ABSTRACT
OBJE CTIVE To mine and analyze t he cardiac adverse drug reaction （ADR）signals induced by febuxostat in post-marketing experience ，and to provide reference for rational drug use in clinic. METHODS Reporting odds ratio （ROR） method was used to mine the ADR signals induced by febuxostat from the FDA Adverse Event Reporting System during the first quarter of 2009 to the fourth quarter of 2020；the information of cardiac disease signals was counted and analyzed. RESULTS A total of 209 ADR signals were detected in 8 282 adverse drug event （ADE）reports with febuxostat as the primary suspected drug ， involving 27 cardiac signals and 754 ADE reports. The most reported signals were symptoms （262 reports），including dizziness ， oedema peripheral，chest pain ，palpitations and gravitational oedema and so on ，followed by coronary atherosclerotic heart disease signal，heart failure signal ，arrhythmia signal ，sudden cardiac death signal （233，157，90，12 reports，respectively）. More than half of the signals were mentioned in the drug instructions ，while the unmentioned signals were mainly kinds of cardiac failure ， arrhythmia and extrasystoles ，etc. The patients with cardiac ADEs who received febuxostat were more male than female ，and the age was 60 and over ；the drug dosage was mostly 40 mg/d or 80 mg/d as recommended in the drug instructions ，and cardiac ADEs mostly occurred within 1 month of medication. CONCLUSIONS Routine attention should be paid to the cardiac safety of febuxostat during medication ，further evaluation and validation of febuxostat-induced cardiac ADR signals are still needed.
ABSTRACT
BACKGROUND: To facilitate long-term safety surveillance of marketing drugs, many spontaneously reporting systems (SRSs) of ADR events have been established world-wide. Since the data collected by SRSs contain sensitive personal health information that should be protected to prevent the identification of individuals, it procures the issue of privacy preserving data publishing (PPDP), that is, how to sanitize (anonymize) raw data before publishing. Although much work has been done on PPDP, very few studies have focused on protecting privacy of SRS data and none of the anonymization methods is favorable for SRS datasets, due to which contain some characteristics such as rare events, multiple individual records, and multi-valued sensitive attributes. METHODS: We propose a new privacy model called MS(k, θ (*) )-bounding for protecting published spontaneous ADE reporting data from privacy attacks. Our model has the flexibility of varying privacy thresholds, i.e., θ (*) , for different sensitive values and takes the characteristics of SRS data into consideration. We also propose an anonymization algorithm for sanitizing the raw data to meet the requirements specified through the proposed model. Our algorithm adopts a greedy-based clustering strategy to group the records into clusters, conforming to an innovative anonymization metric aiming to minimize the privacy risk as well as maintain the data utility for ADR detection. Empirical study was conducted using FAERS dataset from 2004Q1 to 2011Q4. We compared our model with four prevailing methods, including k-anonymity, (X, Y)-anonymity, Multi-sensitive l-diversity, and (α, k)-anonymity, evaluated via two measures, Danger Ratio (DR) and Information Loss (IL), and considered three different scenarios of threshold setting for θ (*) , including uniform setting, level-wise setting and frequency-based setting. We also conducted experiments to inspect the impact of anonymized data on the strengths of discovered ADR signals. RESULTS: With all three different threshold settings for sensitive value, our method can successively prevent the disclosure of sensitive values (nearly all observed DRs are zeros) without sacrificing too much of data utility. With non-uniform threshold setting, level-wise or frequency-based, our MS(k, θ (*))-bounding exhibits the best data utility and the least privacy risk among all the models. The experiments conducted on selected ADR signals from MedWatch show that only very small difference on signal strength (PRR or ROR) were observed. The results show that our method can effectively prevent the disclosure of patient sensitive information without sacrificing data utility for ADR signal detection. CONCLUSIONS: We propose a new privacy model for protecting SRS data that possess some characteristics overlooked by contemporary models and an anonymization algorithm to sanitize SRS data in accordance with the proposed model. Empirical evaluation on the real SRS dataset, i.e., FAERS, shows that our method can effectively solve the privacy problem in SRS data without influencing the ADR signal strength.
