Detect adverse drug reactions for drug Atorvastatin

Adverse drug reactions (ADRs) are big concern for public health. ADRs are one of most common causes to withdraw some drugs from markets. Now two major methods for detecting ADRs are spontaneous reporting system (SRS), and prescription event monitoring (PEM). The World Health Organization (WHO) defines a signal in pharmacovigilance as "any reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously". For spontaneous reporting systems, many machine learning methods are used to detect ADRs, such as Bayesian confidence propagation neural network (BCPNN), decision support methods, genetic algorithms, knowledge based approaches, etc. One limitation is the reporting mechanism to submit ADR reports, which has serious underreporting and is not able to accurately quantify the corresponding risk. Another limitation is hard to detect ADRs with small number of occurrences of each drug-event association in the database. In this paper we propose feature selection approach to detect ADRs from The Health Improvement Network (THIN) database. First a feature matrix, which represents the medical events for the patients before and after taking drugs, is created by linking patients' prescriptions and corresponding medical events together. Then significant features are selected based on feature selection methods, comparing the feature matrix before patients take drugs with one after patients take drugs. Finally the significant ADRs can be detected from thousands of medical events based on corresponding features. Experiments are carried out on the drug Atorvastatin. Good performance is achieved.Comment: Fifth International Symposium on Computational Intelligence and Design (ISCID), 213-216, 2012. arXiv admin note: substantial text overlap with arXiv:1308.514

Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database

Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs are one of most common causes to withdraw some drugs from market. Prescription event monitoring (PEM) is an important approach to detect the adverse drug reactions. The main problem to deal with this method is how to automatically extract the medical events or side effects from high-throughput medical events, which are collected from day to day clinical practice. In this study we propose a novel concept of feature matrix to detect the ADRs. Feature matrix, which is extracted from big medical data from The Health Improvement Network (THIN) database, is created to characterize the medical events for the patients who take drugs. Feature matrix builds the foundation for the irregular and big medical data. Then feature selection methods are performed on feature matrix to detect the significant features. Finally the ADRs can be located based on the significant features. The experiments are carried out on three drugs: Atorvastatin, Alendronate, and Metoclopramide. Major side effects for each drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on computerized methods, further investigation is needed.Comment: International Journal of Information Technology and Computer Science (IJITCS), in print, 201

SAFETY EVALUATION OF STATIN IN YOGYAKARTA, INDONESIA

Background : The 3-hydroxy-3-methylglutaryl-coenzyme (HMG-Co-A) reductase inhibitors, also known as statins, are the most effective class of drugs for lowering serum Low-Density Lipoprotein cholesterol (LDL-c)concentrations. They are first-line agents for patients who require drug therapy to reduce serum LDL-c concentrations. Although these drugs have been very successful in managing the cardiovascular health of many patients, there are also potential adverse effects that have been identified. The most common adverse effects reported include muscle pain or weakness that can progress to rhabdomyolysis and mortality. If detected early, statin-related symptoms are reversible after withdrawal of the statin. Objective : This research was aimed to know the safety of statin used at Public Hospitals in Yogyakarta. Method : This research was observational study with retrospective data collected. The target population are all of diabetes mellitus, cardiovascular and stroke patients that recorded on the medical record of Public Hospitals in Yogyakarta during 2 months. Results : There were 28 patients who used simvastatin and 8 patients who used atorvastatin, experienced adverse effects of statins (n=157). Headache was the most adverse effect which was experienced by the patients. However rhabdomyolysis was not found in this reasearh. Interaction between simvastatin and nifedipine resulted more adverse effects such as headache, insomnia and abdominal pain than with other drugs. Conclusions : Simvastatin, rosuvastatin and atorvastatin were well tolerated use in Yogyakarta, Indonesia. Only 22.9% from 157 patients experienced the adverse effects of statin. Adverse effects because of the interaction between simvastatin and other drugs were experienced by 8.92% patients. The result of this study need to be confirmed with additional study with larger sample sizes and vigilant surveillance to abolish the toxicity of the statin

Statin Induced Myopathy and Myalgia: Time Trend Analysis and Comparison of Risk Associated with Statin Class from 1991–2006

BACKGROUND: Statins are widely used as a cholesterol lowering medication, reduce cardiovascular mortality and morbidity in high risk patients; and only rarely cause serious adverse drug reactions (ADRs). UK primary care databases of morbidity and prescription data, which now cover several million people, have potential for more powerful analytical approaches to study ADRs including adjusting for confounders and examining temporal effects. METHODS: Case-crossover design in detecting statin associated myopathy ADR in 93, 831 patients, using two independent primary care databases (1991-2006). We analysed risk by drug class, by disease code and cumulative year, exploring different cut-off exposure times and confounding by temporality. RESULTS: Using a 12 and 26 week exposure period, large risk ratios (RR) are associated with all classes of statins and fibrates for myopathy: RR 10.6 (9.8-11.4) and 19.9 (17.6-22.6) respectively. At 26 weeks, the largest risks are with fluvastatin RR 33.3 (95% CI 16.8-66.0) and ciprofibrate (with previous statin use) RR 40.5 (95% CI 13.4-122.0). AT 12 weeks the differences between cerivastatin and atorvastatin RR for myopathy were found to be significant, RR 2.05 (95% CI 1.2-3.5), and for rosuvastatin and fluvastatin RR 3.0 (95% CI 1.6-5.7). After 12 months of statin initiation, the relative risk for myopathy for all statins and fibrates increased to 25.7 (95% CI 21.8-30.3). Furthermore, this signal was detected within 2 years of first events being recorded. Our data suggests an annual incidence of statin induced myopathy or myalgia of around 11.4 for 16, 591 patients or 689 per million per year. CONCLUSION: There may be differential risks associated with some classes of statin and fibrate. Myopathy related to statin or fibrate use may persist after a long exposure time (12 months or more). These methods could be applied for early detection of harmful drug side effects, using similar primary care diagnostic and prescribing data

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

Clinical evaluation of rosuvastatin in heart transplant patients with hypercholesterolemia and therapeutic failure of other statin regimens: short-term and long-term efficacy and safety results

[Abstract] We conducted an observational study of 30 heart transplant recipients with serum low-density lipoprotein cholesterol (LDL-c) >100 mg/dl despite previous statin therapy, who were treated with rosuvastatin 10 mg daily (5 mg in case of renal dysfunction). Serum lipids, creatine phosphokinase (CPK), bilirubin, and hepatic enzymes were prospectively measured 2, 4, and 12 weeks after the initiation of the drug. Clinical outcomes of patients who continued on long-term rosuvastatin therapy beyond this 12-week period were reviewed in February 2015. Over the 12-week period following rosuvastatin initiation, serum levels of total cholesterol (TC) and LDL-c and the ratio TC/high-density lipoprotein cholesterol (HDL-c) decreased steadily (P < 0.001). Average absolute reductions of these three parameters were –48.7 mg/dl, –46.6 mg/dl, and –0.9, respectively. Seventeen (57%) achieved a serum LDL-c < 100 mg/dl. No significant changes from baseline were observed in serum levels of triglycerides, HDL-c, hepatic enzymes, bilirubin, or CPK. Twenty-seven (90%) patients continued on long-term therapy with rosuvastatin over a median period of 3.6 years, with no further significant variation in lipid profile. The drug was suspended due to liver toxicity in 1 (3.3%) patient and due to muscle toxicity in 2 (6.7%) patients. All adverse reactions resolved rapidly after rosuvastatin withdrawal. Our study supports rosuvastatin as a reasonable alternative for heart transplant recipients with hypercholesterolemia and therapeutic failure of other statin regimens

Effects of short-term treatment with atorvastatin in smokers with asthma - a randomized controlled trial

&lt;b&gt;Background&lt;/b&gt; The immune modulating properties of statins may benefit smokers with asthma. We tested the hypothesis that short-term treatment with atorvastatin improves lung function or indices of asthma control in smokers with asthma.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Methods&lt;/b&gt; Seventy one smokers with mild to moderate asthma were recruited to a randomized double-blind parallel group trial comparing treatment with atorvastatin (40 mg per day) versus placebo for 4 weeks. After 4 weeks treatment inhaled beclometasone (400 ug per day) was added to both treatment arms for a further 4 weeks. The primary outcome was morning peak expiratory flow after 4 weeks treatment. Secondary outcome measures included indices of asthma control and airway inflammation.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; At 4 weeks, there was no improvement in the atorvastatin group compared to the placebo group in morning peak expiratory flow [-10.67 L/min, 95% CI -38.70 to 17.37, p=0.449], but there was an improvement with atorvastatin in asthma quality of life score [0.52, 95% CI 0.17 to 0.87 p=0.005]. There was no significant improvement with atorvastatin and inhaled beclometasone compared to inhaled beclometasone alone in outcome measures at 8 weeks.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusions&lt;/b&gt; Short-term treatment with atorvastatin does not alter lung function but may improve asthma quality of life in smokers with mild to moderate asthma. Clinicaltrials.gov identifier: NCT0046382

Amiodarone Toxicity: A Case Report

Amiodarone is one of the commonly used drug in arrhythmias. It is first line of treatment for ventricular tachycardia particularly in treatment of supraventicular tachycardia like atrial fibrillation. Amiodarone has a wide range of adverse effects ranging from endocrine to cardiac system. A case of raised thyroid stimulating Hormone (TSH)&nbsp; with amiodarone was reported in our Adverse drug&nbsp; monitoring centre (AMC), in which several other adverse events such as raised raised billirubin and hyponatremia were present. Patient was treated with thyroxine while amiodarone along with atorvastatin and metoprolol were withdrawn. We present a case who developed subclinical hypothyroidism, electrolyte imbalance and liver dysfunction with amiodarone therapy

Epidemiology of Cytochrome P450-mediated Drug-Drug Interactions

Drug-drug interactions (DDIs) comprise an important cause of adverse drug reactions leading to excess hospitalizations. Drug metabolism is catalyzed by 75% by cytochrome P450 (CYP) enzymes and thus they are often involved in pharmacokinetic DDIs. In general, DDIs are studied in randomized controlled clinical trials in selected study populations. The overall aim of the present studies was to perform observational pharmacoepidemiological surveys on CYP-mediated DDIs in diseases important at the population level. The prevalence of co-administrations of four prodrugs (losartan, codeine, tramadol, and clopidogrel), three sulphonylureas (glibenclamide, glimepiride, and glipizide), or two statins (lovastatin and simvastatin) with well established agents altering CYP activity, as well as of statins with fibrates, was studied in Finland utilizing data from a university hospital medication database (inpatients) and the National Prescription Register of the Social Insurance Institution of Finland, Kela (outpatients). Clinical consequences of potential DDIs were estimated by reviewing laboratory data, and information from hospital care and cause-of-death registers. Concomitant use of study substrates with interacting medication was detected in up to one fifth of patients in both hospital and community settings. Potential CYP3A4 interactions in statin users did not manifest in clear adverse laboratory values but pharmacodynamic DDIs between statins and fibrates predisposed patients to muscular toxicity. Sulphonylurea DDIs with CYP2C9 inhibitors increased the risk of hypoglycaemia. CYP3A4 inhibitor use with clopidogrel was not associated with significant changes in mortality but non-fatal thrombosis and haemorrhage complications were seen less often in this group. Concomitant administration of atorvastatin with clopidogrel moderately attenuated the antithrombotic effect by clopidogrel. The overall mortality was increased in CYP3A4 inducer and clopidogrel co-users. Atorvastatin used concomitantly with prodrug clopidogrel seems to be beneficial in terms of total and LDL cholesterol concentrations, and overall mortality compared with clopidogrel use without interacting medication. In conclusion, CYP-mediated DDIs are a common and often unrecognized consequence of irrational drug prescribing.Siirretty Doriast