Killian, Michael

Abstract Aims Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients. Methods and results In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01–1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01–1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13–2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction. Conclusion Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.

Case description: A 67 year old female presented for annual review to the transplant clinic 4 years post orthotopic heart transplant for nonischemic dilated cardiomyopathy. She was asymptomatic. Her medical history included hypothyroidism, hypertension, osteoporosis, restless leg syndrome and calcineurin inhibitor related complications - CKD 3b, impaired glucose tolerance and a left partial glossectomy with neck dissection for stage 3 SCC of the left lateral tongue. Medications included aspirin, atorvastatin, prednisolone, tacrolimus, septrin and ropinirole. Family history was significant for heart failure in her father. She was a nonsmoker and did not consume alcohol. Physical examination was unremarkable. Diagnostic tests are shown below. Discussion: Apical hypertrophic cardiomyopathy (ApHCM) has rarely been described in post cardiac transplant populations. Characterized by increased left ventricular apical wall thickness, it results in a ‘spade-like’ appearance on imaging and is associated with marked T wave inversion on ECG. ApHCM is associated with progression to heart failure, apical aneurysm, ventricular arrhythmias and increased mortality rates. Patients may be asymptomatic at diagnosis. Regarding screening, criteria exist for the selection of donor hearts pre transplant with caution exercised if LVH >13mm. Causes of non HCM hypertrophy in this population include calcineurin inhibitors, hypertension, obesity and graft versus host disease. Data for treatment for ApHCM is lacking however it follows the path of classical HCM, involving the use of beta and calcium channel blockers to reduce demand ischemia and management of sequelae.

Introduction: Cardiac amyloidosis (CA) has been suggested to be common (up to 16%) in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). CA has poorer outcomes and its assessment in all TAVR patients may be costly and challenging. Hypothesis: CA may be screened for with an artificial intelligence (AI) clinical predictive model applied to pre-TAVR ECGs, and high probability (>50%) for CA may be associated with worse clinical outcomes. Methods: In this retrospective analysis of the Mayo institutional National Cardiovascular Disease Registry (NCDR)-TAVR, patients undergoing a TAVR between Jan-2012 and Dec-2017 were included. Pre-TAVR ECGs were analyzed by the AI predictive model for CA embedded in the EMR. EMRs were reviewed to assess for a clinical diagnosis of CA in those identified as high probability (>50%) by the AI tool. Clinical outcomes on 1 year follow-up were extracted from the NCDR-TAVR database and Kaplan-Meier curves were created to compare patients with high CA probability (≥50%) versus those with low probability (<50%). Results: Of 1070 patients who underwent TAVR (mean age 81.1± 8.5 years, 58.2% male), 263 (24.6%) had ≥50% probability for CA on pre-procedure ECG. Only 18 (1.7%) of these had clinical diagnosis of CA. In the survival analysis, high probability of CA by AI tool was associated with higher all-cause mortality (HR 1.60, 95%CI 1.09-2.34, p=0.01) and greater HF admissions (HR 1.59, 95%CI 1.08-2.35, p<0.01). Neither TIA/stroke nor myocardial infarction were statistically significant. MACE (TIA/stroke, myocardial infarction, heart failure admissions) was higher in the ≥50% CA probability group (HR 1.68, 95%CI 1.17-2.40, p<0.01), (Figure) . Conclusion: CA is clinically underdiagnosed in patients undergoing TAVR. AI applied to pre-TAVR ECGs suggesting higher probability for CA identifies a subgroup at higher risk of clinical events, who may benefit from further diagnostic evaluation for CA.

Abstract Background Cardiac masses encompass a wide differential including primary and secondary malignancies and can present with a variety of symptoms, many of which are non-specific. Early identification and classification are important, particularly for cardiac malignancies such as sarcomas as these are aggressive tumours with exceptionally poor prognoses when metastases are present at diagnosis. Case summary We report two cases of patients who presented with dyspnoea and were diagnosed with cardiac sarcomas; the former a primary sarcoma (undifferentiated pleomorphic subtype) and the latter a secondary sarcoma (round cell myxoid liposarcoma) that serve as comparisons for presentation and management of different types of this disease. Computed Tomography (CT) and echocardiography imaging findings are demonstrated showing the typical location and morphology of each subtype. Discussion Cardiac sarcomas are the most common primary cardiac malignancy, of which undifferentiated pleomorphic sarcoma is a common subtype. Undifferentiated pleomorphic sarcomas are aggressive, have a tendency to arise in the left atrium, and can appear similar to benign cardiac masses. Round cell myxoid liposarcomas by contrast are rare causes of secondary cardiac malignancies, metastasizing to the heart from soft tissues. Both diagnoses carry poor prognoses and although rare, are important to recognize as timely intervention with surgery, radiotherapy, and consideration of chemotherapy is key to maximizing survival.