McCoy, Rozalina G.

Objectives To assess the degree to which self‐reported symptoms predict unplanned readmission or emergency department ( ED ) care within 30 days of high‐risk, elderly adults enrolled in a posthospitalization care transition program ( CTP ). Design Retrospective cohort study. Setting Posthospitalization CTP at Mayo Clinic, Rochester, Minnesota, from January 1, 2013, through March 3, 2015. Participants Frail, elderly adults (N = 230; mean age 83.5 ± 8.3, 46.5% male). Measurements Charlson Comorbidity Index ( CCI ) and self‐reported symptoms, measured using the Edmonton Symptom Assessment System ( ESAS ), were ascertained upon CTP enrollment. Results Mean CCI was 3.9 ± 2.3. Of 51 participants returning to the hospital within 30 days of discharge, 13 had ED visits, and 38 were readmitted. Age, sex, and CCI were not significantly different between returning and nonreturning participants, but returning participants were significantly more likely to report shortness of breath ( P = .004), anxiety ( P = .02), depression ( P = .02), and drowsiness ( P = .01). Overall ESAS score was also a significant predictor of hospital return ( P = .01). Conclusion Four self‐reported symptoms and overall ESAS score, but not CCI , ascertained after hospital discharge were strong predictors of hospital return within 30 days. Including symptoms in risk stratification of high‐risk elderly adults may help target interventions and reduce readmissions.

Background: Individualized diabetes management would benefit from prospectively identifying well-controlled patients at risk of losing glycemic control. Objectives: To identify patterns of hemoglobin A1c (HbA1c) change among patients with stable controlled diabetes. Research Design: Cohort study using OptumLabs Data Warehouse, 2001–2013. We develop and apply a machine learning framework that uses a Bayesian estimation of the mixture of generalized linear mixed effect models to discover glycemic trajectories, and a random forest feature contribution method to identify patient characteristics predictive of their future glycemic trajectories. Subjects: The study cohort consisted of 27,005 US adults with type 2 diabetes, age 18 years and older, and stable index HbA1c <7.0%. Measures: HbA1c values during 24 months of observation. Results: We compared models with k=1, 2, 3, 4, 5 trajectories and baseline variables including patient age, sex, race/ethnicity, comorbidities, medications, and HbA1c. The k=3 model had the best fit, reflecting 3 distinct trajectories of glycemic change: (T1) rapidly deteriorating HbA1c among 302 (1.1%) youngest (mean, 55.2 y) patients with lowest mean baseline HbA1c, 6.05%; (T2) gradually deteriorating HbA1c among 902 (3.3%) patients (mean, 56.5 y) with highest mean baseline HbA1c, 6.53%; and (T3) stable glycemic control among 25,800 (95.5%) oldest (mean, 58.5 y) patients with mean baseline HbA1c 6.21%. After 24 months, HbA1c rose to 8.75% in T1 and 8.40% in T2, but remained stable at 6.56% in T3. Conclusions: Patients with controlled type 2 diabetes follow 3 distinct trajectories of glycemic control. This novel application of advanced analytic methods can facilitate individualized and population diabetes care by proactively identifying high risk patients.

OBJECTIVETo examine temporal trends in utilization of glucose-lowering medications, glycemic control, and rate of severe hypoglycemia among patients with type 2 diabetes (T2DM).RESEARCH DESIGN AND METHODSUsing claims data from 1.66 million privately insured and Medicare Advantage patients with T2DM from 2006 to 2013, we estimated the annual 1) age- and sex-standardized proportion of patients who filled each class of agents; 2) age-, sex-, race-, and region-standardized proportion with hemoglobin A1c (HbA1c) &lt;6%, 6 to &lt;7%, 7 to &lt;8%, 8 to &lt;9%, ≥9%; and 3) age- and sex-standardized rate of severe hypoglycemia among those using medications. Proportions were calculated overall and stratified by age-group (18–44, 45–64, 65–74, and ≥75 years) and number of chronic comorbidities (zero, one, and two or more).RESULTSFrom 2006 to 2013, use increased for metformin (from 47.6 to 53.5%), dipeptidyl peptidase 4 inhibitors (0.5 to 14.9%), and insulin (17.1 to 23.0%) but declined for sulfonylureas (38.8 to 30.8%) and thiazolidinediones (28.5 to 5.6%; all P &lt; 0.001). The proportion of patients with HbA1c &lt;7% declined (from 56.4 to 54.2%; P &lt; 0.001) and with HbA1c ≥9% increased (9.9 to 12.2%; P &lt; 0.001). Glycemic control varied by age and was poor among 23.3% of the youngest and 6.3% of the oldest patients in 2013. The overall rate of severe hypoglycemia remained the same (1.3 per 100 person-years; P = 0.72), declined modestly among the oldest patients (from 2.9 to 2.3; P &lt; 0.001), and remained high among those with two or more comorbidities (3.2 to 3.5; P = 0.36).CONCLUSIONSDuring the recent 8-year period, the use of glucose-lowering drugs has changed dramatically among patients with T2DM. Overall glycemic control has not improved and remains poor among nearly a quarter of the youngest patients. The overall rate of severe hypoglycemia remains largely unchanged.

Objective To develop and validate a model of incident type 2 diabetes based solely on administrative data. Data Sources/Study Setting Optum Labs Data Warehouse (OLDW), a national commercial administrative dataset. Study Design HealthImpact model was developed and internally validated using nested case–control study design; n = 473,049 in training cohort and n = 303,025 in internal validation cohort. HealthImpact was externally validated in 2,000,000 adults followed prospectively for 3 years. Only adults ≥18 years were included. Data Collection/Extraction Methods Patients with incident diabetes were identified using HEDIS rules. Control subjects were sampled from patients without diabetes. Medical and pharmacy claims data collected over 3 years prior to index date were used to build the model variables. Principal Findings HealthImpact, scored 0–100, has 48 variables with c‐statistic 0.80815. We identified HealthImpact threshold of 90 as identifying patients at high risk of incident diabetes. HealthImpact had excellent discrimination in external validation cohort (c‐statistic 0.8171). The sensitivity, specificity, positive predictive value, and negative predictive value of HealthImpact >90 for new diagnosis of diabetes within 3 years were 32.35, 94.92, 22.25, and 96.90 percent, respectively. Conclusions HealthImpact is an efficient and effective method of risk stratification for incident diabetes that is not predicated on patient‐provided information or laboratory tests.

ObjectivesPerformance measurement is used by health care providers, payers, and patients. Historically accomplished using administrative data, registries are used increasingly to track and improve care. We assess how measured diabetes care quality differs when calculated using claims versus registry.Data Sources/Study SettingCross‐sectional analysis of administrative claims and electronic health records (EHRs) of patients in a multispecialty integrated health system in 2012 (n = 368,883).Study DesignWe calculated percent of patients attaining glycohemoglobin <8.0 percent, LDL cholesterol <100 mg/dL, blood pressure <140/90 mmHg, and nonsmoking (D4) in cohorts, identified by Medicare Accountable Care Organization/Minnesota Community Measures (ACO‐MNCM; claims‐based), Healthcare Effectiveness Data and Information Set (HEDIS; claims‐based), and registry (EHR‐based).Data Collection/Extraction MethodsClaims were linked to EHR to create a dataset of performance‐eligible patients.Principal FindingsACO‐MNCM, HEDIS, and registry identified 6,475, 6,989, and 6,425 measurement‐eligible patients. Half were common among the methods; discrepancies were due to attribution, age restriction, and encounter requirements. D4 attainment was lower in ACO‐MNCM (36.09 percent) and HEDIS (37.51 percent) compared to registry (43.74 percent) cohorts.ConclusionsRegistry‐ and claims‐based performance measurement methods identify different patients, resulting in different rates of quality metric attainment with implications for innovative population health management.