Predictors Of Delay In Diagnosis and Treatment In Diffuse Large B-Cell Lymphoma and Impact On Survival
Bibliographic record
Abstract
Abstract Background Although diagnostic and treatment delays in solid tumors are known to negatively impact on outcomes, little is known with respect to hematological malignancies. Diffuse Large B-Cell Lymphoma may present with a wide array of symptoms, thus rendering initial diagnosis challenging and time consuming. We evaluated disease-specific, patient-related and socioeconomic factors leading to delays in DLBCL diagnosis and treatment and the respective impact on overall and progression-free survival. Methods A comprehensive clinical database of patients with a new diagnosis or new presentation of transformed DLBCL treated at our center between 2002 and 2010 was utilized. A total 278 patients were included. All patients received at least one cycle of Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone (R-CHOP) immuno-chemotherapy. We defined various time intervals based on Cancer Care Ontario guidelines as follows: patient associated delay – time from symptoms onset to first known contact with a primary care physician (PCP); diagnostic delay – >6 weeks from first PCP contact to initial hematology consultation; and treatment delay – >4 weeks from first hematology consultation to chemotherapy initiation. Results In the population studied (n=278), the median age was 63 and 46% were female. Patients waited a median of 4 weeks (IQR 2-13) before seeking medical attention. A further median of 8 weeks (IQR 4-17) was required for the PCP to diagnose DLBCL or at least to achieve enough clinical suspicion for referral to hematology. From initial hematology consult, a median of 3 weeks (IQR 1-4) elapsed until chemotherapy initiation. In univariate analyses, patients who lacked bone marrow involvement (p=.005), had lower IPI scores (p=.031), higher Charlson comorbidity index (p=.048) and who had initiation of treatment in the outpatient setting (vs. inpatient; p=.021), were more likely to experience diagnostic delays >6 weeks. In multivariable logistic regression analysis, bone marrow involvement (OR=0.41, p=.018), Charlson comorbidity index (OR=1.42, p=.017) and requirement for urgent inpatient chemotherapy administration (OR=0.40, p=.012) remained associated with diagnostic delays. With respect to treatment delays, in univariate analyses, patients who did not have a pathology diagnosis at the time of initial hematology consultation (p<.0001) and those with B symptoms (p=.039) were more likely to experience treatment delays >4 weeks. On multivariable analysis, lack of pathological diagnosis at the time of hematology referral was the only factor that remained associated with treatment delays (OR=8.25, p<0.001). No socioeconomic factors (low income, level of education, and cohabiting alone) predicted for either diagnostic or treatment delays. On Cox multivariable regression analyses, diagnostic (Fig 1) or treatment delays (Fig 2) did not impact on survival or progression-free survival; only IPI score and number of R-CHOP cycles significantly impacted overall survival (HR=1.82, p<.001; HR=0.70 p<.001) and progression-free survival (HR=1.56, p<.001; HR=0.82, p=.004). Conclusion Selected disease and patient-related factors may be associated with delays in management of DLBCL. However, unlike in solid tumor presentations, we can reassure patients that waiting a reasonable time frame to complete diagnostic and staging milestones should not affect their disease course, as long as appropriate chemotherapy dosing is administered. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".