Matthew Lumley

BACKGROUND: There is conflicting evidence on the utility of elective intra-aortic balloon pump (IABP) use during high-risk percutaneous coronary intervention (PCI). Observational series have indicated a reduction in major in-hospital adverse events, although randomized trial evidence does not support this. A recent study has suggested a mortality benefit trend early after PCI, but there are currently no long-term outcome data from randomized trials in this setting. METHODS AND RESULTS: Three hundred one patients with left ventricular impairment (ejection fraction <30%) and severe coronary disease (BCIS-1 jeopardy score ≥8; maximum possible score=12) were randomized to receive PCI with elective IABP support (n=151) or without planned IABP support (n=150). Long-term all-cause mortality was assessed by tracking the databases held at the Office of National Statistics (in England and Wales) and the General Register Office (in Scotland). The groups were balanced in terms of baseline characteristics (left ventricular ejection fraction, 23.6%; BCIS-1 jeopardy score, 10.4) and the amount and type of revascularization performed. Mortality data were available for the entire cohort at a median of 51 months (interquartile range, 41-58) from randomization. All-cause mortality at follow-up was 33% in the overall cohort, with significantly fewer deaths occurring in the elective IABP group (n=42) than in the group that underwent PCI without planned IABP support (n=58) (hazard ratio, 0.66; 95% confidence interval, 0.44-0.98; P=0.039). CONCLUSIONS: In patients with severe ischemic cardiomyopathy treated with PCI, all-cause mortality was 33% at a median of 51 months. Elective IABP use during PCI was associated with a 34% relative reduction in all-cause mortality compared with unsupported PCI. CLINICAL TRIAL REGISTRATION: URL: http://www.isrctn.org. Unique identifier: ISRCTN40553718; and http://www.clinicaltrials.gov. Unique identifier: NCT00910481.

Background: Coronary microvascular dysfunction (MVD) is defined by impaired flow augmentation in response to a pharmacological vasodilator in the presence of nonobstructive coronary artery disease. It is unknown whether diminished coronary vasodilator response correlates with abnormal exercise physiology or inducible myocardial ischemia. Methods: Patients with angina and nonobstructive coronary artery disease had simultaneous coronary pressure and flow velocity measured using a dual sensor-tipped guidewire during rest, supine bicycle exercise, and adenosine-mediated hyperemia. Microvascular resistance (MR) was calculated as coronary pressure divided by flow velocity. Wave intensity analysis quantified the proportion of accelerating wave energy (perfusion efficiency). Global myocardial blood flow and subendocardial:subepicardial perfusion ratio were quantified using 3-Tesla cardiac magnetic resonance imaging during hyperemia and rest; inducible ischemia was defined as hyperemic subendocardial:subepicardial perfusion ratio &lt;1.0. Patients were classified as having MVD if coronary flow reserve &lt;2.5 and controls if coronary flow reserve ≥2.5, with researchers blinded to the classification. Results: Eighty-five patients were enrolled (78% female, 57±10 years), 45 (53%) were classified as having MVD. Of the MVD group, 82% had inducible ischemia compared with 22% of controls ( P &lt;0.001); global myocardial perfusion reserve was 2.01±0.41 and 2.68±0.49 ( P &lt;0.001). In controls, coronary perfusion efficiency improved from rest to exercise and was unchanged during hyperemia (59±11% vs 65±14% vs 57±18%; P =0.02 and P =0.14). In contrast, perfusion efficiency decreased during both forms of stress in MVD (61±12 vs 44±10 vs 42±11%; both P &lt;0.001). Among patients with a coronary flow reserve &lt;2.5, 62% had functional MVD, with normal minimal MR (hyperemic MR&lt;2.5 mmHg/cm/s), and 38% had structural MVD with elevated hyperemic MR. Resting MR was lower in those with functional MVD (4.2±1.0 mmHg/cm/s) than in those with structural MVD (6.9±1.7 mmHg/cm/s) or controls (7.3±2.2 mmHg/cm/s; both P &lt;0.001). During exercise, the structural group had a higher systolic blood pressure (188±25 mmHg) than did those with functional MVD (161±27 mmHg; P =0.004) and controls (156±30 mmHg; P &lt;0.001). Functional and structural MVD had similar stress myocardial perfusion and exercise perfusion efficiency values. Conclusion: In patients with angina and nonobstructive coronary artery disease, diminished coronary flow reserve characterizes a cohort with inducible ischemia and a maladaptive physiological response to exercise. We have identified 2 endotypes of MVD with distinctive systemic vascular responses to exercise; whether endotypes have a different prognosis or require different treatments merits further investigation.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of the undiagnosed may be supported by risk-prediction models relating patient factors to AF risk. However, there exists a need for an implementable risk model that is contemporaneous and informed by routinely collected patient data, reflecting the real-world pathology of AF. METHODS: This study sought to develop and evaluate novel and conventional statistical and machine learning models for risk-predication of AF. This was a retrospective, cohort study of adults (aged ≥30 years) without a history of AF, listed on the Clinical Practice Research Datalink, from January 2006 to December 2016. Models evaluated included published risk models (Framingham, ARIC, CHARGE-AF), machine learning models, which evaluated baseline and time-updated information (neural network, LASSO, random forests, support vector machines), and Cox regression. RESULTS: Analysis of 2,994,837 individuals (3.2% AF) identified time-varying neural networks as the optimal model achieving an AUROC of 0.827 vs. 0.725, with number needed to screen of 9 vs. 13 patients at 75% sensitivity, when compared with the best existing model CHARGE-AF. The optimal model confirmed known baseline risk factors (age, previous cardiovascular disease, antihypertensive medication usage) and identified additional time-varying predictors (proximity of cardiovascular events, body mass index (both levels and changes), pulse pressure, and the frequency of blood pressure measurements). CONCLUSION: The optimal time-varying machine learning model exhibited greater predictive performance than existing AF risk models and reflected known and new patient risk factors for AF.

AIMS: The study aims to systematically assess the diagnostic performance of cardiac magnetic resonance (CMR) and nuclear scintigraphy (index tests) for the diagnosis and differentiation of subtypes of cardiac amyloidosis. METHODS AND RESULTS: MEDLINE and Embase electronic databases were searched for studies evaluating the diagnostic performance of CMR or nuclear scintigraphy in detecting cardiac amyloidosis and subsequently in differentiating transthyretin amyloidosis (ATTR) from immunoglobulin light-chain (AL) amyloidosis. In this meta-analysis, histopathological examination of tissue from endomyocardial biopsy (EMB) or extra-cardiac organs were reference standards. Pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated, and a random effects meta-analysis was used to estimate diagnostic odds ratios. Methodological quality was assessed using a validated instrument. Of the 2947 studies identified, 27 met the criteria for inclusion. Sensitivity and specificity of CMR in diagnosing cardiac amyloidosis was 85.7% and 92.0% against EMB reference and 78.9% and 93.9% with any organ histology reference. Corresponding sensitivity and specificity of nuclear scintigraphy was 88.4% and 87.2% against EMB reference and 82.0% and 98.8% with histology from any organ. CMR was unable to reliably differentiate ATTR from AL amyloidosis (sensitivity 28.1-99.0% and specificity 11.0-60.0%). Sensitivity and specificity of nuclear scintigraphy in the differentiation of ATTR from AL amyloidosis ranged from 90.9% to 91.5% and from 88.6% to 97.1%. Pooled negative likelihood ratio and positive likelihood ratio for scintigraphy in this setting were 0.1 and 8, with EMB reference standard. Study quality assessed by QUADAS-2 was generally poor with evidence of bias. CONCLUSIONS: Cardiac magnetic resonance is a useful test for diagnosing cardiac amyloidosis but is not reliable in further classifying the disease. Nuclear scintigraphy offers strong diagnostic performance in both the detection of cardiac amyloidosis and differentiating ATTR from AL amyloidosis. Our findings support the use of both imaging modalities in a non-invasive diagnostic algorithm that also tests for the presence of monoclonal protein.

Coronary microvascular resistance is increasingly measured as a predictor of clinical outcomes, but there is no accepted gold-standard measurement. We compared the diagnostic accuracy of 2 invasive indices of microvascular resistance, Doppler-derived hyperemic microvascular resistance (hMR) and thermodilution-derived index of microcirculatory resistance (IMR), at predicting microvascular dysfunction. A total of 54 patients (61 ± 10 years) who underwent cardiac catheterization for stable coronary artery disease (n = 10) or acute myocardial infarction (n = 44) had simultaneous intracoronary pressure, Doppler flow velocity and thermodilution flow data acquired from 74 unobstructed vessels, at rest and during hyperemia. Three independent measurements of microvascular function were assessed, using predefined dichotomous thresholds: (1) coronary flow reserve (CFR), the average value of Doppler- and thermodilution-derived CFR; (2) cardiovascular magnetic resonance (CMR) derived myocardial perfusion reserve index; and (3) CMR-derived microvascular obstruction. hMR correlated with IMR (rho = 0.41, p <0.0001). hMR had better diagnostic accuracy than IMR to predict CFR (area under curve [AUC] 0.82 vs 0.58, p <0.001, sensitivity and specificity 77% and 77% vs 51% and 71%) and myocardial perfusion reserve index (AUC 0.85 vs 0.72, p = 0.19, sensitivity and specificity 82% and 80% vs 64% and 75%). In patients with acute myocardial infarction, the AUCs of hMR and IMR at predicting extensive microvascular obstruction were 0.83 and 0.72, respectively (p = 0.22, sensitivity and specificity 78% and 74% vs 44% and 91%). We conclude that these 2 invasive indices of coronary microvascular resistance only correlate modestly and so cannot be considered equivalent. In our study, the correlation between independent invasive and noninvasive measurements of microvascular function was better with hMR than with IMR.