Citations

Artificial intelligence (AI) has emerged as a promising adjunct in the diagnosis and management of infective endocarditis (IE), a disease characterized by diagnostic complexity and significant morbidity. Machine learning (ML) models such as SABIER and SYSUPMIE have demonstrated strong predictive accuracy for early IE diagnosis, embolic risk stratification, and postoperative mortality, surpassing traditional clinical scoring systems. In imaging, AI-enhanced echocardiography and advanced modalities like FDG-PET/CT offer improved sensitivity, specificity, and reduced inter-observer variability, potentially transforming clinical decision making. Additionally, AI-powered microbiological techniques, including MALDI-TOF mass spectrometry combined with ML and neural network-based metagenomic classifiers, show promise in rapidly identifying pathogens and predicting antimicrobial resistance. Despite encouraging early results, widespread adoption faces barriers, including data limitations, interpretability issues, ethical concerns, and the need for robust validation. Future directions include leveraging generative AI as clinical consultative tools, provided their capabilities and limitations are carefully managed. Ultimately, collaborative efforts addressing these challenges could transform IE care, enhancing diagnostic accuracy, clinical outcomes, and patient safety.

Abstract Outcomes of bispecific antibodies in patients with renal impairment (RI) are not well characterized, given the exclusion of these patients from clinical trials. Herein, we evaluated patients with relapsed/refractory multiple myeloma and RI treated with standard-of-care teclistamab. RI was defined as creatinine clearance (CrCl) <40 mL/min. CrCl <30 mL/min or dialysis dependence was defined as severe RI. Of the 384 included patients, 81 (21%) had RI, including 45 (18%) with severe RI, and 18 (5%) on dialysis. Patients with RI were more likely to be older (median age, 71 vs 67 years; P = .002) and have a higher median number of previous lines of therapy (7 vs 6; P = .04). Rates and severity of cytokine release syndrome (51% vs 59%; grade ≥3: 1.2% vs 1%) and immune effector cell–associated neurotoxicity syndrome (16% vs 13%; grade ≥3: 2.5% vs 2.6%) were similar in patients with and without RI, respectively. Patients with RI had higher baseline and day 30 post-teclistamab grade ≥3 anemia and grade ≥3 thrombocytopenia. Renal function did not worsen after teclistamab initiation in most patients with RI outside of the context of disease progression. Overall response rate (52% vs 56%; P = .61) and survival outcomes (median progression-free survival, 4.6 vs 6.5 months; P = .62) were comparable in patients with and without RI, respectively, after a median follow-up of 9.9 months. No differences in overall survival or non-relapse mortality were noted. Our findings suggest that treatment with teclistamab is feasible in patients with RI, including those on dialysis, with a similar safety and efficacy profile to patients without RI.