Schaeferle, Gavin

Abstract Several tools have been proposed for assessing frailty in multiple myeloma (MM), but most are based on clinical trial data sets. There are also limited data on the association between frailty and patient-reported outcomes and on the prognostic value of social determinants of health. This study evaluates the prognostic impact of frailty, based on the cumulative deficit frailty index (FI), and relationship and socioeconomic status (SES) in patients with newly diagnosed MM. This retrospective study included 515 patients with MM seen at Mayo Clinic (Rochester, MN) at diagnosis between 2005 and 2018. The FI was calculated using patient-reported data on activities of daily living and comorbidity data, with items scored as 0, 0.5, or 1, in which 1 indicated a deficit. The FI was calculated by dividing the total score by the number of nonmissing items. Frailty was defined as FI ≥0.15; 61% were nonfrail, and 39% were frail. Frailty and nonmarried/relationship status were associated with higher disease stage, decreased the likelihood of early transplantation, and independently associated with decreased survival. SES was not independently associated with survival. Frail patients reported worse scores for fatigue, pain, and quality of life. Approximately a quarter of patients had a deterioration in frailty status at 3 to 12 months, and <10% had improvement. In conclusion, a cumulative deficit FI was associated with higher symptom burden and decreased survival in a real-world cohort of patients with newly diagnosed MM. Frailty status is dynamic and should be reassessed during treatment. Social support has prognostic value and should be evaluated in clinical practice.

Background: Multiple Myeloma (MM) is predominantly a disease of older adults. However, older patients vary widely in physiological status and treatment tolerance. The simplified frailty index (Facon 2020) incorporates age, ECOG performance status, and comorbidities (Charleston Comorbidity Index [CCI]) to classify patients as frail or non-frail. While frailty is associated with increased toxicity with conventional systemic therapies, its impact on outcomes of older patients treated with bispecific antibodies (BsAbs) has not been established. Thus, we conducted a retrospective study to evaluate the efficacy and toxicity of BsAbs in older patients based on frailty. Methods: This is a single institution study. We included patients with MM ≥65 years who initiated treatment with a BsAb at Mayo Clinic in Rochester, MN from August, 2018 to January, 2024. Patients who received a BsAb on trial or as standard of care were included. For patients who received 2 different BsAbs at different timepoints, we included the first treatment only. We grouped patients into frail and non-frail based on the simplified frailty index (score ≥2 = frail). We evaluated the overall response (ORR) and ≥very good partial response (VGPR) rates, progression-free (PFS) and overall (OS) survival, early mortality (within 90 days) including early non-relapse mortality (NRM), and toxicities including cytokine release syndrome (CRS), immune effector cell associated neurotoxicity syndrome (ICANS) and healthcare utilization. P values were insignificant (>0.05) unless otherwise stated. PFS and OS were calculated from the day of BsAb start. Results: A total of 99 patients age 65-89 years were included. The products used were teclistamab (N=43), talquetamab (N=2), and investigational agents (N=54); 67% were ≥70 and 29% were ≥75 years; 39% were female, 95% were non-Hispanic White and 4% were Black/African American. Seventy patients (71%) were considered frail and 29 (29%) were non-frail. At the time of BsAb start, a higher proportion of frail patients had platelets <75 x 10^9/L (30% vs 8%, P=0.04). Otherwise, there was no significant difference between frail and non-frail patients in age (median: 72 vs 70), hemoglobin <8 g/dL (19% vs 12%), absolute neutrophil count <1 x 10^9/L (6% vs 8%), presence of high-risk FISH (70% vs 60%), ISS stage 3 (32% vs 41%), high LDH (58% vs 61%), presence of extramedullary disease (39% vs 25%), serum creatinine ≥2 mg/dL (15% vs 8%), bone marrow plasma cell% (20% in both), triple- (83% vs 90%) and penta-class refractoriness (63% vs 48%), number of prior lines of therapy (median: 6 in both), prior exposure to BCMA-directed therapy (24% vs 21%, P=0.80) and prior autologous stem cell transplant (73% vs 86%). Step-up dosing was used in 81% vs 48% of patients (P=0.001), and treatment was initiated in the outpatient setting in 46% vs 28% (P=0.12) in frail and non-frail patients, respectively. The median follow-up was 1.9 (95%CI: 1.0-2.3) years. There was no significant difference between frail and non-frail patients in ORR (52% vs 62%), ≥VGPR (46% vs 48%), 1-year PFS (36% vs 48%, P=0.26), or 1-year OS (57% vs 61%, P=0.30), respectively. Age and CCI alone were not associated with PFS or OS, but ECOG >1 was associated with decreased PFS (hazard ratio: 3.1, P=0.001) and OS (hazard ratio: 3.1, P<0.001). There was no significant difference between frail vs non-frail patients in grade 3/4 thrombocytopenia (39% vs 17%, P=0.06), grade 3/4 neutropenia (27% vs 35%), total days of hospitalization (median: 8 vs 5, P=0.35), number of unplanned admissions in 6 months (median: 1 in both), or total hospital days for unplanned admissions (1 vs 2 days). There was no significant difference between frail and non-frail patients in early mortality (23% vs 10%, P=0.17), including early NRM (4% in both), CRS (59% vs 66%), grade 3-4 CRS (3% vs 7%), or CRS recurrence (21% vs 10%). ICANS occurred in 5 (9%) frail patients and 1 non-frail patient (grade 2); among frail patients, grades were: 1 in 3 patients, 3 in 1 patient, and 4 in 1 patient. Similarly, there was no significant difference in CRS/ICANS between the 2 groups when patients with and without step-up dosing were analyzed separately. Conclusion: Frailty, defined using the simplified frailty index, was not significantly associated with inferior efficacy or increased toxicity with BsAb therapy among older patients with MM. Poor performance status predicted inferior PFS and OS, while age and comorbidity scores did not.

Introduction: Atrial fibrillation (AF) is the most prevalent arrhythmia, placing a substantial burden on the healthcare system. Patients with AF are cared for in a variety of settings including primary care and cardiology clinics, while some require cardiac electrophysiology (EP) clinics offering specialized drugs or procedures. Appropriate triage of AF referrals to these specific clinics will lead to timely and appropriate care and reduce inefficiency in the health system. Goal: To develop a machine learning (ML) model to triage AF referrals to a large Cardiology center to either the Cardiology or EP clinic based on likelihood of requiring an invasive EP procedure or initiation of antiarrhythmic drug. Methods: We retrospectively analyzed 6,755 patients who were referred to CV with referral diagnosis of AF (mean age 70 ± 11 years, 66% male). We employed multiple input variables to predict the suitability for EP clinic defined as need for initiating an antiarrhythmic drug or an invasive EP procedure. These encompassed patient demographics, relevant comorbidities, medications, prior procedures, medical notes analyzed using natural language processing (NLP), and Holter monitoring data. Results: For internal referrals, XG boost model performed the best with AUC at 79%, accuracy at 72%, and F1 score at 68%. This model produces true positive (TP) at 68% and true negative (TN) at 74%, which was better in comparison with triaging using NLP only (AUC = 69%, TP = 58%, TN = 61%), Holter only (AUC = 52%, TP = 51%, TN = 77%), and NLP/Holter (AUC = 67%, TP = 60%, TN = 60%). When supplementing ML model with Holter readings on average HR ≥ 100 and maximum HR ≥ 150, the results can be improved to TP at 70% and TN at 73%. Conclusions: Our model effectively identifies patients who are more likely to require specialized EP care, enabling the automation and increased efficiency of the AF referral triage process. Prospective validation studies are paramount in determining the true value of this tool.