Masking effect in the Spanish spontaneous reporting database FEDRA.

چکیده اصلی

PURPOSE: This study aimed to analyse the potential masking effect of drug-event combinations (DECs) with extreme reporting rates and to evaluate the impact on disproportionality analysis and therefore in signal detection. METHODS: An algorithm is proposed, based on the approach established by Juhlin et al., that identifies influential outliers and excludes them through six unmasking strategies to recalculate the Reporting Odds Ratio (ROR). This study was performed in the Spanish spontaneous reporting database FEDRA. The dataset included reports from 1 January 1981 to 17 February 2025 excluding those in which the suspected drug was a vaccine (ATC group J07). RESULTS: A total of 287,145 DECs were analysed. Of these, 0.4% (1,211 out of 287,145) were considered influential outliers. Almost 21% (81,371 out of 389,262) of the FEDRA reports included an influential outlier. About 14% (494 out of 3,447) of the drugs and 5.9% (576 out of 9,745) of the adverse drug reactions in FEDRA were part of an influential outlier. Regarding the disproportionality analysis, the proportion of DECs whose lower limit of the 95% confidence interval of their ROR increased after the different strategies ranged from 14.6 to 48.1%. CONCLUSION: The study demonstrates the existence of a masking effect in FEDRA caused by certain highly reported DECs. Their exclusion according to the proposed unmasking strategies could reveal additional SDRs that may otherwise remain masked during routine signal detection activities. This study aimed to analyse how certain highly reported drug-event combinations (DECs) may obscure relevant safety signals. To address it, a method was developed to identify and remove these extreme DECs to get a clearer picture. The analysis was conducted using data from the Spanish spontaneous reporting database FEDRA, excluding vaccines (ATC group J07). A total of 287,145 DECs were assessed, of which 0.4% were identified as influential outliers. Approximately 14.3% of the drugs and 5.9% of the adverse drug reactions in FEDRA were involved in these extreme DECs. Between 14.6% and 48.1% of the DECs showed an important increase in their lower limit of the 95% confidence interval of their reporting odds ratios. In conclusion, the study demonstrates that certain highly reported drug-adverse drug reaction pairs can hide other important issues. By removing these extreme DECs, the potential to detect true safety signals increases.

ترجمه فارسی چکیده

هدف این مطالعه تحلیل اثر احتمالی ماسک‌کردن ترکیب‌های دارو-رویداد (DEC) با نرخ‌های گزارش‌دهی افراطی و ارزیابی تأثیر آن بر تحلیل نابرابری و در نتیجه تشخیص سیگنال بود. الگوریتمی بر اساس رویکرد Juhlin et al. پیشنهاد شد که خارق‌العاده‌ترین موارد را شناسایی کرده و با استفاده از شش استراتژی ماسک‌برداری، آن‌ها را حذف می‌کند تا نسبت شانس گزارش‌دهی (ROR) را بازمحاسبه نماید. این مطالعه در پایگاه گزارش‌دهی خودکار اسپانیا FEDRA انجام شد. داده‌ها شامل گزارش‌های ۱ ژانویه ۱۹۸۱ تا ۱۷ فوریه ۲۰۲۵ به‌جز آن‌هایی بود که داروی مشکوک واکسن بود (گروه ATC J07). مجموعاً ۲۸۷،۱۴۵ DEC تحلیل شد. از این تعداد، ۰.۴٪ (۱،۲۱۱ مورد) به عنوان خارق‌العاده‌ترین موارد مؤثر شناسایی شدند. حدود ۲۱٪ (۸۱،۳۷۱ مورد) از گزارش‌های FEDRA شامل یک خارق‌العاده‌ترین مورد مؤثر بود. حدود ۱۴٪ (۴۹۴ مورد) از داروها و ۵.۹٪ (۵۷۶ مورد) از واکنش‌های نامطلوب دارویی در FEDRA بخشی از یک خارق‌العاده‌ترین مورد مؤثر بودند. در مورد تحلیل نابرابری، نسبت DECهایی که حد پایین ۹۵٪ اطمینان ROR آن‌ها پس از استراتژی‌های مختلف افزایش یافت، بین ۱۴.۶ تا ۴۸.۱٪ ranged بود.

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