48. The golden touch: using MIDAS to uncover associations between environmental factors and vasculitis flare
Creators: name: Dempsey1, Daniel
name: Scott2, Jennifer
name: Little2, Mark A
name: Wyse1, Jason
Publisher: name: Zenodo
Year: 2022
Subjects: Vasculitis
ANCA
Abstracts
Giant Cell Arthritis
MPA
GPA
EGPA
Takayasu
IgA vasculitis

description: <strong>Background</strong>: Patients with ANCA Vasculitis are at risk of disease flare. We hypothesise that flares may be driven by exposure to one or more environmental factors, such as weather patterns or specific pollutants. Investigating this is challenging because: 1) Flare events can occur at any time – i.e., they are observed <em>unevenly</em>, unlike environmental data that is gathered on a fixed periodic basis (e.g., daily). 2) Exposures to environmental factors likely have a cumulative impact on the risk of flare, and the nature of this cumulative relationship is unknown. 3) There are many environmental exposures that could impact flare; these need to be sifted objectively to isolate those that may elevate risk. This work develops a regression method which can identify correlations between flare risk and cumulative exposures. The methods are demonstrated on synthetic data constructed by reference to a clinical database of patients with ANCA vasculitis. <strong>Methods:</strong> The method is based on the Mixed Data Sampling (MIDAS) model. We generalised this method, in a Bayesian framework, to handle unevenly sampled response data, addressing challenge (1) above. This method incorporates a data-driven approach to determine how an environmental exposure has a cumulative effect over time, addressing challenge (2). A further novel generalization we make to MIDAS regression modelling is to incorporate Bayesian stochastic model search, allowing us to search over the many possible combinations of environmental factors that could impact flare risk. This can tell us with what probability an environmental factor had any influence on a flare event. We illustrated the current state of the model with results from a synthetic data simulation study. The response data was simulated to be heavily imbalanced (20% positive events, 80% non-events) to reflect the real-life rarity of flare events. We also simulated many 'pollutants', but only two of which actually influenced the occurrence of synthetic flares. This was repeated 12 times to evaluate how closely the model can retrieve the parameters we used to create the data. <strong>Results:</strong> The results for the two synthetic pollutants that generated the flares are given in figure 1. The algorithm was highly effective at filtering out the junk pollutants, though note that these results are based on a somewhat idealised scenario. A potential problem can arise from the sometimes periodic (or <em>seasonal</em>) nature of time series. Other simulations revealed that the method may have difficulty determining over what lags the effect is distributed when seasonal peaks occur close together. Regardless, even in such cases, the algorithm was still able to ascertain an effect, which is the important feature. <strong>Conclusions</strong>: We have described a regression method for quantifying the time-lagged association between an unevenly sampled response variable and evenly sampled covariates. This approach was developed to uncover a correlation between the probability of a subject suffering a flare event and their environmental exposure, but we envisage the approach being useful for many other applications. <strong>Disclosures:</strong> None <strong>Figure 1.</strong> The 12 simulation results (in grey), their overall average (in blue) and the actual value (in orange) for the two non-junk synthetic pollutants. The plot displays the inferred <em>Distributed Lag </em>Functions.