LAUSR

Treatment decisions in primary myelofibrosis (PMF) are guided by numerous prognostic systems. Patient specific comorbidities have influence on treatment related survival, and are considered in clinical contexts, but have not been routinely incorporated into current prognostic models. We hypothesized that patient specific comorbidities would inform prognosis and could be incorporated into a quantitative score. All patients with PMF or secondary MF (sMF) with available DNA and comprehensive electronic health record (EHR) data treated at Vanderbilt University Medical Center between 1995-2016 were identified within Vanderbilt's Synthetic Derivative and BioVU Biobank. We recapitulated established PMF risk scores (e.g., DIPSS, DIPSS plus, GPSS, MIPSS 70+) and comorbidities through EHR chart extraction and next generation sequencing (NGS) on biobanked peripheral blood DNA. The impact of comorbidities was assessed via DIPSS-adjusted overall survival using Bonferroni correction. Comorbidities associated with inferior survival include renal failure/dysfunction (hazard ratio [HR] 4.3; 95% CI 2.1-8.9; p = 0.0001), intracranial hemorrhage (HR 28.7; 95% CI 7.0-116.8; p=2.83e-06), invasive fungal infection (HR 41.2; 95% CI 7.2-235.2; p=2.90e-05), chronic encephalopathy (HR 15.1; 95% CI 3.8-59.4; p=0.0001). The extended DIPSS model including all four significant comorbidities showed a significantly higher discriminating power (C-index 0.81; 95% CI 0.78-0.84) than the original DIPSS model (C-index 0.73; 95% CI 0.70-0.77). In summary, we repurposed an institutional biobank to identify and risk-classify an uncommon hematologic malignancy by established (e.g., DIPSS) and other clinical and pathologic factors (e.g., comorbidities) in an unbiased fashion. The inclusion of comorbidities into risk evaluation may augment prognostic capability of future genetics-based scoring systems.