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BackgroundThe Affordable Care Act expanded Medicaid eligibility allowing low-income individuals greater access to health care. However, the uptake of state Medicaid expansion has been variable. It remains unclear how the Medicaid expansion was associated with the temporal trends in use of evidence-based cardiovascular drugs.MethodsWe used the publicly available Medicaid Drug Utilization and Current Population Survey to extract filled prescription rates per 1000 Medicaid beneficiaries of statins, antihypertensives, P2Y12 inhibitors, and direct oral anticoagulants. We defined expander states as those who expanded Medicaid on or before January 1, 2014, and nonexpander states as those who had not expanded by December 31, 2018. Difference-in-differences (DID) analyses were performed to compare the association of the Medicaid expansion with per-capita cardiovascular drug prescription rates in expander versus nonexpander states.ResultsBetween 2011 and 2018, the total number of prescriptions among all Medicaid beneficiaries increased, with gains of 89.7% in statins (11.0 to 20.8 million), 76% in antihypertensives (35.3 to 62.2 million), and 37% in P2Y12 inhibitors (1.7 to 2.3 million). Medicaid expansion was associated with significantly greater increases in quarterly prescriptions (per 1000 Medicaid beneficiaries) of statins (DID estimate [95% CI]: 22.5 [16.5-28.6], P75% of the expander states had increases in prescription rates of both statins and antihypertensives. In contrast, 44% of nonexpander states saw declines in statins and antihypertensives. The Medicaid expansion was not associated with higher direct oral anticoagulants prescription rates (DID estimate [95% CI] 0.9 [-0.3 to 2.1], P=0.142).ConclusionsThe 2014 Medicaid expansion was associated with a significant increase in per-capita utilization of cardiovascular prescription drugs among Medicaid beneficiaries. These gains in utilization may contribute to long-term cardiovascular benefits to lower-income and previously underinsured populations.

Most guidelines have recommended lower home blood pressure (BP) threshold when clinic BP threshold of 140/90 mm Hg is used for diagnosis of hypertension. However, home BP thresholds to define hypertension have never been determined in the general population in the United States. We identified home BP thresholds for stage 1 (BP ≥130/80 mm Hg) hypertension using a regression-based approach in the DHS (Dallas Heart Study; n=5768) and the NCMH study (North Carolina Masked Hypertension; n=420). Home BP thresholds were also assessed using outcome-derived approach based on the composite of all-cause mortality or cardiovascular events in the DHS cohort. For this approach, BP thresholds were identified only for systolic BP because diastolic BP was not associated with the outcome. Among untreated participants, the regression-derived thresholds for home BP corresponding to clinic BP for stage 1 hypertension were 129/80 mm Hg in blacks, 130/80 mm Hg in whites, and 126/78 mm Hg in Hispanics, respectively. The results are similar in the North Carolina cohort. The 11-year composite cardiovascular and mortality events corresponding to clinic systolic BP >130 mm Hg were higher in blacks than in whites and Hispanics (13.3% versus 5.98% versus 5.52%, respectively). Using a race/ethnicity-specific composite outcome in the untreated DHS participants, the outcome-derived home systolic BP thresholds corresponding to stage 1 hypertension were 130 mm Hg in blacks, 129 mm Hg in whites, and 131 mm Hg in Hispanics, respectively. Our data based on both regression-derived and outcome approach support home BP threshold of 130/80 mm Hg for diagnosis of hypertension in blacks, whites, and Hispanics.

BACKGROUND:Readmissions after hospitalization for acute myocardial infarction (AMI) are common. However, the few currently available AMI readmission risk prediction models have poor-to-modest predictive ability and are not readily actionable in real time. We sought to develop an actionable and accurate AMI readmission risk prediction model to identify high-risk patients as early as possible during hospitalization. METHODS AND RESULTS:We used electronic health record data from consecutive AMI hospitalizations from 6 hospitals in north Texas from 2009 to 2010 to derive and validate models predicting all-cause nonelective 30-day readmissions, using stepwise backward selection and 5-fold cross-validation. Of 826 patients hospitalized with AMI, 13% had a 30-day readmission. The first-day AMI model (the AMI "READMITS" score) included 7 predictors: renal function, elevated brain natriuretic peptide, age, diabetes mellitus, nonmale sex, intervention with timely percutaneous coronary intervention, and low systolic blood pressure, had an optimism-corrected C-statistic of 0.73 (95% confidence interval, 0.71-0.74) and was well calibrated. The full-stay AMI model, which included 3 additional predictors (use of intravenous diuretics, anemia on discharge, and discharge to postacute care), had an optimism-corrected C-statistic of 0.75 (95% confidence interval, 0.74-0.76) with minimally improved net reclassification and calibration. Both AMI models outperformed corresponding multicondition readmission models. CONCLUSIONS:The parsimonious AMI READMITS score enables early prospective identification of high-risk AMI patients for targeted readmissions reduction interventions within the first 24 hours of hospitalization. A full-stay AMI readmission model only modestly outperformed the AMI READMITS score in terms of discrimination, but surprisingly did not meaningfully improve reclassification.

Readmissions after hospitalization for acute myocardial infarction (AMI) are common. However, the few currently available AMI readmission risk prediction models have poor-to-modest predictive ability and are not readily actionable in real time. We sought to develop an actionable and accurate AMI readmission risk prediction model to identify high-risk patients as early as possible during hospitalization. We used electronic health record data from consecutive AMI hospitalizations from 6 hospitals in north Texas from 2009 to 2010 to derive and validate models predicting all-cause nonelective 30-day readmissions, using stepwise backward selection and 5-fold cross-validation. Of 826 patients hospitalized with AMI, 13% had a 30-day readmission. The first-day AMI model (the AMI "READMITS" score) included 7 predictors: renal function, elevated brain natriuretic peptide, age, diabetes mellitus, nonmale sex, intervention with timely percutaneous coronary intervention, and low systolic blood pressure, had an optimism-corrected C-statistic of 0.73 (95% confidence interval, 0.71-0.74) and was well calibrated. The full-stay AMI model, which included 3 additional predictors (use of intravenous diuretics, anemia on discharge, and discharge to postacute care), had an optimism-corrected C-statistic of 0.75 (95% confidence interval, 0.74-0.76) with minimally improved net reclassification and calibration. Both AMI models outperformed corresponding multicondition readmission models. The parsimonious AMI READMITS score enables early prospective identification of high-risk AMI patients for targeted readmissions reduction interventions within the first 24 hours of hospitalization. A full-stay AMI readmission model only modestly outperformed the AMI READMITS score in terms of discrimination, but surprisingly did not meaningfully improve reclassification.

BACKGROUND:Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown. METHODS AND RESULTS:We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models. CONCLUSIONS:Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions.

BACKGROUND:Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown. METHODS AND RESULTS:We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models. CONCLUSIONS:Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions.

Background: Readmissions after hospitalization for acute myocardial infarction (AMI) are common, but the few available risk prediction models have poor predictive ability. Including more data from hospitalization may improve risk prediction. Objectives: To assess if an AMI-specific electronic health record (EHR) readmission risk prediction model derived and validated from data through the entire hospital course (‘full stay’ model) outperforms a model using data available only from the first day of hospitalization (‘first day’ model). Methods: EHR data from AMI hospitalizations from 6 diverse hospitals in north Texas from 2009-2010 were used to derive a model predicting all-cause non-elective 30-day readmissions which was then validated using five-fold cross-validation. Results: Of 826 consecutive index AMI admissions, 13% were followed by a 30-day readmission. History of diabetes (AOR 2.41, 95% CI 1.37-4.24), SBP

ObjectiveNoninvasive blood pressure (BP) measurement often triggers a transient rise in BP, known as an alerting reaction. However, the prevalence and prognostic significance of the alerting reaction has never been assessed in the general population.MethodsWe evaluated the association between the alerting reaction and left ventricular mass by MRI and urinary albumin-to-creatinine ratio in the Dallas Heart Study, a large population sample of 3069 individuals. Participants were categorized into four groups based on levels of consecutive BP: first, normal first BP and average third to fifth (avg3-5) BP of less than 140/90 mmHg (control group); second, high first BP of at least 140/90 mmHg and normal (avg3-5) BP (alerting reaction group); third, normal first BP and high (avg3-5) BP; and fourth, high first to fifth BP. Then, associations between BP categories with incident cardiovascular outcomes (coronary heart disease, stroke, atrial fibrillation, heart failure, and cardiovascular death) over a median follow-up period of 9.4 years were assessed.ResultsThe sample-weighted prevalence of isolated hypertension during the first BP measurement was 9.6%. Presence of an alerting reaction was independently associated with increased left ventricular mass, urinary albumin-to-creatinine ratio, cardiovascular events after adjustment for traditional cardiovascular risk factors, and baseline BP (adjusted hazard ratio 1.24, 95% confidence interval 1.07-1.43).ConclusionOur study indicated that the alerting reaction is independently associated with increased cardiovascular and renal complications.

BackgroundMultiple epidemiological studies from Europe and Asia have demonstrated increased cardiovascular risks associated with isolated elevation of home blood pressure (BP) or masked hypertension (MH). Previous studies have not addressed cardiovascular outcomes associated with MH and white-coat hypertension (WCH) in the general population in the United States.ObjectivesThe goal of this study was to determine hypertensive target organ damage and adverse cardiovascular outcomes associated with WCH (high clinic BP, ≥140/90 mm Hg; normal home BP,

The Morisky Medication Adherence Scale (MMAS-8) is a questionnaire developed for screening of non-adherence in patients with several chronic conditions, including uncomplicated hypertension. However, its accuracy in predicting non-adherence in patients with apparent treatment-resistant hypertension (a-TRH) is not known. Accordingly, we performed a retrospective study in 47 patients with a-TRH who had completed the eight-item MMAS during the initial clinic visit. Non-adherence was defined as presence of undetected serum levels of at least one prescribed antihypertensive drug by therapeutic drug monitoring. We found that 26% of patients were considered to have low adherence score (

The Morisky Medication Adherence Scale (MMAS-8) is a questionnaire developed for screening of non-adherence in patients with several chronic conditions, including uncomplicated hypertension. However, its accuracy in predicting non-adherence in patients with apparent treatment-resistant hypertension (a-TRH) is not known. Accordingly, we performed a retrospective study in 47 patients with a-TRH who had completed the eight-item MMAS during the initial clinic visit. Non-adherence was defined as presence of undetected serum levels of at least one prescribed antihypertensive drug by therapeutic drug monitoring. We found that 26% of patients were considered to have low adherence score (