Continuous Glucose Monitor Metrics Are Associated with Emergency Department Visits and Hospitalizations for Hypoglycemia and Hyperglycemia, but Have Low Predictive Value.

Objective: Determine whether continuous glucose monitor (CGM) metrics can provide actionable advance warning of an emergency department (ED) visit or hospitalization for hypoglycemic or hyperglycemic (dysglycemic) events. Research Design and Methods: Two nested case-control studies were conducted among insulin-treated diabetes patients at Kaiser Permanente, who shared their CGM data with their providers. Cases included dysglycemic events identified from ED and hospital records (2016-2021). Controls were selected using incidence density sampling. Multiple CGM metrics were calculated among patients using CGM >70% of the time, using CGM data from two lookback periods (0-7 and 8-14 days) before each event. Generalized estimating equations were specified to estimate odds ratios and C-statistics. Results: Among 3626 CGM users, 108 patients had 154 hypoglycemic events and 165 patients had 335 hyperglycemic events. Approximately 25% of patients had no CGM data during either lookback; these patients had >2 × the odds of a hypoglycemic event and 3-4 × the odds of a hyperglycemic event. While several metrics were strongly associated with a dysglycemic event, none had good discrimination. Conclusion: Several CGM metrics were strongly associated with risk of dysglycemic events, and these can be used to identify higher risk patients. Also, patients who are not using their CGM device may be at elevated risk of adverse outcomes. However, no CGM metric or absence of CGM data had adequate discrimination to reliably provide actionable advance warning of an event and thus justify a rapid intervention.

Severe hypoglycemia and falls in older adults with diabetes: The Diabetes & Aging Study.

Objective: To estimate rates of severe hypoglycemia and falls among older adults with diabetes and evaluate their association. Research Design and Methods: Survey in an age-stratified, random sample adults with diabetes age 65-100 years; respondents were asked about severe hypoglycemia (requiring assistance) and falls in the past 12 months. Prevalence ratios (adjusted for age, sex, race/ethnicity) estimated the increased risk of falls associated with severe hypoglycemia. Results: Among 2,158 survey respondents, 79 (3.7%) reported severe hypoglycemia, of whom 68 (86.1%) had no ED visit or hospitalization for hypoglycemia. Falls were reported by 847 (39.2%), of whom 745 (88.0%) had no fall documented in outpatient or inpatient records. Severe hypoglycemia was associated with a 70% greater prevalence of falls (adjusted prevalence ratio = 1.7 (95% CI, 1.3-2.2)). Conclusion: While clinical documentation of events likely reflects severity or care-seeking behavior, severe hypoglycemia and falls are common, under-reported life-threatening events.

The Growing Role of Technology in the Care of Older Adults With Diabetes.

The integration of technologies such as continuous glucose monitors, insulin pumps, and smart pens into diabetes management has the potential to support the transformation of health care services that provide a higher quality of diabetes care, lower costs and administrative burdens, and greater empowerment for people with diabetes and their caregivers. Among people with diabetes, older adults are a distinct subpopulation in terms of their clinical heterogeneity, care priorities, and technology integration. The scientific evidence and clinical experience with these technologies among older adults are growing but are still modest. In this review, we describe the current knowledge regarding the impact of technology in older adults with diabetes, identify major barriers to the use of existing and emerging technologies, describe areas of care that could be optimized by technology, and identify areas for future research to fulfill the potential promise of evidence-based technology integrated into care for this important population.

Hospice Quality, Race, and Disenrollment in Hospice Enrollees With Dementia.

Background: Racial and ethnic minoritized people with dementia (PWD) are at high risk of disenrollment from hospice, yet little is known about the relationship between hospice quality and racial disparities in disenrollment among PWD. Objective: To assess the association between race and disenrollment between and within hospice quality categories in PWD. Design/Setting/Subjects: Retrospective cohort study of 100% Medicare beneficiaries 65+ enrolled in hospice with a principal diagnosis of dementia, July 2012-December 2017. Race and ethnicity (White/Black/Hispanic/Asian and Pacific Islander [AAPI]) was assessed with the Research Triangle Institute (RTI) algorithm. Hospice quality was assessed with the publicly-available Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey item on overall hospice rating, including a category for hospices exempt from public reporting (unrated). Results: The sample included 673,102 PWD (mean age 86, 66% female, 85% White, 7.3% Black, 6.3% Hispanic, 1.6% AAPI) enrolled in 4371 hospices nationwide. Likelihood of disenrollment was higher in hospices in the lowest quartile of quality ratings (vs. highest quartile) for both White (adjusted odds ratio [AOR] 1.12 [95% confidence interval 1.06-1.19]) and minoritized PWD (AOR range 1.2-1.3) and was substantially higher in unrated hospices (AOR range 1.8-2.0). Within both low- and high-quality hospices, minoritized PWD were more likely to be disenrolled compared with White PWD (AOR range 1.18-1.45). Conclusions: Hospice quality predicts disenrollment, but does not fully explain disparities in disenrollment for minoritized PWD. Efforts to improve racial equity in hospice should focus both on increasing equity in access to high-quality hospices and improving care for racial minoritized PWD in all hospices.

The Epidemiology of Smoking in Older Adults: A National Cohort Study.

BACKGROUND: Older smokers account for the greatest tobacco-related morbidity and mortality in the USA, while quitting smoking remains the single most effective preventive health intervention for reducing the risk of smoking-related illness. Yet, knowledge about patterns of smoking and smoking cessation in older adults is lacking. OBJECTIVE: Assess trends in prevalence of cigarette smoking between 1998 and 2018 and identify patterns and predictors of smoking cessation in US older adults. DESIGN: Retrospective cohort study PARTICIPANTS: Individuals aged 55+ enrolled in the nationally representative Health and Retirement Study, 1998-2018 MAIN MEASURES: Current smoking was assessed with the question: "Do you smoke cigarettes now?" Quitting smoking was defined as having at least two consecutive waves (between 2 and 4 years) in which participants who were current smokers in 1998 reported they were not currently smoking in subsequent waves. KEY RESULTS: Age-adjusted smoking prevalence decreased from 15.9% in 1998 (95% confidence interval (CI) 15.2, 16.7) to 11.2% in 2018 (95% CI 10.4, 12.1). Among 2187 current smokers in 1998 (mean age 64, 56% female), 56% of those living to age 90 had a sustained period of smoking cessation. Smoking less than 10 cigarettes/day was strongly associated with an increased likelihood of quitting smoking (subdistribution hazard ratio 2.3; 95% CI 1.9, 2.8), compared to those who smoked more than 20 cigarettes/day. CONCLUSIONS: Smoking prevalence among older persons has declined and substantial numbers of older smokers succeed in quitting smoking for a sustained period. These findings highlight the need for continued aggressive efforts at tobacco cessation among older persons.

New psychotropic medication use among Medicare beneficiaries with dementia after hospital discharge.

BACKGROUND: Hospitalizations among people with dementia (PWD) may precipitate behavioral changes, leading to the psychotropic medication use despite adverse outcomes and limited efficacy. We sought to determine the incidence of new psychotropic medication use among community-dwelling PWD after hospital discharge and, among new users, the proportion with prolonged use. METHODS: This was a retrospective cohort study using a 20% random sample of Medicare claims in 2017, including hospitalized PWD with traditional and Part D Medicare who were 68 years or older. The primary outcome was incident prescribing at discharge of psychotropics including antipsychotics, sedative-hypnotics, antiepileptics, and antidepressants. This was defined as new prescription fills (i.e., from classes not used in 180 days preadmission) within 7 days of hospital or skilled nursing facility discharge. Prolonged use was defined as the proportion of new users who continued to fill newly prescribed medications beyond 90 days of discharge. RESULTS: The cohort included 117,022 hospitalized PWD with a mean age of 81 years; 63% were female. Preadmission, 63% were using at least 1 psychotropic medication; 10% were using medications from ≥3 psychotropic classes. These included antidepressants (44% preadmission), antiepileptics (29%), sedative-hypnotics (21%), and antipsychotics (11%). The proportion of PWD discharged from the hospital with new psychotropics ranged from 1.9% (antipsychotics) to 2.9% (antiepileptics); 6.6% had at least one new class started. Among new users, prolonged use ranged from 36% (sedative-hypnotics) to 63% (antidepressants); across drug classes, prolonged use occurred in 51%. Predictors of newly initiated psychotropics included length of stay (≥median vs.

Fingerstick glucose monitoring by cognitive impairment status in Veterans Affairs nursing home residents with diabetes.

BACKGROUND: Guidelines recommend nursing home (NH) residents with cognitive impairment receive less intensive glycemic treatment and less frequent fingerstick monitoring. Our objective was to determine whether current practice aligns with guideline recommendations by examining fingerstick frequency in Veterans Affairs (VA) NH residents with diabetes across cognitive impairment levels. METHODS: We identified VA NH residents with diabetes aged ≥65 residing in VA NHs for >30 days between 2016 and 2019. Residents were grouped by cognitive impairment status based on the Cognitive Function Scale: cognitively intact, mild impairment, moderate impairment, and severe impairment. We also categorized residents into mutually exclusive glucose-lowering medication (GLM) categories: (1) no GLMs, (2) metformin only, (3) sulfonylureas/other GLMs (+/- metformin but no insulin), (4) long-acting insulin (+/- oral/other GLMs but no short-acting insulin), and (5) any short-acting insulin. Our outcome was mean daily fingersticks on day 31 of NH admission. RESULTS: Among 13,637 NH residents, mean age was 75 years and mean hemoglobin A1c was 7.0%. The percentage of NH residents on short-acting insulin varied by cognitive status from 22.7% in residents with severe cognitive impairment to 33.9% in residents who were cognitively intact. Mean daily fingersticks overall on day 31 was 1.50 (standard deviation = 1.73). There was a greater range in mean fingersticks across GLM categories compared to cognitive status. Fingersticks ranged widely across GLM categories from 0.39 per day (no GLMs) to 3.08 (short-acting insulin), while fingersticks ranged slightly across levels of cognitive impairment from 1.11 (severe cognitive impairment) to 1.59 (cognitively intact). CONCLUSION: NH residents receive frequent fingersticks regardless of level of cognitive impairment, suggesting that cognitive status is a minor consideration in monitoring decisions. Future studies should determine whether decreasing fingersticks in NH residents with moderate/severe cognitive impairment can reduce burdens without compromising safety.

A comprehensive prognostic tool for older adults: Predicting death, ADL disability, and walking disability simultaneously.

BACKGROUND: Many clinical and financial decisions for older adults depend on the future risk of disability and mortality. Prognostic tools for long-term disability risk in a general population are lacking. We aimed to create a comprehensive prognostic tool that predicts the risk of mortality, of activities of daily living (ADL) disability, and walking disability simultaneously using the same set of variables. METHODS: We conducted a longitudinal analysis of the nationally-representative Health and Retirement Study (HRS). We included community-dwelling adults aged ≥70 years who completed a core interview in the 2000 wave of HRS, with follow-up through 2018. We evaluated 40 predictors encompassing demographics, diseases, physical functioning, and instrumental ADLs. We applied novel methods to optimize three models simultaneously while prioritizing variables that take less time to ascertain during backward stepwise elimination. The death prediction model used Cox regression and both the models for walking disability and for ADL disability used Fine and Gray competing-risk regression. We examined calibration plots and generated optimism-corrected statistics of discrimination using bootstrapping. To simulate unavailable patient data, we also evaluated models excluding one or two variables from the final model. RESULTS: In 6646 HRS participants, 2662 developed walking disability, 3570 developed ADL disability, and 5689 died during a median follow-up of 9.5 years. The final prognostic tool had 16 variables. The optimism-corrected integrated area under the curve (iAUC) was 0.799 for mortality, 0.685 for walking disability, and 0.703 for ADL disability. At each percentile of predicted mortality risk, there was a substantial spread in the predicted risks of walking disability and ADL disability. Discrimination and calibration remained good even when missing one or two predictors from the model. This model is now available on ePrognosis (https://eprognosis.ucsf.edu/alexlee.php) CONCLUSIONS: Given the variability in disability risk for people with similar mortality risks, using individualized risks of disabilities may inform clinical and financial decisions for older adults.

Do functional status and Medicare claims data improve the predictive accuracy of an electronic health record mortality index? Findings from a national Veterans Affairs cohort.

BACKGROUND: Electronic health record (EHR) prediction models may be easier to use in busy clinical settings since EHR data can be auto-populated into models. This study assessed whether adding functional status and/or Medicare claims data (which are often not available in EHRs) improves the accuracy of a previously developed Veterans Affairs (VA) EHR-based mortality index. METHODS: This was a retrospective cohort study of veterans aged 75 years and older enrolled in VA primary care clinics followed from January 2014 to April 2020 (n = 62,014). We randomly split participants into development (n = 49,612) and validation (n = 12,402) cohorts. The primary outcome was all-cause mortality. We performed logistic regression with backward stepwise selection to develop a 100-predictor base model using 854 EHR candidate variables, including demographics, laboratory values, medications, healthcare utilization, diagnosis codes, and vitals. We incorporated functional measures in a base + function model by adding activities of daily living (range 0-5) and instrumental activities of daily living (range 0-7) scores. Medicare data, including healthcare utilization (e.g., emergency department visits, hospitalizations) and diagnosis codes, were incorporated in a base + Medicare model. A base + function + Medicare model included all data elements. We assessed model performance with the c-statistic, reclassification metrics, fraction of new information provided, and calibration plots. RESULTS: In the overall cohort, mean age was 82.6 years and 98.6% were male. At the end of follow-up, 30,263 participants (48.8%) had died. The base model c-statistic was 0.809 (95% CI 0.805-0.812) in the development cohort and 0.804 (95% CI 0.796-0.812) in the validation cohort. Validation cohort c-statistics for the base + function, base + Medicare, and base + function + Medicare models were 0.809 (95% CI 0.801-0.816), 0.811 (95% CI 0.803-0.818), and 0.814 (95% CI 0.807-0.822), respectively. Adding functional status and Medicare data resulted in similarly small improvements among other model performance measures. All models showed excellent calibration. CONCLUSIONS: Incorporation of functional status and Medicare data into a VA EHR-based mortality index led to small but likely clinically insignificant improvements in model performance.

Comparing Machine Learning to Regression Methods for Mortality Prediction Using Veterans Affairs Electronic Health Record Clinical Data.

BACKGROUND: It is unclear whether machine learning methods yield more accurate electronic health record (EHR) prediction models compared with traditional regression methods. OBJECTIVE: The objective of this study was to compare machine learning and traditional regression models for 10-year mortality prediction using EHR data. DESIGN: This was a cohort study. SETTING: Veterans Affairs (VA) EHR data. PARTICIPANTS: Veterans age above 50 with a primary care visit in 2005, divided into separate training and testing cohorts (n= 124,360 each). MEASUREMENTS AND ANALYTIC METHODS: The primary outcome was 10-year all-cause mortality. We considered 924 potential predictors across a wide range of EHR data elements including demographics (3), vital signs (9), medication classes (399), disease diagnoses (293), laboratory results (71), and health care utilization (149). We compared discrimination (c-statistics), calibration metrics, and diagnostic test characteristics (sensitivity, specificity, and positive and negative predictive values) of machine learning and regression models. RESULTS: Our cohort mean age (SD) was 68.2 (10.5), 93.9% were male; 39.4% died within 10 years. Models yielded testing cohort c-statistics between 0.827 and 0.837. Utilizing all 924 predictors, the Gradient Boosting model yielded the highest c-statistic [0.837, 95% confidence interval (CI): 0.835-0.839]. The full (unselected) logistic regression model had the highest c-statistic of regression models (0.833, 95% CI: 0.830-0.835) but showed evidence of overfitting. The discrimination of the stepwise selection logistic model (101 predictors) was similar (0.832, 95% CI: 0.830-0.834) with minimal overfitting. All models were well-calibrated and had similar diagnostic test characteristics. LIMITATION: Our results should be confirmed in non-VA EHRs. CONCLUSION: The differences in c-statistic between the best machine learning model (924-predictor Gradient Boosting) and 101-predictor stepwise logistic models for 10-year mortality prediction were modest, suggesting stepwise regression methods continue to be a reasonable method for VA EHR mortality prediction model development.

Predicting Life Expectancy to Target Cancer Screening Using Electronic Health Record Clinical Data.

BACKGROUND: Guidelines recommend breast and colorectal cancer screening for older adults with a life expectancy >10 years. Most mortality indexes require clinician data entry, presenting a barrier for routine use in care. Electronic health records (EHR) are a rich clinical data source that could be used to create individualized life expectancy predictions to identify patients for cancer screening without data entry. OBJECTIVE: To develop and internally validate a life expectancy calculator from structured EHR data. DESIGN: Retrospective cohort study using national Veteran's Affairs (VA) EHR databases. PATIENTS: Veterans aged 50+ with a primary care visit during 2005. MAIN MEASURES: We assessed demographics, diseases, medications, laboratory results, healthcare utilization, and vital signs 1 year prior to the index visit. Mortality follow-up was complete through 2017. Using the development cohort (80% sample), we used LASSO Cox regression to select ~100 predictors from 913 EHR data elements. In the validation cohort (remaining 20% sample), we calculated the integrated area under the curve (iAUC) and evaluated calibration. KEY RESULTS: In 3,705,122 patients, the mean age was 68 years and the majority were male (97%) and white (85%); nearly half (49%) died. The life expectancy calculator included 93 predictors; age and gender most strongly contributed to discrimination; diseases also contributed significantly while vital signs were negligible. The iAUC was 0.816 (95% confidence interval, 0.815, 0.817) with good calibration. CONCLUSIONS: We developed a life expectancy calculator using VA EHR data with excellent discrimination and calibration. Automated life expectancy prediction using EHR data may improve guideline-concordant breast and colorectal cancer screening by identifying patients with a life expectancy >10 years.

Intensification of Diabetes Medications at Hospital Discharge and Clinical Outcomes in Older Adults in the Veterans Administration Health System.

Importance: Transient elevations of blood glucose levels are common in hospitalized older adults with diabetes and may lead clinicians to discharge patients with more intensive diabetes medications than they were using before hospitalization. Objective: To investigate outcomes associated with intensification of outpatient diabetes medications at discharge. Design, Setting, and Participants: This retrospective cohort study assessed patients 65 years and older with diabetes not taking insulin who were hospitalized in the Veterans Health Administration Health System between January 1, 2011, and September 28, 2016, for common medical conditions. Data analysis was performed from January 1, 2020, to March 31, 2021. Exposure: Discharge with intensified diabetes medications, defined as filling a prescription at hospital discharge for a new or higher-dose medication than was being used before hospitalization. Propensity scores were used to construct a matched cohort of patients who did and did not receive diabetes medication intensifications. Main Outcomes and Measures: Coprimary outcomes of severe hypoglycemia and severe hyperglycemia were assessed at 30 and 365 days using competing risk regressions. Secondary outcomes included all-cause readmissions, mortality, change in hemoglobin A1c (HbA1c) level, and persistent use of intensified medications at 1 year after discharge. Results: The propensity-matched cohort included 5296 older adults with diabetes (mean [SD] age, 73.7 [7.7] years; 5212 [98.4%] male; and 867 [16.4%] Black, 47 [0.9%] Hispanic, 4138 [78.1%] White), equally split between those who did and did not receive diabetes medication intensifications at hospital discharge. Within 30 days, patients who received medication intensifications had a higher risk of severe hypoglycemia (hazard ratio [HR], 2.17; 95% CI, 1.10-4.28), no difference in risk of severe hyperglycemia (HR, 1.00; 95% CI, 0.33-3.08), and a lower risk of death (HR, 0.55; 95% CI, 0.33-0.92). At 1 year, no differences were found in the risk of severe hypoglycemia events, severe hyperglycemia events, or death and no difference in change in HbA1c level was found among those who did vs did not receive intensifications (mean postdischarge HbA1c, 7.72% vs 7.70%; difference-in-differences, 0.02%; 95% CI, -0.12% to 0.16%). At 1 year, 48.0% (591 of 1231) of new oral diabetes medications and 38.5% (548 of 1423) of new insulin prescriptions filled at discharge were no longer being filled. Conclusions and Relevance: In this national cohort study, among older adults hospitalized for common medical conditions, discharge with intensified diabetes medications was associated with an increased short-term risk of severe hypoglycemia events but was not associated with reduced severe hyperglycemia events or improve HbA1c control. These findings indicate that short-term hospitalization may not be an effective time to intervene in long-term diabetes management.

Biomarkers of Kidney Tubule Health, CKD Progression, and Acute Kidney Injury in SPRINT (Systolic Blood Pressure Intervention Trial) Participants.

RATIONALE & OBJECTIVE: The Systolic Blood Pressure Intervention Trial (SPRINT) compared the effect of intensive versus standard systolic blood pressure targets on cardiovascular morbidity and mortality. In this ancillary study, we evaluated the use of exploratory factor analysis (EFA) to combine biomarkers of kidney tubule health in urine and plasma and then study their role in longitudinal estimated glomerular filtration rate (eGFR) change and risk of acute kidney injury (AKI). STUDY DESIGN: Observational cohort nested in a clinical trial. SETTING & PARTICIPANTS: 2,351 SPRINT participants with eGFR < 60 mL/min/1.73 m2 at baseline. EXPOSURE: Levels of neutrophil gelatinase-associated lipocalin (NGAL), interleukin 18 (IL-18), chitinase-3-like protein (YKL-40), kidney injury molecule 1 (KIM-1), monocyte chemoattractant protein 1 (MCP-1), α1-microglobulin (A1M) and ß2-microglobulin (B2M), uromodulin (UMOD), fibroblast growth factor 23 (FGF-23), and intact parathyroid hormone (PTH). OUTCOME: Longitudinal changes in eGFR and risk of AKI. ANALYTICAL APPROACH: We performed EFA to capture different tubule pathophysiologic processes. We used linear mixed effects models to evaluate the association of each factor with longitudinal changes in eGFR. We evaluated the association of the tubular factors scores with AKI using Cox proportional hazards regression. RESULTS: From 10 biomarkers, EFA generated 4 factors reflecting tubule injury/repair (NGAL, IL-18, and YKL-40), tubule injury/fibrosis (KIM-1 and MCP-1), tubule reabsorption (A1M and B2M), and tubule reserve/mineral metabolism (UMOD, FGF-23, and PTH). Each 1-SD higher tubule reserve/mineral metabolism factor score was associated with a 0.58% (95% CI, 0.39%-0.67%) faster eGFR decline independent of baseline eGFR and albuminuria. Both the tubule injury/repair and tubule injury/fibrosis factors were independently associated with future risk of AKI (per 1 SD higher, HRs of 1.18 [95% CI, 1.10-1.37] and 1.23 [95% CI, 1.02-1.48], respectively). LIMITATIONS: The factors require validation in other settings. CONCLUSIONS: EFA allows parsimonious subgrouping of biomarkers into factors that are differentially associated with progressive eGFR decline and AKI. These subgroups may provide insights into the pathological processes driving adverse kidney outcomes.

Association of Urine Biomarkers of Kidney Tubule Injury and Dysfunction With Frailty Index and Cognitive Function in Persons With CKD in SPRINT.

RATIONALE & OBJECTIVE: The associations of the glomerular markers of kidney disease, estimated glomerular filtration rate (eGFR) and albuminuria, with frailty and cognition are well established. However, the relationship of kidney tubule injury and dysfunction with frailty and cognition is unknown. STUDY DESIGN: Observational cross-sectional study. SETTING & PARTICIPANTS: 2,253 participants with eGFR<60mL/min/1.73m2 in the Systolic Blood Pressure Intervention Trial (SPRINT). EXPOSURE: Eight urine biomarkers: interleukin 18 (IL-18), kidney injury molecule 1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), chitinase-3-like protein 1 (YKL-40), monocyte chemoattractant protein 1 (MCP-1), α1-microglobulin (A1M), ß2-microglobulin (B2M), and uromodulin (Umod). OUTCOME: Frailty was measured using a previously validated frailty index (FI), categorized as fit (FI≤0.10), less fit (0.100.21). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). ANALYTICAL APPROACH: Associations between kidney tubule biomarkers with categorical FI were evaluated using multinomial logistic regression with the fit group as the reference. Cognitive function was evaluated using linear regression. Models were adjusted for demographic, behavioral, and clinical variables including eGFR and urine albumin. RESULTS: Three of the 8 urine biomarkers of tubule injury and dysfunction were independently associated with FI. Each 2-fold higher level of urine KIM-1, a marker of tubule injury, was associated with a 1.22 (95% CI, 1.01-1.49) greater odds of being in the frail group. MCP-1, a marker of tubulointerstitial fibrosis, was associated with a 1.30 (95% CI, 1.04-1.64) greater odds of being in the frail group, and A1M, a marker of tubule reabsorptive capacity, was associated with a 1.48 (95% CI, 1.11-1.96) greater odds of being in the frail group. These associations were independent of confounders including eGFR and urine albumin, and were stronger than those of urine albumin with FI (1.15 [95% CI, 0.99-1.34]). Higher urine B2M, another marker of tubule reabsorptive capacity, was associated with worse cognitive scores at baseline (ß: -0.09 [95% CI, -0.17 to-0.01]). Urine albumin was not associated with cognitive function. LIMITATIONS: Cross-sectional design, and FI may not be generalizable in other populations. CONCLUSIONS: Urine biomarkers of tubule injury, fibrosis, and proximal tubule reabsorptive capacity are variably associated with FI and worse cognition, independent of glomerular markers of kidney health. Future studies are needed to validate these results among other patient populations.

Fingerstick Glucose Monitoring in Veterans Affairs Nursing Home Residents with Diabetes Mellitus.

BACKGROUND/OBJECTIVE: Guidelines recommend less intensive glycemic treatment and less frequent glucose monitoring for nursing home (NH) residents. However, little is known about the frequency of fingerstick (FS) glucose monitoring in this population. Our objective was to examine the frequency of FS glucose monitoring in Veterans Affairs (VA) NH residents with diabetes mellitus, type II (T2DM). DESIGN AND SETTING: National retrospective cohort study in 140 VA NHs. PARTICIPANTS: NH residents with T2DM and older than 65 years admitted to VA NHs between 2013 and 2015 following discharge from a VA hospital. MEASUREMENTS: NH residents were classified into five groups based on their highest hypoglycemia risk glucose-lowering medication (GLM) each day: no GLMs; metformin only; sulfonylureas; long-acting insulin; and any short-acting insulin. Our outcome was a daily count of FS measurements. RESULTS: Among 17,474 VA NH residents, mean age was 76 (standard deviation (SD) = 8) years and mean hemoglobin A1c was 7.6% (SD = 1.5%). On day 1 after NH admission, 49% of NH residents were on short-acting insulin, decreasing slightly to 43% at day 90. Overall, NH residents had an average of 1.9 (95% confidence interval (CI) = 1.8-1.9) FS measurements on NH day 1, decreasing to 1.4 (95% CI = 1.3-1.4) by day 90. NH residents on short-acting insulin had the most frequent FS measurements, with 3.0 measurements (95% CI = 2.9-3.0) on day 1, decreasing to 2.6 measurements (95% CI = 2.5-2.7) by day 90. Less frequent FS measurements were seen for NH residents receiving long-acting insulin (2.1 (95% CI = 2.0-2.2) on day 1) and sulfonylureas (1.7 (95% CI = 1.5-1.8) on day 1). Even NH residents on metformin monotherapy had 1.1 (95% CI = 1.1-1.2) measurements on day 1, decreasing to 0.5 (95% CI = 0.4-0.6) measurements on day 90. CONCLUSION: Although guidelines recommend less frequent glucose monitoring for NH residents, we found that many VA NH residents receive frequent FS monitoring. Given the uncertain benefits and potential for substantial patient burdens and harms, our results suggest decreasing FS monitoring may be warranted for many low hypoglycemia risk NH residents.

Severe Hypoglycemia, Cardiac Structure and Function, and Risk of Cardiovascular Events Among Older Adults With Diabetes.

OBJECTIVE: To assess the association of severe hypoglycemia measured at baseline with cardiovascular disease (CVD) among community-dwelling older individuals with diabetes, a group particularly susceptible to hypoglycemia. RESEARCH DESIGN AND METHODS: We included older adults with diabetes from the Atherosclerosis Risk in Communities (ARIC) study who attended visit 5 (2011-2013, baseline). Severe hypoglycemia at baseline was defined with use of first position ICD-9 codes from hospitalizations, emergency department visits, and ambulance calls. We examined cross-sectional associations of severe hypoglycemia with echocardiographic indices of cardiac structure-function. We prospectively evaluated the risks of incident or recurrent CVD (coronary heart disease, stroke, or heart failure) and all-cause mortality, from baseline to 31 December 2018, using negative binomial and Cox regression models. RESULTS: Among 2,193 participants (mean [SD] age 76 [5] years, 57% female, 32% Blacks), 79 had a history of severe hypoglycemia at baseline. Severe hypoglycemia was associated with a lower left ventricular (LV) ejection fraction (adjusted ß-coefficient -3.66% [95% CI -5.54, -1.78]), higher LV end diastolic volume (14.80 mL [95% CI 8.77, 20.84]), higher E-to-A ratio (0.11 [95% CI 0.03, 0.18]), and higher septal E/e' (2.48 [95% CI 1.13, 3.82]). In adjusted models, severe hypoglycemia was associated with incident or recurrent CVD (incidence rate ratio 2.19 (95% CI 1.24, 3.88]) and all-cause mortality (hazard ratio 1.71 [95% CI 1.10, 2.67]) among those without prevalent CVD. CONCLUSIONS: Our findings suggest that a history of severe hypoglycemia is associated with alterations in cardiac function and is an important marker of future cardiovascular risk in older adults.

FGF23, Frailty, and Falls in SPRINT.

BACKGROUND/OBJECTIVES: Chronic kidney disease (CKD) is associated with frailty. Fibroblast growth factor 23 (FGF23) is elevated in CKD and associated with frailty among non-CKD older adults and individuals with human immunodeficiency virus. Whether FGF23 is associated with frailty and falls in CKD is unknown. DESIGN: Cross-sectional and longitudinal observational study. SETTING: Systolic Blood Pressure Intervention Trial (SPRINT), a randomized trial evaluating standard (systolic blood pressure [SBP] <140 mm Hg) versus intensive (SBP <120 mm Hg) blood pressure lowering on cardiovascular and cognitive outcomes among older adults without diabetes mellitus. PARTICIPANTS: A total of 2,376 participants with CKD (estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m2 ). MEASUREMENTS: The exposure variable was intact FGF23. We used multinomial logistic regression to determine the cross-sectional association of intact FGF23 with frailty and Cox proportional hazards analysis to determine the longitudinal association with incident falls. Models were adjusted for demographics, comorbidities, randomization group, antihypertensives, eGFR, mineral metabolism markers, and frailty. RESULTS: After adjustment, the odds ratio for prevalent frailty versus non-frailty per twofold higher FGF23 was 1.34 (95% confidence interval [CI] = 1.01-1.77). FGF23 levels in the highest quartile versus the lowest quartile demonstrated more than a twofold increased fall risk (hazard ratio [HR] = 2.32; 95% CI = 1.26-4.26), and the HR per twofold higher FGF23 was 1.99 (95% CI = 1.48-2.68). CONCLUSION: Among SPRINT participants with CKD, FGF23 was associated with prevalent frailty and falls.

Improving the American Diabetes Association Framework for individualizing treatment in older adults: evaluating life expectancy.

INTRODUCTION: For older adults with type 2 diabetes, the American Diabetes Association (ADA) Framework uses comorbidities and functional status to categorize patients by estimated life expectancy to guide individualization of glycemic treatment. We evaluated whether modifying the ADA Framework by removing three comorbidities and incorporating age could improve life expectancy stratification and better identify patients likely to benefit from intensive treatment. RESEARCH DESIGN AND METHODS: We examined 3166 Health and Retirement Study participants aged ≥65 with diabetes from 1998 to 2004, using a prospective cohort design with mortality follow-up through 2016. We classified participants into one of three ADA Framework categories: Healthy, Intermediate Health, and Poor Health. We created modified categories by excluding comorbidities weakly associated with mortality (hypertension, arthritis, and incontinence). Using Gompertz regression, we estimated life expectancy across age strata for both original and modified ADA Framework categories. RESULTS: The original ADA Framework classified 34% as Healthy (likely to benefit from intensive treatment), 50% as Intermediate Health, and 16% as Poor Health (unlikely to benefit from intensive treatment). Our comorbidity modification reclassified 20% of participants from Intermediate Health to Healthy. Using the modified ADA Framework, median life expectancy of the Healthy varied greatly by age (aged 65-69: 16.3 years; aged ≥80: 7.6 years), indicating differing likelihood of benefit. Additionally, age ≥80 made extended life expectancy unlikely (median life expectancy for Healthy 7.6 years, Intermediate Health 5.9 years, Poor Health 2.5 years), suggesting adults ≥80 are unlikely to benefit from intensive treatment. CONCLUSIONS: Modifying the ADA Framework by incorporating age and focusing on comorbidities associated with mortality improved life expectancy stratification, resulting in different treatment recommendations for many older adults.