Predictors of 30-day recurrent emergency department visits for hyperglycemia in patients with types 1 and 2 diabetes: a population-based cohort study

OBJECTIVES

This study's aims were to describe the outcomes of patients with diabetes presenting with their first ED visit for hyperglycemia, and to identify predictors of recurrent ED visits for hyperglycemia.

METHODS

Using linked databases, we conducted a population-based cohort study of adult and pediatric patients with types 1 and 2 diabetes presenting with a first ED visit for hyperglycemia from April 2010 to March 2020 in Ontario, Canada. We determined the proportion of patients with a recurrent ED visit for hyperglycemia within 30 days of the index visit. Using multivariable regression analysis, we examined clinical and socioeconomic predictors for recurrent visits.

RESULTS

There were 779,632 patients with a first ED visit for hyperglycemia. Mean (SD) age was 64.3 (15.2) years; 47.7% were female. 11.0% had a recurrent visit for hyperglycemia within 30 days. Statistically significant predictors of a recurrent visit included: male sex, type 1 diabetes, regions with fewer visible minority groups and with less education or employment, higher hemoglobin A1C, more family physician or internist visits within the past year, being rostered to a family physician, previous ED visits in the past year, ED or hospitalization within the previous 14 days, access to homecare services, and previous hyperglycemia encounters in the past 5 years. Alcoholism and depression or anxiety were positive predictors for the 18-65 age group.

CONCLUSIONS

This population-level study identifies predictors of recurrent ED visits for hyperglycemia, including male sex, type 1 diabetes, regions with fewer visible minority groups and with less education or employment, higher hemoglobin A1C, higher previous healthcare system utilization (ED visits and hospitalization) for hyperglycemia, being rostered to a family physician, and access to homecare services. Knowledge of these predictors may be used to develop targeted interventions to improve patient outcomes and reduce healthcare system costs.

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies

Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.