L. Wu, Wang, and Evans (2019) introduced the disruption index (DI) which has been designed to capture disruptiveness of individual publications based on dynamic citation networks of publications. In this study, we propose a statistical modelling approach to tackle open questions with the DI: (1) how to consider uncertainty in the calculation of DI values, (2) how to aggregate DI values for paper sets, (3) how to predict DI values using covariates, and (4) how to unambiguously classify papers into either disruptive or not disruptive. A Bayesian multilevel logistic approach is suggested that extends an approach of Figueiredo and Andrade (2019). A reanalysis of sample data from Bornmann and Tekles (2021) and Bittmann, Tekles, and Bornmann (2022) shows that the Bayesian approach is helpful in tackling the open questions. For example, the modelling approach is able to predict disruptive papers (milestone papers in physics) in a good way.