Herbert S. Chase

Mining multi-item drug adverse effect associations in spontaneous reporting systems

BackgroundMulti-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was noted in several prominent pharmacovigilance studies. In this paper we examine the application of a well established data mining method known as association rule mining, which we tailored to the above problem, and demonstrate its value. The method was applied to the FDAs spontaneous adverse event reporting system (AERS) with minimal restrictions and expectations on its output, an experiment that has not been previously done on the scale and generality proposed in this work.ResultsBased on a set of 162,744 reports of suspected ADEs reported to AERS and published in the year 2008, our method identified 1167 multi-item ADE associations. A taxonomy that characterizes the associations was developed based on a representative sample. A significant number (67% of the total) of potential multi-item ADE associations identified were characterized and clinically validated by a domain expert as previously recognized ADE associations. Several potentially novel ADEs were also identified. A smaller proportion (4%) of associations were characterized and validated as known drug-drug interactions.ConclusionsOur findings demonstrate that multi-item ADEs are present and can be extracted from the FDA’s adverse effect reporting system using our methodology, suggesting that our method is a valid approach for the initial identification of multi-item ADEs. The study also revealed several limitations and challenges that can be attributed to both the method and quality of data.

Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions.

OBJECTIVE Data-mining algorithms that can produce accurate signals of potentially novel adverse drug reactions (ADRs) are a central component of pharmacovigilance. We propose a signal-detection strategy that combines the adverse event reporting system (AERS) of the Food and Drug Administration and electronic health records (EHRs) by requiring signaling in both sources. We claim that this approach leads to improved accuracy of signal detection when the goal is to produce a highly selective ranked set of candidate ADRs. MATERIALS AND METHODS Our investigation was based on over 4 million AERS reports and information extracted from 1.2 million EHR narratives. Well-established methodologies were used to generate signals from each source. The study focused on ADRs related to three high-profile serious adverse reactions. A reference standard of over 600 established and plausible ADRs was created and used to evaluate the proposed approach against a comparator. RESULTS The combined signaling system achieved a statistically significant large improvement over AERS (baseline) in the precision of top ranked signals. The average improvement ranged from 31% to almost threefold for different evaluation categories. Using this system, we identified a new association between the agent, rasburicase, and the adverse event, acute pancreatitis, which was supported by clinical review. CONCLUSIONS The results provide promising initial evidence that combining AERS with EHRs via the framework of replicated signaling can improve the accuracy of signal detection for certain operating scenarios. The use of additional EHR data is required to further evaluate the capacity and limits of this system and to extend the generalizability of these results.

Biclustering of adverse drug events in the FDA's spontaneous reporting system.

In this article, we present a new pharmacovigilance data mining technique based on the biclustering paradigm, which is designed to identify drug groups that share a common set of adverse events (AEs) in the spontaneous reporting system (SRS) of the US Food and Drug Administration (FDA). A taxonomy of biclusters is developed, revealing that a significant number of bona fide adverse drug event (ADE) biclusters have been identified. Statistical tests indicate that it is extremely unlikely that the bicluster structures thus discovered, as well as their content, could have arisen by mere chance. Some of the biclusters classified as indeterminate provide support for previously unrecognized and potentially novel ADEs. In addition, we demonstrate the potential importance of the proposed methodology in several important aspects of pharmacovigilance such as providing insight into the etiology of ADEs, facilitating the identification of novel ADEs, suggesting methods and a rationale for aggregating terminologies, highlighting areas of focus, and providing an exploratory tool for data mining.

Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis

BACKGROUND Adverse drug events (ADE) cause considerable harm to patients, and consequently their detection is critical for patient safety. The US Food and Drug Administration maintains an adverse event reporting system (AERS) to facilitate the detection of ADE in drugs. Various data mining approaches have been developed that use AERS to detect signals identifying associations between drugs and ADE. The signals must then be monitored further by domain experts, which is a time-consuming task. OBJECTIVE To develop a new methodology that combines existing data mining algorithms with chemical information by analysis of molecular fingerprints to enhance initial ADE signals generated from AERS, and to provide a decision support mechanism to facilitate the identification of novel adverse events. RESULTS The method achieved a significant improvement in precision in identifying known ADE, and a more than twofold signal enhancement when applied to the ADE rhabdomyolysis. The simplicity of the method assists in highlighting the etiology of the ADE by identifying structurally similar drugs. A set of drugs with strong evidence from both AERS and molecular fingerprint-based modeling is constructed for further analysis. CONCLUSION The results demonstrate that the proposed methodology could be used as a pharmacovigilance decision support tool to facilitate ADE detection.

FGF-23 Levels in Patients with AKI and Risk of Adverse Outcomes

BACKGROUND AND OBJECTIVES Fibroblast growth factor 23 plays an important role in regulating phosphate and vitamin D homeostasis. Elevated levels of fibroblast growth factor 23 are independently associated with mortality in patients with CKD and ESRD. Whether fibroblast growth factor 23 levels are elevated and associated with adverse outcomes in patients with AKI has not been studied. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS This study had 30 participants with AKI, which was defined as an increase in serum creatinine ≥ 0.3 mg/dl or ≥ 50% from baseline, and 30 controls from the general hospital wards and intensive care units. Plasma levels of C-terminal fibroblast growth factor 23 and vitamin D metabolites were measured within 24 hours of AKI onset and 5 days later. The composite endpoint was death or need for renal replacement therapy. RESULTS Enrollment fibroblast growth factor 23 levels were significantly higher among participants with AKI than controls (median [interquartile range]=1471 [224-2534] versus 263 [96-574] RU/ml, P=0.003). Enrollment fibroblast growth factor 23 correlated negatively with 25-hydroxyvitamin D (r=-0.43, P<0.001) and 1,25-dihydroxyvitamin D (r=-0.39, P=0.003) and positively with phosphate (r=0.32, P=0.02) and parathyroid hormone (r=0.37, P=0.005). Among participants with AKI, enrollment fibroblast growth factor 23 (but not other serum parameters) was significantly associated with the composite endpoint, even after adjusting for age and enrollment serum creatinine (11 events; adjusted odds ratio per 1 SD higher ln[fibroblast growth factor 23]=13.73, 95% confidence interval=1.75-107.50). CONCLUSIONS Among patients with AKI, fibroblast growth factor 23 levels are elevated and associated with greater risk of death or need for renal replacement therapy.

Mining electronic health records for adverse drug effects using regression based methods

The identification of post-marketed adverse drug events (ADEs) is paramount to health care. Spontaneous reporting systems (SRS) are currently the mainstay in pharmacovigilance. Recently, electronic health records (EHRs) have emerged as a promising and effective complementary resource to SRS, as they contain a more complete record of the patient, and do not suffer from the reporting biases inherent to SRS. However, mining EHRs for potential ADEs, which typically involves identification of statistical associations between drugs and medical conditions, introduced several other challenges, the main one being the necessity for statistical techniques that account for confounding. The objective of this paper is to present and demonstrate the feasibility of a method based on regression techniques, which is designed for automated large scale mining of ADEs in EHR narratives. To the best of our knowledge this is a first of its kind approach that combines both the use of EHR data, and regression based methods in order to address confounding and identify potential ADEs. Two separate experiments are conducted. The results, which are validated by clinical subject matter experts, demonstrate great promise, as well as highlight additional challenges.

Selecting information in electronic health records for knowledge acquisition

Knowledge acquisition of relations between biomedical entities is critical for many automated biomedical applications, including pharmacovigilance and decision support. Automated acquisition of statistical associations from biomedical and clinical documents has shown some promise. However, acquisition of clinically meaningful relations (i.e. specific associations) remains challenging because textual information is noisy and co-occurrence does not typically determine specific relations. In this work, we focus on acquisition of two types of relations from clinical reports: disease-manifestation related symptom (MRS) and drug-adverse drug event (ADE), and explore the use of filtering by sections of the reports to improve performance. Evaluation indicated that applying the filters improved recall (disease-MRS: from 0.85 to 0.90; drug-ADE: from 0.43 to 0.75) and precision (disease-MRS: from 0.82 to 0.92; drug-ADE: from 0.16 to 0.31). This preliminary study demonstrates that selecting information in narrative electronic reports based on the sections improves the detection of disease-MRS and drug-ADE types of relations. Further investigation of complementary methods, such as more sophisticated statistical methods, more complex temporal models and use of information from other knowledge sources, is needed.

A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records.

OBJECTIVE Electronic health records (EHRs) contain information to detect adverse drug reactions (ADRs), as they contain comprehensive clinical information. A major challenge of using comprehensive information involves confounding. We propose a novel data-driven method to identify ADR signals accurately by adjusting for confounders. MATERIALS AND METHODS We focused on two serious ADRs, rhabdomyolysis and pancreatitis, and used information in 264,155 unique patient records. We identified an ADR using established criteria, selected potential confounders, and then used penalized logistic regressions to estimate confounder-adjusted ADR associations. A reference standard was created to evaluate and compare the precision of the proposed method and four others. RESULTS Precision was 83.3% for rhabdomyolysis and 60.8% for pancreatitis when using the proposed method, and we identified several drug safety signals that are interesting for further clinical review. DISCUSSION The proposed method effectively estimated ADR associations after adjusting for confounders. A main cause of error was probably due to the nature of the dataset in that a substantial number of patients had a single visit only and, therefore, it was not possible to determine correctly the appropriate sequence of events for them. It is likely that performance will be improved with use of EHR data that contain more longitudinal records. CONCLUSIONS This data-driven method is effective in controlling for confounding, resulting in either a higher or similar precision when compared with four comparators, has the unique ability to provide insight into confounders for each specific medication-ADR pair, and can be easily adapted to other EHR systems.

Survival analysis with electronic health record data: Experiments with chronic kidney disease

This article presents a detailed survival analysis for chronic kidney disease (CKD). The analysis is based on the electronic health record (EHR) data comprising almost two decades of clinical observations collected at New York-Presbyterian, a large hospital in New York City with one of the oldest electronic health records in the United States. Our survival analysis approach centers around Bayesian multiresolution hazard modeling, with an objective to capture the changing hazard of CKD over time, adjusted for patient clinical covariates and kidney-related laboratory tests. Special attention is paid to statistical issues common to all EHR data, such as cohort definition, missing data and censoring, variable selection, and potential for joint survival and longitudinal modeling, all of which are discussed alone and within the EHR CKD context.

Vitamin D resistance in chronic kidney disease (CKD)

BackgroundPrevious studies have shown that treatment with ergocalciferol in patients with CKD stage 3 + 4 is not effective with less than 33% of patients achieving a 25-OH vitamin D target of >30 ng/ml. The aim of this study was to test the response to cholecalciferol in CKD. We attempted to replete 25-OH vitamin D to a target level of 40–60 ng/ml using the response to treatment and PTH suppression as an outcome measure.MethodsThis retrospective cohort study identified patients (Stages 2–5 and Transplant) from 2001–2010 who registered at the Chronic Kidney Disease Clinic. Patients received cholecalciferol 10,000 IU capsules weekly as initial therapy. When levels above 40 ng/ml were not achieved, doses were titrated up to a maximum of 50,000 IU weekly. Active vitamin D analogs were also used in some Stage 4–5 CKD patients per practice guidelines. Patients reaching at least one level of 40 ng/mL were designated RESPONDER, and if no level above 40 ng/mL they were designated NON-RESPONDER. Patients were followed for at least 6 months and up to 5 years.Results352 patients were included with a mean follow up of 2.4 years. Of the CKD patients, the initial 25-OH vitamin D in the NON-RESPONDER group was lower than the RESPONDER group (18 vs. 23 ng/ml) (p = 0.03). Among all patients, the initial eGFR in the RESPONDER group was significantly higher than the NON-RESPONDER group (36 vs. 30 ml/min/1.73 m2) (p < 0.001). Over time, the eGFR of the RESPONDER group stabilized or increased (p < 0.001). Over time, the eGFR in the NON-RESPONDER group decreased toward a trajectory of ESRD. Proteinuria did not impact the response to 25-OH vitamin D replacement therapy. There were no identifiable variables associated with the response or lack of response to cholecalciferol treatment.ConclusionsCKD patients treated with cholecalciferol experience treatment resistance in raising vitamin D levels to a pre-selected target level. The mechanism of vitamin D resistance remains unknown and is associated with progressive loss of eGFR. Proteinuria modifies but does not account for the vitamin D resistance.