FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data.

Accurately predicting patients' risk for specific medical outcomes is paramount for effective healthcare management and personalized medicine. While a substantial body of literature addresses the prediction of diverse medical conditions, existing models predominantly focus on singular outcomes, limiting their scope to one disease at a time. However, clinical reality often entails patients concurrently facing multiple health risks across various medical domains. In response to this gap, our study proposes a novel multi-risk framework adept at simultaneous risk prediction for multiple clinical outcomes, including diabetes, mortality, and hypertension. Leveraging a concise set of features extracted from patients' cardiorespiratory fitness data, our framework minimizes computational complexity while maximizing predictive accuracy. Moreover, we integrate a state-of-the-art instance-based interpretability technique into our framework, providing users with comprehensive explanations for each prediction. These explanations afford medical practitioners invaluable insights into the primary health factors influencing individual predictions, fostering greater trust and utility in the underlying prediction models. Our approach thus stands to significantly enhance healthcare decision-making processes, facilitating more targeted interventions and improving patient outcomes in clinical practice. Our prediction framework utilizes an automated machine learning framework, Auto-Weka, to optimize machine learning models and hyper-parameter configurations for the simultaneous prediction of three medical outcomes: diabetes, mortality, and hypertension. Additionally, we employ a local interpretability technique to elucidate predictions generated by our framework. These explanations manifest visually, highlighting key attributes contributing to each instance's prediction for enhanced interpretability. Using automated machine learning techniques, the models simultaneously predict hypertension, mortality, and diabetes risks, utilizing only nine patient features. They achieved an average AUC of 0.90 ± 0.001 on the hypertension dataset, 0.90 ± 0.002 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset through tenfold cross-validation. Additionally, the models demonstrated strong performance with an average AUC of 0.89 ± 0.001 on the hypertension dataset, 0.90 ± 0.001 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset using bootstrap evaluation with 1000 resamples.

Left Upper Extremity Pain, Right Coronary Artery Culprit: A Puzzling Path to Aneurysm Discovery.

Giant coronary artery aneurysm (GCA) is a rare disease afflicting 0.2% of the population. It is primarily attributed to atherosclerosis in adults and Kawasaki disease in children. Other uncommon etiologies include Takayasu arteritis and post-percutaneous coronary intervention.1,2 GCA lacks a universally accepted definition, with proposed criteria including a diameter exceeding 2 cm, 5 cm, or four times the normal vessel size.3 While the majority of GCAs are asymptomatic, a subset of patients present with angina, myocardial infarction from embolization or compression, heart failure due to fistula formation, or even sudden death.1 We report a case of an adult harboring a GCA involving the right coronary artery.

Epidemiology and Prognostic Implications of Coronary Artery Calcium in Asymptomatic Individuals With Prediabetes: A Multicohort Study.

OBJECTIVE: To describe the epidemiology and prognostic value of coronary artery calcium (CAC) in individuals with prediabetes. RESEARCH DESIGN AND METHODS: We pooled participants free of clinical atherosclerotic cardiovascular disease (ASCVD) from four prospective cohorts: the Multi-Ethnic Study of Atherosclerosis, Heinz Nixdorf Recall Study, Framingham Heart Study, and Jackson Heart Study. Two definitions were used for prediabetes: inclusive (fasting plasma glucose [FPG] ≥100 to <126 mg/dL and hemoglobin A1c [HbA1c] ≥5.7% to <6.5%, if available, and no glucose-lowering medications) and restrictive (FPG ≥110 to <126 mg/dL and HbA1c ≥5.7% to <6.5%, if available, among participants not taking glucose-lowering medications). RESULTS: The study included 13,376 participants (mean age 58 years; 54% women; 57% White; 27% Black). The proportions with CAC ≥100 were 17%, 22%, and 37% in those with euglycemia, prediabetes, and diabetes, respectively. Over a median (25th-75th percentile) follow-up time of 14.6 (interquartile range 7.8-16.4) years, individuals with prediabetes and CAC ≥100 had a higher unadjusted 10-year incidence of ASCVD (13.4%) than the overall group of those with diabetes (10.6%). In adjusted analyses, using the inclusive definition of prediabetes, compared with euglycemia, the hazard ratios (HRs) for ASCVD were 0.79 (95% CI 0.62, 1.01) for prediabetes and CAC 0, 0.70 (0.54, 0.89) for prediabetes and CAC 1-99, 1.54 (1.27, 1.88) for prediabetes and CAC ≥100, and 1.64 (1.39, 1.93) for diabetes. Using the restrictive definition, the HR for ASCVD was 1.63 (1.29, 2.06) for prediabetes and CAC ≥100. CONCLUSIONS: CAC ≥100 is frequent among individuals with prediabetes and identifies a high ASCVD risk subgroup in which the adjusted ASCVD risk is similar to that in individuals with diabetes.

Prediction of the development of new coronary atherosclerotic plaques with radiomics.

BACKGROUND: Radiomics is expected to identify imaging features beyond the human eye. We investigated whether radiomics can identify coronary segments that will develop new atherosclerotic plaques on coronary computed tomography angiography (CCTA). METHODS: From a prospective multinational registry of patients with serial CCTA studies at ≥ 2-year intervals, segments without identifiable coronary plaque at baseline were selected and radiomic features were extracted. Cox models using clinical risk factors (Model 1), radiomic features (Model 2) and both clinical risk factors and radiomic features (Model 3) were constructed to predict the development of a coronary plaque, defined as total PV â€‹≥ â€‹1 â€‹mm3, at follow-up CCTA in each segment. RESULTS: In total, 9583 normal coronary segments were identified from 1162 patients (60.3 â€‹± â€‹9.2 years, 55.7% male) and divided 8:2 into training and test sets. At follow-up CCTA, 9.8% of the segments developed new coronary plaque. The predictive power of Models 1 and 2 was not different in both the training and test sets (C-index [95% confidence interval (CI)] of Model 1 vs. Model 2: 0.701 [0.690-0.712] vs. 0.699 [0.0.688-0.710] and 0.696 [0.671-0.725] vs. 0.0.691 [0.667-0.715], respectively, all p â€‹> â€‹0.05). The addition of radiomic features to clinical risk factors improved the predictive power of the Cox model in both the training and test sets (C-index [95% CI] of Model 3: 0.772 [0.762-0.781] and 0.767 [0.751-0.787], respectively, all p â€‹< â€‹00.0001 compared to Models 1 and 2). CONCLUSION: Radiomic features can improve the identification of segments that would develop new coronary atherosclerotic plaque. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT0280341.

Ejection fraction and ventricular volumes on rubidium positron emission tomography: Validation against cardiovascular magnetic resonance.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is the non-invasive gold standard for non-invasively determining left ventricular volumes (LVVs) and ejection fraction (EF). We aimed to assess the accuracy of LVV and left ventricular ejection fraction measured by positron emission tomography (PET) as compared to CMR. METHODS: Patients who underwent both PET and CMR within 1 year were identified from prospective institutional registries. Analysis was performed to evaluate the agreement between the raw and body-surface-area-normalized left ventricular volume (LVV) and EF derived from PET vs. those derived from CMR. RESULTS: The study population consisted of 669 patients (mean age 62 ± 13 years, 65% male). The median (interquartile range [IQR]) duration between CMR and PET imaging was 36 (7-118) days. The median (IQR) EF values were 52% (38-63%) on CMR and 53% (37-65%) on PET (mean difference: 0.53% ± 9.1, P = 0.129) with a strong correlation (Spearman rho = 0.84, P < 0.001; Intraclass Correlation Coefficient 0.84, 95% confidence interval [CI]: 0.82-0.86, P < 0.001; Lin's concordance correlation coefficient was 0.844, 95% CI: 0.822 to 0.865). Results were similar with LVV, normalized LVV/EF, and in subgroups of patients with reduced EF, coronary artery disease scar, and LV hypertrophy as well as in patients with defibrillators. However, PET tended to underestimate LVV compared to CMR. CONCLUSION: Our analysis showed a strong correlation of EF and LVV by PET against a reference standard of CMR, whereas PET significantly underestimated LVV, but not EF, compared to CMR.

Best Practices in Nuclear Imaging for the Diagnosis of Transthyretin Amyloid Cardiomyopathy (ATTR-CM) in KSA: The Eagle Eyes of Local Experts.

Transthyretin amyloid cardiomyopathy (ATTR-CM) is a complex and serious form of heart failure caused by the accumulation of transthyretin amyloid protein in the heart muscle. Variable symptoms of ATTR-CM can lead to a delayed diagnosis. Recognizing the diagnostic indicators is crucial to promptly detect this condition. A targeted literature review was conducted to examine the latest international consensus recommendations on a comprehensive diagnosis of ATTR-CM. Additionally, a panel consisting of nuclear medicine expert consultants (n = 10) and nuclear imaging technicians (n = 2) convened virtually from the Kingdom of Saudi Arabia (KSA) to formulate best practices for ATTR-CM diagnosis. The panel reached a consensus on a standard diagnostic pathway for ATTR-CM, which commences by evaluating the presence of clinical red flags and initiating a cardiac workup to assess the patient's echocardiogram. Cardiac magnetic resonance imaging may be needed, in uncertain cases. When there is a high suspicion of ATTR-CM, patients undergo nuclear scintigraphy and hematologic tests to rule out primary or light-chain amyloidosis. The expert panel emphasized that implementing best practices will support healthcare professionals in KSA to improve their ability to detect and diagnose ATTR-CM more accurately and promptly. Diagnosing ATTR-CM accurately and early can reduce morbidity and mortality rates through appropriate treatment.

Progression of non-obstructive coronary plaque: a practical CCTA-based risk score from the PARADIGM registry.

OBJECTIVES: No clear recommendations are endorsed by the different scientific societies on the clinical use of repeat coronary computed tomography angiography (CCTA) in patients with non-obstructive coronary artery disease (CAD). This study aimed to develop and validate a practical CCTA risk score to predict medium-term disease progression in patients at a low-to-intermediate probability of CAD. METHODS: Patients were part of the Progression of AtheRosclerotic PlAque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry. Specifically, 370 (derivation cohort) and 219 (validation cohort) patients with two repeat, clinically indicated CCTA scans, non-obstructive CAD, and absence of high-risk plaque (≥ 2 high-risk features) at baseline CCTA were included. Disease progression was defined as the new occurrence of ≥ 50% stenosis and/or high-risk plaque at follow-up CCTA. RESULTS: In the derivation cohort, 104 (28%) patients experienced disease progression. The median time interval between the two CCTAs was 3.3 years (2.7-4.8). Odds ratios for disease progression derived from multivariable logistic regression were as follows: 4.59 (95% confidence interval: 1.69-12.48) for the number of plaques with spotty calcification, 3.73 (1.46-9.52) for the number of plaques with low attenuation component, 2.71 (1.62-4.50) for 25-49% stenosis severity, 1.47 (1.17-1.84) for the number of bifurcation plaques, and 1.21 (1.02-1.42) for the time between the two CCTAs. The C-statistics of the model were 0.732 (0.676-0.788) and 0.668 (0.583-0.752) in the derivation and validation cohorts, respectively. CONCLUSIONS: The new CCTA-based risk score is a simple and practical tool that can predict mid-term CAD progression in patients with known non-obstructive CAD. CLINICAL RELEVANCE STATEMENT: The clinical implementation of this new CCTA-based risk score can help promote the management of patients with non-obstructive coronary disease in terms of timing of imaging follow-up and therapeutic strategies. KEY POINTS: • No recommendations are available on the use of repeat CCTA in patients with non-obstructive CAD. • This new CCTA score predicts mid-term CAD progression in patients with non-obstructive stenosis at baseline. • This new CCTA score can help guide the clinical management of patients with non-obstructive CAD.

Rationale and design of the CONFIRM2 (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) study.

BACKGROUND: In the last 15 years, large registries and several randomized clinical trials have demonstrated the diagnostic and prognostic value of coronary computed tomography angiography (CCTA). Advances in CT scanner technology and developments of analytic tools now enable accurate quantification of coronary artery disease (CAD), including total coronary plaque volume and low attenuation plaque volume. The primary aim of CONFIRM2, (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is to perform comprehensive quantification of CCTA findings, including coronary, non-coronary cardiac, non-cardiac vascular, non-cardiac findings, and relate them to clinical variables and cardiovascular clinical outcomes. DESIGN: CONFIRM2 is a multicenter, international observational cohort study designed to evaluate multidimensional associations between quantitative phenotype of cardiovascular disease and future adverse clinical outcomes in subjects undergoing clinically indicated CCTA. The targeted population is heterogenous and includes patients undergoing CCTA for atherosclerotic evaluation, valvular heart disease, congenital heart disease or pre-procedural evaluation. Automated software will be utilized for quantification of coronary plaque, stenosis, vascular morphology and cardiac structures for rapid and reproducible tissue characterization. Up to 30,000 patients will be included from up to 50 international multi-continental clinical CCTA sites and followed for 3-4 years. SUMMARY: CONFIRM2 is one of the largest CCTA studies to establish the clinical value of a multiparametric approach to quantify the phenotype of cardiovascular disease by CCTA using automated imaging solutions.

Simplified Approach to Predicting Obstructive Coronary Disease With Integration of Coronary Calcium: Development and External Validation.

BACKGROUND: The Diamond-Forrester model was used extensively to predict obstructive coronary artery disease (CAD) but overestimates probability in current populations. Coronary artery calcium (CAC) is a useful marker of CAD, which is not routinely integrated with other features. We derived simple likelihood tables, integrating CAC with age, sex, and cardiac chest pain to predict obstructive CAD. METHODS AND RESULTS: The training population included patients from 3 multinational sites (n=2055), with 2 sites for external testing (n=3321). We determined associations between age, sex, cardiac chest pain, and CAC with the presence of obstructive CAD, defined as any stenosis ≥50% on coronary computed tomography angiography. Prediction performance was assessed using area under the receiver-operating characteristic curves (AUCs) and compared with the CAD Consortium models with and without CAC, which require detailed calculations, and the updated Diamond-Forrester model. In external testing, the proposed likelihood tables had higher AUC (0.875 [95% CI, 0.862-0.889]) than the CAD Consortium clinical+CAC score (AUC, 0.868 [95% CI, 0.855-0.881]; P=0.030) and the updated Diamond-Forrester model (AUC, 0.679 [95% CI, 0.658-0.699]; P<0.001). The calibration for the likelihood tables was better than the CAD Consortium model (Brier score, 0.116 versus 0.121; P=0.005). CONCLUSIONS: We have developed and externally validated simple likelihood tables to integrate CAC with age, sex, and cardiac chest pain, demonstrating improved prediction performance compared with other risk models. Our tool affords physicians with the opportunity to rapidly and easily integrate a small number of important features to estimate a patient's likelihood of obstructive CAD as an aid to clinical management.

Unveiling the Hidden Patterns: Multimodality Cardiac Imaging in Takotsubo Cardiomyopathy Diagnosis.

Takotsubo cardiomyopathy, also known as stress cardiomyopathy, is a reversible form of cardiomyopathy characterized by reduced ejection fraction with regional wall motion abnormalities, elevated cardiac enzyme levels, and signs of ischemia on electrocardiogram despite the absence of obstructive epicardial coronary artery disease. It is often preceded by intense emotional or physical illness stressors. This case describes a 65-year-old female patient who likely developed takotsubo cardiomyopathy precipitated by the stress of diverticulitis.

Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging.

BACKGROUND: Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification. We aim to provide an explainable ML approach to predict MACE using clinical, CCTA, and SPECT data. METHODS: Consecutive patients who underwent clinically indicated CCTA and SPECT myocardial imaging for suspected CAD were included and followed up for MACEs. A MACE was defined as a composite outcome that included all-cause mortality, myocardial infarction, or late revascularization. We employed an Automated Machine Learning (AutoML) approach to predict MACE using clinical, CCTA, and SPECT data. Various mainstream models with different sets of hyperparameters have been explored, and critical predictors of risk are obtained using explainable techniques on the global and patient levels. Ten-fold cross-validation was used in training and evaluating the AutoML model. RESULTS: A total of 956 patients were included (mean age 61.1 ±14.2 years, 54% men, 89% hypertension, 81% diabetes, 84% dyslipidemia). Obstructive CAD on CCTA and ischemia on SPECT were observed in 14% of patients, and 11% experienced MACE. ML prediction's sensitivity, specificity, and accuracy in predicting a MACE were 69.61%, 99.77%, and 96.54%, respectively. The top 10 global predictive features included 8 CCTA attributes (segment involvement score, number of vessels with severe plaque ≥70, ≥50% stenosis in the left marginal coronary artery, calcified plaque, ≥50% stenosis in the left circumflex coronary artery, plaque type in the left marginal coronary artery, stenosis degree in the second obtuse marginal of the left circumflex artery, and stenosis category in the marginals of the left circumflex artery) and 2 clinical features (past medical history of MI or left bundle branch block, being an ever smoker). CONCLUSION: ML can accurately predict risk of developing a MACE in patients suspected of CAD undergoing SPECT MPI and CCTA. ML feature-ranking can also show, at a sample- as well as at a patient-level, which features are key in making such a prediction.

Cardiovascular Testing in the United States during the COVID-19 Pandemic: Volume Recovery and Worldwide Comparison.

Purpose: To characterize the recovery of diagnostic cardiovascular procedure volumes in U.S. and non-U.S. facilities in the year following the initial COVID-19 outbreak. Materials and Methods: The International Atomic Energy Agency (IAEA) coordinated a worldwide study called the IAEA Noninvasive Cardiology Protocols Study of COVID-19 2 (INCAPS COVID 2), collecting data from 669 facilities in 107 countries, including 93 facilities in 34 U.S. states, to determine the impact of the pandemic on diagnostic cardiovascular procedure volumes. Participants reported volumes for each diagnostic imaging modality used at their facility for March 2019 (baseline), April 2020, and April 2021. This secondary analysis of INCAPS COVID 2 evaluated differences in changes in procedure volume between U.S. and non-U.S. facilities and among U.S. regions. Factors associated with return to prepandemic volumes in the United States were also analyzed in a multivariable regression analysis. Results: Reduction in procedure volumes in April 2020 compared with baseline was similar for U.S. and non-U.S. facilities (-66% vs -71%, P = .27). U.S. facilities reported greater return to baseline in April 2021 than did all non-U.S. facilities (4% vs -6%, P = .008), but there was no evidence of a difference when comparing U.S. facilities with non-U.S. high-income country (NUHIC) facilities (4% vs 0%, P = .18). U.S. regional differences in return to baseline were observed between the Midwest (11%), Northeast (9%), South (1%), and West (-7%, P = .03), but no studied factors were significant predictors of 2021 change from prepandemic baseline. Conclusion: The reductions in cardiac testing during the early pandemic have recovered within a year to prepandemic baselines in the United States and NUHICs, while procedure volumes remain depressed in lower-income countries.Keywords: SPECT, Cardiac, Epidemiology, Angiography, CT Angiography, CT, Echocardiography, SPECT/CT, MR Imaging, Radionuclide Studies, COVID-19, Cardiovascular Imaging, Diagnostic Cardiovascular Procedure, Cardiovascular Disease, Cardiac Testing Supplemental material is available for this article. © RSNA, 2023.

Deep neural network for identification of impaired microvascular and vasomotor function from stress electrocardiography.

Impaired microvascular and vasomotor function is a common consequence of aging, diabetes, and other risk factors, and is associated with adverse cardiac outcomes. Such impairments are not readily identified by standard clinical methods of cardiovascular testing such as coronary angiography and noninvasive single photon emission tomography (SPECT) myocardial perfusion imaging (MPI). We hypothesized that signals embedded within stress electrocardiograms (ECGs) identify individuals with microvascular and vasomotor dysfunction. Methods: We developed and validated a novel convolutional neural network (CNN) using stress and rest ECG data (ECG-Flow) to identify patients with impaired myocardial flow reserve (MFR) on quantitative positron emission tomography (PET) MPI (N=3887). Diagnostic accuracy was validated with an internal holdout set of patients undergoing stress PET MPI (N=963). The prognostic association of ECG-Flow with mortality was then evaluated in a separate cohort of patients undergoing SPECT MPI (N=5102). Results: ECG-Flow achieved good diagnostic accuracy for impaired MFR in the holdout PET cohort (AUC, sensitivity, specificity: 0.737, 71.1%, 65.7%). Abnormal ECG-Flow was found to be significantly associated with mortality in both PET holdout and SPECT MPI cohorts (adjusted HR 2.12 [95 ρ CI 1.45, 2.10], ρ = 0.0001, and 2.07 [1.82, 2.36], ρ < 0.0001, respectively). Conclusion: Signals predictive of microvascular and vasomotor dysfunction are embedded in stress ECG waveforms. These signals can be identified by deep learning methods and are related to prognosis in patients undergoing both stress PET and SPECT MPI.

Integrated myocardial flow reserve (iMFR) assessment: diffuse atherosclerosis and microvascular dysfunction are more strongly associated with mortality than focally impaired perfusion.

BACKGROUND AND AIMS: Although treatment of ischemia-causing epicardial stenoses may improve symptoms of ischemia, current evidence does not suggest that revascularization improves survival. Conventional myocardial ischemia imaging does not uniquely identify diffuse atherosclerosis, microvascular dysfunction, or nonobstructive epicardial stenoses. We sought to evaluate the prognostic value of integrated myocardial flow reserve (iMFR), a novel noninvasive approach to distinguish the perfusion impact of focal atherosclerosis from diffuse coronary disease. METHODS: This study analyzed a large single-center registry of consecutive patients clinically referred for rest-stress myocardial perfusion positron emission tomography. Cox proportional hazards modeling was used to assess the association of two previously reported and two novel perfusion measures with mortality risk: global stress myocardial blood flow (MBF); global myocardial flow reserve (MFR); and two metrics derived from iMFR analysis: the extents of focal and diffusely impaired perfusion. RESULTS: In total, 6867 patients were included with a median follow-up of 3.4 years [1st-3rd quartiles, 1.9-5.0] and 1444 deaths (21%). Although all evaluated perfusion measures were independently associated with death, diffusely impaired perfusion extent (hazard ratio 2.65, 95%C.I. [2.37-2.97]) and global MFR (HR 2.29, 95%C.I. [2.08-2.52]) were consistently stronger predictors than stress MBF (HR 1.62, 95%C.I. [1.46-1.79]). Focally impaired perfusion extent (HR 1.09, 95%C.I. [1.03-1.16]) was only moderately related to mortality. Diffusely impaired perfusion extent remained a significant independent predictor of death when combined with global MFR (p < 0.0001), providing improved risk stratification (overall net reclassification improvement 0.246, 95%C.I. [0.183-0.310]). CONCLUSIONS: The extent of diffusely impaired perfusion is a strong independent and additive marker of mortality risk beyond traditional risk factors, standard perfusion imaging, and global MFR, while focally impaired perfusion is only moderately related to mortality.