Prediction of black box warning by mining patterns of Convergent Focus Shift in clinical trial study populations using linked public data

Abstract

Objective To link public data resources for predicting post-marketing drug safety label changes by analyzing the Convergent Focus Shift patterns among drug testing trials. Methods We identified 256 top-selling prescription drugs between 2003 and 2013 and divided them into 83 BBW drugs (drugs with at least one black box warning label) and 173 ROBUST drugs (drugs without any black box warning label) based on their FDA black box warning (BBW) records. We retrieved 7499 clinical trials that each had at least one of these drugs for intervention from the ClinicalTrials.gov. We stratified all the trials by pre-marketing or post-marketing status, study phase, and study start date. For each trial, we retrieved drug and disease concepts from clinical trial summaries to model its study population using medParser and SNOMED-CT. Convergent Focus Shift (CFS) pattern was calculated and used to assess the temporal changes in study populations from pre-marketing to post-marketing trials for each drug. Then we selected 68 candidate drugs, 18 with BBW warning and 50 without, that each had at least nine pre-marketing trials and nine post-marketing trials for predictive modeling. A random forest predictive model was developed to predict BBW acquisition incidents based on CFS patterns among these drugs. Pre-and post-marketing trials of BBW and ROBUST drugs were compared to look for their differences in CFS patterns. Results Among the 18 BBW drugs, we consistently observed that the post-marketing trials focused more on recruiting patients with medical conditions previously unconsidered in the pre-marketing trials. In contrast, among the 50 ROBUST drugs, the post-marketing trials involved a variety of medications for testing their associations with target intervention(s). We found it feasible to predict BBW acquisitions using different CFS patterns between the two groups of drugs. Our random forest predictor achieved an AUC of 0.77. We also demonstrated the feasibility of the predictor for identifying long-term BBW acquisition events without compromising prediction accuracy. Conclusions This study contributes a method for post-marketing pharmacovigilance using Convergent Focus Shift (CFS) patterns in clinical trial study populations mined from linked public data resources. These signals are otherwise unavailable from individual data resources. We demonstrated the added value of linked public data and the feasibility of integrating ClinicalTrials.gov summaries and drug safety labels for post-marketing surveillance. Future research is needed to ensure better accessibility and linkage of heterogeneous drug safety data for efficient pharmacovigilance.

Knowing when to act: A call for an open misinformation library to guide actionable surveillance

“…Other examples of large databases that aggregate community contributions like Wikipedia and arXiv tend to be much broader and less structured, but researchers have also found new uses for the information, including prediction or modelling of outcomes such a citation counts, drug safety outcomes, or movie success (Bar-Ilan and Aharony, 2014; Davis and Fromerth, 2007; Ma and Weng, 2016; Mestyán et al., 2013; Moat et al., 2013). Though it might not seem similar, patient data stored in medical records are another example where structured and unstructured information are contributed by a decentralised group and then pooled for the purpose of decision support – including predicting risks by aggregating from the most similar past examples (Longhurst et al., 2014).…”

A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates

“…Far less work has been performed on identifying trials from the information stored in clinical trial registries or on linking clinical trial registries to bibliographic databases [14,[27][28][29]. However, some methods have shown that it is possible to identify meaningful clusters of similar trials within registries [30][31][32], especially in relation to populations [33], and ClinicalTrials.gov data has been used in predicting black box warnings [34].…”

Identifying unreported links between ClinicalTrials.gov trial registrations and their published results

“…[1][2][3] ClinicalTrials.gov has been used as the basis for studies investigating publication and outcome reporting biases, [4][5][6][7] and data from the registry have been repurposed in novel applications, such as evidence synthesis and safety assessments. 8,9 Connecting trial registrations to published articles reporting results of trials is incomplete. Up to half of all registered trials remain unpublished in the 2 years after trial completion.…”

“…http://clinicaltrials.gov is the largest individual trial registry and has grown substantially following mandated use by journals, funding organisations, and regulatory agencies 1–3 http://clinicaltrials.gov. has been used as the basis for studies investigating publication and outcome reporting biases, 4–7 and data from the registry have been repurposed in novel applications, such as evidence synthesis and safety assessments 8,9 …”