ABSTRACT
Predictive prognostic scoring (PS) systems are not primarily applicable to elderly patients with classical Hodgkin lymphoma (cHL). The objective of this study was to develop a PS system for these patients. The derivation cohort (DC) was utilized for model development, consisting of 97 variables. The resulting algorithm was named as Hodgkin's Lymphoma Early Death in the Elderly within 12 months (HEDEL12). Internal and external validation cohorts (IVC and EVC) were employed for validation. A total of 286 patients were evaluated retrospectively. In DC 38 of 178 patients died within the first 12 months and overall survival (OS) at 12-month was 78.6%. Independent predictors of HEDEL12 were female sex, low albumin levels (<3.5 g/dL), and ECOG scores 2-4. According to HEDEL12 scores 0-1, OS at 12- months were 89.8% and 91.0% for IVC and EVC, respectively. The HEDEL12 scoring is useful in predicting the survival of advanced-stage cHL patients.
Predictive prognostic scoring (PS) systems are not applicable to elderly patients with classical Hodgkin lymphomaFemale sex, low albumin levels (<3.5 g/dL), and ECOG scores 2-4 are independent predictors of survival in older advanced stage cHL patients.
ABSTRACT
INTRODUCTION AND OBJECTIVES: A crucial issue when appraising the performance of non-invasive markers is the limitations of the reference standard they are compared to. Digital image analysis (DIA) was suggested as a reproducible approach expressing fibrosis numerically as a proportionate area (PA) (%). We aimed to evaluate ELF test with direct reference to PA (%), thereby explore the improvement in accuracy to discriminate significant fibrosis which may actually have been underestimated by categorical pathological staging. MATERIALS AND METHODS: PA (%) data were obtained by DIA of trichrome-stained liver biopsies of 52 chronic hepatitis patients. Paired serum samples of patients and additional 36 controls were performed to measure ELF test. Diagnostic performance characteristics of ELF test was derived in predicting significant fibrosis in the patient cohort, and also, in distinguishing healthy controls from patients with significant fibrosis. RESULTS: We found an AUROC value of 0.73 for ELF to predict significant fibrosis as assessed by DIA and a lower AUROC value of 0.66 when assessed by conventional pathology. Importantly, ELF test provided considerably high diagnostic accuracy to discriminate healthy controls from patients with significant fibrosis defined by Ishak F≥2 and TPA≥5% (AUROCs 0.93 and 0.94, respectively) with optimal ELF cut-off point of 8.4 for both. CONCLUSIONS: Digital quantification could represent a better reference standard than conventional pathology allowing a better discriminatory capability for ELF test. ELF test provided high diagnostic accuracy to discriminate healthy controls from patients with significant fibrosis suggesting a role as a screening strategy in the community setting.