Management of heart failure is a major health care challenge. Healthcare providers are expected to use best practices described in clinical practice guidelines, which typically consist of a long series of complex rules. For heart failure management, the relevant guidelines are nearly 80 pages long. Due to their complexity, the guidelines are often difficult to fully comply with, which can result in suboptimal medical practices. In this paper, we describe a heart failure treatment adviser system that automates the entire set of rules in the guidelines for heart failure management. The system is based on answer set programming, a form of declarative programming suited for simulating human-style reasoning. Given a patients information, the system is able to generate a set of guideline-compliant recommendations. We conducted a pilot study of the system on 21 real and 10 simulated patients with heart failure. The results show that the system can give treatment recommendations compliant with the guidelines. Out of 187 total recommendations made by the system, 176 were agreed upon by the expert cardiologists. Also, the system missed eight valid recommendations. The reason for the missed and discordant recommendations seems to be insufficient information, differing style, experience, and knowledge of experts in decision-making that were not captured in the system at this time. The system can serve as a point-of-care tool for clinics. Also, it can be used as an educational tool for training physicians and an assessment tool to measure the quality metrics of heart failure care of an institution.

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: 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 <100 mmHg on admission (AOR 2.18, 95% CI 1.68-2.82), elevated Cr (≥2 mg/dL) on admission (AOR 2.56, 95% CI 2.52-6.08), elevated BNP on admission (AOR 6.36, 95% CI 1.65-24.47) and lack of PCI within 24 hours of admission (AOR 1.31, 95% CI 1.02-1.69) were significant predictors of readmission. Our ‘first-day’ AMI readmissions model based on these predictors had good discrimination ( Table ). Adding three other variables from the hospital course - use of IV diuretics (AOR 1.58, 95% CI 1.07-2.31), anemia (hematocrit ≤ 33%) on discharge (AOR 2.04, 95% CI 1.20-3.46), and discharge to post-acute care (AOR 1.50, 95% CI 0.90-2.50) - improved discrimination of the ‘full stay’ AMI model but only modestly improved net reclassification and calibration. Conclusions: A ‘full-stay’ AMI-specific EHR readmission model modestly outperformed a ‘first-day’ EHR model, a multi-condition EHR model, and the CMS AMI model. Surprisingly, incorporating more hospitalization data improved discrimination of the full-stay AMI model but did not meaningfully improve reclassification compared to the first-day model. Readmissions in AMI may be accurately predicted on the first day of hospitalization; waiting until later in hospitalization does not markedly improve risk prediction.

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.

Advertisement calls in frogs have evolved to be species-specific signals of recognition and are therefore considered an essential component of integrative taxonomic approaches to identify species and delineate their distribution range. The species rich genus Microhyla is a particularly challenging group for species identification, discovery and conservation management due to the small size, conserved morphology and wide distribution of its members, necessitating the need for a thorough description of their vocalization. In this study, we provide quantitative description of the vocal behaviour of Microhyla nilphamariensis, a widely distributed south Asian species, from Delhi, India, based on call recordings of 18 individuals and assessment of 21 call properties. Based on the properties measured acrossed 360 calls, we find that a typical advertisement call of M. nilphamariensis lasts for 393.5 ±  57.5 ms, has 17 pulses on average and produce pulses at rate of 39 pulses/s. The overall call dominant frequency was found to be 2.8 KHz and the call spectrum consisted of two dominant frequency peaks centered at 1.6 KHz and 3.6 KHz, ranging between 1.5-4.1 KHz. Apart from its typical advertisement call, our study also reveals the presence of three rare call types, previously unreported in this species. We describe variability in call properties and discuss their relation to body size and temperature. We found that overall dominant frequency 1 (spectral property) was found to be correlated with body size, while first pulse period (temporal property) was found to be correlated with temperature. Further, we compare the vocal repertoire of M. nilphamariensis with that of the congener Microhyla ornata from the western coast of India and Sri Lanka and also compare the call properties of these two populations of M. ornata to investigate intra-specific call variation. We find statistically significant differentiation in their acoustic repertoire in both cases. Based on 18 call properties (out of 20), individuals of each locality clearly segregate on PCA factor plane forming separate groups. Discriminant function analysis (DFA) using PCA factors shows 100% classification success with individuals of each locality getting classified to a discrete group. This confirms significant acoustic differentiation between these species as well as between geographically distant conspecifics. The data generated in this study will be useful for comparative bioacoustic analysis of Microhyla species and can be utilized to monitor populations and devise conservation management plan for threatened species in this group.

Objective

Noninvasive 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.

Methods

We 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.

Results

The 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).

Conclusion

Our study indicated that the alerting reaction is independently associated with increased cardiovascular and renal complications.

While aldosterone antagonists have proven benefit among post-myocardial infarction (MI) patients with low ejection fraction (EF), how this treatment is used among older MI patients in routine practice is not well described. Using ACTION Registry-GWTG linked to Medicare data, we examined 12 080 MI patients ≥65 years with EF ≤40% who were indicated for aldosterone antagonist therapy per current guidelines and without documented contraindications. Of these, 11% (n=1310) were prescribed aldosterone antagonists at discharge. Notably, 10% of patients prescribed an aldosterone antagonist were eligible for, but not concurrently treated with, an angiotensin-converting enzyme inhibitor/angiotensin receptor blocker. Spironolactone was the predominantly prescribed aldosterone antagonist. At 2-year follow-up, aldosterone antagonist use was not associated with lower mortality (unadjusted 39% versus 38%; HR 0.99, 95% CI 0.88-1.33 using inverse probability-weighted propensity adjustment) except in symptomatic HF patients (HR 0.84, 95% CI 0.72-0.99, Pinteraction=0.009). Risks of hyperkalemia were low at 30 days, but significantly higher among patients prescribed aldosterone antagonists (unadjusted 2.3% versus 1.5%; adjusted HR 2.04, 95% CI 1.16-3.60), as was 2-year risk of acute renal failure (unadjusted 6.7% versus 4.8%; adjusted HR 1.39, 95% CI 1.01-1.92) compared with patients not prescribed aldosterone antagonists. Aldosterone antagonist use among eligible older MI patients in routine clinical practice was not associated with lower mortality except in patients with HF symptoms, but was associated with increased risks of hyperkalemia and acute renal failure. These results underscore the importance of close post-discharge monitoring of this patient population.

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 (<6), while the actual prevalence of non-adherence was 51% by therapeutic drug monitoring. Sensitivity of the MMAS-8 was 26% (95% confidence interval, 10.3%-48.4%) with specificity of 75% (95% confidence interval, 53.3%-90.2%). By multivariate analysis, the MMAS-8 score was not an independent predictor of non-adherence, while certain clinical parameters such as heart rate were found to be independent predictors of non-adherence. Our study suggested limited accuracy of the MMAS-8 in detecting medication non-adherence in a-TRH.

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

The 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.

Methods

We 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.

Results

Between 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], P<0.001), antihypertensives (DID estimate [95% CI]: 63.2 [47.3-79.1], P<0.001), and P2Y12 inhibitors (DID estimate [95% CI]: 1.7 [1.2-2.2], P<0.001). Between 2013 and 2018, >75% 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).

Conclusions

The 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.