External Validation of the eRADAR Risk Score for Detecting Undiagnosed Dementia in Two Real-World Healthcare Systems.

BACKGROUND: Fifty percent of people living with dementia are undiagnosed. The electronic health record (EHR) Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) was developed to identify older adults at risk of having undiagnosed dementia using routinely collected clinical data. OBJECTIVE: To externally validate eRADAR in two real-world healthcare systems, including examining performance over time and by race/ethnicity. DESIGN: Retrospective cohort study PARTICIPANTS: 129,315 members of Kaiser Permanente Washington (KPWA), an integrated health system providing insurance coverage and medical care, and 13,444 primary care patients at University of California San Francisco Health (UCSF), an academic medical system, aged 65 years or older without prior EHR documentation of dementia diagnosis or medication. MAIN MEASURES: Performance of eRADAR scores, calculated annually from EHR data (including vital signs, diagnoses, medications, and utilization in the prior 2 years), for predicting EHR documentation of incident dementia diagnosis within 12 months. KEY RESULTS: A total of 7631 dementia diagnoses were observed at KPWA (11.1 per 1000 person-years) and 216 at UCSF (4.6 per 1000 person-years). The area under the curve was 0.84 (95% confidence interval: 0.84-0.85) at KPWA and 0.79 (0.76-0.82) at UCSF. Using the 90th percentile as the cut point for identifying high-risk patients, sensitivity was 54% (53-56%) at KPWA and 44% (38-51%) at UCSF. Performance was similar over time, including across the transition from International Classification of Diseases, version 9 (ICD-9) to ICD-10 codes, and across racial/ethnic groups (though small samples limited precision in some groups). CONCLUSIONS: eRADAR showed strong external validity for detecting undiagnosed dementia in two health systems with different patient populations and differential availability of external healthcare data for risk calculations. In this study, eRADAR demonstrated generalizability from a research sample to real-world clinical populations, transportability across health systems, robustness to temporal changes in healthcare, and similar performance across larger racial/ethnic groups.

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.

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.

Preoperative Medical Testing and Falls in Medicare Beneficiaries Awaiting Cataract Surgery.

PURPOSE: Delaying cataract surgery is associated with an increased risk of falls, but whether routine preoperative testing delays cataract surgery long enough to cause clinical harm is unknown. We sought to determine whether the use of routine preoperative testing leads to harm in the form of delayed surgery and falls in Medicare beneficiaries awaiting cataract surgery. DESIGN: Retrospective, observational cohort study using 2006-2014 Medicare claims. PARTICIPANTS: Medicare beneficiaries 66+ years of age with a Current Procedural Terminology claim for ocular biometry. METHODS: We measured the mean and median number of days between biometry and cataract surgery, calculated the proportion of patients waiting ≥ 30 days or ≥ 90 days for surgery, and determined the odds of sustaining a fall within 90 days of biometry among patients of high-testing physicians (testing performed in ≥ 75% of their patients) compared with patients of low-testing physicians. We also estimated the number of days of delay attributable to high-testing physicians. MAIN OUTCOME MEASURES: Incidence of falls occurring between biometry and surgery, odds of falling within 90 days of biometry, and estimated delay associated with physician testing behavior. RESULTS: Of 248 345 beneficiaries, 16.4% were patients of high-testing physicians. More patients of high-testing physicians waited ≥ 30 days and ≥ 90 days to undergo surgery (31.4% and 8.2% vs. 25.0% and 5.5%, respectively; P < 0.0001 for both). Falls before surgery in patients of high-testing physicians increased by 43% within the 90 days after ocular biometry (1.0% vs. 0.7%; P < 0.0001). The adjusted odds ratio of falling within 90 days of biometry in patients of high-testing physicians versus low-testing physicians was 1.10 (95% confidence interval [CI], 1.03-1.19; P = 0.008). After adjusting for surgical wait time, the odds ratio decreased to 1.07 (95% CI, 1.00-1.15; P = 0.06). The delay associated with having a high-testing physician was approximately 8 days (estimate, 7.97 days; 95% CI, 6.40-9.55 days; P < 0.0001). Other factors associated with delayed surgery included patient race (non-White), Northeast region, ophthalmologist ≤ 40 years of age, and low surgical volume. CONCLUSIONS: Overuse of routine preoperative medical testing by high-testing physicians is associated with delayed surgery and increased falls in cataract patients awaiting surgery.

A Novel Metric for Developing Easy-to-Use and Accurate Clinical Prediction Models: The Time-cost Information Criterion.

BACKGROUND: Guidelines recommend that clinicians use clinical prediction models to estimate future risk to guide decisions. For example, predicted fracture risk is a major factor in the decision to initiate bisphosphonate medications. However, current methods for developing prediction models often lead to models that are accurate but difficult to use in clinical settings. OBJECTIVE: The objective of this study was to develop and test whether a new metric that explicitly balances model accuracy with clinical usability leads to accurate, easier-to-use prediction models. METHODS: We propose a new metric called the Time-cost Information Criterion (TCIC) that will penalize potential predictor variables that take a long time to obtain in clinical settings. To demonstrate how the TCIC can be used to develop models that are easier-to-use in clinical settings, we use data from the 2000 wave of the Health and Retirement Study (n=6311) to develop and compare time to mortality prediction models using a traditional metric (Bayesian Information Criterion or BIC) and the TCIC. RESULTS: We found that the TCIC models utilized predictors that could be obtained more quickly than BIC models while achieving similar discrimination. For example, the TCIC identified a 7-predictor model with a total time-cost of 44 seconds, while the BIC identified a 7-predictor model with a time-cost of 119 seconds. The Harrell C-statistic of the TCIC and BIC 7-predictor models did not differ (0.7065 vs. 0.7088, P=0.11). CONCLUSION: Accounting for the time-costs of potential predictor variables through the use of the TCIC led to the development of an easier-to-use mortality prediction model with similar discrimination.

A fuller picture of COVID-19 prognosis: the added value of vulnerability measures to predict mortality in hospitalised older adults.

BACKGROUND: Although coronavirus disease 2019 (COVID-19) disproportionally affects older adults, the use of conventional triage tools in acute care settings ignores the key aspects of vulnerability. OBJECTIVE: This study aimed to determine the usefulness of adding a rapid vulnerability screening to an illness acuity tool to predict mortality in hospitalised COVID-19 patients. DESIGN: Cohort study. SETTING: Large university hospital dedicated to providing COVID-19 care. PARTICIPANTS: Participants included are 1,428 consecutive inpatients aged ≥50 years. METHODS: Vulnerability was assessed using the modified version of PRO-AGE score (0-7; higher = worse), a validated and easy-to-administer tool that rates physical impairment, recent hospitalisation, acute mental change, weight loss and fatigue. The baseline covariates included age, sex, Charlson comorbidity score and the National Early Warning Score (NEWS), a well-known illness acuity tool. Our outcome was time-to-death within 60 days of admission. RESULTS: The patients had a median age of 66 years, and 58% were male. The incidence of 60-day mortality ranged from 22% to 69% across the quartiles of modified PRO-AGE. In adjusted analysis, compared with modified PRO-AGE scores 0-1 ('lowest quartile'), the hazard ratios (95% confidence interval) for 60-day mortality for modified PRO-AGE scores 2-3, 4 and 5-7 were 1.4 (1.1-1.9), 2.0 (1.5-2.7) and 2.8 (2.1-3.8), respectively. The modified PRO-AGE predicted different mortality risk levels within each stratum of NEWS and improved the discrimination of mortality prediction models. CONCLUSIONS: Adding vulnerability to illness acuity improved accuracy of predicting mortality in hospitalised COVID-19 patients. Combining tools such as PRO-AGE and NEWS may help stratify the risk of mortality from COVID-19.

Identifying Potential Intervention Points for Acute Hypoglycemic Events in Patients With Type 2 Diabetes Using Retrospective Clinical Data.

This retrospective study examined changes in medication orders as a risk factor for future acute hypoglycemic events. The investigators identified factors associated with acute hypoglycemic events resulting in emergency department visits or inpatient admissions. Non-Hispanic Black race, chronic kidney disease, insulin at baseline, and nonprivate insurance were associated with higher risk of an acute hypoglycemic event, whereas age, sex, and A1C were not. After adjustment for other risk factors, changes in insulin orders after A1C measurement were associated with a 1.5 times higher risk of an acute hypoglycemia (adjusted hazard ratio 1.48, 95% CI 1.08-2.03). These results further understanding of risk factors and clinical processes relevant to predicting and preventing acute hypoglycemia.

Patients' Use of Social Media for Diabetes Self-Care: Systematic Review.

BACKGROUND: Patient engagement with diabetes self-care is critical to reducing morbidity and mortality. Social media is one form of digital health that is available for diabetes self-care, although its use for peer-to-peer communication has not been systematically described, and its potential to support patient self-care is unclear. OBJECTIVE: The primary aim of this systematic review was to describe the use of social media among patients (peer-to-peer) to manage diabetes and cardiovascular disease (CVD). The secondary aim was to assess patients' clinical outcomes, behavioral outcomes, quality of life, and self-efficacy resulting from peer-to-peer social media use. METHODS: We conducted a literature search in the following databases: PubMed, EMBASE, Web of Science, CINAHL, and PsycINFO (January 2008 through April 2019). The inclusion criteria were quantitative studies that included peer-to-peer use of social media for self-care of diabetes mellitus (with all subtypes) and CVD, including stroke. RESULTS: After an initial yield of 3066 citations, we selected 91 articles for a full-text review and identified 7 papers that met our inclusion criteria. Of these, 4 studies focused on type 1 diabetes, 1 study included both type 1 and 2 diabetes, and 2 studies included multiple chronic conditions (eg, CVD, diabetes, depression, etc). Our search did not yield any individual studies on CVD alone. Among the selected papers, 2 studies used commercial platforms (Facebook and I Seek You), 3 studies used discussion forums developed specifically for each study, and 2 surveyed patients through different platforms or blogs. There was significant heterogeneity in the study designs, methodologies, and outcomes applied, but all studies showed favorable results on either primary or secondary outcomes. The quality of studies was highly variable. CONCLUSIONS: The future landscape of social media use for patient self-care is promising. However, current use is nascent. Our extensive search yielded only 7 studies, all of which included diabetes, indicating the most interest and demand for peer-to-peer interaction on diabetes self-care. Future research is needed to establish efficacy and safety in recommending social media use among peers for diabetes self-care and other conditions.

Are We Choosing Wisely? Older Adults' Cancer Screening Intentions and Recalled Discussions with Physicians About Stopping.

BACKGROUND: National guidelines recommend against cancer screening for older individuals with less than a 10-year life expectancy, but it is unknown if this population desires ongoing screening. OBJECTIVE: To determine (1) if older individuals with < 10-year life expectancy have future intentions for cancer screening, (2) if they recall a doctor previously suggesting that screening is no longer needed, and (3) individual characteristics associated with intentions to seek screening. DESIGN: National Social life Health and Aging Project (2015-2016), a nationally representative, cross-sectional survey. PARTICIPANTS: Community-dwelling adults 55-97 years old (n = 3816). MAIN MEASURES: Self-reported: (1) mammography and PSA testing within the last 2 years, (2) future intentions to be screened, and (3) discussion with a doctor that screening is no longer needed. Ten-year life expectancy was estimated using the Lee prognostic index. Multivariate logistic regression analysis examined intentions to pursue future screening, adjusting for sociodemographic and health covariates. KEY RESULTS: Among women 75-84 with < 10-year life expectancy, 59% intend on future mammography and 81% recall no conversation with a doctor that mammography may no longer be necessary. Among men 75-84 with < 10-year life expectancy, 54% intend on future PSA screening and 77% recall no discussions that PSA screening may be unnecessary. In adjusted analyses, those reporting recent cancer screening or no recollection that screening may not be necessary were more likely to want future mammography or PSA screening (p < 0.001). CONCLUSION: Over 75% of older individuals with limited life expectancy intend to continue cancer screening, and less than 25% recall discussing with physicians the need for these tests. In addition to public health and education efforts, these results suggest that older adults' recollection of being told by physicians that screening is not necessary may be a modifiable risk factor for reducing overscreening in older adults with limited life expectancy.

The Quality of Family Relationships, Diabetes Self-Care, and Health Outcomes in Older Adults.

The purpose of the study was to investigate the relationship between family support, diabetes self-care, and health outcomes in older, community-dwelling adults. Using the theoretical framework of the Self-Care of Chronic Illness Theory and a cross-sectional design, 60 participants completed questionnaires related to diabetes self-care activities of the individual, supportive and nonsupportive diabetes behaviors of the family, and the quality of family relations. Participants indicated that diabetes self-care behaviors were performed frequently, with exercise reported as the least-performed behavior. Multiple regression analyses revealed that the quality of family relations as measured by the Family Relationship Index contributed significantly (26.0%) to the variability in A1C levels (R 2 = 0.260, F(1, 40) = 14.037, P = 0.001). Neither family supportive behavior nor the quality of family relations contributed to diabetes self-care. It is recommended that health care providers include family members to assess diabetes family support and family relationships in the care of older adults with diabetes.

Functional Impairment and Decline in Middle Age: A Cohort Study.

BACKGROUND: Difficulties with daily functioning are common in middle-aged adults. However, little is known about the epidemiology or clinical course of these problems, including the extent to which they share common features with functional impairment in older adults. OBJECTIVE: To determine the epidemiology and clinical course of functional impairment and decline in middle age. DESIGN: Cohort study. SETTING: The Health and Retirement Study. PARTICIPANTS: 6874 community-dwelling adults aged 50 to 56 years who did not have functional impairment at enrollment. MEASUREMENTS: Impairment in activities of daily living (ADLs), defined as self-reported difficulty performing 1 or more ADLs, assessed every 2 years for a maximum follow-up of 20 years, and impairment in instrumental ADLs (IADLs), defined similarly. Data were analyzed by using multistate models that estimate probabilities of different outcomes. RESULTS: Impairment in ADLs developed in 22% of participants aged 50 to 64 years, in whom further functional transitions were common. Two years after the initial impairment, 4% (95% CI, 3% to 5%) of participants had died, 9% (CI, 8% to 11%) had further ADL decline, 50% (CI, 48% to 52%) had persistent impairment, and 37% (CI, 35% to 39%) had recovered independence. In the 10 years after the initial impairment, 16% (CI, 14% to 18%) had 1 or more episodes of functional decline and 28% (CI, 26% to 30%) recovered from their initial impairment and remained independent throughout this period. The pattern of findings was similar for IADLs. LIMITATION: Functional status was self-reported. CONCLUSION: Functional impairment and decline are common in middle age, as are transitions from impairment to independence and back again. Because functional decline in older adults has similar features, current interventions used for prevention in older adults may hold promise for those in middle age. PRIMARY FUNDING SOURCE: National Institute on Aging and National Center for Advancing Translational Sciences through the University of California, San Francisco, Clinical and Translational Sciences Institute.

Frequency of Screening for Colorectal Cancer by Predicted Life Expectancy Among Adults 76-85 Years.

This study assesses whether colorectal cancer screening varied by predicted life expectancy in a national sample of Veterans Affairs patients aged 76 to 85 years.

The development and validation of an index to predict 10-year mortality risk in a longitudinal cohort of older English adults.

Background: we aimed to develop and validate a population-representative 10-year mortality risk index for older adults in England. Methods: data were from 10,798 men and women aged 50 years and older in the population-based English Longitudinal Study of Ageing in 2002/03, randomly split into development (n = 5,377) and validation cohorts (n = 5,421). Participants were asked about their sociodemographics, health behaviours, comorbidities, and functional status in the home-based interviews. Variables that were independently associated with all-cause mortality through March 2013 in the development cohort were weighted relative to one another to develop risk point scores for the index that was calibrated in the validation cohort. Results: the validated 10-year mortality risk index assigns points for: increasing age (50-59 years: 0 points; 60-64: 1 point; 65-69: 3 points; 70-74: 5 points; 75-79: 7 points; 80-84: 9 points; ≥85: 12 points), male (2 points), no vigorous physical activity (1 point), smoking (2 points), having a diagnosis of cancer (1 point), chronic lung disease (2 points) or heart failure (4 points), and having difficulty preparing a hot meal (2 points), pushing or pulling large objects (1 point) or walking 100 yards (1 point). In the full study cohort, 10-year mortality rates increased from 1.7% (11/664) in those with 0 points to 95% (189/199) among those with ≥16 points. Conclusion: this highly predictive 10-item mortality risk index is valid in the English population aged 50 years and older. It uses simple information that is often available in research studies and patient reports, and does not require biomarker data to predict mortality.

"Everyone else gets ice cream here more often than I do--It burns me up"--Perspectives on Diabetes Care from Nursing Home Residents and their Doctors.

BACKGROUND: To explore the perspectives of nursing home (NH) residents with diabetes and their doctors regarding the burdens of living with diabetes and diabetes treatments. METHODS: Qualitative study of nursing home residents aged 65 and older with diabetes (n = 14) and nursing home physicians (n = 9) at a Department of Veterans Affairs nursing home (known as the Community Living Center). A semi-structured interview was used to elicit nursing home residents' and physicians' perspectives on the burden of diabetes and diabetes treatments. Transcripts were analyzed using constant comparative methods. RESULTS: The mean age of the nursing home residents was 74; Most (93%) were male and 50% self-identified themselves as white. The mean age of nursing home physicians was 39 and 55% were geriatricians. Dietary restrictions, loss of independence and fingersticks/insulin were noted to be the most burdensome aspects of diabetes. Nursing home residents with a more positive outlook were generally more engaged in their care, while nursing home residents with a more pessimistic outlook were less engaged, allowing their physicians to assume complete control of their care. While physicians noted the potential negative impact of dietary restrictions, nursing home residents' comments suggest that physicians underestimate the burden of dietary restrictions. CONCLUSIONS: Veterans Affairs nursing home residents were substantially burdened by their diabetes treatments, especially dietary restrictions and fingerstick monitoring. Since there is little evidence that dietary restrictions improve outcomes, fewer dietary restrictions may be appropriate and lead to lower treatment burdens for nursing home residents with diabetes.

Polypharmacy in the Aging Patient: A Review of Glycemic Control in Older Adults With Type 2 Diabetes.

IMPORTANCE: There is substantial uncertainty about optimal glycemic control in older adults with type 2 diabetes mellitus. OBSERVATIONS: Four large randomized clinical trials (RCTs), ranging in size from 1791 to 11,440 patients, provide the majority of the evidence used to guide diabetes therapy. Most RCTs of intensive vs standard glycemic control excluded adults older than 80 years, used surrogate end points to evaluate microvascular outcomes and provided limited data on which subgroups are most likely to benefit or be harmed by specific therapies. Available data from randomized clinical trials suggest that intensive glycemic control does not reduce major macrovascular events in older adults for at least 10 years. Furthermore, intensive glycemic control does not lead to improved patient-centered microvascular outcomes for at least 8 years. Data from randomized clinical trials consistently suggest that intensive glycemic control immediately increases the risk of severe hypoglycemia 1.5- to 3-fold. Based on these data and observational studies, for the majority of adults older than 65 years, the harms associated with a hemoglobin A1c (HbA1c) target lower than 7.5% or higher than 9% are likely to outweigh the benefits. However, the optimal target depends on patient factors, medications used to reach the target, life expectancy, and patient preferences about treatment. If only medications with low treatment burden and hypoglycemia risk (such as metformin) are required, a lower HbA1c target may be appropriate. If patients strongly prefer to avoid injections or frequent fingerstick monitoring, a higher HbA1c target that obviates the need for insulin may be appropriate. CONCLUSIONS AND RELEVANCE: High-quality evidence about glycemic treatment in older adults is lacking. Optimal decisions need to be made collaboratively with patients, incorporating the likelihood of benefits and harms and patient preferences about treatment and treatment burden. For the majority of older adults, an HbA1c target between 7.5% and 9% will maximize benefits and minimize harms.

Journalists, district attorneys and researchers: why IRBs should get in the middle.

BACKGROUND: Federal regulations in the United States have shaped Institutional Review Boards (IRBs) to focus on protecting individual human subjects. Health services research studies focusing on healthcare institutions such as hospitals or clinics do not have individual human subjects. Since U.S. federal regulations are silent on what type of review, if any, these studies require, different IRBs may approach similar studies differently, resulting in undesirable variation in the review of studies focusing on healthcare institutions. Further, although these studies do not focus on individual human subjects, they may pose risks to participating institutions, as well as individuals who work at those institutions, if identifying information becomes public. DISCUSSION: Using two recent health services research studies conducted in the U.S. as examples, we discuss variations in the level of IRB oversight for studies focusing on institutions rather than individual human subjects. We highlight how lack of IRB guidance poses challenges for researchers who wish to both protect their subjects and work appropriately with the public, journalists or the legal system in the U.S. Competing interests include the public's interest in transparency, the researcher's interest in their science, and the research participants' interests in confidentiality. Potential solutions that may help guide health services researchers to balance these competing interests include: 1) creating consensus guidelines and standard practices that address confidentiality risk to healthcare institutions and their employees; and 2) expanding the IRB role to conduct a streamlined review of health services research studies focusing on healthcare institutions to balance the competing interest of stakeholders on a case-by-case basis. SUMMARY: For health services research studies focusing on healthcare institutions, we outline the competing interests of researchers, healthcare institutions and the public. We propose solutions to decrease undesirable variations in the review of these studies.

Emergency department visits and hospitalizations by tube-fed nursing home residents with varying degrees of cognitive impairment: a national study.

BACKGROUND: Numerous studies indicate that the use of feeding tubes (FT) in persons with advanced cognitive impairment (CI) does not improve clinical outcomes or survival, and results in higher rates of hospitalization and emergency department (ED) visits. It is not clear, however, whether such risk varies by resident level of CI and whether these ED visits and hospitalizations are potentially preventable. The objective of this study was to determine the rates of ED visits, hospitalizations and potentially preventable ambulatory care sensitive (ACS) ED visits and ACS hospitalizations for long-stay NH residents with FTs at differing levels of CI. METHODS: We linked Centers for Medicare and Medicaid Services inpatient & outpatient administrative claims and beneficiary eligibility data with Minimum Data Set (MDS) resident assessment data for nursing home residents with feeding tubes in a 5% random sample of Medicare beneficiaries residing in US nursing facilities in 2006 (n = 3479). Severity of CI was measured using the Cognitive Performance Scale (CPS) and categorized into 4 groups: None/Mild (CPS = 0-1, MMSE = 22-25), Moderate (CPS = 2-3, MMSE = 15-19), Severe (CPS = 4-5, MMSE = 5-7) and Very Severe (CPS = 6, MMSE = 0-4). ED visits, hospitalizations, ACS ED visits and ACS hospitalizations were ascertained from inpatient and outpatient administrative claims. We estimated the risk ratio of each outcome by CI level using over-dispersed Poisson models accounting for potential confounding factors. RESULTS: Twenty-nine percent of our cohort was considered "comatose" and "without any discernible consciousness", suggesting that over 20,000 NH residents in the US with feeding tubes are non-interactive. Approximately 25% of NH residents with FTs required an ED visit or hospitalization, with 44% of hospitalizations and 24% of ED visits being potentially preventable or for an ACS condition. Severity of CI had a significant effect on rates of ACS ED visits, but little effect on ACS hospitalizations. CONCLUSIONS: ED visits and hospitalizations are common in cognitively impaired tube-fed nursing home residents and a substantial proportion of ED visits and hospitalizations are potentially preventable due to ACS conditions.