Elevated Baseline Serum Fibrinogen: Effect on 2-Year Major Adverse Cardiovascular Events Following Percutaneous Coronary Intervention.

BackgroundElevated fibrinogen is associated with short-term major adverse cardiovascular events (MACE) after percutaneous coronary intervention, but the relation with late MACE is unknown.Methods and resultsBaseline demographics and 2-year MACE were recorded among subjects undergoing nonemergent percutaneous coronary intervention. A total of 332 subjects (66.6±19.5 years, 69.9% male, 25.3% acute coronary syndrome) were enrolled. Two-year MACE (periprocedural myocardial infarction 9.0%, rehospitalization 6.3%, revascularization 12.7%, non-periprocedural myocardial infarction 4.5%, stent thrombosis 0.9%, stroke 1.8%, and death 0.6%) were associated with higher fibrinogen (352.8±123.4 mg/dL versus 301.6±110.8 mg/dL; P<0.001), longer total stent length (40.1±25.3 mm versus 32.1±19.3 mm; P=0.004), acute coronary syndrome indication (38.7% versus 17.8%; P<0.001), number of bare-metal stents (0.5±1.1 versus 0.2±0.5; P=0.002), and stent diameter ≤2.5 mm (55.8% versus 38.4%, P=0.003). No relation between platelet reactivity and 2-year MACE was observed. Fibrinogen ≥280 mg/dL (odds ratio [OR] 3.0, confidence interval [CI], 1.6-5.4, P<0.001), total stent length ≥32 mm (OR 2.2, CI, 1.3-3.8, P<0.001), acute coronary syndrome indication (OR 4.1, CI, 2.3-7.5, P<0.001), any bare-metal stents (OR 3.2, CI, 1.6-6.1, P<0.001), and stent diameter ≤2.5 mm (OR 2.0, CI, 1.2-3.5, P=0.010) were independently associated with 2-year MACE. Following a landmark analysis excluding periprocedural myocardial infarction, fibrinogen ≥280 mg/dL remained strongly associated with 2-year MACE (37.0% versus 17.4%, log-rank P<0.001).ConclusionsElevated baseline fibrinogen level is associated with 2-year MACE after percutaneous coronary intervention. Acute coronary syndrome indication for percutaneous coronary intervention, total stent length implanted, and use of bare-metal stents or smaller-diameter stents are also independently associated with 2-year MACE, while measures of on-thienopyridine platelet reactivity are not

Provenance-Centered Dataset of Drug-Drug Interactions

Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank), etc. While each one of these approaches is properly statistically validated, they do not take into consideration the overlap between them as one of their decision making variables. In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide researchers a resource for leveraging the work of others into their prediction methods. As one of the main issues to overcome the usage of external resources is their mappings between drug names and identifiers used, we also provide the set of mappings we curated to be able to compare the multiple sources we aggregate in our dataset.Comment: In Proceedings of the 14th International Semantic Web Conference (ISWC) 201

Learning signals of adverse drug-drug interactions from the unstructured text of electronic health records.

Drug-drug interactions (DDI) account for 30% of all adverse drug reactions, which are the fourth leading cause of death in the US. Current methods for post marketing surveillance primarily use spontaneous reporting systems for learning DDI signals and validate their signals using the structured portions of Electronic Health Records (EHRs). We demonstrate a fast, annotation-based approach, which uses standard odds ratios for identifying signals of DDIs from the textual portion of EHRs directly and which, to our knowledge, is the first effort of its kind. We developed a gold standard of 1,120 DDIs spanning 14 adverse events and 1,164 drugs. Our evaluations on this gold standard using millions of clinical notes from the Stanford Hospital confirm that identifying DDI signals from clinical text is feasible (AUROC=81.5%). We conclude that the text in EHRs contain valuable information for learning DDI signals and has enormous utility in drug surveillance and clinical decision support

Antipsychotics and Torsadogenic Risk: Signals Emerging from the US FDA Adverse Event Reporting System Database

Background: Drug-induced torsades de pointes (TdP) and related clinical entities represent a current regulatory and clinical burden. Objective: As part of the FP7 ARITMO (Arrhythmogenic Potential of Drugs) project, we explored the publicly available US FDA Adverse Event Reporting System (FAERS) database to detect signals of torsadogenicity for antipsychotics (APs). Methods: Four groups of events in decreasing order of drug-attributable risk were identified: (1) TdP, (2) QT-interval abnormalities, (3) ventricular fibrillation/tachycardia, and (4) sudden cardiac death. The reporting odds ratio (ROR) with 95 % confidence interval (CI) was calculated through a cumulative analysis from group 1 to 4. For groups 1+2, ROR was adjusted for age, gender, and concomitant drugs (e.g., antiarrhythmics) and stratified for AZCERT drugs, lists I and II (http://www.azcert.org, as of June 2011). A potential signal of torsadogenicity was defined if a drug met all the following criteria: (a) four or more cases in group 1+2; (b) significant ROR in group 1+2 that persists through the cumulative approach; (c) significant adjusted ROR for group 1+2 in the stratum without AZCERT drugs; (d) not included in AZCERT lists (as of June 2011). Results: Over the 7-year period, 37 APs were reported in 4,794 cases of arrhythmia: 140 (group 1), 883 (group 2), 1,651 (group 3), and 2,120 (group 4). Based on our criteria, the following potential signals of torsadogenicity were found: amisulpride (25 cases; adjusted ROR in the stratum without AZCERT drugs = 43.94, 95 % CI 22.82-84.60), cyamemazine (11; 15.48, 6.87-34.91), and olanzapine (189; 7.74, 6.45-9.30). Conclusions: This pharmacovigilance analysis on the FAERS found 3 potential signals of torsadogenicity for drugs previously unknown for this risk

Is there a role for oral triple therapy in patients with acute coronary syndromes without atrial fibrillation?

© 2018 Bentham Science PublishersBACKGROUND: Acute coronary syndrome (ACS) patients, despite treatment with dual antiplatelet therapy (DAPT), have up to 10% risk of recurrent major adverse cardiac events (MACE) in the short term. METHODS: Here we review studies using more potent antithrombotic agent combinations to reduce this risk, namely triple therapy (TT) with the addition of an oral anticoagulant, PAR-1 antagonist, or cilostazol to DAPT (mainly aspirin and clopidogrel), and discuss the limitations of trials to date. RESULTS: Generally speaking, TT leads to an increase in bleeding. Vorapaxar showed a signal for reducing ischaemic events, but increased intracranial haemorrhage 3-fold in the subacute phase of ACS, although remains an option for secondary prevention beyond the immediate subacute phase, particularly if prasugrel or ticagrelor are not available. Non-vitamin K oral anticoagulants (NOACs) all increased bleeding, with only modest reduction in MACE noted with low dose rivaroxaban. Rivaroxaban can be considered combined with aspirin and clopidogrel in ACS patients at high ischaemic and low bleeding risk, without prior stroke/TIA. The combination of P2Y12 inhibitor and NOAC, without aspirin, looks promising. DAPT may be replaced, not by TT, but by dual therapy comprising a NOAC with a P2Y12 inhibitor. CONCLUSION: More potent antithrombotic regimens increase bleeding and should only be considered on an individual basis, after careful risk stratification. Accurate risk stratification of ACS patients, for both ischaemic and bleeding risk, is essential to allow individualised treatment.Peer reviewe

Nightly treatment of primary insomnia with prolonged release melatonin for 6 months: a randomized placebo controlled trial on age and endogenous melatonin as predictors of efficacy and safety

<p>Background: Melatonin is extensively used in the USA in a non-regulated manner for sleep disorders. Prolonged release melatonin (PRM) is licensed in Europe and other countries for the short term treatment of primary insomnia in patients aged 55 years and over. However, a clear definition of the target patient population and well-controlled studies of long-term efficacy and safety are lacking. It is known that melatonin production declines with age. Some young insomnia patients also may have low melatonin levels. The study investigated whether older age or low melatonin excretion is a better predictor of response to PRM, whether the efficacy observed in short-term studies is sustained during continued treatment and the long term safety of such treatment.</p> <p>Methods: Adult outpatients (791, aged 18-80 years) with primary insomnia, were treated with placebo (2 weeks) and then randomized, double-blind to 3 weeks with PRM or placebo nightly. PRM patients continued whereas placebo completers were re-randomized 1:1 to PRM or placebo for 26 weeks with 2 weeks of single-blind placebo run-out. Main outcome measures were sleep latency derived from a sleep diary, Pittsburgh Sleep Quality Index (PSQI), Quality of Life (World Health Organzaton-5) Clinical Global Impression of Improvement (CGI-I) and adverse effects and vital signs recorded at each visit.</p> <p>Results: On the primary efficacy variable, sleep latency, the effects of PRM (3 weeks) in patients with low endogenous melatonin (6-sulphatoxymelatonin [6-SMT] ≤8 μg/night) regardless of age did not differ from the placebo, whereas PRM significantly reduced sleep latency compared to the placebo in elderly patients regardless of melatonin levels (-19.1 versus -1.7 min; P = 0.002). The effects on sleep latency and additional sleep and daytime parameters that improved with PRM were maintained or enhanced over the 6-month period with no signs of tolerance. Most adverse events were mild in severity with no clinically relevant differences between PRM and placebo for any safety outcome.</p> <p>Conclusions: The results demonstrate short- and long-term efficacy and safety of PRM in elderly insomnia patients. Low melatonin production regardless of age is not useful in predicting responses to melatonin therapy in insomnia. The age cut-off for response warrants further investigation.</p&gt

Genetic and Immune Predictors for Hypersensitivity Syndrome to Antiepileptic Drugs

Hypersensitivity syndrome reactions (HSR) to antiepileptic drugs (AED) are associated with severe clinical cutaneous adverse reactions (SCAR).Our aims are: to assess HSRs to AEDs using the in vitro lymphocyte toxicity assay (LTA) in patients who manifested HSRs clinically, to correlate LTA results with the clinical syndrome, to correlate LTA results with the human leukocyte antigen (HLA) allele B*1502 (HLA-B*1502) positivity in a Han Chinese-Canadian population, and to determine the cytokine network in this population. HSR patients developed fever and cutaneous eruptions in the presence or absence of organ involvement within 8 weeks of exposure to carbamazepine (CBZ), phenytoin (PHY) or lamotrigine (LTG). Control patients received AEDs without presenting HSR. We investigated 10 CBZ-HSR (4 presented with Stevens-Johnson syndrome (SJS)), 24 CBZ-controls, 10 PHY-HSR (4 presented with drug-induced liver injury (DILI)), 24 PHY-controls, 6 LTG-HSR (1 SJS and 1 DILI) and 24 LTG-controls. There were 30 Han Chinese individuals (14 HSR patients and 16 controls) in our cohort. LTA toxicity greater than 12.5%±2.5% was considered positive. Differences among groups were determined by analysis of variance. In addition, we measured cytokine secretion in the patient sera between 1 month and 3 years after the event. All Han Chinese individuals and 30% of Caucasians were genotyped for HLA-B*1502.A perfect correlation (r=0.92) was observed between positive LTA and clinical diagnosis of DILI and SJS/toxic epidermal necrolysis (TEN). HLA-B*1502 positivity in Han Chinese is a predictor of CBZ-HSR and PHY-HSR. HLA-B*1502-negative Han Chinese receiving only CBZ or a combination of CBZ-PHY tolerated the drug(s) clinically, presenting negative CBZ-LTA and PHY-LTA. However, 3 patients presenting negative CBZ-LTA and PHY-LTA, as well as negative HLA-B*1502, showed positive LTG-LTA (38%, 28% and 25%, respectively), implying that they should not be prescribed LTG. Three patients had LTA positive to both PHY and CBZ, and 3 others had LTA positive to both PHY and LTG. Clinically, all six patients presented HSR to both drugs that they tested positive to (cross-reactivity). Patients were grouped based on the clinical presentation of their symptoms as only rash and fever or a triad that characterizes "true" HSR (rash, fever and DILI or SJS/TEN). Levels of pro-inflammatory cytokines were significantly higher in patient sera compared to control sera. More specifically, the highest levels of tumor necrosis factor (TNF)-α was measured in patients presenting "true" HSR, as were the apoptotic markers Fas, caspase 8 activity and M30. We concluded that LTA is sensitive for DILI and SJS/TEN regardless of drug or ethnicity. HSR prediction will prevent AED-induced morbidity. In Han Chinese, HLA-B*1502 positivity is a predictor for CBZ-HSR and PHY-HSR. Its negativity does not predict a negative LTG-HSR. There is cross-reactivity between AEDs. Additionally, T-cell cytokines and chemokines control the pathogenesis of SJS/TEN and DILI, contributing to apoptotic processes in the liver and in the skin

Drug prescription support in dental clinics through drug corpus mining

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients