Development and Validation of the American Heart Association's PREVENT Equations

BACKGROUND

Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD.

METHODS

The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets.

RESULTS

Among 6 612 004 adults included, mean±SD age was 53±12 years, and 56% were women. Over a mean±SD follow-up of 4.8±3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81-1.16) and 0.94 (0.81-1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD- and heart failure-specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004-0.005] and 0.005 [0.004-0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84-1.20] versus 1.39 [1.14-1.65]; P=0.01).

CONCLUSIONS

PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes: An Individual-Participant Data Meta-Analysis

Importance

Chronic kidney disease (low estimated glomerular filtration rate [eGFR] or albuminuria) affects approximately 14% of adults in the US.

Objective

To evaluate associations of lower eGFR based on creatinine alone, lower eGFR based on creatinine combined with cystatin C, and more severe albuminuria with adverse kidney outcomes, cardiovascular outcomes, and other health outcomes.

Design, Setting, and Participants

Individual-participant data meta-analysis of 27 503 140 individuals from 114 global cohorts (eGFR based on creatinine alone) and 720 736 individuals from 20 cohorts (eGFR based on creatinine and cystatin C) and 9 067 753 individuals from 114 cohorts (albuminuria) from 1980 to 2021.

Exposures

The Chronic Kidney Disease Epidemiology Collaboration 2021 equations for eGFR based on creatinine alone and eGFR based on creatinine and cystatin C; and albuminuria estimated as urine albumin to creatinine ratio (UACR).

Main Outcomes and Measures

The risk of kidney failure requiring replacement therapy, all-cause mortality, cardiovascular mortality, acute kidney injury, any hospitalization, coronary heart disease, stroke, heart failure, atrial fibrillation, and peripheral artery disease. The analyses were performed within each cohort and summarized with random-effects meta-analyses.

Results

Within the population using eGFR based on creatinine alone (mean age, 54 years [SD, 17 years]; 51% were women; mean follow-up time, 4.8 years [SD, 3.3 years]), the mean eGFR was 90 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 11 mg/g (IQR, 8-16 mg/g). Within the population using eGFR based on creatinine and cystatin C (mean age, 59 years [SD, 12 years]; 53% were women; mean follow-up time, 10.8 years [SD, 4.1 years]), the mean eGFR was 88 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 9 mg/g (IQR, 6-18 mg/g). Lower eGFR (whether based on creatinine alone or based on creatinine and cystatin C) and higher UACR were each significantly associated with higher risk for each of the 10 adverse outcomes, including those in the mildest categories of chronic kidney disease. For example, among people with a UACR less than 10 mg/g, an eGFR of 45 to 59 mL/min/1.73 m2 based on creatinine alone was associated with significantly higher hospitalization rates compared with an eGFR of 90 to 104 mL/min/1.73 m2 (adjusted hazard ratio, 1.3 [95% CI, 1.2-1.3]; 161 vs 79 events per 1000 person-years; excess absolute risk, 22 events per 1000 person-years [95% CI, 19-25 events per 1000 person-years]).

Conclusions and Relevance

In this retrospective analysis of 114 cohorts, lower eGFR based on creatinine alone, lower eGFR based on creatinine and cystatin C, and more severe UACR were each associated with increased rates of 10 adverse outcomes, including adverse kidney outcomes, cardiovascular diseases, and hospitalizations.

Prevalence of use and characteristics of users of home-based medical care in Medicare Advantage

BACKGROUND/OBJECTIVES

Home-based medical care (HBMC) is longitudinal medical care provided by physicians, advanced practice providers, and, often, inter-professional care teams to patients in their homes. Our objective is to determine the prevalence of HBMC among older adults (≥65) insured by a Medicare Advantage (MA) plan and compare characteristics of those who receive HBMC to those who do not.

METHODS

Study used de-identified medical claims and enrollment records for MA beneficiaries during calendar years 2017 and 2018 linked with socioeconomic status data in the OptumLabs Data Warehouse. We defined a cohort of MA beneficiaries age ≥65 receiving HBMC for at least 2 months during 2017-2018, described the cohort using demographic, utilization, and comorbidity data and compared it to a 5% random sample of a population of MA beneficiaries age ≥65 not receiving HBMC (No HBMC).

RESULTS

Overall, 1.45% of the study cohort age ≥65 received HBMC. Compared to No HBMC (n = 132,147), those receiving HBMC (n = 38,800) were more likely to be: older (46.6% vs. 11.9% age 85+); female (70.8% vs. 58.5%); Black (12.3% vs. 11.3%); urban (90.3% vs. 81.3%); experience hospitalization (38.0% vs. 13.3%), emergency department visit (58.3% vs. 26.9%), ambulance trip (44.1% vs. 9.6%), skilled nursing facility (37.6% vs. 6.4%), or hospice care admission (21.1% vs. 3.5%). They also were more likely to experience a wide range of chronic conditions including dementia (58.1% vs. 5.2%), morbidity burden (Charlson score 3.4 vs. 1.8), and serious illness (77.1% vs. 29.5%). All comparisons p < 0.0001.

CONCLUSIONS

MA beneficiaries who received HBMC are older, experience greater chronic and serious illness burden, and higher levels of facility-based care than those who did not receive HBMC. MA plans need strategies to identify patients that would benefit from HBMC and develop approaches to deliver such care to this impactful, often invisible population.

Health systems and telemedicine adoption for diabetes and hypertension care

OBJECTIVES

The COVID-19 pandemic accelerated telemedicine use nationally, but differences across health systems are understudied. We examine telemedicine use for adults with diabetes and/or hypertension across 10 health systems and analyze practice and patient characteristics associated with greater use.

STUDY DESIGN

Encounter-level data from the AMGA Optum Data Warehouse for March 13, 2020, to December 31, 2020, were analyzed, which included 3,016,761 clinical encounters from 764,521 adults with diabetes and/or hypertension attributed to 1 of 1207 practice sites with at least 50 system-attributed patients.

METHODS

Linear spline regression estimated whether practice size and ownership were associated with telemedicine during the adoption (weeks 0-4), de-adoption (weeks 5-12), and maintenance (weeks 13-42) periods, controlling for patient socioeconomic and clinical characteristics.

RESULTS

Telemedicine use peaked at 11% to 42% of weekly encounters after 4 weeks. In adjusted analyses, small practices had lower telemedicine use for adults with diabetes during the maintenance period compared with larger practices. Practice ownership was not associated with telemedicine use. Practices with higher proportions of Black patients continued to expand telemedicine use during the de-adoption and maintenance periods.

CONCLUSIONS

Practice ownership was not associated with telemedicine use during first months of the pandemic. Small practices de-adopted telemedicine to a greater degree than medium and large practices. Technical support for small practices, irrespective of their ownership, could enable telemedicine use for adults with diabetes and/or hypertension.

An Electronic Health Record-Compatible Model to Predict Personalized Treatment Effects From the Diabetes Prevention Program: A Cross-Evidence Synthesis Approach Using Clinical Trial and Real-World Data

OBJECTIVE

To develop an electronic health record (EHR)-based risk tool that provides point-of-care estimates of diabetes risk to support targeting interventions to patients most likely to benefit.

PATIENTS AND METHODS

A risk prediction model was developed and validated in a large observational database of patients with an index visit date between January 1, 2012, and December 31, 2016, with treatment effect estimates from risk-based reanalysis of clinical trial data. The risk model development cohort included 1.1 million patients with prediabetes from the OptumLabs Data Warehouse (OLDW); the validation cohort included a distinct sample of 1.1 million patients in OLDW. The randomly assigned clinical trial cohort included 3081 people from the Diabetes Prevention Program (DPP) study.

RESULTS

Eleven variables reliably obtainable from the EHR were used to predict diabetes risk. This model validated well in the OLDW (C statistic = 0.76; observed 3-year diabetes rate was 1.8% (95% confidence interval [CI], 1.7 to 1.9) in the lowest-risk quarter and 19.6% (19.4 to 19.8) in the highest-risk quarter). In the DPP, the hazard ratio (HR) for lifestyle modification was constant across all levels of risk (HR, 0.43; 95% CI, 0.35 to 0.53), whereas the HR for metformin was highly risk dependent (HR, 1.1; 95% CI, 0.61 to 2.0 in the lowest-risk quarter vs HR, 0.45; 95% CI, 0.35 to 0.59 in the highest-risk quarter). Fifty-three percent of the benefits of population-wide dissemination of the DPP lifestyle modification and 73% of the benefits of population-wide metformin therapy can be obtained by targeting the highest-risk quarter of patients.

CONCLUSION

The Tufts-Predictive Analytics and Comparative Effectiveness DPP Risk model is an EHR-compatible tool that might support targeted diabetes prevention to more efficiently realize the benefits of the DPP interventions.

Variation in Initiation, Engagement, and Retention on Medications for Opioid Use Disorder Based on Health Insurance Plan Design

BACKGROUND

The association between cost-sharing and receipt of medication for opioid use disorder (MOUD) is unknown.

METHODS

We constructed a cohort of 10,513 commercially insured individuals with a new diagnosis of opioid use disorder and information on insurance cost-sharing in a large national deidentified claims database. We examined 4 cost-sharing measures: (1) pharmacy deductible; (2) medical service deductible; (3) pharmacy medication copay; and (4) medical office copay. We measured MOUD (naltrexone, buprenorphine, or methadone) initiation (within 14 d of diagnosis), engagement (second receipt within 34 d of first), and 6-month retention (continuous receipt without 14-d gap). We used multivariable logistic regression to assess the association between cost-sharing and MOUD initiation, engagement, and retention. We calculated total out-of-pocket costs in the 30 days following MOUD initiation for each type of MOUD.

RESULTS

Of 10,513 individuals with incident opioid use disorder, 1202 (11%) initiated MOUD, 742 (7%) engaged, and 253 (2%) were retained in MOUD at 6 months. A high ($1000+) medical deductible was associated with a lower odds of initiation compared with no deductible (odds ratio: 0.85, 95% confidence interval: 0.74-0.98). We found no significant associations between other cost-sharing measures for initiation, engagement, or retention. Median initial 30-day out-of-pocket costs ranged from $100 for methadone to $710 for extended-release naltrexone.

CONCLUSIONS

Among insurance plan cost-sharing measures, only medical services deductible showed an association with decreased MOUD initiation. Policy and benefit design should consider ways to reduce cost barriers to initiation and retention in MOUD.

Potential barriers to filling buprenorphine and naltrexone prescriptions among a retrospective cohort of individuals with opioid use disorder

INTRODUCTION

Medications for opioid use disorder (MOUD) are highly effective, but barriers along the cascade of care for opioid use disorder (OUD) from diagnosis to treatment limit their reach. For individuals desiring MOUD, the final step in the cascade is filling a written prescription, and fill rates have not been described.

METHODS

We used data from a large de-identified database linking individuals' electronic medical records (EMR) and administrative claims data and employed a previously developed algorithm to identify individuals with a new diagnosis of OUD. We included individuals with a prescription for buprenorphine or naltrexone recorded in the EMR. The outcome was a prescription fill within 30 days as reported in claims data. We compared demographic and clinical characteristics between those who did and did not fill the prescription and used a Kaplan-Meier curve to assess whether fill rates differed based on patient copay.

RESULTS

We identified 264 individuals with a new diagnosis of OUD who had a prescription written for buprenorphine or oral naltrexone. Of these, 70% (184) filled the prescription within 30 days, and more than half (57%) filled the prescription on the day it was written. Individuals with prescription copay at or below the mean had a 75% fill rate at 30 days compared with 63% for those with copay above the mean (p < 0.05) and this difference was consistent across fill times (log rank p-value <0.05).

CONCLUSIONS

It is alarming that nearly 1 in 3 MOUD prescriptions go unfilled. More research is needed to understand and reduce barriers to this final step of the OUD cascade of care.

Chronic Kidney Disease Testing Among Primary Care Patients With Type 2 Diabetes Across 24 U.S. Health Care Organizations

OBJECTIVE

Clinical guidelines for people with diabetes recommend chronic kidney disease (CKD) testing at least annually using estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (uACR). We aimed to understand CKD testing among people with type 2 diabetes in the U.S.

RESEARCH DESIGN AND METHODS

Electronic health record data were analyzed from 513,165 adults with type 2 diabetes receiving primary care from 24 health care organizations and 1,164 clinical practice sites. We assessed the percentage of patients with both one or more eGFRs and one or more uACRs and each test individually in the 1, 2, and 3 years ending September 2019 by health care organization and clinical practice site. Elevated albuminuria was defined as uACR ≥30 mg/g.

RESULTS

The 1-year median testing rate across organizations was 51.6% for both uACR and eGFR, 89.5% for eGFR, and 52.9% for uACR. uACR testing varied (10th-90th percentile) from 44.7 to 63.3% across organizations and from 13.3 to 75.4% across sites. Over 3 years, the median testing rate for uACR across organizations was 73.7%. Overall, the prevalence of detected elevated albuminuria was 15%. The average prevalence of detected elevated albuminuria increased linearly with uACR testing rates at sites, with estimated prevalence of 6%, 15%, and 30% at uACR testing rates of 20%, 50%, and 100%, respectively.

CONCLUSIONS

While eGFR testing rates are uniformly high among people with type 2 diabetes, testing rates for uACR are suboptimal and highly variable across and within the organizations examined. Guideline-recommended uACR testing should increase detection of CKD.

Measuring What Matters: Beyond Quality Performance Measures in Caring for Adults with Obesity

Obesity is a chronic disease that poses serious health and societal burdens. Although guidelines exist for obesity management in primary care, evaluating the success of obesity treatment programs is hampered by lack of established, robust quality measures. This study aimed to develop, and test for feasibility, measures for operational tracking, quality performance, and patient-centered care in the context of a national collaborative to develop a model for obesity management in the US primary care setting. The authors developed and evaluated 7 measures used to track the care of patients with overweight or obesity (n = 226,727 at baseline) receiving care within 10 health care organizations (HCOs). Measure categories included: (1) operational tracking (obesity/overweight prevalence and prevalence of obesity-related complications); (2) quality performance (obesity diagnosis, change in weight over time, anti-obesity medication prescriptions, and assessment of obesity-related complications); and (3) patient-centered care (patient-reported outcomes). Measures were tested for feasibility, variability across HCOs, ability to detect differences over time, and value to the HCOs. All measures were feasible to collect, provided value to the participating HCOs, and demonstrated variation and ability to detect differences over time (eg, rates of documented diagnosis of obesity classes 1, 2, and 3 increased from 29%, 46%, and 66%, respectively, at baseline to 35%, 53%, and 71% at study end). This study confirmed the feasibility and perceived value of 7 operational, performance, and patient-centered measures collected in primary care practices in 10 HCOs over an 18-month period.

Female Nutritional Status across the Life-Span in Sub-Saharan Africa. 2. Causes and Consequences

This article reviews existing data concerning the causes and consequences of female malnutrition in sub-Saharan Africa. As in most parts of the world, the primary cause of female malnutrition is household food insecurity compounded by low household and individual incomes. Gender-specific factors that further undermine women's nutritional status are the severe physiological burden of frequent child-bearing and the continuous long hours of energy-intensive work. Negative consequences of malnutrition among females include high rates of mortality and morbidity, impaired learning, low birthweights, and reduced energy for discretionary activities. We question the conclusion of other studies that African women have developed special “adaptive mechanisms” to compensate for nutritional deprivation, and recommend that further research investigate the hidden individual and societal costs of malnutrition among women.

Female Nutritional Status across the Life-Span in Sub-Saharan Africa. 1. Prevalence Patterns

This article reviews and synthesizes existing nutritional studies that provide gender-disaggregated data from sub-Saharan Africa. The analytic focus is on female nutritional status across the life-span. However, it was found that available data are biased towards preschool children and women of reproductive age. As in other economically disadvantaged parts of the world, the two most prevalent nutritional deficiencies among females in sub-Saharan Africa are iron-deficiency anaemia and protein-energy malnutrition. In comparison with other regions of the world, sub-Saharan African females seem to be nutritionally better off than females in South Asia, but as malnourished as, or more malnourished than, females elsewhere. Indirect indicators of nutritional status, such as birthweight and maternal mortality, suggest that the nutritional situation of women in Western Africa is poorer than that of women in Eastern and Southern Africa. In comparison with males in sub-Saharan Africa, however, no consistent pattern of female nutritional disadvantage was found.

Using gender-based analyses to understand physical inactivity among women in Yellowstone County, Montana

Physical inactivity contributes to many health problems. Gender, the socially constructed roles and activities deemed appropriate for men and women, is an important factor in women's physical inactivity. To better understand how gender influences participation in leisure-time physical activity, a gender analysis was conducted using sex-disaggregated data from a county-wide health assessment phone survey and a qualitative analysis of focus group transcripts. From this gender analysis, several gender-based constraints emerged, including women's roles as caregivers, which left little time or energy for physical activity, women's leisure time activities and hobbies, which were less active than men's hobbies, and expectations for women's appearance that made them uncomfortable sweating in front of strangers. Gender-based opportunities included women's enjoyment of activity as a social connection, less rigid gender roles for younger women, and a sense of responsibility to set a good example for their families. The gender analysis was used to gain a deeper understanding of gender-based constraints and opportunities related to physical activity. This understanding is being used in the next step of our research to develop a gender-specific intervention to promote physical activity in women that addresses the underlying causes of physical inactivity through accommodation or transformation of those gender norms.

Using telehealth to provide diabetes care to patients in rural Montana: findings from the promoting realistic individual self-management program

OBJECTIVE

The objectives of this study were to demonstrate the feasibility of telehealth technology to provide a team approach to diabetes care for rural patients and determine its effect on patient outcomes when compared with face-to-face diabetes visits.

MATERIALS AND METHODS

An evaluation of a patient-centered interdisciplinary team approach to diabetes management compared telehealth with face-to-face visits on receipt of recommended preventive guidelines, vascular risk factor control, patient satisfaction, and diabetes self-management at baseline and 1, 2, and 3 years postintervention.

RESULTS

One-year postintervention the receipt of recommended dilated eye exams increased 31% and 43% among telehealth and face-to-face patients, respectively (p=0.28). Control of two or more risk factors increased 37% and 69% (p=0.21). Patient diabetes care satisfaction rates increased 191% and 131% among telehealth and face-to-face patients, respectively (p=0.51). A comparison of telehealth with face-to-face patients resulted in increased self-reported blood glucose monitoring as instructed (97% vs. 89%; p=0.63) and increased dietary adherence (244% vs. 159%; p=0.86), respectively. Receipt of a monofilament foot test showed a significantly greater improvement among face-to-face patients (17% vs. 35%; p=0.01) at 1 year postintervention, but this difference disappeared in years 2 and 3.

CONCLUSIONS

Telehealth proved to be an effective mode for the provision of diabetes care to rural patients. Few differences were detected in the delivery of a team approach to diabetes management via telehealth compared with face-to-face visits on receipt of preventive care services, vascular risk factor control, patient satisfaction, and patient self-management. A team approach using telehealth may be a viable strategy for addressing the unique challenges faced by patients living in rural communities.

An analysis of data management tools for diabetes self-management: can smart phone technology keep up?

In this issue of Journal of Diabetes Science and Technology, Rao and colleagues present a comparison of three iPhone diabetes data management applications: the Diamedic Diabetes Logbook, Blood Sugar Diabetes Control, and WaveSense Diabetes Manager. These applications provide patients the ability to enter blood glucose readings manually, view graphs and simple statistics, and email data to health care providers. While these applications show promise, they are limited in their current forms. All require manual data entry and none convert insulin-to-carbohydrate ratios to insulin dose. Future development of these types of technology should consider integration with blood glucose meters and expanded calculation capabilities, as well as monitoring of other risk factors, e.g., blood pressure and lipids, and tracking of preventive examinations, e.g., eye, foot, and renal.