Temporal trends of abnormal myocardial perfusion imaging in a cohort of Italian subjects: Relation with cardiovascular risk factors

Age-Specific Cardiovascular Risk Factors for Major Adverse Cardiac Events in Patients Undergoing Myocardial Perfusion Imaging

BACKGROUND

The prevalence of traditional cardiovascular risk factors shows different age-specific patterns. It is not known whether the prognostic impact of risk factors is similarly age-specific. We evaluated the profiles of cardiovascular risk factors and their prognostic impact on coronary artery disease (CAD) in relation to age.

METHODS

We included 3667 patients with suspected or known CAD undergoing stress myocardial perfusion imaging (MPI). We evaluated the risk for major adverse cardiac events (MACE) within three years from the index MPI in patients belonging to three groups according to age tertile distribution: <59, 59-68, and >68 years. Gender, body mass index, diabetes, hypertension, dyslipidemia, family history of CAD, smoking, angina, dyspnea, previous CAD, and MPI outcome were assessed as risk factors by a multivariable Cox's regression.

RESULTS

The three-year risk of MACE increased progressively with age and was 9%, 13%, and 18% for each group, respectively (p < 0.0001). Dyspnea and abnormal MPI outcome were significant risk factors for all age groups. Diabetes and smoking were significant from the age of 59 onwards, while hypertension resulted significant for patients older than 68 years.

CONCLUSIONS

The number of risk factors was significantly associated with the occurrence of MACE increase with age. It is noteworthy that a personal history of CAD was not useful for risk stratification, while MPI results were.

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.

External validation of the CRAX2MACE model in an Italian cohort of patients with suspected coronary artery disease undergoing stress myocardial perfusion imaging

BACKGROUND

Prevention and development of diagnostic and therapeutic techniques reduced morbidity and mortality for coronary artery disease (CAD). In this context, the cardiovascular risk assessment for major adverse cardiac events (MACE) at 2-year (CRAX2MACE) model for prediction of 2-year major adverse cardiac events was developed. We performed an external validation of this model.

METHODS

We included 1003 patients with suspected CAD undergoing stress-rest single-photon emission computed tomography myocardial perfusion imaging at our academic center between March 2015 and April 2019.

RESULTS

Considering the occurrence of MACE (death from any cause, acute myocardial infarction, or late coronary revascularization), for the CRAX2MACE model the area under the receiver operating characteristic curve was 0.612 and the Brier score was 0.061. The Hosmer-Lemeshow test estimated a non-optimal fit (χ2 28, P < .001). Considering only hard events (cardiac death, acute myocardial infarction), the external validation of the CRAX2MACE model revealed a Brier score of 0.053 and an area under the receiver operating characteristic curve of 0.621. Hosmer-Lemeshow test was calculated by deciles and showed a poor fit (χ2 31, P < .001).

CONCLUSION

CRAX2MACE model had a limited value for predicting 2-year major adverse cardiovascular events in an external validation cohort of patients with suspected CAD.

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

BACKGROUND

We evaluated if risk scores commonly used to predict the absence of significant stenosis at coronary computed tomography (CT) angiography are useful to predict a normal stress myocardial perfusion imaging (MPI) study.

METHODS

Our cohort included a total of 1422 consecutive patients with zero coronary artery calcium score (ZCS) who underwent 82Rb PET/CT for evaluation of suspected coronary artery disease (CAD). Predictive models were constructed as reported by Genders et al. and Alshahrani et al., and the probability of abnormal summed stress score (SSS) and of reduced myocardial perfusion reserve (MPR) based on these risk scores was assessed.

RESULTS

In the overall population, the prevalence of abnormal SSS was 0.10 and the prevalence of reduced MPR was 0.17 (both P < .001).The observed frequencies of abnormal SSS and reduced MPR vs the probabilities predicted by the Genders and Alshahrani models were above the diagonal identity line, highlighting an underestimation of the observed occurrence by these models. The areas under the receiver operating characteristic curve of the Genders and Alshahrani models indicated lack of discriminative ability for predicting abnormal SSS (0.547 and 0.527) and reduced MPR (0.509 and 0.538). The Hosmer-Lemeshow test revealed that both models underestimated the observed occurrence of abnormal SSS and reduced MPR.

CONCLUSIONS

Available models were unable to identify among patients with ZCS those with a low probability of a normal stress MPI study. Thus, an optimal approach to rule out from MPI patients without detectable coronary calcium still needs to be improved.

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.

Pretest models for predicting abnormal stress single-photon emission computed tomography myocardial perfusion imaging

BACKGROUND

The frequency of abnormal stress SPECT myocardial perfusion imaging (MPS) decreased over the past decades despite an increase in the prevalence of cardiovascular risk factors. These findings strengthen the need to develop more effective strategies for appropriately referring patients with suspected coronary artery disease (CAD) to cardiac imaging. The aim of this study was to develop pretest assessment models for predicting abnormal stress MPS.

METHODS

We included 5,601 consecutive patients with suspected CAD, who underwent stress MPS at our academic center. Two different models were considered: a basic model including age, gender, and anginal symptoms; and a clinical model including also diabetes, hypertension, hypercholesterolemia, smoking, and family history of CAD.

RESULTS

In patients with abnormal MPS, the basic model classified more than 75% of patients as intermediate risk, whereas only 23% were incorrectly classified as low risk. In patients with normal MPS, 45% were correctly classified as low risk and none as high risk. Basic and clinical models had a limited discriminating capacity (area under the receiver operating characteristic curve 0.644 for basic model and 0.647 for clinical model) between individuals with and without abnormal stress MPS. The decision curve analysis demonstrates a high net benefit across a range of threshold probabilities from ~ 15% to ~30% for both models.

CONCLUSIONS

A pretest risk stratification based on traditional cardiovascular risk factors has a limited value for predicting an abnormal stress MPS in patients with suspected CAD. However, selecting a proper threshold probability enhances the appropriateness of referral to stress MPS.

Research on the Method of Predicting Fractional Flow Reserve Based on Multiple Independent Risk Factors

The use of diameter stenosis (DS), as revealed by coronary angiography, for predicting fractional flow reserve (FFR) usually results in a high error rate of detection. In this study, we investigated a method for predicting FFR in patients with coronary stenosis based on multiple independent risk factors. The aim of the study was to improve the accuracy of detection. First, we searched the existing literature to identify multiple independent risk factors and then calculated the corresponding odds ratios. The improved analytic hierarchy process (IAHP) was then used to determine the weighted value of each independent risk factor, based on the corresponding odds ratio. Next, we developed a novel method, based on the top seven independent risk factors with the highest weighted values, to predict FFR. This model was then used to predict the FFR of 253 patients with coronary stenosis, and the results were then compared with previous methods (DS alone and a simplified scoring system). In addition to DS, we identified a range of other independent risk factors, with the highest weighted values, for predicting FFR, including gender, body mass index, location of stenosis, type of coronary artery distribution, left ventricular ejection fraction, and left myocardial mass. The area under the receiver-operating characteristic curve (AUC) for the newly developed method was 84.3% (95% CI: 79.2–89.4%), which was larger than 65.3% (95% CI: 61.5–69.1%) of DS alone and 74.8% (95% CI: 68.4–81.2%) of the existing simplified scoring system. The newly developed method, based on multiple independent risk factors, effectively improves the prediction accuracy for FFR.

Diagnostic performance of myocardial perfusion imaging with conventional and CZT single-photon emission computed tomography in detecting coronary artery disease: A meta-analysis

BACKGROUND

We performed a meta-analysis to compare the diagnostic performance of conventional SPECT (C-SPECT) and cadmium-zinc-telluride (CZT)-SPECT systems in detecting angiographically proven coronary artery disease (CAD).

METHODS

Studies published between January 2000 and February 2018 were identified by database search. We included studies assessing C-SPECT or CZT-SPECT as a diagnostic test to evaluate patients for the presence of CAD, defined as at least 50% diameter stenosis on invasive coronary angiography. A study was eligible regardless of whether patients were referred for suspected or known CAD.

RESULTS

We identified 40 eligible articles (25 C-SPECT and 15 CZT-SPECT studies) including 7334 patients (4997 in C-SPECT and 2337 in CZT-SPECT studies). The pooled sensitivity and specificity were 85% and 66% for C-SPECT and 89% and 69% for CZT-SPECT imaging studies. The area under the curve was slightly higher for CZT-SPECT (0.89) compared to C-SPECT (0.83); accordingly, the summary diagnostic OR was 17 for CZT-SPECT and 11 for C-SPECT. The accuracy of the two tests slightly differs between C-SPECT and CZT-SPECT (chi-square 11.28, P < .05). At meta-regression analysis, no significant association between both sensitivity and specificity and demographical and clinical variables considered was found for C-SPECT and CZT-SPECT studies.

CONCLUSIONS

C-SPECT and CZT-SPECT have good diagnostic performance in detecting angiographic proven CAD, with a slightly higher accuracy for CZT-SPECT. This result supports the use of the novel gamma cameras in clinical routine practices also considering the improvements in acquisition time and radiation exposure reduction.