Comparison of Khorana vs. ONKOTEV predictive score to individualize anticoagulant prophylaxis in outpatients with cancer.

Background: Based on the Khorana score, guidelines recommend anticoagulation for primary prophylaxis (PP) in outpatients with cancer with an intermediate-to-high risk of venous thromboembolism (VTE). ONKOTEV score has been prospectively externally validated as novel risk assessment model (RAM) with good discriminatory performances but no direct comparisons with Khorana Score are available.

Methods: Using the ONKOTEV validation dataset (n = 425), we applied generalized decision curve analysis (gDCA) which integrates the principles of evidence-based medicine with treatment effects, model accuracy and patient preferences (weighted as the relative value [RV] of avoiding VTE versus major bleeding [MB]).

High certainty evidence is stable and trustworthy, whereas evidence of moderate or lower certainty may be equally prone to being unstable.

Objectives: To assess to what extent the overall quality of evidence indicates changes to observe intervention effect estimates when new data become available.

Methods: We conducted a meta-epidemiological study. We obtained evidence from meta-analyses of randomized trials of Cochrane reviews addressing the same health-care question that was updated with inclusion of additional data between January 2016 and May 2021.

Decision theoretical foundations of clinical practice guidelines: an extension of the ASH thrombophilia guidelines.

Decision analysis can play an essential role in informing practice guidelines. The American Society of Hematology (ASH) thrombophilia guidelines have made a significant step forward in demonstrating how decision modeling integrated within Grading of Recommendations Assessment, Developing, and Evaluation (GRADE) methodology can advance the field of guideline development. Although the ASH model was transparent and understandable, it does, however, suffer from certain limitations that may have generated potentially wrong recommendations.

Converting IMPROVE bleeding and VTE risk assessment models into a fast-and-frugal decision tree for optimal hospital VTE prophylaxis.

Current hospital venous thromboembolism (VTE) prophylaxis for medical patients is characterized by both underuse and overuse. The American Society of Hematology (ASH) has endorsed the use of risk assessment models (RAMs) as an approach to individualize VTE prophylaxis by balancing overuse (excessive risk of bleeding) and underuse (risk of avoidable VTE). ASH has endorsed IMPROVE (International Medical Prevention Registry on Venous Thromboembolism) risk assessment models, the only RAMs to assess short-term bleeding and VTE risk in acutely ill medical inpatients.

Distinguishing ASH clinical practice guidelines from other forms of ASH clinical advice.

The American Society of Hematology (ASH) develops a variety of resources that provide guidance to clinicians on the diagnosis and management of blood diseases. These resources include clinical practice guidelines (CPGs) and other forms of clinical advice. Although both ASH CPGs and other forms of clinical advice provide recommendations, they differ with respect to the methods underpinning their development, the principal type of recommendations they offer, their transparency and concordance with published evidence, and the time and resources required for their development.

Methods proposed for monitoring the implementation of evidence-based research: a cross-sectional study.

Objectives: Evidence-based research (EBR) is the systematic and transparent use of prior research to inform a new study so that it answers questions that matter in a valid, efficient, and accessible manner. This study surveyed experts about existing (e.g.

Improving methods of clinical practice guidelines: From guidelines to pathways to fast-and-frugal trees and decision analysis to develop individualised patient care.

Background: Current methods for developing clinical practice guidelines have several limitations: they are characterised by the "black box" operation-a process with defined inputs and outputs but an incomplete understanding of its internal workings; they have "the integration problem"-a lack of framework for explicitly integrating factors such as patient preferences and trade-offs between benefits and harms; they generate one recommendation at a time that typically are not connected in a coherent analytical framework; and they apply to "average" patients, while clinicians and their patients seek advice tailored to individual circumstances.

Methods: We propose augmenting the current guideline development method by converting evidence-based pathways into fast-and-frugal decision trees (FFTs) and integrating them with generalised decision curve analysis to formulate clear, individualised management recommendations.

Results: We illustrate the process by developing recommendations for the management of heparin-induced thrombocytopenia (HIT).

Decision curve analysis based on summary data.

Background: To realize the potential of precision medicine, predictive models should be integrated within the framework of decision analysis, such as the decision curve analysis (DCA). To date, its application has required individual patient data (IPD) that are often unavailable. Performing DCA using aggregate data without requiring IPD may advance the goals of precision medicine.

Guideline panel social dynamics influence the development of clinical practice recommendations: a mixed-methods systematic review.

Objectives: To synthesize empirical studies that investigate the cognitive and social processes involved in the deliberation process of guideline development meetings and determine the distribution of deliberated topics.

Study Design And Setting: We conducted a mixed-method systematic review using a convergent segregated approach. We searched for empirical studies that investigate the intragroup dynamics of guideline development meetings indexed in bibliographic databases.

Medical Decision-Making and Artificial Intelligence.

In this chapter, we discuss the potential role that artificial intelligence (AI) may have in medical decision-making, the pros and cons, and the limitations and biases that might be introduced when using these novel techniques. As computing becomes more powerful and models continue to grow increasingly more complex, the potential of AI to improve decision-making is increasingly promising. Within many medical fields, however, at the time of this writing (September 2023), the promise of AI is yet to translate into everyday reality.

Which Threshold Model?

As outlined in the Preface (and Chap. 1 and other chapters), this book espoused two fundamental views. The first view consists of the proposal that the threshold model represents a method to address the Sorites paradox, which is a consequence of a relationship between scientific evidence (that exists on a continuum of credibility) and decision-making (that is categorical, yes/no exercises).

Hybrid and Dual-Processing Threshold Decision Models.

In the previous chapters, we presented various derivations of the threshold model based on the same disease outcomes. We assumed that a decision-maker would calculate the threshold based on either mortality or morbidity outcomes. Basinga and van den Ende derived the threshold by combining both mortality and morbidity outcomes.

Using Decision Curve Analysis to Evaluate Testing and/or Predictive Modeling.

In this chapter, we extend the threshold model to evaluate the value of diagnostic tests or predictive models over a range of all possible thresholds by using decision curve analysis (DCA). DCA has been developed within the expected utility theory (EUT) and expected regret theory (ERT) framework.

Formulating Management Strategies Using Fast-and-Frugal Trees (A Decision Tool to Transform Clinical Practice Guidelines and Clinical Pathways into Decision Support at the Point of Care).

Clinical management is rarely based on the collection of one data item. Instead, it is typically characterized by the continuous collection and evaluation of clinical data (symptoms, signs, laboratory, imaging tests, etc.) to establish a platform for further management decisions.

Decision-Making When Diagnostic Testing is Available.

When a decision-maker has the option of diagnostic testing, they face a typical dilemma: (1) do not administer treatment and do not test, (2) test and decide to administer treatment based on the test result, and (3) administer treatment without testing. In this chapter, we will discuss the theory behind threshold modeling when diagnostic testing is available; we will illustrate the approach by presenting a case vignette.

Making Decisions When no Further Diagnostic Testing is Available (Expected Regret Theory Threshold Model).

In Chap. 2 , we illustrated the application of the expected utility theory (EUT) to rational decision-making when no further diagnostic testing is available. In this chapter, we apply regret theory to the decision problems discussed in Chap.

Making Decisions When No Further Diagnostic Testing is Available.

In this chapter, we illustrate how evidence about treatments' benefits and harms can be integrated to enable rational decision-making even under considerable clinical uncertainty.

Evidence and Decision-Making.

Today, every country struggles to provide adequate health care to its citizens. Globally, an average of $8.3 trillion or 10% of gross domestic product (GDP) is annually spent on health services.

Generalised decision curve analysis for explicit comparison of treatment effects.

Rationale: Decision curve analysis (DCA) helps integrate prediction models with treatment assessments to guide personalised therapeutic choices among multiple treatment options. However, the current versions of DCA do not explicitly model treatment effects in the analysis but implicitly or holistically assess therapeutic benefits and harms. In addition, the existing DCA cannot allow the comparison of multiple treatments using a standard metric.