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Introduction: Decision aids are software or paper-based tools that enable patients and health care practitioners to work together to make optimal treatment decisions. Although there are decades of favorable research for the use of decision aids, there is wide variation in their development and implementation. Objective: To review the literature and provide a summary of best practices for the design, implementation, and assessment of decision aids. Methods: A literature search and screening process was employed with a focus on systematic review articles. The resulting articles were synthesized and summarized into recommendations regarding the optimal design, implementation, and appraisal of shared decision-making tools. Results: Designers of decision aids should initially focus on engagement by involving health care practitioners and patients to ensure the decision aids are pertinent to the situation and not perceived as time consuming. The International Patient Decision Aids Standards help ensure quality in design. Patients should be able to select information in a manner that suits them and view it in a way that allows them to evaluate trade-offs. In reporting of statistical risks, format bias should be avoided through careful and consistent choice of format and by adding visual representations. Decision aids should be tested in real settings with iterative improvements. For appraisal of the implemented decision aids, consider using the Control Preference and Decisional Conflict scales. Discussion: Further research is recommended regarding optimal engagement of users with decision aids and exploration of the distinct opportunities that computerized decision aids and online patient communities may present. Conclusion: Decision aids can be a valuable tool for shared medical decision making. Their quality and usefulness can be maximized by involving users in their creation and by attending to key considerations in their design, implementation, and appraisal. [ABSTRACT FROM AUTHOR]

No clinical risk score is universally accepted for coronary artery disease. In 603 patients (mean age, 61.2 ± 12.3 yr) with stable coronary artery disease, we investigated the predictive power of clinical risk scores derived from the Framingham, the Long-term Intervention with Pravastatin in Ischemic Disease (LIPID), and the Vienna and Ludwigshafen Coronary Artery Disease (VILCAD) studies. Secondary outcomes were the recurrence of an acute thrombotic event (coronary events, strokes, or transient ischemic attacks), or heart failure or death. The primary outcome was the combination of secondary outcomes. During follow-up (duration, 2.08 ± 0.97 yr), 42 patients had an acute thrombotic event; 22, heart failure or death; and 60, the primary outcome. The Framingham score predicted acute thrombotic events: hazard ratio (HR)=1.05; 95% confidence interval (CI), 1.01-1.08; P=0.03; net reclassification index (NRI, calculated to evaluate improvement in prediction gained by adding different risk scores to models constructed with variables excluded from the calculation of that score)=9.7% (95% CI, 9.6-9.8). The LIPID (HR=1.13; 95% CI, 1.04-1.22; P=0.005) and VILCAD scores (HR=1.99; 95% CI, 1.48-2.67; P <0.001) predicted heart failure or death with NRIs of 5.8% (95% CI, 5.7-5.9) and 18.6% (95% CI, 18.3-18.9), respectively. The primary outcome was predicted by the LIPID (HR=1.1; 95% CI, 1.03-1.17; P=0.005) and VILCAD scores (HR=1.39; 95% CI, 1.13-1.70; P=0.003). The NRIs (95% CIs) were 3.4% (3.3-3.5) and 19.4% (19.3-19.6), respectively. We conclude that the accuracy of these risk scores varies in accordance with the outcome studied. [ABSTRACT FROM AUTHOR]

Objectives: A uniform rationalized management protocol for pregnancies of unknown location (PUL) is lacking. We developed a two-step triage protocol to select PUL at high risk of ectopic pregnancy (EP), based on serum progesterone level at presentation (step 1) and the serum human chorionic gonadotropin (hCG) ratio, defined as the ratio of hCG at 48 h to hCG at presentation (step 2).Methods: This was a cohort study of 2753 PUL (301 EP), involving a secondary analysis of prospectively and consecutively collected PUL data from two London-based university teaching hospitals. Using a chronological split we used 1449 PUL for development and 1304 for validation. We aimed to assign PUL as low risk with high confidence (high negative predictive value (NPV)) while classifying most EP as high risk (high sensitivity). The first triage step assigned PUL as low risk using a threshold of serum progesterone at presentation. The remaining PUL were triaged using a novel logistic regression risk model based on hCG ratio and initial serum progesterone (second step), defining low risk as an estimated EP risk of < 5%.Results: On validation, initial serum progesterone ≤ 2 nmol/L (step 1) classified 16.1% PUL as low risk. Second-step classification with the risk model selected an additional 46.0% of all PUL as low risk. Overall, the two-step protocol classified 62.1% of PUL as low risk, with an NPV of 98.6% and a sensitivity of 92.0%. When the risk model was used in isolation (i.e. without the first step), 60.5% of PUL were classified as low risk with 99.1% NPV and 94.9% sensitivity.Conclusion: PUL can be classified efficiently into being either high or low risk for complications using a two-step protocol involving initial progesterone and hCG levels and the hCG ratio. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT Objectives A uniform rationalized management protocol for pregnancies of unknown location (PUL) is lacking. We developed a two-step triage protocol to select PUL at high risk of ectopic pregnancy (EP), based on serum progesterone level at presentation (step 1) and the serum human chorionic gonadotropin (hCG) ratio, defined as the ratio of hCG at 48 h to hCG at presentation (step 2). Methods This was a cohort study of 2753 PUL (301 EP), involving a secondary analysis of prospectively and consecutively collected PUL data from two London-based university teaching hospitals. Using a chronological split we used 1449 PUL for development and 1304 for validation. We aimed to assign PUL as low risk with high confidence (high negative predictive value (NPV)) while classifying most EP as high risk (high sensitivity). The first triage step assigned PUL as low risk using a threshold of serum progesterone at presentation. The remaining PUL were triaged using a novel logistic regression risk model based on hCG ratio and initial serum progesterone (second step), defining low risk as an estimated EP risk of < 5%. Results On validation, initial serum progesterone ≤ 2 nmol/L (step 1) classified 16.1% PUL as low risk. Second-step classification with the risk model selected an additional 46.0% of all PUL as low risk. Overall, the two-step protocol classified 62.1% of PUL as low risk, with an NPV of 98.6% and a sensitivity of 92.0%. When the risk model was used in isolation (i.e. without the first step), 60.5% of PUL were classified as low risk with 99.1% NPV and 94.9% sensitivity. Conclusion PUL can be classified efficiently into being either high or low risk for complications using a two-step protocol involving initial progesterone and hCG levels and the hCG ratio. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

This paper reports on the application of decision counseling based on the analytic hierarchy process (AHP) to assist men in deciding whether or not to schedule a prostate cancer screening exam. The study is based on data that we collected from 129 men enrolled in the intervention arm of two randomized, controlled trials. First, we administered a baseline survey to gather data on participant sociodemographic characteristics and perceptions about prostate cancer and screening. Subsequently, a health educator conducted a session with each man to review an informational booklet on prostate cancer screening. Then, the health educator used an AHP-based decision process that identified the most important factors (both pro or con) that might influence prostate cancer screening preferences, clarified preferences related to scheduling a prostate screening exam, and elicited a scheduling decision. We performed univariable and multivariable logistic regression analyses to identify variables associated with the decision. Of the 129 men enrolled, 85 men (66 percent) decided to schedule a prostate cancer screening exam. Multivariable analyses showed that preference strengths and favorable perceptions of prostate cancer screening predicted the decision to screen. [ABSTRACT FROM AUTHOR]