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. In 2019, the USA spent nearly 18% ($3.2 trillion) of its GDP on health care, projected to reach $6.2 trillion by 2028.

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. 2 .

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.

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.

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.

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.

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. Here, we summarize the role of AI in medical decision-making (diagnosis, prognosis, and treatment).

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).

The Pglyrp1-Regulated Microbiome Enhances Experimental Allergic Asthma.

Changes in intestinal or respiratory microbiomes in infants correlate with increased incidence of asthma, but the causative role of microbiome in the susceptibility to asthma and the host genes that regulate these changes in microbiome are mostly unknown. In this study, we show that decreased responsiveness to allergic asthma in Pglyrp1 -/- mice (lacking bactericidal peptidoglycan recognition protein 1) could be transferred to germ-free wild-type mice by colonization of mothers and newborns with microbiota from Pglyrp1 -/- mice. These colonized mice had decreased airway resistance and fewer inflammatory cells, less severe histopathology, and lower levels of IgE and proallergic cytokines and chemokines in the lungs. This microbiome-dependent decreased responsiveness to asthma was most pronounced in colonized germ-free BALB/c mice (genetically predisposed to asthma), only partially evident in outbred germ-free Swiss Webster mice, and marginal in conventional BALB/c mice following depletion of microbiome with antibiotics. Mice with a low asthmatic response colonized with microbiota from Pglyrp1 -/- mice had increased abundance of Bacteroidetes and decreased abundance of Firmicutes, Tenericutes, Deferribacteres, and Spirochaetes in the feces and increased abundance of Pasteurella in the oropharynx. These changes in bacterial abundance in the feces and oropharynx correlated with lower asthmatic responses in the lungs. Thus, our results show that Pglyrp1 enhances allergic asthmatic responses primarily through its effect on the host intestinal microbiome and identify several bacteria that may increase or decrease sensitivity to asthma. This effect of microbiome is strong in asthma-prone BALB/c mice and weak in asthma-resistant outbred mice and requires germ-free conditions before colonization with microbiota from Pglyrp1 -/- mice.

Monte Carlo decision curve analysis using aggregate data.

BACKGROUND: Decision curve analysis (DCA) is an increasingly used method for evaluating diagnostic tests and predictive models, but its application requires individual patient data. The Monte Carlo (MC) method can be used to simulate probabilities and outcomes of individual patients and offers an attractive option for application of DCA. MATERIALS AND METHODS: We constructed a MC decision model to simulate individual probabilities of outcomes of interest. These probabilities were contrasted against the threshold probability at which a decision-maker is indifferent between key management strategies: treat all, treat none or use predictive model to guide treatment. We compared the results of DCA with MC simulated data against the results of DCA based on actual individual patient data for three decision models published in the literature: (i) statins for primary prevention of cardiovascular disease, (ii) hospice referral for terminally ill patients and (iii) prostate cancer surgery. RESULTS: The results of MC DCA and patient data DCA were identical. To the extent that patient data DCA were used to inform decisions about statin use, referral to hospice or prostate surgery, the results indicate that MC DCA could have also been used. As long as the aggregate parameters on distribution of the probability of outcomes and treatment effects are accurately described in the published reports, the MC DCA will generate indistinguishable results from individual patient data DCA. CONCLUSIONS: We provide a simple, easy-to-use model, which can facilitate wider use of DCA and better evaluation of diagnostic tests and predictive models that rely only on aggregate data reported in the literature.

Peptidoglycan recognition proteins kill bacteria by inducing oxidative, thiol, and metal stress.

Mammalian Peptidoglycan Recognition Proteins (PGRPs) are a family of evolutionary conserved bactericidal innate immunity proteins, but the mechanism through which they kill bacteria is unclear. We previously proposed that PGRPs are bactericidal due to induction of reactive oxygen species (ROS), a mechanism of killing that was also postulated, and later refuted, for several bactericidal antibiotics. Here, using whole genome expression arrays, qRT-PCR, and biochemical tests we show that in both Escherichia coli and Bacillus subtilis PGRPs induce a transcriptomic signature characteristic of oxidative stress, as well as correlated biochemical changes. However, induction of ROS was required, but not sufficient for PGRP killing. PGRPs also induced depletion of intracellular thiols and increased cytosolic concentrations of zinc and copper, as evidenced by transcriptome changes and supported by direct measurements. Depletion of thiols and elevated concentrations of metals were also required, but by themselves not sufficient, for bacterial killing. Chemical treatment studies demonstrated that efficient bacterial killing can be recapitulated only by the simultaneous addition of agents leading to production of ROS, depletion of thiols, and elevation of intracellular metal concentrations. These results identify a novel mechanism of bacterial killing by innate immunity proteins, which depends on synergistic effect of oxidative, thiol, and metal stress and differs from bacterial killing by antibiotics. These results offer potential targets for developing new antibacterial agents that would kill antibiotic-resistant bacteria.

When to perform hepatic resection for intermediate-stage hepatocellular carcinoma.

UNLABELLED: Transcatheter arterial chemoembolization (TACE) is the first-line therapy recommended for patients with intermediate hepatocellular carcinoma (HCC). However, in clinical practice, these patients are often referred to surgical teams to be evaluated for hepatectomy. After making a treatment decision (e.g., TACE or surgery), physicians may discover that the alternative treatment would have been preferable, which may bring a sense of regret. Under this premise, it is postulated that the optimal decision will be the one associated with the least amount of regret. Regret-based decision curve analysis (Regret-DCA) was performed on a Cox's regression model developed on 247 patients with cirrhosis resected for intermediate HCC. Physician preferences on surgery versus TACE were elicited in terms of regret; threshold probabilities (Pt) were calculated to identify the probability of survival for which physicians are uncertain of whether or not to perform a surgery. A survey among surgeons and hepatologists regarding three hypothetical clinical cases of intermediate HCC was performed to assess treatment preference domains. The 3- and 5-year overall survival rates after hepatectomy were 48.7% and 33.8%, respectively. Child-Pugh score, tumor number, and esophageal varices were independent predictors of survival (P<0.05). Regret-DCA showed that for physicians with Pt values of 3-year survival between 35% and 70%, the optimal strategy is to rely on the prediction model; for physicians with Pt<35%, surgery should be offered to all patients; and for Pt values>70%, the least regretful strategy is to perform TACE on all patients. The survey showed a significant separation among physicians' preferences, indicating that surgeons and hepatologists can uniformly act according to the regret threshold model. CONCLUSION: Regret theory provides a new perspective for treatment-related decisions applicable to the setting of intermediate HCC.

When is rational to order a diagnostic test, or prescribe treatment: the threshold model as an explanation of practice variation.

BACKGROUND: The threshold model represents an important advance in the field of medical decision-making. It is a linchpin between evidence (which exists on the continuum of credibility) and decision-making (which is a categorical exercise - we decide to act or not act). The threshold concept is closely related to the question of rational decision-making. When should the physician act, that is order a diagnostic test, or prescribe treatment? The threshold model embodies the decision theoretic rationality that says the most rational decision is to prescribe treatment when the expected treatment benefit outweighs its expected harms. However, the well-documented large variation in the way physicians order diagnostic tests or decide to administer treatments is consistent with a notion that physicians' individual action thresholds vary. METHODS: We present a narrative review summarizing the existing literature on physicians' use of a threshold strategy for decision-making. RESULTS: We found that the observed variation in decision action thresholds is partially due to the way people integrate benefits and harms. That is, explanation of variation in clinical practice can be reduced to a consideration of thresholds. Limited evidence suggests that non-expected utility threshold (non-EUT) models, such as regret-based and dual-processing models, may explain current medical practice better. However, inclusion of costs and recognition of risk attitudes towards uncertain treatment effects and comorbidities may improve the explanatory and predictive value of the EUT-based threshold models. CONCLUSIONS: The decision when to act is closely related to the question of rational choice. We conclude that the medical community has not yet fully defined criteria for rational clinical decision-making. The traditional notion of rationality rooted in EUT may need to be supplemented by reflective rationality, which strives to integrate all aspects of medical practice - medical, humanistic and socio-economic - within a coherent reasoning system.

How do physicians decide to treat: an empirical evaluation of the threshold model.

BACKGROUND: According to the threshold model, when faced with a decision under diagnostic uncertainty, physicians should administer treatment if the probability of disease is above a specified threshold and withhold treatment otherwise. The objectives of the present study are to a) evaluate if physicians act according to a threshold model, b) examine which of the existing threshold models [expected utility theory model (EUT), regret-based threshold model, or dual-processing theory] explains the physicians' decision-making best. METHODS: A survey employing realistic clinical treatment vignettes for patients with pulmonary embolism and acute myeloid leukemia was administered to forty-one practicing physicians across different medical specialties. Participants were randomly assigned to the order of presentation of the case vignettes and re-randomized to the order of "high" versus "low" threshold case. The main outcome measure was the proportion of physicians who would or would not prescribe treatment in relation to perceived changes in threshold probability. RESULTS: Fewer physicians choose to treat as the benefit/harms ratio decreased (i.e. the threshold increased) and more physicians administered treatment as the benefit/harms ratio increased (and the threshold decreased). When compared to the actual treatment recommendations, we found that the regret model was marginally superior to the EUT model [Odds ratio (OR) = 1.49; 95% confidence interval (CI) 1.00 to 2.23; p = 0.056]. The dual-processing model was statistically significantly superior to both EUT model [OR = 1.75, 95% CI 1.67 to 4.08; p < 0.001] and regret model [OR = 2.61, 95% CI 1.11 to 2.77; p = 0.018]. CONCLUSIONS: We provide the first empirical evidence that physicians' decision-making can be explained by the threshold model. Of the threshold models tested, the dual-processing theory of decision-making provides the best explanation for the observed empirical results.

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. METHODS: We present a statistical framework demonstrating that DCA can be conducted by using only the mean and standard deviation (SD) from the raw probabilities of the predictive model. We tested our theoretical framework by performing extensive simulations and comparing the aggregate-based DCA with IPD DCA. The latter was conducted using IPD from four predictive models that employed logistic regression, Cox or competing risk time-to-event modeling including (a) statins for primary prevention of cardiovascular disease (n = 4859), (b) hospice referral for terminally ill patients (n = 9104), (c) use of thromboprophylaxis for preventing venous thromboembolism in patients with cancer (n = 425) and (d) prevention of sinusoidal obstruction syndrome after hematopoietic cell transplantation (SCT) (n = 80). RESULTS: Simulations assuming perfect calibration showed that regardless of which probability distributions informed the predictive models, the differences in DCA were negligible. Similarly, for the adequately powered models, the results of DCA based on the summary data were similar to IPD-derived DCA. The inherent instability of the predictive models, based on the smaller sample sizes, resulted in a somewhat larger discrepancy between aggregate and IPD-based DCA. CONCLUSIONS: DCA informed by adequately powered and well-calibrated models using only summary statistical estimates (mean and SD) approximates well models using IPD. Use of aggregate data will facilitate broader integration of predictive with decision modeling toward the goals of individualized decision-making.

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

ABSTRACT: 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. That is, the panel considered 2 models separately: after 3 to 6 months of index venous thromboembolism (VTE), the panel compared thrombophilia testing (A) vs discontinuing anticoagulants (B) and testing (A) vs recommending indefinite anticoagulation to all patients (C), instead of considering all relevant options simultaneously (A vs B vs C). Our study aimed to avoid what we refer to as the omitted choice bias by integrating 2 ASH models into a single unifying threshold decision model. We analyzed 6 ASH panel's recommendations related to the testing for thrombophilia in settings of "provoked" vs "unprovoked" VTE and low vs high bleeding risk (total 12 recommendations). Our model disagreed with the ASH guideline panels' recommendations in 4 of the 12 recommendations we considered. Considering all 3 options simultaneously, our model provided results that would have produced sounder recommendations for patient care. By revisiting the ASH guidelines methodology, we have not only improved the recommendations for thrombophilia but also provided a method that can be easily applied to other clinical problems and promises to improve the current guidelines' methodology.

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). We converted evidence-based pathways for HIT, developed by the American Society of Hematology, into an FFT. Here, we consider only thrombotic complications and major bleeding. We leveraged the predictive potential of FFTs to compare the effects of argatroban, bivalirudin, fondaparinux, and direct oral anticoagulants (DOACs) using generalised decision curve analysis. We found that DOACs were superior to other treatments if the FFT-predicted probability of HIT exceeded 3%. CONCLUSIONS: The proposed analytical framework connects guidelines, pathways, FFTs, and decision analysis, offering risk-tailored personalised recommendations and addressing current guideline development critiques.

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

ABSTRACT: 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. ASH, however, notes that no RAMs have been thoroughly analyzed for their effect on patient outcomes. We aimed to validate the IMPROVE models and adapt them into a simple, fast-and-frugal (FFT) decision tree to evaluate the impact of VTE prevention on health outcomes and costs. We used 3 methods: the "best evidence" from ASH guidelines, a "learning health system paradigm" combining guideline and real-world data from the Medical University of South Carolina (MUSC), and a "real-world data" approach based solely on MUSC data retrospectively extracted from electronic records. We found that the most effective VTE prevention strategy used the FFT decision tree based on an IMPROVE VTE score of ≥2 or ≥4 and a bleeding score of <7. This method could prevent 45% of unnecessary treatments, saving ∼$5 million annually for patients such as the MUSC cohort. We recommend integrating IMPROVE models into hospital electronic medical records as a point-of-care tool, thereby enhancing VTE prevention in hospitalized medical patients.