The Dutch list of essential drugs for undergraduate medical education: A modified Delphi study.

AIMS: Prescribing errors among junior doctors are common in clinical practice because many lack prescribing competence after graduation. This is in part due to inadequate education in clinical pharmacology and therapeutics (CP&T) in the undergraduate medical curriculum. To support CP&T education, it is important to determine which drugs medical undergraduates should be able to prescribe safely and effectively without direct supervision by the time they graduate. Currently, there is no such list with broad-based consensus. Therefore, the aim was to reach consensus on a list of essential drugs for undergraduate medical education in the Netherlands. METHODS: A two-round modified Delphi study was conducted among pharmacists, medical specialists, junior doctors and pharmacotherapy teachers from all eight Dutch academic hospitals. Participants were asked to indicate whether it was essential that medical graduates could prescribe specific drugs included on a preliminary list. Drugs for which ≥80% of all respondents agreed or strongly agreed were included in the final list. RESULTS: In all, 42 (65%) participants completed the two Delphi rounds. A total of 132 drugs (39%) from the preliminary list and two (3%) newly proposed drugs were included. CONCLUSIONS: This is the first Delphi consensus study to identify the drugs that Dutch junior doctors should be able to prescribe safely and effectively without direct supervision. This list can be used to harmonize and support the teaching and assessment of CP&T. Moreover, this study shows that a Delphi method is suitable to reach consensus on such a list, and could be used for a European list.

The Development of Practice Recommendations for Drug-Disease Interactions by Literature Review and Expert Opinion.

BACKGROUND: Drug-disease interactions negatively affect the benefit/risk ratio of drugs for specific populations. In these conditions drugs should be avoided, adjusted, or accompanied by extra monitoring. The motivation for many drug-disease interactions in the Summary of Product Characteristics (SmPC) is sometimes insufficiently supported by (accessible) evidence. As a consequence the translation of SmPC to clinical practice may lead to non-specific recommendations. For the translation of this information to the real world, it is necessary to evaluate the available knowledge about drug-disease interactions, and to formulate specific recommendations for prescribers and pharmacists. The aim of this paper is to describe a standardized method how to develop practice recommendations for drug-disease interactions by literature review and expert opinion. METHODS: The development of recommendations for drug-disease interactions will follow a six-step plan involving a multidisciplinary expert panel (1). The scope of the drug-disease interaction will be specified by defining the disease and by describing relevant effects of this drug-disease interaction. Drugs possibly involved in this drug-disease interaction are selected by checking the official product information, literature, and expert opinion (2). Evidence will be collected from the official product information, guidelines, handbooks, and primary literature (3). Study characteristics and outcomes will be evaluated and presented in standardized reports, including preliminary conclusions on the clinical relevance and practice recommendations (4). The multidisciplinary expert panel will discuss the reports and will either adopt or adjust the conclusions (5). Practice recommendations will be integrated in clinical decision support systems and published (6). The results of the evaluated drug-disease interactions will remain up-to-date by screening new risk information, periodic literature review, and (re)assessments initiated by health care providers. ACTIONABLE RECOMMENDATIONS: The practice recommendations will result in advices for specific DDSI. The content and considerations of these DDSIs will be published and implemented in all Clinical Decision Support Systems in the Netherlands. DISCUSSION: The recommendations result in professional guidance in the context of individual patient care. The professional will be supported in the decision making in concerning pharmacotherapy for the treatment of a medical problem, and the clinical risks of the proposed medication in combination with specific diseases.

Handling categories properly: a novel objective of clinical research.

A major objective of clinical research is to study outcome effects in subgroups. Such effects generally have stepping functions that are not strictly linear. Analyzing stepping functions in linear models thus raises the risk of underestimating the effects. In the past few years, recoding subgroup properties from continuous variables into categorical ones has been recommended as a solution to the problem. The objectives of this study were to demonstrate from examples how recoding works and to show that stepping functions, if used as continuous variables, do not produce significant effects, whereas they produce very significant effects after recoding. In the first example, the effects on physical strength were assessed in 60 subjects of different races. A linear regression in SPSS with race as the independent and physical strength score as the dependent variable showed that race was not a significant predictor of physical strength. Using the process of recoding, the variable race into categorical dummy variables showed that compared with the presence of Hispanic race, the black and white races were significant positive predictors (P = 0.0001 and 0.004 respectively) and Asian race is a significant negative predictor (P = 0.050). In the second example, the effects of numbers of comedications on admissions to a hospital resulting from adverse drug effects were assessed. A logistic regression in SPSS with numbers of comedications as the independent variable showed that comedications was not a significant predictor of iatrogenic admission. Using again the process of recoding for categorical dummy variables showed that comedication was a very significant predictor of iatrogenic admission with P = 0.004. Categorical variables are currently rarely analyzed in a proper way. Mostly they are analyzed in the form of continuous variables. This approach does not always fit the data patterns causing negative results as demonstrated in the examples of this article. We recommend that such variables be recoded into categorical dummy variables.

Time-dependent predictors in clinical research, performance of a novel method.

Individual patients' predictors of survival may change across time, because people may change their lifestyles. Standard statistical methods do not allow adjustments for time-dependent predictors. In the past decade, time-dependent factor analysis has been introduced as a novel approach adequate for the purpose. Using examples from survival studies, we assess the performance of the novel method. SPSS statistical software is used (SPSS Inc., Chicago, IL). Cox regression is a major simplification of real life; it assumes that the ratio of the risks of dying in parallel groups is constant over time. It is, therefore, inadequate to analyze, for example, the effect of elevated low-density lipoprotein cholesterol on survival, because the relative hazard of dying is different in the first, second, and third decades. The time-dependent Cox regression model allowing for nonproportional hazards is applied and provides a better precision than the usual Cox regression (P = 0.117 versus 0.0001). Elevated blood pressure produces the highest risk at the time it is highest. An overall analysis of the effect of blood pressure on survival is not significant, but after adjustment for the periods with highest blood pressures using the segmented time-dependent Cox regression method, blood pressure is a significant predictor of survival (P = 0.04). In a long-term therapeutic study, treatment modality is a significant predictor of survival, but after the inclusion of the time-dependent low-density lipoprotein cholesterol variable, the precision of the estimate improves from a P value of 0.02 to 0.0001. Predictors of survival may change across time, e.g., the effect of smoking, cholesterol, and increased blood pressure in cardiovascular research and patients' frailty in oncology research. Analytical models for survival analysis adjusting such changes are welcome. The time-dependent and segmented time-dependent predictors are adequate for the purpose. The usual multiple Cox regression model can include both time-dependent and time-independent predictors.

Cardiovascular research: data dispersion issues.

Biological processes are full of variations and so are responses to therapy as measured in clinical research. Estimators of clinical efficacy are, therefore, usually reported with a measure of uncertainty, otherwise called dispersion. This study aimed to review both the flaws of data reports without measure of dispersion and those with over-dispersion.EXAMPLES OF ESTIMATORS COMMONLY REPORTED WITHOUT A MEASURE OF DISPERSION INCLUDE: number needed to treat;reproducibility of quantitative diagnostic tests;sensitivity/specificity;Markov predictors;risk profiles predicted from multiple logistic models.Data with large differences between response magnitudes can be assessed for over-dispersion by goodness of fit tests. The χ(2) goodness of fit test allows adjustment for over-dispersion.For most clinical estimators, the calculation of standard errors or confidence intervals is possible. Sometimes, the choice is deliberately made not to use the data fully, but to skip the standard errors and to use the summary measures only. The problem with this approach is that it may suggest inflated results. We recommend that analytical methods in clinical research should always attempt to include a measure of dispersion in the data. When large differences exist in the data, the presence of over-dispersion should be assessed and appropriate adjustments made.