Search

BackgroundMachine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk.MethodsWe included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD).ResultsDuring mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making. [ABSTRACT FROM AUTHOR]

Background: Computed tomography attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only utilized for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and evaluate these measures for all-cause mortality (ACM) risk stratification., Methods: We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry (four sites), to define chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle (SM), subcutaneous, intramuscular (IMAT), visceral (VAT), and epicardial (EAT) adipose tissues were quantified between automatically-identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation, and indexed volumes were evaluated for predicting ACM, adjusting for established risk factors and 18 other body compositions measures via Cox regression models and Kaplan-Meier curves., Findings: End-to-end processing time was <2 minutes/scan with no user interaction. Of 9918 patients studied, 5451(55%) were male. During median 2.5 years follow-up, 610 (6.2%) patients died. High VAT, EAT and IMAT attenuation were associated with increased ACM risk (adjusted hazard ratio (HR) [95% confidence interval] for VAT: 2.39 [1.92, 2.96], p<0.0001; EAT: 1.55 [1.26, 1.90], p<0.0001; IMAT: 1.30 [1.06, 1.60], p=0.0124). Patients with high bone attenuation were at lower risk of death as compared to subjects with lower bone attenuation (adjusted HR 0.77 [0.62, 0.95], p=0.0159). Likewise, high SM volume index was associated with a lower risk of death (adjusted HR 0.56 [0.44, 0.71], p<0.0001)., Interpretations: CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers which can be automatically measured and offer important additional prognostic value., Competing Interests: Declaration of interests RJM received grant support and consulting fees from Pfizer. TDR received research grant support from GE Healthcare and Advanced Accelerator Applications. AJE received speaker fees from Ionetix, consulting fees from W. L. Gore & Associates, authorship fees from Wolters Kluwer Healthcare. AJE served on a scientific advisory board for Canon Medical Systems and received grants from Attralus, Bruker, Canon Medical Systems, Eidos Therapeutics, Intellia Therapeutics, Ionis Pharmaceuticals, Neovasc, Pfizer, Roche Medical Systems, and W. L. Gore & Associates. EJM received grant support from and is a consultant for GE Healthcare. MB was supported by a research award from the Kosciuszko Foundation – The American Centre of Polish Culture. DB and PS participate in software royalties for QPS software at Cedars-Sinai Medical Center. DB served as a consultant for GE Healthcare. PS received research grant support from Siemens Medical Systems, and consulting fees from Synektik S.A. Other Authors declared no competing interests.

Background: While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment., Methods: Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs)., Results: Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all)., Conclusion: In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.

Unlabelled: The purposes of this study were (a) to assess the feasibility of diastolic function (DFx) evaluation using standard 16-frame postexercise gated (99m)Tc-sestamibi myocardial perfusion SPECT (MPS), (b) to determine the relationship of the 2 common DFx parameters, peak filling rate (PFR) and time to peak filling (TTPF), to clinical and systolic function (SFx) variables in patients with normal myocardial perfusion and SFx, and (c) to derive and validate normal limits., Methods: Ninety patients (71 men; age, 30-79 y) with normal exercise gated MPS were studied. None had hypertension, diabetes, rest electrocardiogram abnormality, or known cardiac disease. All patients reached > or = 85% of maximum predicted heart rate (HR). The population was randomized into derivation (n = 50) and validation (n = 40) groups. Univariable and multivariable approaches were deployed to assess the influence of clinical and functional variables on DFx parameters., Results: PFR and TTPF were assessed in all patients. Mean values of PFR and TTPF in the whole study population were 2.62 +/- 0.46 end-diastolic volumes per second (EDV/s) and 164.6 +/- 21.7 ms, respectively. By applying a 2-SD cutoff to the mean values in the derivation group, the threshold for abnormal PFR and the threshold for abnormal TTPF were < 1.71 EDV/s and > 216.7 ms, respectively. The normalcy rates in the validation group for PFR and TTPF were both 100%. The PFR showed weak but significant correlations with age, EDV, end-systolic volume, left ventricular ejection fraction (LVEF), and poststress HR. However, TTPF did not correlate with these parameters. Final normal thresholds determined from the combined populations were PFR = 1.70 EDV/s and TTPF = 208 ms. Multivariable analysis showed that age, sex, LVEF, and HR are strong predictors for PFR, whereas TTPF was not influenced by any clinical or SFx variable., Conclusion: With a new algorithm in QGS, assessment of LV DFx is feasible using 16-frame gated MPS even without bad-beat rejection, resulting in normal limits similar to those reported with gated blood-pool studies. However, due to the dependency of PFR on SFx parameters, sex, HR, and age, TTPF appears to be a stable and more useful parameter with this approach. The clinical usefulness of these findings requires further study.