Randomized Evaluation of a Remote Management Program to Improve Guideline-directed Medical Therapy: The Diabetes Remote Intervention to Improve Use of Evidence-based Medications (DRIVE) Trial.

BACKGROUND: Several sodium-glucose transport protein 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) reduce cardiovascular (CV) events and improve kidney outcomes in patients with type 2 diabetes (T2D); however, utilization remains low despite guideline recommendations. METHODS: A randomized, remote implementation trial in the Mass General Brigham network enrolled patients with T2D at high CV and /or kidney risk. Patients eligible for, but not prescribed, SGLT2i or GLP-1 RA were randomly assigned to simultaneous virtual patient education with concurrent prescription of SGLT2i or GLP-1 RA ("simultaneous") or two months of virtual education followed by medication prescription ("education-first") delivered by a multi-disciplinary team driven by non-licensed navigators and clinical pharmacists who prescribed SGLT2i or GLP-1 RA using a standardized treatment algorithm. The primary outcome was the proportion of patients with prescriptions for either SGLT2i or GLP-1 RA by 6 months. RESULTS: Between March 2021 and December 2022, 200 patients were randomized. Mean age was 66.5 years, 36.5% were female, 22.0% were non-White. Overall, 30.0% had cardiovascular CV disease, 5.0% had cerebrovascular disease, and 1.5% had both. Mean estimated glomerular filtration rate (eGFR) 77.9 mL/min/1.73m2 and mean urine/albumin creatinine ratio (UACR) 88.6mg/g. After two months, 69/200 (34.5%) patients received a new prescription for either SGLT2i or GLP-1 RA: 53.4% of patients in the simultaneous arm vs. 8.3% of patients were in the education-first arm (p<0.001). After six months, 128/200 (64.0%) received a new prescription: 69.8 % of patients in the simultaneous arm vs. 56.0% of patients in education-first (p<0.001). Patient self-report of taking SGLT2i or GLP-1 RA within six months of trial entry was similarly higher in the simultaneous versus education-first arm (69 /116; 59.5% vs 37/84; 44.0%; p<0.001) Median time to first prescription was 24 (IQR 13, 50) vs 85 days (IQR 65, 106), respectively (p<0.001). CONCLUSIONS: In this randomized trial, a remote team-based program that identifies patients with T2D and high CV or kidney risk, provides virtual education, and prescribes SGLT2i or GLP-1 RA improves GDMT. These findings support greater utilization of virtual team-based approaches to optimize chronic disease management.

Methods, rationale, and design for a remote pharmacist and navigator-driven disease management program to improve guideline-directed medical therapy in patients with type 2 diabetes at elevated cardiovascular and/or kidney risk.

AIM: Describe the rationale for and design of Diabetes Remote Intervention to improVe use of Evidence-based medications (DRIVE), a remote medication management program designed to initiate and titrate guideline-directed medical therapy (GDMT) in patients with type 2 diabetes (T2D) at elevated cardiovascular (CV) and/or kidney risk by leveraging non-physician providers. METHODS: An electronic health record based algorithm is used to identify patients with T2D and either established atherosclerotic CV disease (ASCVD), high risk for ASCVD, chronic kidney disease, and/or heart failure within our health system. Patients are invited to participate and randomly assigned to either simultaneous education and medication management, or a period of education prior to medication management. Patient navigators (trained, non-licensed staff) are the primary points of contact while a pharmacist or nurse practitioner reviews and authorizes each medication initiation and titration under an institution-approved collaborative drug therapy management protocol with supervision from a cardiologist and/or endocrinologist. Patient engagement is managed through software to support communication, automation, workflow, and standardization. CONCLUSION: We are testing a remote, navigator-driven, pharmacist-led, and physician-overseen management strategy to optimize GDMT for T2D as a population-level strategy to close the gap between guidelines and clinical practice for patients with T2D at elevated CV and/or kidney risk.

Lipoprotein(a), C-Reactive Protein, and Cardiovascular Risk in Primary and Secondary Prevention Populations.

Importance: Elevated lipoprotein(a) (Lp[a]) is a putative causal risk factor for atherosclerotic cardiovascular disease (ASCVD). There are conflicting data as to whether Lp(a) may increase cardiovascular risk only in the presence of concomitant inflammation. Objective: To investigate whether Lp(a) is associated with cardiovascular risk independent of high-sensitivity C-reactive protein (hs-CRP) in both primary and secondary prevention populations. Design, Setting, and Participants: This cohort study uses data from 3 distinct cohorts, 1 population-based cohort and 2 randomized clinical trials. Participants included individuals from the UK Biobank (data from 2006-2010) without prevalent ASCVD, participants in the FOURIER (TIMI 59) trial (data from 2013-2017) who had baseline Lp(a) and hs-CRP data, and participants in the SAVOR-TIMI 53 trial (data from 2010-2013) who had prevalent ASCVD and baseline values for Lp(a) and hs-CRP. The data analysis took place from November 2022 to November 2023. Exposure: Baseline plasma Lp(a), considered either as a continuous variable or dichotomized at 125 nmol/L. Main Outcomes and Measures: Risk of major adverse cardiovascular events (MACE) (composite of cardiovascular death, myocardial infarction [MI], or ischemic stroke), the individual MACE components, and peripheral artery disease (PAD). Results: Among 357 220 individuals in the UK Biobank without prevalent ASCVD, 232 699 (65%) had low hs-CRP (<2 mg/L), and 124 521 (35%) had high hs-CRP (≥2 mg/L) values. In a Cox proportional hazard model adjusted for ASCVD risk factors, higher Lp(a) was associated with increased cardiovascular risk regardless of baseline hs-CRP value for MACE (hs-CRP ≥2 mg/L: hazard ratio [HR] per 50-nmol/L higher Lp[a], 1.05; 95% CI, 1.04-1.07; P < .001; for hs-CRP <2 mg/L: HR, 1.05; 95% CI, 1.04-1.07; P < .001; P = .80 for interaction), as well as MI, ischemic stroke, and PAD individually. Among 34 020 individuals in the FOURIER and SAVOR trials with baseline cardiometabolic disease, there were 17 643 (52%) with low and 16 377 (48%) with high baseline hs-CRP values. In Cox proportional hazard models using aggregated data from FOURIER and SAVOR, higher baseline Lp(a) was associated with increased cardiovascular risk regardless of baseline hs-CRP for MACE (hs-CRP ≥2 mg/L: HR per 50-nmol/L higher Lp[a], 1.02; 95% CI, 1.00-1.05; P = .04; hs-CRP <2 mg/L: HR, 1.05; 95% CI, 1.02-1.08; P < .001; P = .16 for interaction), MI, and PAD. Conclusions and Relevance: In this study, higher levels of Lp(a) were associated with MACE, MI, and PAD in both primary and secondary prevention populations regardless of baseline hs-CRP value.

Novel Polygenic Risk Score and Established Clinical Risk Factors for Risk Estimation of Aortic Stenosis.

Importance: Polygenic risk scores (PRSs) have proven to be as strong as or stronger than established clinical risk factors for many cardiovascular phenotypes. Whether this is true for aortic stenosis remains unknown. Objective: To develop a novel aortic stenosis PRS and compare its aortic stenosis risk estimation to established clinical risk factors. Design, Setting, and Participants: This was a longitudinal cohort study using data from the Million Veteran Program (MVP; 2011-2020), UK Biobank (2006-2010), and 6 Thrombolysis in Myocardial Infarction (TIMI) trials, including DECLARE-TIMI 58 (2013-2018), FOURIER (TIMI 59; 2013-2017), PEGASUS-TIMI 54 (2010-2014), SAVOR-TIMI 53 (2010-2013), SOLID-TIMI 52 (2009-2014), and ENGAGE AF-TIMI 48 (2008-2013), which were a mix of population-based and randomized clinical trials. Individuals from UK Biobank and the MVP meeting a previously validated case/control definition for aortic stenosis were included. All individuals from TIMI trials were included unless they had a documented preexisting aortic valve replacement. Analysis took place from January 2022 to December 2023. Exposures: PRS for aortic stenosis (developed using data from MVP and validated in UK Biobank) and other previously validated cardiovascular PRSs, defined either as a continuous variable or as low (bottom 20%), intermediate, and high (top 20%), and clinical risk factors. Main Outcomes: Aortic stenosis (defined using International Classification of Diseases or Current Procedural Terminology codes in UK Biobank and MVP or safety event data in the TIMI trials). Results: The median (IQR) age in MVP was 67 (57-73) years, and 135 140 of 147 104 participants (92%) were male. The median (IQR) age in the TIMI trials was 66 (54-78) years, and 45 524 of 59 866 participants (71%) were male. The best aortic stenosis PRS incorporated 5 170 041 single-nucleotide variants and was associated with aortic stenosis in both the MVP testing sample (odds ratio, 1.41; 95% CI, 1.37-1.45 per 1 SD PRS; P = 4.6 × 10-116) and TIMI trials (hazard ratio, 1.44; 95% CI, 1.27-1.62 per 1 SD PRS; P = 3.2 × 10-9). Among genetic and clinical risk factors, the aortic stenosis PRS performed comparably to most risk factors besides age, and within a given age range, the combination of clinical and genetic risk factors was additive, providing a 3- to 4-fold increased gradient of risk of aortic stenosis. However, the addition of the aortic stenosis PRS to a model including clinical risk factors only improved risk discrimination of aortic stenosis by 0.01 to 0.02 (C index in MVP: 0.78 with clinical risk factors, 0.79 with risk factors and aortic stenosis PRS; C index in TIMI: 0.71 with clinical risk factors, 0.73 with risk factors and aortic stenosis PRS). Conclusions: This study developed and validated 1 of the first aortic stenosis PRSs. While aortic stenosis genetic risk was independent from clinical risk factors and performed comparably to all other risk factors besides age, genetic risk resulted in only a small improvement in overall aortic stenosis risk discrimination beyond age and clinical risk factors. This work sets the stage for further development of an aortic stenosis PRS.

Retrieval Augmented Generation Enabled Generative Pre-Trained Transformer 4 (GPT-4) Performance for Clinical Trial Screening.

Background: Subject screening is a key aspect of all clinical trials; however, traditionally, it is a labor-intensive and error-prone task, demanding significant time and resources. With the advent of large language models (LLMs) and related technologies, a paradigm shift in natural language processing capabilities offers a promising avenue for increasing both quality and efficiency of screening efforts. This study aimed to test the Retrieval-Augmented Generation (RAG) process enabled Generative Pretrained Transformer Version 4 (GPT-4) to accurately identify and report on inclusion and exclusion criteria for a clinical trial. Methods: The Co-Operative Program for Implementation of Optimal Therapy in Heart Failure (COPILOT-HF) trial aims to recruit patients with symptomatic heart failure. As part of the screening process, a list of potentially eligible patients is created through an electronic health record (EHR) query. Currently, structured data in the EHR can only be used to determine 5 out of 6 inclusion and 5 out of 17 exclusion criteria. Trained, but non-licensed, study staff complete manual chart review to determine patient eligibility and record their assessment of the inclusion and exclusion criteria. We obtained the structured assessments completed by the study staff and clinical notes for the past two years and developed a workflow of clinical note-based question answering system powered by RAG architecture and GPT-4 that we named RECTIFIER (RAG-Enabled Clinical Trial Infrastructure for Inclusion Exclusion Review). We used notes from 100 patients as a development dataset, 282 patients as a validation dataset, and 1894 patients as a test set. An expert clinician completed a blinded review of patients' charts to answer the eligibility questions and determine the "gold standard" answers. We calculated the sensitivity, specificity, accuracy, and Matthews correlation coefficient (MCC) for each question and screening method. We also performed bootstrapping to calculate the confidence intervals for each statistic. Results: Both RECTIFIER and study staff answers closely aligned with the expert clinician answers across criteria with accuracy ranging between 97.9% and 100% (MCC 0.837 and 1) for RECTIFIER and 91.7% and 100% (MCC 0.644 and 1) for study staff. RECTIFIER performed better than study staff to determine the inclusion criteria of "symptomatic heart failure" with an accuracy of 97.9% vs 91.7% and an MCC of 0.924 vs 0.721, respectively. Overall, the sensitivity and specificity of determining eligibility for the RECTIFIER was 92.3% (CI) and 93.9% (CI), and study staff was 90.1% (CI) and 83.6% (CI), respectively. Conclusion: GPT-4 based solutions have the potential to improve efficiency and reduce costs in clinical trial screening. When incorporating new tools such as RECTIFIER, it is important to consider the potential hazards of automating the screening process and set up appropriate mitigation strategies such as final clinician review before patient engagement.

Formative Perceptions of a Digital Pill System to Measure Adherence to Heart Failure Pharmacotherapy: Mixed Methods Study.

BACKGROUND: Heart failure (HF) affects 6.2 million Americans and is a leading cause of hospitalization. The mainstay of the management of HF is adherence to pharmacotherapy. Despite the effectiveness of HF pharmacotherapy, effectiveness is closely linked to adherence. Measuring adherence to HF pharmacotherapy is difficult; most clinical measures use indirect strategies such as calculating pharmacy refill data or using self-report. While helpful in guiding treatment adjustments, indirect measures of adherence may miss the detection of suboptimal adherence and co-occurring structural barriers associated with nonadherence. Digital pill systems (DPSs), which use an ingestible radiofrequency emitter to directly measure medication ingestions in real-time, represent a strategy for measuring and responding to nonadherence in the context of HF pharmacotherapy. Previous work has demonstrated the feasibility of using DPSs to measure adherence in other chronic diseases, but this strategy has yet to be leveraged for individuals with HF. OBJECTIVE: We aim to explore through qualitative interviews the facilitators and barriers to using DPS technology to monitor pharmacotherapy adherence among patients with HF. METHODS: We conducted individual, semistructured qualitative interviews and quantitative assessments between April and August 2022. A total of 20 patients with HF who were admitted to the general medical or cardiology service at an urban quaternary care hospital participated in this study. Participants completed a qualitative interview exploring the overall acceptability of and willingness to use DPS technology for adherence monitoring and perceived barriers to DPS use. Quantitative assessments evaluated HF history, existing medication adherence strategies, and attitudes toward technology. We analyzed qualitative data using applied thematic analysis and NVivo software (QSR International). RESULTS: Most participants (12/20, 60%) in qualitative interviews reported a willingness to use the DPS to measure HF medication adherence. Overall, the DPS was viewed as useful for increasing accountability and reinforcing adherence behaviors. Perceived barriers included technological issues, a lack of need, additional costs, and privacy concerns. Most were open to sharing adherence data with providers to bolster clinical care and decision-making. Reminder messages following detected nonadherence were perceived as a key feature, and customization was desired. Suggested improvements are primarily related to the design and usability of the Reader (a wearable device). CONCLUSIONS: Overall, individuals with HF perceived the DPS to be an acceptable and useful tool for measuring medication adherence. Accurate, real-time ingestion data can guide adherence counseling to optimize adherence management and inform tailored behavioral interventions to support adherence among patients with HF.

Natriuretic peptides, body mass index and heart failure risk: Pooled analyses of SAVOR-TIMI 53, DECLARE-TIMI 58 and CAMELLIA-TIMI 61.

AIM: N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations are lower in patients with obesity. The interaction between body mass index (BMI) and NT-proBNP with respect to heart failure risk remains incompletely defined. METHODS AND RESULTS: Data were pooled across three randomized clinical trials enrolling predominantly patients who were overweight or obese with established cardiometabolic disease: SAVOR-TIMI 53, DECLARE-TIMI 58 and CAMELLIA-TIMI 61. Hospitalization for heart failure (HHF) was examined across strata of baseline BMI and NT-proBNP. The effect of dapagliflozin versus placebo was assessed for a treatment interaction across BMI categories in patients with or without an elevated baseline NT-proBNP (≥125 pg/ml). Among 24 455 patients, the median NT-proBNP was 96 (interquartile range [IQR]: 43-225) pg/ml and the median BMI was 33 (IQR 29-37) kg/m2, with 68% of patients having a BMI ≥30 kg/m2. There was a significant inverse association between NT-proBNP and BMI which persisted after adjustment for all clinical variables (p < 0.001). Within any range of NT-proBNP, those at higher BMI had higher risk of HHF at 2 years (comparing BMI <30 vs. ≥40 kg/m2 for NT-proBNP ranges of <125, 125-<450 and ≥450 pg/ml: 0.0% vs. 0.6%, 1.3% vs. 4.0%, and 8.1% vs. 13.8%, respectively), which persisted after multivariable adjustment (adjusted hazard ratio [HRadj] 7.47, 95% confidence interval [CI] 3.16-17.66, HRadj 3.22 [95% CI 2.13-4.86], and HRadj 1.87 [95% CI 1.35-2.60], respectively). In DECLARE-TIMI 58, dapagliflozin versus placebo consistently reduced HHF across BMI categories in those with an elevated NT-proBNP (p-trend for HR across BMI = 0.60), with a pattern of greater absolute risk reduction (ARR) at higher BMI (ARR for BMI <30 to ≥40 kg/m2: 2.2% to 4.7%; p-trend = 0.059). CONCLUSIONS: The risk of HHF varies across BMI categories for any given range of circulating NT-proBNP. These findings showcase the importance of considering BMI when applying NT-proBNP for heart failure risk stratification, particularly for patients with low-level elevations in NT-proBNP (125-<450 pg/ml) where there appears to be a clinically meaningful absolute and relative risk gradient.

Implementation of a Scalable Online Weight Management Programme in Clinical Settings: Protocol for the PROPS 2.0 Programme (Partnerships for Reducing Overweight and Obesity with Patient-Centered Strategies 2.0).

INTRODUCTION: There is an urgent need for scalable strategies for treating overweight and obesity in clinical settings. PROPS 2.0 (Partnerships for Reducing Overweight and Obesity with Patient-Centered Strategies 2.0) aims to adapt and implement the combined intervention from the PROPS Study at scale, in a diverse cross-section of patients and providers. METHODS AND ANALYSIS: We are implementing PROPS 2.0 across a variety of clinics at Brigham and Women's Hospital, targeting enrolment of 5000 patients. Providers can refer patients or patients can self-refer. Eligible patients must be ≥20 years old and have a body mass index (BMI) of ≥30 kg/m2 or a BMI of 25-29.9 kg/m2 plus another cardiovascular risk factor or obesity-related condition. After enrolment, patients register for the RestoreHealth online programme/app (HealthFleet Inc.) and participate for 12 months. Patients can engage with the programme and receive personalized feedback from a coach. Patient navigators help to enrol patients, enter updates in the electronic health record, and refer patients to additional resources. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework is guiding the evaluation. ETHICS AND DISSEMINATION: The Mass General Brigham Human Research Committee approved this protocol. An implementation guide will be created and disseminated, to help other sites adopt the intervention in the future. TRIAL REGISTRATION NUMBER: NCT0555925.

Risk Assessment of Kidney Disease Progression and Efficacy of SGLT2 Inhibition in Patients With Type 2 Diabetes.

OBJECTIVE: To develop a risk assessment tool to identify patients with type 2 diabetes (T2D) at higher risk for kidney disease progression and who might benefit more from sodium-glucose cotransporter 2 (SGLT2) inhibition. RESEARCH DESIGN AND METHODS: A total of 41,204 patients with T2D from four Thrombolysis In Myocardial Infarction (TIMI) clinical trials were divided into derivation (70%) and validation cohorts (30%). Candidate predictors of kidney disease progression (composite of sustained ≥40% decline in estimated glomerular filtration rate [eGFR], end-stage kidney disease, or kidney death) were selected with multivariable Cox regression. Efficacy of dapagliflozin was assessed by risk categories (low: <0.5%; intermediate: 0.5 to <2%; high: ≥2%) in Dapagliflozin Effect on Cardiovascular Events (DECLARE)-TIMI 58. RESULTS: There were 695 events over a median follow-up of 2.4 years. The final model comprised eight independent predictors of kidney disease progression: atherosclerotic cardiovascular disease, heart failure, systolic blood pressure, T2D duration, glycated hemoglobin, eGFR, urine albumin-to-creatinine ratio, and hemoglobin. The c-indices were 0.798 (95% CI, 0.774-0.821) and 0.798 (95% CI, 0.765-0.831) in the derivation and validation cohort, respectively. The calibration plot slope (deciles of predicted vs. observed risk) was 0.98 (95% CI, 0.93-1.04) in the validation cohort. Whereas relative risk reductions with dapagliflozin did not differ across risk categories, there was greater absolute risk reduction in patients with higher baseline risk, with a 3.5% absolute risk reduction in kidney disease progression at 4 years in the highest risk group (≥1%/year). Results were similar with the 2022 Chronic Kidney Disease Prognosis Consortium risk prediction model. CONCLUSIONS: Risk models for kidney disease progression can be applied in patients with T2D to stratify risk and identify those who experience a greater magnitude of benefit from SGLT2 inhibition.

Frequency, compliance, and yield of cardiac testing after high-sensitivity troponin accelerated diagnostic protocol implementation.

BACKGROUND: Among persons presenting to the emergency department with suspected acute myocardial infarction (MI), cardiac troponin (cTn) testing is commonly used to detect acute myocardial injury. Accelerated diagnostic protocols (ADPs) guide clinicians to integrate cTn results with other clinical information to decide whether to order further diagnostic testing. OBJECTIVE: To determine the change in the rate and yield of stress test or coronary CT angiogram following cTn measurement in patients with chest pain presenting to the emergency department pre- and post-transition to a high-sensitivity (hs-cTn) assay in an updated ADP. METHODS: Using electronic health records, we examined visits for chest pain at five emergency departments affiliated with an integrated academic health system 1-year pre- and post-hs-cTn assay transition. Outcomes included stress test or coronary imaging frequency, ADP compliance among those with additional testing, and diagnostic yield (ratio of positive tests to total tests). RESULTS: There were 7564 patient-visits for chest pain, including 3665 in the pre- and 3899 in the post-period. Following the updated ADP using hs-cTn, 862 (23.5 per 100 patient visits) visits led to subsequent testing versus 1085 (27.8 per 100 patient visits) in the pre-hs-cTn period, (P < 0.001). Among those who were tested, the protocol-compliant rate fell from 80.9% to 46.5% (P < 0.001), but the yield of those tests rose from 24.5% to 29.2% (P = 0.07). Among tests that were noncompliant with ADP guidance, yield was similar pre- and post-updated hs-cTn ADP implementation (pre 13.0%, post 15.4% (P = 0.43). CONCLUSION: Implementation of hs-cTn supported by an updated ADP was associated with a lower rate of stress testing and coronary CT angiogram.

Assessment of Atherothrombotic Risk in Patients With Type 2 Diabetes Mellitus.

BACKGROUND: Risk of atherothrombotic events is not uniform in patients with type 2 diabetes mellitus (T2DM). Tailored risk assessment may help guide selection of pharmacotherapies for cardiovascular primary and secondary prevention. OBJECTIVES: The purpose of this study was to develop a risk model for atherothrombosis in patients with T2DM. METHODS: We developed and validated a risk model for myocardial infarction (MI) or ischemic stroke (IS) in a pooled cohort of 42,181 patients with T2DM from 4 TIMI (Thrombolysis In Myocardial Infarction) clinical trial cohorts. Candidate variables were assessed with multivariable Cox regression, and independent variables (P < 0.05) were retained in the final model. Discrimination and calibration were assessed. Treatment interactions with dapagliflozin (sodium-glucose cotransporter-2 inhibitor) and evolocumab (proprotein convertase subtilisin/kexin type 9 inhibitor) were explored in the DECLARE-TIMI 58 (Dapagliflozin Effect on CardiovascuLAR Events-Thrombolysis In Myocardial Infarction 58) and FOURIER (Further Cardiovascular Outcomes Research with PCSK9 Inhibition in Subjects with Elevated Risk) trials, respectively. RESULTS: Sixteen variables were independent predictors of MI or IS. The model identified a >8-fold gradient of MI or IS rates between the top vs bottom risk quintiles in the validation cohort (3-year Kaplan-Meier rate: 14.9% vs 1.4%; P < 0.0001). C-indexes were 0.704 and 0.706 in the derivation and validation cohorts, respectively. The model was well-calibrated in both primary and secondary prevention. Absolute reduction in the rates of MI or IS tended to be greater in patients with higher baseline predicted risk for both dapagliflozin (absolute risk reduction: 2.1% vs 0.2%) and evolocumab (absolute risk reduction: 3.2% vs 1.0%). CONCLUSIONS: We developed and validated a risk score for atherothrombotic events, leveraging 16 routinely assessed clinical variables in patients with T2DM. The score has the potential to improve risk assessment and inform clinical decision-making.

Dapagliflozin Improves Heart Failure Symptoms and Physical Limitations Across the Full Range of Ejection Fraction: Pooled Patient-Level Analysis From DEFINE-HF and PRESERVED-HF Trials.

BACKGROUND: Patients with heart failure (HF) have a high burden of symptoms and physical limitations, regardless of ejection fraction (EF). Whether the benefits of SGLT2 (sodium-glucose cotransporter-2) inhibitors on these outcomes vary across the full range of EF remains unclear. METHODS: Patient-level data were pooled from the DEFINE-HF trial (Dapagliflozin Effects on Biomarkers, Symptoms, and Functional Status in Patients With Heart Failure With Reduced Ejection Fraction) of 263 participants with reduced EF (≤40%), and PRESERVED-HF trial (Effects of Dapagliflozin on Biomarkers, Symptoms and Functional Status in Patients With Preserved Ejection Fraction Heart Failure) of 324 participants with preserved EF (≥45%). Both were randomized, double-blind 12-week trials of dapagliflozin versus placebo, recruiting participants with New York Heart Association class II or higher and elevated natriuretic peptides. The effect of dapagliflozin on the change in the Kansas City Cardiomyopathy Questionnaire (KCCQ) Clinical Summary Score (CSS) at 12 weeks was tested with ANCOVA adjusted for sex, baseline KCCQ, EF, atrial fibrillation, estimated glomerular filtration rate, and type 2 diabetes. Interaction of dapagliflozin effects on KCCQ-CSS by EF was assessed using EF both categorically and continuously with restricted cubic spline. Responder analyses, examining proportions of patients with deterioration, and clinically meaningful improvements in KCCQ-CSS were conducted using logistic regression. RESULTS: Of 587 patients randomized (293 dapagliflozin, 294 placebo), EF was ≤40, >40-≤60, and >60% in 262 (45%), 199 (34%), and 126 (21%), respectively. Dapagliflozin improved KCCQ-CSS at 12 weeks (placebo-adjusted difference 5.0 points [95% CI, 2.6-7.5]; P<0.001). This was consistent in participants with EF≤40 (4.6 points [95% CI, 1.0-8.1]; P=0.01), >40 to ≤60 (4.9 points [95% CI, 0.8-9.0]; P=0.02) and >60% (6.8 points [95% CI, 1.5-12.1]; P=0.01; Pinteraction=0.79). Benefits of dapagliflozin on KCCQ-CSS were also consistent when analyzing EF continuously (Pinteraction=0.94). In responder analyses, fewer dapagliflozin-treated patients had deterioration and more had small, moderate, and large KCCQ-CSS improvements versus placebo; these results were also consistent regardless of EF (all Pinteractionvalues nonsignificant). CONCLUSIONS: In patients with HF, dapagliflozin significantly improves symptoms and physical limitations after 12 weeks of treatment, with consistent and clinically meaningful benefits across the full range of EF. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifiers: NCT02653482 and NCT03030235.

Remote Cardiovascular Hypertension Program Enhanced Blood Pressure Control During the COVID-19 Pandemic.

Background The COVID-19 pandemic disrupted traditional health care; one fallout was a drastic decrease in blood pressure (BP) assessment. We analyzed the pandemic's impact on our existing remote hypertension management program's effectiveness and adaptability. Methods and Results This retrospective observational analysis evaluated BP control in an entirely remote management program before and during the pandemic. A team of pharmacists, nurse practitioners, physicians, and nonlicensed navigators used an evidence-based clinical algorithm to optimize hypertensive treatment. The algorithm was adapted during the pandemic to simplify BP control. Overall, 1256 patients (605 enrolled in the 6 months before the pandemic shutdown in March 2020 and 651 in the 6 months after) were a median age of 63 years old, 57% female, and 38.2% non-White. Among enrolled patients with sustained hypertension, 51.1% reached BP goals. Within this group, rates of achieving goal BP improved to 94.6% during the pandemic from 75.8% prepandemic (P<0.0001). Mean baseline home BP was 141.7/81.9 mm Hg during the pandemic and 139.8/82.2 prepandemic, and fell ≈16/9 mm Hg in both periods (P<0.0001). Maintenance during the pandemic was achieved earlier (median 11.8 versus 19.6 weeks, P<0.0001), with more frequent monthly calls (8.2 versus 3.1, P<0.0001) and more monthly home BP recordings per patient (32.4 versus 18.9, P<0.0001), compared with the prepandemic period. Conclusions A remote clinical management program was successfully adapted and delivered significant improvements in BP control and increased home BP monitoring despite a nationally observed disruption of traditional hypertension care. Such programs have the potential to transform hypertension management and care delivery.

Results of a Remotely Delivered Hypertension and Lipid Program in More Than 10 000 Patients Across a Diverse Health Care Network.

Importance: Blood pressure (BP) and cholesterol control remain challenging. Remote care can deliver more effective care outside of traditional clinician-patient settings but scaling and ensuring access to care among diverse populations remains elusive. Objective: To implement and evaluate a remote hypertension and cholesterol management program across a diverse health care network. Design, Setting, and Participants: Between January 2018 and July 2021, 20 454 patients in a large integrated health network were screened; 18 444 were approached, and 10 803 were enrolled in a comprehensive remote hypertension and cholesterol program (3658 patients with hypertension, 8103 patients with cholesterol, and 958 patients with both). A total of 1266 patients requested education only without medication titration. Enrolled patients received education, home BP device integration, and medication titration. Nonlicensed navigators and pharmacists, supported by cardiovascular clinicians, coordinated care using standardized algorithms, task management and automation software, and omnichannel communication. BP and laboratory test results were actively monitored. Main Outcomes and Measures: Changes in BP and low-density lipoprotein cholesterol (LDL-C). Results: The mean (SD) age among 10 803 patients was 65 (11.4) years; 6009 participants (56%) were female; 1321 (12%) identified as Black, 1190 (11%) as Hispanic, 7758 (72%) as White, and 1727 (16%) as another or multiple races (including American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, unknown, other, and declined to respond; consolidated owing to small numbers); and 142 (11%) reported a preferred language other than English. A total of 424 482 BP readings and 139 263 laboratory reports were collected. In the hypertension program, the mean (SD) office BP prior to enrollment was 150/83 (18/10) mm Hg, and the mean (SD) home BP was 145/83 (20/12) mm Hg. For those engaged in remote medication management, the mean (SD) clinic BP 6 and 12 months after enrollment decreased by 8.7/3.8 (21.4/12.4) and 9.7/5.2 (22.2/12.6) mm Hg, respectively. In the education-only cohort, BP changed by a mean (SD) -1.5/-0.7 (23.0/11.1) and by +0.2/-1.9 (30.3/11.2) mm Hg, respectively (P < .001 for between cohort difference). In the lipids program, patients in remote medication management experienced a reduction in LDL-C by a mean (SD) 35.4 (43.1) and 37.5 (43.9) mg/dL at 6 and 12 months, respectively, while the education-only cohort experienced a mean (SD) reduction in LDL-C of 9.3 (34.3) and 10.2 (35.5) mg/dL at 6 and 12 months, respectively (P < .001). Similar rates of enrollment and reductions in BP and lipids were observed across different racial, ethnic, and primary language groups. Conclusions and Relevance: The results of this study indicate that a standardized remote BP and cholesterol management program may help optimize guideline-directed therapy at scale, reduce cardiovascular risk, and minimize the need for in-person visits among diverse populations.

A polygenic risk score predicts atrial fibrillation in cardiovascular disease.

AIMS: Interest in targeted screening programmes for atrial fibrillation (AF) has increased, yet the role of genetics in identifying patients at highest risk of developing AF is unclear. METHODS AND RESULTS: A total of 36,662 subjects without prior AF were analyzed from four TIMI trials. Subjects were divided into quintiles using a validated polygenic risk score (PRS) for AF. Clinical risk for AF was calculated using the CHARGE-AF model. Kaplan-Meier event rates, adjusted hazard ratios (HRs), C-indices, and net reclassification improvement were used to determine if the addition of the PRS improved prediction compared with clinical risk and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Over 2.3 years, 1018 new AF cases developed. AF PRS predicted a significant risk gradient for AF with a 40% increased risk per 1-SD increase in PRS [HR: 1.40 (1.32-1.49); P < 0.001]. Those with high AF PRS (top 20%) were more than two-fold more likely to develop AF [HR 2.45 (1.99-3.03), P < 0.001] compared with low PRS (bottom 20%). Furthermore, PRS provided an additional gradient of risk stratification on top of the CHARGE-AF clinical risk score, ranging from a 3-year incidence of 1.3% in patients with low clinical and genetic risk to 8.7% in patients with high clinical and genetic risk. The subgroup of patients with high clinical risk, high PRS, and elevated NT-proBNP had an AF risk of 16.7% over 3 years. The C-index with the CHARGE-AF clinical risk score alone was 0.65, which improved to 0.67 (P < 0.001) with the addition of NT-proBNP, and increased further to 0.70 (P < 0.001) with the addition of the PRS. CONCLUSION: In patients with cardiovascular conditions, AF PRS is a strong independent predictor of incident AF that provides complementary predictive value when added to a validated clinical risk score and NT-proBNP.

A remote hypertension management program clinical algorithm.

INTRODUCTION: Hypertension is the leading risk factor for death, affecting over one billion people worldwide, yet control rates are poor and stagnant. We developed a remote hypertension management program that leverages digitally transmitted home blood pressure (BP) measurements, algorithmic care pathways, and patient-navigator communications to aid patients in achieving guideline-directed BP goals. METHODS: Patients with uncontrolled hypertension are identified through provider referrals and electronic health record screening aided by population health managers within the Mass General Brigham (MGB) health system. Non-licensed patient navigators supervised by pharmacists, nurse practitioners, and physicians engage and educate patients. Patients receive cellular or Bluetooth-enabled BP devices with which they monitor and transmit scheduled home BP readings. Evidence-based medication changes are made according to a custom hypertension algorithm approved within a collaborative drug therapy management (CDTM) agreement with MGB and implemented by pharmacists. Using patient-specific characteristics, we developed different pathways to optimize medication regimens. The renin-angiotensin-aldosterone system-blocker pathway prescribed ARBs/ACE inhibitors first for patients with diabetes, impaired renal function, and microalbuminuria; the standard pathway started patients on calcium channel blockers. Regimens were escalated frequently, adding thiazide-type diuretics, and including beta blockers and mineralocorticoid receptor antagonists if needed. DISCUSSION: We have developed an algorithmic approach for the remote management of hypertension with demonstrated success. A focus on algorithmic decision-making streamlines tasks and responsibilities, easing the potential for scalability of this model. As the backbone of our remote management program, this clinical algorithm can improve BP control and innovate the management of hypertension in large populations.

I2b2-etl: Python application for importing electronic health data into the informatics for integrating biology and the bedside platform.

MOTIVATION: The i2b2 platform is used at major academic health institutions and research consortia for querying for electronic health data. However, a major obstacle for wider utilization of the platform is the complexity of data loading that entails a steep curve of learning the platform's complex data schemas. To address this problem, we have developed the i2b2-etl package that simplifies the data loading process, which will facilitate wider deployment and utilization of the platform. RESULTS: We have implemented i2b2-etl as a Python application that imports ontology and patient data using simplified input file schemas and provides inbuilt record number de-identification and data validation. We describe a real-world deployment of i2b2-etl for a population-management initiative at MassGeneral Brigham. AVAILABILITY AND IMPLEMENTATION: i2b2-etl is a free, open-source application implemented in Python available under the Mozilla 2 license. The application can be downloaded as compiled docker images. A live demo is available at https://i2b2clinical.org/demo-i2b2etl/ (username: demo, password: Etl@2021). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis.

Analysis of health data typically requires development of queries using structured query language (SQL) by a data-analyst. As the SQL queries are manually created, they are prone to errors. In addition, accurate implementation of the queries depends on effective communication with clinical experts, that further makes the analysis error prone. As a potential resolution, we explore an alternative approach wherein a graphical interface that automatically generates the SQL queries is used to perform the analysis. The latter allows clinical experts to directly perform complex queries on the data, despite their unfamiliarity with SQL syntax. The interface provides an intuitive understanding of the query logic which makes the analysis transparent and comprehensible to the clinical study-staff, thereby enhancing the transparency and validity of the analysis. This study demonstrates the feasibility of using a user-friendly interface that automatically generate SQL for analysis of health data. It outlines challenges that will be useful for designing user-friendly tools to improve transparency and reproducibility of data analysis.