Parsons, Susan K.

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.

PURPOSE Routine collection of sexual orientation (SO) and gender identity (GI; collectively SOGI) in cancer clinics advances cancer care equity. METHODS In 2022, NCI Community Oncology Research Program (NCORP) practice groups were asked about routine collection of SOGI data in the electronic health record. The proportions of practice groups reporting collection of SO and/or GI data were calculated, and practice group characteristics were assessed for associations. RESULTS Of 271 practice groups nationwide, 42% (n = 112) collect SO data, 58% (n = 157) collect GI data, and 35% (n = 96) collect both. In multivariate analyses, SO data collection was associated with practice groups having minority outreach staff (odds ratio [OR], 2.07 [95% CI, 1.12 to 3.81]; P = .02); GI data collection was associated with practice groups located in the Northeastern United States (OR, 2.08 [95% CI, 0.73 to 5.91]; P = .045), and those with a higher proportion of new patients who were White (OR, 1.02 [95% CI, 1.01 to 1.04]; P < .001). Practice groups in the South were least likely to collect SOGI data (OR, 0.49 [95% CI, 0.26 to 0.94]; P = .004). There were no statistically significant differences in SO and/or GI collection on the basis of the practice group's proportion of Medicaid/Medicare patients, number of new patients with cancer per year, or practice ownership. CONCLUSION Slightly over one third of NCORP practice groups report routinely collecting SOGI data. There are regional differences in data collection, underscoring the need to craft targeted, region-specific interventions focused on boosting the capture and recording of SOGI data in an affirming manner.

PURPOSE Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort). RESULTS In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis. CONCLUSION The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.

Background: Prognostic modeling allows personalized risk prediction for individual patients (pt). The A-HIPI model in advanced stage classical Hodgkin lymphoma (AS-HL), developed and validated by the HoLISTIC Consortium (www.hodgkinconsortium.org), generates the individualized probability of a progression-free survival (PFS) event or death (OS) within the first 5 years (y) from diagnosis in pts based on continuous variables (www.qxmd.com/calculate/calculator_869/a-hipi). Clinical prognostic tools in lymphoma (eg, IPS, IPI, FLIPI, etc) typically use groupings of categorical values to define risk. Grouping a continuous value often results in loss of information, and most tools are not predictive for individual pts. However, discrete groupings have clinical utility & practicality for a) defining pt populations for clinical trials & real world studies, b) stratification within clinical trials, and c) crafting treatment guidelines. We studied potential approaches for utilizing the A-HIPI model to generate risk groups with input on strengths & limitations from the HoLISTIC modeling team & clinical experts. Methods: The A-HIPI model was developed via TRIPOD guidelines on 4,022 pts treated on 8 international clinical trials for AS-HL (Rodday. JCO 2023). External validation was performed on a dataset of 1,431 pts from 4 prospective registries. The 5y PFS (PFS5) in the A-HIPI development dataset was 77% (95% CI: 76-78); the 5y OS (OS5) was 92% (95% CI: 91-93). This represented the average outcome for a pt with AS-HL pt naïve to other clinical data at presentation. The distribution of PFS5 & OS5 predictions were heavily skewed (ie, asymmetric distribution) in both the A-HIPI discovery and validation dataset. While not unexpected due to the excellent PFS & OS in this disease setting, this presents challenges in the delineation of risk groups as depicted below . Three approaches were examined for the generation of A-HIPI risk groups. Proposed cutoffs were defined using the distribution of A-HIPI risk scores and data from the model-building cohort (ie, clinical trials). Validation was done using the A-HIPI validation cohort (ie, HL registries). Results: Approach 1: Risk groups based on clinical thresholds. Clinicians were queried what estimates of PFS5 would constitute high vs low risk. The positive, right-skewed distribution of A-HIPI risk scores limited this approach ( Figure), as cutoffs of PFS5 &lt;70 and PFS5&gt; 90 would only identify 15% and &lt;1% of the population, respectively. Approach 2: Risk groups based on deviation from “average” pt. The 5-y PFS was 77% (95% CI: 76-78). We explored defining “standard risk” based on this confidence interval as well as clinical boundaries, with pts above or below this classified as decreased or increased risk, respectively. Results of a +/-5% clinical boundary are presented in the Table. Approach 3: Risk groups based on “ranking” of pts. Here we ranked the A-HIPI risk scores of the 4022 AS-HL pts in the model building cohort and used the distribution of the risk scores as a benchmark. The risk profile for a future pt is then compared to this distribution (eg, how do you rank compared to your peers). Continuous results are often presented this way (eg, tertiles or quartiles). It also allows flexibility for the user to define the size of the risk groups and/or clinical threshold of interest. Application of this approach showed good alignment between the predicted model percentiles and the observed distribution of scores in the validation cohort ( Table). This is also reflected in calibration curves presented in the primary manuscript. An online application (eg, R-Shiny) will be provided at the meeting to allow users to define their own cutoffs to aid in pt prognostication as well as identify populations for clinical trial development. Conclusions: There are challenges with defining risk groups from individual risk prediction modeling in AS-HL. Different applications and purposes, the skewed distribution of events/risk estimates, as well as varying clinical definitions of high risk, make it challenging to define consensus expert-based risk groupings for AS-HL. A flexible “rank-based” approach appeared to provide the most clinical utility & data granularity, which may be leveraged for clinical trial design and pt stratification. Further analysis and discussion of how to optimally define high-risk and low-risk populations in AS-HL will be needed as new therapeutic options emerge.

Background: Chemotherapy in combination with radiotherapy (RT) is the standard for early-stage (ES) Hodgkin lymphoma (cHL). Despite high cure rates (12-year overall survival (OS) &gt;93%), there is room to improve short and long-term outcomes for children and adults with ES cHL. Incorporation of immunotherapy (IO) into front line treatment of ES cHL is an opportunity to both improve progression-free survival (PFS) and maintain OS, while minimizing long-term morbidity and mortality by reducing exposure to RT and high-dose chemotherapy. Study Design and Methods: HL leaders of the NCTN groups, expert HL researchers and physicians, and representatives from the Cancer Therapy Evaluation Program (CTEP) formed a working group in April 2020 with the goals of harmonizing treatment approaches for ES cHL across pediatric and adult patients and reaching consensus regarding the optimal study design for incorporating IO therapy into frontline treatment. Study champions identified from each of the North American cooperative groups [Children's Oncology Group (COG), SWOG, ECOG-ACRIN, Alliance, NRG] and experts in imaging, radiation oncology, lymphoma biology and patient-reported outcomes (PROs) were included. The resulting COG-led clinical trial, AHOD2131, represents the largest ES cHL trial in the history of North American cooperative groups and the first to enroll patients across the age continuum. AHOD2131 (NCT05675410; Figure) is a randomized, phase 3 trial enrolling patients ages 5 to 60 years with newly diagnosed, previously untreated stage I and II cHL and will investigate the addition of the CD30-antibody drug conjugate brentuximab vedotin (Bv) with PD-1 blockade (nivolumab) compared to standard chemotherapy +/- RT. Since the April 2023 activation, 97 North American sites have activated the trial, and 21 participants have enrolled. Target enrollment is 1875 patients over 5 years of accrual, for an estimated 1782 eligible and evaluable patients. The primary objective is to compare the 3-year PFS of patients with ES cHL treated through a response-adapted, superiority design with either standard therapy or with an IO approach (BV + nivolumab). All patients will be stratified as favorable or unfavorable based on disease risk features at enrollment. Based on initial response assessment by PET/CT (via central review) after 2 cycles of ABVD, patients were classified to PET2 positive (SER, defined as 5 Point Score 4 or 5, ~15% of patients) and PET negative (RER). Those who are PET positive will receive involved site RT (ISRT). The SER and RER patients will be randomized to standard chemotherapy vs. IO therapy respectively. In addition to the primary outcome of PFS, there are 11 secondary and 10 exploratory aims. The secondary aims focus on OS, PFS of subgroups, and event-free survival at 12 years, physician-reported treatment-related adverse event (AE) rates, patient-reported AE using pediatric and adult versions of Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE), and the capture of representation by self-report and the use of electronic PRO data collections. The exploratory aims include the assessment of imaging central review as compared to local institutional review and the use of convolutional neural networks through artificial-intelligence machine learning. The study will examine the effect of therapy on acute and long-term adverse effects including health-related quality of life (HRQoL) and OS for up to 12 years. The study will compare patient self-report and provider AE reports and evaluate the associations of self-reported race/ethnicity and social determinants of health (SDOH)and post-progression/post-relapse OS. AHOD2131 strengthens the effort between the North American cooperative groups to conduct collaborative clinical trials and aims to harmonize an improved standard of care for ES cHL across the age continuum.

Purpose: Improved predictive tools are desired in classic Hodgkin lymphoma (cHL) to improve prognostication and to better risk stratify patients (pts) for clinical trials. In younger pts who tolerate intensive therapies, focus has been on limiting late treatment toxicity through risk-adapted, PET-guided treatment strategy. In older pts, acute treatment toxicities are limiting factors for optimal disease control. Due to these fundamental differences, predictive tools may perform differently in younger and older pts. In particular, tools developed in younger pts may be suboptimal in older populations and vice versa. The primary objective of this study was to assess the performance of the A-HIPI (development was limited to 18-65 years of age) against the international prognostic score (IPS) for modelling overall survival (OS) and progression-free survival (PFS) in younger and older pts with cHL. Methods: This study utilized data from the population-based, nationwide Danish National Lymphoma registry. Pts with advanced-stage cHL (Ann Arbor stage III-IV or IIB with extra nodal disease) were included and categorized by age in two groups: ages ≤65 years vs. &gt;65 years. In the primary analysis, all pts regardless of therapy were included. A sensitivity analysis restricting to pts starting on BEACOPP or ABVD was performed (Only ABVD in the &gt;65 cohort). Concordance index (C-index) and calibration were used to assess model performance and generalizability. Calibration was visualized using calibration plots and quantified by calibration in the large (CITL) and calibration slope (CS) at 5-years post diagnosis. Results: A total of 893 advanced-stage HL pts fulfilled the inclusion criteria (634 in the ≤65 cohort and 259 in the &gt;65 cohort). The 5-year OS and PFS in the &gt;65-year cohort were 44% (Confidence intervals (CI); 37-51%) and 35% (CI; 30-42%), respectively. Fifty-six pts in the &gt;65 cohort were classified as low-intermediate-risk (IPS; 1-2), 128 intermediate-risk (IPS; 3-4), and 75 high-risk (IPS; 5-7). For the ≤65 cohort, the 5-year OS and PFS was 88% (CI; 85-90%) and 76% (CI; 73-80%), respectively. Twenty-three pts were classified as low-risk (IPS; 0), 244 low-intermediate-risk, 286 intermediate-risk, and 81 high-risk. The A-HIPI had a C-index of 0.74 and 0.63 for OS and PFS, respectively in the ≤65 years cohort vs. 0.65 and 0.64 for OS and PFS, respectively, in the &gt;65 cohort. The IPS had a C-index in the ≤65 cohort of 0.66 and 0.60 for OS and PFS, respectively, vs. 0.58 and 0.59 for OS and PFS, respectively, in the &gt;65 cohort (i.e., the IPS performed better in the ≤65 cohort in terms of OS and PFS but was inferior to the A-HIPI). When restricting to pts treated with ABVD or BEACOPP, the A-HIPI C-index in the &gt;65 cohort (116 pts) were reduced to 0.55 for OS and 0.54 for PFS. For the ≤65 cohort (595 pts), the A-HIPI C-index was stable at 0.75 and 0.62 for OS and PFS, respectively. In terms of A-HIPI calibration in the &gt;65 cohort, CITL and CS were -0.78 and 0.39 for OS vs. -0.98 and 0.80 for PFS. In the ≤65 cohort, CITL and CS were -0.06 and 0.99 for OS vs. 0.21 and 1.18 for PFS. Visual inspection of the calibration plot (Figure 1) for the &gt;65 cohort showed that the A-HIPI underestimated the observed proportion for both OS and PFS (i.e., the observed events were higher than the predicted). Calibration plot for the ≤65 cohort (Figure 2) indicated that the A-HIPI accurately modelled the observed proportion for both OS and PFS (i.e., the predicted probability was consistent with the observed proportion). Conclusion: The newly developed A-HIPI was validated in a large population-based cohort of Danish pts with advanced-stage cHL. Performance of the A-HIPI was high in the ≤65 cohort and superior to the IPS for OS prediction. Calibration of A-HIPI was excellent in pts ages ≤65 years. However, A-HIPI performed significantly worse among pts &gt;65 years and attained poor performance in pts receiving ABVD or BEACOPP, which is not surprising since patients older than 65 were not included in the development. This study emphasizes the importance of further efforts to develop or update models that apply specifically in older individuals. The inclusion of additional variables reflecting comorbidities and pts fitness into a prognostic model may be necessary to effectively deliver accurate prognoses and risk assessments for the older pt.

Background: To be useful across the broadest range of patients and clinical settings, clinical prediction models should be externally validated in different data sets and clinical settings and refined over time. In 2022, the HoLISTIC Consortium (www.hodgkinconsortium.org) developed and validated the A-HIPI model via TRIPOD guidelines for adults 18-65 with advanced stage classic Hodgkin Lymphoma (AS-HL) (Rodday et al., JCO 2023). The A-HIPI model, based on individual patient (pt) data from more than 5000 patients (pts) from North America, Europe, and Australia, predicts progression-free survival and overall survival within the first 5 years (PFS5 and OS5) based on pre-treatment pt and disease characteristics (www.qxmd.com/calculate/calculator_869/a-hipi). In collaboration with Brazilian clinical experts and researchers, we describe the performance of the A-HIPI in the Brazilian Prospective HL registry. Methods: The Brazilian Prospective HL registry (NCT02589548) was originally established in 2009 with the goal of evaluating risk factors, treatment, and disease outcomes. By 2022, 1357 pts with classic HL were registered with a median follow-up of 5 years for surviving patients (Biasoli I. HemaSphere 2022). 5-year PFS and OS were calculated using the Kaplan-Meier method. Using the methodology for validation and calibration outlined in by Rodday et al., calculated risk scores were calculated for PFS5 and OS5. Harrell c-statistic and the Uno c-statistic were calculated to evaluate discrimination (i.e., differentiation of those at higher risk of having an event from those at lower risk) and plots of deciles of predicted vs. observed outcomes were created to assess calibration (i.e., how similar the predicted absolute risk of the model is to the true (observed) risk in an external cohort). In the primary A-HIPI analysis, laboratory values outside of permitted range were excluded and not imputed. This was checked in sensitivity analysis using multiple imputation for missing values; no substantial differences were noted. Thus, in the current validation, out-of-range lab values were excluded. There were no exclusions in the current analysis for early death, defined as deaths that occurred from the onset of first treatment until 30 days after its end. Pts with HIV were excluded from the assembled cohort. Results: 694 patients with AS-HL with the inclusion criteria of the original study were included in the present study. The median age was 30.9 years (range, 18-65) at diagnosis with 46% of the pts being female. 230 (33%) patients were classified as Stage IIB, 190 with Stage III (27.4%), and 274 (39.5%) with Stage IV. Bulky disease (&gt;10 cm) was present in 226 patients (33%). Median hemoglobin was 11.6 g/dL (8-16.3), median albumin 3.63 g/dL (1.41-5.46), and median lymphocyte count was 1.5 x 10 3 µL (0.10-4.76). The 5-y PFS (PFS5) in the A-HIPI Brazilian validation dataset was 68.4% (95% CI: 64.7-71.9%) and the 5-y OS (OS5) was 86.0% (95% CI: 82.8-88.6).Harrell c-statistics for the A-HIPI model were 0.60 (95% CI, .56-.64) for 5-year PFS and 0.69 (95%CI, .64-.74) for 5-year OS and Uno c-statistics for the A-HIPI model were 0.63 (95% CI, .58-.68) for 5-year PFS and 0.72 (95%CI, .65-.78) for 5-year OS. Calibration plots (see figures) show the predicted A-HIPI model estimates were well calibrated with the observed outcomes in the Brazilian HL registry. Conclusions: Despite important differences between the original development cohort for A-HIPI and the Brazilian registry in patient and clinical characteristics (e.g., younger age at diagnosis, greater proportion of Stage IV disease) and the well characterized impact of income maldistribution and socio-economic status on clinical outcomes (Biasoli et al., Int J Cancer 2018), the A-HIPI clinical prediction tool performed well in the Brazilian Prospective Registry. This included evidence of adequate discrimination and calibration. Future studies are planned, including the impact of treatment selection and response, use of interim imaging, and supportive care may underscore important differences in outcomes among adults with AS-HL.

Background: There are a lack of clinical prediction tools in early-stage Hodgkin lymphoma (E-HL). We developed and validated a modern-day model, known as E-HIPI, to predict progression-free survival (PFS) within the first 5 years (y) incorporating detailed individual patient (pt) data from international clinical trials and prospective registry data that were standardized, normalized & harmonized as part of the HoLISTIC Consortium (www.hodgkinconsortium.org). Methods: Model development utilized Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines (Moons, Ann Intern Med 2015) on a cohort of 3,080 newly-diagnosed E-HL pts treated on 4 seminal phase 3 clinical trials (NCIC: Meyer, NEJM 2012; RAPID: Radford, NEJM 2015; EORTC-GELA H9U: Ferme, E ur J Cancer 2017; EORTC/LYSA/FIL H10: Andre, JCO 2017). External validation was done in an independent cohort of 462 E-HL pts from a large registry (Princess Margaret, Toronto). Pts with classic HL, stage I or II disease, and ages 18-65 y were included. The primary outcome was PFS. Cox models were utilized with follow-up truncated at 5 years. Baseline predictors were: sex, stage, B symptoms, histology, number & location of nodal sites, and continuous data values of age, maximum tumor diameter (MTD), white blood cell count (WBC), lymphocyte count, hemoglobin, albumin & erythrocyte sedimentation rate (ESR). Multiple imputation was used for missing data. To consider possible non-linear relationships for continuous variables we examined plots & cubic splines. We used backward elimination ( P&lt;0.1) to develop the models & internal validation with 200 bootstrap samples was conducted to estimate optimism & correct for overfitting. The final prediction equations applied optimism corrections to beta coefficients & hazard ratios (HR). Sensitivity analyses included model results stratified by PET staging (vs CT) and each HL trial. The models were evaluated in the external validation cohort & compared using c-statistics. Model scores were also grouped by tertiles for visualization in Kaplan-Meier (KM) curves. Results: Median age in the development cohort was 35y; 51% were female; 81% had nodular sclerosis & 77% stage II disease; 26% had B symptoms; 32% had mediastinal bulk (≥10cm or &gt;1/3 diameter) and the mean MTD was 6.4 cm (interquartile range (IQR), 3.9-8.2). Median follow-up was 60 months (IQR 45-75). KM estimates at 5y was 90.7% (264 events) for PFS, and 97.1% (78 events) for overall survival (OS). After backward elimination, significant variables retained were age, sex, nodal location (cervical region), MTD & albumin. Stage, ESR, or the number of nodal sites were not significant. Age was modeled with piecewise linear splines due to its non-linear relationship. See the Table for the final prediction model and optimism-corrected HRs. Optimism-corrected c-statistics in the development model was 0.63. Sensitivity analyses of stratification by type of imaging at staging or by each clinical trial demonstrated no change in model parameters or c-statistics. Most baseline characteristics and outcomes of the external validation cohort were similar besides median follow-up time (74 months, IQR 37-129). C-statistics for the E-HIPI model in external validation was 0.63. In addition, observed 5y outcomes stratified by tertile of predicted risk in the external cohort separated pts into a low & higher-risk group, which comprised 1/3 & 2/3 of pts, respectively ( Figure). Finally, model diagnostics revealed the potential for time dependency in the retained covariates. Further assessment of differential prediction between early (&lt;2y) vs. later (2-5y) PFS events is ongoing. Conclusion: Following TRIPOD methodology, we rigorously developed and externally validated the E-HIPI among &gt;3,500 E-HL pts.Discrete risk groups were delineated, and we identified several novel factors, including nodal location, sex & key continuous variables that predict 5y PFS. Additional model refinement is underway, including formal calibration & external validation via other HL registries (Stanford, Mayo/Iowa SPORE). A web-based calculator to simplify application of the E-HIPI will also be presented at ASH. Future planned modeling includes integration of differing treatments & response-adapted imaging via multistate modeling to enrich individualized survival estimates and simulation modeling to predict late consequences.

AbstractAdolescents and young adults (AYA) with lymphoma experience treatment‐related effects in the short and long term that impact their quality of life and survivorship experience. The effort to improve outcomes for AYA lymphoma survivors requires understanding the available literature, identifying current knowledge deficits, designing better clinical trials incorporating the patient perspective, using novel tools to bridge data gaps and building survivorship guidelines that translate research to clinical practice. This review article summarizes the current state of lymphoma treatment‐related outcomes in AYAs and provides future direction.