Patel, Krishna

ABSTRACT Aims Exercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition “fitness” score, then validated its utility in two external populations. Methods and Results We included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing ≥7 minutes on a Bruce protocol) then developed a body composition “fitness” score. We then assessed the associations of “fitness” score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 – 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition “fitness” score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 – 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition “fitness” score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 – 0.79, p<0.001). Conclusions We demonstrated that a comprehensive body composition “fitness” score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed. Graphical Abstract Overview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition “fitness” score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.

Abstract Background and Aims Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET for attenuation correction and potentially contain valuable data about body tissue composition. Deep learning and image processing were combined to automatically quantify skeletal muscle (SM), bone and adipose tissue from these scans and then evaluate their associations with death or myocardial infarction (MI). Methods In PET MPI from three sites, deep learning quantified SM, bone, epicardial adipose tissue (EAT), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT). Sex-specific thresholds for abnormal values were established. Associations with death or MI were evaluated using unadjusted and multivariable models adjusted for clinical and imaging factors. Results This study included 10 085 patients, with median age 68 (interquartile range 59–76) and 5767 (57%) male. Body tissue segmentations were completed in 102 ± 4 s. Higher VAT density was associated with an increased risk of death or MI in both unadjusted [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.37–1.43] and adjusted (HR 1.24, 95% CI 1.19–1.28) analyses, with similar findings for IMAT, SAT, and EAT. Patients with elevated VAT density and reduced myocardial flow reserve had a significantly increased risk of death or MI (adjusted HR 2.49, 95% CI 2.23–2.77). Conclusions Volumetric body tissue composition can be obtained rapidly and automatically from standard cardiac PET/CT. This new information provides a detailed, quantitative assessment of sarcopenia and cardiometabolic health for physicians.

Background: The Kansas City Cardiomyopathy Questionnaire (KCCQ) has been psychometrically evaluated in multiple heart failure (HF) populations, but the comparability of its psychometric properties between men and women is unknown. Methods: Data from 3 clinical trials (1 in stable HF with preserved ejection fraction, 1 each in stable and acute HF with reduced ejection fraction) and 1 prospective cohort study (stable HF with reduced ejection fraction), incorporating 6773 men and 3612 women with HF, were used to compare the construct validity, internal and test-retest reliability, ability to detect change, predict mortality and hospitalizations and minimally important differences between the 2 sexes. Interactions of the KCCQ overall summary and subdomain scores by sex were independently examined. Results: The KCCQ-Overall Summary score correlated well with New York Heart Association functional class in both sexes across patients with stable (correlation coefficient: −0.40 in men versus −0.49 in women) and acute (−0.37 in men versus −0.34 in women) HF. All KCCQ subdomains demonstrated concordant relationships with relevant comparison standards with no significant interactions by sex in 19 of 21 of these construct validity analyses. All KCCQ scores were equally predictive and other psychometric evaluations showed similar results by sex: test-retest reliability (intraclass correlation coefficient 0.94 in men versus 0.92 in women), responsive to change (standardized response mean 1.01 in both sexes), as were the minimally important differences and internal reliability. Conclusions: The psychometric properties of the KCCQ, in terms of validity, prognosis, reliability, and sensitivity to change, are comparable in men and women with HF with preserved ejection fraction and HF with reduced ejection fraction.