Skip to main content
Abstracts PO84

Symptom Burden and Advanced Stage Predict Delayed Presentation in Non-Hodgkin Lymphoma: A Nigerian Cohort Study

Akunwata Chima 1,2, Shokunbi Wuraola 1,2

Introduction/Background/Significance: Delayed presentation in lymphoma patients worsens outcomes, particularly in resource-limited settings like Nigeria. This study examines factors associated with delayed hospital presentation in NHL patients at a tertiary center.

Materials and Methods/Case Presentation/Objective: We conducted a retrospective analysis of 171 NHL patients (mean age: 47.2 years, SD: 17.9; 84.2% < 65 years; male-to-female ratio: 1.45:1) diagnosed at University College Hospital, Ibadan, Nigeria, between 2007 and 2019. Data collected included symptom count (e.g., fever, weight loss, lymph node swelling), time from symptom awareness to presentation, disease stage (I–II vs. III–IV), and ECOG performance status (ECOG 0–1 vs. ECOG ≥2). Symptom burden was categorized as low (1–2 symptoms, 43.9%) or high ( >2 symptoms, 56.1%). Late presentation was defined as ≥8 weeks from symptom awareness. Logistic regression was performed to assess predictors of late presentation, including symptom burden, stage, age, sex, and ECOG status.

Results/Description/Main Outcome Measures: Overall, 52.6% of patients presented late (≥8 weeks), with a mean delay of 10.8 weeks. High symptom burden ( >2 symptoms) was a significant predictor of late presentation (adjusted Odds Ratio [OR]: 2.95, 95% CI: 1.37–7.31; p = 0.02). Advanced stage (III–IV, 76.5%) also significantly predicted delayed presentation (OR: 3.28, p = 0.01). While most patients (94.7%) had a good performance status (ECOG 0–1), age and sex were not significant predictors (p > 0.05).

Conclusions: In Nigerian non-Hodgkin lymphoma patients, high symptom burden and advanced disease stage are significant independent predictors of delayed presentation, despite a predominantly good performance status. These findings highlight the need for targeted public awareness campaigns to improve early symptom recognition and facilitate timely diagnostic access in resource-constrained settings.