Diagnostic value of clinical risk scores for predicting normal stress myocardial perfusion imaging in subjects without coronary artery calcium

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

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.

Prognostic value of heart rate reserve in patients with suspected coronary artery disease undergoing stress myocardial perfusion imaging

BACKGROUND

Chronotropic incompetence is common in patients with cardiovascular disease and is associated with increased risk of adverse events. We assessed the incremental prognostic value of heart rate reserve (HRR) over stress myocardial perfusion single-photon emission computed tomography (MPS) findings in patients with suspected coronary artery disease (CAD).

METHODS

We studied 866 patients with suspected CAD undergoing exercise stress-MPS as part of their diagnostic program. The primary study endpoint was all-cause mortality. All patients were followed for at least 5 years. HRR was calculated as the difference between peak exercise and resting HR, divided by the difference of age-predicted maximal and resting HR and expressed as percentage.

RESULTS

During 7 years follow-up, 61 deaths occurred, with a 7% cumulative event rate. Patients experiencing death were older (P < .001), and had a higher prevalence of male gender (P < .001) and diabetes (P < .05). Patients with event also had lower values of HRR (65% ± 27% vs 73% ± 18%, P < .0001) and higher prevalence of stress-induced myocardial ischemia (25% vs 8%, P < .0001). Male gender, HRR and stress-induced ischemia were independent predictors of all-cause mortality (all P < .01). HRR improved the prognostic power of a model including clinical data and MPS findings, increasing the global χ2 from 66 to 82 (P < .005).

CONCLUSIONS

Chronotropic incompetence has independent and incremental prognostic value in predicting all-cause mortality in patients with suspected CAD undergoing exercise stress-MPS. Hence, the evaluation of HRR may further improve patients' risk stratification.

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

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.