Development and Pilot Evaluation of a Decision Aid for Small Kidney Masses

Decisional Conflict Among Patients Newly Diagnosed With Clinical T1 Renal Masses: A Prospective Study

PURPOSE

Because multiple management options exist for clinical T1 renal masses, patients may experience a state of uncertainty about the course of action to pursue (ie, decisional conflict). To better support patients, we examined patient, clinical, and decision-making factors associated with decisional conflict among patients newly diagnosed with clinical T1 renal masses suspicious for kidney cancer.

MATERIALS AND METHODS

From a prospective clinical trial, participants completed the Decisional Conflict Scale (DCS), scored 0 to 100 with < 25 associated with implementing decisions, at 2 time points during the initial decision-making period. The trial further characterized patient demographics, health status, tumor burden, and patient-centered communication, while a subcohort completed additional questionnaires on decision-making. Associations of patient, clinical, and decision-making factors with DCS scores were evaluated using generalized estimating equations to account for repeated measures per patient.

RESULTS

Of 274 enrollees, 250 completed a DCS survey; 74% had masses ≤ 4 cm in size, while 11% had high-complexity tumors. Model-based estimated mean DCS score across both time points was 17.6 (95% CI 16.0-19.3), though 50% reported a DCS score ≥ 25 at least once. On multivariable analysis, DCS scores increased with age (+2.64, 95% CI 1.04-4.23), high- vs low-complexity tumors (+6.50, 95% CI 0.35-12.65), and cystic vs solid masses (+9.78, 95% CI 5.27-14.28). Among decision-making factors, DCS scores decreased with higher self-efficacy (-3.31, 95% CI -5.77 to -0.86]) and information-seeking behavior (-4.44, 95% CI -7.32 to -1.56). DCS scores decreased with higher patient-centered communication scores (-8.89, 95% CI -11.85 to -5.94).

CONCLUSIONS

In addition to patient and clinical factors, decision-making factors and patient-centered communication relate with decisional conflict, highlighting potential avenues to better support patient decision-making for clinical T1 renal masses.

Development and validation of a multicenter Cox regression model to predict all-cause mortality in patients with renal masses suspicious for renal cancer

OBJECTIVE

Life expectancy models are useful tools to support clinical decision-making. Prior models have not been used widely in clinical practice for patients with renal masses. We sought to develop and validate a model to predict life expectancy following the detection of a localized renal mass suspicious for renal cell carcinoma.

MATERIALS AND METHODS

Using retrospective data from 2 large centers, we identified patients diagnosed with clinically localized renal parenchymal masses from 1998 to 2018. After 2:1 random sampling into a derivation and validation cohort stratified by site, we used age, sex, log-transformed tumor size, simplified cardiovascular index and planned treatment to fit a Cox regression model to predict all-cause mortality from the time of diagnosis. The model's discrimination was evaluated using a C-statistic, and calibration was evaluated visually at 1, 5, and 10 years.

RESULTS

We identified 2,667 patients (1,386 at Corewell Health and 1,281 at Johns Hopkins) with renal masses. Of these, 420 (16%) died with a median follow-up of 5.2 years (interquartile range 2.2-8.3). Statistically significant predictors in the multivariable Cox regression model were age (hazard ratio [HR] 1.04; 95% confidence interval [CI] 1.03-1.05); male sex (HR 1.40; 95% CI 1.08-1.81); log-transformed tumor size (HR 1.71; 95% CI 1.30-2.24); cardiovascular index (HR 1.48; 95% CI 1.32-1.67), and planned treatment (HR: 0.10, 95% CI: 0.06-0.18 for kidney-sparing intervention and HR: 0.20, 95% CI: 0.11-0.35 for radical nephrectomy vs. no intervention). The model achieved a C-statistic of 0.74 in the derivation cohort and 0.73 in the validation cohort. The model was well-calibrated at 1, 5, and 10 years of follow-up.

CONCLUSIONS

For patients with localized renal masses, accurate determination of life expectancy is essential for decision-making regarding intervention vs. active surveillance as a primary treatment modality. We have made available a simple tool for this purpose.

An Evaluation of a Web-Based Decision Aid for Treatment Planning of Small Kidney Tumors: Pilot Randomized Controlled Trial

Background

Surgery is the most common treatment for localized small kidney masses (SKMs) up to 4 cm, despite a lack of evidence for improved overall survival. Nonsurgical management options are gaining recognition, as evidence supports the indolence of most SKMs. Decision aids (DAs) have been shown to improve patient comprehension of the trade-offs of treatment options and overall decision quality, and may improve consideration of all major options according to individual health priorities and preferences.

Objective

This pilot randomized controlled trial (RCT) primarily aims to evaluate the impact of a new web-based DA on treatment decisions for patients with SKM; that is, selection of surgical versus nonsurgical treatment options. Secondary objectives include an assessment of decision-making outcomes: decisional conflict, decision satisfaction, and an understanding of individual preferences for treatment that incorporate the trade-offs associated with surgical versus nonsurgical interventions.

Methods

Three phases comprise the construction and evaluation of a new web-based DA on SKM treatment. In phase 1, this DA was developed in print format through a multidisciplinary design committee incorporating patient focus groups. Phase 2 was an observational study on patient knowledge and decision-making measures after randomization to receive the printed DA or institutional educational materials, which identified further educational needs applied to a web-based DA. Phase 3 will preliminarily evaluate the web-based DA: in a pilot RCT, 50 adults diagnosed with SKMs will receive the web-based DA or an existing web-based institutional website at urology clinics at a large academic medical center. The web-based DA applies risk communication and information about diagnosis and treatment options, elicits preferences regarding treatment options, and provides a set of options to consider with their doctor based on a decision-analytic model of benefits/harm analysis that accounts for comorbidity, age group, and tumor features. Questionnaires and treatment decision data will be gathered before and after viewing the educational material.

Results

This phase will consist of a pilot RCT from August 2022 to January 2023 to establish feasibility and preliminarily evaluate decision outcomes. Previous study phases from 2018 to 2020 supported the feasibility of providing the printed DA in urology clinics before clinical consultation and demonstrated increased patient knowledge about the diagnosis and treatment options and greater likelihood of favoring nonsurgical treatment just before consultation. This study was funded by the National Cancer Institute. Recruitment will begin in August 2022.

Conclusions

A web-based DA has been designed to address educational needs for patients making treatment decisions for SKM, accounting for comorbidities and treatment-related benefits and risks. Outcomes from the pilot trial will evaluate the potential of a web-based DA in personalizing treatment decisions and in helping patients weigh attributes of surgical versus nonsurgical treatment options for their SKMs.

Trial Registration

ClinicalTrials.gov NCT05387863; https://clinicaltrials.gov/ct2/show/NCT05387863

International Registered Report Identifier (IRRID)

PRR1-10.2196/41451