An Automated System Combining Safety Signal Detection and Prioritization from Healthcare Databases: A Pilot Study

Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review

PURPOSE

Signal detection is a crucial step in the discovery of post-marketing adverse drug reactions. There is a growing interest in using routinely collected data to complement established spontaneous report analyses. This work aims to systematically review the methods for drug safety signal detection using routinely collected healthcare data and their performance, both in general and for specific types of drugs and outcomes.

METHODS

We conducted a systematic review following the PRISMA guidelines, and registered a protocol in PROSPERO. MEDLINE, EMBASE, PubMed, Web of Science, Scopus, and the Cochrane Library were searched until July 13, 2021.

RESULTS

The review included 101 articles, among which there were 39 methodological works, 25 performance assessment papers, and 24 observational studies. Methods included adaptations from those used with spontaneous reports, traditional epidemiological designs, methods specific to signal detection with real-world data. More recently, implementations of machine learning have been studied in the literature. Twenty-five studies evaluated method performances, 16 of them using the area under the curve (AUC) for a range of positive and negative controls as their main measure. Despite the likelihood that performance measurement could vary by drug-event pair, only 10 studies reported performance stratified by drugs and outcomes, in a heterogeneous manner. The replicability of the performance assessment results was limited due to lack of transparency in reporting and the lack of a gold standard reference set.

CONCLUSIONS

A variety of methods have been described in the literature for signal detection with routinely collected data. No method showed superior performance in all papers and across all drugs and outcomes, performance assessment and reporting were heterogeneous. However, there is limited evidence that self-controlled designs, high dimensional propensity scores, and machine learning can achieve higher performances than other methods.

Concomitant Medication Use With Xiyanping Injection and the Risk of Suspected Allergic Reactions: A Nested Case–Control Study Based on China’s National Medical Insurance Database

Introduction: Xiyanping injection (XYP), a type of Traditional Chinese Medicine, is widely used and often applied in combination with other medications in treating bronchitis, tonsillitis, and bacillary dysentery in China. In recent years, an elevated risk of allergic reactions has been observed following XYP, but whether concomitant medication use contributes to this risk is still unknown.

Objective: This study aims to investigate the association between the concomitant use of XYP and the 25 most frequently co-applied medications with suspected allergic reactions for China’s patients receiving XYP.

Methods: A nested case–control study was conducted using the sampling data from 2015 China’s Urban Employees Basic Medical Insurance and Urban Residents Basic Medical Insurance database. Four anti-allergic marker drugs were used to evaluate suspected allergic reactions. Univariate analyses and multivariable conditional logistic regression were conducted, and results were reported as odds ratios (ORs) with a 95% confidence interval (CI). Sensitivity analyses were performed on the expanded sample by including those prescribed with anti-allergic marker drugs on the same day as XYP and then stopped XYP on the next day.

Results: Out of 57,612 participants with XYP prescription, we obtained 949 matched case–control pairs. Multivariable conditional logistic regression revealed that seven concomitant medications including gentamicin [OR = 4.29; 95% CI (2.52, 7.30)], cefoperazone-sulbactam [OR = 4.26; 95% CI (1.40, 13.01)], lidocaine [OR = 2.76; 95% CI (1.79, 4.25)], aminophylline [OR = 1.73; 95% CI (1.05, 2.85)], ribavirin [OR = 1.54; 95% CI (1.13, 2.10)], potassium chloride [OR = 1.45; 95% CI (1.10, 1.91)], and vitamin C [OR = 1.32; 95% CI (1.03, 1.70)] were associated with increased risk, while cefathiamidine [OR = 0.29; 95% CI (0.16, 0.51)] was associated with reduced risk. Sensitivity analysis on 2,438 matched pairs revealed similar findings.

Conclusion: Increased risks for suspected allergic reactions were found for the concomitant use of XYP with seven medications. Our data suggest that gentamicin, cefoperazone-sulbactam, lidocaine, and ribavirin should be applied with precautions for patients receiving XYP, and further studies on drug interactions and allergy mechanisms are warranted.

High-Dimensional Propensity Score-Adjusted Case-Crossover for Discovering Adverse Drug Reactions from Computerized Administrative Healthcare Databases

INTRODUCTION

Increasing availability of medico-administrative databases has prompted the development of automated pharmacovigilance signal detection methodologies. Self-controlled approaches have recently been proposed. They account for time-independent confounding factors that may not be recorded. So far, large numbers of drugs have been screened either univariately or with LASSO penalized regressions.

OBJECTIVE

We propose and assess a new method that combines the case-crossover self-controlled design with propensity scores (propensity score-adjusted case-crossover) built from high-dimensional data-driven variable selection, to account for co-medications or possibly other measured confounders.

METHODS

Comparison with the univariate and LASSO case-crossover was performed from simulations and a real-data study. Multiple regressions (LASSO, propensity score-adjusted case-crossover) accounted for co-medications and no other covariates. For the univariate and propensity score-adjusted case-crossover methods, the detection threshold was based on a false discovery rate procedure, while for LASSO, it relied on the Akaike Information Criterion. For the real-data study, two drug safety experts evaluated the signals generated from the analysis of 4099 patients with acute myocardial infarction from the French national health database.

RESULTS

On simulations, our approach ranked the signals similarly to the LASSO and better than the univariate method while controlling the false discovery rate at the prespecified level, contrary to the univariate method. The LASSO provided the best sensitivity at the cost of larger false discovery rate estimates. On the application, our approach showed similar performances to the LASSO and better performances than the univariate method. It highlighted 43 signals out of 609 drug candidates: 22 (51%) were considered as potentially pharmacologically relevant, including seven (16%) regarded as highly relevant.

CONCLUSIONS

Our findings show the interest of a propensity score combined with a case-crossover for pharmacovigilance. They also confirm that indication bias remains a challenge when mining medico-administrative databases.

Automation in signal management in pharmacovigilance-an insight

Drugs are the imperial part of modern society, but along with their therapeutic effects, drugs can also cause adverse effects, which can be mild to morbid. Pharmacovigilance is the process of collection, detection, assessment, monitoring and prevention of adverse drug events in both clinical trials as well as in the post-marketing phase. The recent trends in increasing unknown adverse events, known as signals, have raised the need to develop an ideal system for monitoring and detecting the potential signals timely. The process of signal management comprises of techniques to identify individual case safety reports systematically. Automated signal detection is highly based upon the data mining of the spontaneous reporting system such as reports from health care professional, observational studies, medical literature or from social media. If a signal is not managed properly, it can become an identical risk associated with the drug which can be hazardous for the patient safety and may have fatal outcomes which may impact health care system adversely. Once a signal is detected quantitatively, it can be further processed by the signal management team for the qualitative analysis and further evaluations. The main components of automated signal detection are data extraction, data acquisition, data selection, and data analysis and data evaluation. This system must be developed in the correct format and context, which eventually emphasizes the quality of data collected and leads to the optimal decision-making based upon the scientific evaluation.