A New Relational Database Including Clinical Data and Myocardial Perfusion Imaging Findings in Coronary Artery Disease

Eur J Nucl Med Mol Imaging

Acampa

et al. 2020

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Purpose We assessed the effects of the COVID-19 pandemic on myocardial perfusion imaging (MPI) for ischemic heart disease during the lockdown imposed by the Italian Government. Methods We retrospectively reviewed the number and the findings of stress single-photon emission computed tomography (SPECT)-MPI performed between February and May 2020 during the COVID-19 pandemic at the University of Napoli Federico II. The number and the findings of stress SPECT-MPI studies acquired in the corresponding months of the years 2017, 2018, and 2019 were also evaluated for direct comparison. Results The number of stress SPECT-MPI studies performed during the COVID-19 pandemic ( n = 123) was significantly lower ( P < 0.0001) compared with the mean yearly number of procedures performed in the corresponding months of the years 2017, 2018, and 2019 ( n = 413). Yet, the percentage of abnormal stress SPECT-MPI studies was similar ( P = 0.65) during the pandemic (36%) compared with the mean percentage value of the corresponding period of the years 2017, 2018, and 2019 (34%). Conclusion The number of stress SPECT-MPI studies was significantly reduced during the COVID-19 pandemic compared with the corresponding months of the previous 3 years. The lack of difference in the prevalence of abnormal SPECT-MPI studies between the two study periods strongly suggests that many patients with potentially abnormal imaging test have been missed during the pandemic.

Section: Methodsmentioning

confidence: 99%

Effects of the COVID-19 pandemic on myocardial perfusion imaging for ischemic heart disease

Nappi

Eur J Nucl Med Mol Imaging

Acampa

et al. 2020

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“…For patients undergoing more than one PET/CT study, only the earliest procedure was considered. All patients were part of ongoing prospective dedicated database [ 14 ]. This study complies with the Declaration of Helsinki.…”

Section: Methodsmentioning

confidence: 99%

A Comparison among Different Machine Learning Pretest Approaches to Predict Stress-Induced Ischemia at PET/CT Myocardial Perfusion Imaging

Computational and Mathematical Methods in Medicine

Petretta

Assante

et al. 2021

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Traditional approach for predicting coronary artery disease (CAD) is based on demographic data, symptoms such as chest pain and dyspnea, and comorbidity related to cardiovascular diseases. Usually, these variables are analyzed by logistic regression to quantifying their relationship with the outcome; nevertheless, their predictive value is limited. In the present study, we aimed to investigate the value of different machine learning (ML) techniques for the evaluation of suspected CAD; having as gold standard, the presence of stress-induced ischemia by 82Rb positron emission tomography/computed tomography (PET/CT) myocardial perfusion imaging (MPI) ML was chosen on their clinical use and on the fact that they are representative of different classes of algorithms, such as deterministic (Support vector machine and Naïve Bayes), adaptive (ADA and AdaBoost), and decision tree (Random Forest, rpart, and XGBoost). The study population included 2503 consecutive patients, who underwent MPI for suspected CAD. To testing ML performances, data were split randomly into two parts: training/test (80%) and validation (20%). For training/test, we applied a 5-fold cross-validation, repeated 2 times. With this subset, we performed the tuning of free parameters for each algorithm. For all metrics, the best performance in training/test was observed for AdaBoost. The Naïve Bayes ML resulted to be more efficient in validation approach. The logistic and rpart algorithms showed similar metric values for the training/test and validation approaches. These results are encouraging and indicate that the ML algorithms can improve the evaluation of pretest probability of stress-induced myocardial ischemia.

“…These patients were part of an ongoing prospective dedicated database. 22 The criteria used for patient selection were the same reported in the J-ACCESS study. 7,8 More in detail, the inclusion criteria were C 20 years of age and stress and rest ECG-gated SPECT performed for suspected or known coronary artery disease (CAD).…”

Section: Patientsmentioning

confidence: 99%

External validation and update of the J-ACCESS model in an Italian cohort of patients undergoing stress myocardial perfusion imaging

Petretta¹,

Journal of Nuclear Cardiology

Assante

et al. 2023

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Background Cardiovascular risk models are based on traditional risk factors and investigations such as imaging tests. External validation is important to determine reproducibility and generalizability of a prediction model. We performed an external validation of t the Japanese Assessment of Cardiac Events and Survival Study by Quantitative Gated SPECT (J-ACCESS) model, developed from a cohort of patients undergoing stress myocardial perfusion imaging. Methods We included 3623 patients with suspected or known coronary artery disease undergoing stress single-photon emission computer tomography (SPECT) myocardial perfusion imaging at our academic center between January 2001 and December 2019. Results In our study population, the J-ACCESS model underestimated the risk of major adverse cardiac events (cardiac death, nonfatal myocardial infarction, and severe heart failure requiring hospitalization) within three-year follow-up. The recalibrations and updated of the model slightly improved the initial performance: C-statistics increased from 0.664 to 0.666 and Brier score decreased from 0.075 to 0.073. Hosmer–Lemeshow test indicated a logistic regression fit only for the calibration slope (P = .45) and updated model (P = .22). In the update model, the intercept, diabetes, and severity of myocardial perfusion defects categorized coefficients were comparable with J-ACCESS. Conclusion The external validation of the J-ACCESS model as well as recalibration models have a limited value for predicting of three-year major adverse cardiac events in our patients. The performance in predicting risk of the updated model resulted superimposable to the calibration slope model.