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.

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.

SummaryBackground 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=20.43, P,0.001) and 1,25-dihydroxyvitamin D (r=20.39, 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.

Electronic health records (EHRs) are an important source of data for detection of adverse drug reactions (ADRs). However, adverse events are frequently due not to medications but to the patients’ underlying conditions. Mining to detect ADRs from EHR data must account for confounders. We developed an automated method using natural-language processing (NLP) and a knowledge source to differentiate cases in which the patient’s disease is responsible for the event rather than a drug. Our method was applied to 199,920 hospitalization records, concentrating on two serious ADRs: rhabdomyolysis (n = 687) and agranulocytosis (n = 772). Our method automatically identified 75% of the cases, those with disease etiology. The sensitivity and specificity were 93.8% (confidence interval: 88.9-96.7%) and 91.8% (confidence interval: 84.0-96.2%), respectively. The method resulted in considerable saving of time: for every 1 h spent in development, there was a saving of at least 20 h in manual review. The review of the remaining 25% of the cases therefore became more feasible, allowing us to identify the medications that had caused the ADRs.

In this paper 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 in FDA’s spontaneous reporting system. A taxonomy of biclusters is developed, revealing that a significant number of bone fide adverse drug event (ADE) biclusters are identified. Statistical tests indicate that it is extremely unlikely that the discovered bicluster structures as well as their content arose by chance. Some of the biclusters classified as indeterminate provide support for previously unrecognized and potentially novel ADEs. In addition, we demonstrate the importance of the proposed methodology to several important aspects of pharmacovigilance such as: providing insight into the etiology of ADEs, facilitating the identification of novel ADEs, suggesting methods and rational for aggregating terminologies, highlighting areas of focus, and as a data exploratory tool.

Sodium movement across the luminal membrane of the toad bladder is the rate-limiting step for active transepitheliaI transport. Recent studies suggest that changes in intracellular sodium regulate the Na permeability of the luminal border, either directly or indirectly via increases in cell calcium induced by the high intracellular sodium. To test these proposals, we measured Na movement across the luminal membrane (the Na influx) and found that it is reduced when intracellular Na is increased by ouabain or by removal of external potassium. Removal of serosal sodium also reduced the influx, suggesting that the Na gradient across the serosal border rather than the cell Na concentration is the critical factor. Because in tissues such as muscle and nerve a steep transmembrane sodium gradient is necessary to maintain low cytosolic calcium, it is possible that a reduction in the sodium gradient in the toad bladder reduces luminal permeability by increasing the cell calcium activity. We found that the inhibition of the influx by ouabain or low serosal Na was prevented, in part, by removal of serosal calcium. To test for the existence of a sodium-calcium exchanger, we studied calcium transport in isolated basolateral membrane vesicles and found that calcium uptake was proportional to the outward directed sodium gradient. Uptake was not the result of a sodium diffusion potential. Calcium efflux from preloaded vesicles was accelerated by an inward directed sodium gradient. Preliminary kinetic analysis showed that the sodium gradient changes the Vm, x but not the Km of calcium transport. These results suggest that the effect of intracellular sodium on the luminal sodium.permeability is due to changes in intracellular calcium.

BackgroundDiagnostic accuracy might be improved by algorithms that searched patients’ clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus this study was to determine if patients with MS could be identified from their clinical notes prior to the initial recognition by their healthcare providers.MethodsAn MS-enriched cohort of patients with well-established MS (n = 165) and controls (n = 545), was generated from the adult outpatient clinic. A random sample cohort was generated from randomly selected patients (n = 2289) from the same adult outpatient clinic, some of whom had MS (n = 16). Patients’ notes were extracted from the data warehouse and signs and symptoms mapped to UMLS terms using MedLEE. Approximately 1000 MS-related terms occurred significantly more frequently in MS patients’ notes than controls’. Synonymous terms were manually clustered into 50 buckets and used as classification features. Patients were classified as MS or not using Naïve Bayes classification.ResultsClassification of patients known to have MS using notes of the MS-enriched cohort entered after the initial ICD9[MS] code yielded an ROC AUC, sensitivity, and specificity of 0.90 [0.87-0.93], 0.75[0.66-0.82], and 0.91 [0.87-0.93], respectively. Similar classification accuracy was achieved using the notes from the random sample cohort. Classification of patients not yet known to have MS using notes of the MS-enriched cohort entered before the initial ICD9[MS] documentation identified 40% [23–59%] as having MS. Manual review of the EHR of 45 patients of the random sample cohort classified as having MS but lacking an ICD9[MS] code identified four who might have unrecognized MS.ConclusionsDiagnostic accuracy might be improved by mining patients’ clinical notes for signs and symptoms of specific diseases using NLP. Using this approach, we identified patients with MS early in the course of their disease which could potentially shorten the time to diagnosis. This approach could also be applied to other diseases often missed by primary care providers such as cancer. Whether implementing computerized diagnostic support ultimately shortens the time from earliest symptoms to formal recognition of the disease remains to be seen.

The results demonstrate that the proposed methodology could be used as a pharmacovigilance decision support tool to facilitate ADE detection.