Providence Health & Services (since 2016: Providence St. Joseph Health) is a not-for-profit, Catholic health care system operating multiple hospitals across seven states, with headquarters in Renton, Washington. The health system includes 51 hospitals, more than 800 non-acute facilities and numerous other health, supportive housing and educational services on the west coast of the United States (Alaska, Washington, Oregon, and California) as well as Montana, New Mexico, and Texas. Providence Health & Services was founded by the Sisters of Providence in 1859.
B-cell maturation antigen (BCMA)-targeting therapies provide a new approach to treating multiple myeloma (MM). Alnuctamab (ALNUC) is a 2 + 1 immunoglobulin G1-based bispecific antibody binding BCMA and CD3ε receptors on myeloma and T cells, respectively. CC-93269-MM-001 is a first-in-human, phase 1 dose escalation/expansion study investigating ALNUC in relapsed/refractory MM. Patients had ≥3 prior regimens, disease progression ≤60 days of last regimen, and were BCMA-directed therapy-naïve. ALNUC was administered intravenously (IV) and subcutaneously (SC); however, SC was selected for further evaluation due to the more favorable safety profile. Ninety-five patients received ALNUC SC; at data cutoff, 44.2% remained on treatment and median follow-up was 8.0 months. The recommended phase 2 dose was 30 mg. The most common treatment emergent adverse events (any grade/grade 3/4) were CRS (57.9%/0%), and neutropenia (53.7%/43.2%). Infections were also frequent (64.2%/14.7%). ORR was 58.9% for all ALNUC SC-treated patients and 71.4% for the 30-mg cohort; 47/95 (49.5%) were measurable residual disease (MRD) negative. Overall, the safety and efficacy of ALNUC SC were comparable to other BCMA-targeted therapies. These results support improved safety of SC versus IV, and corroborate a step-up dosing strategy to mitigate CRS. Importantly, a schedule that de-intensifies over time provides favorable toxicity that may be applicable to other bispecific engagers.
Transoral incisionless fundoplication (TIF) is FDA-approved for patients with hiatal hernia < 2 cm. The American Foregut Society (AFS) recommends TIF only for those with Hill Grade (HG) ≤ 2 and advises cruroplasty for HG ≥ 3, regardless of hernia size. However, many patients may decline or be unfit for surgery. We conducted a systematic review and individual patient data (IPD) pooled analysis to assess the efficacy of TIF 2.0 in patients with severe GERD and HG III anatomy. A systematic search of PubMed and EMBASE through May 2023 identified studies evaluating TIF 2.0. Studies involving cruroplasty (cTIF) or other endoscopic therapies were excluded. Authors of eligible studies were contacted to provide IPD for patients with HG III. The primary outcome was complete cessation of proton pump inhibitors (PPIs). Secondary outcomes included technical success, GERD metrics, HG classification post-TIF, adverse events, and need for reintervention. Twenty-three studies met inclusion criteria and 4 provided IPD for 28 patients with HG III anatomy. Mean age and BMI were 51.1 years and 26.5 kg/m², respectively. Technical success was 100
Interactivity is crucial for effective data visualizations. However, it is often challenging to implement interactions for existing static visualizations, since the underlying code and data for existing static visualizations are often not available, and it also takes significant time and effort to enable interactions for them even if the original code and data are available. To fill this gap, we propose Athanor, a novel approach to transform existing static visualizations into interactive ones using multimodal large language models (MLLMs) and natural language instructions. Our approach introduces three key innovations: (1) an action-modification interaction design space that maps visualization interactions into user actions and corresponding adjustments, (2) a multi-agent requirement analyzer that translates natural language instructions into an actionable operational space, and (3) a visualization abstraction transformer that converts static visualizations into flexible and interactive representations regardless of their underlying implementation. Athanor allows users to effortlessly author interactions through natural language instructions, eliminating the need for programming. We conducted two case studies and in-depth interviews with target users to evaluate our approach. The results demonstrate the effectiveness and usability of our approach in allowing users to conveniently enable flexible interactions for static visualizations.
