Patient-specific comorbidities as prognostic variables for survival in myelofibrosis

Andrew L. Sochacki, Cosmin Adrian Bejan, Shilin Zhao, Ameet Patel, Ashwin Kishtagari, Travis P. Spaulding, Alexander J. Silver, Shannon S. Stockton, Kelly Pugh, R. Dixon Dorand, Manasa Bhatta, Nicholas Strayer, Siwei Zhang, Christina A. Snider, Thomas Stricker, Aziz Nazha, Alexander G. Bick, Yaomin Xu, Michael R. Savona

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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 myelofibrosis with available DNA and comprehensive electronic health record (EHR) data treated at Vanderbilt University Medical Center between 1995 and 2016 were identified within Vanderbilt’s Synthetic Derivative and BioVU Biobank. We recapitulated established PMF risk scores (eg, Dynamic International Prognostic Scoring System [DIPSS], DIPSS plus, Genetics-Based Prognostic Scoring System, Mutation-Enhanced International Prognostic Scoring System 70+) and comorbidities through EHR chart extraction and next-generation sequencing 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% confidence interval [95% CI], 2.1-8.9; P = .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), and chronic encephalopathy (HR, 15.1; 95% CI, 3.8-59.4; P = .0001). The extended DIPSS model including all 4 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 (eg, DIPSS) and other clinical and pathologic factors (eg, comorbidities) in an unbiased fashion. The inclusion of comorbidities into risk evaluation may augment prognostic capability of future genetics-based scoring systems.

Original languageEnglish
Pages (from-to)756-767
Number of pages12
JournalBlood advances
Volume7
Issue number5
DOIs
StatePublished - 14 Mar 2023
Externally publishedYes

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