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Development and validation of a Bayesian survival model for inclusion body myositis.

ABSTRACT: BACKGROUND:Associations between disease characteristics and payer-relevant outcomes can be difficult to establish for rare and progressive chronic diseases with sparse available data. We developed an exploratory bridging model to predict premature mortality from disease characteristics, and using inclusion body myositis (IBM) as a representative case study. METHODS:Candidate variables that may be potentially associated with premature mortality were identified by disease experts and from the IBM literature. Interdependency between candidate variables in IBM patients were assessed using existing patient-level data. A Bayesian survival model for the IBM population was developed with identified variables as predictors for premature mortality in the model. For model selection and external validation, model predictions were compared to published mortality data in IBM patient cohorts. After validation, the final model was used to simulate the increased risk of premature death in IBM patients. Baseline survival was based on age- and gender-specific survival curves for the general population in Western countries as reported by the World Health Organisation. RESULTS:Presence of dysphagia, aspiration pneumonia, falls, being wheelchair-bound and 6-min walking distance (6MWD in meters) were identified as candidate variables to be used as predictors for premature mortality based on inputs received from disease experts and literature. There was limited correlation between these functional performance measures, which were therefore treated as independent variables in the model. Based on the Bayesian survival model, among all candidate variables, presence of dysphagia and decrease in 6MWD [m] were associated with poorer survival with contributing hazard ratios (HR) 1.61 (95% credible interval [CrI]: 0.84-3.50) and 2.48 (95% CrI: 1.27-5.00) respectively. Excess mortality simulated in an IBM cohort vs. an age- and gender matched general-population cohort was 4.03 (95% prediction interval 1.37-10.61). CONCLUSIONS:For IBM patients, results suggest an increased risk of premature death compared with the general population of the same age and gender. In the absence of hard data, bridging modelling generated survival predictions by combining relevant information. The methodological principle would be applicable to the analysis of associations between disease characteristics and payer-relevant outcomes in progressive chronic and rare diseases. Studies with lifetime follow-up would be needed to confirm the modelling results.

SUBMITTER: Capkun G

PROVIDER: S-EPMC6836518 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Development and validation of a Bayesian survival model for inclusion body myositis.

Capkun Gorana G Schmidt Jens J Ghosh Shubhro S Sharma Harsh H Obadia Thomas T de Vera Ana A Risson Valery V Amzal Billy B

Theoretical biology & medical modelling 20191107 1

<h4>Background</h4>Associations between disease characteristics and payer-relevant outcomes can be difficult to establish for rare and progressive chronic diseases with sparse available data. We developed an exploratory bridging model to predict premature mortality from disease characteristics, and using inclusion body myositis (IBM) as a representative case study.<h4>Methods</h4>Candidate variables that may be potentially associated with premature mortality were identified by disease experts an ...[more]

PMID: 31694651

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Project description:BackgroundInclusion body myositis (IBM) is an inflammatory myopathy clinically characterized by proximal and distal muscle weakness, with inflammatory infiltrates, rimmed vacuoles and mitochondrial changes in muscle histopathology. There is scarce knowledge on IBM aetiology, and non-established biomarkers or effective treatments are available, partly due to the lack of validated disease models.MethodsWe have performed transcriptomics and functional validation of IBM muscle pathological hallmarks in fibroblasts from IBM patients (n = 14) and healthy controls (n = 12), paired by age and sex. The results comprise an mRNA-seq, together with functional inflammatory, autophagy, mitochondrial and metabolic changes between patients and controls.ResultsGene expression profile of IBM vs control fibroblasts revealed 778 differentially expressed genes (P-value adj < 0.05) related to inflammation, mitochondria, cell cycle regulation and metabolism. Functionally, an increased inflammatory profile was observed in IBM fibroblasts with higher supernatant cytokine secretion (three-fold increase). Autophagy was reduced considering basal protein mediators (18.4% reduced), time-course autophagosome formation (LC3BII 39% reduced, P-value < 0.05), and autophagosome microscopic evaluation. Mitochondria displayed reduced genetic content (by 33.9%, P-value < 0.05) and function (30.2%-decrease in respiration, 45.6%-decline in enzymatic activity (P-value < 0.001), 14.3%-higher oxidative stress, 135.2%-increased antioxidant defence (P-value < 0.05), 11.6%-reduced mitochondrial membrane potential (P-value < 0.05) and 42.8%-reduced mitochondrial elongation (P-value < 0.05)). In accordance, at the metabolite level, organic acid showed a 1.8-fold change increase, with conserved amino acid profile. Correlating to disease evolution, oxidative stress and inflammation emerge as potential markers of prognosis.ConclusionsThese findings confirm the presence of molecular disturbances in peripheral tissues from IBM patients and prompt patients' derived fibroblasts as a promising disease model, which may eventually be exported to other neuromuscular disorders. We additionally identify new molecular players in IBM associated with disease progression, setting the path to deepen in disease aetiology, in the identification of novel biomarkers or in the standardization of biomimetic platforms to assay new therapeutic strategies for preclinical studies.

Dataset Information

Development and validation of a Bayesian survival model for inclusion body myositis.

Publications

Development and validation of a Bayesian survival model for inclusion body myositis.

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