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Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model.

ABSTRACT: To develop and externally validate a prediction model for the 6-month risk of a severe hypoglycemic event among individuals with pharmacologically treated diabetes.The development cohort consisted of 31,674 Kaiser Permanente Colorado members with pharmacologically treated diabetes (2007-2015). The validation cohorts consisted of 38,764 Kaiser Permanente Northwest members and 12,035 HealthPartners members. Variables were chosen that would be available in electronic health records. We developed 16-variable and 6-variable models, using a Cox counting model process that allows for the inclusion of multiple 6-month observation periods per person.Across the three cohorts, there were 850,992 6-month observation periods, and 10,448 periods with at least one severe hypoglycemic event. The six-variable model contained age, diabetes type, HgbA1c, eGFR, history of a hypoglycemic event in the prior year, and insulin use. Both prediction models performed well, with good calibration and c-statistics of 0.84 and 0.81 for the 16-variable and 6-variable models, respectively. In the external validation cohorts, the c-statistics were 0.80-0.84.We developed and validated two prediction models for predicting the 6-month risk of hypoglycemia. The 16-variable model had slightly better performance than the 6-variable model, but in some practice settings, use of the simpler model may be preferred.

SUBMITTER: Schroeder EB

PROVIDER: S-EPMC5516886 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model.

Schroeder Emily B EB Xu Stan S Goodrich Glenn K GK Nichols Gregory A GA O'Connor Patrick J PJ Steiner John F JF

Journal of diabetes and its complications 20170411 7

<h4>Aims</h4>To develop and externally validate a prediction model for the 6-month risk of a severe hypoglycemic event among individuals with pharmacologically treated diabetes.<h4>Methods</h4>The development cohort consisted of 31,674 Kaiser Permanente Colorado members with pharmacologically treated diabetes (2007-2015). The validation cohorts consisted of 38,764 Kaiser Permanente Northwest members and 12,035 HealthPartners members. Variables were chosen that would be available in electronic he ...[more]

PMID: 28462891

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Project description:ImportanceIntensive glucose-lowering treatment among patients with non-insulin-requiring type 2 diabetes may increase the risk of hypoglycemia.ObjectivesTo estimate the prevalence of intensive treatment and the association between intensive treatment, clinical complexity, and incidence of severe hypoglycemia among adults with type 2 diabetes who are not using insulin.Design, setting, and participantsRetrospective analysis of administrative, pharmacy, and laboratory data from the OptumLabs Data Warehouse from January 1, 2001, through December 31, 2013. The study included nonpregnant adults 18 years or older with type 2 diabetes who achieved and maintained a hemoglobin A1c (HbA1c) level less than 7.0% without use of insulin and had no episodes of severe hypoglycemia or hyperglycemia in the prior 12 months.Main outcomes and measuresRisk-adjusted probability of intensive treatment and incident severe hypoglycemia, stratified by patient clinical complexity. Intensive treatment was defined as use of more glucose-lowering medications than recommended by practice guidelines at specific index HbA1c levels. Severe hypoglycemia was ascertained by ambulatory, emergency department, and hospital claims for hypoglycemia during the 2 years after the index HbA1c test. Patients were categorized as having high vs low clinical complexity if they were 75 years or older, had dementia or end-stage renal disease, or had 3 or more serious chronic conditions.ResultsOf 31 542 eligible patients (median age, 58 years; interquartile range, 51-65 years; 15 483 women [49.1%]; 18 188 white [57.7%]), 3910 (12.4%) had clinical complexity. The risk-adjusted probability of intensive treatment was 25.7% (95% CI, 25.1%-26.2%) in patients with low clinical complexity and 20.8% (95% CI, 19.4%-22.2%) in patients with high clinical complexity. In patients with low clinical complexity, the risk-adjusted probability of severe hypoglycemia during the subsequent 2 years was 1.02% (95% CI, 0.87%-1.17%) with standard treatment and 1.30% (95% CI, 0.98%-1.62%) with intensive treatment (absolute difference, 0.28%; 95% CI, -0.10% to 0.66%). In patients with high clinical complexity, intensive treatment significantly increased the risk-adjusted probability of severe hypoglycemia from 1.74% (95% CI, 1.28%-2.20%) with standard treatment to 3.04% (95% CI, 1.91%-4.18%) with intensive treatment (absolute difference, 1.30%; 95% CI, 0.10%-2.50%).Conclusions and relevanceMore than 20% of patients with type 2 diabetes received intensive treatment that may be unnecessary. Among patients with high clinical complexity, intensive treatment nearly doubles the risk of severe hypoglycemia.

Project description:ImportanceEstimating mortality risk in older adults with dementia is important for guiding decisions such as cancer screening, treatment of new and chronic medical conditions, and advance care planning.ObjectiveTo develop and externally validate a mortality prediction model in community-dwelling older adults with dementia.Design, setting, and participantsThis cohort study included community-dwelling participants (aged ≥65 years) in the Health and Retirement Study (HRS) from 1998 to 2016 (derivation cohort) and National Health and Aging Trends Study (NHATS) from 2011 to 2019 (validation cohort).ExposuresCandidate predictors included demographics, behavioral/health factors, functional measures (eg, activities of daily living [ADL] and instrumental activities of daily living [IADL]), and chronic conditions.Main outcomes and measuresThe primary outcome was time to all-cause death. We used Cox proportional hazards regression with backward selection and multiple imputation for model development. Model performance was assessed by discrimination (integrated area under the receiver operating characteristic curve [iAUC]) and calibration (plots of predicted and observed mortality).ResultsOf 4267 participants with probable dementia in HRS, the mean (SD) age was 82.2 (7.6) years, 2930 (survey-weighted 69.4%) were female, and 785 (survey-weighted 12.1%) identified as Black. Median (IQR) follow-up time was 3.9 (2.0-6.8) years, and 3466 (81.2%) participants died by end of follow-up. The final model included age, sex, body mass index, smoking status, ADL dependency count, IADL difficulty count, difficulty walking several blocks, participation in vigorous physical activity, and chronic conditions (cancer, heart disease, diabetes, lung disease). The optimism-corrected iAUC after bootstrap internal validation was 0.76 (95% CI, 0.75-0.76) with time-specific AUC of 0.73 (95% CI, 0.70-0.75) at 1 year, 0.75 (95% CI, 0.73-0.77) at 5 years, and 0.84 (95% CI, 0.82-0.85) at 10 years. On external validation in NHATS (n = 2404), AUC was 0.73 (95% CI, 0.70-0.76) at 1 year and 0.74 (95% CI, 0.71-0.76) at 5 years. Calibration plots suggested good calibration across the range of predicted risk from 1 to 10 years.Conclusions and relevanceWe developed and externally validated a mortality prediction model in community-dwelling older adults with dementia that showed good discrimination and calibration. The mortality risk estimates may help guide discussions regarding treatment decisions and advance care planning.

Project description:BackgroundHospital admissions for severe hypoglycemia are associated with significant healthcare costs, decreased quality of life, and increased morbidity and mortality, especially for older adults with diabetes. Understanding the reasons for hypoglycemia hospitalization is essential for the development of effective interventions; yet, the causes and precipitants of hypoglycemia are not well understood.MethodsWe conducted a qualitative study of non-nursing home patients aged 65 years or older without cognitive dysfunction admitted to a single tertiary-referral hospital with diabetes-related hypoglycemia. During the hospitalization, we conducted one-on-one, in-depth, semi-structured interviews to explore: (1) experiences with diabetes management among patients hospitalized for severe hypoglycemia; and (2) factors contributing and leading to the hypoglycemic event. Major themes and sub-themes were extracted using the constant comparative method by 3 study authors.ResultsAmong the 17 participants interviewed, the mean age was 78.9 years of age, 76.5% were female, 64.7% African American, 64.7% on insulin, and patients had an average of 13 chronic conditions. Patients reported: (1) surprise at hypoglycemia despite living with diabetes for many years; (2) adequate support, knowledge, and preparedness for hypoglycemia; (3) challenges balancing a diet that minimizes hyperglycemia and prevents hypoglycemia; (4) the belief that hyperglycemia necessitates medical intervention, but hypoglycemia does not; and (5) tension between clinician-prescribed treatment plans and self-management based on patients' experience. Notably, participants did not report the previously cited reasons for hypoglycemia, such as food insecurity, lack of support or knowledge, or treatment errors.ConclusionsOur findings suggest that some hypoglycemic events may not be preventable, but in order to reduce the risk of hypoglycemia in older individuals at risk: (1) healthcare systems need to shift from their general emphasis on the avoidance of hyperglycemia towards the prevention of hypoglycemia; and (2) clinicians and patients need to work together to design treatment regimens that fit within patient capacity and are flexible enough to accommodate life's demands.

Dataset Information

Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model.

Publications

Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model.

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