Primer: using decision analysis to improve clinical decision making in urology

A Clinical Decision Aid to Support Personalized Treatment Selection for Patients with Clinical T1 Renal Masses: Results from a Multi-institutional Competing-risks Analysis

BACKGROUND

Personalized treatment for clinical T1 renal cortical masses (RCMs) should take into account competing risks related to tumor and patient characteristics.

OBJECTIVE

To develop treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-d Clavien grade ≥3 complications across radical nephrectomy (RN), partial nephrectomy (PN), thermal ablation (TA), and active surveillance (AS).

DESIGN, SETTING, AND PARTICIPANTS

Pretreatment clinical and radiological features were collected for consecutive adult patients treated with initial RN, PN, TA, or AS for RCMs at four high-volume referral centers (2000-2019).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Prediction models used competing-risks regression for CSM and OCM and logistic regression for 90-d Clavien grade ≥3 complications. Performance was assessed using bootstrap validation.

RESULTS AND LIMITATIONS

The cohort comprised 5300 patients treated with RN (n = 1277), PN (n = 2967), TA (n = 476), or AS (n = 580). Over median follow-up of 5.2 yr (interquartile range 2.5-8.7), there were 117 CSM, 607 OCM, and 198 complication events. The C index for the predictive models was 0.80 for CSM, 0.77 for OCM, and 0.64 for complications. Predictions from the fitted models are provided in an online calculator (https://small-renal-mass-risk-calculator.fredhutch.org). To illustrate, a hypothetical 74-yr-old male with a 4.5-cm RCM, body mass index of 32 kg/m², estimated glomerular filtration rate of 50 ml/min, Eastern Cooperative Oncology Group performance status of 3, and Charlson comorbidity index of 3 has predicted 5-yr CSM of 2.9-5.6% across treatments, but 5-yr OCM of 29% and risk of 90-d Clavien grade 3-5 complications of 1.9% for RN, 5.8% for PN, and 3.6% for TA. Limitations include selection bias, heterogeneity in practice across treatment sites and the study time period, and lack of control for surgeon/hospital volume.

CONCLUSIONS

We present a risk calculator incorporating pretreatment features to estimate treatment-specific competing risks of mortality and complications for use during shared decision-making and personalized treatment selection for RCMs.

PATIENT SUMMARY

We present a risk calculator that generates personalized estimates of the risks of death from cancer or other causes and of complications for surgical, ablation, and surveillance treatment options for patients with stage 1 kidney tumors.

Urinary miR-183 and miR-205 do not surpass PCA3 in urine as predictive markers for prostate biopsy outcome despite their highly dysregulated expression in prostate cancer tissue

BACKGROUND

MicroRNAs (miRNAs) have shown to be promising novel biomarkers in various cancers. We aimed to translate the results of an own previous tissue-based miRNA profile of prostate carcinoma (PCa) with upregulated miR-183 and downregulated miR-205 into a urine-based testing procedure for diagnosis of PCa.

METHODS

Urine sediments were prepared from urine samples collected after a standardized digital-rectal examination (DRE) of patients undergoing prostate biopsy with PSA (prostate-specific antigen) values <20 μg/L in consecutive order. According to the sample-size calculation (α=0.05, power=0.95), 38 patients each with PCa and without PCa were randomly enrolled in this study. PCA3 (prostate cancer associated 3) in urine as Food and Drug Administration-approved assay was determined as reference standard for comparison. The miRNAs were measured by RT-qPCR using TaqMan assays and normalized using different approaches.

RESULTS

Both miRNAs were correlated to the mRNA PSA concentrations in the sediments indicating a relationship to the released prostate cells after DRE. However, they had no discriminating capacity between patients with and without PCa. In contrast, PCA3 clearly differentiated between these two patients groups. There was also no significant correlation between miRNAs and standard clinicopathologic variables like Gleason score and serum PSA.

CONCLUSIONS

The data of our study show that miR-183 and miR-205 failed to detect early and aggressive PCa despite their highly dysregulated expression in cancer tissue. Our results and the critical evaluation of the few data of other studies raise serious doubts concerning the capability of urinary miRNAs to replace or improve PCA3 as predictive marker for prostate biopsy outcome.

The clinical decision analysis using decision tree

The clinical decision analysis (CDA) has used to overcome complexity and uncertainty in medical problems. The CDA is a tool allowing decision-makers to apply evidence-based medicine to make objective clinical decisions when faced with complex situations. The usefulness and limitation including six steps in conducting CDA were reviewed. The application of CDA results should be done under shared decision with patients' value.

Tumor markers in prostate cancer I: blood-based markers

The introduction of total prostate specific antigen (total PSA) testing in blood has revolutionized the detection and management of men with prostate cancer (PCa). The objective of this review was to discuss the challenges of PCa biomarker research, definition of the type of PCa biomarkers, the statistical considerations for biomarker discovery and validation, and to review the literature regarding total PSA velocity and novel blood-based biomarkers.

METHODS

An English-language literature review of the Medline database (1990 to August 2010) of published data on blood-based biomarkers and PCa was undertaken.

RESULTS

The inherent biological variability of total PSA levels affects the interpretation of any single result. Men who will eventually develop PCa have increased total PSA levels years or decades before the cancer is diagnosed. Total PSA velocity improves predictiveness of total PSA only marginally, limiting its value for PCa screening and prognostication. The combination of PSA molecular forms and other biomarkers improve PCa detection substantially. Several novel blood-based biomarkers such as human glandular kallikrein 2 (hK2), urokinase plasminogen activator (uPA) and its receptor (uPAR), transforming growth factor-beta 1 (TGF-β1); interleukin-6 (IL-6) and its receptor (IL-6R) may help PCa diagnosis, staging, prognostication, and monitoring. Panels of biomarkers that capture the biologic potential of PCa are in the process of being validated for PCa prognostication.

CONCLUSIONS

PSA is a strong prognostic marker for long-term risk of clinically relevant cancer. However, there is a need for novel biomarkers that aid clinical decision making about biopsy and initial treatment. There is no doubt that progress will continue based on the integrated collaboration of researchers, clinicians and biomedical firms.

Statistical consideration for clinical biomarker research in bladder cancer

OBJECTIVE

To critically review and illustrate current methodological and statistical considerations for bladder cancer biomarker discovery and evaluation.

METHODS

Original, review, and methodological articles, and editorials were reviewed and summarized.

RESULTS

Biomarkers may be useful at multiple stages of bladder cancer management: early detection, diagnosis, staging, prognosis, and treatment; however, few novel biomarkers are currently used in clinical practice. The reasons for this disjunction are many and reflect the long and difficult pathway from candidate biomarker discovery to clinical assay, and the lack of coherent and comprehensive processes (pipelines) for biomarker development. Conceptually, the development of new biomarkers should be a process that is similar to therapeutic drug evaluation-a highly regulated process with carefully regulated phases from discovery to human applications. In a further effort to address the pervasive problem of inadequacies in the design, analysis, and reporting of biomarker prognostic studies, a set of reporting recommendations are discussed. For example, biomarkers should provide unique information that adds to known clinical and pathologic information. Conventional multivariable analyses are not sufficient to demonstrate improved prediction of outcomes. Predictive models, including or excluding any new putative biomarker, need to show clinically significant improvement of performance in order to claim any real benefit. Towards this end, proper model building, avoidance of overfitting, and external validation are crucial. In addition, it is important to choose appropriate performance measures dependent on outcome and prediction type and to avoid the use of cutpoints. Biomarkers providing a continuous score provide potentially more useful information than cutpoints since risk fits a continuum model. Combination of complementary and independent biomarkers is likely to better capture the biological potential of a tumor than any single biomarker. Finally, methods that incorporate clinical consequences such as decision curve analysis are crucial to the evaluation of biomarkers.

CONCLUSIONS

Attention to sound design and statistical practice should be delivered as early as possible and will help maximize the promise of biomarkers for patient care. Studies should include a measure of predictive accuracy and clinical decision-analysis. External validation using data from an independent cohort provides the strongest evidence that a model is valid. There is a need for adequately assessed clinical biomarkers in bladder cancer.

Critical review of prostate cancer predictive tools

Prostate cancer is a very complex disease, and the decision-making process requires the clinician to balance clinical benefits, life expectancy, comorbidities and potential treatment-related side effects. Accurate prediction of clinical outcomes may help in the difficult process of making decisions related to prostate cancer. In this review, we discuss attributes of predictive tools and systematically review those available for prostate cancer. Types of tools include probability formulas, look-up and propensity scoring tables, risk-class stratification prediction tools, classification and regression tree analysis, nomograms and artificial neural networks. Criteria to evaluate tools include discrimination, calibration, generalizability, level of complexity, decision analysis and ability to account for competing risks and conditional probabilities. The available predictive tools and their features, with a focus on nomograms, are described. While some tools are well-calibrated, few have been externally validated or directly compared with other tools. In addition, the clinical consequences of applying predictive tools need thorough assessment. Nevertheless, predictive tools can facilitate medical decision-making by showing patients tailored predictions of their outcomes with various alternatives. Additionally, accurate tools may improve clinical trial design.

The use of clinical utility assessments in early clinical development

A quickly realizable benefit of model-based drug development is in reducing uncertainty in risk/benefit, comprising individually of safety and effectiveness, two key attributes of a product evaluated for regulatory approval, marketing, and use. In this review, we investigate gaps and opportunities in using fundamental decision analytic principles in drug development and present a quantitative clinical pharmacology framework for the application of such aids for early clinical development decision making. We anticipate that implementation of such emerging tools will enable sufficient scientific understanding of the two attributes to facilitate the early termination of compounds with less than desirable risk/benefit profiles and continuance of compounds with acceptable risk/benefit profiles.

Decision analysis in pediatric hematology

Decision analysis is a simulation, model-based research technique in which investigators combine information from a variety of sources to create a mathematical model representing a clinical decision. This tool can be used to address many clinical dilemmas in pediatric hematology for which traditional clinical trials are unfeasible or impossible. This article outlines the basic steps of performing and analyzing a decision analysis tree and describes several decision analyses published in the field of pediatric hematology and how to evaluate and judge the decision analysis literature.

Decision analysis and Markov modeling in urology

PURPOSE

The process of decision making in medicine has become increasingly complex. This has developed as the result of increasing amounts of data, often without direct information or answers regarding a specific clinical problem. The use of mathematical models has grown and they are commonly used in all areas. We describe and discuss the application of decision analysis and Markov modeling in urology.

MATERIALS AND METHODS

We define decision analysis and Markov models, providing a background and primer to educate the urologist. In addition, we performed a complete MEDLINE database search for all decision analyses in all disciplines of urology, serving as a reference summarizing the current status of the literature.

RESULTS

The review provides urologists with the ability to critically evaluate studies involving decision analysis and Markov models. We identified 107 publications using decision analysis or Markov modeling in urology. A total of 36 studies used Markov models, whereas the remainder used standard decision analytical models. All areas of urology, including oncology, pediatrics, andrology, endourology, reconstruction, transplantation and erectile dysfunction, were represented.

CONCLUSIONS

Decision analysis and Markov modeling are widely used approaches in the urological literature. Understanding the fundamentals of these tools is critical to the practicing urologist.