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Background: Pulmonary hypertension (PH) is a life-threatening disease affecting up to 1% of the global population. Diagnosis can be challenging and is often delayed due to the need for advanced imaging and invasive procedures. The use of artificial intelligence applied to ECGs (ECG-AI) has been shown to detect subtle patterns in voltage-time data and may be a valuable tool for the early detection of PH. Hypothesis and Purpose: To evaluate the performance of a previously trained, ECG-AI algorithm to detect PH (ECG-AI PH) using real-world data (RWD) collected in a multicenter, validation study. A joint primary hypothesis required sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) to exceed the null values of 76%, 70%, 10% and 90%. Study Design and Methods: This retrospective validation study was conducted at 5 geographically diverse U.S. health systems. Adult subjects were eligible for inclusion if they had a 12-lead ECG paired with an echocardiogram (Echo) in which tricuspid regurgitation velocity (TRV) was recorded, following presentation with dyspnea. Patients were classified according to echocardiographic criteria as either PH (PH+, TRV >3.4 m/s) or controls (PH-, TRV ≤2.8 m/s) to simulate the real-world use of ECG-AI, where a positive result could lead to a follow-up Echo. The study database was locked before processing the digital ECGs with ECG-AI PH. Performance was also estimated in a subset of subjects that later had a right heart catheterization using mPAP ≥ 20mmHg as the definition of PH. Results: A total of 14281 subjects (53% female, 63% aged 50+ years) met the inclusion criteria, including 3019 PH+ cases and 11262 PH- controls (Figure). Sn and Sp were 84.0% (95% CI: 82.6%, 85.3%) and 71.7% (95% CI: 70.9%, 72.6%), respectively. The positive and negative predictive values were 18.9% (95% CI: 18.4%, 19.4%) and 98.3% (95% CI: 98.1%, 98.4%), respectively, at 7.3% prevalence. Each endpoint met pre-defined performance criteria. In the subset of 1683 subjects with a follow up RHC, performance remained robust (Sn 85% (1155/1358); Sp 46% (151/325)). Conclusion: While ECG-AI PH was first developed as an investigational tool to detect PH, continued development as software as a medical device for clinical use demonstrated that the algorithm retained strong performance to detect PH in diverse, non-overlapping clinical settings and patient populations.

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.

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.

Abstract Introduction: Daratumumab is approved across lines of therapy for multiple myeloma (MM) by subcutaneous administration (DARA SC) or by intravenous administration (DARA IV). In clinical studies, the administration time ranges from 3-5 minutes for DARA SC versus 3-7 hours for DARA IV, and DARA SC is associated with reduced rates of administration-related reactions (ARRs). These characteristics of DARA SC are associated with benefits for clinics in terms of safety and clinic time. We previously reported DARA administration characteristics at Mayo Clinic infusion centers: DARA SC had shorter total clinic and total chair times compared with DARA IV, and DARA SC was associated with very low rates of ARR-related events (Soefje S, et al. EHA Library. 2021). Based upon this analysis, the treatment plan was modified to reduce the mandated observation time for DARA SC to improve clinic efficiency. Here, we describe updated clinical administration characteristics for DARA SC at Mayo Clinic infusion centers before and after the reduction in procedure-mandated observation time, versus DARA IV, using a novel empirical data extraction approach from Electronic Health Records (EHR). Methods: Patients ≥18 years of age with an ICD-9/10 diagnosis code of MM and a first DARA treatment between April 5, 2017 and June 22, 2021 were identified in Mayo Clinic's EHR database. Data were extracted using a scheduling and pharmacy software that tracked patient movement through appointments at a Mayo Clinic infusion center. On May 3, 2021, the Mayo treatment plan was amended to shorten the mandated post-administration observation time for DARA SC from 4 to 2 hours for Dose 1 and from 1 hour to 30 minutes for Doses 2 and 3, with no mandated observation time for Doses 4+. In this analysis, data were captured for patients initiating DARA IV throughout the defined treatment window. For DARA SC, data were captured for patients starting DARA SC therapy on the initial treatment plan (before May 3, 2021; DARA SC initial) and after the change to shorten the mandated post-administration observation time for DARA SC (after May 3, 2021; DARA SC shortened). Time-based measures included: total clinic time (check-in time through patient check-out); total chair time (time from infusion room entry to infusion room exit or check-out time, including order review, pharmacy preparation time, and post-administration observation time); and observation time (time from the end of medication administration to infusion room exit or patient check-out). The medication administration time documented for DARA SC was 0 minutes due to the default duration for SC injections in the EHR. Results: In total, 755 (DARA IV, n=586; DARA SC initial, n=145; DARA SC shortened, n=24) patients received DARA treatment for MM. For all doses combined, the median total clinic time was 2.9 hours shorter for DARA SC compared with DARA IV (DARA IV, 4.9 hrs; DARA SC initial, 2.0 hrs). The median total clinic time for DARA SC and DARA IV was highest at Dose 1 (DARA IV, 9.5 hrs; DARA SC initial, 6.1 hrs; DARA SC shortened, 4.4 hrs) and lower for subsequent doses (Dose 4+: DARA IV, 4.6 hrs; DARA SC initial, 1.7 hrs; DARA SC shortened, 1.6 hrs; Figure). Similarly, the median total chair time was 2.7 hours shorter in the DARA SC group compared with the DARA IV group for all doses combined (DARA IV, 4.1 hrs; DARA SC initial, 1.4 hrs). The median total chair time for DARA SC and DARA IV was highest at Dose 1 (DARA IV, 8.8 hrs; DARA SC initial, 5.6 hrs; DARA SC shortened, 3.9) and lower for subsequent doses (Dose 4+: DARA IV, 3.7 hrs; DARA SC initial, 1.1 hrs; DARA SC shortened, 1.1 hrs). Conclusion: Marked reductions in the amount of time spent both in clinic and in chair were observed with DARA SC compared with DARA IV, with additional time savings observed following the Mayo procedure change to reduce the DARA SC mandated observation time. These favorable DARA SC administration characteristics may indicate a reduction in the burden on both clinical resources and patients in Mayo Clinic infusion centers. These results add to the growing body of evidence supporting the use of DARA SC as an efficient and convenient treatment administration option for patients with MM. Figure 1 Figure 1. Disclosures Soefje: Beigene: Consultancy; Pfizer: Speakers Bureau; Janssen: Consultancy, Research Funding. Carpenter: nference Inc.: Current Employment; Janssen: Consultancy. Carlson: nference Inc.: Current Employment; Janssen: Consultancy. Awasthi: nference Inc.: Current Employment, Current holder of individual stocks in a privately-held company; Janssen: Consultancy. Lin: Janssen: Current Employment. Kaila: Janssen Scientific Affairs, LLC: Current Employment. Kayal: nference Inc.: Current Employment; Janssen: Consultancy. Kirkup: nference Inc.: Current holder of individual stocks in a privately-held company, Ended employment in the past 24 months; Path AI: Current Employment, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company. Wagner: nference Inc.: Current Employment; Janssen: Consultancy. Gray: Janssen Scientific Affairs, LLC: Current Employment, Current holder of individual stocks in a privately-held company. Kumar: Oncopeptides: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Bluebird Bio: Consultancy; Beigene: Consultancy; Tenebio: Research Funding; Roche-Genentech: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.