Wagner, Tyler

ABSTRACT Pulmonary arterial hypertension (PAH) is a progressive disease with significant morbidity and mortality. Due to nonspecific symptoms, diagnosis can be challenging and subject to substantial delays. Using data from Mayo Clinic′s electronic health records, we looked at causes of delayed diagnosis and whether earlier diagnosis means better outcomes. This retrospective cohort study included adults with PAH confirmed by right heart catheterization (RHC) between 2015 and 2019. Univariate and multivariate analyses evaluated the association of 229 clinical and laboratory features with time to PAH diagnosis and survival outcomes. Early diagnosis was defined as < 6 months and a delayed diagnosis as ≥ 22.2 months from an eligible event. Survival probability was determined using Kaplan‐Meier analysis . The study enrolled 160 patients. The most common initial PAH symptoms were dyspnea (42.5%) and fatigue (16.9%). It took a median of 7, 13, 26, and 123 days from eligible event to the first X‐ray, electrocardiogram, echocardiogram, and RHC, respectively. Factors most closely associated with delayed diagnosis were chronic obstructive pulmonary disease, normal creatinine, high systolic blood pressure, acute respiratory infection, normal iron, normal diastolic blood pressure, and being male aged 18.5 to < 25 years. Compared with delayed diagnosis patients (22.6%), more early diagnosis patients saw a pulmonologist (35.3%) before another provider. After 5 years, 73% of patients in the early diagnosis and 31% in the delayed diagnosis group were still alive. This study identified factors associated with a delayed diagnosis of PAH and found that earlier diagnosis and treatment initiation were associated with significantly improved survival.

Introduction: Pulmonary hypertension (PH) is a life-threatening, progressive disease with non-specific symptoms, often leading to delayed diagnosis. Early identification of World Health Organization Group 1 (Pulmonary Arterial Hypertension, PAH) and Group 4 (Chronic Thromboembolic Pulmonary Hypertension, CTEPH) is essential, as effective therapies can improve outcomes. Hypothesis: An electrocardiogram-based AI algorithm for PH detection (ECG-AI PH) may enable earlier diagnosis and reduce healthcare utilization, including hospitalizations and procedures. Methods: Retrospective analysis used a de-identified data platform of >7M clinical records from a multistate integrated health system. Adult precapillary PH patients (mPAP >20 mmHg, PVR >2 WU, PCWP ≤15 mmHg) were identified as PAH or CTEPH based on ICD codes, use of approved therapies, or surgical interventions (for CTEPH) between 2002 and 2024. ECG-AI PH was applied to ECGs within 30 days of diagnostic right heart catheterization, using a 5:1 randomly sampled PH-negative control cohort. Training set patients were excluded. Clinical event frequency was compared between two intervals: from first possible PH symptom (dyspnea, syncope, chest pain, fatigue, lower limb swelling) to diagnosis, and from symptom onset to first positive ECG-AI PH prediction. Results: A total of 1882 PAH and 359 CTEPH patients met inclusion criteria. Of these, 1340 PAH and 258 CTEPH patients had symptom codes prior to diagnosis. Both groups showed prolonged intervals from symptom onset to diagnosis, with multiple diagnostic procedures and hospitalizations (Figure). ECG-AI PH performance evaluation on the test set included 647 PAH and 152 CTEPH patients. ECG-AI PH achieved AUCs of 0.90 and 0.89 for PAH and CTEPH, sensitivities of 80.3% and 76.8%, and specificities of 83.4% and 82.4%. Among those tested, 576 PAH and 95 CTEPH patients had a positive ECG-AI PH prediction after symptom onset but before diagnosis. Compared to the current patient journey, the interval between initial symptoms and a positive output from ECG-AI PH was shorter and had fewer diagnostic tests/visits. Conclusion: ECG-AI PH demonstrated strong performance in detecting PAH and CTEPH. It may reduce diagnostic delays, support earlier PH-focused screening (e.g., echocardiograms evaluating the right heart), enable earlier intervention, and reduce pre-diagnosis healthcare burden, benefitting both patient outcomes and healthcare system efficiency.

Background Teclistamab, the first-in-class B-cell maturation antigen (BCMA) x CD3 bispecific therapy was recently approved by the FDA for the treatment of triple-class exposed relapsed or refractory multiple myeloma (MM) after ≥ 4 prior lines of therapy. Because of the risk of cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), the step-up dosing (SUD) of teclistamab is commonly administered in an inpatient setting among early users. Mayo Clinic has a well-established hospital-based outpatient program for novel immunotherapies. Since the FDA approval, teclistamab SUD has been administered as part of this program. Each dose of the SUD is administered in an outpatient setting, and the patient is given a remote monitoring kit to regularly measure vital signs and stay connected with the command center for signs and symptoms of CRS and ICANS throughout the entire SUD period. This study aimed to evaluate early safety outcomes and healthcare resource utilization during SUD in real-world (RW) patients who initiated teclistamab under the outpatient administration model across 3 Mayo Clinic locations (Rochester, MN, Phoenix/Scottsdale, AZ, Jacksonville, FL). Methods This was a retrospective study using Mayo Clinic's electronic medical records from October 26, 2022 to June 12, 2023. Eligible patients were adults diagnosed with MM who had initiated commercial teclistamab at any of the 3 Mayo Clinic locations. Patient characteristics, rates and severity of CRS and ICANS, as well as healthcare resource utilization during SUD were described. Time between teclistamab administration and patient check-out was reported for SUD and treatment doses, respectively. Results At the time of data cutoff, 39 patients received at least 1 teclistamab dose across the 3 locations (median age 67.2 years; male: 74%; White: 87%; non-Hispanic: 92%), including 36 patients who initiated teclistamab SUD directly in an outpatient setting. A total of 8 (21%) patients had MM with high-risk cytogenetics and 14 (36%) had prior exposure to other BCMA-targeted therapies. Prevalent comorbidities prior to receiving teclistamab included anemia (77%), hypertension (56%), lytic bone lesions (51%), neutropenia (49%), and hypogammaglobulinemia (41%). Renal impairment or failure was observed in 31% of the patients (Table 1). Almost all patients received acetaminophen (95%), diphenhydramine (95%), and dexamethasone (92%) as pre-medications on the same day as teclistamab administrations during SUD. Of the teclistamab doses with clinic time data, the majority (62%) of doses during SUD required 30 minutes to 1 hour of clinic time for monitoring between administration and check-out. After SUD, the clinic time for weekly treatment doses decreased to less than 30 minutes for most (79%) doses (Figure 1). In patients who completed SUD by data cutoff (n = 37), most patients (70%) received the SUD on a 3-day dose interval (i.e., day 1, 4, 7). The mean time between the first and third doses was 6.3 days. Among the 37 patients with complete SUD, 12 (32%) developed CRS. The highest CRS grade was grade 1 for 10 patients; 1 patient had a grade 2 CRS, and 1 patient had a grade 4 CRS. The patient with grade 4 CRS concurrently decompensated during a dialysis session and all symptoms were considered for CRS grading. A total of 6 patients had recurrent CRS, including 1 patient with 3 events during SUD. All patients who developed CRS during SUD (n=12) were admitted to the hospital for treatment, with a total of 19 admissions across all patients and a median length of stay of 1.7 days per admission. Among patients who were admitted for CRS (n=12), the majority were treated with acetaminophen (92%), diphenhydramine (83%) and dexamethasone (83%); tocilizumab was given to the 2 patients with CRS grade 2 or higher. Only 1 ICANS event (grade 2) was observed in 1 patient during SUD, after dose 3, and the patient was admitted to the hospital for treatment. Conclusion This study evaluated early RW safety outcomes of teclistamab under an outpatient administration model and found the CRS rate and severity to be comparable with other RW evidence generated from various data sources. Outcomes related to teclistamab SUD from Mayo Clinic supported the safety and feasibility of outpatient administration model as a potential option to reduce healthcare resource utilization and improve treatment experiences.

136 Background: Studies using International Classification of Diseases (ICD) codes have aimed to characterize the safety profile of cancer therapies in the Real-World Data setting, but this approach has limitations including an inability to illustrate patient journey and offer insights into drug-Adverse Event (AE) causality. Using data from a cohort of ~9000 patients treated with immune checkpoint inhibitors (ICI) at Mayo Clinic, we recently demonstrated the feasibility of applying Augmented Curation (AC), a natural language processing (NLP)-based innovation, on unstructured electronic health record (EHR) data to detect and characterize immune related AEs (irAEs). While the ability to identify and extract clinical details from EHRs has been previously demonstrated, our approach focuses on applying these techniques to identify implied textual causality of drug-event pairs. This study aims to build upon our prior findings and compare the accuracy, speed, and resource savings enabled by AC analysis versus manual review of patient notes. Methods: We compared the prevalence of adrenal insufficiency, colitis, and hypophysitis (A.C.H) among ICI patients at Mayo Clinic using ICD codes and AC which leverages SciBERT to perform scientific NLP tasks. AC models trained to identify drug-adverse event relationships were used to assess irAEs and subsequent treatments and demonstrated strong performance with F1 scores of 0.85. Manual curation of relevant clinical data, i.e. medications and adverse events, was employed to create a “gold standard” A.C.H. cohort. Time required for manual review compared to AC was documented for overall comparison. Corticosteroid/immunosuppressant use and ICI discontinuations were used as proxies of severity. Results: In our cohort, 540 unique patients experienced at least one of the A.C.H. irAEs. A.C.H patients were found to receive corticosteroids for their respective irAE 79% of the time and discontinued the ICI due to the irAE 7.7% of the time. 6.1% of Colitis patients were treated with a 2L immunosuppressant. Similar to the previous study, AC showed higher sensitivity, PPV and NPV compared to ICD codes alone within the A.C.H. cohort. Sentence extraction and entity classification using AC models on all 9,000-patients took ~10 minutes, whereas manual review of this cohort would take roughly 9 weeks (~10 minutes per patient). Conclusions: The results from this study reinforce that AC models are a valuable tool to comprehensively extract information and to impute relationships between entities, such as therapies and AEs. The AC models demonstrated better accuracy at a much greater speed compared to manual review of patient EHRs. Leveraging AC models provides the ability to manipulate unstructured clinical text married to structured EHR data at scale while minimizing need for manual curation.

Temporal inference from laboratory testing results and triangulation with clinical outcomes extracted from unstructured electronic health record (EHR) provider notes is integral to advancing precision medicine. Here, we studied 246 SARS-CoV-2 PCR-positive (COVID pos ) patients and propensity-matched 2460 SARS-CoV-2 PCR-negative (COVID neg ) patients subjected to around 700,000 lab tests cumulatively across 194 assays. Compared to COVID neg patients at the time of diagnostic testing, COVID pos patients tended to have higher plasma fibrinogen levels and lower platelet counts. However, as the infection evolves, COVID pos patients distinctively show declining fibrinogen, increasing platelet counts, and lower white blood cell counts. Augmented curation of EHRs suggests that only a minority of COVID pos patients develop thromboembolism, and rarely, disseminated intravascular coagulopathy (DIC), with patients generally not displaying platelet reductions typical of consumptive coagulopathies. These temporal trends provide fine-grained resolution into COVID-19 associated coagulopathy (CAC) and set the stage for personalizing thromboprophylaxis.