Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model
Introduction
Hypoglycemia is a potentially life-threatening complication of diabetes treatment, particularly in individuals treated with insulin or sulfonylurea drugs that stimulate insulin secretion.1., 2., 3., 4. A clinically useful prediction tool for hypoglycemia would potentially enable providers and healthcare systems to identify patients at high risk and initiate anticipatory interventions to reduce the risk of severe hypoglycemia through goal modification, medication changes, or focused patient education.
A recent review by the VA QUERI program examined predictors of severe hypoglycemia in adults with type 2 diabetes.1 Important risk factors for severe hypoglycemia included intensive glycemic control, history of hypoglycemia, renal insufficiency, history of microvascular complications, longer diabetes duration, lower education level, African American race, and history of dementia. Gender, age, and lower body mass index (BMI) were not consistently associated with risk of hypoglycemia, although higher age and lower BMI were associated with increased risk of hypoglycemia in the two largest individual studies. However, this review did not describe a clinically useful and externally validated prediction rule. Using data from DCCT/EDIC on 1441 individuals with type 1 diabetes, Lagani et al. developed a prediction rule for hypoglycemia.2 They used a separate cohort of 393 individuals with type 2 diabetes as a validation data set. Their model included five variables, several of which are not easily collected from electronic medical records (marital status, strict vs. standard insulin regimen, total insulin daily dose, family history of type 2 diabetes, and past history of severe hypoglycemia).
For individuals using frequent home blood glucose monitoring or continuous glucose monitors, either with or without an insulin pump, computer algorithms have been developed to predict the very short term risk of hypoglycemia (over the next minutes to hours).5., 6. However, models based on clinical risk factors commonly available in electronic health records (EHRs) to predict the longer term risk of severe hypoglycemia (over days to months) are lacking.
The aims of this study were: (1) to develop a multivariable model to predict the 6-month risk of severe hypoglycemia requiring medical intervention among individuals receiving pharmacologic treatment for diabetes, within one integrated health care delivery system (Kaiser Permanente Colorado) using information available in the electronic health record and other available clinical data sources; and (2) to externally validate the prediction model at two other sites (Kaiser Permanente Northwest and HealthPartners).
Section snippets
Study population
This study included three members of the SUPREME-DM (SUrveillance, PREvention, and ManagEment of Diabetes Mellitus) consortium, a group of 11 member organizations of the Health Care Services Research Network (HCSRN).7., 8. The development cohort was based in Kaiser Permanente Colorado (KPCO) which serves the Denver-Boulder metropolitan areas. We used two validation cohorts, based in Kaiser Permanente Northwest (KPNW; serving the Portland, OR and Vancouver, WA metropolitan areas) and
Model development
There were 31,674 individuals in the KPCO cohort (Appendix Fig. 1), and 325,529 observation periods, with a mean of 10.3 observation periods per individual. There were 4366 observation periods with severe hypoglycemia events, and total of 4727 severe hypoglycemia events.
The baseline variables at the time of the first observation period are shown in Table 1. Just over 95% of the cohort had type 2 diabetes. The unadjusted hazard ratios and hazard ratios from the full 16-variable model are shown
Discussion
We developed 16-variable and 6-variable models to predict the 6-month risk of severe hypoglycemia among individuals receiving pharmacologic treatment for diabetes. Both models showed good calibration and discrimination, with c-statistics of 0.84 and 0.81. The 16-variable model performed somewhat better, but at the cost of greater complexity.
The individual predictors showed similar relationships to prior studies.1., 25., 26., 27. Age and hemoglobin A1c both had U-shaped relationships, with the
Conclusions
In conclusion, we developed and validated 16-variable and 6-variable models to predict the 6-month risk of severe hypoglycemia among a population of individuals with pharmacologically treatment diabetes. These models should be evaluated in other patient populations, but appear to be promising tools to help quantify hypoglycemia risk, and indicate the need for preventive measures.
Acknowledgements
Funding: This work was supported by the Agency for Healthcare Research and Quality [grant numbers R01HS022963 and R01HS019859]. E.B.S. is supported by the National Institute for Diabetes and Digestive and Kidney Diseases [grant number 1K23DK099237-01]. P.J.O. receives support from the National Institute for Diabetes and Digestive and Kidney Diseases [grant number P30DK092924]. The funders had no role in the study's design, conduct, and reporting. The content is solely the responsibility of the
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The other authors have no conflicts of interest to disclose.