Citations

Abstract Monoclonal B-cell lymphocytosis (MBL) progresses to chronic lymphocytic leukemia (CLL) requiring therapy at 1% to 5% per year. Improved prediction of progression would greatly benefit individuals with MBL. Patients with CLL separate into 3 distinct epigenetic subtypes (epitypes) with high prognostic significance, and recently the intermediate epitype has been shown to be enriched for high-risk immunoglobulin lambda variable (IGLV) 3-21 rearrangements, impacting outcomes for these patients. Here, we employed this combined strategy to generate the epigenetic and light chain immunoglobulin (ELCLV3-21) signature to classify 219 individuals with MBL. The ELCLV3-21 high-risk signature distinguished MBL individuals with a high probability of progression (39.9% and 71.1% at 5 and 10 years, respectively). ELCLV3-21 improved the accuracy of predicting time to therapy for individuals with MBL compared with other established prognostic indicators, including the CLL international prognostic index (c-statistic, 0.767 vs 0.668, respectively). Comparing ELCLV3-21 risk groups in MBL vs a cohort of 226 patients with CLL revealed ELCLV3-21 high-risk individuals with MBL had significantly shorter time to therapy (P = .003) and reduced overall survival (P = .03) compared with ELCLV3-21 low-risk individuals with CLL. These results highlight the power of the ELCLV3-21 approach to identify individuals with a higher likelihood of adverse clinical outcome and may provide a more accurate approach to classify individuals with small B-cell clones.

Introduction: Rilonacept treatment in RHAPSODY resolved active pericarditis recurrences, and long-term treatment led to sustained risk reduction. Prior analysis linked greater baseline Late Gadolinium Enhancement (LGE), with more rapid recurrence upon rilonacept suspension after 12 weeks of treatment. Serial cardiac magnetic resonance (CMR) imaging (T2-STIR, LGE) enabled longitudinal assessment for tracking clinical improvement, guiding decision-making, and predicting patient outcomes after treatment cessation. Methods: At the long-term extension (LTE) 18-month decision milestone (18MDM), investigators chose, based on clinical status, to continue rilonacept, suspend rilonacept/observe, or discontinue the LTE. An imaging core lab blinded to clinical data measured pericardial thickness and graded pericardial edema (T2-STIR) and LGE at baseline and 18MDM. Pericarditis recurrence was assessed clinically following rilonacept suspension. Results: Baseline and 18MDM CMRs were available for 13 patients. Reductions in pericardial thickness, T2-STIR, and LGE from baseline to 18MDM while on treatment are provided in Figure . CMRs were obtained in 7/8 patients suspending rilonacept at 18MDM: LGE was none/trace, and T2-STIR was negative; yet, 5/7 (71%) had pericarditis recurrence within 1-4 months of rilonacept suspension despite prophylactic colchicine (n=2). Conclusions: Continued clinical improvement during prolonged rilonacept treatment corresponded with improvement on CMR, including reduced pericardial thickness, resolution of pericardial edema on T2-STIR, and resolution of LGE. Negative/trace LGE at 18MDM while on treatment did not predict absence of pericarditis recurrence upon subsequent rilonacept suspension in this size-limited subgroup. Larger prospective studies examining CMR parameters in guiding RP treatment duration decisions and informing associated clinical outcomes are warranted.

Abstract Objectives Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters. Materials and methods This qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software. Results We completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply–demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias. Discussion This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers. Conclusion Artificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers.