Tips for Teachers of Evidence-based Medicine: Making Sense of Decision Analysis Using a Decision Tree

Decision analysis is a tool that clinicians can use to choose an option that maximizes the overall net benefit to a patient. It is an explicit, quantitative, and systematic approach to decision making under conditions of uncertainty. In this article, we present two teaching tips aimed at helping clinical learners understand the use and relevance of decision analysis. The first tip demonstrates the structure of a decision tree. With this tree, a clinician may identify the optimal choice among complicated options by calculating probabilities of events and incorporating patient valuations of possible outcomes. The second tip demonstrates how to address uncertainty regarding the estimates used in a decision tree. We field tested the tips twice with interns and senior residents. Teacher preparatory time was approximately 90 minutes. The field test utilized a board and a calculator. Two handouts were prepared. Learners identified the importance of incorporating values into the decision-making process as well as the role of uncertainty. The educational objectives appeared to be reached. These teaching tips introduce clinical learners to decision analysis in a fashion aimed to illustrate principles of clinical reasoning and how patient values can be actively incorporated into complex decision making

Severe Acute Respiratory Syndrome–associated Coronavirus Infection

Whether severe acute respiratory syndrome–associated coronavirus (SARS-CoV) infection can be asymptomatic is unclear. We examined the seroprevalence of SARS-CoV among 674 healthcare workers from a hospital in which a SARS outbreak had occurred. A total of 353 (52%) experienced mild self-limiting illnesses, and 321 (48%) were asymptomatic throughout the course of these observations. None of these healthcare workers had antibody to SARS CoV, indicating that subclinical or mild infection attributable to SARS CoV in adults is rare

Human Metapneumovirus Detection in Patients with Severe Acute Respiratory Syndrome

We used a combination approach of conventional virus isolation and molecular techniques to detect human metapneumovirus (HMPV) in patients with severe acute respiratory syndrome (SARS). Of the 48 study patients, 25 (52.1%) were infected with HMPV; 6 of these 25 patients were also infected with coronavirus, and another 5 patients (10.4%) were infected with coronavirus alone. Using this combination approach, we found that human laryngeal carcinoma (HEp-2) cells were superior to rhesus monkey kidney (LLC-MK2) cells commonly used in previous studies for isolation of HMPV. These widely available HEp-2 cells should be included in conjunction with a molecular method for cell culture followup to detect HMPV, particularly in patients with SARS

How to optimise antimicrobial prescriptions in the Intensive Care Unit: principles of individualised dosing using pharmacokinetics and pharmacodynamics

Optimising antimicrobial dosing for critically ill patients is highly challenging and when it is not achieved can lead to worse patient outcomes. To this end, use of dosing regimens recommended in package inserts from drug manufacturers is frequently insufficient to guide dosing in these patients appropriately. Whilst the effect of critical illness pathophysiology on the pharmacokinetic (PK) behaviour of antimicrobials can be profound, the variability of these changes between patients is still being quantified. The PK effects of hypoproteinaemia, organ dysfunction and the presence of augmented renal clearance may lead to plasma antimicrobial concentrations that are difficult to predict at the bedside, which may result in excess toxicity or suboptimal bacterial killing. This paper outlines the factors that affect pharmacokinetics in critically ill patients and how knowledge of these factors can increase the likelihood of achieving optimal antimicrobial plasma concentrations. In selected settings, we advocate individualised dosing of renally cleared antimicrobials using physiological data such as measured creatinine clearance and published non-renal clearance data. Where such data do not exist, therapeutic drug monitoring may be a useful alternative and has been associated with significant clinical benefits, although it is not currently widely available

Principles of antibacterial dosing in continuous renal replacement therapy

To outline the concepts involved in optimizing antibacterial dosing in critically ill patients with acute renal failure undergoing continuous renal replacement therapy (CRRT), provide a strategy for optimizing dosing, and summarize the data required to implement the strategy. Data Sources: MEDLINE search from February 1986 to 2008. Data Extraction and Synthesis: Optimal dosing of antibacterials is dependent on achieving pharmacokinetic targets associated with maximal killing of bacteria and improved outcomes. The initial dose is dependent on the volume of distribution. Maintenance doses are dependent on clearance. Both should be adjusted according to the pharmacokinetic target associated with optimal bacterial killing, when known. The volume of distribution of some antibacterials is altered by critical illness or acute renal failure or both. Clearance by CRRT is dependent on the dose and mode of CRRT and the sieving or saturation coefficient of the drug. Both sieving and saturation coefficient are related to the plasma protein binding and thus may be altered in renal failure. Conclusions: Appropriate dose calculation requires knowledge of the pharmacokinetic target and the usual minimum inhibitory concentration of the suspected organism in the patient’s locality (or if unavailable, the break point for the organism), published pharmacokinetic data (volume of distribution, non-CRRT clearance) on critically ill patients receiving CRRT (which may differ substantially from noncritically ill patients or those without renal failure), the sieving or saturation coefficient of the relevant drug in critically ill patients, the dose and mode of CRRT being used, and the actual dose of CRRT that is delivered. This large number of variables results in considerable inter- and intrapatient heterogeneity in dose requirements. This article provides basic principles and relevant data to guide the clinician in prescribing individualized dosing regimes.