Jeffery, Molly Moore

Background: Physical function (PF) is an important outcome that can inform cancer treatment tolerability from a clinical and regulatory perspective. PF can be assessed through different modalities: patient-reported (PRO), clinician-reported (ClinRo), performance tests (PerfO), and digital sensors (wearables). No standard approach for PF measurement has been defined, and each modality bears unique implementation challenges. The Integrating 4 Measures to Assess Physical Function study (In4M, NCT05214144) characterized the performance of these PF modalities in patients receiving cytotoxic chemotherapy, with a goal of guiding an assessment strategy in future cancer trials. Here we present feasibility results of the In4M study in patients with lymphoma. Methods: In4M is a prospective observational study with longitudinal PF assessment of PROs, ClinRo, PerfO, and wearable sensor data for over 9 months in patients initiating chemotherapy for lymphoma or breast cancer at Mayo Clinic and Yale University. Patient-reported PF was assessed using several validated ePRO instruments, ClinRo utilized clinician-reported Eastern Cooperative Oncology Group (ECOG) performance status, PerfO was measured via 6-minute walk test (6MWT), and digital sensor data was obtained from a wearable (Fitbit) provided to all participants. We evaluated the feasibility of implementing these PF modalities through compliance outcomes and a study-specific exit survey. Descriptive statistics included medians and interquartile ranges (IQR) for continuous variables and frequencies for categorical variables. We used a mixed model to estimate mean number of Fitbit compliant days per week. Results: Among 66 patients with lymphoma in the study (median age was 57 years; 52% were male), 90% self-reported as White, 4.9% as Black, 1.6% as Asian, 1.6% as American Indian or Alaska Native, and 1.6% as multiple races. Ethnicity was reported as non-Hispanic/Latino by 93% of patients. Education status was reported as more than high school by 92% of patients, and 49% reported full-time employment at time of enrollment. The overall ePROs completion rate was 80.7% for the entire study period. Baseline ePRO completion rate was 93.9%. Completion rate while undergoing chemotherapy was 91.3% and 75.4% in the post-treatment follow-up. The median time to complete ePROs was 4.1 minutes (IQR 2.8-5.9), ranging from 3.1 minutes for surveys with 37 questions to 5.6 minutes for surveys with 55 questions (survey length varied by timepoint). The baseline 6MWT was completed by 91% of patients. The most common reasons for not completing the baseline 6MWT were: patient refusal (50%, 3/6), patient not physically present (33%, 2/6), and unable to complete due to fatigue (17%, 1/6). The 3-month 6MWT was completed by 66% (40/61) of patients, with the most common reasons for missingness being: patient not physically present (38%, 8/21) and unable to complete due to fatigue (14%, 3/21). When considering the days when Fitbit was used, the median wear time was 21.3 hours (IQR 18.9-22.5) per day. The proportion of compliant days (>10 hours of wear time during waking hours) was 53% (IQR 20-84). The median of participants' average compliant days per week was 5.4 (IQR 4.3-6.1) days/week after enrollment, with a slight downward trend over time (-0.017 compliant days per week [95% CI: -0.021, -0.012, p <0.001] over each consecutive week in the study). The exit survey was completed by 39 (59%) patients. Most participants (85%) indicated they did not find the ePRO surveys burdensome to complete. Using the Fitbit (charging and syncing) was found to be manageable by most patients (87% and 84%, respectively). 60.8% of patients did not find the wearable bothersome to use, while the main themes reported by the remaining patients were related to the operation of the wearable itself (functions, small text size, forgetting login information) or discomfort (band material, wearing at night). Conclusion: Multi-modal assessment of PF with PRO, ClinRo, PerfO and wearable sensor data in patients with lymphoma undergoing treatment is feasible. Designing lymphoma therapeutic trials that incorporate at least one PF assessment modality, or assessment with a parsimonious combination of modalities, is achievable and may yield valuable insights into treatment tolerability for clinical and regulatory purposes.

Abstract Background The US Food and Drug Administration (FDA) released a draft guidance document detailing core patient-reported outcomes in cancer clinical trials, including physical function (PF). This study aimed to develop analytic methods and visualizations of patient-reported PF in patients with cancer. Methods We applied an estimand framework to a patient-reported tolerability endpoint to develop data summaries cross-sectionally and over time, along with visualizations. We accomplished this through iterative feedback with clinicians, statisticians, and FDA stakeholders using three clinical trial datasets in hematologic malignancies. Graphical approaches were applied to three datasets in hematologic malignancies: (1) patients with myeloproliferative neoplasms enrolled in MPN-RC 111/112 trials completed EORTC QLQ-C30 over 12 months; (2) patients with hematologic malignancies undergoing CAR-T cell therapy or autologous transplant who completed FACT questionnaires over 6 months; and (3) patients with multiple myeloma or amyloidosis who completed the PROMIS-29 questionnaire over 6 months. Zoom polls were administered to two stakeholder groups (clinicians/clinical investigators and patient advocates) to elicit feedback. Results Visualizations included stacked bar charts, line plots of arithmetic mean changes from baseline, pie charts, waffle plots, and waterfall plots of PF data. Graphics considered scaled scores and individual items and included delineation of PRO completion rate at each time point. Confidence intervals and reference lines were included as applicable, and colorblind accessible colors were implemented to ensure inclusivity of all visualizations. Data summaries over time reporting “worst” change were difficult to interpret. In terms of stakeholders’ preference, patients preferred stacked bar charts while clinicians equally favored stacked bar charts and line plots; both patients and clinicians preferred waterfall plots to pie charts. Patient feedback highlighted the need for various graphics to convey group level trends and granular individual-patient level information. Conclusion Patient-reported PF informs the evaluation of treatment tolerability in cancer trials. Data summaries and visualizations of physical function developed through an iterative process were reviewed favorably by patients, clinicians and FDA stakeholders in this study. Future work to systematically assess accuracy of interpretation of the various analytic and visualization methods is a necessary next step across clinical, regulatory, payer and patient stakeholders. Trial registration NCT01259817, NCT01259856

AbstractObjectiveTransgender and gender‐diverse (TGD) individuals have a gender identity or expression that differs from the sex assigned to them at birth. They are an underserved population who experience health care inequities. Our primary objective was to identify if there are treatment differences between TGD and cisgender lesbian/gay/bisexual/queer (LGBQ) or heterosexual individuals presenting with abdominal pain to the emergency department (ED).MethodsRetrospective observational cohort study of patients ≥12 years of age presenting to 21 EDs within a health care system with a chief complaint of abdominal pain between 2018 and 2022. TGD patients were matched 1:1:1:1 to cisgender LGBQ women and men and cisgender heterosexual women and men, respectively. Propensity score matching covariates included age, ED site, mental health history, and gastrointestinal history. The primary outcome was pain assessment within 60 min of arrival. The secondary outcome was analgesics administered in the ED.ResultsWe identified 300 TGD patients, of whom 300 TGD patients were successfully matched for a total cohort of 1300 patients. The median (IQR) age was 25 (20–32) years and most patients were treated in a community ED (58.2%). There was no difference between groups in pain assessment within 60 min of arrival (59.0% TGD vs. 63.2% non TGD, p = 0.19). There were no differences in the number of times pain was assessed (median [IQR] 2 [1–3] vs. 2 [1–4], p = 0.31) or the severity of pain between groups (5.5 [4–7] vs. 6 [4–7], p = 0.11). TGD patients were more likely to receive nonsteroidal anti‐inflammatory drugs (32.0% vs. 24.9%, p = 0.015) and less likely to receive opioids than non‐TGD patients (24.7% vs. 36.9%, p = <0.001). TGD and nonbinary patients, along with LGBQ cisgender women (24.7%) and heterosexual cisgender women (34%), were less likely to receive opioids than LGBQ cisgender men (54%) and heterosexual cisgender men (42.3%, p < 0.01).ConclusionThere was no difference in frequency of pain assessment, regardless of gender identity or sexual orientation. More cisgender men, compared to TGD and cisgender women, received opioids for their pain.

1564 Background: Physical function (PF) is a core outcome in cancer trials that can inform treatment tolerability. PF can be assessed across modalities including by patient (pt) report, clinician (clin) report, performance tests and digital sensors. The optimal approach for PF measurement has not been defined. Methods: In4M is a prospective observational study evaluating PF in pts initiating chemotherapy for breast cancer or lymphoma followed over 9 months. We used an electronic platform (Hugo) to aggregate serially assessed pt-reported PF, clin- and pt-reported ECOG performance status (PS), 6-minute walk test (6MWT) and wearable (Fitbit) data. We evaluated feasibility in all pts; associations among PF measures and the ability of PF measures to detect change were evaluated in pts with both baseline (BL) and 3-month (3M) data. We used Pearson correlations to assess the relationship of average daily steps with measures of PF. To assess change, we compared each PF measure from BL to 3M using a 2-sided α=.05 paired t-test. Results: Of the208 pts enrolled, 193 (93%) and 159 (76%) completed surveys, and 121 (58%) and 124 (60%) provided wearable data (evaluable: ≥1 day data) at BL and 3M, respectively. When requiring a stringent BL with 4+ days of data with ≥1 weekend day to be evaluable, 88 (42%) and 120 (58%) provided wearable data at BL and 3M. At both BL and 3M, pt-reported PF was generally more highly correlated with wearable-derived daily steps than clin-reported PF and 6MWT were (Table). Significant declines in PF were observed for pt- and clin-reported PF (all p<0.05), but not other modalities. Conclusions: Longitudinalassessment of multi-modal PF data including pragmatic wearable data collection was feasible in pts receiving chemotherapy. Pt-reported PF was the only modality that was both correlated with step count and able to detect change. Additional wearable metrics, correlations among PF measurements and outcomes, and clinically meaningful change scores will be reported. Optimal PF measurement may improve future reporting of treatment tolerability. Clinical trial information: NCT05214144 . [Table: see text]

11059 Background: Some opioid stewardship policies could negatively affect patients with cancer pain. However, patients with cancer are also at risk for opioid related harms, especially with persistent use. We assessed opioid prescribing trends in the context of cancer treatment and patient reported pain in a large health system in Connecticut. Methods: We conducted a retrospective cohort study of opioid-naïve adult patients with solid tumor malignancies diagnosed from 2016 through 2020 within the Yale New Haven Health System. We identified new (≥1 opioid prescription in 0-6 months following diagnosis) and additional (0-6 and 6-9 months) opioid prescriptions. The analysis evaluated all eligible patients as well as two clinical cohorts: patients treated surgically and patients with metastatic cancer. For patients with metastatic cancer and a documented pain score in the electronic health record flowsheet data, we further stratified by any (any score≥1) or no (all scores=0) pain in the 6 months since diagnosis and prior to first opioid, if given. For these patients, we noted therapeutic class of the first opioid. We used a logistic model adjusted for patient demographics to calculate predicted probability of opioid prescription and change over time. Results: A total of 10,868 patients met study criteria. Overall, we observed a decline in new opioid prescribing from 69.0% to 62.7% (p<.001) (Table). Additional opioid use also declined from 23.4% to 20.2% (p=.02). In the surgery cohort, new opioid prescribing fell from 95.7% to 88.2% (p<.001), while additional opioid use was stable over time (approximately 12%). For patients with metastatic cancer with any documented pain, new opioid prescribing was stable over time (approximately 56%). For those with documented pain scores=0, new opioid prescribing declined from 59.7% to 34.2% (p<.001). In these patients, the class listed on the first opioid prescription was analgesia in 86.6% and cough in 13.4%. Conclusions: Overall, and in patients treated with oncological surgeries, our study in a single large US health system suggests a modest, gradual decline in opioid prescribing for patients with cancer over time. On the other hand, among patients with metastatic cancer, opioid prescribing remained stable for those with documented pain and declined steeply for those without documented pain. [Table: see text]

Abstract Background The impact of ongoing efforts to decrease opioid use on patients with cancer remains undefined. Our objective was to determine trends in new and additional opioid use in patients with and without cancer. Methods This retrospective cohort study used data from Surveillance, Epidemiology, and End Results program–Medicare for opioid-naive patients with solid tumor malignancies diagnosed from 2012 through 2017 and a random sample of patients without cancer. We identified 238 470 eligible patients with cancer and further focused on 4 clinical strata: patients without cancer, patients with metastatic cancer, patients with nonmetastatic cancer treated with surgery alone (“surgery alone”), and patients with nonmetastatic cancer treated with surgery plus chemotherapy or radiation therapy (“surgery+”). We identified new, early additional, and long-term additional opioid use and calculated the change in predicted probability of these outcomes from 2012 to 2017. Results New opioid use was higher in patients with cancer (46.4%) than in those without (6.9%) (P &lt; .001). From 2012 to 2017, the predicted probability of new opioid use was more stable in the cancer strata (relative declines: 0.1% surgery alone; 2.4% surgery+; 8.8% metastatic cancer), than in the noncancer stratum (20.0%) (P &lt; .001 for each cancer to noncancer comparison). Early additional use declined among surgery patients (‒14.9% and ‒17.5% for surgery alone and surgery+, respectively) but was stable among patients with metastatic disease (‒2.8%, P = .50). Conclusions Opioid prescribing declined over time at a slower rate in patients with cancer than in patients without cancer. Our study suggests important but tempered effects of the changing opioid climate on patients with cancer.

Background/aims There has been growing interest in better understanding the potential of observational research methods in medical product evaluation and regulatory decision-making. Previously, we used linked claims and electronic health record data to emulate two ongoing randomized controlled trials, characterizing the populations and results of each randomized controlled trial prior to publication of its results. Here, our objective was to compare the populations and results from the emulated trials with those of the now-published randomized controlled trials. Methods This study compared participants’ demographic and clinical characteristics and study results between the emulated trials, which used structured data from OptumLabs Data Warehouse, and the published PRONOUNCE and GRADE trials. First, we examined the feasibility of implementing the baseline participant characteristics included in the published PRONOUNCE and GRADE trials’ using real-world data and classified each variable as ascertainable, partially ascertainable, or not ascertainable. Second, we compared the emulated trials and published randomized controlled trials for baseline patient characteristics (concordance determined using standardized mean differences <0.20) and results of the primary and secondary endpoints (concordance determined by direction of effect estimates and statistical significance). Results The PRONOUNCE trial enrolled 544 participants, and the emulated trial included 2226 propensity score-matched participants. In the PRONOUNCE trial publication, one of the 32 baseline participant characteristics was listed as an exclusion criterion on ClinicalTrials.gov but was ultimately not used. Among the remaining 31 characteristics, 9 (29.0%) were ascertainable, 11 (35.5%) were partially ascertainable, and 10 (32.2%) were not ascertainable using structured data from OptumLabs. For one additional variable, the PRONOUNCE trial did not provide sufficient detail to allow its ascertainment. Of the nine variables that were ascertainable, values in the emulated trial and published randomized controlled trial were discordant for 6 (66.7%). The primary endpoint of time from randomization to the first major adverse cardiovascular event and secondary endpoints of nonfatal myocardial infarction and stroke were concordant between the emulated trial and published randomized controlled trial. The GRADE trial enrolled 5047 participants, and the emulated trial included 7540 participants. In the GRADE trial publication, 8 of 34 (23.5%) baseline participant characteristics were ascertainable, 14 (41.2%) were partially ascertainable, and 11 (32.4%) were not ascertainable using structured data from OptumLabs. For one variable, the GRADE trial did not provide sufficient detail to allow for ascertainment. Of the eight variables that were ascertainable, values in the emulated trial and published randomized controlled trial were discordant for 4 (50.0%). The primary endpoint of time to hemoglobin A1c ≥7.0% was mostly concordant between the emulated trial and the published randomized controlled trial. Conclusion Despite challenges, observational methods and real-world data can be leveraged in certain important situations for a more timely evaluation of drug effectiveness and safety in more diverse and representative patient populations.

Background: Recent literature has found rapid uptake of short-acting filgrastim biosimilars but slower uptake of other biosimilars, such as infliximab, in both Medicare and privately insured enrollees. Objectives: To describe patient, provider, and health plan characteristics associated with a switch to biosimilar among existing infliximab patients. Research Design: We constructed a retrospective panel dataset of patients undergoing active infliximab treatments and the choice of infliximab drug for each infusion. We used mixed logit regression controlling for patient, provider, and health plan characteristics as well as time-fixed effects. Subjects: Medicare Advantage and privately insured enrollees with evidence of active infliximab treatments between 2016 and 2020 (n=357,430). Measures: Our primary outcome of interest was to switch from infliximab originator to one of the infliximab biosimilars. Exposure variables of interest variables such as out-of-pocket, site of care, and in-network deductible. Results: Our study found nominally low switching among existing infliximab originator users (3.4%). We found that patients who previously received 1 infliximab originator infusion were 63.7% more likely to switch to biosimilar compared with patients who previously received administration of 20 infliximab originators. We found that biosimilar’s placement as health’s plan preferred drug was attributed to higher likelihood of biosimilar use (odds ratio: 1.666; P -value=0.001). We did not observe any statistically significant effect among out-of-pocket amount or deductible with respect to switch to infliximab biosimilar. Conclusions: To encourage uptake and switch to biosimilar, policymakers should consider targeted policies that include leveraging health plan tools such as placement of biosimilar as preferred drug and aim to educate patients on the clinical equivalence between infliximab biosimilar and originator.

ImportancePrevious studies have found that hospitals participating in the 340B Drug Pricing Program have higher Medicare Part B spending and expansion into affluent neighborhoods. Less is known about the association of 340B participation with spending by commercial insurance, where reimbursements are higher than Medicare.ObjectiveTo use the Affordable Care Act expansion of eligibility for the 340B Drug Pricing Program to study the association between participation and spending on outpatient-administered oncological drugs for commercially insured patients.Design, Setting, and ParticipantsThis cohort study included a balanced panel hospital cohort containing new and never 340B program participants between 2007 and 2019; more recent data were not included to avoid the effect of disruptions in care due to the COVID-19 pandemic. Descriptive analyses documented spending trends for patients receiving common outpatient-administered biologics used in cancer treatments (bevacizumab, filgrastim, pegfilgrastim, rituximab, and trastuzumab) at 340B (treated) and non-340B (control) hospitals. A difference-in-differences model assessed changes in episode drug spending. Analyses were conducted between December 2021 and June 2022.ExposureNew 340B program participation between 2010 and 2016.Main Outcome and MeasuresTotal drug episode spending, with control variables including total billed units, drug, calendar-year fixed effects, and hospital fixed effects.ResultsOf 95 127 included episodes (56 917 [59.8%] episodes in female patients) across 478 hospitals, patients seen in 340B and non-340B hospitals were similar in sex and drug used, and 340B hospital patients were older than non-340B patients (median [IQR] age for all patients, 61 [51-71] years). New 340B participating hospitals were more likely to be small (&amp;lt;50 beds) and more likely to be in rural settings. In the difference-in-differences analysis, total episode drug spending increased by $4074.69 (95% CI, $1592.84-$6556.70; P = .001) in the year following start of 340B program participation relative to nonparticipants. Heterogeneous group time effects were seen, with earlier participants less likely to have increased episode spending.Conclusions and RelevanceIn this cohort study, new 340B participation was associated with statistically significant higher oncological drug episode spending compared with nonparticipants after changes in 340B inclusion rules in 2010. These findings raise questions about unintended consequences of the 340B program on drug spending from the commercially insured population.