Predictors of major adverse cardiac events among patients with chest pain and low HEART score in the emergency department

AIM

For patients who present to the emergency departments (ED) with undifferentiated chest pain, the risk of major adverse cardiac events (MACE) may be underestimated in low-HEART score patients. We aimed to identify characteristics of patients who were classified as low risk by HEART score but subsequently developed MACE at 6 weeks.

METHODS

We studied a multiethnic cohort of patients who presented with chest pain arousing suspicion of acute coronary syndrome to EDs in the Netherlands and Singapore. Patients were risk-stratified using HEART score and followed up for MACE at 6 weeks. Risk factors of developing MACE despite low HEART scores (scores 0-3) were identified using logistic and Cox regression models.

RESULTS

Among 1376 (39.8%) patients with low HEART scores, 63 (4.6%) developed MACE at 6 weeks. More males (53/806, 6.6%) than females (10/570, 2.8%) with low HEART score developed MACE. There was no difference in outcomes between ethnic groups. Among low-HEART score patients with 2 points for history, 21% developed MACE. Among low-HEART score patients with 1 point for troponin, 50% developed MACE, while 100% of those with 2 points for troponin developed MACE. After adjusting for HEART score and potential confounders, male sex was independently associated with increased odds (OR 4.12, 95%CI 2.14-8.78) and hazards (HR 3.93, 95%CI 1.98-7.79) of developing MACE despite low HEART score.

CONCLUSION

Male sex, highly suspicious history and elevated troponin were disproportionately associated with MACE. These characteristics should prompt clinicians to consider further investigation before discharge.

The prognostic value of automated coronary calcium derived by a deep learning approach on non-ECG gated CT images from 82Rb-PET/CT myocardial perfusion imaging

BACKGROUND

Assessment of both coronary artery calcium(CAC) scores and myocardial perfusion imaging(MPI) in patients suspected of coronary artery disease(CAD) provides incremental prognostic information. We used an automated method to determine CAC scores on low-dose attenuation correction CT(LDACT) images gathered during MPI in one single assessment. The prognostic value of this automated CAC score is unknown, we therefore investigated the association of this automated CAC scores and major adverse cardiovascular events(MACE) in a large chest-pain cohort.

METHOD

We analyzed 747 symptomatic patients referred for ⁸²RubidiumPET/CT, without a history of coronary revascularization. Ischemia was defined as a summed difference score≥2. We used a validated deep learning(DL) method to determine CAC scores. For survival analysis CAC scores were dichotomized as low(<400) and high(≥400). MACE was defined as all cause death, late revascularization (>90 days after scanning) or nonfatal myocardial infarction. Cox proportional hazard analysis were performed to identify predictors of MACE.

RESULTS

During 4 years follow-up, 115 MACEs were observed. High CAC scores showed higher cumulative event rates, irrespective of ischemia (nonischemic: 25.8% vs 11.9% and ischemic: 57.6% vs 23.4%, P-values <0.001). Multivariable cox regression revealed both high CAC scores (HR 2.19 95%CI 1.43-3.35) and ischemia (HR 2.56 95%CI 1.71-3.35) as independent predictors of MACE. Addition of automated CAC scores showed a net reclassification improvement of 0.13(0.022-0.245).

CONCLUSION

Automatically derived CAC scores determined during a single imaging session are independently associated with MACE. This validated DL method could improve risk stratification and subsequently lead to more personalized treatment in patients suspected of CAD.

Plasma extracellular vesicle proteins are associated with stress-induced myocardial ischemia in women presenting with chest pain

Diagnosing stable ischemic heart disease (IHD) is challenging, especially in females. Currently, no blood test is available. Plasma extracellular vesicles (EV) are emerging as potential biomarker source. We therefore aimed to identify stress induced ischemia due to stable IHD with plasma extracellular vesicle protein levels in chest pain patients. We analyzed 450 patients suspected for stable IHD who were referred for ⁸²Rb PET/CT in the outpatient clinic. Blood samples were collected before PET/CT and plasma EVs were isolated in 3 plasma subfractions named: TEX, HDL, LDL. In total 6 proteins were quantified in each of these subfractions using immuno-bead assays. CD14 and CystatinC protein levels were independent significant predictors of stress-induced ischemia in the LDL and the HDL subfraction and SerpinC1 and SerpinG1 protein levels in the HDL fraction. Subgroup-analysis on sex revealed that these associations were completely attributed to the associations in women. None of the significant EV proteins remained significant in men. Plasma EV proteins levels are associated with the presence of stable IHD in females presenting with chest pain. This finding, if confirmed in larger cohort studies could be a crucial step in improving diagnostic assessment of women with suspected IHD.

Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols

Background Although several deep learning (DL) calcium scoring methods have achieved excellent performance for specific CT protocols, their performance in a range of CT examination types is unknown. Purpose To evaluate the performance of a DL method for automatic calcium scoring across a wide range of CT examination types and to investigate whether the method can adapt to different types of CT examinations when representative images are added to the existing training data set. Materials and Methods The study included 7240 participants who underwent various types of nonenhanced CT examinations that included the heart: coronary artery calcium (CAC) scoring CT, diagnostic CT of the chest, PET attenuation correction CT, radiation therapy treatment planning CT, CAC screening CT, and low-dose CT of the chest. CAC and thoracic aorta calcification (TAC) were quantified using a convolutional neural network trained with (a) 1181 low-dose chest CT examinations (baseline), (b) a small set of examinations of the respective type supplemented to the baseline (data specific), and (c) a combination of examinations of all available types (combined). Supplemental training sets contained 199-568 CT images depending on the calcium burden of each population. The DL algorithm performance was evaluated with intraclass correlation coefficients (ICCs) between DL and manual (Agatston) CAC and (volume) TAC scoring and with linearly weighted κ values for cardiovascular risk categories (Agatston score; cardiovascular disease risk categories: 0, 1-10, 11-100, 101-400, >400). Results At baseline, the DL algorithm yielded ICCs of 0.79-0.97 for CAC and 0.66-0.98 for TAC across the range of different types of CT examinations. ICCs improved to 0.84-0.99 (CAC) and 0.92-0.99 (TAC) for CT protocol-specific training and to 0.85-0.99 (CAC) and 0.96-0.99 (TAC) for combined training. For assignment of cardiovascular disease risk category, the κ value for all test CT scans was 0.90 (95% confidence interval [CI]: 0.89, 0.91) for the baseline training. It increased to 0.92 (95% CI: 0.91, 0.93) for both data-specific and combined training. Conclusion A deep learning calcium scoring algorithm for quantification of coronary and thoracic calcium was robust, despite substantial differences in CT protocol and variations in subject population. Augmenting the algorithm training with CT protocol-specific images further improved algorithm performance. © RSNA, 2020 See also the editorial by Vannier in this issue.

Automated calcium scores collected during myocardial perfusion imaging improve identification of obstructive coronary artery disease

Background

Myocardial perfusion imaging (MPI) is an accurate noninvasive test for patients with suspected obstructive coronary artery disease (CAD) and coronary artery calcium (CAC) score is known to be a powerful predictor of cardiovascular events. Collection of CAC scores simultaneously with MPI is unexplored.

Aim

We aimed to investigate whether automatically derived CAC scores during myocardial perfusion imaging would further improve the diagnostic accuracy of MPI to detect obstructive CAD.

Methods

We analyzed 150 consecutive patients without a history of coronary revascularization with suspected obstructive CAD who were referred for 82Rb PET/CT and available coronary angiographic data. Myocardial perfusion was evaluated both semi quantitatively as well as quantitatively according to the European guidelines. CAC scores were automatically derived from the low-dose attenuation correction CT scans using previously developed software based on deep learning. Obstructive CAD was defined as stenosis >70% (or >50% in the left main coronary artery) and/or fractional flow reserve (FFR) ≤0.80.

Results

In total 58% of patients had obstructive CAD of which seventy-four percent were male. Addition of CAC scores to MPI and clinical predictors significantly improved the diagnostic accuracy of MPI to detect obstructive CAD. The area under the curve (AUC) increased from 0.87 to 0.91 (p: 0.025). Sensitivity and specificity analysis showed an incremental decrease in false negative tests with our MPI + CAC approach (n = 14 to n = 4), as a consequence an increase in false positive tests was seen (n = 11 to n = 28).

Conclusion

CAC scores collected simultaneously with MPI improve the detection of obstructive coronary artery disease in patients without a history of coronary revascularization.

Clinical and prognostic significance of eosinophilia and inv(16)/t(16;16) in pediatric acute myelomonocytic leukemia (AML-M4)

BACKGROUND

The cytogenetic aberrations inv(16)(p13.1q22)/t(16;16)(p13.1;q22), frequently detected in acute myelomonocytic leukemia with eosinophilia (FAB type M4eo), are generally considered a prognostically favorable subgroup. M4eo comprises a distinct morphology compared to M4 without eosinophilia (M4eo-) and therefore may be indicative for a different pathogenesis.

PROCEDURES

Morphology and cytogenetic/molecular analyses of a Dutch cohort of pediatric acute myelomonocytic leukemia (AML-M4) patients were performed and studied in order to analyze the association between the presence of eosinophilia morphology (M4eo+), inv(16)/t(16;16) (inv(16)+), clinical features, and outcome.

RESULTS

Of the 119 included patients with available combined morphological and cytogenetic results, 60% had M4eo- without inv(16) (inv(16)-), 10% had M4eo-/inv(16)+, 13% had M4eo+/inv(16)-, and 17% had M4eo+/inv(16)+. M4eo+ was significantly associated with the presence of inv(16)/t(16;16) (P < 0.001). Patients with M4eo+ had no significantly superior outcome compared with patients with M4eo-, whereas patients with inv(16)+ had significantly superior probabilities of event-free survival and probabilities of overall survival compared with patients without inv(16)-. Patients with M4eo+/inv(16)+ had no significantly better outcome than those with M4eo-/inv(16)+.

CONCLUSION

The prognostically favorable impact of distinct morphology with eosinophilia probably relies on its association with inv(16)/t(16;16). Simultaneous presence of both eosinophilia and inv(16) was not associated with superior outcome in our study. These results may be relevant for risk-group classification and risk-group adapted treatment and underline the importance of accurate cytogenetic analysis.

Sex-Based Differences in the Performance of the HEART Score in Patients Presenting to the Emergency Department With Acute Chest Pain

Background

Sex‐based differences in clinical presentation, pathophysiology, and outcomes of patients with acute chest pain are increasingly being recognized, but are not implemented in guidelines and clinical prediction tools. We evaluated the performance of the HEART score in women versus men, because sex‐based differences may exist among the algorithm's components: history, electrocardiogram, age, risk factors, and admission troponin level.

Methods and Results

The HEART score was retrospectively assessed in 831 women and 1084 men presenting to the emergency department with acute chest pain, assigning patients to the low‐, intermediate‐, or high‐risk category for the occurrence of major adverse cardiac events (MACE) within 6 weeks. MACE, consisting of myocardial infarction, coronary revascularization, and all‐cause death, also included events during index visit. Six‐week MACE rates were 2 times lower in women than men (10.0% versus 20.8%; P<0.01). Despite similar discriminatory accuracy of the HEART score among women and men (c‐statistic, 0.80 [0.75–0.84] versus 0.77 [0.74–0.81]; P=0.43), 6‐week MACE rates were significantly lower in women than men across all HEART risk categories: 2.1% versus 6.5% (P<0.01) in the low‐risk category, 12.7% versus 21.3% (P<0.01) in intermediate‐risk category, and 53.1% versus 77.0% (P=0.02) in the high‐risk category. The HEART score‐adjusted risk ratio for men was 1.6 (1.3–2.0; P<0.01).

Conclusions

The markedly higher 6‐week MACE risk in men across all HEART risk categories should be taken into account when using the HEART score to guide clinical decision making; early discharge with a low‐risk HEART score appears less safe for men than women with acute chest pain.