Algorithms for optimizing drug therapy

BACKGROUND: Drug therapy has become increasingly efficient, with more drugs available for treatment of an ever-growing number of conditions. Yet, drug use is reported to be sub optimal in several aspects, such as dosage, patient's adherence and outcome of therapy. The aim of the current study was to investigate the possibility to optimize drug therapy using computer programs, available on the Internet. METHODS: One hundred and ten officially endorsed text documents, published between 1996 and 2004, containing guidelines for drug therapy in 246 disorders, were analyzed with regard to information about patient-, disease- and drug-related factors and relationships between these factors. This information was used to construct algorithms for identifying optimum treatment in each of the studied disorders. These algorithms were categorized in order to define as few models as possible that still could accommodate the identified factors and the relationships between them. The resulting program prototypes were implemented in HTML (user interface) and JavaScript (program logic). RESULTS: Three types of algorithms were sufficient for the intended purpose. The simplest type is a list of factors, each of which implies that the particular patient should or should not receive treatment. This is adequate in situations where only one treatment exists. The second type, a more elaborate model, is required when treatment can by provided using drugs from different pharmacological classes and the selection of drug class is dependent on patient characteristics. An easily implemented set of if-then statements was able to manage the identified information in such instances. The third type was needed in the few situations where the selection and dosage of drugs were depending on the degree to which one or more patient-specific factors were present. In these cases the implementation of an established decision model based on fuzzy sets was required. Computer programs based on one of these three models could be constructed regarding all but one of the studied disorders. The single exception was depression, where reliable relationships between patient characteristics, drug classes and outcome of therapy remain to be defined. CONCLUSION: Algorithms for optimizing drug therapy can, with presumably rare exceptions, be developed for any disorder, using standard Internet programming methods

The role of guidelines and the patient's life-style in GPs' management of hypercholesterolaemia

BACKGROUND: Recent Swedish and joint European guidelines on hyperlipidaemia stress the high coronary risk for patients with already established arterio-sclerotic disease (secondary prevention) or diabetes. For the remaining group, calculation of the ten-year risk for coronary events using the Framingham equation is suggested. There is evidence that use of and adherence to guidelines is incomplete and that tools for risk estimations are seldom used. Intuitive risk estimates are difficult and systematically biased. The purpose of the study was to examine how GPs use knowledge of guidelines in their decisions to recommend or not recommend a cholesterol-lowering drug and the reasons for their decisions. METHODS: Twenty GPs were exposed to six case vignettes presented on a computer. In the course of six screens, successively more information was added to the case. The doctors were instructed to think aloud while processing the cases (Think-Aloud Protocols) and finally to decide for or against drug treatment. After the six cases they were asked to describe how they usually reason when they meet patients with high cholesterol values (Free-Report Protocols). The two sets of protocols were coded for cause-effect relations that were supposed to reflect the doctors' knowledge of guidelines. The Think-Aloud Protocols were also searched for reasons for the decisions to prescribe or not to prescribe. RESULTS: According to the protocols, the GPs were well aware of the importance of previous coronary heart disease and diabetes in their decisions. On the other hand, only a few doctors mentioned other arterio-sclerotic diseases like stroke and peripheral artery disease as variables affecting their decisions. There were several instances when the doctors' decisions apparently deviated from their knowledge of the guidelines. The arguments for the decisions in these cases often concerned aspects of the patient's life-style like smoking or overweight- either as risk-increasing factors or as alternative strategies for intervention. CONCLUSIONS: Coding verbal protocols for knowledge and for decision arguments seems to be a valuable tool for increasing our understanding of how guidelines are used in the on treatment of hypercholesterolaemia. By analysing arguments for treatment decisions it was often possible to understand why departures from the guidelines were made. While the need for decision support is obvious, the current guidelines may be too simple in some respects

Estimating the Impact of Adding C-Reactive Protein as a Criterion for Lipid Lowering Treatment in the United States

BACKGROUND: There is growing interest in using C-reactive protein (CRP) levels to help select patients for lipid lowering therapy—although this practice is not yet supported by evidence of benefit in a randomized trial. OBJECTIVE: To estimate the number of Americans potentially affected if a CRP criteria were adopted as an additional indication for lipid lowering therapy. To provide context, we also determined how well current lipid lowering guidelines are being implemented. METHODS: We analyzed nationally representative data to determine how many Americans age 35 and older meet current National Cholesterol Education Program (NCEP) treatment criteria (a combination of risk factors and their Framingham risk score). We then determined how many of the remaining individuals would meet criteria for treatment using 2 different CRP-based strategies: (1) narrow: treat individuals at intermediate risk (i.e., 2 or more risk factors and an estimated 10–20% risk of coronary artery disease over the next 10 years) with CRP > 3 mg/L and (2) broad: treat all individuals with CRP > 3 mg/L. DATA SOURCE: Analyses are based on the 2,778 individuals participating in the 1999–2002 National Health and Nutrition Examination Survey with complete data on cardiac risk factors, fasting lipid levels, CRP, and use of lipid lowering agents. MAIN MEASURES: The estimated number and proportion of American adults meeting NCEP criteria who take lipid-lowering drugs, and the additional number who would be eligible based on CRP testing. RESULTS: About 53 of the 153 million Americans aged 35 and older meet current NCEP criteria (that do not involve CRP) for lipid-lowering treatment. Sixty-five percent, however, are not currently being treated, even among those at highest risk (i.e., patients with established heart disease or its risk equivalent)—62% are untreated. Adopting the narrow and broad CRP strategies would make an additional 2.1 and 25.3 million Americans eligible for treatment, respectively. The latter strategy would make over half the adults age 35 and older eligible for lipid-lowering therapy, with most of the additionally eligible (57%) coming from the lowest NCEP heart risk category (i.e., 0–1 risk factors). CONCLUSION: There is substantial underuse of lipid lowering therapy for American adults at high risk for coronary disease. Rather than adopting CRP-based strategies, which would make millions more lower risk patients eligible for treatment (and for whom treatment benefit has not yet been demonstrated in a randomized trial), we should ensure the treatment of currently defined high-risk patients for whom the benefit of therapy is established

Development of a context model to prioritize drug safety alerts in CPOE systems

Background: Computerized physician order entry systems (CPOE) can reduce the number of medication errors and adverse drug events (ADEs) in healthcare institutions. Unfortunately, they tend to produce a large number of partly irrelevant alerts, in turn leading to alert overload and causing alert fatigue. The objective of this work is to identify factors that can be used to prioritize and present alerts depending on the 'context' of a clinical situation. Methods: We used a combination of literature searches and expert interviews to identify and validate the possible context factors. The internal validation of the context factors was performed by calculating the inter-rater agreement of two researcher's classification of 33 relevant articles. Results: We developed a context model containing 20 factors. We grouped these context factors into three categories: characteristics of the patient or case (e. g. clinical status of the patient); characteristics of the organizational unit or user (e. g. professional experience of the user); and alert characteristics (e. g. severity of the effect). The internal validation resulted in nearly perfect agreement (Cohen's Kappa value of 0.97). Conclusion: To our knowledge, this is the first structured attempt to develop a comprehensive context model for prioritizing drug safety alerts in CPOE systems. The outcome of this work can be used to develop future tailored drug safety alerting in CPOE systems