BackgroundA medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health.DescriptionThe Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term ‘adverse event’ denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data. For example, using the data extracted from the Vaccine Adverse Event Reporting System (VAERS), OAE was used to analyse vaccine adverse events associated with the administrations of different types of influenza vaccines. OAE has also been used to represent and classify the vaccine adverse events cited in package inserts of FDA-licensed human vaccines in the USA.ConclusionOAE is a biomedical ontology that logically defines and classifies various adverse events occurring after medical interventions. OAE has successfully been applied in several adverse event studies. The OAE ontological framework provides a platform for systematic representation and analysis of adverse events and of the factors (e.g., vaccinee age) important for determining their clinical outcomes.

BackgroundNeuropathy often occurs following drug treatment such as chemotherapy. Severe instances of neuropathy can result in cessation of life-saving chemotherapy treatment.ResultsTo support data representation and analysis of drug-associated neuropathy adverse events (AEs), we developed the Ontology of Drug Neuropathy Adverse Events (ODNAE). ODNAE extends the Ontology of Adverse Events (OAE). Our combinatorial approach identified 215 US FDA-licensed small molecule drugs that induce signs and symptoms of various types of neuropathy. ODNAE imports related drugs from the Drug Ontology (DrON) with their chemical ingredients defined in ChEBI. ODNAE includes 139 drug mechanisms of action from NDF-RT and 186 biological processes represented in the Gene Ontology (GO). In total ODNAE contains 1579 terms. Our analysis of the ODNAE knowledge base shows neuropathy-inducing drugs classified under specific molecular entity groups, especially carbon, pnictogen, chalcogen, and heterocyclic compounds. The carbon drug group includes 127 organic chemical drugs. Thirty nine receptor agonist and antagonist terms were identified, including 4 pairs (31 drugs) of agonists and antagonists that share targets (e.g., adrenergic receptor, dopamine, serotonin, and sex hormone receptor). Many drugs regulate neurological system processes (e.g., negative regulation of dopamine or serotonin uptake). SPARQL scripts were used to query the ODNAE ontology knowledge base.ConclusionsODNAE is an effective platform for building a drug-induced neuropathy knowledge base and for analyzing the underlying mechanisms of drug-induced neuropathy. The ODNAE-based methods used in this study can also be extended to the representation and study of other categories of adverse events.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0069-x) contains supplementary material, which is available to authorized users.

Background Sleep disruption is common in inflammatory bowel diseases (IBD). However, studies demonstrating a similar prevalence in irritable bowel syndrome (IBS) suggest that nighttime disruption due to diarrhea and abdominal pain may be key drivers of poor sleep quality. Whether inflammation is associated with poor sleep independently has not been examined previously. Methods This single center study included subjects with IBD recruited to an ongoing prospective registry who completed a questionnaire assessing sleep quality and mood. Inflammatory marker levels (C-reactive protein (CRP), erythrocyte sedimentation rate (ESR)) and clinical disease activity including nighttime disruption on the day of enrollment was obtained from the medical record. Logistic regression models were used to identify predictors of sleep quality. Results The study included 131 subjects (72 women) with a median age of IBD diagnosis of 25 years. Twenty-three subjects (19%) had a high C-reactive protein level (≥ 8mg/dL). Poor sleep was more common in those with high CRP levels than with normal values (70% vs. 39%, p=0.009). This association remained significant on multivariate analysis (Odds ratio (OR) 4.12, 95% confidence interval (CI) 1.38 – 12.29). Adjusting for the presence of nighttime disruption did not significant alter this association (OR 3.16, 95% CI 1.01 – 9.90). High CRP correlated with poor sleep even in patients not experiencing nocturnal symptoms (n=101, OR 4.89, 95% CI 1.24 – 19.36). Conclusion High CRP is associated with poor sleep quality in IBD independent of the presence of nighttime disruptions, suggesting a relationship exists between circulating inflammatory markers and sleep.

POAF was associated with increased major morbidity, possibly from worsening right heart failure leading to increased renal failure and unplanned right ventricular assist device placement. This led to patients with POAF having longer intensive care unit and hospital stays and more frequent discharges to a facility.