Objective: Patients with severe symptomatic aortic stenosis are assessed for coronary artery disease (CAD) prior to transcatheter aortic valve implantation (TAVI) with treatment implications. Invasive coronary angiography (ICA) is the recommended modality but is associated with peri-procedural complications. Integrating machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) into existing TAVI-planning CT protocol may aid exclusion of significant CAD and thus avoiding ICA in selected patients. Materials and Methods: A single-center, retrospective study was conducted, 41 TAVI candidates with both TAVI-planning CT and ICA performed were analyzed. CT datasets were evaluated by a ML-based CT-FFR software. Beta-blocker and nitroglycerin were not administered in these patients. The primary outcome was to identify significant CAD. The diagnostic performance of CT-FFR was compared against ICA. Results: On per-patient level, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy were 89%, 94%, 80%, 97% and 93%, respectively. On per-vessel level, the sensitivity, specificity, PPV, NPV and diagnostic accuracy were 75%, 94%, 67%, 96% and 92%, respectively. The area under the receiver operative characteristics curve per individual coronary vessels yielded overall 0.90 (95% confidence interval 85%–95%). ICA may be avoided in up to 80% of patients if CT-FFR results were negative. Conclusion ML-based CT-FFR can provide accurate screening capabilities for significant CAD thus avoiding ICA. Its integration to existing TAVI-planning CT is feasible with the potential of improving the safety and efficiency of pre-TAVI CAD assessment.

Objective: Data on early-onset dementia in Chinese is limited. This study aimed to report the diagnostic profiles and characteristics of patients with early-onset dementia in a university-affiliated cognitive disorder clinic in Hong Kong. Methods: We prospectively collected data of consecutive patients who were referred between January 2012 and December 2018. All patients were referred for diagnostic evaluation of cognitive symptoms. Patients with symptom-onset at age 65 or before were recruited. We excluded patients with (1) cognitive deficits referable to an isolated event or toxin and (2) significant mood disorders. Results: Of the 93 patients included, four patients had temporal lobe epilepsy mimicking dementia. Three patients had cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), one patient had Niemann-Pick disease type C and two patients had undetermined aetiology. The remaining 83 patients had primary degenerative dementia. The most frequent diagnosis wasAlzheimer’s disease (AD) (70%), followed by frontotemporal dementia (FTD) (22%) and parkinsonian disorders (8%). The mean age of symptom onset was 57.8 ± 5.8 years.Ten (17%) AD patients had non-amnestic presentation. Fifteen FTD patients consented for mutation screening in the GRN (progranulin), MAPT (microtubule-associated protein tau) and C9orf72 genes, none were positive. Conclusions: Early-onset dementia had a broader differential diagnoses than late-onset dementia, and includes a number of rare hereditary diseases. Patients with suspected early-onset dementia should be thoroughly evaluated to identify any treatable causes.