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Drug Safety Monitoring in Children: Performance of Signal Detection Algorithms and Impact of Age Stratification.

ABSTRACT: Spontaneous reports of suspected adverse drug reactions (ADRs) can be analyzed to yield additional drug safety evidence for the pediatric population. Signal detection algorithms (SDAs) are required for these analyses; however, the performance of SDAs in the pediatric population specifically is unknown. We tested the performance of two SDAs on pediatric data from the US FDA Adverse Event Reporting System (FAERS) and investigated the impact of age stratification and age adjustment on the performance of SDAs.We tested the performance of two established SDAs: the proportional reporting ratio (PRR) and the empirical Bayes geometric mean (EBGM) on a pediatric dataset from FAERS (2004-2012). We compared the performance of the SDAs with a published pediatric-specific reference set by calculating diagnostic test-related statistics, including the area under the curve (AUC) of receiver operating characteristics. Impact of age stratification and age-adjustment on the performance of the SDAs was assessed. Age adjustment was performed by pooling (Mantel-Hanszel) stratum-specific estimates.A total of 115,674 pediatric reports (patients aged 0-18 years) comprising 893,587 drug-event combinations (DECs) were analysed. Crude values of the AUC were similar for both SDAs: 0.731 (PRR) and 0.745 (EBGM). Stratification unmasked four DECs, e.g., 'ibuprofen and thrombocytopenia'. Age adjustment did not improve performance.The performance of the two tested SDAs was similar in the pediatric population. Age adjustment does not improve performance and is therefore not recommended to be performed routinely. Stratification can reveal new associations, and therefore is recommended when either drug use is age-specific or when an age-specific risk is suspected.

SUBMITTER: Osokogu OU

PROVIDER: S-EPMC4982893 | biostudies-other | 2016 Sep

REPOSITORIES: biostudies-other

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Drug Safety Monitoring in Children: Performance of Signal Detection Algorithms and Impact of Age Stratification.

Osokogu Osemeke U OU Dodd Caitlin C Pacurariu Alexandra A Kaguelidou Florentia F Weibel Daniel D Sturkenboom Miriam C J M MC

Drug safety 20160901 9

<h4>Introduction</h4>Spontaneous reports of suspected adverse drug reactions (ADRs) can be analyzed to yield additional drug safety evidence for the pediatric population. Signal detection algorithms (SDAs) are required for these analyses; however, the performance of SDAs in the pediatric population specifically is unknown. We tested the performance of two SDAs on pediatric data from the US FDA Adverse Event Reporting System (FAERS) and investigated the impact of age stratification and age adjust ...[more]

PMID: 27255487

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Project description:BackgroundOutbreak detection algorithms play an important role in effective automated surveillance. Although many algorithms have been designed to improve the performance of outbreak detection, few published studies have examined how epidemic features of infectious disease impact on the detection performance of algorithms. This study compared the performance of three outbreak detection algorithms stratified by epidemic features of infectious disease and examined the relationship between epidemic features and performance of outbreak detection algorithms.MethodsExponentially weighted moving average (EWMA), cumulative sum (CUSUM) and moving percentile method (MPM) algorithms were applied. We inserted simulated outbreaks into notifiable infectious disease data in China Infectious Disease Automated-alert and Response System (CIDARS), and compared the performance of the three algorithms with optimized parameters at a fixed false alarm rate of 5% classified by epidemic features of infectious disease. Multiple linear regression was adopted to analyse the relationship of the algorithms' sensitivity and timeliness with the epidemic features of infectious diseases.ResultsThe MPM had better detection performance than EWMA and CUSUM through all simulated outbreaks, with or without stratification by epidemic features (incubation period, baseline counts and outbreak magnitude). The epidemic features were associated with both sensitivity and timeliness. Compared with long incubation, short incubation had lower probability (?*?=?-0.13, P?<?0.001) but needed shorter time to detect outbreaks (?*?=?-0.57, P?<?0.001). Lower baseline counts were associated with higher probability (?*?=?-0.20, P?<?0.001) and longer time (?*?=?0.14, P?<?0.001). The larger outbreak magnitude was correlated with higher probability (?*?=?0.55, P?<?0.001) and shorter time (?*?=?-0.23, P?<?0.001).ConclusionsThe results of this study suggest that the MPM is a prior algorithm for outbreak detection and differences of epidemic features in detection performance should be considered in automatic surveillance practice.

Dataset Information

Drug Safety Monitoring in Children: Performance of Signal Detection Algorithms and Impact of Age Stratification.

Publications

Drug Safety Monitoring in Children: Performance of Signal Detection Algorithms and Impact of Age Stratification.

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