AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening.

Background Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites. Per scan, 32 structures were segmented with a multistructure model. For each structure, 15 clinically interpretable radiomic features were quantified. Four general codes describing abnormalities reported by NLST radiologists were applied to identify extrapulmonary significant incidental findings on the CT scans. Death at 2-year and 10-year follow-up and the presence of extrapulmonary significant incidental findings were predicted with ensemble AI models, and individualized structure risk scores were evaluated. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the performance of the models for prediction of ACM and extrapulmonary significant incidental findings. The Pearson χ2 test and Kruskal-Wallis rank sum test were used for statistical analyses. Results A total of 24 401 participants (median age, 61 years [IQR, 57-65 years]; 14 468 male) were included. In 3880 of 24 401 participants (16%), 4283 extrapulmonary significant incidental findings were reported. During the 10-year follow-up, 3389 of 24 401 participants (14%) died. CAC had the highest feature importance for predicting the three study end points. The 10-year ACM model demonstrated the best AUC performance (0.72; per-year mortality of 2.6% above and 0.8% below the risk threshold), followed by 2-year ACM (0.71; per-year mortality of 1.13% above and 0.3% below the risk threshold) and prediction of extrapulmonary significant incidental findings (0.70; probability of occurrence of 25.4% above and 9.6% below the threshold). Conclusion A fully automated AI model indicated extrapulmonary structures at risk on chest CT scans and predicted ACM with explanations. ClinicalTrials.gov Identifier: NCT00047385 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yanagawa and Hata in this issue.

CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients.

PURPOSE: This study aimed to compare the predictive value of CT attenuation-corrected stress total perfusion deficit (AC-sTPD) and non-corrected stress TPD (NC-sTPD) for major adverse cardiac events (MACE) in obese patients undergoing cadmium zinc telluride (CZT) SPECT myocardial perfusion imaging (MPI). METHODS: The study included 4,585 patients who underwent CZT SPECT/CT MPI for clinical indications (chest pain: 56%, shortness of breath: 13%, other: 32%) at Yale New Haven Hospital (age: 64 ± 12 years, 45% female, body mass index [BMI]: 30.0 ± 6.3 kg/m2, prior coronary artery disease: 18%). The association between AC-sTPD or NC-sTPD and MACE defined as the composite end point of mortality, nonfatal myocardial infarction or late coronary revascularization (> 90 days after SPECT) was evaluated with survival analysis. RESULTS: During a median follow-up of 25 months, 453 patients (10%) experienced MACE. In patients with BMI ≥ 35 kg/m2 (n = 931), those with AC-sTPD ≥ 3% had worse MACE-free survival than those with AC-sTPD < 3% (HR: 2.23, 95% CI: 1.40 - 3.55, p = 0.002) with no difference in MACE-free survival between patients with NC-sTPD ≥ 3% and NC-sTPD < 3% (HR:1.06, 95% CI:0.67 - 1.68, p = 0.78). AC-sTPD had higher AUC than NC-sTPD for the detection of 2-year MACE in patients with BMI ≥ 35 kg/m2 (0.631 versus 0.541, p = 0.01). In the overall cohort AC-sTPD had a higher ROC area under the curve (AUC, 0.641) than NC-sTPD (0.608; P = 0.01) for detection of 2-year MACE. In patients with BMI ≥ 35 kg/m2 AC sTPD provided significant incremental prognostic value beyond NC sTPD (net reclassification index: 0.14 [95% CI: 0.20 - 0.28]). CONCLUSIONS: AC sTPD outperformed NC sTPD in predicting MACE in patients undergoing SPECT MPI with BMI ≥ 35 kg/m2. These findings highlight the superior prognostic value of AC-sTPD in this patient population and underscore the importance of CT attenuation correction.

Downward myocardial creep during stress PET imaging is inversely associated with mortality.

PURPOSE: The myocardial creep is a phenomenon in which the heart moves from its original position during stress-dynamic PET myocardial perfusion imaging (MPI) that can confound myocardial blood flow measurements. Therefore, myocardial motion correction is important to obtain reliable myocardial flow quantification. However, the clinical importance of the magnitude of myocardial creep has not been explored. We aimed to explore the prognostic value of myocardial creep quantified by an automated motion correction algorithm beyond traditional PET-MPI imaging variables. METHODS: Consecutive patients undergoing regadenoson rest-stress [82Rb]Cl PET-MPI were included. A newly developed 3D motion correction algorithm quantified myocardial creep, the maximum motion at stress during the first pass (60 s), in each direction. All-cause mortality (ACM) served as the primary endpoint. RESULTS: A total of 4,276 patients (median age 71 years; 60% male) were analyzed, and 1,007 ACM events were documented during a 5-year median follow-up. Processing time for automatic motion correction was < 12 s per patient. Myocardial creep in the superior to inferior (downward) direction was greater than the other directions (median, 4.2 mm vs. 1.3-1.7 mm). Annual mortality rates adjusted for age and sex were reduced with a larger downward creep, with a 4.2-fold ratio between the first (0 mm motion) and 10th decile (11 mm motion) (mortality, 7.9% vs. 1.9%/year). Downward creep was associated with lower ACM after full adjustment for clinical and imaging parameters (adjusted hazard ratio, 0.93; 95%CI, 0.91-0.95; p < 0.001). Adding downward creep to the standard PET-MPI imaging model significantly improved ACM prediction (area under the receiver operating characteristics curve, 0.790 vs. 0.775; p < 0.001), but other directions did not (p > 0.5). CONCLUSIONS: Downward myocardial creep during regadenoson stress carries additional information for the prediction of ACM beyond conventional flow and perfusion PET-MPI. This novel imaging biomarker is quantified automatically and rapidly from stress dynamic PET-MPI.

Comparative predictors of mortality among patients referred for stress single-photon emission computed tomography versus positron emission tomography myocardial perfusion imaging.

BACKGROUND: There is currently little information regarding the usage and comparative predictors of mortality among patients referred for single-photon emission computed tomography (SPECT) versus positron emission tomography (PET) myocardial perfusion imaging (MPI) within multimodality imaging laboratories. METHODS: We compared the clinical characteristics and mortality outcomes among 15,718 patients referred for SPECT-MPI and 6202 patients referred for PET-MPI between 2008 and 2017. RESULTS: Approximately two-thirds of MPI studies were performed using SPECT-MPI. The PET-MPI group was substantially older and included more patients with known coronary artery disease (CAD), hypertension, diabetes, and myocardial ischemia. The annualized mortality rate was also higher in the PET-MPI group, and this difference persisted after propensity matching 3615 SPECT-MPI and 3615 PET-MPI patients to have similar clinical profiles. Among the SPECT-MPI patients, the most potent predictor of mortality was exercise ability and performance, including consideration of patients' mode of stress testing and exercise duration. Among the PET-MPI patients, myocardial flow reserve (MFR) was the most potent predictor of mortality. CONCLUSIONS: In our real-world setting, PET-MPI was more commonly employed among older patients with more cardiac risk factors than SPECT-MPI patients. The most potent predictors of mortality in our SPECT and PET-MPI groups were variables exclusive to each test: exercise ability/capacity for SPECT-MPI patients and MFR for PET-MPI patients.

Is typical angina still prognostically important? The influence of "treatment bias" upon prognostic assessments.

BACKGROUND: Since typical angina has become less frequent, it is unclear if this symptom still has prognostic significance. METHODS: We evaluated 38,383 patients undergoing stress/rest SPECT myocardial perfusion imaging followed for a median of 10.9 years. After dividing patients by clinical symptoms, we evaluated the magnitude of myocardial ischemia and subsequent mortality among medically treated versus revascularized subgroups following testing. RESULTS: Patients with typical angina had more frequent and greater ischemia than other symptom groups, but not higher mortality. Among typical angina patients, those who underwent early revascularization had substantially greater ischemia than the medically treated subgroup, including a far higher proportion with severe ischemia (44.9% vs 4.3%, P < 0.001) and transient ischemic dilation of the LV (31.3% vs 4.7%, P < 0.001). Nevertheless, the revascularized typical angina subgroup had a lower adjusted mortality risk than the medically treated subgroup (HR = 0.72, 95% CI: 0.57-0.92, P = 0.009) CONCLUSIONS: Typical angina is associated with substantially more ischemia than other clinical symptoms. However, the high referral of patients with typical angina patients with ischemia to early revascularization resulted in this group having a lower rather than higher mortality risk versus other symptom groups. These findings illustrate the need to account for "treatment bias" among prognostic studies.

Incremental prognostic value of stress phase entropy over standard PET myocardial perfusion imaging variables.

PURPOSE: Phase analysis can assess left ventricular dyssynchrony. The independent prognostic value of phase variables over positron emission tomography myocardial perfusion imaging (PET-MPI) variables including myocardial flow reserve (MFR) has not been studied. The aim of this study was to explore the prognostic value of phase variables for predicting mortality over standard PET-MPI variables. METHODS: Consecutive patients who underwent pharmacological stress-rest 82Rb PET study were enrolled. All PET-MPI variables including phase variables (phase entropy, phase bandwidth, and phase standard deviation) were automatically obtained by QPET software (Cedars-Sinai, Los Angeles, CA). Cox proportional hazard analyses were used to assess associations with all-cause mortality (ACM). RESULTS: In a total of 3963 patients (median age 71 years; 57% male), 923 patients (23%) died during a median follow-up of 5 years. Annualized mortality rates increased with stress phase entropy, with a 4.6-fold difference between the lowest and highest decile groups of entropy (2.6 vs. 12.0%/year). Abnormal stress phase entropy (optimal cutoff value, 43.8%) stratified ACM risk in patients with normal and impaired MFR (both p < 0.001). Among three phase variables, only stress phase entropy was significantly associated with ACM after the adjustment of standard clinical and PET-MPI variables including MFR and stress-rest change of phase variables, whether modeled as binary variables (adjusted hazard ratio, 1.44 for abnormal entropy [> 43.8%]; 95%CI, 1.18-1.75; p < 0.001) or continuous variables (adjusted hazard ratio, 1.05 per 5% increase; 95%CI, 1.01-1.10; p = 0.030). The addition of stress phase entropy to the standard PET-MPI variables significantly improved the discriminatory power for ACM prediction (p < 0.001), but the other phase variables did not (p > 0.1). CONCLUSION: Stress phase entropy is independently and incrementally associated with ACM beyond standard PET-MPI variables including MFR. Phase entropy can be obtained automatically and included in clinical reporting of PET-MPI studies to improve patient risk prediction.

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.

PURPOSE: Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS: From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS: Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS: Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. METHODS: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (n = 332) or low likelihood of CAD (n = 179). RESULTS: Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both). CONCLUSION: Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.

Diagnostic accuracy of bone scintigraphy imaging for transthyretin cardiac amyloidosis: systematic review and meta-analysis.

BACKGROUND: Bone scintigraphy imaging is frequently used to investigate patients with suspected transthyretin cardiac amyloidosis (ATTR-CM). However, the reported accuracy for interpretation approaches has changed over time. We performed a systematic review and meta-analysis to determine the diagnostic accuracy of visual planar grading, heart-to-contralateral (HCL) ratio, and quantitative analysis of SPECT imaging and evaluate reasons for shifts in reported accuracy. METHODS: We performed a systematic review to identify studies of the diagnostic accuracy of bone scintigraphy for ATTR-CM from 1990 until February 2023 using PUBMED and EMBASE. Studies were reviewed separately by two authors for inclusion and for risk of bias assessment. Summary receiver operating characteristic curves and operating points were determined with hierarchical modeling. RESULTS: Out of a total of 428 identified studies, 119 were reviewed in detail and 23 were included in the final analysis. The studies included a total of 3954 patients, with ATTR-CM diagnosed in 1337 (39.6%) patients and prevalence ranging from 21 to 73%. Visual planar grading and quantitative analysis had higher diagnostic accuracy (.99) than HCL ratio (.96). Quantitative analysis of SPECT imaging had the highest specificity (97%) followed by planar visual grade (96%) and HCL ratio (93%). ATTR-CM prevalence accounted for some of the observed between study heterogeneity. CONCLUSIONS: Bone scintigraphy imaging is highly accurate for identifying patients with ATTR-CM, with between study heterogeneity in part explained by differences in disease prevalence. We identified small differences in specificity, which may have important clinical implications when applied to low-risk screening populations.

External validation of the CRAX2MACE model.

BACKGROUND: Single-photon emission computed tomography (SPECT) myocardial perfusion is frequently used to predict risk of major adverse cardiovascular events (MACE). We performed an external validation of the CRAX2MACE score, developed to estimate 2-year risk of MACE in patients with suspected coronary artery disease (CAD). METHODS: Patients who underwent clinically indicated SPECT with available follow-up for MACE were included (N = 2,985). The prediction performance for MACE (revascularization, myocardial infarction, or death) within 2 years for CRAX2MACE was compared with stress and ischemic total perfusion deficit (TPD) using area under the receiver operating characteristic curve (AUC). Calibration was assessed with calibration plots, Brier score, and the Hosmer-Lemeshow test. RESULTS: MACE occurred within 2 years in 243 (8.1%) patients. The AUC for CRAX2MACE (0.710, 95% CI 0.677-0.743) was significantly higher compared to stress TPD (AUC 0.669, 95% CI 0.632-0.706, P = .010) and ischemic TPD (AUC 0.664, 95% CI 0.627-0.700, P < .001). The model had acceptable goodness-of-fit (P = .103) and was well-calibrated with Brier score of 0.071. CONCLUSION: CRAX2MACE had higher predictive performance for 2-year MACE than quantitative perfusion in an external population. The current model is simple to use and could be implemented to assist physicians when estimating patient risk.

Decline in typical angina among patients referred for cardiac stress testing.

OBJECTIVE: To evaluate temporal trends in the prevalence of typical angina and its clinical correlates among patients referred for stress/rest SPECT myocardial perfusion imaging (MPI). PATIENTS AND METHODS: We evaluated the prevalence of chest pain symptoms and their relationship to inducible myocardial ischemia among 61,717 patients undergoing stress/rest SPECT-MPI between January 2, 1991 and December 31, 2017. We also assessed the relationship between chest pain symptom and angiographic findings among 6,579 patients undergoing coronary CT angiography between 2011 and 2017. RESULTS: The prevalence of typical angina among SPECT-MPI patients declined from 16.2% between 1991 and 1997 to 3.1% between 2011 and 2017, while the prevalence of dyspnea without any chest pain increased from 5.9 to 14.5% over the same period. The frequency of inducible myocardial ischemia declined over time within all symptom groups, but its frequency among current patients (2011-2017) with typical angina was approximately three-fold higher versus other symptom groups (28.4% versus 8.6%, p < 0.001). Overall, patients with typical angina had a higher prevalence of obstructive CAD on CCTA than those with other clinical symptoms, but 33.3% of typical angina patients had no coronary stenoses, 31.1% had 1-49% stenoses, and 35.4% had ≥ 50% stenoses. CONCLUSIONS: The prevalence of typical angina has declined to a very low level among contemporary patients referred for noninvasive cardiac tests. The angiographic findings among current typical angina patients are now quite heterogeneous, with one-third of such patients having normal coronary angiograms. However, typical angina remains associated with a substantially higher frequency of inducible myocardial ischemia compared to patients with other cardiac symptoms.

Increasing frequency of dyspnea among patients referred for cardiac stress testing.

OBJECTIVE: To assess the frequency, change in prevalence, and prognostic significance of dyspnea among contemporary patients referred for cardiac stress testing. PATIENTS AND METHODS: We evaluated the prevalence of dyspnea and its relationship to all-cause mortality among 33,564 patients undergoing stress/rest SPECT-MPI between January 1, 2002 and December 31, 2017. Dyspnea was assessed as a single-item question. Patients were divided into three temporal groups. RESULTS: The overall prevalence of dyspnea in our cohort was 30.2%. However, there was a stepwise increase in the temporal prevalence of dyspnea, which was present in 25.6% of patients studied between 2002 and 2006, 30.5% of patients studied between 2007 and 2011, and 38.7% of patients studied between 2012 and 2017. There was a temporal increase in the prevalence of dyspnea in each age, symptom, and risk factor subgroup. The adjusted hazard ratio for mortality was higher among patients with dyspnea vs those without dyspnea both among all patients, and within each chest pain subgroup. CONCLUSIONS: Dyspnea has become increasingly prevalent among patients referred for cardiac stress testing and is now present among nearly two-fifths of contemporary cohorts referred for stress-rest SPECT-MPI. Prospective study is needed to standardize the assessment of dyspnea and evaluate the reasons for its increasing prevalence.

Development and validation of ischemia risk scores.

BACKGROUND: The likelihood of ischemia on myocardial perfusion imaging is central to physician decisions regarding test selection, but dedicated risk scores are lacking. We derived and validated two novel ischemia risk scores to support physician decision making. METHODS: Risk scores were derived using 15,186 patients and validated with 2,995 patients from a different center. Logistic regression was used to assess associations with ischemia to derive point-based and calculated ischemia scores. Predictive performance for ischemia was assessed using area under the receiver operating characteristic curve (AUC) and compared with the CAD consortium basic and clinical models. RESULTS: During derivation, the calculated ischemia risk score (0.801) had higher AUC compared to the point-based score (0.786, p < 0.001). During validation, the calculated ischemia score (0.716, 95% CI 0.684- 0.748) had higher AUC compared to the point-based ischemia score (0.699, 95% CI 0.666- 0.732, p = 0.016) and the clinical CAD model (AUC 0.667, 95% CI 0.633- 0.701, p = 0.002). Calibration for both ischemia scores was good in both populations (Brier score  < 0.100). CONCLUSIONS: We developed two novel risk scores for predicting probability of ischemia on MPI which demonstrated high accuracy during model derivation and in external testing. These scores could support physician decisions regarding diagnostic testing strategies.

Hot spot imaging in cardiovascular diseases: an information statement from SNMMI, ASNC, and EANM.

This information statement from the Society of Nuclear Medicine and Molecular Imaging, American Society of Nuclear Cardiology, and European Association of Nuclear Medicine describes the performance, interpretation, and reporting of hot spot imaging in nuclear cardiology. The field of nuclear cardiology has historically focused on cold spot imaging for the interpretation of myocardial ischemia and infarction. Hot spot imaging has been an important part of nuclear medicine, particularly for oncology or infection indications, and the use of hot spot imaging in nuclear cardiology continues to expand. This document focuses on image acquisition and processing, methods of quantification, indications, protocols, and reporting of hot spot imaging. Indications discussed include myocardial viability, myocardial inflammation, device or valve infection, large vessel vasculitis, valve calcification and vulnerable plaques, and cardiac amyloidosis. This document contextualizes the foundations of image quantification and highlights reporting in each indication for the cardiac nuclear imager.

Prevalence, incidence and clinical outcomes of epicardial coronary artery disease among transthyretin amyloidosis cardiomyopathy patients.

BACKGROUND: Transthyretin amyloidosis cardiomyopathy (ATTR-CM) patients are often older and may be at risk for obstructive epicardial coronary artery disease (oeCAD). While ATTR-CM may cause small vessel coronary disease, the prevalence and clinical significance of oeCAD is not well described. METHODS AND RESULTS: The prevalence and incidence of oeCAD and its association with all-cause mortality and hospitalization among 133 ATTR-CM patients with ≥ 1-year follow-up was evaluated. The mean age was 78 ± 9 years, 119 (89%) were male, 116 (87%) had wild-type and 17 (13%) had hereditary subtypes. Seventy-two (54%) patients underwent oeCAD investigations, with 30 (42%) receiving a positive diagnosis. Among patients with a positive oeCAD diagnosis, 23 (77%) were diagnosed prior to ATTR-CM diagnosis, 6 (20%) at the time of ATTR-CM diagnosis, and 1 (3%) after ATTR-CM diagnosis. Baseline characteristics between patients with and without oeCAD were similar. Among patients with oeCAD, only 2 (7%) required additional investigations, intervention or hospitalization after ATTR-CM diagnosis. After a median follow-up of 27 months there were 37 (28%) deaths in the study population, including 5 patients with oeCAD (17%). Fifty-six (42%) patients in the study population required hospitalization, including 10 patients with oeCAD (33%). There was no significant difference in the rates of death or hospitalization among ATTR-CM patients with and without oeCAD, and oeCAD was not significantly associated with either outcome by univariable regression analysis. CONCLUSIONS: While oeCAD is prevalent in ATTR-CM patients, this diagnosis is frequently known at time of ATTR-CM diagnosis and characteristics are similar to patients without oeCAD.

Amyloidosis and COVID-19: experience from an amyloid program in Canada.

Severe acute respiratory syndrome coronavirus (SARS-CoV2) and associated COVID-19 infection continue to impact patients globally. Patients with underlying health conditions are at heightened risk of adverse outcomes from COVID-19; however, research involving patients with rare health conditions remains scarce. The amyloidoses are a rare grouping of protein deposition diseases. Light-chain and transthyretin amyloidosis are the most common disease forms, often present with systemic involvement of vital organs including the heart, nerves, kidneys, and GI tracts of affected individuals. The Amyloidosis Program of Calgary examined 152 ATTR patients and 103 AL patients analyzing rates of vaccination, COVID-19 testing, infection outcomes, influence referrals, and excess deaths. Results showed 15 total PCR-confirmed COVID-19 infections in the tested population of amyloid patients, with a higher frequency of infections among patient with AL compared to the ATTR cohort (26.2% vs 5.1%). Four patients (26.6%) required hospital admission for COVID-19 infection, 2 ATTR, and 2 AL patients. Of the confirmed cases, 1 (0.07%) unvaccinated ATTR patient died of a COVID-19 infection. An excess of deaths was found in both the ATTR and AL cohorts when comparing pre-pandemic years 2018 and 2019 to the pandemic years of 2020 and 2021. The finding suggests that amyloidosis patients are likely at a high risk for severe COVID-19 infection and mortality, especially those of advanced age, those on an active treatment with chemotherapy, and those with concomitant B-cell or plasma cell disorder. The impact of virtual healthcare visits and pandemic measures on the excess of deaths observed requires further research.

Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology.

Artificial intelligence (AI) techniques have emerged as a highly efficient approach to accurately and rapidly interpret diagnostic imaging and may play a vital role in nuclear cardiology. In nuclear cardiology, there are many clinical, stress, and imaging variables potentially available, which need to be optimally integrated to predict the presence of obstructive coronary artery disease (CAD) or predict the risk of cardiovascular events. In spite of clinical awareness of a large number of potential variables, it is difficult for physicians to integrate multiple features consistently and objectively. Machine learning (ML) is particularly well suited to integrating this vast array of information to provide patient-specific predictions. Deep learning (DL), a branch of ML characterized by a multi-layered convolutional model architecture, can extract information directly from images and identify latent image features associated with a specific prediction. This review will discuss the latest AI applications to disease diagnosis and risk prediction in nuclear cardiology with a focus on potential clinical applications.

Prognostic value of early left ventricular ejection fraction reserve during regadenoson stress solid-state SPECT-MPI.

BACKGROUND: We hypothesized early post-stress left ventricular ejection fraction reserve (EFR) on solid-state-SPECT is associated with major cardiac adverse events (MACE). METHODS: 151 patients (70 ± 12 years, male 50%) undergoing same-day rest/regadenoson stress 99mTc-sestamibi solid-state SPECT were followed for MACE. Rest imaging was performed in the upright and supine positions. Early stress imaging was started 2 minutes after the regadenoson injection in the supine position and followed by late stress acquisition in the upright position. Total perfusion deficit (TPD) and functional parameters were quantified automatically. EFR, ∆end-diastolic volume (EDV), and end-systolic volume (ESV) were calculated as the difference between stress and rest values in the same position. EFR < 0%, ∆EDV ≥ 5 ml, or ∆ESV ≥ 5 ml was defined as abnormal. RESULTS: During the follow-up (mean 3.2 years), 28 MACE occurred (19%). In Kaplan-Meier analysis, there was a significantly decreased event-free survival in patients with early EFR < 0% (P = 0.004). Similarly, there was a decreased event-free survival in patients with ∆ESV ≥ 5 ml at early stress (P = 0.003). However, EFR, ∆EDV, and ∆ESV at late stress were not associated with MACE-free survival. Cox proportional hazards model adjusting for clinical information and stress TPD demonstrated that EFR, ∆EDV, and ∆ESV at early stress were significantly associated with MACE (P < 0.05 for all). CONCLUSIONS: Reduced early post-stress EFR on vasodilator stress solid-state SPECT is associated with MACE.

The prevalence and predictors of inducible myocardial ischemia among patients referred for radionuclide stress testing.

BACKGROUND: The frequency of inducible myocardial ischemia has declined in contemporary stress test cohorts, suggesting a need to re-evaluate its optimal use. To-date, however, a comprehensive analysis of the most potent predictors of myocardial ischemia among cardiac stress test patients has not been conducted. METHODS: We assessed 27,615 patients referred for stress-rest SPECT myocardial perfusion imaging between January 1, 2004 and December 31, 2017. Chi-square analysis was used to ascertain the most potent predictors of ischemia. RESULTS: Among our cohort, CAD status (presence/absence of known CAD), rest left ventricular ejection fraction (LVEF), and typical angina were the most potent predictors of ischemia. The frequency of ischemia was only 6.6% among patients with an LVEF > 55% but 38.1% for patients with LVEF < 45% (P < 0.001). The frequency of myocardial ischemia was fourfold higher among patients with known CAD vs no known CAD (28.0% vs 6.5%, P < 0.001) and approximately threefold higher among patients with typical angina vs patients with atypical symptoms (P < 0.001). CONCLUSIONS: The frequency of myocardial ischemia varies markedly according to the common clinical parameters and is particularly high among patients with known CAD, low LVEF, and typical angina. These observations may be used to develop more cost-effective strategies for referring patients for cardiac stress testing.

Quantitative Assessment of Cardiac Hypermetabolism and Perfusion for Diagnosis of Cardiac Sarcoidosis.

BACKGROUND: Quantitative assessment of cardiac hypermetabolism from 18Flourodeoxy glucose (FDG) positron emission tomography (PET) may improve diagnosis of cardiac sarcoidosis (CS). We assessed different approaches for quantification of cardiac hypermetabolism and perfusion in patients with suspected CS. METHODS AND RESULTS: Consecutive patients undergoing 18FDG PET assessment for possible CS between January 2014 and March 2019 were included. Cardiac hypermetabolism was quantified using maximal standardized uptake value (SUVMAX), cardiometabolic activity (CMA) and volume of inflammation, using relative thresholds (1.3× and 1.5× left ventricular blood pool [LVBP] activity), and absolute thresholds (SUVMAX > 2.7 and 4.1). Diagnosis of CS was established using the Japanese Ministry of Health and Wellness criteria. In total, 69 patients were studied, with definite or possible CS in 29(42.0%) patients. CMA above 1.5× LVBP SUVMAX had the highest area under the receiver operating characteristic curve (AUC 0.92). Quantitative parameters using relative thresholds had higher AUC compared to absolute thresholds (p < 0.01). Interobserver variability was low for CMA, with excellent agreement regarding absence of activity (Kappa 0.970). CONCLUSIONS: Quantitation with scan-specific thresholds has superior diagnostic accuracy compared to absolute thresholds. Based on the potential clinical benefit, programs should consider quantification of cardiac hypermetabolism when interpreting 18F-FDG PET studies for CS.