The Impact of Age on the Outcome of Primary Treatment for Classical Hodgkin's Lymphoma: 70 Years of Age Is a Clinical Relevant Cutpoint and the Most Important Predictor of Overall Survival
Bibliographic record
Abstract
Introduction: Classical Hodgkin lymphoma (cHL) typically affects younger patients but 15-35% are >60 years. The age used to define an elderly population has varied but age 60 is frequently used. A clinically relevant definition of older age could be based on the use of alternate treatments due to different efficacy and/or toxicity. Treatment outcomes may also be influenced by tumor biology and patient comorbidity that vary with age. We evaluated the effect of age on treatment outcomes in cHL. Methods: All cHL patients treated at our centre between Jan 1999 and Dec 2015 were retrospectively analyzed. Clinical data were obtained from prospectively collected Lymphoma database and additional data was manually retrieved. Treatment for localized disease was combined modality (2-4 cycles of ABVD and potentially 6 cycles for bulk disease > 10 cm; radiation doses 20-35 Gy) with advanced disease typically receiving chemotherapy alone (ABVD 6-8 cycles). Older patients received individualized treatment. We used the Hematopoietic Cell Transplantation-specific Comorbidity Index (HCT-CI), (Sorror Blood 2005) as the elements can be abstracted retrospectively. Results: 607 patients were identified; 14% were >60 years and 6% were age >70. Baseline characteristics are outlined in table 1. Patients >70 presented more frequently with high-risk HCT-CI and worse ECOG PS. Patients > 60 presented more frequently with advanced stage (61-70 age group: 40%; >70 years: 46%). 65% of the patients age >70 presented with an IPS of >3. Chemotherapy alone approaches were used more commonly in older patients (age 61-70: 40%; age 70+: 51%) than in those <60 years (25%). Within the whole cohort 12 patients received non-anthracycline based treatment (<60: n=4; 61-70: n=1; and >70: n=7).For patients <60 and >70 this decision was made due to prior comorbidities that precluded the use of standard treatment, and for the patient in the 61-70 was because of acute toxicity with ABVD-based chemotherapy. Treatment was discontinued in 33% of the patients > 70 (77% due to toxicity), 21% in the 61-70 years group (30% toxicity) and 6% in patients <60 (29% toxicity). Patients > 70 had higher rates of grade 3-5 febrile neutropenia (28% versus 15% [age 61-70] and 7% [age <60]). Bleomycin toxicity was more common in older patients (age >70: grade 3-4 events 12% of the patients with discontinuation in 66%; age 61-70: 13% with discontinuation rate of 20%) compared to a 1% rate of grade 3-5 events in age <60. There was a grade 5 episode of febrile neutropenia in >70 group and 1 death related to bleomycin in age <60. With a median follow up of 8.6 years, the 10-year OS and PFS were 80.5% and 71.2%, respectively. By age-group, the 10-year OS was 88% (<60 years), 57% (61-70 years) and 15% (age >70 years); (p<0.001) (Figure 1a-b). In multivariable analysis for OS, age 61-70 (HR 2.44, p=0.002) and age >70 (HR 5.72, p=0.001), non-anthracycline based chemotherapy (HR 3.69, p<0.001), high-risk HCT-CI (HR 3.03, p=0.001) and ECOG 2-4 (HR 1.8, p=0.017), were significant. Age >70, type of chemotherapy, high-risk HCT-CI, and advanced stage were significant for PFS in the multivariable analysis (Table 2a-b). Death due to disease or toxicity at 10 years was 13.6% (age <60: 9.7%, 61-70 years: 23.2%; and for age > 70: 50.7%; [p=<0.001]) (Figure 2a-b). Multivariable analysis for cause-specific survival identified age >70 years (HR 4.04, p=<0.001), extranodal disease (HR 2.57, p=0.001) and ECOG 2-4 (HR 2.10, p=0.010) as significant predictors(Table 3). Conclusions: Age >70 years is a clinically relevant age cutoff as it has additional prognostic significance and greater rates of treatment discontinuation and toxicity compared to age 60. The HCT-CI is a useful predictor of outcome in cHL and should be validated prospectively. In multivariable analysis, age, type of treatment, comorbidity and ECOG performance status are independent predictors of OS. Further studies are ongoing to validate these findings and assess biologic differences in older versus younger cHL patients. Disclosures Tsang: Nordic Nanovector: Research Funding. Kridel:Gilead Sciences: Research Funding. Kukreti:Celgene: Honoraria; Amgen: Honoraria; Takeda: Honoraria. Prica:Celgene: Honoraria; Janssen: Honoraria. Kuruvilla:Janssen: Research Funding; Roche: Honoraria; Novartis: Honoraria; Merck: Honoraria; Gilead: Honoraria; Seattle Genetics: Consultancy; Roche: Consultancy; Merck: Consultancy; Karyopharm: Consultancy; Celgene: Honoraria; BMS: Honoraria; BMS: Consultancy; Abbvie: Consultancy; Gilead: Consultancy; Astra Zeneca: Honoraria; Janssen: Honoraria; Roche: Research Funding; Amgen: Honoraria; Seattle Genetics: Honoraria; Karyopharm: Honoraria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".