Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

BACKGROUND: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems. OBJECTIVE: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score. DESIGN: Retrospective, cohort study. PARTICIPANTS: Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19. MAIN MEASURES: One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically. KEY RESULTS: Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum. CONCLUSIONS: These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.

Blood pressure outcomes at 18 months in primary care patients prescribed remote physiological monitoring for hypertension: a prospective cohort study.

This pragmatic matched cohort study using EHR data extended the follow up to 18 months for BP outcomes comparing individuals prescribed remote patient monitoring (n = 288) and temporally-matched controls (n = 1152) from six primary care practices. After 18 months, the RPM-prescribed cohort had greater BP control < 140/90 mm Hg (RPM cohort: 71.5%, control cohort: 51.9%, p < 0.001) and lower systolic BP (131.6 versus 136.0 mm Hg, p = 0.004) using office and home measurements. BP control at 18 months assessed by office measurements only was also higher in the RPM group (62.2% versus 51.9%, p = 0.004).

Changes in Age Distribution and Maternal Mortality in a Subset of the US, 2014-2021.

INTRODUCTION: This study aimed to determine the association between changes in age distribution and maternal mortality rates (MMR) in a subset of the United States between 2014 and 2021. METHODS: A serial cross-sectional analysis of birthing individuals aged 15-44 years from 2014 to 2021 was performed. States that had not adopted the pregnancy checkbox as of 2014 were excluded from the primary analysis. A significant inflection point in MMR was identified in 2019 with the Joinpoint Regression Program, so all analyses were stratified: 2014-2019 and 2019-2021. The Kitagawa decomposition was applied to quantify the contribution from (1) changes in age distribution and (2) changes in age-specific MMR (ASMR) to total MMR. Data analysis occurred between 2022 and 2023. RESULTS: From 2014 to 2021, the mean (standard deviation) age of birthing individuals changed from 28.3 (5.8) to 29.4 (5.7) years. The MMR (95% CI) increased significantly from 16.5 (15.8-18.5) to 18.9 (17.4-20.5) per 100,000 live births from 2014 to 2019 with acceleration in MMR to 31.8 (30.0-33.8) by 2021. The change in maternal age distribution contributed to 36% of the total change in the MMR from 2014 to 2019 and 4% from 2019 to 2021. Age-specific MMR components increased significantly for those aged 25-29 years and 30-34 years from 2014 to 2019. All 5-year age strata except the 15-19 year old group saw increases in age-specific MMR from 2019 to 2021. CONCLUSIONS: MMR increased significantly from 2014 to 2021 with rapid increase after 2019. However, older age of birthing individuals explained only a minority of the increased MMR in both periods. The greatest contribution to MMR arose from increases in age-specific MMR.

Reducing Care Overuse in Older Patients Using Professional Norms and Accountability : A Cluster Randomized Controlled Trial.

BACKGROUND: Effective strategies are needed to curtail overuse that may lead to harm. OBJECTIVE: To evaluate the effects of clinician decision support redirecting attention to harms and engaging social and reputational concerns on overuse in older primary care patients. DESIGN: 18-month, single-blind, pragmatic, cluster randomized trial, constrained randomization. (ClinicalTrials.gov: NCT04289753). SETTING: 60 primary care internal medicine, family medicine and geriatrics practices within a health system from 1 September 2020 to 28 February 2022. PARTICIPANTS: 371 primary care clinicians and their older adult patients from participating practices. INTERVENTION: Behavioral science-informed, point-of-care, clinical decision support tools plus brief case-based education addressing the 3 primary clinical outcomes (187 clinicians from 30 clinics) were compared with brief case-based education alone (187 clinicians from 30 clinics). Decision support was designed to increase salience of potential harms, convey social norms, and promote accountability. MEASUREMENTS: Prostate-specific antigen (PSA) testing in men aged 76 years and older without previous prostate cancer, urine testing for nonspecific reasons in women aged 65 years and older, and overtreatment of diabetes with hypoglycemic agents in patients aged 75 years and older and hemoglobin A1c (HbA1c) less than 7%. RESULTS: At randomization, mean clinic annual PSA testing, unspecified urine testing, and diabetes overtreatment rates were 24.9, 23.9, and 16.8 per 100 patients, respectively. After 18 months of intervention, the intervention group had lower adjusted difference-in-differences in annual rates of PSA testing (-8.7 [95% CI, -10.2 to -7.1]), unspecified urine testing (-5.5 [CI, -7.0 to -3.6]), and diabetes overtreatment (-1.4 [CI, -2.9 to -0.03]) compared with education only. Safety measures did not show increased emergency care related to urinary tract infections or hyperglycemia. An HbA1c greater than 9.0% was more common with the intervention among previously overtreated diabetes patients (adjusted difference-in-differences, 0.47 per 100 patients [95% CI, 0.04 to 1.20]). LIMITATION: A single health system limits generalizability; electronic health data limit ability to differentiate between overtesting and underdocumentation. CONCLUSION: Decision support designed to increase clinicians' attention to possible harms, social norms, and reputational concerns reduced unspecified testing compared with offering traditional case-based education alone. Small decreases in diabetes overtreatment may also result in higher rates of uncontrolled diabetes. PRIMARY FUNDING SOURCE: National Institute on Aging.

Social and psychosocial determinants of racial and ethnic differences in cardiovascular health: The MASALA and MESA studies.

Background: Social and psychosocial determinants are associated with cardiovascular health (CVH). Objectives: To quantify the contributions of social and psychosocial factors to racial/ethnic differences in CVH. Methods: In the Multi-Ethnic Study of Atherosclerosis and Mediators of Atherosclerosis in South Asians Living in America cohorts, Kitagawa-Blinder-Oaxaca decomposition quantified the contributions of social and psychosocial factors to differences in mean CVH score (range 0-14) in Black, Chinese, Hispanic, or South Asian compared with White participants. Results: Among 7,978 adults (mean age 61 [SD 10] years, 52 % female), there were 1,892 Black (mean CVH score for decomposition analysis 7.96 [SD 2.1]), 804 Chinese (CVH 9.69 [1.8]), 1,496 Hispanic (CVH 8.00 [2.1]), 1,164 South Asian (CVH 9.16 [2.0]), and 2,622 White (CVH 8.91 [2.1]) participants. The factors that were associated with the largest magnitude of explained differences in mean CVH score were income for Black participants (if mean income in Black participants were equal to White participants, Black participants' mean CVH score would be 0.14 [SE 0.05] points higher); place of birth for Chinese participants (if proportion of US-born and foreign-born individuals among Chinese adults were equivalent to White participants, Chinese participants' mean CVH score would be 0.22 [0.10] points lower); and education for Hispanic and South Asian participants (if educational attainment were equivalent to White participants, Hispanic and South Asian participants' mean CVH score would be 0.55 [0.11] points higher and 0.37 [0.11] points lower, respectively). Conclusions: In these multiethnic US cohorts, social and psychosocial factors were associated with racial/ethnic differences in CVH.

Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning.

It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Of the 15,565 participants in our dataset, 2170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI 0.782-0.844) vs 0.792 (CI 0.760-0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.

A Proposed Pediatric Clinical Cardiovascular Health Reference Standard.

INTRODUCTION: Clinical cardiovascular health is a construct that includes 4 health factors-systolic and diastolic blood pressure, fasting glucose, total cholesterol, and body mass index-which together provide an evidence-based, more holistic view of cardiovascular health risk in adults than each component separately. Currently, no pediatric version of this construct exists. This study sought to develop sex-specific charts of clinical cardiovascular health for age to describe current patterns of clinical cardiovascular health throughout childhood. METHODS: Data were used from children and adolescents aged 8-19 years in six pooled childhood cohorts (19,261 participants, collected between 1972 and 2010) to create reference standards for fasting glucose and total cholesterol. Using the models for glucose and cholesterol as well as previously published reference standards for body mass index and blood pressure, clinical cardiovascular health charts were developed. All models were estimated using sex-specific random-effects linear regression, and modeling was performed during 2020-2022. RESULTS: Models were created to generate charts with smoothed means, percentiles, and standard deviations of clinical cardiovascular health for each year of childhood. For example, a 10-year-old girl with a body mass index of 16 kg/m2 (30th percentile), blood pressure of 100/60 mm Hg (46th/50th), glucose of 80 mg/dL (31st), and total cholesterol of 160 mg/dL (46th) (lower implies better) would have a clinical cardiovascular health percentile of 62 (higher implies better). CONCLUSIONS: Clinical cardiovascular health charts based on pediatric data offer a standardized approach to express clinical cardiovascular health as an age- and sex-standardized percentile for clinicians to assess cardiovascular health in childhood to consider preventive approaches at early ages and proactively optimize lifetime trajectories of cardiovascular health.

Rural-urban disparities in pregestational and gestational diabetes in pregnancy: Serial, cross-sectional analysis of over 12 million pregnancies.

OBJECTIVE: To compare trends in pregestational (DM) and gestational diabetes (GDM) in pregnancy in rural and urban areas in the USA, because pregnant women living in rural areas face unique challenges that contribute to rural-urban disparities in adverse pregnancy outcomes. DESIGN: Serial, cross-sectional analysis. SETTING: US National Center for Health Statistics (NCHS) Natality Files from 2011 to 2019. POPULATION: A total of 12 401 888 singleton live births to nulliparous women aged 15-44 years. METHODS: We calculated the frequency (95% confidence interval [CI]) per 1000 live births, the mean annual percentage change (APC), and unadjusted and age-adjusted rate ratios (aRR) of DM and GDM in rural compared with urban maternal residence (reference) per the NCHS Urban-Rural Classification Scheme overall, and by delivery year, reported race and ethnicity, and US region (effect measure modification). MAIN OUTCOME MEASURES: The outcomes (modelled separately) were diagnoses of DM and GDM. RESULTS: From 2011 to 2019, there were increases in both the frequency (per 1000 live births; mean APC, 95% CI per year) of DM and GDM in rural areas (DM: 7.6 to 10.4 per 1000 live births; APC 2.8%, 95% CI 2.2%-3.4%; and GDM: 41.4 to 58.7 per 1000 live births; APC 3.1%, 95% CI 2.6%-3.6%) and urban areas (DM: 6.1 to 8.4 per 1000 live births; APC 3.3%, 95% CI 2.2%-4.4%; and GDM: 40.8 to 61.2 per 1000 live births; APC 3.9%, 95% CI 3.3%-4.6%). Individuals living in rural areas were at higher risk of DM (aRR 1.48, 95% CI 1.45%-1.51%) and GDM versus those in urban areas (aRR 1.17, 95% CI 1.16%-1.18%). The increased risk was similar each year for DM (interaction p = 0.8), but widened over time for GDM (interaction p < 0.01). The rural-urban disparity for DM was wider for individuals who identified as Hispanic race/ethnicity and in the South and West (interaction p < 0.01 for all); and for GDM the rural-urban disparity was generally wider for similar factors (i.e. Hispanic race/ethnicity, and in the South; interaction p < 0.05 for all). CONCLUSIONS: The frequency of DM and GDM increased in both rural and urban areas of the USA from 2011 to 2019 among nulliparous pregnant women. Significant rural-urban disparities existed for DM and GDM, and increased over time for GDM. These rural-urban disparities were generally worse among those of Hispanic race/ethnicity and in women who lived in the South. These findings have implications for delivering equitable diabetes care in pregnancy in rural US communities.

Body Mass Index, Adverse Pregnancy Outcomes, and Cardiovascular Disease Risk.

BACKGROUND: Obesity is a well-established risk factor for both adverse pregnancy outcomes (APOs) and cardiovascular disease (CVD). However, it is not known whether APOs are mediators or markers of the obesity-CVD relationship. This study examined the association between body mass index, APOs, and postpartum CVD risk factors. METHODS: The sample included adults from the nuMoM2b (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-To-Be) Heart Health Study who were enrolled in their first trimester (6 weeks-13 weeks 6 days gestation) from 8 United States sites. Participants had a follow-up visit at 3.7 years postpartum. APOs, which included hypertensive disorders of pregnancy, preterm birth, small-for-gestational-age birth, and gestational diabetes, were centrally adjudicated. Mediation analyses estimated the association between early pregnancy body mass index and postpartum CVD risk factors (hypertension, hyperlipidemia, and diabetes) and the proportion mediated by each APO adjusted for demographics and baseline health behaviors, psychosocial stressors, and CVD risk factor levels. RESULTS: Among 4216 participants enrolled, mean±SD maternal age was 27±6 years. Early pregnancy prevalence of overweight was 25%, and obesity was 22%. Hypertensive disorders of pregnancy occurred in 15%, preterm birth in 8%, small-for-gestational-age birth in 11%, and gestational diabetes in 4%. Early pregnancy obesity, compared with normal body mass index, was associated with significantly higher incidence of postpartum hypertension (adjusted odds ratio, 1.14 [95% CI, 1.10-1.18]), hyperlipidemia (1.11 [95% CI, 1.08-1.14]), and diabetes (1.03 [95% CI, 1.01-1.04]) even after adjustment for baseline CVD risk factor levels. APOs were associated with higher incidence of postpartum hypertension (1.97 [95% CI, 1.61-2.40]) and hyperlipidemia (1.31 [95% CI, 1.03-1.67]). Hypertensive disorders of pregnancy mediated a small proportion of the association between obesity and incident hypertension (13% [11%-15%]) and did not mediate associations with incident hyperlipidemia or diabetes. There was no significant mediation by preterm birth or small-for-gestational-age birth. CONCLUSIONS: There was heterogeneity across APO subtypes in their association with postpartum CVD risk factors and mediation of the association between early pregnancy obesity and postpartum CVD risk factors. However, only a small or nonsignificant proportion of the association between obesity and CVD risk factors was mediated by any of the APOs, suggesting APOs are a marker of prepregnancy CVD risk and not a predominant cause of postpartum CVD risk.

Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning.

Background: It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Objective: To develop a ASCVD risk prediction model that incorporates longitudinal risk factors using deep learning. Methods: Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Results: Of the 15,565 participants in our dataset, 2,170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI: 0.782-0.844) vs 0.792 (CI: 0.760-0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Conclusion: Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.

Blood pressure outcomes at 12 months in primary care patients prescribed remote physiological monitoring for hypertension: a prospective cohort study.

Remote patient monitoring (RPM) for hypertension enables automatic transmission of blood pressure (BP) and pulse into the electronic health record (EHR), but its effectiveness in primary care is unknown. This pragmatic matched cohort study using EHR data compared BP outcomes between individuals prescribed RPM and temporally-matched controls from six primary care practices. We retrospectively created a cohort of 288 Medicare-enrolled patients prescribed BP RPM (cases) and 1152 propensity score-matched controls (1:4). Matching was based on age, sex, systolic blood pressure (SBP), marital status, and other characteristics. Outcomes at 3, 6, 9 and 12 months were: controlling high BP (most recent BP < 140/90 mm Hg), antihypertensive medication intensification, and most recent SBP assessed using: all measurements, and office measurements only. At baseline, RPM-prescribed patients and controls had similar ages and systolic BP. BP control diverged at 3 months (RPM: 72.2%, control: 51%, p < 0.001). This difference persisted but decreased over follow-up. After 12 months, the RPM-prescribed cohort had greater BP control (RPM: 71.5%, control: 58.1%, p < 0.001) and lower SBP (132.3 versus 136.5 mm Hg, p = 0.003) using all measurements, but they did not differ using only office measurements (12 month BP control: 60.8% versus 58.1%, p = 0.44; SBP: 135.9 versus 136.5 mm Hg, p = 0.91). At 12 months, the most recent BP measurements were more current for RPM-prescribed patients (median [IQR] 8 [0-109] versus 134 [56-239] days). Net increases in antihypertensive medications by 12 months were similar. Implementation of RPM in primary care could inform hypertension management strategies and increase hypertension control. Registration: ClinicalTrials.gov identifier: NCT05562921.

The 2017 American College of Cardiology/American Heart Association Hypertension Guideline and Blood Pressure in Older Adults.

INTRODUCTION: The 2017 American College of Cardiology/American Heart Association blood pressure guideline redefined hypertension and lowered the blood pressure treatment target. Empirical data on the guideline's impact are needed. METHODS: Data were analyzed from Atherosclerosis Risk in Communities study participants who attended baseline pre-guideline (2016-2017) and post-guideline (2018-2019) visits with baseline systolic blood pressure between 120 and 159 mmHg. Participants were grouped according to baseline systolic blood pressure by change in classification under the new guideline as follows: not reclassified (120-129 mmHg), reclassified to Stage 1 hypertension (130-139 mmHg), and reclassified to Stage 2 hypertension (140-159 mmHg). Means and 95% CIs for systolic blood pressure changes between baseline and follow-up, changes in antihypertensive use, and percentages that achieved the post-guideline recommendation (systolic blood pressure <130 mmHg) were calculated. Analyses were performed in 2021-2022. RESULTS: Among 2,193 community-dwelling Atherosclerosis Risk in Communities participants aged 71-95 years at baseline, systolic blood pressure changes between baseline and follow-up visits differed among participants not reclassified (+4.1 mmHg, 95% CI=3.0, 5.3 mmHg), reclassified to Stage 1 hypertension (-1.1 mmHg, 95% CI= -2.2, 0.1 mmHg), and reclassified to Stage 2 hypertension (-5.7 mmHg, 95% CI= -6.8, -4.7 mmHg). Antihypertensive use changed from 77.3% to 78.4% (p=0.25) among participants reclassified to Stage 1 hypertension and from 78.3% to 81.4% (p<0.01) among participants reclassified to Stage 2 hypertension. At follow-up, 41.8% of the Stage 1 and 22.4% of the Stage 2 hypertension groups reached the systolic blood pressure <130 mmHg goal. CONCLUSIONS: There were small decreases in systolic blood pressure and increases in antihypertensive therapy among older adults reclassified to Stage 2 hypertension but not among those reclassified to Stage 1 hypertension by the 2017 American College of Cardiology/American Heart Association guideline.

Cardiovascular health trajectories from age 2-12: a pediatric electronic health record study.

PURPOSE: Many children have non-ideal cardiovascular health (CVH), but little is known about the course of CVH in early childhood. We identified CVH trajectories in children and assess the generalizability of these trajectories in an external sample. METHODS: We used data spanning 2010-2018 from children aged 2-12 years within the Chicago Area Patient-Centered Outcomes Research Network-an electronic health record network. Four clinical systems comprised the derivation sample and a fifth the validation sample. Body mass index, blood pressure, cholesterol, and blood glucose were categorized as ideal, intermediate, and poor using clinical measurements, laboratory readings, and International Classification of Diseases diagnosis codes and summed for an overall CVH score. Group-based trajectory modeling was used to create CVH score trajectories which were assessed for classification accuracy in the validation sample. RESULTS: Using data from 122,363 children (47% female, 47% non-Hispanic White) three trajectories were identified: 59.5% maintained high levels of clinical CVH, 23.4% had high levels of CVH that declined, and 17.1% had intermediate levels of CVH that further declined with age. A similar classification emerged when the trajectories were fitted in the validation sample. CONCLUSIONS: Stratification of CVH was present by age 2, implicating the need for early life and preconception prevention strategies.

Using electronic health record data to link families: an illustrative example using intergenerational patterns of obesity.

OBJECTIVE: Electronic health record (EHR) data are a valuable resource for population health research but lack critical information such as relationships between individuals. Emergency contacts in EHRs can be used to link family members, creating a population that is more representative of a community than traditional family cohorts. MATERIALS AND METHODS: We revised a published algorithm: relationship inference from the electronic health record (RIFTEHR). Our version, Pythonic RIFTEHR (P-RIFTEHR), identifies a patient's emergency contacts, matches them to existing patients (when available) using network graphs, checks for conflicts, and infers new relationships. P-RIFTEHR was run on December 15, 2021 in the Northwestern Medicine Electronic Data Warehouse (NMEDW) on approximately 2.95 million individuals and was validated using the existing link between children born at NM hospitals and their mothers. As proof-of-concept, we modeled the association between parent and child obesity using logistic regression. RESULTS: The P-RIFTEHR algorithm matched 1 157 454 individuals in 448 278 families. The median family size was 2, the largest was 32 persons, and 247 families spanned 4 generations or more. Validation of the mother-child pairs resulted in 95.1% sensitivity. Children were 2 times more likely to be obese if a parent is obese (OR: 2.30; 95% CI, 2.23-2.37). CONCLUSION: P-RIFTEHR can identify familiar relationships in a large, diverse population in an integrated health system. Estimates of parent-child inheritability of obesity using family structures identified by the algorithm were consistent with previously published estimates from traditional cohort studies.

Prospective Cohort Study of Remote Patient Monitoring with and without Care Coordination for Hypertension in Primary Care.

BACKGROUND: Out-of-office blood pressure (BP) measurements contribute valuable information for guiding clinical management of hypertension. Measurements from home devices can be directly transmitted to patients' electronic health record for use in remote monitoring programs. OBJECTIVE: This study aimed to compare in primary care practice care coordinator-assisted implementation of remote patient monitoring (RPM) for hypertension to RPM implementation alone and to usual care. METHODS: This was a pragmatic observational cohort study. Patients aged 65 to 85 years with Medicare insurance from two populations were included: those with uncontrolled hypertension and a general hypertension group seeing primary care physicians (PCPs) within one health system. Exposures were clinic-level availability of RPM plus care coordination, RPM alone, or usual care. At two clinics (13 PCPs), nurse care coordinators with PCP approval offered RPM to patients with uncontrolled office BP and assisted with initiation. At two clinics (39 PCPs), RPM was at PCPs' discretion. Twenty clinics continued usual care. Main measures were controlling high BP (<140/90 mm Hg), last office systolic blood pressure (SBP), and proportion with antihypertensive medication intensification. RESULTS: Among the Medicare cohorts with uncontrolled hypertension, 16.7% (39/234) of patients from the care coordination clinics were prescribed RPM versus <1% (4/600) at noncare coordination sites. RPM-enrolled care coordination group patients had higher baseline SBP than the noncare coordination group (148.8 vs. 140.0 mm Hg). After 6 months, in the uncontrolled hypertension cohorts the prevalences of controlling high BP were 32.5% (RPM with care coordination), 30.7 % (RPM alone), and 27.1% (usual care); multivariable adjusted odds ratios (95% confidence interval) were 1.63 (1.12-2.39; p = 0.011) and 1.29 (0.98-1.69; p = 0.068) compared with usual care, respectively. CONCLUSION: Care coordination facilitated RPM enrollment among poorly controlled hypertension patients and may improve hypertension control in primary care among Medicare patients.

Remote physiologic monitoring for hypertension in primary care: a prospective pragmatic pilot study in electronic health records using propensity score matching.

Objectives: Since 2019, the Centers for Medicare and Medicaid Services covers remote physiologic monitoring (RPM) for blood pressure (BP) per hypertension diagnosis and treatment guidelines. Here, we integrated Omron VitalSight RPM into the health system's electronic health record to transmit BP and pulse without manual entry, assessed feasibility, and used pragmatic prospective matched cohort studies to assess initial effects in (1) uncontrolled (last two office BP ≥140/90 mmHg) and (2) general (diagnosed hypertension or last office BP ≥140/90 mmHg) hypertension patient populations. Materials and Methods: Seventeen clinicians at two internal medicine practices were oriented. Eligible patients were aged 65-85 years had Medicare insurance with ≥1 office visit in the previous year. We prospectively identified matched controls (age, sex, BP, and number of office visits in previous year) from other primary care practices within the health system and estimated the association between RPM availability (clinic-level) and patient BP outcomes after 6 months. ClinicalTrials.gov: NCT04604925. Results: Feasibility. Uptake was low at pilot clinics: 10 physicians prescribed RPM to 118 patients during the 6-month pilot. This included 7% (14/207) of the prespecified uncontrolled hypertension cohort and 3.3% (78/2356) of the general hypertension cohort. Surveyed clinicians (n = 4) reported changing their patients' medical treatment in response to RPM BPs, although they recommended having a dedicated RN or LPN to review BP readings. Effectiveness. At 6 months, BP control was greater at pilot practices than among matched controls (uncontrolled: 31.4% vs 22.8%; P = .007; general: 64.0% vs 59.7%; P < .001). Systolic BP at last office visit did not differ (mean [SD] 146.0 [15.7] vs 147.1 [15.6]; P = .48) in the uncontrolled population, and was lower in the general population (131.8 [15.7] vs 132.8 [15.9]; P = .04).The frequency of antihypertensive medication changes was similar in both groups (uncontrolled P = .986; general P = .218). Discussion and Conclusions: Uptake notwithstanding, RPM may have improved BP control. A potential mechanism is increased physician awareness of and attention to uncontrolled hypertension. Barriers to RPM use among physicians require further study.

Comparative effectiveness of a mindfulness-based intervention (M-Body) on depressive symptoms: study protocol of a randomized controlled trial in a Federally Qualified Health Center (FQHC).

BACKGROUND: Mindfulness-based interventions have been shown to improve psychological outcomes including stress, anxiety, and depression in general population studies. However, effectiveness has not been sufficiently examined in racially and ethnically diverse community-based settings. We will evaluate the effectiveness and implementation of a mindfulness-based intervention on depressive symptoms among predominantly Black women at a Federally Qualified Health Center in a metropolitan city. METHODS: In this 2-armed, stratified, individually randomized group-treated controlled trial, 274 English-speaking participants with depressive symptoms ages 18-65 years old will be randomly assigned to (1) eight weekly, 90-min group sessions of a mindfulness-based intervention (M-Body), or (2) enhanced usual care. Exclusion criteria include suicidal ideation in 30 days prior to enrollment and regular (>4x/week) meditation practice. Study metrics will be assessed at baseline and 2, 4, and 6 months after baseline, through clinical interviews, self-report surveys, and stress biomarker data including blood pressure, heart rate, and stress related biomarkers. The primary study outcome is depressive symptom score after 6 months. DISCUSSION: If M-Body is found to be an effective intervention for adults with depressive symptoms, this accessible, scalable treatment will widely increase access to mental health treatment in underserved, racial/ethnic minority communities. TRIAL REGISTRATION: ClinicalTrials.gov NCT03620721. Registered on 8 August 2018.

Association of statin use with outcomes of patients admitted with COVID-19: an analysis of electronic health records using superlearner.

IMPORTANCE: Statin use prior to hospitalization for Coronavirus Disease 2019 (COVID-19) is hypothesized to improve inpatient outcomes including mortality, but prior findings from large observational studies have been inconsistent, due in part to confounding. Recent advances in statistics, including incorporation of machine learning techniques into augmented inverse probability weighting with targeted maximum likelihood estimation, address baseline covariate imbalance while maximizing statistical efficiency. OBJECTIVE: To estimate the association of antecedent statin use with progression to severe inpatient outcomes among patients admitted for COVD-19. DESIGN, SETTING AND PARTICIPANTS: We retrospectively analyzed electronic health records (EHR) from individuals ≥ 40-years-old who were admitted between March 2020 and September 2022 for ≥ 24 h and tested positive for SARS-CoV-2 infection in the 30 days before to 7 days after admission. EXPOSURE: Antecedent statin use-statin prescription ≥ 30 days prior to COVID-19 admission. MAIN OUTCOME: Composite end point of in-hospital death, intubation, and intensive care unit (ICU) admission. RESULTS: Of 15,524 eligible COVID-19 patients, 4412 (20%) were antecedent statin users. Compared with non-users, statin users were older (72.9 (SD: 12.6) versus 65.6 (SD: 14.5) years) and more likely to be male (54% vs. 51%), White (76% vs. 71%), and have ≥ 1 medical comorbidity (99% vs. 86%). Unadjusted analysis demonstrated that a lower proportion of antecedent users experienced the composite outcome (14.8% vs 19.3%), ICU admission (13.9% vs 18.3%), intubation (5.1% vs 8.3%) and inpatient deaths (4.4% vs 5.2%) compared with non-users. Risk differences adjusted for labs and demographics were estimated using augmented inverse probability weighting with targeted maximum likelihood estimation using Super Learner. Statin users still had lower rates of the composite outcome (adjusted risk difference: - 3.4%; 95% CI: - 4.6% to - 2.1%), ICU admissions (- 3.3%; - 4.5% to - 2.1%), and intubation (- 1.9%; - 2.8% to - 1.0%) but comparable inpatient deaths (0.6%; - 1.3% to 0.1%). CONCLUSIONS AND RELEVANCE: After controlling for confounding using doubly robust methods, antecedent statin use was associated with minimally lower risk of severe COVID-19-related outcomes, ICU admission and intubation, however, we were not able to corroborate a statin-associated mortality benefit.

Comparison of Sodium-Glucose Cotransporter-2 Inhibitor and Glucagon-Like Peptide-1 Receptor Agonist Prescribing in Patients With Diabetes Mellitus With and Without Cardiovascular Disease.

Sodium-glucose cotransporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP1-RAs) reduce cardiovascular events and mortality in patients with type 2 diabetes mellitus (T2DM). We sought to describe trends in prescribing for SGLT2is and GLP1-RAs in diverse care settings, including (1) the outpatient clinics of a midwestern integrated health system and (2) small- and medium-sized community-based primary care practices and health centers in 3 midwestern states. We included adults with T2DM and ≥1 outpatient clinic visit. The outcomes of interest were annual active prescription rates for SGLT2is and GLP1-RAs (separately). In the integrated health system, 22,672 patients met the case definition of T2DM. From 2013 to 2019, the overall prescription rate for SGLT2is increased from 1% to 15% (absolute difference [AD] 14%, 95% confidence interval [CI] 13% to 15%, p <0.01). The GLP1-RA prescription rate was stable at 10% (AD 0%, 95% CI -1% to 1%, p = 0.9). In community-based primary care practices, 43,340 patients met the case definition of T2DM. From 2013 to 2017, the SGLT2i prescription rate increased from 3% to 7% (AD 4%, 95% CI 3% to 6%, p <0.01), whereas the GLP1-RA prescription rate was stable at 2% to 3% (AD 1%, 95% CI -1 to 1%, p = 0.40). In a fully adjusted regression model, non-Hispanic Black patients had lower odds of SGLT2i or GLP1-RA prescription (odds ratio 0.56, 95% CI 0.34 to 0.89, p = 0.016). In conclusion, the increase in prescription rates was greater for SGLT2is than for GLP1-RAs in patients with T2DM in a large integrated medical center and community primary care practices. Overall, prescription rates for eligible patients were low, and racial disparities were observed.