Drug Dictionary Research Articles

Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.

To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases. We developed methods to generate drug codelists and tested this using the Clinical Practice Research Datalink (CPRD) Aurum database, accounting for missing data in the database. We generated codelists for: (1) cardiovascular disease and (2) inhaled Chronic Obstructive Pulmonary Disease (COPD) therapies, applying them to a sample cohort of 335931 COPD patients. We compared searching all drug dictionary variables (A) against searching only (B) chemical or (C) ontological variables. In Search A, we identified 165150 patients prescribed cardiovascular drugs (49.2% of cohort), and 317963 prescribed COPD inhalers (94.7% of cohort). Evaluating output per search strategy, Search C missed numerous prescriptions, including vasodilator anti-hypertensives (A and B:19696 prescriptions; C:1145) and SAMA inhalers (A and B:35310; C:564). We recommend the full search (A) for comprehensiveness. There are special considerations when generating adaptable and generalizable drug codelists, including fluctuating status, cohort-specific drug indications, underlying hierarchical ontology, and statistical analyses. Methods must have end-to-end clinical input, and be standardizable, reproducible, and understandable to all researchers across data contexts.

A Survey on “Medicines at Your Fingertips”

Medicines at Your Fingertips is a website aimed to provide all the information about any medicine, it’s side effects and all the other health related information. All the data of the medicines is stored in our database and it is fetched during the execution of user’s request. We have created the frontend using HTML, CSS, JS, JQUERY, JQUERY UI and Bootstrap. The Backend is built using Django Framework of Python. Our website contains 6 main components which are: Chatbot: An intelligent chatbot which will give any information that the user has asked. The AI chatbot is created using the Pytorch library of python. It will also help the user to navigate from the whole website. Drugs a to Z, a drug dictionary to give all the information about the desired medicine. There are 2 ways to search the medicine. Pill Finder: To search the medicine alphabetically or using the search bar Phonetic Search: To search the medicine using voice command. Drugs By Condition: It contains the information about all types of health conditions, their causes and treatment along with some medicines which are used to treat them. Side Effects: It also has alphabetically sorted medicines which gives the information about the side effects of the particular medicine along with a search bar. First Aid: This part consists of 3 components which gives the information about first aid treatments and My Med List to set reminders for the doses of your medicine.

Automated Drug Coding Using Artificial Intelligence: An Evaluation of WHODrug Koda on Adverse Event Reports.

IntroductionCoding medicinal products described on adverse event (AE) reports to specific entries in standardised drug dictionaries, such as WHODrug Global, is a time-consuming step in case processing activities despite its potential for automation. Many organisations are already partially automating drug coding using text-processing methods and synonym lists, however addressing challenges such as misspellings, abbreviations or ambiguous trade names requires more advanced methods. WHODrug Koda is a drug coding engine using text-processing algorithms, built-in coding rules and machine learning to code drug verbatims to WHODrug Global.ObjectiveOur aim was to evaluate the drug coding performance of WHODrug Koda on AE reports from VigiBase, the World Health Organization’s global database of individual case safety reports, in terms of level of automation and coding quality.MethodsKoda was evaluated on 4.8 million drug entries from VigiBase. Automation level was computed as the proportion of drug entries automatically coded by Koda and was compared to a simple case-insensitive text-matching algorithm. Coding quality was evaluated in terms of coding accuracy, by comparing Koda’s prediction to the WHODrug entries found on the AE reports in VigiBase. To better understand the cases in which Koda’s coding results did not match with the WHODrug entries in VigiBase, a manual assessment of 600 samples of disagreeing encodings was performed by two teams of expert drug coders.ResultsCompared with a simple direct-match baseline, Koda can increase the automation level from 61% to 89%, while providing high coding quality with an accuracy of 97%.ConclusionsEven though Koda was designed for use in clinical trials, Koda achieves automation level and coding quality for drug coding of AE reports comparable with the performance observed in a previous evaluation of Koda on clinical trial data. Koda can thus help organisations to automate their drug coding of AE reports to a large degree.

Combating TKI resistance in CML by inhibiting the PI3K/Akt/mTOR pathway in combination with TKIs: a review.

Chronic myeloid leukemia (CML), a myeloproliferative hematopoietic cancer, is caused by a genetic translocation between chromosomes 9 and 22. This translocation produces a small Philadelphia chromosome, which contains the Bcr-Abl oncogene. The Bcr-Abl oncogene encodes the BCR-ABL protein, upregulates various signaling pathways (JAK-STAT, MAPK/ERK, and PI3K/Akt/mTOR), and out of which the specifically highly active pathway is the PI3K/Akt/mTOR pathway. Among early treatments for CML, tyrosine kinase inhibitors (TKIs) were found to be the most effective, but drug resistance against kinase inhibitors led to the discovery of novel alternative therapies. At this point, the PI3K/Akt/mTOR pathway components became new targets due to stimulation of this pathway in TKIs-resistant CML patients. The current review article deals with reviewing the scientific literature on the PI3K/Akt/mTOR pathway inhibitors listed in the National Cancer Institute (NCI) drug dictionary and proved effective against multiple cancers. And out of those enlisted inhibitors, the US FDA has also approved some PI3K inhibitors (Idelalisib, Copanlisib, and Duvelisib) and mTOR inhibitors (Everolimus, Sirolimus, and Temsirolimus) for cancer therapy. So far, several inhibitors have been tested, and further investigations are still ongoing. Even in Imatinib, Nilotinib, and Ponatinib-resistant CML cells, a dual PI3K/mTOR inhibitor, BEZ235, showed antiproliferative activity. Therefore, by considering the literature data of these reviews and further examining some of the reported inhibitors, which proved effective against the PI3K/Akt/mTOR signaling pathway in multiple cancers, may improve the therapeutic approaches towards TKI-resistant CML cells where the respective signaling pathway gets upregulated.

Racial/Ethnic Differences in Prenatal Supplement and Medication Use in Low-Risk Pregnant Women.

This study aimed to describe the overall quantity and type of supplements and medications used during pregnancy in a low-risk cohort and to examine any racial/ethnic differences in intake. We used data from 2,164 racially/ethnically diverse, nonobese, and low-risk pregnant women participating without pre-pregnancy chronic conditions in a prospective cohort study at 12 sites across the United States. Medication data were self-reported as free text in enrollment, follow-up visit questionnaires, and abstracted from medical records at delivery. Supplements and medications data were mapped to their active ingredients and categorized into corresponding classes using the Slone Drug Dictionary. The total number and classes of supplements and medications consumed during pregnancy were calculated. Modified Poisson regression models were used to estimate the racial/ethnic differences in supplements and medications intake. All models were adjusted for maternal sociodemographic factors and study site. 98% of women took at least one supplement during pregnancy, with prenatal vitamins/multivitamins being most common. While only 31% reported taking no medications during pregnancy, 23% took one, 18% took two, and 28% took three or more. The percentage of women taking at least one medication during pregnancy was highest among non-Hispanic white women and lowest among Asians (84 vs. 55%, p < 0.001). All racial/ethnic groups reported taking the same top four medication classes including central nervous system agents, gastrointestinal drugs, anti-infective agents, and antihistamines. Compared with non-Hispanic white women, Hispanic (adjusted relative risk [aRR]: 0.84, 95% confidence interval [CI]: 0.71-0.98), and Asian women (aRR: 0.83, 95% CI: 0.70-0.98) were less likely to take central nervous system agents, as well as gastrointestinal drugs (Hispanics aRR: 0.79, 95% CI: 0.66-0.94; Asians aRR = 0.75, 95% CI: 0.63-0.90), and antihistamines (Hispanics aRR: 0.65, 95% CI: 0.47-0.92). Supplement intake was nearly universal. Medication use was also common among this low-risk pregnancy cohort and differed by race/ethnicity. NCT00912132. · In women without chronic conditions, medication use is common.. · Racial/ethnic differences exist in prenatal medications use.. · Almost all women use supplements during pregnancy..

Autocorrect on Drugs e-Dictionary Search Module Using Levenshtein Distance Algorithm

The Dictionary of Medicine in the form of a physical book has many drawbacks, one of them is its thickness makes it impractical to be carried. This becomes a motivation to develop drug dictionary applications in the form of a Drugs e-Dictionary. One of the developed Drugs e-Dictionary uses A-Z index-based approach to discover any drug terms. This approach is less effective and less efficient timewise. Therefore, it is necessary to add a search function that has an autocorrect feature to aid the user. The purpose of this study is to build a search module that has an autocorrect feature on Drugs e-Dictionary using the Levenshtein Distance algorithm. The methodology or the stages of this research divided into the construction of a search module on Drugs e-Dictionary, implementation of the Levenshtein Distance algorithm, and autocorrect validation test. The results of the algorithm implementation show that the search module with the autocorrect feature can detect typing errors in the inputted terms by producing the closest drug term output in the database, then automatically provide suggestions for improvement and display the results of the improved drug terms to the user. it reaches 90% accuracy of inputted query, with 90% precision and 90% recall.

Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer

BackgroundHospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to find cases of drug-adverse event (AE) relations. PurposeThe objective of this paper is to develop a natural language processing (NLP) framework to detect drug-AE relations from unstructured hospital discharge summaries. Basic proceduresAn NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE) terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert review of discharge summaries from a tertiary hospital in Singapore in 2011. Main findingsA total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries) by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600 records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations. Principal conclusionsAdverse drug reactions are a significant contributor to health care costs and utilization. Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting the results of the algorithm as part of an iterative machine learning process, is an important step towards use of hospital discharge summaries for an active pharmacovigilance program.

A hybrid approach for named entity recognition in Chinese electronic medical record

BackgroundWith the rapid spread of electronic medical records and the arrival of medical big data era, the application of natural language processing technology in biomedicine has become a hot research topic.MethodsIn this paper, firstly, BiLSTM-CRF model is applied to medical named entity recognition on Chinese electronic medical record. According to the characteristics of Chinese electronic medical records, obtain the low-dimensional word vector of each word in units of sentences. And then input the word vector to BiLSTM to realize automatic extraction of sentence features. And then CRF performs sentence-level word tagging. Secondly, attention mechanism is added between the BiLSTM and the CRF to construct Attention-BiLSTM-CRF model, which can leverage document-level information to alleviate tagging inconsistency. In addition, this paper proposes an entity auto-correct algorithm to rectify entities according to historical entity information. At last, a drug dictionary and post-processing rules are well-built to rectify entities, to further improve performance.ResultsThe final F1 scores of the BiLSTM-CRF and Attention-BiLSTM-CRF model on given test dataset are 90.15 and 90.82% respectively, both of which are higher than 89.26%, which is the best F1 score on the test dataset except ours.ConclusionOur approach can be used to recognize medical named entity on Chinese electronic medical records and achieves the state-of-the-art performance on the given test dataset.

Rational design and identification of immuno-oncology drug combinations

BackgroundClinical trials investigating immuno-oncology (IO) drug combinations are largely based on empiricism or limited non-clinical evaluations. This study identified the current combination IO drug clinical trials and investigated how tumour molecular profiling can help rationalise IO drug combinations. MethodsIO targets were identified via PubMed search and expert opinion. IO drugs were compiled by searching the National Cancer Institute Drug Dictionary and pharmaceutical pipelines, August 2016. Combination IO trials were obtained by searching doublet IO drug combinations in www.clinicaltrials.gov from September to November 2016. IO target gene expressions were extracted from The Cancer Genome Atlas (TCGA) data set and compared with normal tissues from the Genotype-Tissue Expression database. Differentially expressed genes for each cancer were determined using the Wilcoxon rank-sum test, and p-values were corrected for multiple testing. ResultsIn total, 178 IO targets were identified; 90 targets have either regulatory approved or investigational therapeutics. In total, 410 combination trials involving ≥2 IO drugs were identified: skin (n = 102) and genitourinary (n = 41) malignancies have the largest number of combination IO trials; 109 trials involved >2 disease sites. Summative patient accrual estimates among all trials are 71,345. Trials combining cytotoxic T lymphocyte antigen 4 (CTLA4) with programmed cell death protein 1 (n = 79) and CTLA4 with programmed cell death ligand 1 (n = 44) are the most common. Gene expression data from TCGA were mined to extract the 178 IO targets in 9089 tumours originating from 19 cancer types. IO target expression–clustered heatmap analysis identified several promising drug combinations. ConclusionOur review highlights the great interest in combination IO clinical trials. Our analysis can enrich IO combination therapy selection.

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials.

Drug adverse events (AEs) are a major health threat to patients seeking medical treatment and a significant barrier in drug discovery and development. AEs are now required to be submitted during clinical trials and can be extracted from ClinicalTrials.gov (https://clinicaltrials.gov/), a database of clinical studies around the world. By extracting drug and AE information from ClinicalTrials.gov and structuring it into a database, drug-AEs could be established for future drug development and repositioning. To our knowledge, current AE databases contain mainly U.S. Food and Drug Administration (FDA)-approved drugs. However, our database contains both FDA-approved and experimental compounds extracted from ClinicalTrials.gov. Our database contains 8,161 clinical trials of 3,102,675 patients and 713,103 reported AEs. We extracted the information from ClinicalTrials.gov using a set of python scripts, and then used regular expressions and a drug dictionary to process and structure relevant information into a relational database. We performed data mining and pattern analysis of drug-AEs in our database. Our database can serve as a tool to assist researchers to discover drug-AE relationships for developing, repositioning, and repurposing drugs.

Bonnes pratiques pour la prescription de thymorégulateurs à l’adolescence : une revue de la littérature

Resume L’adolescence est un moment singulier du processus de maturation de l’individu ; les medicaments psychotropes peuvent avoir chez l’adolescent des profils d’efficacite et de tolerance differents de ceux notes a d’autres âges de la vie. Limitant notre propos a une seule classe pharmacologique, nous nous posons dans ce travail la question suivante : dans quelles indications et selon quelles modalites les thymoregulateurs devraient-ils etre prescrits aux adolescents ? Revue de la litterature Le lithium dispose de la plus ancienne autorisation de mise sur le marche (AMM) en France et aux Etats-Unis dans le cadre du trouble bipolaire a l’adolescence ; il presenterait egalement, hors AMM, un interet dans les troubles du comportement. Le lithium necessite une surveillance biologique reguliere et exige une bonne observance. Aucun anticonvulsivant ne possede l’AMM dans le cadre du trouble bipolaire a l’adolescence ; pourtant l’American Academy of Child and Adolescent Psychiatry (AACAP) recommande le divalproate de sodium comme un des traitements de premiere intention de la manie. Enfin, les antipsychotiques de deuxieme generation (ADG) ont obtenu une AMM pour le trouble bipolaire : en France, uniquement l’aripiprazole, mais aux Etats-Unis egalement la risperidone, l’olanzapine et la quetiapine. De plus, les ADG figurent dans les recommandations consacrees aux troubles du comportement de l’adolescent et publiees par l’AACAP et le National Institute for Health and Clinical Excellence. Leurs effets secondaires sont metaboliques, moteurs et cognitifs. Discussion Les donnees epidemiologiques de prescription de psychotropes se trouvent partiellement en concordance avec les recommandations precitees. D’une part, il existe effectivement une tendance pour un usage preferentiel des ADG, mais d’autre part, le lithium n’est quasiment pas prescrit chez l’adolescent, plus rarement que les anticonvulsivants (a visee thymoregulatrice). Ainsi, malgre l’absence d’AMM, ces dernieres molecules sont parfois preferees au lithium – en raison de sa dangerosite en cas d’inobservance ou d’intoxication volontaire –, ou aux ADG – en raison de leur profil de tolerance. En tout cas, quel que soit le medicament prescrit, une question cruciale est celle de l’observance et de l’alliance therapeutique, avec l’adolescent mais aussi avec ses parents.

Adverse drug event reporting systems: a systematic review.

Adverse drug events (ADEs) are harmful and unintended consequences of medications. Their reporting is essential for drug safety monitoring and research, but it has not been standardized internationally. Our aim was to synthesize information about the type and variety of data collected within ADE reporting systems. We developed a systematic search strategy, applied it to four electronic databases, and completed an electronic grey literature search. Two authors reviewed titles and abstracts, and all eligible full-texts. We extracted data using a standardized form, and discussed disagreements until reaching consensus. We synthesized data by collapsing data elements, eliminating duplicate fields and identifying relationships between reporting concepts and data fields using visual analysis software. We identified 108 ADE reporting systems containing 1782 unique data fields. We mapped them to 33 reporting concepts describing patient information, the ADE, concomitant and suspect drugs, and the reporter. While reporting concepts were fairly consistent, we found variability in data fields and corresponding response options. Few systems clarified the terminology used, and many used multiple drug and disease dictionaries such as the Medical Dictionary for Regulatory Activities (MedDRA). We found substantial variability in the data fields used to report ADEs, limiting the comparability of ADE data collected using different reporting systems, and undermining efforts to aggregate data across cohorts. The development of a common standardized data set that can be evaluated with regard to data quality, comparability and reporting rates is likely to optimize ADE data and drug safety surveillance.

Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries

Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries is a time-consuming task and adding new drugs to existing drug dictionaries immediately after they are developed is also a challenge. In recent years, word embeddings that contain rich latent semantic information of words have been widely used to improve the performance of various natural language processing tasks. However, they have not been used in DNR systems. Compared to the semantic features based on drug dictionaries, the advantage of word embeddings lies in that learning them is unsupervised. In this paper, we investigate the effect of semantic features based on word embeddings on DNR and compare them with semantic features based on three drug dictionaries. We propose a conditional random fields (CRF)-based system for DNR. The skip-gram model, an unsupervised algorithm, is used to induce word embeddings on about 17.3 GigaByte (GB) unlabeled biomedical texts collected from MEDLINE (National Library of Medicine, Bethesda, MD, USA). The system is evaluated on the drug-drug interaction extraction (DDIExtraction) 2013 corpus. Experimental results show that word embeddings significantly improve the performance of the DNR system and they are competitive with semantic features based on drug dictionaries. F-score is improved by 2.92 percentage points when word embeddings are added into the baseline system. It is comparative with the improvements from semantic features based on drug dictionaries. Furthermore, word embeddings are complementary to the semantic features based on drug dictionaries. When both word embeddings and semantic features based on drug dictionaries are added, the system achieves the best performance with an F-score of 78.37%, which outperforms the best system of the DDIExtraction 2013 challenge by 6.87 percentage points.

Database: A New Article Type in CPT: Pharmacometrics & Systems Pharmacology.

Database: A New Article Type in CPT: Pharmacometrics & Systems Pharmacology.

RATIONALISATION OF PAEDIATRIC DRUG DOSING AGE RANGES: REDUCING CONFUSION

Aim To produce clear and consistent age ranges that could be used in a paediatric drug dictionary designed to support an electronic prescribing system. The intention was to define age ranges that were mutually exclusive and coterminous. The secondary aim was to use the rationalised age ranges in the development of a paediatric drug dictionary for national use. Method All age ranges were collated from the British National Formulary for Children, Guy9s and St Thomas9, King9s College and University Lewisham Hospital Paediatric Formulary and the hospital9s local guidelines. An experienced team of paediatric clinicians and pharmacists then produced mutually exclusive and coterminous age ranges that all of the original age ranges could be grouped under. The team then sought to describe the age ranges in an understandable written format and that would be usable by an electronic prescribing system. Results The project team developed a set of rules, principles and conventions that were used to describe age related dosing information in an electronic prescribing system. The rules and principles developed were: age ranges should be mutually exclusive and coterminous; age ranges should have precise upper and lower limits; time units should be used in the expression of all age ranges; acceptable time units are days, weeks, months and years; the birth date is ‘day 0’ whatever time of day the child is born; a ‘transition day’ is the day on which the child attains the next significant age and is included in the upper limit of an age range; when a child reaches 18 years old the adult drug dictionary will apply. The conventions developed were applied in an age range spreadsheet including columns for the designated age range expression and the exact number of days that expression signifies to enable the computer system to understand the definition; the age range expressions for drug doses are of the form “ to ; a neonate is a child of 0 days to 28 days; the first month of life is always 28 days, 2 months is 56 days, 3 months is 13 weeks (91 days); other than for the first 3 months of life in the period up to one year ‘a month’=30 days; other than for children in the first 3 months of life age ranges in the period up to 5 years are expressed in months; for ages over 5 years the limits are expressed in years. Gestational age ranges need further definition. Conclusion Mutually exclusive and coterminous age ranges were developed. These age ranges also align to the WHO growth tables. The rules and principles developed are suitable for use in an electronic prescribing system; however, the achievement of plain language understandable definitions was considerably more difficult. This was confounded by the lack of standardisation found in Summary of Product Characteristics information sheets produced by drug companies.

Implementation of standardized pediatric i.v. medication concentrations.

The development and implementation of a comprehensive and standardized list of pediatric i.v. medication concentrations across a large healthcare system are described. In accordance with National Patient Safety Goals, facilities affiliated with the Hospital Corporation of America system had independently standardized and limited the number of drug concentrations in use. This resulted in variation among facilities, which prevented the systemwide standardization of drug dictionaries within the computerized pharmacy and prescriber-order-entry systems, complicated the movement of providers among facilities, and contributed to inconsistency in medication prescribing. A team of experts collaborated to create a comprehensive standard list that included 119 medications and 372 concentrations for pediatric i.v. medications. Implementation of this standard list was driven through a financial incentive from the malpractice insurance provider; facilities that completed the required activities for this optional program could apply for a credit of a portion of their malpractice insurance cost. For the standardization of pediatric i.v. medications, required activities included approval of the standard medication list, incorporation of this list into facility pharmacy dictionaries, and update of all smart pump software to include only the new standard medications and concentrations. Of the 145 facilities that were eligible for the implementation of standard pediatric i.v. medication concentrations, 141 (97%) completed all requirements and received the 2% malpractice insurance cost credit. The use of a financial incentive strategy, in the form of a malpractice insurance credit, successfully motivated the implementation of standardized pediatric medication concentrations across a large healthcare system.

Sleep Related Problems and Urological Symptoms: Testing the Hypothesis of Bidirectionality in a Longitudinal, Population Based Study

We evaluate the bidirectional association between urological symptoms (urinary incontinence, lower urinary tract symptoms and nocturia) and sleep related variables. Data were obtained from a prospective cohort study of 1,610 men and 2,535 women who completed baseline (2002 to 2005) and followup (2006 to 2010) phases of the BACH (Boston Area Community Health) Survey, a population based random sample survey. Sleep restriction (5 hours or less per night), restless sleep, sleep medication use and urological symptoms were assessed by self-report. Urinary incontinence was defined as weekly leakage or moderate/severe leakage, lower urinary tract symptoms (overall, obstructive, irritative) were defined by the AUA-SI (American Urological Association symptom index) and nocturia was defined as urinary frequency 2 or more times per night. At the 5-year followup 10.0%, 8.5% and 16.0% of subjects newly reported lower urinary tract symptoms, urinary incontinence and nocturia, respectively, and 24.2%, 13.3% and 11.6% newly reported poor sleep quality, sleep restriction and use of sleep medication, respectively. Controlling for confounders, the odds of urological symptoms developing were consistently increased for subjects who reported poor sleep quality and sleep restriction at baseline, but only baseline nocturia was positively associated with incident sleep related problems at followup. Body mass index, a potential mediator, reduced selected associations between sleep and incident urinary incontinence and irritative symptoms, but C-reactive protein did not. These data suggest that self-reported sleep related problems and urological symptoms are linked bidirectionally, and that body mass index may be a factor in the relationship between sleep and the development of urological symptoms.

Stevens–Johnson Syndrome/Toxic Epidermal Necrolysis: Are Drug Dictionaries Correctly Informing Physicians Regarding the Risk?

Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) are severe drug reactions associated with high mortality and multiple incapacitating sequelae. In the past 20 years, two large multinational case control studies, published in 1995 and 2008, had identified different degrees of drug association with SJS/TEN: 'strongly associated', 'associated', 'suspected' and 'not suspected' medications. The aim of this study was to check the adequacy of mention of risk of SJS/TEN in the drug dictionaries most widely used by physicians in five European countries. In each country one expert investigator looked at the most widely used drug dictionary (2009 edition) for mentions of risk of SJS/TEN. This was done for a predefined list of medications with a different degree of risk. The presence and clarity or absence of warning was compared with available evidence provided by published results from case-control studies. The five countries participating in the RegiSCAR group: Austria, France, Germany, The Netherlands and the UK. A total of 3,268 drug descriptions of medications for systemic use were analysed, including all brands of 14 'strongly associated' drugs, 5 'associated' drugs and 12 widely used drugs with no established association. Discrepancies were found by country, and between descriptions for different brands of the same generic. Among 522 descriptions of 14 'strongly associated' drugs, only 5 did not mention the risk. For the 1,013 descriptions of 'associated' drugs, 3 % did not mention the risk. One-third of 'not suspected' drugs contained a specific or less specific warning (e.g. bullous cutaneous eruption). Warnings for 'strongly associated' medications were often as imprecise as those for 'not suspected' drugs. Information on the risk of SJS/TEN in drug dictionaries needs improvement to enhance the quality of advice given by general physicians and to raise the understanding of risk by patients.

The role of organizational context and individual nurse characteristics in explaining variation in use of information technologies in evidence based practice

BackgroundThere is growing awareness of the role of information technology in evidence-based practice. The purpose of this study was to investigate the role of organizational context and nurse characteristics in explaining variation in nurses’ use of personal digital assistants (PDAs) and mobile Tablet PCs for accessing evidence-based information. The Promoting Action on Research Implementation in Health Services (PARIHS) model provided the framework for studying the impact of providing nurses with PDA-supported, evidence-based practice resources, and for studying the organizational, technological, and human resource variables that impact nurses’ use patterns.MethodsA survey design was used, involving baseline and follow-up questionnaires. The setting included 24 organizations representing three sectors: hospitals, long-term care (LTC) facilities, and community organizations (home care and public health). The sample consisted of 710 participants (response rate 58%) at Time 1, and 469 for whom both Time 1 and Time 2 follow-up data were obtained (response rate 66%). A hierarchical regression model (HLM) was used to evaluate the effect of predictors from all levels simultaneously.ResultsThe Chi square result indicated PDA users reported using their device more frequently than Tablet PC users (p = 0.001). Frequency of device use was explained by ‘breadth of device functions’ and PDA versus Tablet PC. Frequency of Best Practice Guideline use was explained by ‘willingness to implement research,’ ‘structural and electronic resources,’ ‘organizational slack time,’ ‘breadth of device functions’ (positive effects), and ‘slack staff’ (negative effect). Frequency of Nursing Plus database use was explained by ‘culture,’ ‘structural and electronic resources,’ and ‘breadth of device functions’ (positive effects), and ‘slack staff’ (negative). ‘Organizational culture’ (positive), ‘breadth of device functions’ (positive), and ‘slack staff ‘(negative) were associated with frequency of Lexi/PEPID drug dictionary use.ConclusionAccess to PDAs and Tablet PCs supported nurses’ self-reported use of information resources. Several of the organizational context variables and one individual nurse variable explained variation in the frequency of information resource use.

Associations of Commonly Used Medications with Urinary Incontinence in a Community Based Sample

We examined the association between the use of medications and the prevalence of urinary incontinence in gender specific analyses of a community based, representative sample. A population based epidemiological study was conducted of 5,503 men and women 30 to 79 years old residing in Boston, Massachusetts (baseline data collected from 2002 to 2005). Urological symptoms were ascertained in a 2-hour, in person interview. Urinary incontinence was defined as urine leakage occurring weekly or more often during the last year. Medications used in the last month were considered current use. Associations of 20+ medications and prevalent urinary incontinence were examined using multivariate logistic regression (ORs and 95% CIs) with adjustments for known urinary incontinence risk factors. The prevalence of urinary incontinence in the analysis sample was 9.0% in women and 4.6% in men. For women the prevalence was highest among users of certain antihistamines (28.4%) and angiotensin II receptor blockers (22.9%). For men the prevalence was highest among angiotensin II receptor blocker (22.2%) and loop diuretic (19.1%) users. After final multivariate adjustment there were significant positive associations for certain antihistamines, beta receptor agonists, angiotensin II receptor blockers and estrogens with urinary incontinence in women (all ORs greater than 1.7), and a borderline significant association for anticonvulsants (OR 1.75; 95% CI 1.00, 3.07). Among men only anticonvulsants were associated with urinary incontinence after final adjustments (OR 2.50; 95% CI 1.24, 5.03), although angiotensin II receptor blockers showed an adjusted association of borderline significance (OR 2.21; 95% CI 0.96, 5.10). Although a cross-sectional analysis cannot determine causality, our analysis suggests certain medications should be further examined in longitudinal analyses of risk to determine their influence on urological symptoms.