Achievement of low-risk status is a treatment goal in pulmonary arterial hypertension (PAH). Risk assessment often is performed using multiparameter tools, such as the Registry to Evaluate Early and ...Long-Term PAH Disease Management (REVEAL) risk calculator. Risk calculators that assess fewer variables without compromising validity may expedite risk assessment in the routine clinic setting. We describe the development and validation of REVEAL Lite 2, an abridged version of REVEAL 2.0.
Can a simplified version of the REVEAL 2.0 risk assessment calculator for patients with PAH be developed and validated?
REVEAL Lite 2 includes six noninvasive variables—functional class (FC), vital signs (systolic BP SBP and heart rate), 6-min walk distance (6MWD), brain natriuretic peptide (BNP)/N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and renal insufficiency (by estimated glomerular filtration rate eGFR)—and was validated in a series of analyses (Kaplan-Meier, concordance index, Cox proportional hazard model, and multivariate analysis).
REVEAL Lite 2 approximates REVEAL 2.0 at discriminating low, intermediate, and high risk for 1-year mortality in patients in the REVEAL registry. The model indicated that the most highly predictive REVEAL Lite 2 parameter was BNP/NT-proBNP, followed by 6MWD and FC. Even if multiple, less predictive variables (heart rate, SBP, eGFR) were missing, REVEAL Lite 2 still discriminated among risk groups.
REVEAL Lite 2, an abridged version of REVEAL 2.0, provides a simplified method of risk assessment that can be implemented routinely in daily clinical practice. REVEAL Lite 2 is a robust tool that provides discrimination among patients at low, intermediate, and high risk of 1-year mortality.
ClinicalTrials.gov; No.: NCT00370214; URL: www.clinicaltrials.gov;
Patients with heart failure and preserved ejection fraction (HFpEF) have a high burden of symptoms and functional limitations, and have a poor quality of life. By targeting cardiometabolic ...abmormalities, sodium glucose cotransporter 2 (SGLT2) inhibitors may improve these impairments. In this multicenter, randomized trial of patients with HFpEF (NCT03030235), we evaluated whether the SGLT2 inhibitor dapagliflozin improves the primary endpoint of Kansas City Cardiomyopathy Questionnaire Clinical Summary Score (KCCQ-CS), a measure of heart failure-related health status, at 12 weeks after treatment initiation. Secondary endpoints included the 6-minute walk test (6MWT), KCCQ Overall Summary Score (KCCQ-OS), clinically meaningful changes in KCCQ-CS and -OS, and changes in weight, natriuretic peptides, glycated hemoglobin and systolic blood pressure. In total, 324 patients were randomized to dapagliflozin or placebo. Dapagliflozin improved KCCQ-CS (effect size, 5.8 points (95% confidence interval (CI) 2.3-9.2, P = 0.001), meeting the predefined primary endpoint, due to improvements in both KCCQ total symptom score (KCCQ-TS) (5.8 points (95% CI 2.0-9.6, P = 0.003)) and physical limitations scores (5.3 points (95% CI 0.7-10.0, P = 0.026)). Dapagliflozin also improved 6MWT (mean effect size of 20.1 m (95% CI 5.6-34.7, P = 0.007)), KCCQ-OS (4.5 points (95% CI 1.1-7.8, P = 0.009)), proportion of participants with 5-point or greater improvements in KCCQ-OS (odds ratio (OR) = 1.73 (95% CI 1.05-2.85, P = 0.03)) and reduced weight (mean effect size, 0.72 kg (95% CI 0.01-1.42, P = 0.046)). There were no significant differences in other secondary endpoints. Adverse events were similar between dapagliflozin and placebo (44 (27.2%) versus 38 (23.5%) patients, respectively). These results indicate that 12 weeks of dapagliflozin treatment significantly improved patient-reported symptoms, physical limitations and exercise function and was well tolerated in chronic HFpEF.
Standardized donor‐derived cell‐free DNA (dd‐cfDNA) testing has been introduced into clinical use to monitor kidney transplant recipients for rejection. This report describes the performance of this ...dd‐cfDNA assay to detect allograft rejection in samples from heart transplant (HT) recipients undergoing surveillance monitoring across the United States. Venous blood was longitudinally sampled from 740 HT recipients from 26 centers and in a single‐center cohort of 33 patients at high risk for antibody‐mediated rejection (AMR). Plasma dd‐cfDNA was quantified by using targeted amplification and sequencing of a single nucleotide polymorphism panel. The dd‐cfDNA levels were correlated to paired events of biopsy‐based diagnosis of rejection. The median dd‐cfDNA was 0.07% in reference HT recipients (2164 samples) and 0.17% in samples classified as acute rejection (35 samples; P = .005). At a 0.2% threshold, dd‐cfDNA had a 44% sensitivity to detect rejection and a 97% negative predictive value. In the cohort at risk for AMR (11 samples), dd‐cfDNA levels were elevated 3‐fold in AMR compared with patients without AMR (99 samples, P = .004). The standardized dd‐cfDNA test identified acute rejection in samples from a broad population of HT recipients. The reported test performance characteristics will guide the next stage of clinical utility studies of the dd‐cfDNA assay.
A large multicenter study in a broad heart transplant surveillance population demonstrates the ability of standardized donor‐ derived cell‐free DNA testing to identify both T cell–mediated and antibody‐mediated acute rejection with a high negative predictive value.
Artificial intelligence (AI) refers to the ability of machines to perform intelligent tasks, and machine learning (ML) is a subset of AI describing the ability of machines to learn independently and ...make accurate predictions. The application of AI combined with "big data" from the electronic health records, is poised to impact how we take care of patients. In recent years, an expanding body of literature has been published using ML in cardiovascular health care, including mechanical circulatory support (MCS). This primer article provides an overview for clinicians on relevant concepts of ML and AI, reviews predictive modeling concepts in ML and provides contextual reference to how AI is being adapted in the field of MCS. Lastly, it explains how these methods could be incorporated in the practices of medicine to improve patient outcomes.
Abstract Objectives This study investigates the use of a Bayesian statistical model to address the limited predictive capacity of existing risk scores derived from multivariate analyses. This is ...based on the hypothesis that it is necessary to consider the interrelationships and conditional probabilities among independent variables to achieve sufficient statistical accuracy. Background Right ventricular failure (RVF) continues to be a major adverse event following left ventricular assist device (LVAD) implantation. Methods Data used for this study were derived from 10,909 adult patients from the Inter-Agency Registry for Mechanically Assisted Circulatory Support (INTERMACS) who had a primary LVAD implanted between December 2006 and March 2014. An initial set of 176 pre-implantation variables were considered. RVF post-implant was categorized as acute (<48 h), early (48 h to 14 daysays), and late (>14 days) in onset. For each of these endpoints, a separate tree-augmented naïve Bayes model was constructed using the most predictive variables employing an open source Bayesian inference engine. Results The acute RVF model consisted of 33 variables including systolic pulmonary artery pressure (PAP), white blood cell count, left ventricular ejection fraction, cardiac index, sodium levels, and lymphocyte percentage. The early RVF model consisted of 34 variables, including systolic PAP, pre-albumin, lactate dehydrogenase level, INTERMACS profile, right ventricular ejection fraction, pro-B-type natriuretic peptide, age, heart rate, tricuspid regurgitation, and body mass index. The late RVF model included 33 variables and was predicted mostly by peripheral vascular resistance, model for end-stage liver disease score, albumin level, lymphocyte percentage, and mean and diastolic PAP. The accuracy of all Bayesian models was between 91% and 97%, with an area under the receiver operator characteristics curve between 0.83 and 0.90, sensitivity of 90%, and specificity between 98% and 99%, significantly outperforming previously published risk scores. Conclusions A Bayesian prognostic model of RVF based on the large, multicenter INTERMACS registry provided highly accurate predictions of acute, early, and late RVF on the basis of pre-operative variables. These models may facilitate clinical decision making while screening candidates for LVAD therapy.
Mitral regurgitation (MR) determines pathophysiology and outcome in advanced heart failure. The impact of left ventricular assist device (LVAD) placement on clinically significant MR and its ...contribution to long-term outcomes has been sparsely evaluated.
We evaluated the effect of clinically significant MR on patients implanted in the MOMENTUM 3 trial with either the HeartMate II (HMII) or the HeartMate 3 (HM3) at 2 years. Clinical significance was defined as moderate or severe grade MR determined by site-based echocardiograms.
Of 927 patients with LVAD implants without a prior or concomitant mitral valve procedure, 403 (43.5%) had clinically significant MR at baseline. At 1-month of support, residual MR was present in 6.2% of patients with HM3 and 14.3% of patients with HMII (relative risk = 0.43; 95% CI, 0.22–0.84; p = 0.01) with a low rate of worsening at 2 years. Residual MR at 1-month post-implant did not impact 2-year mortality for either the HM3 (hazard ratio HR,1.41; 95% CI, 0.52–3.89; p = 0.50) or HMII (HR, 0.91; 95% CI, 0.37–2.26; p = 0.84) LVAD. The presence or absence of baseline MR did not influence mortality (HM3 HR, 0.86; 95% CI, 0.56–1.33; p = 0.50; HMII HR, 0.81; 95% CI, 0.54–1.22; p = 0.32), major adverse events or functional capacity. In multivariate analysis, severe baseline MR (p = 0.001), larger left ventricular dimension (p = 0.002), and implantation with the HMII instead of the HM3 LVAD (p = 0.05) were independently associated with an increased likelihood of persistent MR post-implant.
Hemodynamic unloading after LVAD implantation improves clinically significant MR early, sustainably, and to a greater extent with the HM3 LVAD. Neither baseline nor residual MR influence outcomes after LVAD implantation.
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