Optimising Cardiology Ambulatory Care Pathways: A Comprehensive Approach to Admission Avoidance and Timely Intervention in a Post-Pandemic Healthcare Landscape.

INTRODUCTION: Cardiovascular diseases are a substantial burden on healthcare systems, contributing significantly to avoidable hospital admissions. We propose a Cardiology Ambulatory Care Pathway. METHODS: Conducted a 1 month study redirecting admission streams from primary and emergency care, into a Cardiology Ambulatory Care Hub providing triage in Hot Clinic, and access to a Multi-Modal Testing Platform. RESULTS: 98 patients were referred to the Ambulatory Care Hub, 91 of which avoided admission. 52 patients received care in the cardiology hub, 38 of which required further testing. CONCLUSION: We successfully streamlined various service streams, reducing admissions, and improving patient outcomes. Outpatient CTCA, ambulatory ECG, and echocardiography proved instrumental. We project a cost saving of £53,379 per month in bed days (£640,556 annual saving).

A cloud-based learning module for biomarker discovery.

This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on basic principles in biomarker discovery in an interactive format that uses appropriate cloud resources for data access and analyses. In collaboration with Google Cloud, Deloitte Consulting and NIGMS, the Rhode Island INBRE Molecular Informatics Core developed a cloud-based training module for biomarker discovery. The module consists of nine submodules covering various topics on biomarker discovery and assessment and is deployed on the Google Cloud Platform and available for public use through the NIGMS Sandbox. The submodules are written as a series of Jupyter Notebooks utilizing R and Bioconductor for biomarker and omics data analysis. The submodules cover the following topics: 1) introduction to biomarkers; 2) introduction to R data structures; 3) introduction to linear models; 4) introduction to exploratory analysis; 5) rat renal ischemia-reperfusion injury case study; (6) linear and logistic regression for comparison of quantitative biomarkers; 7) exploratory analysis of proteomics IRI data; 8) identification of IRI biomarkers from proteomic data; and 9) machine learning methods for biomarker discovery. Each notebook includes an in-line quiz for self-assessment on the submodule topic and an overview video is available on YouTube (https://www.youtube.com/watch?v=2-Q9Ax8EW84). This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Identifying and training deep learning neural networks on biomedical-related datasets.

This manuscript describes the development of a resources module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Natriuretic Peptides to Classify Risk of Atrial Fibrillation Detection After Stroke: Analysis of the BIOSIGNAL and PRECISE Cohort Studies.

BACKGROUND AND OBJECTIVES: Prolonged cardiac monitoring (PCM) increases atrial fibrillation (AF) detection after ischemic stroke, but access is limited, and it is burdensome for patients. Our objective was to assess whether midregional proatrial natriuretic peptide (MR-proANP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) could classify people who are unlikely to have AF after ischemic stroke and allow better targeting of PCM. METHODS: We analyzed people from the Biomarker Signature of Stroke Aetiology (BIOSIGNAL) study with ischemic stroke, no known AF, and ≥3 days cardiac monitoring. External validation was performed in the Preventing Recurrent Cardioembolic Stroke: Right Approach, Right Patient (PRECISE) study of 28 days of cardiac monitoring in people with ischemic stroke or transient ischemic attack and no known AF. The main outcome is no AF detection. We assessed the discriminatory value of MR-proANP and NT-proBNP combined with clinical variables to identify people with no AF. A decision curve analysis was performed with combined data to determine the net reduction in people who would undergo PCM using the models based on a 15% threshold probability for AF detection. RESULTS: We included 621 people from the BIOSIGNAL study. The clinical multivariable prediction model included age, NIH Stroke Scale score, lipid-lowering therapy, creatinine, and smoking status. The area under the receiver-operating characteristic curve (AUROC) for clinical variables was 0.68 (95% CI 0.62-0.74), which improved with the addition of log10MR-proANP (0.72, 0.66-0.78; p = 0.001) or log10NT-proBNP (0.71, 0.65-0.77; p = 0.009). Performance was similar for the models with log10MR-proANP vs log10NT-proBNP (p = 0.28). In 239 people from the PRECISE study, the AUROC for clinical variables was 0.68 (0.59-0.76), which improved with the addition of log10NT-proBNP (0.73, 0.65-0.82; p < 0.001) or log10MR-proANP (0.79, 0.72-0.86; p < 0.001). Performance was better for the model with log10MR-proANP vs log10NT-proBNP (p = 0.03). The models could reduce the number of people who would undergo PCM by 30% (clinical and log10MR-proANP), 27% (clinical and log10NT-proBNP), or 20% (clinical only). DISCUSSION: MR-proANP and NT-proBNP help classify people who are unlikely to have AF after ischemic stroke. Measuring MR-proANP or NT-proBNP could reduce the number of people who need PCM by 30%, without reducing the amount of AF detected. TRIAL REGISTRATION INFORMATION: NCT02274727; clinicaltrials.gov/study/NCT02274727.

Cloud-based introduction to BASH programming for biologists.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning', https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial authored by National Institute of General Medical Sciences: NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research at the beginning of this supplement. This module delivers learning materials introducing the utility of the BASH (Bourne Again Shell) programming language for genomic data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. The next-generation sequencing revolution has generated massive amounts of novel biological data from a multitude of platforms that survey an ever-growing list of genomic modalities. These data require significant downstream computational and statistical analyses to glean meaningful biological insights. However, the skill sets required to generate these data are vastly different from the skills required to analyze these data. Bench scientists that generate next-generation data often lack the training required to perform analysis of these datasets and require support from bioinformatics specialists. Dedicated computational training is required to empower biologists in the area of genomic data analysis, however, learning to efficiently leverage a command line interface is a significant barrier in learning how to leverage common analytical tools. Cloud platforms have the potential to democratize access to the technical tools and computational resources necessary to work with modern sequencing data, providing an effective framework for bioinformatics education. This module aims to provide an interactive platform that slowly builds technical skills and knowledge needed to interact with genomics data on the command line in the Cloud. The sandbox format of this module enables users to move through the material at their own pace and test their grasp of the material with knowledge self-checks before building on that material in the next sub-module. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Low Natriuretic Peptide Levels and Outcomes in Patients With Heart Failure and Preserved Ejection Fraction.

BACKGROUND: Although some patients with heart failure (HF) with mildly reduced/preserved ejection fraction have low natriuretic peptide levels, there are no large-scale systematic studies of how common these individuals are or what happens to them. OBJECTIVES: The purpose of this study was to examine the proportion of patients in the I-PRESERVE (Irbesartan in Heart Failure with Preserved Ejection Fraction) trial with an N-terminal pro-B-type natriuretic peptide (NT-proBNP) level <125 pg/mL, their clinical characteristics, and outcomes. METHODS: I- PRESERVE enrolled patients with symptomatic HF and a LVEF ≥45% but who did not have NT-proBNP or body mass index inclusion/exclusion criteria. Baseline NT-proBNP was measured after enrollment but not reported to investigators. The primary outcome in this analysis was the composite of cardiovascular death or HF hospitalization. RESULTS: Overall, 808 of 3,480 patients (23.2%) had NT-proBNP <125 pg/mL. Patients with a low NT-proBNP were younger (68.6 years vs 72.6 years; P < 0.001), were less often men (36.1% vs 40.9%; P = 0.015), and had a higher body mass index (48.4% vs 38.7% obese; P < 0.001) than those with a higher NT-proBNP level. Patients with a low NT-proBNP had less atrial fibrillation (8.5% vs 35.1%; P < 0.001), myocardial infarction, diabetes, chronic obstructive pulmonary disease, and anemia but better kidney function. Patients with a lower NT-proBNP level had less marked echocardiographic abnormalities and were less likely to experience cardiovascular death or HF hospitalization; adjusted HR: 0.35 (95% CI: 0.27-0.46; P < 0.001). However, health status was similarly impaired in patients with lower and higher NT-proBNP levels (median Minnesota Living with Heart Failure Questionnaire 43 vs 43; P = 0.95). CONCLUSIONS: Almost one-quarter of patients with HF with mildly reduced/preserved ejection fraction had a low NT-proBNP level. Although these patients have a favorable prognosis, compared to those with a high NT-proBNP level, they have similarly impaired health status which should be a target for treatment. (Irbesartan in Heart Failure With Preserved Systolic Function [I- PRESERVE]; NCT00095238).

A cloud-based training module for efficient de novo transcriptome assembly using Nextflow and Google cloud.

This study describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" (https://github.com/NIGMS/NIGMS-Sandbox). The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on de novo transcriptome assembly using Nextflow in an interactive format that uses appropriate cloud resources for data access and analysis. Cloud computing is a powerful new means by which biomedical researchers can access resources and capacity that were previously either unattainable or prohibitively expensive. To take advantage of these resources, however, the biomedical research community needs new skills and knowledge. We present here a cloud-based training module, developed in conjunction with Google Cloud, Deloitte Consulting, and the NIH STRIDES Program, that uses the biological problem of de novo transcriptome assembly to demonstrate and teach the concepts of computational workflows (using Nextflow) and cost- and resource-efficient use of Cloud services (using Google Cloud Platform). Our work highlights the reduced necessity of on-site computing resources and the accessibility of cloud-based infrastructure for bioinformatics applications.

Empagliflozin to prevent progressive adverse remodelling after myocardial infarction (EMPRESS-MI): rationale and design.

AIMS: Patients with a reduced left ventricular ejection fraction (LVEF) following an acute myocardial infarction (MI) are at risk of progressive adverse cardiac remodelling that can lead to the development of heart failure and death. The early addition of a sodium-glucose cotransporter 2 (SGLT2) inhibitor to standard treatment may delay or prevent progressive adverse remodelling in these patients. METHODS AND RESULTS: EMpagliflozin to PREvent worSening of left ventricular volumes and Systolic function after Myocardial Infarction (EMPRESS-MI) is a randomized, double-blind, placebo-controlled, multi-centre trial designed to assess the effect of empagliflozin on cardiac remodelling evaluated using cardiovascular magnetic resonance (CMR) in 100 patients with left ventricular systolic dysfunction following MI. Eligible patients were those ≥12 h and ≤14 days following acute MI, with an LVEF <45% by CMR. Patients were randomized to empagliflozin 10 mg once a day or matching placebo. The primary outcome will be change in left ventricular end-systolic volume indexed to body surface area over 24 weeks from randomization. Secondary endpoints include measures of left ventricular and atrial volumes, left ventricular mass, LVEF, and circulating cardiac biomarkers. CONCLUSIONS: EMPRESS-MI will assess the effect of the SGLT2 inhibitor empagliflozin on cardiac remodelling in patients with left ventricular systolic dysfunction after an acute MI.

Predictors of heart failure readmission and all-cause mortality in patients with acute heart failure.

BACKGROUND: Predischarge risk stratification of patients with acute heart failure (AHF) could facilitate tailored treatment and follow-up, however, simple scores to predict short-term risk for HF readmission or death are lacking. METHODS: We sought to develop a congestion-focused risk score using data from a prospective, two-center observational study in adults hospitalized for AHF. Laboratory data were collected on admission. Patients underwent physical examination, 4-zone, and in a subset 8-zone, lung ultrasound (LUS), and echocardiography at baseline. A second LUS was performed before discharge in a subset of patients. The primary endpoint was the composite of HF hospitalization or all-cause death. RESULTS: Among 350 patients (median age 75 years, 43% women), 88 participants (25%) were hospitalized or died within 90 days after discharge. A stepwise Cox regression model selected four significant independent predictors of the composite outcome, and each was assigned points proportional to its regression coefficient: NT-proBNP ≥2000 pg/mL (admission) (3 points), systolic blood pressure < 120 mmHg (baseline) (2 points), left atrial volume index ≥60 mL/m2 (baseline) (1 point) and ≥ 9 B-lines on predischarge 4-zone LUS (3 points). This risk score provided adequate risk discrimination for the composite outcome (HR 1.48 per 1 point increase, 95% confidence interval: 1.32-1.67, p < 0.001, C-statistic: 0.70). In a subset of patients with 8-zone LUS data (n = 176), results were similar (C-statistic: 0.72). CONCLUSIONS: A four-variable risk score integrating clinical, laboratory and ultrasound data may provide a simple approach for risk discrimination for 90-day adverse outcomes in patients with AHF if validated in future investigations.

Dapagliflozin and Days of Full Health Lost in the DAPA-HF Trial.

BACKGROUND: Conventional time-to-first-event analyses cannot incorporate recurrent hospitalizations and patient well-being in a single outcome. OBJECTIVES: To overcome this limitation, we tested an integrated measure that includes days lost from death and hospitalization, and additional days of full health lost through diminished well-being. METHODS: The effect of dapagliflozin on this integrated measure was assessed in the DAPA-HF (Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure) trial, which examined the efficacy of dapagliflozin, compared with placebo, in patients with NYHA functional class II to IV heart failure and a left ventricular ejection fraction ≤40%. RESULTS: Over 360 days, patients in the dapagliflozin group (n = 2,127) lost 10.6 ± 1.0 (2.9%) of potential follow-up days through cardiovascular death and heart failure hospitalization, compared with 14.4 ± 1.0 days (4.0%) in the placebo group (n = 2,108), and this component of all measures of days lost accounted for the greatest between-treatment difference (-3.8 days [95% CI: -6.6 to -1.0 days]). Patients receiving dapagliflozin also had fewer days lost to death and hospitalization from all causes vs placebo (15.5 ± 1.1 days [4.3%] vs 20.3 ± 1.1 days [5.6%]). When additional days of full health lost (ie, adjusted for Kansas City Cardiomyopathy Questionnaire-overall summary score) were added, total days lost were 110.6 ± 1.6 days (30.7%) with dapagliflozin vs 116.9 ± 1.6 days (32.5%) with placebo. The difference in all measures between the 2 groups increased over time (ie, days lost by death and hospitalization -0.9 days [-0.7%] at 120 days, -2.3 days [-1.0%] at 240 days, and -4.8 days [-1.3%] at 360 days). CONCLUSIONS: Dapagliflozin reduced the total days of potential full health lost due to death, hospitalizations, and impaired well-being, and this benefit increased over time during the first year. (Study to Evaluate the Effect of Dapagliflozin on the Incidence of Worsening Heart Failure or Cardiovascular Death in Patients With Chronic Heart Failure; NCT03036124).

Prognostic Models for Mortality and Morbidity in Heart Failure With Preserved Ejection Fraction.

Importance: Accurate risk prediction of morbidity and mortality in patients with heart failure with preserved ejection fraction (HFpEF) may help clinicians risk stratify and inform care decisions. Objective: To develop and validate a novel prediction model for clinical outcomes in patients with HFpEF using routinely collected variables and to compare it with a biomarker-driven approach. Design, Setting, and Participants: Data were used from the Dapagliflozin Evaluation to Improve the Lives of Patients With Preserved Ejection Fraction Heart Failure (DELIVER) trial to derive the prediction model, and data from the Angiotensin Receptor Neprilysin Inhibition in Heart Failure With Preserved Ejection Fraction (PARAGON-HF) and the Irbesartan in Heart Failure With Preserved Ejection Fraction Study (I-PRESERVE) trials were used to validate it. The outcomes were the composite of HF hospitalization (HFH) or cardiovascular death, cardiovascular death, and all-cause death. A total of 30 baseline candidate variables were selected in a stepwise fashion using multivariable analyses to create the models. Data were analyzed from January 2023 to June 2023. Exposures: Models to estimate the 1-year and 2-year risk of cardiovascular death or hospitalization for heart failure, cardiovascular death, and all-cause death. Results: Data from 6263 individuals in the DELIVER trial were used to derive the prediction model and data from 4796 individuals in the PARAGON-HF trial and 4128 individuals in the I-PRESERVE trial were used to validate it. The final prediction model for the composite outcome included 11 variables: N-terminal pro-brain natriuretic peptide (NT-proBNP) level, HFH within the past 6 months, creatinine level, diabetes, geographic region, HF duration, treatment with a sodium-glucose cotransporter 2 inhibitor, chronic obstructive pulmonary disease, transient ischemic attack/stroke, any previous HFH, and heart rate. This model showed good discrimination (C statistic at 1 year, 0.73; 95% CI, 0.71-0.75) in both validation cohorts (C statistic at 1 year, 0.71; 95% CI, 0.69-0.74 in PARAGON-HF and 0.75; 95% CI, 0.73-0.78 in I-PRESERVE) and calibration. The model showed similar discrimination to a biomarker-driven model including high-sensitivity cardiac troponin T and significantly better discrimination than the Meta-Analysis Global Group in Chronic (MAGGIC) risk score (C statistic at 1 year, 0.60; 95% CI, 0.58-0.63; delta C statistic, 0.13; 95% CI, 0.10-0.15; P < .001) and NT-proBNP level alone (C statistic at 1 year, 0.66; 95% CI, 0.64-0.68; delta C statistic, 0.07; 95% CI, 0.05-0.08; P < .001). Models derived for the prediction of all-cause and cardiovascular death also performed well. An online calculator was created to allow calculation of an individual's risk. Conclusions and Relevance: In this prognostic study, a robust prediction model for clinical outcomes in HFpEF was developed and validated using routinely collected variables. The model performed better than NT-proBNP level alone. The model may help clinicians to identify high-risk patients and guide treatment decisions in HFpEF.

Canadian Association of Radiologists Head and Neck Imaging Referral Guideline.

The Canadian Association of Radiologists (CAR) Head and Neck Expert Panel consists of radiologists, a laryngologist and laryngeal surgeon, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 11 clinical/diagnostic scenarios, a systematic rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 17 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop 26 recommendation statements across the 11 scenarios. This guideline presents the methods of development and the referral recommendations for sinus disease, tinnitus, thyroid and parathyroid disease, neck mass of unknown origin, acute sialadenitis, chronic salivary conditions, and temporomandibular joint dysfunction.

Using a decision tree to predict the number of COVID cases: a tutorial for beginners.

This manuscript describes the development of a module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox . The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on machine learning and decision tree concepts in an interactive format that uses appropriate cloud resources for data access and analyses. Machine learning (ML) is an important tool in biomedical research and can lead to improvements in diagnosis, treatment, and prevention of diseases. During the COVID pandemic ML was used for predictions at the patient and community levels. Given its ubiquity, it is important that future doctors, researchers and teachers get acquainted with ML and its contributions to research. Our goal is to make it easier for everyone to learn about machine learning. The learning module we present here is based on a small COVID dataset, videos, annotated code and the use of Google Colab or the Google Cloud Platform (GCP). The benefit of these platforms is that students do not have to set up a programming environment on their computer which saves time and is also an important democratization factor. The module focuses on learning the basics of decision trees by applying them to COVID data. It introduces basic terminology used in supervised machine learning and its relevance to research. Our experience with biology students at San Francisco State University suggests that the material increases interest in ML.

Effects of renin-angiotensin system blockers on outcomes from COVID-19: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND AND AIMS: Randomized controlled trials (RCTs) have assessed the effects of renin-angiotensin system (RAS) blockers in adults with coronavirus disease 2019 (COVID-19). This meta-analysis provides estimates of the safety and efficacy of treatment with (vs. without) RAS blockers from these trials. METHODS: PubMed, Web of Science, and ClinicalTrials.gov were searched (1 March-12 April 2023). Event/patient numbers were extracted, comparing angiotensin-converting enzyme (ACE) inhibitor/angiotensin-receptor blocker (ARB) treatment with no treatment, for the outcomes: intensive care unit (ICU) admission, mechanical ventilation, vasopressor use, acute kidney injury (AKI), renal replacement therapy (RRT), acute myocardial infarction, stroke/transient ischaemic attack, heart failure, thromboembolic events, and all-cause death. Fixed-effects meta-analysis estimates were pooled. RESULTS: Sixteen RCTs including 3492 patients were analysed. Compared with discontinuation of RAS blockers, continuation was not associated with increased risk of ICU [risk ratio (RR) 0.96, 0.66-1.41], ventilation (RR 0.77, 0.55-1.09), vasopressors (RR 0.92, 0.58-1.44), AKI (RR 1.01, 0.40-2.56), RRT (RR 1.01, 0.46-2.21), or thromboembolic events (RR 1.07, 0.36-3.19). RAS blocker initiation was not associated with increased risk of ICU (RR 0.71, 0.47-1.08), ventilation (RR 1.12, 0.91-1.38), AKI (RR 1.28, 0.89-1.86), RRT (RR 1.66, 0.89-3.12), or thromboembolic events (RR 1.20, 0.06-23.70), although vasopressor use increased (RR 1.27, 1.02-1.57). The RR for all-cause death in the continuation/discontinuation trials was 1.24 (0.80-1.92), and 1.22 (0.96-1.55) in the initiation trials. In patients with severe/critical COVID-19, RAS blocker initiation increased the risk of all-cause death (RR 1.31, 1.01-1.72). CONCLUSION: ACE inhibitors and ARBs may be continued in non-severe COVID-19 infection, where indicated. Conversely, initiation of RAS blockers may be harmful in critically ill patients.PROSPERO registration number: CRD42023408926.

Neuropeptide Y is elevated in heart failure and is an independent predictor of outcomes.

AIMS: Neuropeptide Y (NPY) is the most abundant neuropeptide found in the heart and is released alongside norepinephrine following prolonged sympathetic activation, a process that is implicated in the pathophysiology of heart failure (HF). In patients with severely impaired left ventricular ejection fraction (LVEF) undergoing cardiac resynchronization therapy, higher levels of NPY measured in coronary sinus blood, are associated with poorer outcome. The aim was to examine the association of peripheral venous NPY levels and outcomes in a HF population with a range of LVEF, using a highly sensitive and specific assay. METHODS AND RESULTS: The association between NPY and the composite outcome of cardiovascular death or HF hospitalization, its components, and all-cause mortality was examined using Cox regression analyses among 833 patients using a threshold of elevated NPY identified through binary recursive partitioning adjusted for prognostic variables including estimated glomerular filtration rate (eGFR), ejection fraction and B-type natriuretic peptide (BNP). The mean value of NPY was 25.8 ± 18.2 pg/ml. Patients with high NPY levels (≥29 pg/ml) compared with low values were older (73 ± 10 vs. 71 ± 11 years), more often male (58.5% vs. 55.6%), had higher BNP levels (583 [261-1096] vs. 440 [227-829] pg/ml), lower eGFR (46.4 ± 13.9 vs. 52.4 ± 11.7 ml/min/1.73 m2 ), and were more often treated with diuretics. There was no associated risk of HF hospitalization with NPY levels ≥29 vs. <29 pg/ml. Higher NPY levels were associated with a greater risk of cardiovascular and all-cause death (adjusted hazard ratio 1.56 [95% confidence interval 1.21-2.10], p = 0.003 and 1.30 [1.04-1.62], p = 0.02, respectively). There was no associated risk of HF hospitalization with higher NPY levels. CONCLUSIONS: Peripherally measured NPY is an independent predictor of all-cause and cardiovascular death even after adjustment for other prognostic variables, including BNP.

A novel, small-volume subcutaneous furosemide formulation delivered by an abdominal patch infusor device in patients with heart failure: results of two phase I studies.

AIMS: Subcutaneous (SC) furosemide has potential advantages over intravenous (IV) furosemide by enabling self-administration or administration by a lay caregiver, such as facilitating early discharge, preventing hospitalizations, and in palliative care. A high-concentration, pH-neutral furosemide formulation has been developed for SC administration via a small patch infusor pump. We aimed to compare the bioavailability, pharmacokinetic (PK), and pharmacodynamic (PD) profiles of a new SC furosemide formulation with conventional IV furosemide and describe the first use of a bespoke mini-pump to administer this formulation. METHODS AND RESULTS: A novel pH-neutral formulation of SC furosemide containing 80 mg furosemide in ∼2.7 mL (infused over 5 h) was investigated. The first study was a PK/PD study of SC furosemide compared with 80 mg IV furosemide administered as a bolus in ambulatory patients with heart failure (HF). The primary outcome was absolute bioavailability of SC compared with IV furosemide. The second study investigated the same SC furosemide preparation delivered by a patch infusor in patients hospitalized with HF. Primary outcome measures were treatment-emergent adverse events, infusion site pain, device performance, and PK measurements.The absolute bioavailability of SC furosemide in comparison to IV furosemide was 112%, resulting in equivalent diuresis and natriuresis. When SC furosemide was administered via the patch pump, there were no treatment-emergent adverse events and 95% of participants reported no/minor discomfort at the infusion site. CONCLUSION: The novel preparation of SC furosemide had similar bioavailability to IV furosemide. Administration via a patch pump was feasible and well tolerated.

Biomarker discovery process at binomial decision point (2BDP): Analytical pipeline to construct biomarker panel.

A clinical incident is typically manifested by several molecular events; therefore, it seems logical that a successful diagnosis, prognosis, or stratification of a clinical landmark require multiple biomarkers. In this report, we presented a machine learning pipeline, namely "Biomarker discovery process at binomial decision point" (2BDP) that took an integrative approach in systematically curating independent variables (e.g., multiple molecular markers) to explain an output variable (e.g., clinical landmark) of binary in nature. In a logical sequence, 2BDP includes feature selection, unsupervised model development and cross validation. In the present work, the efficiency of 2BDP was demonstrated by finding three biomarker panels that independently explained three stages of Alzheimer's disease (AD) marked as Braak stages I, II and III, respectively. We designed three assortments from the entire cohort based on these Braak stages; subsequently, each assortment was split into two populations at Braak score I, II or III. 2BDP systematically integrated random forest and logistic regression fitting model to find biomarker panels with minimum features that explained these three assortments, e.g., significantly differentiated two populations segregated by Braak stage I, II or III, respectively. Thereafter, the efficacies of these panels were measured by the area under the curve (AUC) values of the receiver operating characteristic (ROC) plot. The AUC-ROC was calculated by two cross-validation methods. Final set of gene markers was a mix of novel and a priori established AD signatures. These markers were weighted by unique coefficients and linearly connected in a group of 2-10 to explain Braak stage I, II or III by AUC ≥ 0.8. Small sample size and a lack of distinctly recruited Training and Test sets were the limitations of the present undertaking; yet 2BDP demonstrated its capability to curate a panel of optimum numbers of biomarkers to describe the outcome variable with high efficacy.