Multivariate joint modeling characterizes how markers of growth and lung function decline predict cystic fibrosis pulmonary exacerbation onset.

Multivariate joint modeling characterizes how markers of growth and lung function decline predict cystic fibrosis pulmonary exacerbation onset.

Active collaboration with Dr. Rhonda Szczesniak, Department of Biostatistics Epidemiology and the department of Pulmonary Medicine at Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, United States.

Objectives: To fit a univariate joint model (JM) of lung-function decline and pulmonary exacerbation (PE) onset and contrast its predictive performance with a class of multivariate JMs that include combinations of growth markers as additional submodels.

Study Design and Setting: Longitudinal cohort study on 17,100 patients aged 6-20 years (US CF Registry; 2003-2015). Primary outcomes included longitudinal lung function (FEV1), BMI, weight-for-age and height-for-age percentiles and onset of PE. Relevant demographic/clinical covariates were included in each submodel. We implemented a joint model of FEV1 and time-to-PE and four multivariate JMs including growth outcomes.
Results: All five JMs indicated a negative association between FEV1 and hazard of PE onset (HR from univariate joint model: 0.97, P < 0.0001), and all had reasonable predictive accuracy (AUC > 0.70 for cross-validations). Jointly modeling only FEV1 and PE onset yielded the most accurate (AUC = 0.75) and precise predictions (narrowest IQR from cross-validations). Dynamic predictions were accurate across forecast horizons (0.5-, 1-, and 2-years) but precision improved with age.

Conclusion: Including growth markers via multivariate JMs did not yield gains in prediction performance but joint modeling is a useful approach for monitoring CF disease progression.

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Eleni-Rosalina Andrinopoulou
Assistant professor in Biostatistics