Upshaw, Jenica

Abstract Background: Despite recent therapeutic advances in AS cHL, PET-adaptive chemotherapy-based regimens remain a first-line treatment option, especially for patients (pts) without access to novel agents. Most PET-adaptive regiments start with either 2 cycles of escBEACOPP (escBEACOPP2) or ABVD (ABVD2) prior to interim PET assessment (iPET), followed by treatment de-escalation or intensification by iPET result. There have been no direct comparisons of escBEACOPP2 vs ABVD2 PET-adaptive regimens. Additionally, the AS-Hodgkin Lymphoma International Prognostication Index (A-HIPI) score (Rodday JCO 2023) has not been fully explored in escBEACOPP2 regimens. Therefore, we utilized multiple analytic approaches to compare outcomes of PET-adaptive escBEACOPP2 and ABVD2 regimens, while adjusting for and assessing the effect of the baseline A-HIPI score. Methods: Through the global HoLISTIC Consortium (www.hodgkinconsortium.com), we obtained individual patient data from 4 AS clinical trials of newly diagnosed cHL pts treated with PET-adaptive escBEACOPP2 (AHL2011) or ABVD2 (SWOG0816, RATHL, HD0607) regimens. Pts were restricted to age 18 to 65 years (y), stage IIB-IV, and PET-adapted treatment arms. Positive iPET was based on Deauville score >3. Treatment effects of escBEACOPP2 vs ABVD2 (reference) on 5y progression-free survival (PFS) and overall survival (OS) were first evaluated using Cox models. Importantly, baseline disease risk was adjusted for using the 5y PFS or OS A-HIPI score (including stage, sex, age, bulk, lymphocyte count, albumin, white blood cell count), where higher scores indicate higher predicted risk (scale 1-100). The A-HIPI was modeled per 1 standard deviation (SD) increase. Our multistate model (MSM) comprised 4 health states: diagnosis, sustained remission at 1yr, treatment failure, and death from any cause. Pts all started in the diagnosis state and transitioned to other states without return to prior states. We assessed the impact of treatment on transitions (except binary sustained remission at 1y) that involved ≥5 pts per treatment group, adjusting for A-HIPI for all transitions. All effects are reported as adjusted hazard ratios (aHRs) with 95% confidence intervals (CI). Results: 2,381 AS cHL pts were included (n=372 for escBEACOPP2, n=2009 for ABVD2). For escBEACOPP2 and ABVD2 groups (median follow-up 60 and 56 months), respectively, the mean baseline A-HIPI scores (i.e., predicted 5y PFS event rate) were 25.1 (SD=6.3) and 22.9 (SD=6.5), the rates of positive iPET were 11.3% and 17.0%, 5y PFS was 87.0% and 79.5%, and 5y OS was 96.3% and 94.6%. In Cox models, escBEACOPP2 was associated with significantly improved PFS compared to ABVD2 (aHR=0.54, 95% CI=0.39-0.73); the aHR for OS was 0.67 (95% CI=0.37-1.19). Notably, higher risk A-HIPI score was associated with significantly worse PFS (aHR=1.42, 95% CI=1.31-1.54) and OS (aHR=1.60, 95% CI=1.47-1.76), independent of treatment. In the MSM, the effect of escBEACOPP2 compared to ABVD2 regimens was modeled in 3 transitions with adjustment for A-HIPI score: those treated with escBEACOPP2 had lower likelihood of treatment failure with (aHR=0.38, 95% CI=0.22-0.66) or without (aHR=0.61, 95% CI=0.40-0.92) sustained remission at 1y; the aHR for treatment failure to death was 0.55 (95% CI=0.25-1.22). Higher risk baseline A-HIPI score was associated with higher likelihood of treatment failure and death without sustained remission at 1y (aHR=1.43, 95% CI=1.28-1.61 and aHR=2.50, 95% CI=1.80-3.49, respectively). Furthermore, higher risk baseline A-HIPI score was associated with higher likelihood of treatment failure after achieving sustained remission at 1y (aHR=1.29, 95% CI=1.12-1.48) as well as death after treatment failure (aHR=1.54, 95% CI=1.27-1.86). Conclusions: Comparing newly diagnosed adult AS cHL PET-adaptive regimens with adjustment for baseline A-HIPI score, we found pts treated with escBEACOPP2 on AHL2011 had improved PFS versus pooled pt data from 3 ABVD2 trials. Using MSM, we further demonstrated that escBEACOPP2 was associated with a lower likelihood of treatment failure with or without achieving sustained remission at 1yr. In addition, baseline A-HIPI score was associated with PFS and OS, which was independent of treatment. Finally, higher baseline A-HIPI scores maintained prognostic impact throughout the disease course, including increased risk of treatment failure for pts in remission at 1yr and death after treatment failure.

Abstract Background: Although progression-free survival (PFS) in early-stage (ES) classic Hodgkin Lymphoma (cHL) is high, there remains a subset of patients (pts) at higher risk of worse outcomes, particularly in those with unfavorable risk disease. Leveraging pt-level data from within the HoLISTIC (Hodgkin Lymphoma International STudy for Individual Care) Consortium, the E-HIPI was developed in clinical trial pts and validated in real-world registry pts to predict 2-year (y) PFS (Rodday. NEJM Evidence 2025). Continuous, objective, and readily measurable pre-treatment variables were considered, with sex, maximum tumor diameter, albumin, and hemoglobin included as significant predictors. Using the E-HIPI, interactive calculators for pt risk, comparison, and stratification were created (https://rtools.mayo.edu/holistic_ehipi/). Although the E-HIPI was developed and validated in both unfavorable and favorable ES cHL pts, the clinical predictors are primarily markers of unfavorable disease. In the development and initial validation cohorts, 2y PFS was 93.7% and 90.3%, respectively, the C-statistic was 0.63 in both cohorts, calibration was strong with slight underprediction of events in the validation cohort, and the E-HIPI outperformed the historic binary classification of patients as favorable or unfavorable using EORTC criteria. However, since pts in the original E-HIPI development and validation were primarily treated with ABVD chemotherapy protocols, additional validation is needed, particularly among pts with unfavorable ES cHL who were treated initially with more intensive chemotherapeutic platforms (e.g., BEACOPP-based). Thus, we conducted an external validation of the E-HIPI in pts treated on recent unfavorable ES cHL GHSG trials. Methods: The validation cohort included 2367 adults (ages 18 to 60y) with newly-diagnosed unfavorable cHL from randomized phase 3 trials conducted by the GHSG (HD14 von Tresckow. JCO 2012; and HD17 Borchmann. Lancet 2021). We calculated the predicted probability of 2y PFS events based on the E-HIPI model equation (scale 1-100%). Model performance was assessed using discrimination and calibration. Discrimination, which is the model's ability to separate high and low risk pts, was assessed using Harrell's C-statistic. Calibration, which is the model's ability to accurately predict absolute risk, was assessed by comparing observed and predicted probabilities of 2y PFS events within quintiles of predicted probabilities and estimating calibration-in-the-large (predicted minus observed event rate) and the calibration slope. Results: For pts treated on unfavorable GHSG trials, mean age was 33y, 95% had stage II disease, mean baseline E-HIPI score (i.e., predicted 2y PFS event rate) was 6.0% (SD=2.3%), and the observed 2y PFS rate was 96.2% (95%CI: 95.4-97.0%). The C-statistic was 0.65 (95% CI: 0.59-0.70), calibration-in-the-large was 0.02, and the calibration slope was 1.08 (95% CI: 0.52, 1.64). Within the lowest risk E-HIPI quintile, the observed 2y PFS event rate was 1.6%, compared with 6.1% in the highest risk quintile. Conclusions: In external validation of the recently published E-HIPI in pts with cHL treated on unfavorable GHSG trials using more intensive BEACOPP-based protocols, discrimination was similar to the original development and validation cohorts. Collectively, the E-HIPI is a treatment-agnostic prediction tool across varied treatment regimens for unfavorable ES cHL pts. Better 2y PFS in the GHSG trials than in the E-HIPI development and initial validation cohorts may have contributed to mild misclassification, likely reflecting differences in treatment regimens. This may be resolved with recalibration or incorporating granular treatment variables into future models. Additional research is needed to understand E-HIPI performance in pts with favorable disease who have even lower event rates as well as to more fully predict individualized pt outcomes across varied chemotherapy platforms and radiation received for all ES cHL pts.

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: 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 <70 and PFS5> 90 would only identify 15% and <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.

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. >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 >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 >65 cohort). The 5-year OS and PFS in the >65-year cohort were 44% (Confidence intervals (CI); 37-51%) and 35% (CI; 30-42%), respectively. Fifty-six pts in the >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 >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 >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 >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 >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 >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 >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 (>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.