Dataset Information

Bayesian profiling multiple imputation for missing hemoglobin values in electronic health records.

ABSTRACT: Electronic health records (EHRs) are increasingly used for clinical and comparative effectiveness research, but suffer from missing data. Motivated by health services research on diabetes care, we seek to increase the quality of EHRs by focusing on missing values of longitudinal glycosylated hemoglobin (A1c), a key risk factor for diabetes complications and adverse events. Under the framework of multiple imputation (MI), we propose an individualized Bayesian latent profiling approach to capture A1c measurement trajectories subject to missingness. The proposed method is applied to EHRs of adult patients with diabetes in a large academic Midwestern health system between 2003 and 2013 and had Medicare A and B coverage. We combine MI inferences to evaluate the association of A1c levels with the incidence of acute adverse health events and examine patient heterogeneity across identified patient profiles. We investigate different missingness mechanisms and perform imputation diagnostics. Our approach is computationally efficient and fits flexible models that provide useful clinical insights.

SUBMITTER: Si Y

PROVIDER: S-EPMC9600600 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Bayesian profiling multiple imputation for missing hemoglobin values in electronic health records.

Si Yajuan Y Palta Mari M Smith Maureen M

The annals of applied statistics 20201219 4

Electronic health records (EHRs) are increasingly used for clinical and comparative effectiveness research, but suffer from missing data. Motivated by health services research on diabetes care, we seek to increase the quality of EHRs by focusing on missing values of longitudinal glycosylated hemoglobin (A1c), a key risk factor for diabetes complications and adverse events. Under the framework of multiple imputation (MI), we propose an individualized Bayesian latent profiling approach to capture ...[more]

PMID: 36303710

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Project description:Evaluating appropriate methodologies for imputation of missing outcome data from electronic medical records (EMRs) is crucial but lacking for observational studies. Using US EMR in people with type 2 diabetes treated over 12 and 24 months with dipeptidyl peptidase 4 inhibitors (DPP-4i, n = 38,483) and glucagon-like peptide 1 receptor agonists (GLP-1RA, n = 8,977), predictors of missingness of disease biomarker (HbA1c) were explored. Robustness of multiple imputation (MI) by chained equations, two-fold MI (MI-2F) and MI with Monte Carlo Markov Chain were compared to complete case analyses for drawing inferences. Compared to younger people (age quartile Q1), those in age quartile Q3 and Q4 were less likely to have missing HbA1c by 25-32% (range of OR CI: 0.55-0.88) at 6-month follow-up and by 26-39% (range of OR CI: 0.50-0.80) at 12-month follow-up. People with HbA1c ≥ 7.5% at baseline were 12% (OR CI: 0.83, 0.93) and 14% (OR CI: 0.77, 0.97) less likely to have missing data at 6-month follow-up in the DPP-4i and GLP-1RA groups, respectively. All imputation methods provided similar HbA1c distributions during follow-up as observed with complete case analyses. The clinical inferences based on absolute change in HbA1c and by proportion of people reducing HbA1c to a clinically acceptable level (≤ 7%) were also similar between imputed data and complete case analyses. MI-2F method provided marginally smaller mean difference between observed and imputed data with relatively smaller standard error of difference, compared to other methods, while evaluating for consistency through artificial within-sample analyses. The established MI techniques can be reliably employed for missing outcome data imputations in large EMR-based relational databases, leading to efficiently designing and drawing robust clinical inferences in pharmaco-epidemiological studies.Supplementary informationThe online version contains supplementary material available at 10.1007/s41666-022-00119-w.

Project description:BACKGROUND:Electronic health record (EHR) systems generate large datasets that can significantly enrich the development of medical predictive models. Several attempts have been made to investigate the effect of glycated hemoglobin (HbA1c) elevation on the prediction of diabetes onset. However, there is still a need for validation of these models using EHR data collected from different populations. OBJECTIVE:The aim of this study is to perform a replication study to validate, evaluate, and identify the strengths and weaknesses of replicating a predictive model that employed multiple logistic regression with EHR data to forecast the levels of HbA1c. The original study used data from a population in the United States and this differentiated replication used a population in Saudi Arabia. METHODS:A total of 3 models were developed and compared with the model created in the original study. The models were trained and tested using a larger dataset from Saudi Arabia with 36,378 records. The 10-fold cross-validation approach was used for measuring the performance of the models. RESULTS:Applying the method employed in the original study achieved an accuracy of 74% to 75% when using the dataset collected from Saudi Arabia, compared with 77% obtained from using the population from the United States. The results also show a different ranking of importance for the predictors between the original study and the replication. The order of importance for the predictors with our population, from the most to the least importance, is age, random blood sugar, estimated glomerular filtration rate, total cholesterol, non-high-density lipoprotein, and body mass index. CONCLUSIONS:This replication study shows that direct use of the models (calculators) created using multiple logistic regression to predict the level of HbA1c may not be appropriate for all populations. This study reveals that the weighting of the predictors needs to be calibrated to the population used. However, the study does confirm that replicating the original study using a different population can help with predicting the levels of HbA1c by using the predictors that are routinely collected and stored in hospital EHR systems.

Dataset Information

Bayesian profiling multiple imputation for missing hemoglobin values in electronic health records.

Publications

Bayesian profiling multiple imputation for missing hemoglobin values in electronic health records.

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