The rate of death was 1.5% before the checklist was introduced and declined to 0.8% afterward (P = 0.003). Inpatient complications occurred in 11.0% of patients at baseline and in 7.0% after introduction of the checklist (P<0.001). Conclusions Implementation of the checklist was associated with concomitant reductions in the rates of death and complications among patients at least 16 years of age who were undergoing noncardiac surgery in a diverse group of hospitals.

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A Surgical Safety Checklist to Reduce Morbidity and Mortality in a Global Population

Authors Jan L. Brozek, MD, PhD – Department of Clinical Epidemiology & Biostatistics and Medicine, McMaster University, Hamilton, Canada Jean Bousquet, MD, PhD – Service des Maladies Respiratoires, Hopital Arnaud de Villeneuve, Montpellier, France, INSERM, CESP U1018, Respiratory and Environmental Epidemiology Team, France, and WHO Collaborating Center for Rhinitis and Asthma Carlos E. Baena-Cagnani, MD – Faculty of Medicine, Catholic University of Cordoba, Cordoba, Argentina Sergio Bonini, MD – Institute of Neurobiology and Molecular Medicine – CNR, Rome, Italy and Department of Medicine, Second University of Naples, Naples, Italy G. Walter Canonica, MD – Allergy & Respiratory Diseases, DIMI, Department of Internal Medicine, University of Genoa, Genoa, Italy Thomas B. Casale, MD – Division of Allergy and Immunology, Department of Medicine, Creighton University, Omaha, Nebraska, USA Roy Gerth van Wijk, MD, PhD – Section of Allergology, Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, the Netherlands Ken Ohta, MD, PhD – Division of Respiratory Medicine and Allergology, Department of Medicine, Teikyo University School of Medicine, Tokyo, Japan Torsten Zuberbier, MD – Department of Dermatology and Allergy, Charite Universitatsmedizin Berlin, Berlin, Germany Holger J. Schunemann, MD, PhD, MSc – Department of Clinical Epidemiology & Biostatistics and Medicine, McMaster University, Hamilton, Canada

/pdf/allergic-rhinitis-and-its-impact-on-asthma-20b4c7y0pr.pdf

Allergic Rhinitis and Its Impact on Asthma

To improve patient care and facilitate clinical research, the International League Against Epilepsy (ILAE) appointed a Task Force to formulate a consensus definition of drug resistant epilepsy. The overall framework of the definition has two "hierarchical" levels: Level 1 provides a general scheme to categorize response to each therapeutic intervention, including a minimum dataset of knowledge about the intervention that would be needed; Level 2 provides a core definition of drug resistant epilepsy using a set of essential criteria based on the categorization of response (from Level 1) to trials of antiepileptic drugs. It is proposed as a testable hypothesis that drug resistant epilepsy is defined as failure of adequate trials of two tolerated, appropriately chosen and used antiepileptic drug schedules (whether as monotherapies or in combination) to achieve sustained seizure freedom. This definition can be further refined when new evidence emerges. The rationale behind the definition and the principles governing its proper use are discussed, and examples to illustrate its application in clinical practice are provided.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1528-1167.2009.02397.x

Definition of drug resistant epilepsy: consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies.

Objective To ascertain the current burden of adverse drug reactions (ADRs) through a prospective analysis of all admissions to hospital. Design Prospective observational study. Setting Two large general hospitals in Merseyside, England. Participants 18 820 patients aged > 16 years admitted over six months and assessed for cause of admission. Main outcome measures Prevalence of admissions due to an ADR, length of stay, avoidability, and outcome. Results There were 1225 admissions related to an ADR, giving a prevalence of 6.5%, with the ADR directly leading to the admission in 80% of cases. The median bed stay was eight days, accounting for 4% of the hospital bed capacity. The projected annual cost of such admissions to the NHS is £466m (€706m, $847m). The overall fatality was 0.15%. Most reactions were either definitely or possibly avoidable. Drugs most commonly implicated in causing these admissions included low dose aspirin, diuretics, warfarin, and non-steroidal anti-inflammatory drugs other than aspirin, the most common reaction being gastrointestinal bleeding. Conclusion The burden of ADRs on the NHS is high, accounting for considerable morbidity, mortality, and extra costs. Although many of the implicated drugs have proved benefit, measures need to be put into place to reduce the burden of ADRs and thereby further improve the benefit:harm ratio of the drugs.

https://www.bmj.com/content/bmj/329/7456/15.full.pdf

Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients

Adverse drug reactions: definitions, diagnosis, and management.

Background: The detection of new drug safety signals is of growing importance with ever more new drugs becoming available and exposure to medicines increasing. The task of evaluating information relating to safety lies with national agencies and, for international data, with the World Health Organization Programme for International Drug Monitoring.

A Retrospective Evaluation of a Data Mining Approach to Aid Finding New Adverse Drug Reaction Signals in the WHO International Database

Large collections of electronic patient records provide a vast but still underutilised source of information on the real world use of medicines. They are maintained primarily for the purpose of patient administration, but contain a broad range of clinical information highly relevant for data analysis. While they are a standard resource for epidemiological confirmatory studies, their use in the context of exploratory data analysis is still limited. In this paper, we present a framework for open-ended pattern discovery in large patient records repositories. At the core is a graphical statistical approach to summarising and visualising the temporal association between the prescription of a drug and the occurrence of a medical event. The graphical overview contrasts the observed and expected number of occurrences of the medical event in different time periods both before and after the prescription of interest. In order to effectively screen for important temporal relationships, we introduce a new measure of temporal association, which contrasts the observed-to-expected ratio in a time period immediately after the prescription to the observed-to-expected ratio in a control period 2 years earlier. An important feature of both the observed-to-expected graph and the measure of temporal association is a statistical shrinkage towards the null hypothesis of no association, which provides protection against highlighting spurious associations. We demonstrate the usefulness of the proposed pattern discovery methodology by a set of examples from a collection of over two million patient records in the United Kingdom. The identified patterns include temporal relationships between drug prescriptions and medical events suggestive of persistent and transient risks of adverse events, possible beneficial effects of drugs, periodic co-occurrence, and systematic tendencies of patients to switch from one medication to another.

Temporal pattern discovery in longitudinal electronic patient records

Post-marketing drug safety data sets are often massive, and entail problems with heterogeneity and selection bias. Nevertheless, quantitative methods have proven a very useful aid to help clinical experts in screening for previously unknown associations in these data sets. The WHO international drug safety database is the world's largest data set of its kind with over three million reports on suspected adverse drug reaction incidents. Since 1998, an exploratory data analysis method has been in routine use to screen for quantitative associations in this data set. This method was originally based on large sample approximations and limited to pairwise associations, but in this article we propose more accurate credibility interval estimates and extend the method to allow for the analysis of more complex quantitative associations. The accuracy of the proposed credibility intervals is evaluated through comparison to precise Monte Carlo simulations. In addition, we propose a Mantel-Haenszel-type adjustment to control for suspected confounders.

Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events.

The WHO database contains over 2.5 million case reports, analysis of this data set is performed with the intention of signal detection. This paper presents an overview of the quantitative method used to highlight dependencies in this data set. The method Bayesian confidence propagation neural network (BCPNN) is used to highlight dependencies in the data set. The method uses Bayesian statistics implemented in a neural network architecture to analyse all reported drug adverse reaction combinations. This method is now in routine use for drug adverse reaction signal detection. Also this approach has been extended to highlight drug group effects and look for higher order dependencies in the WHO data. Quantitatively unexpectedly strong relationships in the data are highlighted relative to general reporting of suspected adverse effects; these associations are then clinically assessed.

I. Ralph Edwards

Papers

Adverse drug reactions: definitions, diagnosis, and management.

A Retrospective Evaluation of a Data Mining Approach to Aid Finding New Adverse Drug Reaction Signals in the WHO International Database

Temporal pattern discovery in longitudinal electronic patient records

Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events.

A data mining approach for signal detection and analysis.