Early and accurate identification of adverse drug events (ADEs) is critically important for public health. We have developed a novel approach for predicting ADEs, called predictive pharmacosafety networks (PPNs). PPNs integrate the network structure formed by known drug-ADE relationships with information on specific drugs and adverse events to predict likely unknown ADEs. Rather than waiting for sufficient post-market evidence to accumulate for a given ADE, this predictive approach relies on leveraging existing, contextual drug safety information, thereby having the potential to identify certain ADEs earlier. We constructed a network representation of drug-ADE associations for 809 drugs and 852 ADEs on the basis of a snapshot of a widely used drug safety database from 2005 and supplemented these data with additional pharmacological information. We trained a logistic regression model to predict unknown drug-ADE associations that were not listed in the 2005 snapshot. We evaluated the model's performance by comparing these predictions with the new drug-ADE associations that appeared in a 2010 snapshot of the same drug safety database. The proposed model achieved an AUROC (area under the receiver operating characteristic curve) statistic of 0.87, with a sensitivity of 0.42 given a specificity of 0.95. These findings suggest that predictive network methods can be useful for predicting unknown ADEs.

Drug-drug interactions (DDIs) can lead to serious and potentially lethal adverse events. In recent years, several drugs have been withdrawn from the market due to interaction-related adverse events (AEs). Current methods for detecting DDIs rely on the accumulation of sufficient clinical evidence in the post-market stage – a lengthy process that often takes years, during which time numerous patients may suffer from the adverse effects of the DDI. Detection methods are further hindered by the extremely large combinatoric space of possible drug-drug-AE combinations. There is therefore a practical need for predictive tools that can identify potential DDIs years in advance, enabling drug safety professionals to better prioritize their limited investigative resources and take appropriate regulatory action. To meet this need, we describe Predictive Pharmacointeraction Networks (PPINs) – a novel approach that predicts unknown DDIs by exploiting the network structure of all known DDIs, together with other intrinsic and taxonomic properties of drugs and AEs. We constructed an 856-drug DDI network from a 2009 snapshot of a widely-used drug safety database, and used it to develop PPIN models for predicting future DDIs. We compared the DDIs predicted based solely on these 2009 data, with newly reported DDIs that appeared in a 2012 snapshot of the same database. Using a standard multivariate approach to combine predictors, the PPIN model achieved an AUROC (area under the receiver operating characteristic curve) of 0.81 with a sensitivity of 48% given a specificity of 90%. An analysis of DDIs by severity level revealed that the model was most effective for predicting “contraindicated” DDIs (AUROC = 0.92) and less effective for “minor” DDIs (AUROC = 0.63). These results indicate that network based methods can be useful for predicting unknown drug-drug interactions.

This study of once-daily gentamicin represents the largest sample size of pre-term infants published to date. The proposed regimen is simple and yields a high proportion of desirable levels. We recommend it for use in preterm and term newborns.

No abstract

Objective To characterize heparin-induced thrombocytopenia (HIT) at a single pediatric center including the prevalence and the accuracy of the 4Ts scoring system as a predictor of HIT. Study design In this retrospective cohort study, we identified 155 consecutive patients < 21 years old with sufficient data for 4Ts scoring. The 4Ts scoring system is a validated pretest tool in adults that predicts the likelihood of HIT using clinical features. Hospital-wide exposure to unfractionated (UFH) and low molecular weight heparin (LMWH) was determined by querying the hospital pharmacy database. Results The majority of patients with suspected HIT (61.2%) were on surgical services. Initial 4Ts scoring predicted the risk of HIT as 3 (2%) had high risk 4Ts scores, 114 (73%) had intermediate risk 4Ts scores, and the remaining 38 (25%) had low risk 4Ts scores. HIT was confirmed in 0/38 patients with low risk 4Ts scores, 2/114 patients with intermediate-risk 4Ts scores and all three patients with high-risk 4Ts scores presented with HIT with thrombosis. Of 12 positive HIT screening tests, results were falsely positive in 66.6% of patients with intermediate risk 4Ts scores and 100% of patients with low risk 4Ts scores. The prevalence of HIT was 0.058% and HIT with thrombosis was 0.046% in pediatric patients on UFH. Conclusions The incidence of HIT appears significantly lower in pediatric patients compared with adults. Application of the 4Ts system as a pretest tool may reduce laboratory evaluation for HIT in heparin-exposed children with low risk 4Ts scores, decreasing unnecessary further testing, intervention and cost.