …”

Diagnostic with incomplete nominal/discrete data

“…An account of available software facilitated modelling using MI in diabetes studies is given in ref. [17] Despite regarded as "state of the art", EM and MI techniques are computationally very intensive, especially MI, which is rather a statistical experiment featuring an imputation method. Apart from the design, the biggest contributor to the problem is the multitude of model parameters as their number is dependent on the number of problem dimensions and can grow explosively with model complexity.…”

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records

“…A study by Rose et al [18] discussed the correlation between RBS and HbA1c levels. Stanley et al [19] used a linear regression model for imputation of missing HbA1c data. Their model calculates HbA1c levels for patient records with missing HbA1c values as continuous and categorical values and uses 4 predictors extracted from an EHR system: RBS, FBS, along with age and gender, as predictors to calculate the level of HbA1c for a diabetic population.…”