Decision curve analysis based on summary data

Improving Guideline Development Processes: Integrating Evidence Estimation and Decision-Analytical Frameworks

RATIONALE, AIMS AND OBJECTIVES

Despite using state-of-the-art methodologies like Grades of Recommendation, Assessment, Development and Evaluation (GRADE), current guideline development frameworks still rely heavily on panellists' intuitive integration of evidence related to the benefits and harms/burdens of health interventions. This leads to the 'black-box' and 'integration' problems, highlighting the lack of transparency in guideline decision-making. Combined with humans' limited capacity to process the large volumes of information presented in Summary of Findings (SoF) tables-the primary output of systematic reviews that underpin guideline recommendations-this reliance on non-explicit processes raises concerns about the trustworthiness of clinical practice guidelines.

METHODS

SoF tables provide the best available evidence, derived from frequentist or Bayesian estimation frameworks. Decision analysis, which integrates both types of estimates but considers intervention consequences, is the only analytical approach that combines multiple outcomes (benefits, harms and costs) into a single metric to support decision-making. Such analysis seeks to identify the optimal decision by balancing harms, benefits and uncertainties. This paper leverages the PICO format (Population, Intervention, Comparison(s), Outcome) as a conceptual basis for deriving SoF tables. Subsequently, we propose a solution to GRADE's "black-box" and "integration" problems by matching PICO-based SoF with decision models.

RESULTS

We succeeded in connecting the PICO framework to simple decision-analytical models, restricted to time frames supported by empirically verifiable evidence, to calculate which competing intervention offers the greatest benefit (net differences in expected utility; ΔEU). The single metric [ΔEU] enabled a simple, transparent and easy-to-understand assessment of the superiority of competing management strategies across multiple outcomes (considering both benefits and harms), addressing the 'black-box' and 'integration' problems. Completing a SoF-based decision model takes about 10 min. Not surprisingly, the recommendations based on ΔEU may differ from the intuitive recommendations of panels.

CONCLUSION

We propose that incorporating the straightforward and transparent modelling into guideline panels' decision-making processes will enhance their intuitive judgements, resulting in more trustworthy recommendations. Given the simplicity of calculating ΔEU, we advocate for its immediate inclusion in systematic reviews and SoF tables.

Synergistic potential of CDH3 in targeting CRC metastasis and enhancing immunotherapy

BACKGROUND

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, particularly due to advanced-stage metastasis. P-cadherin (CDH3), a potential therapeutic target, is highly expressed in CRC tissues and associated with poor prognosis and metastasis. However, the mechanisms underlying its role in CRC progression and its translational potential remain poorly understood.

MATERIALS AND METHODS

This study integrated multiple public databases (TCGA, HCMDB, UALCAN, HPA, UniProt, cBioPortal, and GEO) to evaluate CDH3 expression, construct a prognostic model, and perform functional and translational analyses. Immunohistochemistry was used to validate CDH3 protein expression in clinical samples. Additional analyses included correlations with clinicopathological parameters, immune infiltration (TIDE, TISIDB), functional enrichment (KEGG, GSEA), drug sensitivity (GSCA), and molecular docking (MOE). Single-cell sequencing (CancerSEA, HPA) was also conducted to explore CDH3's role at the single-cell level.

RESULTS

CDH3 expression was significantly elevated in CRC tissues and correlated with poor prognosis, recurrence, and metastasis. CDH3 expression was associated with the infiltration of resting immune cells, particularly dendritic cells, and enrichment analysis revealed its critical role in CRC metastasis through extracellular matrix (ECM) and local adhesion pathways. Notably, afatinib emerged as a promising candidate for targeting CDH3 via "drug repositioning," a process involving the repurposing of existing drugs for new therapeutic applications.

CONCLUSION

This study provides novel insights into CDH3's role in CRC metastasis and its potential as a therapeutic target. The translational potential of CDH3, including its integration with immunotherapy and drug repositioning strategies, offers a promising avenue for the treatment of metastatic CRC.

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.