A Comparison of the Timing of Treatment Initiation for Older Versus Younger Patients with Follicular Lymphoma
Bibliographic record
Abstract
Introduction: Older FL patients have a worse prognosis than younger FL patients1,2. Data regarding whether older patients (aged 60+) are more likely to be initially managed with a watch and wait (WW) approach are conflicting3-5. While it has been reported that 22% of patients with stage 2+ FL are managed with WW despite meeting at least one GELF criterion (GELFc) at diagnosis, it is unknown whether this is more or less likely to occur in older patients. We postulate that treatment of older and younger patients differs and may influence outcomes. Thus, we examined the interplay between the presence of GELFc at diagnosis and timing of therapy for older vs younger patients. We defined older as > 75 years based on the FL international prognostic index 24 (FLIPI24) risk model6. Methods: A single-center, retrospective cohort study identified patients with grade 1-3A FL by searching the electronic health records (EHR) querying the term “follicular lymphoma” in the “diagnosis” category (Sep. 2013 - Jan. 2023) and reviewing paper-based records from Jan. 2008 - Aug. 2013.Time from diagnosis to first treatment (TTFT) was defined as time from pathological diagnosis until first treatment, including systemic therapy and/or radiation. Patients were stratified according to age (<75 vs. 75+) and censored at the time they met GELFc, were diagnosed with transformed FL, or died. Patients with stage 1 FL were excluded from TTFT and progression-free survival (PFS) analyses (defined as time from initiation of therapy until clinical or radiological progression, whichever occurred first); those with stage 2 disease were included unless they received definitive radiotherapy. If a patient with stage 1 disease progressed to a higher stage while on observation, they were included in the TTFT cohort with survival follow-up for the analysis beginning at the time of progression. All patients were included in the overall survival (OS) and descriptive analyses. Results: The EHR search yielded 368 charts, 205 were excluded (high-grade FL (n=35), diagnosis prior to 2008 (n=68), referral for clinical trial (n=48), isolated cutaneous/GI FL (n=7), FL in situ (n=4), prior diagnosis of other non-Hodgkin lymphoma (n=4), discordant lymphoma (n=2), pediatric FL (n=2), FL was not the final diagnosis (n=18), unavailable pathology report (n=10) or patient records (n=7)). 25 more cases were identified from paper-based records for a total of 188 patients. Median age at diagnosis was 62 years (range, 30-94), 50% were female. Most were diagnosed with stage 3/4 disease (68.4%, n=128), 38.2% (n=63) had a high-risk FLIPI score, and 40% (n=72) met at least one GELFc within three months of diagnosis. Median TTFT was not reached (NR) for younger and older patients and did not differ significantly (log-rank p value=0.51). PFS was significantly shorter for older patients (log-rank p-value =0.021) (median 2.4 vs 2.4 years). OS was also significantly shorter for older patients (log-rank p-value <0.0001) (median OS 11.3 years vs. NR). Cox regression model with sex and Charlson comorbidity index (CCI) as covariates identified male sex as a significant covariate for PFS (univariate: HR 1.98, p=0.03, 95%CI 1.07-3.68; multivariate: HR 2.27, p=0.0099, 95%CI 1.22-4.26) but not CCI. CCI was significantly associated with a shorter OS on univariate but not on multivariate analysis including age. A similar percentage of older and younger patients were initially managed with observation (55.6% vs. 59.9%, p=0.80) even though older patients were more likely to meet GELFc at diagnosis (57.6% vs. 36.1%, p=0.037). When therapy was initiated, most patients in both groups received CIT (58.3% vs. 56.6%, p= 0.99) but younger patients more often received R-CHOP (23%) or BR (27%), while older patients more often received R-CVP (36.1%). Discussion: In our cohort, younger and older patients without GELFc had similar TTFT. However, older patients had a significantly shorter PFS and OS. Older patients were just as likely to be managed by WW despite the higher number of older patients with at least one GELFc at diagnosis, which could have contributed to their poorer outcome. Our findings align with previous studies showing inferior outcomes for older FL patients and suggest that this may in part be due to late initiation of therapy. Our results are likely influenced by institutional practices, expanding this study to other institutions is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".