Prognostication of metastatic death in uveal melanoma patients: A Markov multi-state model

Eleuteri, Antonio ORCID: 0000-0003-0718-6672, Taktak, Azzam FG, Coupland, Sarah E ORCID: 0000-0002-1464-2069, Heimann, Heinrich, Kalirai, Helen ORCID: 0000-0002-4440-2576 and Damato, Bertil
(2018) Prognostication of metastatic death in uveal melanoma patients: A Markov multi-state model. COMPUTERS IN BIOLOGY AND MEDICINE, 102. pp. 151-156.

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<h4>Background/aims</h4>Uveal melanoma is fatal in almost 50% of patients. We previously developed a prognostic model to predict all-cause mortality. The aim of this study was to improve our model by predicting metastatic death as a cause-specific event distinct from other causes of death.<h4>Methods</h4>Patients treated in Liverpool were included if they resided in England, Scotland or Wales and if their uveal melanoma involved the choroid. They were flagged at the National Health Service Cancer Registry, which automatically informed us of the date and cause of death of any deceased patients. A semiparametric Markov multi-state model was fitted. Two different baseline hazard rates were assumed, with state transition-specific covariates. For both failure types, age at treatment and sex were used. For the metastatic death case, these factors were added: anterior margin position, largest basal tumour diameter, tumour thickness, extra-ocular extension, presence of epithelioid melanoma cells, presence of closed connective tissue loops, increased mitotic count, chromosome 3 loss, and chromosome 8q gain. Missing data required a multiple-imputation procedure.<h4>Results</h4>The cohort comprised 4161 patients, 893 of whom died of metastastic disease with another 772 dying of other causes. The optimism-corrected, bootstrapped C-index for metastatic death prediction was 0.86, denoting very good discriminative performance. Bootstrapped calibration curves at two and five years also showed very good performance.<h4>Conclusions</h4>Our improved model provides reliable, personalised metastatic death prognostication using clinical, histological and genetic information, and it can be used as a decision support tool to individualize patient care in a clinical environment.

Item Type: Article
Uncontrolled Keywords: Uveal melanoma, Competing risks, Markov model, Cox model, Multiple imputation, Bootstrap
Depositing User: Symplectic Admin
Date Deposited: 04 Nov 2019 09:48
Last Modified: 19 Jan 2023 01:08
DOI: 10.1016/j.compbiomed.2018.09.024
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