Williams, Annalynn M

Abstract Background: Rural/nonmetropolitan (nonmetro) counties face known barriers to comprehensive cancer care compared with metropolitan (metro) counties, which may impact the outcomes for lymphoma patients. We evaluated the impact metro vs nonmetro status on outcomes after a diagnosis of non-Hodgkin lymphoma (NHL) in the context of clinical, demographic, and lifestyle factors. Methods: Of 7724 participants enrolled in the prospective LEO cohort study from 2015-2020, 7610 (98.5%) had their county of residence at diagnosis assigned a RUCC (2013) code. We compared outcomes of participants from metro (defined as RUCC 1-3, which are central counties with ≥1 urban areas of ≥50,000) vs nonmetro counties (RUCC 4-9, which are counties with smaller urban areas as well as completely rural). We estimated hazard ratios (HRs) and 95% confidence intervals (CI) from univariable and multivariable Cox models for associations of metro status with overall survival (OS). Cause of death (lymphoma [including treatment related], non-lymphoma, unknown) was assessed using a competing risk approach. Results: Compared with metro counties (N=6096), LEO participants from nonmetro counties (N=1514) were older (median age 64 vs 62 years; p<0.001), more likely to be non-Hispanic white (94% vs 75%; p<0.001) and married (84% vs 75%; p<0.001), and less likely to have completed graduate or professional school education (16% vs 27%; p<0.001). There were no notable differences (>5%) by metro status for sex, employment status, NHL subtype, stage, performance status, abnormal lactate dehydrogenase (LDH), international prognostic index (IPI), number of comorbidities, time from diagnosis to treatment interval (DTI) or participation in a clinical trial. Compared with LEO participants from metro counties, those from nonmetro counties had inferior OS at 5 years (74.5% vs 78.4%; HR=1.23, CI 1.10-1.37; p<0.001). The OS association only slightly attenuated after adjustment for age, sex, race/ethnicity and NHL subtype (HR=1.20, CI 1.07-1.34; p=0.002), and held in models that adjusted for clinical prognostic factors (stage, NHL subtype, IPI, DTI), socioeconomic status (SES; education, marital status), health status (comorbidity, frailty), or lifestyle factors (smoking, body mass index). Using a competing risk model, the cumulative incidence of death at 5 years due to lymphoma (16.9% vs 14.1%; p=0.002) and non-lymphoma (5.9% vs 3.9%; p=0.002) was higher for participants from nonmetro vs metro counties, respectively. For participants <65 years, nonmetro vs metro status had inferior OS (HR=1.40, CI 1.17-1.67; p<0.001). This association remained after adjustment for age, sex, race/ethnicity and NHL subtype (HR=1.36, CI 1.14-1.63; p<0.001), as well as after adjustment for clinical prognostic, SES, health status, or lifestyle factors. The cumulative incidence of death at 5 years due to lymphoma was higher in participants from nonmetro vs metro counties (15.1% vs 11.5%; p=0.002), while there was no difference by metro status for non-lymphoma related deaths (2.1% vs 1.7%; p=0.47). In contrast, for participants age 65+ years, there was no differences by metro status for OS (HR=1.04, CI 0.91-1.20; p=0.56). After adjustment for age, sex, race/ethnicity and NHL subtype, participants from nonmetro areas had inferior OS (HR=1.12, 0.97-1.29) although this was not statistically significant (p=0.13), and was attenuated in models adjusting for clinical, health status, or lifestyle factors. The cumulative incidence of death at 5 years due to lymphoma was no different in participants from nonmetro vs metro counties (18.9% vs 18.0%; p=0.37), while non-lymphoma related deaths were higher in nonmetro counties (9.9% vs 6.8%; p=0.017). Conclusion: LEO participants from nonmetro vs metro counties had inferior OS overall, although this was predominately observed for younger (<65 years) participants. These associations did not appear to be explained by demographic, NHL subtype, clinical, SES, health status, or lifestyle factors. Of particular note, the risk of death due to lymphoma was higher in nonmetro areas for participants <65 years but not those 65+ years, while the risk of death due to other causes was higher in nonmetro areas for participants 65+ years but not those <65 years. These findings require further study, particularly related to barriers to care, and suggest the potential need for age-specific strategies to optimize outcomes for NHL patients from nonmetro/rural areas.

Abstract Introduction The LEO Comorbidity Index (LCI), comprising ten equally weighted comorbidity categories (respiratory, cardiovascular, digestive, hepatic, renal, autoimmune, diabetes, cancer history, HIV, and stroke), has demonstrated prognostic utility in patients with non-Hodgkin lymphomas. However, its predictive accuracy may vary with increasing age, due to age-related rises in comorbidity burden and physiological vulnerability. To investigate these dynamics, we evaluated the age-stratified predictive performance of LCI in patients with large B-cell lymphoma (LBCL), with a focused analysis on individuals aged 80+ years. We also examined which specific comorbidities most strongly influenced overall survival (OS) and developed a simplified, clinically applicable index tailored to this population. Methods We first included adults ≥40 years with newly diagnosed LBCL and recorded comorbidities from the LEO registry (USA, 2015–2020; n=1,652) to assess age-stratified predictive performance of LCI using the C-index. Next, we focused on patients 80+ years (n=263), pooled from the LEO cohort (n=147) and NiHiL registry (restricted to General Hospital, Prague, Czech Republic, 2010–2023; n=116). Comorbidities were collected from patient self-reports (LEO) and/or medical records (LEO/NiHiL). Univariate and multivariate Cox models were used. The primary endpoint was OS. Results Among the total LBCL population (n=1,652), LCI predictive performance improved by age category with C-index for patients <65 of 0.584, 65–79 years 0.579, and ≥80 years 0.647, indicating stronger discrimination in the 80+ group. Subsequent analysis focused on patients aged 80+ years (n=263; median age 83). Within this cohort, 34% had ECOG performance status (PS) 2–4, 66% stage III–IV disease, 59% elevated lactate dehydrogenase, 35% >1 extranodal site involved, and 58% International Prognostic Index (IPI) score of 3–5. A total of 377 comorbidities were reported, averaging 1.43 per patient (1.22 in LEO; 1.61 in NiHiL). LCI distribution was: 0 points in 24% (n=62), 1 point in 33% (n=88), and 2–5 points in 43% (n=113). Higher LCI scores had worse ECOG PS 2–4: 19% (LCI=0), 33% (LCI=1), and 43% (LCI ≥2; P=0.01), and inferior OS (median 6.9 years LCI=0, 3.0 years LCI=1, and 2.8 years LCI≥2, resp.; P=0.02). Presence of ≥1 comorbidity was associated with shorter OS compared to no comorbidities (HR=1.67, P=0.01). The LCI was an independent predictor of OS after adjusting for age (HR=1.18, P=0.01, C-index 0.623), IPI (HR=1.16, P=0.03, C-index 0.628) as well as for recently proposed SENIOR-IPI (HR=1.14, P=0.049, C-index 0.665). When evaluating individual comorbidities, heart disease (HR=1.54, P=0.01), stroke (HR=1.89, P=0.03), and recent malignancy (HR=1.99, P=0.02) were independent predictors of OS. Based on these findings, we developed a simplified LCI (sLCI) for use in 80+ patients incorporating only the three comorbidities (1 point each). Patients were categorized as sLCI: 0 (49%, n=129), 1 (44%, n=115), and 2–3 points (7%, n=19). sLCI scores 0, 1, 2–3 were associated with ECOG PS 2–4 27%, 37%, and 63%, respectively (P=0.01) and inferior OS: median 5.3 years (0), 2.5 years (1), and 1.4 years (2–3; P<0.01). sLCI remained an independent and strong predictor of OS after adjusting for age (HR=1.71, P<0.01, C-index 0.636), IPI (HR=1.66, P<0.01, C-index 0.635), and SENIOR-IPI (HR=1.60, P<0.04, C-index 0.659). The results were consistent in both NiHiL and LEO cohorts and remained significant among patients treated with anthracyclines (sLCI adjusted for age: HR=1.58, P<0.01, C-index 0.607; for IPI: HR=1.53, P=0.01, C-index 0.623; for SENIOR-IPI: HR=1.51, P=0.01, C-index 0.633). Conclusion Comorbidity burden becomes increasingly prognostic with higher age in LBCL patients. In the 80+ individuals, comorbidities – particularly heart disease, recent cancer, and stroke – have an independent impact on OS. While the original LCI captures the impact of broadly defined comorbidities, its prognostic utility attenuates in the 80+ patients, likely reflecting limited relevance of indolent chronic conditions in this population's constrained survival horizon. The sLCI, focused on the most prognostically relevant comorbidities, offers practical risk stratification. Incorporation of sLCI into clinical decision-making – alongside other prognostic indices – could better identify high-risk individuals and guide treatment strategies. Funding: NU21-03-00411, U01 CA195568.

ABSTRACT Newly diagnosed patients with non‐Hodgkin lymphoma (NHL) often have a history of other diseases, and these comorbidities can impact patient treatment and management options, as well as overall survival (OS). We developed the Lymphoma Epidemiology of Outcomes (LEO) comorbidity index (LCI) as a sum of 10 comorbidities adapted from the Self‐Report‐Generated Charlson Comorbidity Index (SRG‐CCI) for use in the LEO cohort, a national prospective study of newly diagnosed NHL. Of the 5502 participants with self‐reported comorbidity data, 3107 (56.4%) were male and the mean age at diagnosis was 60.9 years (range, 18–99 years). The LCI ranged from 0 to 6, with 48.6% having 0, 30.2% having 1, 21.2% having 2 or more comorbidities. With a median follow‐up of 62.4 months among surviving participants, 2099 patients had an event and 1219 died. Continuous LCI similarly predicted both 1‐year mortality (c‐statistic = 0.654) and OS (c‐statistic = 0.655), while it showed a weaker but still statistically significant predictive ability for lymphoma‐specific (c‐statistics = 0.617) and event‐free (c‐statistic = 0.574) survival. Participants with 1 (HR = 1.21, 95% CI 1.05–1.39) and 2+ (HR = 1.80, 95% CI 1.56–2.08) comorbidities had inferior OS compared to those with no comorbidities after adjustment for age and sex (c‐statistic = 0.654), and performance strengthened after adjustment for the International Prognostic Index (c‐statistic = 0.672). LCI predicted OS most strongly in marginal zone (c‐statistics = 0.748) and weakest in T‐cell (c‐statistic = 0.579) lymphoma. The cumulative incidence of death due to lymphoma, lymphoma treatment, and other causes all increased with increasing comorbidities, with the greatest increase observed for death due to other causes. The LCI performs comparable to other published comorbidity indices, supporting its use in the LEO cohort to better model real‐world outcomes and more generally providing an approach to implementing comorbidity indices in cancer survivorship cohorts.

Introduction: Patients with non-Hodgkin Lymphoma (NHL), especially adolescents and young adults, can face late effects from treatment, including decreased fertility. Due to time constraints prior to treatment initiation and sometimes financial limitations, implementing fertility counseling and referral-based guideline care remains challenging. We aim to understand the occurrence and predictors of fertility counseling and preservation in patients with NHL in the Lymphoma Epidemiology of Outcomes (LEO) cohort study. Methods: The LEO cohort prospectively enrolled newly diagnosed patients with NHL from 2015-2020 at 8 academic medical centers in the US. Clinical. Treatment data were abstracted from medical records, pathology was reviewed and classified by a LEO pathologist, and participants completed a self-administered questionnaire. Participants were then prospectively followed to identify outcomes and collect additional patient reported data, including a survivorship questionnaire at follow-up 3 years (FU3) after diagnosis that included items on fertility counseling and preservation. The questionnaire contained two fertility questions analyzed: whether healthcare providers discussed the impact of lymphoma treatment on fertility, and whether providers discussed the process of fertility preservation. This analysis was based on participants aged 18-50 years at enrollment and who completed the fertility questions at FU3. Women who had undergone natural or procedural menopause and men who had undergone vasectomy pre-treatment were excluded. We estimated the association of demographic and clinical factors with fertility preservation using odds ratios (ORs) and 95% confidence intervals (CI) from univariate and multivariable logistic regression models. Results: Seventy-seven patients were included in the analysis: 36 females (46%) and 41 males (53%). The median age of diagnosis was 40 years (IQR 30-45), and 36 (47%) participants were in the age 18-39-year category. Participant self-identified race/ethnicity was 69% non-Hispanic White, 22% Hispanic, 4% Black and 3% Asian. Sixty (78%) patients were treated with alkylators, 12 (15.6%) with non-alkylators, and 5 (6.5%) with radiation. Fifty-five (71%) of the patients were classified as having aggressive lymphoma, with follicular, extranodal marginal zone, primary cutaneous follicle center, and nodal marginal zone B-cell lymphoma defined as indolent and remaining subtypes aggressive. Around 6% of female patients underwent fertility preservation after lymphoma diagnosis, compared to 12.2% of male patients. All the patients who had undergone fertility preservation after lymphoma diagnosis were in the 18-39-year age group. On univariate analysis, females were more likely to have undergone fertility toxicity and preservation discussions compared to males, with 72% of females and 59% of males recalling a discussion regarding fertility (OR 1.8, 95% CI 0.7-4.9, p=0.21). Those age 18-39 were significantly more likely to have undergone fertility discussion compared to those aged 40-50 (OR 10.2, 95% CI 3.3-39.2, p<0.001). Patients treated with alkylating therapy were significantly more likely to undergo fertility discussion when compared to those on non-alkylating therapy (OR 9.0, 95% CI 2.3-45.5, p<0.01). In addition, 75% of those with aggressive lymphoma compared to 41% of patients with indolent lymphoma had fertility discussion (OR 4.2, 95% CI 1.5-12.4, p=0.01). White patients were significantly less likely than Hispanic, Black and Asian patients to have fertility discussion (OR 0.2, 95% CI 0.04-0.6, p<0.01). In the multivariable analysis, alkylating therapy (OR 8.9, 95% CI 1.6-66.7) and age 18-39 years (OR 10.9, 95% CI 2.9-55.3) remained significant predictors of fertility discussion; White race was also trending towards significance (OR 0.2, 95% CI 0.04-1.0, p=0.08). Aggressive lymphoma subtype was not a significant predictor of fertility discussion. Conclusions: In this cohort of lymphoma survivors 3 years post-diagnosis, we found that Hispanic patients, those diagnosed at a younger age and exposed to alkylating chemotherapy agents were significantly more likely to undergo discussions regarding the effects of lymphoma treatment on fertility. Our study highlights the importance of all cancer patients of childbearing age having the opportunity to address fertility concerns during clinic visits.

Background: Financial toxicity (FT), or the negative impact of medical expenses experienced by patients, is a known adverse effect of cancer treatment that result in worse health outcomes for patients. However, there is limited literature investigating FT in lymphoma with even less data on predictors such as age, gender, and socioeconomic factors. As lymphoma treatment options expand, it is vital to understand the impact of these treatments on patients' financial well-being. We investigate FT through patient reported outcomes from the Lymphoma Epidemiology of Outcomes (LEO), a large multi-institution prospective cohort study of non-Hodgkin lymphoma (NHL). Methods: Patients with newly diagnosed NHL were enrolled into the LEO cohort from 2015-2020. Clinical, pathology and treatment data were abstracted from medical records, and participants were offered self-administered surveys at enrollment and selected follow-up time points. Participants were included if they completed at least four of six survey questions on FT adapted from the Medical Expenditure Panel Survey (MEPS) within the first year after diagnosis. Questions focused on either material hardship (unable to cover costs; file for bankruptcy; borrow money; work extra job or hours) or psychological stress (worried about paying large medical bills). We evaluated FT as answering positively to any question (any vs none). Associations with FT were evaluated using chi-squared tests. All analyses were performed using R (v4.2.2). Results: 3202 patients completed at least four survey questions relating to FT. Mean age at diagnosis was 62 years (18-94), 56% were male, and majority identified as white (90%). Characteristics associated with FT are reported (p<0.001). FT was more likely in younger patients: 18-25 years (64%, 30/47), 26-44 years (73%, 299/313), and 45-64 years (58%, 799/1375) as compared to those 65-79 years (36%, 459/1272) and ≥80 years (35%, 69/195). FT was highest in patients without a high school diploma (54%, 45/84), followed by patients with a high school diploma (52%, 209/402), and an associate's degree (57%, 358/627). It was lowest in patients with a bachelor's (45%, 224/296) or post-graduate degree (39.6%, 216/545). Higher FT was seen in those receiving disability income (65%, 47/72); whereas less FT was seen in retired patients (36%, 299/828). FT rates inversely correlated with income: 65% for income < $21,000 (184/283), 61% for $21,000- $39,999 (224/370) and 60% for $40,000-$65,999 (293/491). Lower FT noted for income range of $66,000-$106,000 (47%, 323/681) or > $106,000 (37%, 324/872). Patients separated from a partner (82.9%, 19/23) had the highest FT, followed by patients living with a partner (64%, 48/75), divorced (56%, 103/184), and never married (53.4%, 79/148). Patients who were married (47%, 793/1672) or widowed (41%, 60/146) reported the lowest FT. Higher FT was reported in DLBCL (55%, 631/1146), MCL (55%, 172/315), and TCL (53%, 119/226) when compared to FL (44%, 327/746), MZL (44%, 137/316), and other NHL (44%, 200/453). This is likely related to treatment as more FT was seen in patients who received an anthracycline and/or alkylator containing therapy (54%, 1066/1963) versus non-systemic (43%, 120/278) and observation (35%, 168/483). In multivariable analyses, FT strongly correlated with age at diagnosis, household income, and treatment type. The odds ratio and 95% confidence intervals for the most significant category within each variable are as follows: age per 1 year (0.96, [0.95-0.97]), income > $106,000 vs < $21,000 (0.28, [0.21-0.38]), and observation vs anthracycline and/or alkylator (0.56, [0.44-0.71]). Conclusion: Nearly 50% of patients with lymphoma experience FT, seen as material hardship and psychological stress, within the first year of diagnosis. Patients with a younger age (18-44), less education, and lower household income reported higher levels of FT; whereas those who were retired or married/widowed reported lower FT. Lymphoma subtypes that require more anthracycline and/or alkylator containing therapy imposed higher FT. Understanding these risk factors can help clinicians identify patients who may benefit from additional resources during lymphoma management.

Background: Indolent non-Hodgkin lymphomas (iNHL) are incurable and disproportionately affect adults age >65 in whom frailty and functional impairment can impact disease management. The Vulnerable Elders Survey (VES-13) is a 13-item screening tool that incorporates age, self-rated health, physical function limitations, and functional disabilities to identify vulnerable older community-dwelling adults. Adults with a VES-13 score of >3 have a fourfold increased risk of death or functional decline over 2 years as compared to adults with a score <3 (Saliba et al, JAGS 2001). Our team previously showed that VES-13 >3 was associated with a higher risk of 1-year mortality in NHL (OR 3.26, 95% CI 2.15-4.96, Fama et al, ASH 2019), including in adults age <65. In this study, we determined whether VES-13 scores >3 were associated with quality of life (QOL) changes in adults with iNHL across the age spectrum. Methods: We used the Lymphoma Epidemiology of Outcomes (LEO) Cohort Study to identify adults enrolled from 2015-2020 with grade 1-3A follicular and marginal zone lymphoma (FL, MZL) who completed the VES-13 at enrollment and QOL surveys at enrollment and follow-up. The LEO Cohort Study enrolls participants within 6 months of diagnosis and collects information on disease status, QOL, health behaviors, and patient-reported functional assessment. For this analysis we included QOL measured using the Functional Assessment of Cancer Therapy-General (FACT-G) at years 1 and 2 post-enrollment. The FACT-G total score was the sum of 4 subscales (range): physical (PWB, 0-28), social/family (SFWB, 0-28), emotional (EWB, 0-24), and functional (FWB, 0-28) well-being. A higher FACT-G total score (range 0-108) indicated better overall QOL. VES-13 scores ranged from 0 (lowest risk for death or decline) to 10+ (highest risk). Generalized linear mixed models evaluated changes in QOL from baseline based on VES-13 score >3 (vulnerable) vs <3 (non-vulnerable), within groups by age at enrollment >65 and <65. Results: Our study included 1,186 participants: 42% age >65, 54% female, 84% non-Hispanic White, 10% Hispanic, 3.3% Black, and 1.4% Asian/Pacific Islander. Most (68%) were diagnosed with FL. FLIPI was high risk for 20%, and MALT-IPI was high risk for 23%. In the VES-13 <3 group, the mean (SD) baseline FACT-G total score was 86.5 (12.0), PWB 21.0 (3.5), SFWB 24.3 (4.3), EWB 18.8 (3.7), and FWB 22.3 (4.9). In the VES-13 >3 group, the mean (SD) baseline FACT-G total score was 70.4 (16.5), PWB 15.4 (5.6), SFWB 22.3 (5.5), EWB 17.5 (4.5), and FWB 15.3 (6.4); all significantly lower than the VES-13 >3 group (all p<0.01). In adults <65, 63 (9%) had a VES-13 >3, which was associated with a significant improvement from baseline in PWB at years 1 and 2 when compared to VES-13 <3 (changes in baseline PWB were 2.1, 2.9 for VES-13 >3 vs 0.2, 0.1 for VES-13 <3, all p<0.05). Similarly, we observed improvement in FWB from baseline at years 1 and 2 in the VES-13 >3 vs <3 group (2.4, 2.9 vs 0.4, -0.1, respectively all p<0.05). The VES-13 >3 group had a decline in their EWB at 1 year vs the VES <3 group (-0.5 vs 1.2, respectively, p=0.003). At year 2, total FACT-G improved from baseline in the VES-13 >3 group (5.6 for VES-13 >3 vs -0.5 for VES-13 <3, p=0.002). SFWB changes from baseline did not differ between groups. In adults >65, 83 (17%) had a VES-13 >3, which was associated with a significant improvement from baseline PWB (1.3, 1.5 for VES-13 >3 vs. 0.1, -0.1 for VES-13 <3), FWB (3.3, 2.2 for VES-13 >3 vs. 0.5, 0.1 for VES-13 <3), and total FACT-G score (5.2, 3.4 for VES-13 >3 vs. 1.0, -0.4 for VES-13 <3) when compared to VES-13 <3 at years 1 and 2 (all p<0.05). SFWB and EWB changes from baseline did not differ between groups. While there was improvement from baseline PWB and FWB in the VES-13 >3 group, QOL scores across all subscales remained below the VES-13 <3 group in the study period for both age groups. Statistical significance was maintained in models adjusted for baseline characteristics, including initial treatment strategy (observation, systemic, vs local) for both age groups. Conclusion: Although the vulnerable group reported worse QOL throughout the study period compared to the non-vulnerable group, survivors in the vulnerable group saw greater improvement in their physical and functional well-being at 1- and 2-years post-diagnosis. Additional analyses are underway to identify drivers of improvement in physical and functional well-being in vulnerable adults diagnosed with iNHL.

Background: Although advanced stage cHL typically has excellent disease outcomes, 20-30% of patients (pts) experience treatment failure. In addition, decision-making is challenging given varying treatment options and incomplete prognostic data across the disease course. Pre-treatment factors, such as those incorporated into the Advanced Stage Hodgkin Lymphoma International Prognostication Index (A-HIPI), predict 5 year (y) overall survival (OS) and progression-free survival (PFS) (Rodday JCO 2023). Other than interim PET (iPET) results, it is unknown what factors influence cHL disease states mid-therapy. Using multistate modeling (MSM) and individual pt data from the HoLISTIC Consortium, we refined prognostication across the cHL disease course, specifically assessing the relationships between A-HIPI, iPET and end of treatment (EOT) response, and whether the A-HIPI and iPET provide independent prognostic information. Methods: We analyzed 1240 pts aged 18-65y with newly diagnosed Stage IIB, III or IV cHL treated on 2 advanced stage PET-adapted trials (RATHL, SWOG0816). All pts first received 2 cycles of ABVD; those with a negative iPET (Deauville score (DS) ≤3) received ABVD or AVD, and those with a positive iPET (DS >3) received BEACOPP. In contrast to other methods, MSMs incorporate multiple disease states into one model, account for censoring and competing risks, and estimate transitions between disease states using different covariates. Our MSM had 6 states: diagnosis, negative iPET, positive iPET, EOT non-progression (e.g., DS ≤ 3), treatment failure, and death. Treatment failure included progression or relapse. All pts start at the diagnosis state and can transition to other states without return to prior states. Transition states with <5 events were excluded. The MSM was estimated using a multivariable Cox model with censoring at 5y. Covariates were the 5y PFS A-HIPI (comprised of stage and continuous age, lymphocyte count and albumin), which was included for all transitions, and iPET, which was included for transitions from EOT non-progression and from treatment failure. Higher scores on the 5y PFS A-HIPI indicate lower risk of progression or death; the A-HIPI was modeled for a 1 standard deviation (SD) increase. We calculated probabilities of 5y treatment failure for 4 sample pts based on low (70) and high (85) A-HIPI scores, positive and negative iPET, and EOT non-progression. Results: Median age was 33y (Q1=25, Q3=45), 56% were male, 23% were stage IIB, 40% III and 37% IV, and mean A-HIPI was 77 (SD=7). Median follow-up was 75 months (Q1=53, Q3=92). At iPET, 80% (95% CI 78%, 83%) were negative and 16% (95% CI 14%, 18%) were positive. At EOT, 90% (95% CI 89%, 92%) were in the non-progression state. At 5y, 75% (95% CI 73%, 78%) were in the non-progression state, 16% (95% CI 14%, 19%) had experienced treatment failure, and 9% (7%, 11%) had died. Better A-HIPI scores (per SD) were associated with lower rates of transitioning from diagnosis to positive iPET (HR=0.80, p<0.01) and from positive iPET to treatment failure (HR=0.58, p=0.04). Better A-HIPI scores (per SD) were also associated with lower rates of transitioning from EOT non-progression to treatment failure (HR=0.78, p<0.01) and from treatment failure to death (HR=0.64, p<0.01), adjusting for iPET. Positive iPET was associated with an increased rate of transitioning from EOT non-progression to treatment failure (HR=1.90, p<0.01), adjusting for A-HIPI. The probability of 5y treatment failure for 4 sample pts was: 25% for low A-HIPI and positive iPET; 20% for high A-HIPI and positive iPET; 13% for low A-HIPI and negative iPET; and 12% for high A-HIPI and negative iPET. Conclusions: We created a novel MSM that refines prognostication across the cHL disease course by incorporating pre- and post-treatment factors (e.g., iPET) to estimate transitions to future disease states. Although the A-HIPI was developed using pre-treatment factors to predict 5y OS and PFS, we found that it was also associated with transitions to interim disease states, including iPET and EOT response. The A-HIPI and iPET each provided independent prognostic information, supporting the use of both in estimating cHL outcomes. Future analyses will develop clinical prediction models using MSMs that also incorporate varying frontline and salvage treatments and treatment-specific late effects, with the goal of providing individualized information across the disease course.

Background: QOL can be affected by disease burden, side effects of treatment, and psychosocial effects of living with cancer. Although MZL is considered incurable, we previously found that patients diagnosed from 2002-2015 had a good QOL both at baseline (BL) and the first decade after diagnosis, regardless of treatment choice (Bommier et al., submitted). Here, we evaluate whether these findings can be extended to the 2015-2020 treatment era and a more diverse US cohort. Methods: Adults with newly diagnosed MZL from 8 academic centers were prospectively enrolled within 6 months of diagnosis from 2015-2020 in the LEO cohort (NCT02736357). Participants were actively followed for disease progression/relapse, retreatment and death. QOL was measured at BL, and at 1, 2, 3 and 5 years after diagnosis using the Functional Assessment of Cancer Therapy-Lymphoma (FACT-Lym) scale (range 0-164), which consists of the FACT-General (FACT-G, range 0-104) plus a lymphoma-specific scale (range 0-60). Total FACT-G measures 4 QOL domains (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being (WB). We also used the Trial Outcome Index (TOI; range 0-112), which consists of FACT-G physical and functional WB plus the lymphoma-specific scale and is commonly used as an endpoint in treatment trials. All MZL patients with both BL and at least one follow-up QOL questionnaire were eligible for analysis. Patients were grouped by initial management: active surveillance (observation), local/antibiotic therapy (radiation, surgery, or anti- H. pylori therapy), or systemic therapy (chemotherapy, targeted agents, or antibody therapy). Change in QOL scores over time was analyzed using mixed models for repeated measurements, and initial treatment groups were compared. Results: Of 384 patients, 60% were female, 91% were White, 6.6% were Black/African American, and 13% were Hispanic (all races). The median age was 64 years (range 19-94). For initial management, 28% (N=106) were observed, 31% (N=118) received local/antibiotic therapy, and 42% (N=160) received systemic therapy. Across treatment groups, there were no significant differences by age, gender, race, or performance status, while there were differences by Hispanic ethnicity (p=0.006), Ann Arbor stage (p<0.001) and MALT-IPI score (p<0.001) (Table). At BL, patients receiving systemic therapy had lower physical WB (p=0.008), emotional WB (p=0.04), functional WB (p=0.03), FACT-G total score (p=0.008), lymphoma subscale (p<0.001), FACT-Lym (p=0.001) and TOI (p<0.001) compared to observation and local/antibiotic therapy (Table). While FACT-G total scores were largely stable through 5 years after diagnosis, social/family WB declined from BL for patients initially receiving either local/antibiotic therapy (mean at 1-year -1.4, p<0.05; 2-year -2.1, p<0.05; 3-year -1.6, p<0.05; 5-year-2.9, p<0.05) or systemic therapy (mean at 2-year -1.2 , p<0.05; 3-year -1.4, p<0.05; and 5-year -1.2, p<0.05), while this was stable for observed patients. In patients receiving initial systemic therapy, there was a transient improvement in the lymphoma-specific scale (mean +1.7, p<0.05) and TOI (mean +3.7, p<0.05). Otherwise, QOL remained stable over time and global QoL (FACT-G, FACT-Lym), TOI, Lymphoma-specific scale (Figure), and the other FACT-G subscales (physical, emotional, or functional WB) did not differ by initial treatment strategy. Conclusions: In the diverse LEO cohort from the recent treatment era, we confirm our prior findings for newly diagnosed MZL patients that QOL is globally stable over the first 5 years after diagnosis; emotional WB is not associated with initial treatment type, providing evidence that being initially observed does not negatively impact emotional health; and there is a decline in social/family WB (support from friends and family, family acceptance of illness, family communication, close to partner), which supports the need to consider interventions for patients to address these unmet needs. In addition, this study extends these findings to the lymphoma-specific scale, FACT-Lym, and TOI, which all showing largely stable trends in the 5 years after diagnosis and suggest that recent MZL treatment strategies, including systemic therapies, neither impair nor improve QOL.

Background: Understanding the symptoms and quality of life (QOL) over time of adults with follicular lymphoma (FL) is important for treatment decision-making and clinical management. However, there are limited population-level data on the long-term QOL of adults with FL. We aim to describe the real-world, long-term QOL of adults with newly diagnosed FL up to 5/6 years after diagnosis. Methods: We used the Mayo Clinic/Iowa Molecular Epidemiology Resource (MER) and the multi-institutional Lymphoma Epidemiology of Outcomes (LEO) to identify adults with grade 1-3A FL who completed QOL surveys at baseline and follow-up. Participants with lymphoma were prospectively enrolled within 9 months of diagnosis in the MER cohort from 2002-2015 and within 6 months of diagnosis in the multi-institutional LEO cohort study across 8 cancer centers from 2015-2020. The LEO/MER cohort studies systematically collected information on disease status, QOL, health behaviors, and functional assessment. Treatments, disease relapses, and deaths were verified by medical record review. Treating physicians determined clinical management. QOL was measured using the Functional Assessment of Cancer Therapy-General (FACT-G) at years 1, 2, 3, and 5/6 post-diagnosis. A higher FACT-G total score (range 0-108) indicated better QOL across 4 subscales (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being. We categorized participants based on their frontline management at time of diagnosis: “observation” (surveillance), “treatment” (systemic treatment with immunotherapy +/- chemotherapy), and “local” (radiation) groups. We employed a generalized linear mixed model to evaluate and compare the changes in QOL scores (as a continuous variable) from baseline for the observation, treatment, and local groups, adjusting for sex, race/ethnicity, age, FLIPI risk, and cohort (i.e., LEO or MER). We also evaluated QOL changes by FLIPI risk. Results: Our study included 1,544 participants with FL and QOL data. At the time of enrollment, median age was 61 years (range 19-91), 88% were non-Hispanic White, and 49% were female. Based on initial management, 529 (34%) were in the observation group, 880 (57%) were in the treatment group, and 135 (9%) were in the local group. FLIPI risk was high for 17% in the observation group, 29.3% in the treatment group, and 3% in the local group. The mean (standard deviation) baseline FACT-G total score was lowest in the treatment group with the following baseline scores: 86 (13) for the observation group, 83 (14) for the treatment group, and 88 (12) for the local group (Table 1). The observation and local groups reported a greater worsening of their baseline FACT-G total score vs the treatment group at both the 2-year (-1.2, -0.8 vs +1.8, respectively) and 5/6 year (-5.4, -6 vs. -1.6, respectively) timepoints (Figure 1). This appeared to be driven by worsened physical and functional well-being in the observation (-1.3, -2.2, respectively) and local (-0.9, -2.4, respectively) groups vs the treatment group (0, -0.3, respectively). These differences were statistically significant between the observation vs treatment groups. There were no statistically significant differences in the other subscales, i.e., social/family and emotional well-being, by frontline management. Social/family well-being decreased across all groups by 5/6 years (-2.3 in observation, -2.2 in treatment, and -3 in local, p<0.05). Regardless of FLIPI risk, the total FACT-G scores decreased within each FLIPI risk group at 5/6 years (-4.4 for low, -3.8 for intermediate, and -4.9 for high, p<0.05). This was primarily due to worsened social/family well-being (-2.4 for low, -2.2 for intermediate, and -3 for high, p<0.05) at 5/6 years. There was no statistically significant difference in QOL between FLIPI risk groups. Conclusion: This is one of the first and largest studies with real-world longitudinal QOL data for FL. Our study suggests that frontline systemic treatment initially improved QOL and resulted in a lower degree of QOL decline over time. Social/family well-being decreased over time regardless of frontline management or FLIPI risk. Further research is warranted to explore the impact of timing and specific systemic treatments on QOL, incorporate the patient/caregiver experience to address social/family well-being, and identify the clinical significance of our findings.