Search

Delays in diagnosis of peripartum cardiomyopathy (PPCM) are common and are associated with worse outcomes; however, few studies have addressed methods for improving early detection.We hypothesized that easily accessible data (heart rate [HR] and electrocardiograms [ECGs]) could identify women with more severe PPCM and at increased risk of adverse outcomes.Clinical data, including HR and ECG, from patients diagnosed with PPCM between January 1998 and July 2016 at our institution were collected and analyzed. Linear and logistic regression were used to analyze the relationship between HR at diagnosis and the left ventricular ejection fraction (LVEF) at diagnosis. Outcomes included overall mortality, recovery status, and major adverse cardiac events.Among 82 patients meeting inclusion criteria, the overall mean LVEF at diagnosis was 26 ± 11.1%. Sinus tachycardia (HR 100) was present in a total of 50 patients (60.9%) at the time of diagnosis. In linear regression, HR significantly predicted lower LVEF (F = 30.00, p .0001). With age-adjusted logistic regression, elevated HR at diagnosis was associated with a fivefold higher risk of overall mortality when initial HR was110 beats per minute (adjusted odds ratio 5.35, confidence interval 1.23-23.28), p = .025).In this study, sinus tachycardia in women with PPCM was associated with lower LVEF at the time of diagnosis. Tachycardia in the peripartum period should raise concern for cardiomyopathy and may be an early indicator of adverse prognosis.

The value of utilizing a multigene pharmacogenetic panel to tailor pharmacotherapy is contingent on the prevalence of prescribed medications with an actionable pharmacogenetic association. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has categorized over 35 gene-drug pairs as "level A," for which there is sufficiently strong evidence to recommend that genetic information be used to guide drug prescribing. The opportunity to use genetic information to tailor pharmacotherapy among adult patients was determined by elucidating the exposure to CPIC level A drugs among 11 Implementing Genomics In Practice Network (IGNITE)-affiliated health systems across the US. Inpatient and/or outpatient electronic-prescribing data were collected between January 1, 2011 and December 31, 2016 for patients ≥ 18 years of age who had at least one medical encounter that was eligible for drug prescribing in a calendar year. A median of ~ 7.2 million adult patients was available for assessment of drug prescribing per year. From 2011 to 2016, the annual estimated prevalence of exposure to at least one CPIC level A drug prescribed to unique patients ranged between 15,719 (95% confidence interval (CI): 15,658-15,781) in 2011 to 17,335 (CI: 17,283-17,386) in 2016 per 100,000 patients. The estimated annual exposure to at least 2 drugs was above 7,200 per 100,000 patients in most years of the study, reaching an apex of 7,660 (CI: 7,632-7,687) per 100,000 patients in 2014. An estimated 4,748 per 100,000 prescribing events were potentially eligible for a genotype-guided intervention. Results from this study show that a significant portion of adults treated at medical institutions across the United States is exposed to medications for which genetic information, if available, should be used to guide prescribing.

Background: Major gaps exist in the routine initiation and dose up-titration of guideline-directed medical therapies (GDMT) for patients with heart failure with reduced ejection fraction. Without novel approaches to improve prescribing, the cumulative benefits of heart failure with reduced ejection fraction treatment will be largely unrealized. Direct-to-consumer marketing and shared decision making reflect a culture where patients are increasingly involved in treatment choices, creating opportunities for prescribing interventions that engage patients. Methods: The EPIC-HF (Electronically Delivered, Patient-Activation Tool for Intensification of Medications for Chronic Heart Failure with Reduced Ejection Fraction) trial randomized patients with heart failure with reduced ejection fraction from a diverse health system to usual care versus patient activation tools—a 3-minute video and 1-page checklist—delivered electronically 1 week before, 3 days before, and 24 hours before a cardiology clinic visit. The tools encouraged patients to work collaboratively with their clinicians to “make one positive change” in heart failure with reduced ejection fraction prescribing. The primary endpoint was the percentage of patients with GDMT medication initiations and dose intensifications from immediately preceding the cardiology clinic visit to 30 days after, compared with usual care during the same period. Results: EPIC-HF enrolled 306 patients, 290 of whom attended a clinic visit during the study period: 145 were sent the patient activation tools and 145 were controls. The median age of patients was 65 years; 29% were female, 11% were Black, 7% were Hispanic, and the median ejection fraction was 32%. Preclinic data revealed significant GDMT opportunities, with no patients on target doses of β-blocker, sacubitril/valsartan, and mineralocorticoid receptor antagonists. From immediately preceding the cardiology clinic visit to 30 days after, 49.0% in the intervention and 29.7% in the control experienced an initiation or intensification of their GDMT ( P =0.001). The majority of these changes were made at the clinician encounter itself and involved dose uptitrations. There were no deaths and no significant differences in hospitalization or emergency department visits at 30 days between groups. Conclusions: A patient activation tool delivered electronically before a cardiology clinic visit improved clinician intensification of GDMT. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03334188.

Background Peripartum cardiomyopathy (PPCM) causes significant morbidity and mortality in childbearing women. Delays in diagnosis lead to worse outcomes; however, no validated risk prediction model exists. We sought to validate a previously described model and identify novel risk factors for PPCM presenting at the time of delivery. Methods and Results Administrative hospital records from 5,277,932 patients from 8 states were screened for PPCM, identified by International Classification of Disease-9 Clinical Modification codes (674.5x) at the time of delivery. Demographics, comorbidities, procedures, and outcomes were quantified. Performance of a previously published regression model alone and with the addition of novel PPCM-associated characteristics was assessed using receiver operating characteristic area under the curve (AUC) analysis. Novel risk factors were identified using multivariate logistic regression and the likelihood ratio test. In total, 1186 women with PPCM were studied, including 535 of 4,003,912 delivering mothers (0.013%) in the derivation set compared with 651 of 5,277,932 (0.012%) in the validation set. The previously published risk prediction model performed well in both the derivation (area under the curve 0.822) and validation datasets (area under the curve 0.802). Novel PPCM-associated characteristics in the combined cohort included diabetes mellitus (odds ratio [OR] of PPCM 1.93, 95% confidence interval [CI] 1.23–3.02, P = .004), mood disorders (OR 1.74, 95% CI 1.22–2.47, P = .002), obesity (OR 1.92, 95% CI 1.45–2.55, P Conclusions This is the first validated risk prediction model to identify women at increased risk for PPCM at the time of delivery. Diabetes mellitus, obesity, mood disorders, and lower socioeconomic status are risk factors associated with PPCM. This model may be useful for identifying women at risk and preventing delays in diagnosis.

Background The objective was to describe the design of a population-level electronic health record (EHR) and insurance claims-based surveillance system of adolescents and adults with congenital heart defects (CHDs) in Colorado and to evaluate the bias introduced by duplicate cases across data sources. Methods The Colorado CHD Surveillance System ascertained individuals aged 11-64 years with a CHD based on International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic coding between 2011 and 2013 from a diverse network of health care systems and an All Payer Claims Database (APCD). A probability-based identity reconciliation algorithm identified duplicate cases. Logistic regression was conducted to investigate bias introduced by duplicate cases on the relationship between CHD severity (severe compared to moderate/mild) and adverse outcomes including all-cause mortality, inpatient hospitalization, and major adverse cardiac events (myocardial infarction, congestive heart failure, or cerebrovascular event). Sensitivity analyses were conducted to investigate bias introduced by the sole use or exclusion of APCD data. Results A total of 12,293 unique cases were identified, of which 3,476 had a within or between data source duplicate. Duplicate cases were more likely to be in the youngest age group and have private health insurance, a severe heart defect, a CHD comorbidity, and higher health care utilization. We found that failure to resolve duplicate cases between data sources would inflate the relationship between CHD severity and both morbidity and mortality outcomes by ~15%. Sensitivity analyses indicate that scenarios in which APCD was excluded from case finding or relied upon as the sole source of case finding would also result in an overestimation of the relationship between a CHD severity and major adverse outcomes. Discussion Aggregated EHR- and claims-based surveillance systems of adolescents and adults with CHD that fail to account for duplicate records will introduce considerable bias into research findings. Conclusion Population-level surveillance systems for rare chronic conditions, such as congenital heart disease, based on aggregation of EHR and claims data require sophisticated identity reconciliation methods to prevent bias introduced by duplicate cases.

In recent years, the genomics community has witnessed the growth of large research biobanks, which collect DNA samples for research purposes. Depending on how and where the samples are genotyped, biobanks also offer the potential opportunity to return actionable genomic results to the clinical setting. We developed a preemptive clinical pharmacogenomic implementation initiative via a health system-wide research biobank at the University of Colorado. Here, we describe how preemptive return of clinical pharmacogenomic results via a research biobank is feasible, particularly when coupled with strong institutional support to maximize the impact and efficiency of biobank resources, a multidisciplinary implementation team, automated clinical decision support tools, and proactive strategies to engage stakeholders early in the clinical decision support tool development process.

Purpose Little is known about how many insured patients receive pharmacogenetic testing. We describe trends of single-gene pharmacogenetic testing in a US managed care population, and demographic and clinical characteristics of patients who received a test. Methods We leveraged a random sample of nearly 11 million patients from a data set of paid medical and pharmacy claims to identify patients with at least one claim indicating receipt of at least one of these single-gene pharmacogenetic tests: CYP2C19, CYP2D6, CYP2C9, VKORC1, UGT1A1, and HLA class 1 typing. Results From 1 January 2013 to 30 September 2017, 5712 patients received at least one pharmacogenetic test (55% female; mean age = 43 years). The median number of tests per patient was 3 (mean = 2.7, max = 12); 54% were processed through Managed Medicare/Medicaid, while 45% were processed through commercial insurance. The total number of pharmacogenetic tests received more than doubled from 2013 (n = 1955) to 2015 (n = 4192), then decreased slightly in 2016 (n = 3946). The most common test was CYP2C19 (n = 4719), and “long-term (current) use of other medications” was the most common diagnosis. Conclusion Pharmacogenetic testing through patients’ insurance was low, but more than doubled from 2013 to 2016. This study highlights the need to better understand utilization patterns and insurance coverage for pharmacogenetic tests.

Background: Prior prognostic models for acute decompensated HF (ADHF) have incorporated race as a covariate may not have completely account for the social and biological factors that underlie the racial disparities in outcomes. We developed race-specific models to improve risk prediction and better identify race-specific predictors of mortality in patients with ADHF using machine learning (ML) methods. Methods: Race-specific ML-models for in-hospital mortality were developed and validated in ADHF patients enrolled in the GWTG-HF registry between 2007 to 2020 (14,586 Black and 50,351 Non-Black adults) using over 40 candidate variables. External validation was performed among participants from the ARIC study with ADHF (n=1,115 Black and 2,028 Non-Black adults). The discrimination and calibration was compared with a previously validated risk score and cohort-specific logistic regression (LR) models. The changes in model reclassification performance with addition of zip-code level data of socioeconomic (SES) parameters was also assessed. Results: In the GWTG-HF cohort, the ML models had superior performance in both Black and Non-Black participants compared with the traditional GWTG-HF risk score ( Table ). The superior performance of the ML-model was also observed in the external validation cohort as compared with the other LR models ( Table ). Addition of measures of SES did not meaningfully change the performance but improved the reclassification metrics of the ML-models with net up-classification of risk in Black [NRI = 0.21 (0.07, 0.35)] and down-classification in Non-Black patients [NRI = -0.08 (-0.12, -0.04)]. SES was responsible for 21.7% of the total attributable mortality risk in Black adults compared to 0.4% in Non-Black adults. Conclusion: Race-specific, ML-based mortality models demonstrated superior performance when compared to traditional HF mortality risk models. Socioeconomic distress is an important contributor to risk in Black adults.

The development of treatments for heart failure (HF) is challenged by burdensome clinical trials. Reducing the need for extensive data collection and increasing opportunities for data compatibility between trials may improve efficiency and reduce resource burden. The Heart Failure Collaboratory (HFC) multi-stakeholder consortium sought to create a lean case report form (CRF) for use in HF clinical trials evaluating cardiac devices. The HFC convened patients, clinicians, clinical researchers, the U.S. Food and Drug Administration (FDA), payers, industry partners, and statisticians to create a consensus core CRF. Eight recent clinical trial CRFs for the treatment of HF from 6 industry partners were analyzed. All CRF elements were systematically reviewed. Those elements deemed critical for data collection in HF clinical trials were used to construct the final, harmonized CRF. The original CRFs included 176 distinct data items covering demographics, vital signs, physical examination, medical history, laboratory and imaging testing, device therapy, medications, functional and quality of life assessment, and outcome events. The resulting, minimally inclusive CRF device contains 75 baseline data items and 6 events, with separate modular additions that can be used depending on the additional detail required for a particular intervention. The consensus electronic form is now freely available for use in clinical trials. Creation of a core CRF is important to improve clinical trial efficiency in HF device development in the United States. This living document intends to reduce clinical trial administrative burden, increase evidence integrity, and improve comparability of clinical data between trials.

Introduction The Surgical Risk Preoperative Assessment System is a parsimonious, universal surgical risk calculator integrated into our local electronic health record. We determined how many of its eight preoperative risk predictor variables could be automatically obtained from the electronic health record. This has implications for the usability and adoption of Surgical Risk Preoperative Assessment System, serving as an example of use of electronic health record data for populating clinical decision support tools. Methods We quantified the availability and accuracy in the electronic health record of the eight Surgical Risk Preoperative Assessment System predictor variables (patient age, American Society of Anesthesiology physical status classification, functional health status, sepsis, work Relative Value Unit, in-/outpatient operation, surgeon specialty, emergency status) at the patient’s preoperative encounter of 5205 patients entered into the American College of Surgeons National Surgical Quality Improvement Program. Accuracy was determined by comparing the electronic health record data to the same patient’s National Surgical Quality Improvement Program data, used as the “gold standard.” Acceptable accuracy was defined as a Kappa statistic or Pearson correlation coefficient ≥0.8 when comparing electronic health record and National Surgical Quality Improvement Program data. Acceptable availability was defined as presence of the variable in the electronic health record at the preoperative encounter ≥95% of the time. Results Of the eight predictor variables, six had acceptable accuracy. Only preoperative sepsis and functional health status had Kappa statistics Conclusions Processes need to be developed to populate more of the Surgical Risk Preoperative Assessment System preoperative predictor variables in the patient’s electronic health record prior to the preoperative visit to lessen the burden on the busy surgeon and encourage more widespread use of Surgical Risk Preoperative Assessment System.

Objectives The purpose of this study was to compare the effectiveness of bucindolol with that of metoprolol succinate for the maintenance of sinus rhythm in a genetically defined heart failure (HF) population with atrial fibrillation (AF). Background Bucindolol is a beta-blocker whose unique pharmacologic properties provide greater benefit in HF patients with reduced ejection fraction (HFrEF) who have the beta1-adrenergic receptor (ADRB1) Arg389Arg genotype. Methods A total of 267 HFrEF patients with a left ventricular ejection fraction (LVEF) Results The hazard ratio (HR) for the primary endpoint was 1.01 (95% confidence interval [CI]: 0.71 to 1.42), but trends for bucindolol benefit were observed in several subgroups. Precision therapeutic phenotyping revealed that a differential response to bucindolol was associated with the interval of time from the initial diagnoses of AF and HF to randomization and with the onset of AF relative to that of the initial HF diagnosis. In a cohort whose first AF and HF diagnoses were Conclusions Pharmacogenetically guided bucindolol therapy did not reduce the recurrence of AF/AFL or ACM compared to that of metoprolol therapy in HFrEF patients, but populations were identified who merited further investigation in future phase 3 trials.

Objectives This study sought to investigate sex differences in outcomes and responses to spironolactone in patients with heart failure with preserved ejection fraction (HFpEF). Background HFpEF affects women more frequently than men. Sex differences in responses to effects of mineralocorticoid antagonists have not been reported. Methods This was an exploratory, post hoc, non-pre-specified analysis of the TOPCAT (Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function) trial. Subjects with symptomatic HF and a left ventricular ejection fraction ≥45% were randomized to spironolactone or placebo therapy. Subjects enrolled from the Americas were analyzed. The primary outcome was a composite of cardiovascular (CV) death, cardiac arrest, or HF hospitalization. Secondary outcomes included all-cause mortality, CV, and non-CV mortality and CV, HF, and non-CV hospitalization. Sex differences in outcomes and treatment effects were determined using time-to-event analysis. Results In total, 882 of 1,767 subjects (49.9%) were women. Women were older with fewer comorbidities but worse patient-reported outcomes. There were no sex differences in outcomes in the placebo arm or in response to spironolactone for the primary outcome or its components. Spironolactone therapy was associated with reduced all-cause mortality in women (hazard ratio: 0.66; p = 0.01) but not in men (pinteraction = 0.02). Conclusions In TOPCAT, women and men presented with different clinical profiles and similar clinical outcomes. The interaction between spironolactone and sex in TOPCAT overall and in the present analysis was nonsignificant for the primary outcome, but there was a reduction in all-cause mortality associated with spironolactone therapy in women, with a significant interaction between sex and treatment arm. Prospective evaluation is needed to determine whether spironolactone therapy may be effective for treatment of HFpEF in women. (Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function [TOPCAT]; NCT00094302)

This study assessed the performance of an automated speech analysis technology in detecting pulmonary fluid overload in patients with acute decompensated heart failure (ADHF).Pulmonary edema is the main cause of heart failure (HF)-related hospitalizations and a key predictor of poor postdischarge prognosis. Frequent monitoring is often recommended, but signs of decompensation are often missed. Voice and sound analysis technologies have been shown to successfully identify clinical conditions that affect vocal cord vibration mechanics.Adult patients with ADHF (n = 40) recorded 5 sentences, in 1 of 3 languages, using HearO, a proprietary speech processing and analysis application, upon admission (wet) to and discharge (dry) from the hospital. Recordings were analyzed for 5 distinct speech measures (SMs), each a distinct time, frequency resolution, and linear versus perceptual (ear) model; mean change from baseline SMs was calculated.In total, 1,484 recordings were analyzed. Discharge recordings were successfully tagged as distinctly different from baseline (wet) in 94% of cases, with distinct differences shown for all 5 SMs in 87.5% of cases. The largest change from baseline was documented for SM2 (218%). Unsupervised, blinded clustering of untagged admission and discharge recordings of 9 patients was further demonstrated for all 5 SMs.Automated speech analysis technology can identify voice alterations reflective of HF status. This platform is expected to provide a valuable contribution to in-person and remote follow-up of patients with HF, by alerting to imminent deterioration, thereby reducing hospitalization rates. (Clinical Evaluation of Cordio App in Adult Patients With CHF; NCT03266029).

Background Actigraphy-based measurements of physiologic parameters may enable design of patient-centric heart failure (HF) clinical trials. Recently, the Heart Failure Collaboratory (HFC) focused on recommendations for meaningful change and use of actigraphy as an endpoint in HF clinical trials. We aimed to evaluate randomized controlled trials (RCTs) that have quantified the impact of HF interventions using actigraphy. Methods Using a scoping review strategy, we evaluated the use of actigraphy in HF RCTs. Studies were identified through electronic searches of Embase, OVID MEDLINE, PubMed, and Cochrane review. Data on trial characteristics and results were collected. Results We identified 11 RCTs with a total of 1,455 participants. The risk of bias across the included trials was overall high. All trials had the primary outcomes reflecting measures of either physical activity (n=8), sleep (n=2), or both (n=1). Five trials evaluated response to pharmacologic therapies compared to placebo, three evaluated physical activity interventions, two evaluated group or cognitive therapy, and one evaluated sleep-ventilation strategy. Sample sizes ranged from 30 to 619 participants. There was significant heterogeneity relating to device type, body placement site, and handling of missing actigraphy data. Duration of monitoring ranged from 48 hours to 12 weeks. None of the studies evaluating pharmacologic therapies (n=5) demonstrated a significant improvement of actigraphy-based primary endpoints measurements. Conclusions There is significant heterogeneity in the use, methodology, and results of actigraphy-based HF RCTs. Our results highlight the need to develop, standardize, and validate actigraphy-specific outcomes for use in HF clinical trials

Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2–4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. We customized Illumina’s Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections. Viral infections affect the body in many ways, including via changes to the epigenome, the sum of chemical modifications to an individual’s collection of genes that affect gene activity. Here, we analyzed the epigenome in blood samples from people with and without COVID-19 to determine whether we could find changes consistent with SARS-CoV-2 infection. Using a combination of statistical and machine learning techniques, we identify markers of SARS-CoV-2 infection as well as of severity and progression of COVID-19 disease. These signals of disease progression were present from the initial blood draw when first walking into the hospital. Together, these approaches demonstrate the potential of measuring the epigenome for monitoring SARS-CoV-2 status and severity. Konigsberg et al. profile DNA methylation in blood samples from SARS-CoV-2 cases and controls. The authors use machine learning to classify infected vs. non-infected individuals and predict clinical outcomes related to disease severity.

Objective:To evaluate outcomes in patients with Turner Syndrome, especially those with cardiac conditions, compared to those without Turner syndrome.Design:Retrospective cohort study utilising hospitalisation data from 2006 to 2012. Conditional logistic regression models are used to analyse outcomes of interest: all-cause mortality, increased length of stay, and discharge to home.Participants:We identified 2978 women with Turner syndrome, matched to 11,912 controls by primary diagnosis.Results:Patients with Turner syndrome were more likely to experience inpatient mortality (odds ratio 1.44, 95% confidence interval 1.02–2.02, p = 0.04) and increased length of stay (OR 1.31, CI 1.18–1.46, p = 0.03) than primary diagnosis matched controls, after adjusting for age, race, insurance status, and Charlson comorbidity index. Patients with Turner syndrome were 32% less likely to be discharged to home (OR 0.68, CI 0.60–0.78, p < 0.001). When restricting the sample of patients to those admitted with a cardiac diagnosis, the likelihood of mortality (OR 3.10, CI 1.27–7.57, p = 0.01) and prolonged length of stay (OR 1.42, CI 1.03–1.95, p = 0.03) further increased, while the likelihood of discharge to home further decreased (OR 0.55, CI 0.38–0.80, p = 0.001) in Turner syndrome compared to primary diagnosis matched controls. Specifically, patients with congenital heart disease were more likely to have prolonged length of stay (OR: 1.53, CI 1.18–2.00, p = 0.002), but not increased mortality or decreased discharge to home.Conclusions:Hospitalised women with Turner syndrome carry a higher risk of adverse outcomes even when presenting otherwise similarly as controls, an important consideration for those treating them in these settings.

Background: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment and patient adherence to prevention guidelines. We investigated the diet domain in FHS (Framingham Heart Study), CHS (Cardiovascular Heart Study), and the ARIC study (Atherosclerosis Risk in Communities) to identify potential lifestyle and behavioral factors associated with coronary heart disease, HF, and stroke. Methods: We used machine learning feature selection based on random forest analysis to identify potential risk factors associated with coronary heart disease, stroke, and HF in FHS. We evaluated the significance of selected variables using univariable and multivariable Cox proportional hazards analysis adjusted for known cardiovascular risks. Findings from FHS were then validated using CHS and ARIC. Results: We identified multiple dietary and behavioral risk factors for cardiovascular disease outcomes including marital status, red meat consumption, whole milk consumption, and coffee consumption. Among these dietary variables, increasing coffee consumption was associated with decreasing long-term risk of HF congruently in FHS, ARIC, and CHS. Conclusions: Higher coffee intake was found to be associated with reduced risk of HF in all three studies. Further study is warranted to better define the role, possible causality, and potential mechanism of coffee consumption as a potential modifiable risk factor for HF.

Objectives The aim of this study was to determine whether patients with heart failure with reduced ejection fraction (HFrEF) due to nonischemic etiology eligible for cardiac resynchronization therapy (CRT) benefit from an implantable cardioverter-defibrillator (ICD). Background It is uncertain whether CRT with an ICD (CRT-D) compared to without an ICD (CRT-P) is associated with a survival benefit in patients with nonischemic etiologies of HFrEF. Methods Analyses of the COMPANION (Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure) trial were performed, using Cox proportional hazards modeling stratified by HFrEF etiology of nonischemic cardiomyopathy (NICM) or ischemic cardiomyopathy (ICM). The primary outcome was all-cause mortality (ACM), and secondary outcomes were the combination of cardiovascular mortality or heart failure hospitalization and sudden cardiac death. Results Among patients randomized to CRT (n = 1,212), 236 (19.5%) died, 131 and 105 in the CRT-P and CRT-D arms, respectively. The unadjusted and adjusted hazard ratios (HRs) for CRT-D versus CRT-P were both 0.84 (95% confidence interval [CI]: 0.65 to 1.09) for ACM, with a significant device-etiology interaction (pinteraction = 0.015 adjusted; pinteraction = 0.040 unadjusted). In patients with NICM (n = 555), CRT-D versus CRT-P was associated with reduced ACM (adjusted HR: 0.54; 95% CI: 0.34 to 0.86), while patients with ICM (n = 657) did not exhibit a between-device reduction in ACM (adjusted HR: 1.05; 95% CI: 0.77 to 1.44). The effects of CRT-D versus CRT-P on sudden cardiac death (advantage CRT-D) and cardiovascular mortality or heart failure hospitalization (no difference between CRT-P and CRT-D) were similar between the 2 HFrEF etiologies. Conclusions COMPANION patients with NICM exhibited a decrease in ACM associated with CRT-D but not CRT-P treatment, whereas patients with ICM did not.

This cross-sectional study assesses potential opportunities for genotype-guided prescribing in pediatric populations among multiple health systems by examining the prevalence of prescriptions for each drug with the highest level of evidence and estimating the prevalence of potentially actionable prescribing decisions., Key Points Question What is the opportunity for genotype-guided prescribing among pediatric patients in the US? Findings In this serial cross-sectional study of annual prescribing data at 16 health systems representing approximately 2.9 million pediatric patients per year from 2011 to 2017, the annual prevalence of exposure to at least 1 Clinical Pharmacogenetics Implementation Consortium level A drug ranged from 7987 to 10 629 per 100 000 pediatric patients, with increasing prevalence before reaching a plateau in 2014. The medications with the highest potential for actionability were analgesics (oxycodone, codeine, and tramadol), the antiemetic ondansetron, and antidepressants (citalopram, escitalopram, and amitriptyline). Meaning These findings suggest that ample opportunity exists for genotype-guided prescribing among pediatric patients in the US, especially for drugs metabolized by CYP2D6 or CYP2C19., Importance Genotype-guided prescribing in pediatrics could prevent adverse drug reactions and improve therapeutic response. Clinical pharmacogenetic implementation guidelines are available for many medications commonly prescribed to children. Frequencies of medication prescription and actionable genotypes (genotypes where a prescribing change may be indicated) inform the potential value of pharmacogenetic implementation. Objective To assess potential opportunities for genotype-guided prescribing in pediatric populations among multiple health systems by examining the prevalence of prescriptions for each drug with the highest level of evidence (Clinical Pharmacogenetics Implementation Consortium level A) and estimating the prevalence of potential actionable prescribing decisions. Design, Setting, and Participants This serial cross-sectional study of prescribing prevalences in 16 health systems included electronic health records data from pediatric inpatient and outpatient encounters from January 1, 2011, to December 31, 2017. The health systems included academic medical centers with free-standing children’s hospitals and community hospitals that were part of an adult health care system. Participants included approximately 2.9 million patients younger than 21 years observed per year. Data were analyzed from June 5, 2018, to April 14, 2020. Exposures Prescription of 38 level A medications based on electronic health records. Main Outcomes and Measures Annual prevalence of level A medication prescribing and estimated actionable exposures, calculated by combining estimated site-year prevalences across sites with each site weighted equally. Results Data from approximately 2.9 million pediatric patients (median age, 8 [interquartile range, 2-16] years; 50.7% female, 62.3% White) were analyzed for a typical calendar year. The annual prescribing prevalence of at least 1 level A drug ranged from 7987 to 10 629 per 100 000 patients with increasing trends from 2011 to 2014. The most prescribed level A drug was the antiemetic ondansetron (annual prevalence of exposure, 8107 [95% CI, 8077-8137] per 100 000 children). Among commonly prescribed opioids, annual prevalence per 100 000 patients was 295 (95% CI, 273-317) for tramadol, 571 (95% CI, 557-586) for codeine, and 2116 (95% CI, 2097-2135) for oxycodone. The antidepressants citalopram, escitalopram, and amitriptyline were also commonly prescribed (annual prevalence, approximately 250 per 100 000 patients for each). Estimated prevalences of actionable exposures were highest for oxycodone and ondansetron (>300 per 100 000 patients annually). CYP2D6 and CYP2C19 substrates were more frequently prescribed than medications influenced by other genes. Conclusions and Relevance These findings suggest that opportunities for pharmacogenetic implementation among pediatric patients in the US are abundant. As expected, the greatest opportunity exists with implementing CYP2D6 and CYP2C19 pharmacogenetic guidance for commonly prescribed antiemetics, analgesics, and antidepressants.

Introduction: Several clinical phenotypes have been described in patients with heart failure with preserved ejection fraction (HFpEF). This heterogeneity may explain why no therapies have improved outcomes in clinical trials of HFpEF patients. In a secondary analysis of TOPCAT, the authors discovered improved mortality in female HFpEF patients treated with spironolactone. The objective of this study was to determine if outcomes for spironolactone in women with HFpEF varied by female-specific phenotypes. Methods: Using 10 clinical variables, a latent class analysis (LCA) of 5196 female patients from 7 clinical trials (BEST, IPRESERVE, MOCHA, NEAT-HFpEF, RELAX, TOPCAT, and SOLVD) was used to identify phenotypes across the LVEF spectrum, which included 3 distinct phenotypes of HFpEF patients. A post-hoc analysis of TOPCAT was completed to analyze the outcomes of spironolactone versus placebo by female phenotype. The outcomes were cardiovascular (CV) mortality, heart failure (HF) hospitalizations, all-cause mortality, and CV hospitalizations. Results: Table 1 shows characteristics of 3 female HFpEF phenotypes: phenotype 1 “aging hypertensive”, phenotype 2 “younger hypertensive”, and phenotype 3 “obesity phenotype”. 887 female patients from the TOPCAT trial were analyzed according to these phenotypes. Of these, only phenotype 1 showed a benefit of spironolactone therapy compared to placebo with regards to the primary outcome of CV mortality and HF hospitalizations (20.8% vs 30.2%, p Conclusion: Older, hypertensive women with HFpEF show a significant reduction in CV mortality and HF hospitalizations when treated with spironolactone vs. placebo, whereas other women did not. Prior studies show that this phenotype is often associated with a higher mortality, thus spironolactone may be particularly important in this patient population.

Integrating clinical decision support (CDS) across the continuum of population-, encounter-, and precision-level care domains may improve hospital and clinic workflow efficiency. Due to the diversity and volume of electronic health record data, complexity of medical and operational knowledge, and specifics of target user workflows, the development and implementation of comprehensive CDS is challenging. Additionally, many providers have an incomplete understanding of the full capabilities of current CDS to potentially improve the quality and efficiency of care delivery. These varied requirements necessitate a multidisciplinary team approach to CDS development for successful integration. Here, we present a practical overview of current and evolving applications of CDS approaches in a large academic setting and discuss the successes and challenges. We demonstrate that implementing CDS tools in the context of linked population-, encounter-, and precision-level care provides an opportunity to integrate complex algorithms at each level into a unified mechanism to improve patient management.

Background Clinical decision support (CDS) design best practices are intended to provide a narrative representation of factors that influence the success of CDS tools. However, they provide incomplete direction on evidence-based implementation principles. Objective This study aims to describe an integrated approach toward applying an existing implementation science (IS) framework with CDS design best practices to improve the effectiveness, sustainability, and reproducibility of CDS implementations. Methods We selected the Practical Robust Implementation and Sustainability Model (PRISM) IS framework. We identified areas where PRISM and CDS design best practices complemented each other and defined methods to address each. Lessons learned from applying these methods were then used to further refine the integrated approach. Results Our integrated approach to applying PRISM with CDS design best practices consists of 5 key phases that iteratively interact and inform each other: multilevel stakeholder engagement, designing the CDS, design and usability testing, thoughtful deployment, and performance evaluation and maintenance. The approach is led by a dedicated implementation team that includes clinical informatics and analyst builder expertise. Conclusions Integrating PRISM with CDS design best practices extends user-centered design and accounts for the multilevel, interacting, and dynamic factors that influence CDS implementation in health care. Integrating PRISM with CDS design best practices synthesizes the many known contextual factors that can influence the success of CDS tools, thereby enhancing the reproducibility and sustainability of CDS implementations. Others can adapt this approach to their situation to maximize and sustain CDS implementation success.

Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.

BACKGROUND Limited consideration of clinical decision support (CDS) design best practices, such as a user-centered design, is often cited as a key barrier to CDS adoption and effectiveness. The application of CDS best practices is resource intensive; thus, institutions often rely on commercially available CDS tools that are created to meet the generalized needs of many institutions and are not user centered. Beyond resource availability, insufficient guidance on how to address key aspects of implementation, such as contextual factors, may also limit the application of CDS best practices. An implementation science (IS) framework could provide needed guidance and increase the reproducibility of CDS implementations. OBJECTIVE This study aims to compare the effectiveness of an enhanced CDS tool informed by CDS best practices and an IS framework with a generic, commercially available CDS tool. METHODS We conducted an explanatory sequential mixed methods study. An IS-enhanced and commercial CDS alert were compared in a cluster randomized trial across 28 primary care clinics. Both alerts aimed to improve beta-blocker prescribing for heart failure. The enhanced alert was informed by CDS best practices and the Practical, Robust, Implementation, and Sustainability Model (PRISM) IS framework, whereas the commercial alert followed vendor-supplied specifications. Following PRISM, the enhanced alert was informed by iterative, multilevel stakeholder input and the dynamic interactions of the internal and external environment. Outcomes aligned with PRISM’s evaluation measures, including patient reach, clinician adoption, and changes in prescribing behavior. Clinicians exposed to each alert were interviewed to identify design features that might influence adoption. The interviews were analyzed using a thematic approach. RESULTS Between March 15 and August 23, 2019, the enhanced alert fired for 61 patients (106 alerts, 87 clinicians) and the commercial alert fired for 26 patients (59 alerts, 31 clinicians). The adoption and effectiveness of the enhanced alert were significantly higher than those of the commercial alert (62% vs 29% alerts adopted, PP=.006). Of the 21 clinicians interviewed, most stated that they preferred the enhanced alert. CONCLUSIONS The results of this study suggest that applying CDS best practices with an IS framework to create CDS tools improves implementation success compared with a commercially available tool. CLINICALTRIAL ClinicalTrials.gov NCT04028557; http://clinicaltrials.gov/ct2/show/NCT04028557

SUMMARY Using serial analysis of myocardial gene expression employing endomyocardial biopsy starting material in a dilated cardiomyopathy cohort, we show that the SARS-CoV-2 cardiac myocyte receptor ACE2 is upregulated with remodeling and with reverse remodeling down-regulates into the normal range. The proteases responsible for virus-cell membrane fusion were expressed but not regulated with remodeling. In addition, a new candidate for CoV-2 cell binding and entry was identified, the integrin ITGA5. The upregulation in ACE2 in remodeled LVs may explain worse outcomes in COVID-19 patients with underlying myocardial disorders, and counteracting ACE2 upregulation is a possible therapeutic approach to minimizing cardiac damage., Graphical abstract, Highlights 1. The SARS-CoV-2 cellular receptor ACE2 and 5 proteases implicated in fusion of virus and cell membranes that are vital to cell entry were expressed at the mRNA level in RNA extracted from septal endomyocardial biopsies of F/NDC and nonfailing (NF) control patients. 2. ACE 2 was upregulated by 1.97 fold in 46 F/NDC patients compared to NF, but proteases showed similar degrees of expression. 3. On LV reverse remodeling effected by beta-blocking agents, ACE2 expression, in the presence of unchanged doses of ACE inhibitors or ARBs, downregulated into the normal range. 4. ITGA5, an integrin that binds to ACE2 and to a motif in the CoV-2 spike protein binding domain, was expressed in both NF and F/NDC, upregulated in the latter at baseline and decreased in expression on reverse remodeling similar to ACE2, and is a candidate for facilitating CoV-2 binding and cell entry in LV myocardium.

Background Lisinopril and losartan manufacturer labels recommend twice-daily dosing (BID) if once-daily (QDay) is insufficient to lower blood pressure (BP). Methods and results Retrospective cohort study of patients taking QDay lisinopril and losartan who experienced a dose-doubling (index date). A text-processing tool categorized BID and QDay groups at the index date based on administration instructions. We excluded: pregnant/hospice, regimens other than BID/QDay, and without BP measurements -6 months/+12 months of the index date. The most proximal BP measurements -6 months and +2 weeks to 12 months of the index date were used to evaluate BP differences. Propensity scores were generated, and differences in BP and adverse events (angioedema, acute kidney injury, hyperkalemia) between BID/QDay groups were analyzed within dosing cohorts using inverse propensity of treatment-weighted regression models. Of 11,210 and 6,051 patients who met all criteria for lisinopril and losartan, 784 (7.0%) and 453 (7.5%) were taking BID, respectively. BID patients were older and had higher comorbidity and medication burdens. There were no differences in systolic/diastolic BP between BID and QDay, with absolute differences in mean systolic BP ranging from -1.8 to 0.7 mmHg and diastolic BP ranging from -1.1 to 0.1 mmHg (all 95% confidence intervals [CI] cross 0). Lisinopril 10mg BID was associated with an increased odds of angioedema compared to lisinopril 20mg QDay (odds ratio 2.27, 95%CI 1.13–4.58). Conclusions Adjusted models do not support improved effectiveness or safety of BID lisinopril and losartan.

Little is known about the relation between socioeconomic factors and health outcomes in adults and adolescents with congenital heart defects (CHD). Population-level data from the Colorado CHD surveillance system from 2011 to 2013 was used to examine the association between area deprivation and outcomes including hospitalizations, emergency department visits, cardiac procedures, all-cause and cardiac-related mortality, and major adverse cardiac events. Socioeconomic context was measured by the Area Deprivation Index at census tract level. Missing race/ethnicity was imputed using the Bayesian Improved Surname Geocoding algorithm. Generalized linear models were utilized to examine health disparities across deprivation quintiles after adjusting for insurance type, race/ethnicity, age, gender, urbanicity, and CHD severity in 5,748 patients. Cases residing in the most deprived quintile had 51% higher odds of inpatient admission, 74% higher odds of emergency department visit, 41% higher odds of cardiac surgeries, and 45% higher odds of major adverse cardiac events compared with cases in the least deprived quintile. Further, rates of hospitalizations, emergency department admissions, and cardiac surgeries were elevated in the most deprived compared with the least deprived quintile. Mortality was not significantly different across quintiles. In conclusion, findings suggest significant health equity issues for adolescent and adults with CHD based on area-based deprivation.

Background Heart failure with reduced ejection fraction (HFrEF) benefits from initiation and intensification of multiple pharmacotherapies. Unfortunately, there are major gaps in the routine use of these drugs. Without novel approaches to improve prescribing, the cumulative benefits of HFrEF treatment will be largely unrealized. Direct-to-consumer marketing and shared decision making reflect a culture where patients are increasingly involved in treatment choices, creating opportunities for prescribing interventions that engage patients. Hypothesis Encouraging patients to engage providers in HFrEF prescribing decisions will improve the use of guideline-directed medical therapies. Design The Electronically delivered, Patient-activation tool for Intensification of Chronic medications for Heart Failure with reduced ejection fraction (EPIC-HF) trial randomizes patients with HFrEF to usual care versus patient-activation tools—a 3-minute video and 1-page checklist—delivered prior to cardiology clinic visits that encourage patients to work collaboratively with their clinicians to intensify HFrEF prescribing. The study assesses the effectiveness of the EPIC-HF intervention to improve guideline-directed medical therapy in the month after its delivery while using an implementation design to also understand the reach, adoption, implementation, and maintenance of this approach within the context of real-world care delivery. Study enrollment was completed in January 2020, with a total 305 patients. Baseline data revealed significant opportunities, with Summary The EPIC-HF trial assesses the implementation, effectiveness, and safety of patient engagement in HFrEF prescribing decisions. If successful, the tool can be easily disseminated and may inform similar interventions for other chronic conditions.

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC&rsquo, s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also conducted to obtain an impression of the usability and potential limitations.

BACKGROUND Clinical decision support (CDS) design best practices are intended to provide a narrative representation of factors that influence the success of CDS tools. However, they provide incomplete direction on evidence-based implementation principles. OBJECTIVE This study aims to describe an integrated approach toward applying an existing implementation science (IS) framework with CDS design best practices to improve the effectiveness, sustainability, and reproducibility of CDS implementations. METHODS We selected the Practical Robust Implementation and Sustainability Model (PRISM) IS framework. We identified areas where PRISM and CDS design best practices complemented each other and defined methods to address each. Lessons learned from applying these methods were then used to further refine the integrated approach. RESULTS Our integrated approach to applying PRISM with CDS design best practices consists of 5 key phases that iteratively interact and inform each other: multilevel stakeholder engagement, designing the CDS, design and usability testing, thoughtful deployment, and performance evaluation and maintenance. The approach is led by a dedicated implementation team that includes clinical informatics and analyst builder expertise. CONCLUSIONS Integrating PRISM with CDS design best practices extends user-centered design and accounts for the multilevel, interacting, and dynamic factors that influence CDS implementation in health care. Integrating PRISM with CDS design best practices synthesizes the many known contextual factors that can influence the success of CDS tools, thereby enhancing the reproducibility and sustainability of CDS implementations. Others can adapt this approach to their situation to maximize and sustain CDS implementation success.

Background Elective percutaneous coronary interventions (PCI) are difficult to discriminate from non-elective PCI in administrative data due to non-specific encounter codes, limiting the ability to track outcomes, ensure appropriate medical management, and/or perform research on patients who undergo elective PCI. The objective of this study was to assess the abilities of several algorithms to identify elective PCI procedures using administrative data containing diagnostic, utilization, and/or procedural codes. Methods and results For this retrospective study, administrative databases in an integrated healthcare delivery system were queried between 1/1/2015 and 6/31/2016 to identify patients who had an encounter for a PCI. Using clinical criteria, each encounter was classified via chart review as a valid PCI, then as elective or non-elective. Cases were tested against nine pre-determined algorithms. Performance statistics (sensitivity, specificity, positive predictive value, and negative predictive value) and associated 95% confidence intervals (CI) were calculated. Of 521 PCI encounters reviewed, 497 were valid PCI, 93 of which were elective. An algorithm that excluded emergency room visit events had the highest sensitivity (97.9%, 95%CI 92.5%-99.7%) while an algorithm that included events occurring within 90 days of a cardiologist visit and coronary angiogram or stress test had the highest positive predictive value (62.2%, 95%CI 50.8%-72.7%). Conclusions Without an encounter code specific for elective PCI, an algorithm excluding procedures associated with an emergency room visit had the highest sensitivity to identify elective PCI. This offers a reasonable approach to identify elective PCI from administrative data.

Background Patients undergoing lead extraction for infected devices have worse outcomes compared with those with noninfected devices. We assessed predictors of in‐hospital mortality and procedure‐related major adverse events ( MAEs ) in a large cohort undergoing lead extraction. Methods and Results Deidentified hospital records procedure from 7 states between 1994 and 2013 were aggregated and International Classification of Disease, Ninth Revision (ICD‐9) procedure codes were used to identify hospital records reporting lead extraction. MAEs included death, cardiac tamponade, hemothorax, and need for emergent cardiac surgery. Predictors of in‐hospital MAEs for infected compared with noninfected leads were identified using multivariate regression. Associations between outcomes and specific microbe were also assessed. In total, 57 220 discharges specified lead extraction. Infected leads accounted for the minority of total lead extractions compared with fractured leads (16.1 versus 59.8%, 25.7% not reported). There were 3298 MAE s (5.8%) including 980 deaths (1.7%). Multivariate predictors of MAE included black race, atrial fibrillation, anemia, heart failure, and admission via either hospital transfer or emergency department versus home (all P P Conclusions Patients undergoing extraction of infected leads have higher in‐hospital mortality and adverse events compared with noninfected leads. Streptococcus, anemia, and heart failure are predictors of adverse outcomes.

To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis.The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables. Phenogroup 1 had higher burden of co-morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non-cardiac co-morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co-morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all-cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non-echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants.Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes.

ObjectiveWomen with pregnancies complicated by hypertensive disorders of pregnancy (HDP) have increased long-term cardiovascular (CV) risk. We sought to determine if they demonstrate increased short-term CV risk.MethodsUsing administrative records, all hospital-based deliveries in Florida from 2004 to 2010 and subsequent readmission to any Florida hospital within 3 years of index delivery were identified. Deliveries and clinical diagnoses were determined using International Classification of Diseases, Ninth Revision, Clinical Modification codes. HDP included pregnancies complicated by gestational hypertension, pre-eclampsia or eclampsia. Outcomes were CV readmission (acute myocardial infarction, stroke or heart failure), non-CV readmission and any readmission within 3 years of delivery excluding subsequent deliveries. Associations were determined using multivariate logistic regression.ResultsAmong 1 452 926 records from delivering mothers of singleton infants (mean age 27.2±6.2 years; 52% white, 23% African American (AA), 18% Hispanic), there were 4054 CV and 259 252 non-CV readmissions. Women with HDP had higher CV readmission rates (6.4 vs 2.5/1000 deliveries; PConclusionWomen with HDP had twice the risk of CV readmission within 3 years of delivery, with higher rates among AA women. More work is needed to explore preventive strategies for HDP-associated events.

Myocardial H 2 receptor activation contributes to heart failure (HF) in preclinical models, and H 2 receptor antagonists are associated with decreased HF incidence. This study evaluated whether H 2 histamine receptor (HRH2) single nucleotide polymorphisms (SNPs) are associated with HF incidence and whether myocardial transcript abundance is associated with HF recovery. The association of SNPs in HRH2 with incident HF was characterized using Cox proportional hazards regression among participants in the Multi-Ethnic Study of Atherosclerosis. Differences in myocardial HRH2 transcripts were characterized in participants with dilated cardiomyopathy comparing 6 “super-responders” with 6 nonresponders to β blockade in the Beta-Blocker Effect on Remodeling and Gene Expression Trial. In MESA, no candidate SNP was associated with HF in black, Hispanic, or white participants. The rs2241562 minor allele was present only in Chinese participants and the adjusted HF hazard among those with 1 or more copies of this allele was 3.7, 95% confidence interval 1.0 to 13.4. In BORG, super-responders to β blockade had higher levels of myocardial HRH2 transcript at baseline compared with nonresponders (fragments per kilobase per transcript per million mapped reads: Variant 2, 5.5 ± 1.1 compared with 3.2 ± 0.8 in nonresponders, p = 0.002; Variant 1 + 2, 32.1 ± 7.4 compared with 23.3 ± 4.2 in nonresponders, p = 0.04). In conclusion, t he presence of a minor allele at rs2241562 was associated with increased HF incidence in Chinese participants. Differences in myocardial HRH2 transcript abundance were seen in participants with dilated cardiomyopathy who responded to β blockade. These observations support the hypothesis that HRH2 is involved in the pathogenesis of HF.

Diagnosis, prognosis, treatment, and development of new therapies for diseases or syndromes depend on a reliable means of identifying phenotypes associated with distinct predictive probabilities for these various objectives. Left ventricular ejection fraction (LVEF) provides the current basis for combined functional and structural phenotyping in heart failure by classifying patients as those with heart failure with reduced ejection fraction (HFrEF) and those with heart failure with preserved ejection fraction (HFpEF). Recently the utility of LVEF as the major phenotypic determinant of heart failure has been challenged based on its load dependency and measurement variability. We review the history of the development and adoption of LVEF as a critical measurement of LV function and structure and demonstrate that, in chronic heart failure, load dependency is not an important practical issue, and we provide hemodynamic and molecular biomarker evidence that LVEF is superior or equal to more unwieldy methods of identifying phenotypes of ventricular remodeling. We conclude that, because it reliably measures both left ventricular function and structure, LVEF remains the best current method of assessing pathologic remodeling in heart failure in both individual clinical and multicenter group settings. Because of the present and future importance of left ventricular phenotyping in heart failure, LVEF should be measured by using the most accurate technology and methodologic refinements available, and improved characterization methods should continue to be sought.

Patients with congestive heart failure (CHF) require complex medical management across the continuum of care. Electronic health records (EHR) are currently used for traditional tasks of documentation, reviewing and managing test results, computerized order entry, and billing. Unfortunately many clinicians view EHR as merely digitized versions of paper charts, which create additional work and cognitive burden without improving quality or efficiency of care. In fact, EHR are revolutionizing the care of chronic diseases such as CHF. This review describes how appropriate use of technologies offered by EHR can help standardize CHF care, promote adherence to evidence-based guidelines, optimize workflow efficiency, improve performance metrics, and facilitate patient engagement. This review discusses a number of tools including documentation templates, telehealth and telemedicine, health information exchange, order sets, clinical decision support, registries, and analytics. Where available, evidence of their potential utility in management of CHF is presented. Together these EHR tools can also be used to enhance quality improvement, patient management, and clinical research as part of a learning health care system model. This review describes how existing EHR tools can support patients, cardiologists, and care teams to deliver consistent, high-quality, coordinated, patient-centered, and guideline-concordant care of CHF.

BACKGROUND: The electrocardiogram or ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scale data could substantially expand the clinical inferences derived from the ECG, while at the same time preserving interpretability for medical decision-making. METHODS AND RESULTS: We identified 36,186 ECGs from the UCSF database that would enable training of models for estimation of cardiac structure or function or detection of disease. We segmented the ECG into standard component waveforms and intervals using a novel combination of convolutional neural networks (CNN) and Hidden Markov Models (HMM) and evaluated this segmentation by comparing resulting electrical intervals against 141,864 measurements produced during the clinical workflow. We then built a patient-level “ECG profile”, a 725-element feature vector and used this profile to train and interpret machine learning models for examples of cardiac structure (left ventricular mass, left atrial volume, mitral annulus e-prime) and disease (pulmonary arterial hypertension (PAH), hypertrophic cardiomyopathy (HCM), cardiac amyloid (CA), and mitral valve prolapse (MVP)). ECG measurements derived from the CNN-HMM segmentation agreed with clinical estimates, with median absolute deviations (MAD) as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Models trained using patient-level ECG profiles enabled surprising quantitative estimates of left ventricular mass and mitral annulus e’ velocity (MAD of 16% and 19%, respectively) with good discrimination for left ventricular hypertrophy and diastolic dysfunction as binary traits. Model performance using our approach for disease detection demonstrated AUROC of 0.94 for PAH, 0.91 for HCM, 0.86 for CA, and 0.77 for MVP. CONCLUSIONS: Modern machine learning methods can extend the 12-lead ECG to quantitative applications well beyond its current uses while preserving the transparency that is so fundamental to clinical care.

BackgroundTo improve user-centred design efforts and efficiency; there is a need to disseminate information on modern day clinician preferences for technologies such as computerised clinical decision support (CDS).ObjectiveTo describe clinician perceptions regarding beneficial features of CDS for chronic medications in primary care.MethodsThis study included focus groups and clinicians individually describing their ideal CDS. Three focus groups were conducted including prescribing clinicians from a variety of disciplines. Outcome measures included identification of favourable features and unintended consequences of CDS for chronic medication management in primary care. We transcribed recordings, performed thematic qualitative analysis and generated counts when possible.ResultsThere were 21 participants who identified four categories of beneficial CDS features during the group discussion: non-interruptive alerts, clinically relevant and customisable support, presentation of pertinent clinical information and optimises workflow. Non-interruptive alerts were broadly defined as passive alerts that a user chooses to review, whereas interruptive were active or disruptive alerts that interrupted workflow and one is forced to review before completing a task. The CDS features identified in the individual descriptions were consistent with the focus group discussion, with the exception of non-interruptive alerts. In the individual descriptions, 12 clinicians preferred interruptive CDS compared with seven clinicians describing non-interruptive CDS.ConclusionClinicians identified CDS for chronic medications beneficial when they are clinically relevant and customisable, present pertinent clinical information (eg, labs, vitals) and improve their workflow. Although clinicians preferred passive, non-interruptive alerts, most acknowledged that these may not be widely seen and may be less effective. These features align with literature describing best practices in CDS design and emphasise those features clinicians prioritise, which should be considered when designing CDS for medication management in primary care. These findings highlight the disparity between the current state of CDS design and clinician-stated design features associated with beneficial CDS.