Featured Publications

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.

Janssen R&D

Conclusions • Treatments for HR+/HER2-low mBC varied, with many patients receiving multiple LOT shortly after 1L and experiencing diminishing time on treatment. Treatment patterns were similar but clinical outcomes shorter in HR+/IHC 0 vs HER2-low disease. • These results highlight a need for effective HER2 targeted therapies that extend duration of clinical benefit earlier in the disease management pathway. • The role of HER2 targeted ADCs in IHC 0 (specifically HER2 null) needs to be further investigated.

Daiichi Sankyo, Inc

Background To describe outcomes of high-risk patients with coronavirus disease 2019 (COVID-19) treated with sotrovimab, other monoclonal antibodies (mAbs), or antivirals, and patients who did not receive early COVID-19 treatment. We also evaluate the comparative effectiveness of sotrovimab versus no treatment in preventing severe clinical outcomes. Methods This observational retrospective cohort study analyzed Mayo Clinic electronic health records. Non-hospitalized adult patients diagnosed with COVID-19 from May 26, 2021 and April 23, 2022 and at high risk of COVID-19 progression were eligible. The primary outcome was 29-day all-cause hospitalization and/or death. Outcomes were described for patients treated with sotrovimab, other mAbs, or antivirals, and eligible but untreated patients, and compared between sotrovimab-treated and propensity score (PS)-matched untreated cohorts. Results We included 35,485 patients (sotrovimab, 1369; other mAbs, 6488; antivirals, 133; high-risk untreated, 27,495). A low proportion of patients treated with sotrovimab (n = 33/1369, 2.4%), other mAbs (n = 147/6488, 2.3%), or antivirals (n = 2/133, 1.5%) experienced all-cause hospitalization or death. Among high-risk untreated patients, the percentage of all-cause hospitalization or death was 3.3% (n = 910/27,495). In the PS-matched analysis, 2.5% (n = 21/854) of sotrovimab-treated patients experienced all-cause hospitalization and/or death versus 2.8% (n = 48/1708) of untreated patients (difference, –0.4%; p = 0.66). Significantly fewer sotrovimab-treated patients required intensive care unit admission (0.5% vs 1.8%; difference, –1.3%; p = 0.002) or respiratory support (3.5% vs 8.7%; difference, –5.2%; p < 0.001). Conclusions There was no significant difference in the proportion of sotrovimab-treated and PS-matched untreated patients experiencing 29-day all-cause hospitalization or mortality, although significantly fewer sotrovimab-treated patients required intensive care unit admission or respiratory support.

Background Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG. Methods The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage–time data, with patients classified as “PH-likely” or “PH-unlikely” (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6–18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. Results Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. Conclusion The PH-EDA can detect PH at diagnosis and 6–18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.

Background: Progression-free survival (PFS) is frequently measured in oncology clinical trials. In analyses outside of the trial setting, various strategies have been utilized to assess real-world PFS (rwPFS), including manual abstraction of clinical records, natural language processing (NLP) of oncology notes and radiology reports, and longitudinal analyses of radiologic images. Here we develop and validate a new semi-automated workflow that combines NLP of clinical notes with structured electronic health record data to facilitate the evaluation of rwPFS in patients with metastatic breast cancer (mBC). Methods: This study analyzes de-identified EHR data using nference nSights. The data is extracted following privacy-preserving protocols and is HIPAA compliant. The overall cohort included 316 patients with HR-positive, HER2-negative mBC who initiated Palbociclib and Letrozole combination therapy between January 1, 2015 and December 31, 2021. We developed and implemented an ensemble of deep-learning NLP frameworks to capture progression events from unstructured clinical notes and radiology reports. A change in the line of therapy, as determined by structured drug order/administration records, was also considered a progression event. We used manually curated “ground-truth” datasets to evaluate the performance of the progression-event capture workflow at the levels of both sentences (N = 1000) and patients (N = 100) by calculating sensitivity, specificity, precision, accuracy, and F1 scores. Progression events and censoring events (death, loss to follow-up, end of study period) were considered to compute rwPFS. Results: At the sentence level, progression events were captured from clinical notes and radiology reports with a sensitivity of 99.8%, specificity of 96.7%, and accuracy of 98.2% (Table). At the patient level, initial progression was correctly captured within a window of +/-30 days with a sensitivity of 92.5%, specificity of 83.0%, and accuracy of 88.0% (Table). In a sample of 100 patients, the median rwPFS was determined to be 25 months (95% CI; 15-35 months) by manual curation and 22 months (95% CI; 15-35 months) by the semi-automated workflow. In the overall cohort, median rwPFS was 20 months (95% CI; 18-25 months). Conclusions: An ensemble of NLP algorithms extracted progression events from clinical notes and radiology reports with high accuracy. A semi-automated workflow enabled rapid and accurate determination of rwPFS in mBC patients receiving a combination chemotherapy regimen. Further evaluation of this workflow to estimate rwPFS in other cancers and therapeutic settings is warranted.