Lopez, Camden L

AbstractThree sets of criteria (International Society of Amyloidosis [ISA], Palladini and Kastritis) were independently developed for staging, progression and response criteria to predict renal survival in patients with AL amyloidosis. We evaluated these criteria using a cohort of 495 newly diagnosed AL amyloidosis patients with renal involvement using time to event competing risk analysis at baseline, 3, 6 and 12 months after treatment. Only Palladini and Kastritis had a staging system and both predicted a higher risk of end stage renal disease (ESRD) in the stage III vs stage I patients but only the Palladini model was predictive for stage II patients. At 3 months, risk of ESRD was significantly higher for Palladini and ISA renal progression (hazard ratio [HR] 2.8 [95% CI: 1.5–5.3, p = .001] and 2.5 [CI: 1.4–4.6, p = .004, respectively]), but renal response was not significantly protective; conversely, the risk of ESRD was not significantly higher for the Kastritis renal progression, but was significantly protective for the Kastritis renal responders (HR 0.38 [95% CI: 0.17–0.84], p = .017). Both progression and response with ISA, Palladini and Kastritis criteria were predictive of ESRD at 6 months and 12 months. While the Palladini staging criteria at baseline, and the ISA and Palladini criteria for progression at 3 months performed better than the Kastritis criteria at baseline and 3 months post‐treatment, the Kastritis criteria performed better for response 3 months after treatment. All three sets of criteria performed well at and after 6 months post‐treatment. These differences are important when choosing endpoints for clinical trials.

Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.