Aurel Cami

Predicting Adverse Drug Events Using Pharmacological Network Models

A network-based method that uses available pharmacosafety data can predict yet-to-be-discovered adverse drug events to help reduce drug-associated morbidity and mortality. The Power of Prediction We’ve all done it: googled a combination of medical terms to describe how we feel after taking a new medication. The result is a seemingly infinite list of Web sites telling us that the nausea is normal, or that the headaches warrant another visit to the doctor. Oftentimes, important adverse effects of drugs are discovered and added to the drug label only years after a drug goes on the market. But what if scientists could know about certain adverse drug effects before they are clinically discovered? Cami and colleagues develop a mathematical approach to predicting such adverse events associated with the drugs we take, in hopes of reducing drug-related morbidity—and mortality. After its release to the market, any given drug undergoes rigorous evaluation to determine associated ADEs (adverse drug effects). This post hoc analysis is usually unable to detect rare or delayed-onset ADEs until enough clinical evidence accumulates–a process that may take years. The method devised by Cami and coauthors does not need to wait for such evidence to accumulate. Instead, it can inform drug safety practitioners early on of likely ADEs that will be detected down the line. The authors first collected a “snapshot” of 809 drugs and their 852 related adverse events that had been documented in 2005. These drug-safety associations were combined with taxonomic and biological data to construct a network that is reminiscent of a web. Cami et al. then used this drug-ADE network to train a logistic regression predictive model—basically creating a formula that would indicate the likelihood of unknown side effects of any drug in the network. The predictive capabilities of the model were prospectively validated using drug-ADE associations newly reported between 2006 and 2010. Such prospective evaluation preserves the chronological order of drug adverse event reporting, making it a realistic method for predicting future ADEs. With their network, the authors were able to predict with high specificity seven of eight drug ADEs identified by pharmacological experts as having emerged after 2005, including the relationship between the anti-diabetes drug rosiglitazone (Avandia) and heart attack. The benefit for patients? With this powerful model in place, certain unknown adverse drug effects may be discovered earlier, helping to prevent drug-related morbidity and mortality through appropriate consumer label warnings. 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.

Linking Electronic Health Record-Extracted Psychosocial Data in Real-Time to Risk of Readmission for Heart Failure

BACKGROUND Knowledge of psychosocial characteristics that helps to identify patients at increased risk for readmission for heart failure (HF) may facilitate timely and targeted care. OBJECTIVE We hypothesized that certain psychosocial characteristics extracted from the electronic health record (EHR) would be associated with an increased risk for hospital readmission within the next 30 days. METHODS We identified 15 psychosocial predictors of readmission. Eleven of these were extracted from the EHR (six from structured data sources and five from unstructured clinical notes). We then analyzed their association with the likelihood of hospital readmission within the next 30 days among 729 patients admitted for HF. Finally, we developed a multivariable predictive model to recognize individuals at high risk for readmission. RESULTS We found five characteristics-dementia, depression, adherence, declining/refusal of services, and missed clinical appointments-that were associated with an increased risk for hospital readmission: the first four features were captured from unstructured clinical notes, while the last item was captured from a structured data source. CONCLUSIONS Unstructured clinical notes contain important knowledge on the relationship between psychosocial risk factors and an increased risk of readmission for HF that would otherwise have been missed if only structured data were considered. Gathering this EHR-based knowledge can be automated, thus enabling timely and targeted care.

Pharmacointeraction Network Models Predict Unknown Drug-Drug Interactions

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.

Incidence and Patterns of Extended-Course Antibiotic Therapy in Patients Evaluated for Lyme Disease

BACKGROUND Most patients with Lyme disease (LD) can be treated effectively with 2-4 weeks of antibiotics. The Infectious Disease Society of America guidelines do not currently recommend extended treatment even in patients with persistent symptoms. METHODS To estimate the incidence of extended use of antibiotics in patients evaluated for LD, we retrospectively analyzed claims from a nationwide US health insurance plan in 14 high-prevalence states over 2 periods: 2004-2006 and 2010-2012. RESULTS As measured by payer claims, the incidence of extended antibiotic therapy among patients evaluated for LD was higher in 2010-2012 (14.72 per 100 000 person-years; n = 684) than in 2004-2006 (9.94 per 100 000 person-years; n = 394) (P < .001). Among these patients, 48.8% were treated with ≥2 antibiotics in 2010-2012 and 29.9% in 2004-2006 (P < .001). In each study period, a distinct small group of providers (roughly 3%-4%) made the diagnosis in >20% of the patients who were evaluated for LD and prescribed extended antibiotic treatment. CONCLUSIONS Insurance claims data suggest that the use of extended courses of antibiotics and multiple antibiotics in the treatment of LD has increased in recent years.

Concordance and predictive value of two adverse drug event data sets.

BackgroundAccurate prediction of adverse drug events (ADEs) is an important means of controlling and reducing drug-related morbidity and mortality. Since no single “gold standard” ADE data set exists, a range of different drug safety data sets are currently used for developing ADE prediction models. There is a critical need to assess the degree of concordance between these various ADE data sets and to validate ADE prediction models against multiple reference standards.MethodsWe systematically evaluated the concordance of two widely used ADE data sets – Lexi-comp from 2010 and SIDER from 2012. The strength of the association between ADE (drug) counts in Lexi-comp and SIDER was assessed using Spearman rank correlation, while the differences between the two data sets were characterized in terms of drug categories, ADE categories and ADE frequencies. We also performed a comparative validation of the Predictive Pharmacosafety Networks (PPN) model using both ADE data sets. The predictive power of PPN using each of the two validation sets was assessed using the area under Receiver Operating Characteristic curve (AUROC).ResultsThe correlations between the counts of ADEs and drugs in the two data sets were 0.84 (95% CI: 0.82-0.86) and 0.92 (95% CI: 0.91-0.93), respectively. Relative to an earlier snapshot of Lexi-comp from 2005, Lexi-comp 2010 and SIDER 2012 introduced a mean of 1,973 and 4,810 new drug-ADE associations per year, respectively. The difference between these two data sets was most pronounced for Nervous System and Anti-infective drugs, Gastrointestinal and Nervous System ADEs, and postmarketing ADEs. A minor difference of 1.1% was found in the AUROC of PPN when SIDER 2012 was used for validation instead of Lexi-comp 2010.ConclusionsIn conclusion, the ADE and drug counts in Lexi-comp and SIDER data sets were highly correlated and the choice of validation set did not greatly affect the overall prediction performance of PPN. Our results also suggest that it is important to be aware of the differences that exist among ADE data sets, especially in modeling applications focused on specific drug and ADE categories.

Erratum to: Provider patient-sharing networks and multiple-provider prescribing of benzodiazepines

Erratum to: J Gen Intern Med DOI: 10.1007/s11606-015-3470-8 The odds ratio for the relationship between median care density and multiple prescribing of benzodiazepine was incorrectly reported in the text as 0.76 (95% CI 0.60 – 0.95; p=0.028). It should have read, “Backward stepwise logistic regression revealed that median care density was negatively associated with the risk of an overlapping benzodiazepine prescription (OR 0.76; 95% CI 0.60 – 0.95; p=0.0178) after adjusting for covariates (Table 3).” In the Discussion, it should have also read the following: “Consistent with these studies, in univariate analysis, we showed a significant relationship between the number of prescribers and overlapping benzodiazepine prescriptions; however, the magnitude of the association was reduced when considered alongside care density in multivariate analysis.”