Wagner, Tyler E

ABSTRACT Clinical worsening events are increasingly recognized as a meaningful outcome in pulmonary arterial hypertension (PAH). We applied machine‐learning models to real‐world data to identify clinical features that may predict clinical worsening events in PAH. Data were obtained retrospectively from the electronic health records of adults diagnosed with PAH at Mayo Clinic locations (January 2015–December 2019). Machine‐learning LASSO regularized logistic regression models were developed to analyze the association between 100 clinical features and occurrence of clinical worsening events. In total, 455 patients were included (mean age 62.1 years; 59.3% female). Of these, 232 (51.0%) experienced a clinical worsening event after a median (quartile 1, quartile 3) of 10.9 (4.1, 21.4) months. The best‐performing model had an area under the curve of 0.78, sensitivity of 78%, and specificity of 59%. The model identified 11 clinical features associated with clinical worsening events in PAH with a non‐zero coefficient: baseline age, body mass index (BMI), creatinine level, red cell (erythrocyte) distribution width (RDW), mean pulmonary arterial pressure, pulmonary vascular resistance, sodium concentration, and QT interval, along with dyspnea, number of clinic visits, and other forms of heart disease within a 1‐month window before a clinical worsening event. Baseline RDW and BMI, and number of clinic visits within the 1‐month window were statistically significantly associated with clinical worsening events. Our study used machine‐learning models to identify clinical features associated with risk of clinical worsening events in PAH. Automation of risk prediction could lead to earlier therapeutic intervention to optimize patient outcomes.

PURPOSE Teclistamab is initiated with a step-up dosing (SUD) schedule to mitigate the risk of cytokine release syndrome (CRS) and immune effector cell–associated neurotoxicity syndrome (ICANS). Early teclistamab users commonly received SUD in a hospital setting. This study aimed to evaluate safety and health care resource utilization (HRU) in real-world patients with multiple myeloma who initiated teclistamab SUD in an outpatient setting. METHODS This was a retrospective study using Mayo Clinic's electronic medical records from October 26, 2022, to October 31, 2023. Patient characteristics were summarized for all patients treated with teclistamab and separately for patients who started SUD outpatient. SUD pattern, safety, HRU, and post-SUD dosing schedule were described in patients with complete SUD. RESULTS At data cutoff, 65 patients received ≥1 teclistamab dose, including 58 patients who initiated SUD outpatient (median age, 69.2 years; male, 63.8%; White, 89.7%). Among 57 patients who completed SUD in an outpatient setting, all received premedications on the days of teclistamab administrations per label recommendation; 18 (31.6%) developed CRS (13 grade 1, four grade 2, and one grade 4) and two developed ICANS (one each with grade 2 and 4). All CRS and ICANS resolved with supportive care and all patients continued treatment. Eighteen patients were admitted to the hospital for CRS treatment, with a median CRS-related hospital stay of 2 days per admission. Most (60%) doses during SUD required <1 hour clinic time between administration and checkout. Post-SUD, clinic time for treatment doses decreased to <30 minutes for most doses (82%). CONCLUSION Outcomes of this study support outpatient administration as a safe and feasible option for teclistamab SUD to potentially reduce HRU and improve patient experiences.

PURPOSE Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is critical for characterizing the benefit/risk profile of ICI therapies beyond the clinical trial population. Diagnosis codes, such as International Classification of Diseases codes, do not comprehensively illustrate a patient's care journey and offer no insight into drug-irAE causality. This study aims to capture the relationship between ICIs and irAEs more accurately by using augmented curation (AC), a natural language processing–based innovation, on unstructured data in electronic health records. METHODS In a cohort of 9,290 patients treated with ICIs at Mayo Clinic from 2005 to 2021, we compared the prevalence of irAEs using diagnosis codes and AC models, which classify drug-irAE pairs in clinical notes with implied textual causality. Four illustrative irAEs with high patient impact—myocarditis, encephalitis, pneumonitis, and severe cutaneous adverse reactions, abbreviated as MEPS—were analyzed using corticosteroid administration and ICI discontinuation as proxies of severity. RESULTS For MEPS, only 70% (n = 118) of patients found by AC were also identified by diagnosis codes. Using AC models, patients with MEPS received corticosteroids for their respective irAE 82% of the time and permanently discontinued the ICI because of the irAE 35.9% (n = 115) of the time. CONCLUSION Overall, AC models enabled more accurate identification and assessment of patient impact of ICI-induced irAEs not found using diagnosis codes, demonstrating a novel and more efficient strategy to assess real-world clinical outcomes in patients treated with ICIs.

PURPOSE: Median duration of daratumumab (DARA) administration for treatment of multiple myeloma is 3-7 hours for the intravenous formulation (DARA IV) and 3-5 minutes for the subcutaneous formulation (DARA SC). Here, we describe clinical administration characteristics of DARA using a novel method for data extraction from electronic health records. METHODS: Time-based measurements were extracted using a scheduling/pharmacy software program that tracked patient movement through appointments for patients initiating DARA in Mayo Clinic infusion centers from April 5, 2017, to October 14, 2021. Cohorts included patients who received DARA IV or DARA SC, or converted from DARA IV to DARA SC. The DARA SC cohort was further analyzed before (DARA SC initial) and after (DARA SC shortened) a reduction in the postadministration observation time mandated by the treatment plan. Events associated with administration-related reactions (ARRs) were also identified. RESULTS: Median total clinic times were 2.7-3.0 hours shorter for DARA SC versus DARA IV. Median clinic times were highest at dose 1 and decreased with subsequent doses. Median total chair times were 2.7-2.8 hours shorter for DARA SC versus DARA IV. Incidences of ARR-related events with DARA SC were low across doses. CONCLUSION: Reduced clinic times were observed with DARA SC, indicating that use of DARA SC as a treatment option results in time savings that may free clinic resources. Furthermore, novel methods of electronic health record data extraction can provide insights that may help inform clinic resource optimization.