Risk prediction for severe hypoglycemia in a type 2 diabetes population with previous non-severe hypoglycemia |
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| Affiliation: | 1. Department of Internal Medicine and Center for Value-Based Care Research, Cleveland Clinic Community Care, Cleveland Clinic, United States of America;2. Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, United States of America;3. Department of Endocrinology, Endocrinology and Metabolism Institute, Cleveland Clinic, United States of America;1. Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN;2. Eli Lilly and Company, Vienna, Austria;1. Department of Veterans Affairs-New Jersey Health Care System, East Orange, NJ, USA;2. Louis Stokes Department of Veterans Affairs Medical Center, Cleveland, OH, USA;3. School of Medicine, Case Western Reserve University, Cleveland, OH, USA;4. Department of Biostatistics and Epidemiology, Rutgers University – School of Public Health, Piscataway, NJ, USA;5. Department of Physiology, Pharmacology, & Neuroscience, Rutgers University-New Jersey Medical School, Newark, NJ, USA;1. Internal Medicine Department, Hospital Marina Baixa, Alicante, Spain;2. Internal Medicine Department, Hospital de Fuenlabrada, Madrid, Spain;3. Internal Medicine Department, Hospital de Zafra, Badajoz, Spain;4. Internal Medicine Department, Hospital Juan Ramón Jiménez, Huelva, Spain;5. Centro de Investigación Operativa, Universidad Miguel Hernández, Sant Joan D''Alacant, Alicante, Spain;6. Internal Medicine Department, Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain;7. Internal Medicine Department, Hospital San Juan de Dios, Tenerife. Spain;8. Internal Medicine Department, Hospital Regional Universitario, FIMABIS, Málaga, Spain |
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| Abstract: | Background/AimEpisodes of non-severe hypoglycemia can be captured through diagnoses documented in the electronic medical record. We aimed to create a clinically useful prediction model for a severe hypoglycemia event, requiring an emergency department visit or hospitalization, in patients with Type 2 diabetes with a history of non-severe hypoglycemia.MethodsUsing electronic medical record data from 50,439 patients with Type 2 diabetes in one health system, number of severe hypoglycemia events and associated patient characteristics from 2006 to 2015 were previously defined. Using the landmarking method, a dynamic prediction model was built using the subset of 1876 patients who had a documented non-severe hypoglycemia diagnosis code, using logistic regression to obtain landmark-specific odds of severe hypoglycemia in this group. For model performance, the bootstrap procedure was employed for internal validation and area under the curve (AUC) and index of prediction accuracy (IPA) were calculated.ResultsGlycosylated hemoglobin (HbA1c) less than 7% (53 mmol/mol) was associated with increased odds ratio (OR) of severe hypoglycemia at 3 months (OR 1.92 95% Confidence Interval (CI) 1.19–3.10 at HbA1c 5% (31 mmol/mol) and OR 1.21, CI 1.03–1.41 at HbA1c 6%(42 mmol/mol).) History of non-severe hypoglycemia within the past 3 months increased odds for severe hypoglycemia (OR 2.58 95% CI 1.80–3.70) as did Black race, insulin use with the past 3 months, and comorbidities. Metformin and sulfonlylurea use in the past 3 months, increasing age and body mass index had lower odds of a future severe hypoglycemia event. For the prediction model for 3 month risk of severe hypoglycemia, the AUC was 0.890 (CI 0.843–0.907) and the IPA was 10.8% (CI 4.4% - 12.4%).ConclusionIn patients with a documented diagnosis of non-severe hypoglycemia, a dynamic prediction model identifies patients with Type 2 diabetes with 3-month increased risk of severe hypoglycemia, allowing for preventive efforts, such as medication changes, at the point of care. |
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