Remote-Use Applications of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised Clinical Outcome Assessment Tool: A Scoping Review.

OBJECTIVES: In 2021, the US Congress passed the Accelerating Access to Critical Therapies for Amyotrophic Lateral Sclerosis Act. The law encourages development of "tools, methods, and processes" to improve clinical trial efficiency for neurodegenerative diseases. The Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) is an outcome measure administered during in-person clinic visits and used to support investigational studies for persons living with amyotrophic lateral sclerosis. Availability of a standardized, remote-use version of the ALSFRS-R may promote more inclusive, decentralized clinical trials. A scoping literature review was conducted to identify existing remote-use ALSFRS-R tools, synthesize feasibility and comparability of administration modes, and summarize barriers and facilitators to inform development of a standardized remote-use ALSFRS-R tool. METHODS: Included studies reported comparisons between remote and in-person, clinician-reported, ALSFRS-R administration and were published in English (2002-2022). References were identified by searching peer-reviewed and gray literature. Twelve studies met the inclusion criteria and were analyzed to compare findings within and across modes of administration. RESULTS: Remote modes of ALSFRS-R administration were categorized into 4 nonmutually exclusive categories: telephone (n = 6), videoconferencing (n = 3), computer or online platforms (n = 3), mobile applications and wearables (n = 2), and 1 unspecified telemedicine modality (n = 1). Studies comparing in-person to telephone or videoconferencing administration reported high ALSFRS-R rating correlations and nonsignificant between-mode differences. CONCLUSIONS: There is insufficient information in the ALSFRS-R literature to support remote clinician administration for collecting high quality data. Future research should engage persons living with amyotrophic lateral sclerosis, care partners, and providers to develop a standardized remote-use ALSFRS-R version.

Artificial intelligence coronary computed tomography, coronary computed tomography angiography using fractional flow reserve, and physician visual interpretation in the per-vessel prediction of abnormal invasive adenosine fractional flow reserve.

Aims: A comparison of diagnostic performance comparing AI-QCTISCHEMIA, coronary computed tomography angiography using fractional flow reserve (CT-FFR), and physician visual interpretation on the prediction of invasive adenosine FFR have not been evaluated. Furthermore, the coronary plaque characteristics impacting these tests have not been assessed. Methods and results: In a single centre, 43-month retrospective review of 442 patients referred for coronary computed tomography angiography and CT-FFR, 44 patients with CT-FFR had 54 vessels assessed using intracoronary adenosine FFR within 60 days. A comparison of the diagnostic performance among these three techniques for the prediction of FFR ≤ 0.80 was reported. The mean age of the study population was 65 years, 76.9% were male, and the median coronary artery calcium was 654. When analysing the per-vessel ischaemia prediction, AI-QCTISCHEMIA had greater specificity, positive predictive value (PPV), diagnostic accuracy, and area under the curve (AUC) vs. CT-FFR and physician visual interpretation CAD-RADS. The AUC for AI-QCTISCHEMIA was 0.91 vs. 0.76 for CT-FFR and 0.62 for CAD-RADS ≥ 3. Plaque characteristics that were different in false positive vs. true positive cases for AI-QCTISCHEMIA were max stenosis diameter % (54% vs. 67%, P < 0.01); for CT-FFR were maximum stenosis diameter % (40% vs. 65%, P < 0.001), total non-calcified plaque (9% vs. 13%, P < 0.01); and for physician visual interpretation CAD-RADS ≥ 3 were total non-calcified plaque (8% vs. 12%, P < 0.01), lumen volume (681 vs. 510 mm3, P = 0.02), maximum stenosis diameter % (40% vs. 62%, P < 0.001), total plaque (19% vs. 33%, P = 0.002), and total calcified plaque (11% vs. 22%, P = 0.003). Conclusion: Regarding per-vessel prediction of FFR ≤ 0.8, AI-QCTISCHEMIA revealed greater specificity, PPV, accuracy, and AUC vs. CT-FFR and physician visual interpretation CAD-RADS ≥ 3.

A Novel Earbud Detects Aortic Stenosis Murmur Before and After Transcatheter Aortic Valve Replacement.

Noninvasive infrasonic hemodynography using the MindMics earbuds captures low-frequency acoustic vibrations throughout the cardiac cycle. In an n-of-1 analysis, we propose a new method of assessing severe aortic stenosis by using infrasonic hemodynography to detect its characteristic systolic ejection murmur before and after transcatheter aortic valve replacement.