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Background: The use of an artificial intelligence electrocardiography (AI-ECG) algorithm has demonstrated its reliability in predicting the risk of atrial fibrillation (AF) within the general population. Objectives: This study aimed to determine the effectiveness of the AI-ECG score in identifying patients with chronic lymphocytic leukemia (CLL) who are at high risk of developing AF. Methods: We estimated the probability of AF based on AI-ECG among patients with CLL extracted from the Mayo Clinic CLL database. Additionally, we computed the Mayo Clinic CLL AF risk score and determined its ability to predict AF. Results: Among 754 newly diagnosed patients with CLL, 71.4% were male (median age = 69 years). The median baseline AI-ECG score was 0.02 (range = 0-0.93), with a value ≥0.1 indicating high risk. Over a median follow-up of 5.8 years, the estimated 10-year cumulative risk of AF was 26.1%. Patients with an AI-ECG score of ≥0.1 had a significantly higher risk of AF (HR: 3.9; 95% CI: 2.6-5.7; P < 0.001). This heightened risk remained significant (HR: 2.5; 95% CI: 1.6-3.9; P < 0.001) even after adjusting for the Mayo CLL AF risk score, heart failure, chronic kidney disease, and CLL therapy. In a second cohort of CLL patients treated with a Bruton tyrosine kinase inhibitor (n = 220), a pretreatment AI-ECG score ≥0.1 showed a nonsignificant increase in the risk of AF (HR: 1.7; 95% CI: 0.8-3.6; P = 0.19). Conclusions: An AI-ECG algorithm, in conjunction with the Mayo CLL AF risk score, can predict the risk of AF in patients with newly diagnosed CLL. Additional studies are needed to determine the role of AI-ECG in predicting AF risk in CLL patients treated with a Bruton tyrosine kinase inhibitor.
RESUMO
ABSTRACT: High-count monoclonal B-cell lymphocytosis (HCMBL) is a precursor condition to chronic lymphocytic leukemia (CLL). We have shown that among individuals with HCMBL, the CLL-International Prognostic Index (CLL-IPI) is prognostic for time-to-first therapy (TTFT). Little is known about the prognostic impact of somatically mutated genes among individuals with HCMBL. We sequenced DNA from 371 individuals with HCMBL using a targeted sequencing panel of 59 recurrently mutated genes in CLL to identify high-impact mutations. We compared the sequencing results with that of our treatment-naïve CLL cohort (N = 855) and used Cox regression to estimate hazard ratios and 95% confidence intervals (CIs) for associations with TTFT. The frequencies of any mutated genes were lower in HCMBL (52%) than CLL (70%). At 10 years, 37% of individuals with HCMBL with any mutated gene had progressed requiring treatment compared with 10% among individuals with HCMBL with no mutations; this led to 5.4-fold shorter TTFT (95% CI, 2.6-11.0) among HCMBL with any mutated gene vs none, independent of CLL-IPI. When considering individuals with low risk of progression according to CLL-IPI, those with HCMBL with any mutations had 4.3-fold shorter TTFT (95% CI, 1.6-11.8) vs those with none. Finally, when considering both CLL-IPI and any mutated gene status, we observed individuals with HCMBL who were high risk for both prognostic factors had worse prognosis than patients with low-risk CLL (ie, 5-year progression rate of 32% vs 21%, respectively). Among HCMBL, the frequency of somatically mutated genes at diagnosis is lower than that of CLL. Accounting for both the number of mutated genes and CLL-IPI can identify individuals with HCMBL with more aggressive clinical course.