The threshold model revisited

Decision threshold models in medical decision making: a scoping literature review

BACKGROUND

Decision thresholds play important role in medical decision-making. Individual decision-making differences may be attributable to differences in subjective judgments or cognitive processes that are captured through the decision thresholds. This systematic scoping review sought to characterize the literature on non-expected utility decision thresholds in medical decision-making by identifying commonly used theoretical paradigms and contextual and subjective factors that inform decision thresholds.

METHODS

A structured search designed around three concepts-individual decision-maker, decision threshold, and medical decision-was conducted in MEDLINE (Ovid) and Scopus databases from inception to July 2023. ProQuest (Dissertations and Theses) database was searched to August 2023. The protocol, developed a priori, was registered on Open Science Framework and PRISMA-ScR guidelines were followed for reporting on this study. Titles and abstracts of 1,618 articles and the full texts for the 228 included articles were reviewed by two independent reviewers. 95 articles were included in the analysis. A single reviewer used a pilot-tested data collection tool to extract study and author characteristics, article type, objectives, theoretical paradigm, contextual or subjective factors, decision-maker, and type of medical decision.

RESULTS

Of the 95 included articles, 68 identified a theoretical paradigm in their approach to decision thresholds. The most common paradigms included regret theory, hybrid theory, and dual processing theory. Contextual and subjective factors that influence decision thresholds were identified in 44 articles.

CONCLUSIONS

Our scoping review is the first to systematically characterizes the available literature on decision thresholds within medical decision-making. This study offers an important characterization of the literature through the identification of the theoretical paradigms for non-expected utility decision thresholds. Moreover, this study provides insight into the various contextual and subjective factors that have been documented within the literature to influence decision thresholds, as well as these factors juxtapose theoretical paradigms.

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]). The aim is to select the most optimal treatment strategy among multiple options: "no treatment", "treat all patients with DOAC/LMVH", or "use ONKOTEV/KHORANA scores to guide PP with DOAC/LMWH".

RESULTS

Results showed that ONKOTEV-guided PP (using DOAC or LMWH) remained the most optimal strategy for wide range assumption of treatment efficacy and patient's preference. For those patients, who value avoiding VTE more than MB, then offering DOAC to all patients represents the best strategy. When MBs are feared more than the morbidity of VTE, ONKOTEV-guided PP (DOAC) represents the best management strategy. In all cases, ONKOTEV outperformed Khorana for individualized VTE prevention.

CONCLUSIONS

When the two predictive models are integrated within a decision analysis framework, ONKOTEV appears superior to Khorana Score in guiding individualized prevention of cancer-related VTE in outpatients with cancer. The findings herein reported provide cutting edge insights in cancer care and support the spread of ONKOTEV score in the ambulatory cancer setting.

fNIRS Changes in the Middle Temporal and Occipital Cortices After a Cochlear Implant

OBJECTIVES

The relationship between the middle temporal (MTG) and occipital cortices in post-lingually deaf (PLD) individuals is unclear. This study aimed to investigate changes in the MTG and occipital cortices excitability and their effects on the occipital cortex in individuals with PLD after receiving a cochlear implant (CI).

METHODS

Twenty-six individuals with severe-to-profound binaural sensorineural PLD were assessed clinically. Nine individuals had received a unilateral cochlear implant over 6 months, while 17 had not. Brodmann area 19 (BA19, extra-striate occipital cortex) and MTG (auditory-related area of cortex) were selected as regions of interest. The excitability of the ROI was observed and compared in the surgery and no-surgery groups by functional near-infrared spectroscopy (fNIRS) in the resting state, and correlations between connectivity of the MTG and occipital cortex, and as well as the duration of time that had elapsed following CI surgery, were investigated.

RESULTS

fNIRS revealed enhanced global cortical connectivity in the BA19 and MTG on the operative side (p < 0.05) and the connectivity between BA19 and the MTG also increased (p < 0.05). The connectivity between the MTG and BA19 was positively correlated with the duration of cochlear implantation, as was the case for BA18.

CONCLUSION

There was evidence for remodeling of the cerebral cortex: increased excitability was observed in the MTG and BA19, and their connectivity was enhanced, indicating a synergistic effect. Moreover, the MTG may further stimulate the visual cortex by strengthening their connectivity after CI.

LEVEL OF EVIDENCE

3 Laryngoscope, 2024.

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.

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.

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.

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.

Crimson clues: advancing endometriosis detection and management with novel blood biomarkers

Endometriosis is an inflammatory condition affecting approximately 10% of the female-born population. Despite its prevalence, the lack of noninvasive biomarkers has contributed to an established global diagnostic delay. The intricate pathophysiology of this enigmatic disease may leave signatures in the blood, which, when detected, can be used as noninvasive biomarkers. This review provides an update on how investigators are utilizing the established disease pathways and innovative methodologies, including genome-wide association studies, next-generation sequencing, and machine learning, to unravel the clues left in the blood to develop blood biomarkers. Many blood biomarkers show promise in the discovery phase, but because of a lack of standardized and robust methodologies, they rarely progress to the development stages. However, we are now seeing biomarkers being validated with high diagnostic accuracy and improvements in standardization protocols, providing promise for the future of endometriosis blood biomarkers.

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.

AIMS AND OBJECTIVES

To develop a generalised version of DCA (gDCA) by decomposing holistically assessed net benefits and harms into patient preferences versus empirical evidence (as obtained in the trials, meta-analyses of clinical studies, etc.) to allow individualised comparison of single or multiple treatments using a common metric.

METHODS

We reformulated DCA by (1) decomposing holistic, implicit utilities into specific utilities related to treatment effects and patient's relative values (RV) about disease outcomes versus treatment harms, (2) explicitly modelling each treatment effect at the level of probabilities and/or utilities (outcomes) in a decision tree, and (3) avoiding scaling effects employed in the original DCA to enable comparison of treatment effects against the common metrics. We used data from a published network meta-analysis of randomised trials to inform the use of statin treatment according to Framingham Risk Model.

RESULTS

We illustrate the analysis by modelling the effects of three statins in the primary prevention of cardiovascular disease. We performed simultaneous comparisons against standard metrics (RV) for all treatments. We examined for which RV values, a predictive model for guiding personalised treatment, outperformed the strategies of treating everyone or treating no one. We found that the magnitude of benefits (efficacy) seems more important than the simple ratio of efficacy/harms.

CONCLUSION

We describe gDCA for evaluating single or multiple treatments to help tailor therapy toward individual risk characteristics. gDCA further helps integrate the principles of evidence-based medicine with decision analysis.

Piloting a new method to estimate action thresholds in medicine through intuitive weighing

OBJECTIVES

In clinical decision-making, physicians take actions such as prescribing treatment only when the probability of disease is sufficiently high. The lowest probability at which the action will be considered, is the action threshold. Such thresholds play an important role whenever decisions have to be taken under uncertainty. However, while several methods to estimate action thresholds exist, few methods give satisfactory results or have been adopted in clinical practice. We piloted the adapted nominal group technique (aNGT), a new prescriptive method based on a formal consensus technique adapted for use in clinical decision-making.

DESIGN, SETTING AND PARTICIPANTS

We applied this method in groups of postgraduate students using three scenarios: treat for rifampicin-resistant tuberculosis (RR-TB), switch to second-line HIV treatment and isolate for SARS-CoV-2 infection.

INTERVENTIONS

The participants first summarise all harms of wrongly taking action when none is required and wrongly not taking action when it would have been useful. Then they rate the statements on these harms, discuss their importance in the decision-making process, and finally weigh the statements against each other.

MAIN OUTCOME MEASURES

The resulting consensus threshold is estimated as the relative weights of the harms of the false positives divided by the total harm, and averaged out over participants. In some applications, the thresholds are compared with an existing method based on clinical vignettes.

RESULTS

The resulting action thresholds were just over 50% for RR-TB treatment, between 20% and 50% for switching HIV treatment and 43% for COVID-19 isolation. These results were considered acceptable to all participants. Between sessions variation was low for RR-TB and moderate for HIV. Threshold estimates were moderately lower with the method based on clinical vignettes.

CONCLUSIONS

The aNGT gives sensible results in our pilot and has the potential to estimate action thresholds, in an efficient manner, while involving all relevant stakeholders. Further research is needed to study the value of the method in clinical decision-making and its ability to generate acceptable thresholds that stakeholders can agree on.

The therapeutic threshold in clinical decision-making for TB

Because TB control is still hampered by the limitations of diagnostic tools, diagnostic uncertainty is common. The decision to offer treatment is based on clinical decision-making. The therapeutic threshold, test threshold and test-treatment threshold can guide in making these decisions. This review summarizes the literature on methods to estimate the therapeutic threshold that have been applied for TB. Only five studies estimated the threshold for the diagnosis of TB. The therapeutic threshold can be estimated by prescriptive methods, based on calculations, and by descriptive methods, deriving the threshold from observing clinical practice. Test and test-treatment thresholds can be calculated using the therapeutic threshold and the characteristics of an available diagnostic test. Estimates of the therapeutic threshold for pulmonary TB from intuitive descriptive approaches (20%-50%) are higher than theoretical prescriptive calculations (2%-3%). In conclusion, estimates of the therapeutic threshold for pulmonary TB depend on the method used. Other methods exist within the field of decision-making that have yet to be implemented or adapted as tools to estimate the TB therapeutic threshold. Because clinical decision-making is a core element of TB management, it is necessary to find a new, clinician-friendly way to unbiasedly estimate context-specific, agreed upon therapeutic thresholds.

Patients, doctors and risk attitudes

A lively topic of debate in decision theory over recent years concerns our understanding of the different risk attitudes exhibited by decision makers. There is ample evidence that risk-averse and risk-seeking behaviours are widespread, and a growing consensus that such behaviour is rationally permissible. In the context of clinical medicine, this matter is complicated by the fact that healthcare professionals must often make choices for the benefit of their patients, but the norms of rational choice are conventionally grounded in a decision maker's own desires, beliefs and actions. The presence of both doctor and patient raises the question of whose risk attitude matters for the choice at hand and what to do when these diverge. Must doctors make risky choices when treating risk-seeking patients? Ought they to be risk averse in general when choosing on behalf of others? In this paper, I will argue that healthcare professionals ought to adopt a deferential approach, whereby it is the risk attitude of the patient that matters in medical decision making. I will show how familiar arguments for widely held anti-paternalistic views about medicine can be straightforwardly extended to include not only patients' evaluations of possible health states, but also their attitudes to risk. However, I will also show that this deferential view needs further refinement: patients' higher-order attitudes towards their risk attitudes must be considered in order to avoid some counterexamples and to accommodate different views about what sort of attitudes risk attitudes actually are.

Decomposing clinical practice guidelines panels' deliberation into decision theoretical constructs

RATIONALE, AIMS AND OBJECTIVES: The development of clinical practice guidelines (CPG) suffers from the lack of an explicit and transparent framework for synthesising the key elements necessary to formulate practice recommendations. We matched deliberations of the American Society of Haematology (ASH) CPG panel for the management of pulmonary embolism (PE) with the corresponding decision-theoretical constructs to assess agreement of the panel recommendations with explicit decision modelling.

METHODS

Five constructs were identified of which three were used to reformulate the panel's recommendations: (1) standard, expected utility threshold (EUT) decision model; (2) acceptable regret threshold model (ARg) to determine the frequency of tolerable false negative (FN) or false positive (FP) recommendations, and (3) fast-and-frugal tree (FFT) decision trees to formulate the entire strategy for management of PE. We compared four management strategies: withhold testing versus d-dimer → computerized pulmonary angiography (CTPA) ('ASH-Low') versus CTPA→ d-dimer ('ASH-High') versus treat without testing.

RESULTS

Different models generated different recommendations. For example, according to EUT, testing should be withheld for prior probability PE < 0.13%, a clinically untenable threshold which is up to 15 times (2/0.13) below the ASH guidelines threshold of ruling out PE (at post probability of PE ≤ 2%). Three models only agreed that the 'ASH low' strategy should be used for the range of pretest probabilities of PE between 0.13% and 13.27% and that the 'ASH high' management should be employed in a narrow range of the prior PE probabilities between 90.85% and 93.07%. For all other prior probabilities of PE, choosing one model did not ensure coherence with other models.

CONCLUSIONS

CPG panels rely on various decision-theoretical strategies to develop its recommendations. Decomposing CPG panels' deliberation can provide insights if the panels' deliberation retains a necessary coherence in developing guidelines. CPG recommendations often do not agree with the EUT decision analysis, widely used in medical decision-making modelling.

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.

How to Make Life and Death Medical Decisions? On the Occasion of Play/Drama as Health Care Protection Method of Decision Making Using by Patients with Pancreatic Cancer

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Large-scale, pragmatic randomized trials in the era of big data, precision medicine and machine learning. Valid and necessary, or outdated and a waste of resources?

During the past decade, more and more large-scale pragmatic clinical trials have been carried out in orthopedic trauma surgery. This trend is fueled by the common belief that the larger the numbers in a trial, the broader the eligibility criteria, and the less strict the regimentation of local treatment standards by protocol, the more trustworthy the findings would be. However, it must also be taken into account that the precision of an outcome measurement does not depend on the sample size alone, but the homogeneity of the studied population. Consequently, a small trial with stringent entry and assessment criteria may offer similarly precise answers as a large trial with less strict entry and assessment criteria because of the basic mathematical correlation between standard deviation and standard error of the mean. There is now a lively and controversial debate about the role of randomized controlled trials (RCT) in an era of stratified medicine driven by the ever increasing understanding and clinical measurability of molecular pathways, making a certain intervention more effective in patients who show a distinct genetic variant. Cluster and pattern recognition by artificial intelligence (AI) and its methodological variety applied to huge datasets and population-based cohorts further propel the spiral of knowledge. Advanced adaptive RCT concepts like enrichment designs, basket and bucket trials, master protocols etc. were developed to combine classic principles of the scientific method with big data, the latter of which have not arrived yet in trauma care. In spite of all biomedical and methodological achievements made, surprisingly such key questions remain unanswered as a) is a certain treatment causally responsible for making a difference in patient-centered outcomes compared to placebo, a control treatment, or the standard of care, b) do the results of a controlled experiment are relevant enough to change clinical practice, and c) under which conditions and assumptions shall we conduct large-scale pragmatic RCTs, focused confirmatory RCTs, or personalized analyses with or without AI support.

Ethics of uncertainty

Uncertainty is inherent in clinical medicine. However, just because absolute certainty is unachievable does not mean that rational and optimal decisions cannot be made. It is argued that we need to distinguish legitimate from illegitimate scientific uncertainties that are generated by manufacturing doubts aiming to create mis- and disinformation. The attempt to create doubts implies that actions under uncertainties are impossible. Such a belief ultimately harms public, which requires reasoned actions within a context of genuine scientific and medical uncertainties. The latter indicates that rational decisions, even in the absence of guaranteed absolute certainty, are not only possible but, on average, beneficial both for society and individuals.

Determinants of usefulness in professional behaviour change interventions: observational study of a 15-year national program

OBJECTIVE

Educational, and audit and feedback interventions are effective in promoting health professional behaviour change and evidence adoption. However, we lack evidence to pinpoint which particular features make them most effective. Our objective is to identify determinants of quality in professional behaviour change interventions, as perceived by participants.

DESIGN

We performed a comparative observational study using data from the Veterans' Medicines Advice and Therapeutics Education Services program, a nation-wide Australian Government Department of Veterans' Affairs funded program that provides medicines advice and promotes physician adoption of best practices by use of a multifaceted intervention (educational material and a feedback document containing individual patient information).

SETTING

Primary care practices providing care to Australian veterans.

PARTICIPANTS

General practitioners (GPs) targeted by 51 distinct behaviour change interventions, implemented between November 2004 and June 2018.

PRIMARY AND SECONDARY OUTCOME MEASURES

We extracted features related to presentation (number of images, tables and characters), content (polarity and subjectivity using sentiment analysis, number of external links and medicine mentions) and the use of five behaviour change techniques (prompt/cues, goal setting, discrepancy between current behaviour and goal, information about health consequences, feedback on behaviour). The main outcome was perceived usefulness, extracted from postintervention survey.

RESULTS

On average, each intervention was delivered to 9667 GPs. Prompt and goal setting strategies in the audit and feedback were independently correlated to perceived usefulness (p=0.030 and p=0.005, respectively). The number of distinct behaviour change techniques in the audit and feedback was correlated with improved usefulness (Pearson's coefficient 0.45 (0.19, 0.65), p=0.001). No presentation or content features in the educational material were correlated with perceived usefulness.

CONCLUSIONS

The finding provides additional evidence encouraging the use of behaviour change techniques, in particular prompt and goal setting, in audit and feedback interventions.

Direct oral anticoagulant for the prevention of thrombosis in ambulatory patients with cancer: A systematic review and meta-analysis

BACKGROUND

It is unclear if direct oral anticoagulant (DOAC) is efficacious and safe for prophylaxis of venous thromboembolism (VTE) in ambulatory patients with cancer.

METHODS

We performed a systematic review using EMBASE, MEDLINE, and CENTRAL. Inclusion criteria included adult ambulatory patients with cancer, prophylactic use of DOAC, and randomized controlled trials. Exclusion criteria included pediatric patients, inpatient or postoperative setting, therapeutic indication of DOAC, or non-phase III randomized controlled trial. Two authors screened/reviewed articles and abstracted the data. Meta-analysis was performed using random-effects model. Efficacy outcome included overall and symptomatic VTE incidence during the first 6 months. Safety outcomes included major bleeding and clinically relevant non-major bleeding (CRNMB) incidence during the on-treatment period. Subgroup analysis was performed for intermediate- and high-risk Khorana score.

RESULTS

A total of 202 records were identified and 28 full-text articles were assessed. Two studies with 1415 participants were included for meta-analysis. For DOAC vs placebo, the relative risks for overall and symptomatic VTE incidence by 6 months were 0.56 (0.35-0.89) and 0.58 (0.29-1.13), respectively. The relative risks for major bleeding and CRNMB while on-treatment were 1.96 (0.80-4.82) and 1.28 (0.74-2.20), respectively. Patients with high-risk Khorana score (3+) derived the largest absolute risk reduction of VTE.

CONCLUSIONS

Low-dose DOAC reduces the rate of overall VTE in higher risk cancer patients starting systemic chemotherapy. It may reduce the rate of symptomatic VTE but increase the likelihood of bleeding.

The threshold model revisited

BACKGROUND

The threshold model represents one of the most significant advances in the field of medical decision-making, yet it often does not apply to the most common class of clinical problems, which include health outcomes as a part of definition of disease. In addition, the original threshold model did not take a decision-maker's values and preferences explicitly into account.

METHODS

We reformulated the threshold model by (1) applying it to those clinical scenarios, which define disease according to outcomes that treatment is designed to affect, (2) taking into account a decision-maker's values.

RESULTS

We showed that when outcomes (eg, morbidity) are integral part of definition of disease, the classic threshold model does not apply (as this leads to double counting of outcomes in the probabilities and utilities branches of the model). To avoid double counting, the model can be appropriately analysed by assuming diagnosis is certain (P = 1). This results in deriving a different threshold-the threshold for outcome of disease (M_t ) instead of threshold for probability of disease (P_t ) above which benefits of treatment outweigh its harms. We found that M_t  ≤ P_t , which may explain differences between normative models and actual behaviour in practice. When a decision-maker values outcomes related to benefit and harms differently, the new threshold model generates decision thresholds that could be descriptively more accurate.

CONCLUSIONS

Calculation of the threshold depends on careful disease versus utility definitions and a decision-maker's values and preferences.