Abstract
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 (i.e., 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 0–100 with <25 associated with implementing decisions, at two timepoints during the initial decision-making period. The trial further characterized patient demographics, health status, tumor burden, and patient-centered communication while a sub-cohort 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 timepoints 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– −0.86]) and information-seeking behavior (−4.44, 95% CI −7.32– −1.56). DCS scores decreased with higher patient-centered communication scores (−8.89, 95% CI −11.85– −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.
Keywords: early-stage kidney cancer, small renal masses, medical decision-making, decisional conflict
Introduction
The widespread use of cross-sectional imaging has led to the increased detection of clinical T1 renal masses suspicious for early-stage kidney cancer.1,2 Surgery has been the standard practice historically; however, for small renal masses (SRMs) ≤4 cm in size, up to 20– 30% are benign with 20% harboring aggressive disease and <3% metastasizing within 5 years.3,4 Furthermore, for older adults with significant comorbidity, the potential survival benefit for surgery versus expectant management appears to be minimal.5–7 Accordingly, new treatments have emerged including minimally invasive surgery, partial nephrectomy, and thermal ablation, which each carry unique considerations.3,8,9 More recently, active surveillance (i.e., serial imaging and delayed intervention if needed) has been shown to be safe with comparable cancer-specific survival for appropriately-selected patients.10
Given the multitude of management options for patients with SRMs, decision-making has become more complex, which can create uncertainty in the course of action to pursue (i.e., decisional conflict). Several studies have examined the role of decisional conflict in patient decision-making, particularly for cancer surgery.11–14 These studies underscore the relationship between decisional conflict and decision quality, satisfaction, and regret. Elevated decisional conflict has also been associated with worse quality of life.12,15 In response to this challenge, decision aids and shared decision-making have been recommended and developed,16,17 but their effectiveness depends on a clear understanding of both the factors in decision-making and the specific decision support needs of patients. Prior research has been conducted to identify drivers of treatment decision-making including kidney cancer-specific, clinician, and contextual factors (e.g., age, comorbidities, tumor size/complexity, geography, practice patterns).18–21 Comparatively, the decision-making experience from the patient’s perspective remains poorly defined. In a single prior study that surveyed patients with newly diagnosed SRMs, patients reported variable levels of decisional conflict with less conflict associated with more knowledge and shared decision-making.12
Understanding the decision-making experience from the patient’s perspective is imperative. As part of a prospective, hybrid clinical trial evaluating the impact of renal mass biopsy on decision-making, we examined patient, clinical, and decision-making factors and their relationship with decisional conflict among patients newly diagnosed with clinical T1 renal masses, hypothesizing that factors within each of these 3 domains relate to higher or lower decisional conflict. By identifying factors potentially driving decisional conflict, we can develop strategies to improve the patient decision-making experience.
Methods
2.1. Cohort Characteristics and Data Collection
From June 2018 through June 2022, we prospectively enrolled patients onto GRADE-SRM (Genomic Risk Assessment and Decisional Evaluation for Small Renal Masses), a prospective, comparative, non-randomized, hybrid clinical trial (NCT03819569) evaluating the potential for renal mass biopsy to yield genomic information and impact patient decision-making for patients with newly diagnosed T1 renal masses suspicious for kidney cancer. Inclusion criteria included ages 18–95, a newly diagnosed renal mass ≤7 cm in diameter on cross-sectional imaging evaluated at UNC, and willingness to participate in tissue collection (i.e., blood, renal mass biopsy, or surgical specimen) and patient-reported outcome questionnaires. Exclusion criteria included previous renal mass biopsy, evidence of advanced kidney cancer (e.g., clinically suspicious lymph nodes, direct invasion into adjacent structures), metastatic cancer, or organ transplant candidate or recipient. Patients meeting these criteria and who agreed to participate provided informed consent.
Participants received a baseline survey within 1 week of their initial clinical evaluation (timepoint 1) with a urologic oncologist at UNC and again approximately 4–8 weeks later (timepoint 2). The second survey was administered either 1–2 weeks after renal mass biopsy or just prior to treatment (surgery or ablation). Surveys were completed through mixed modes depending on patient preference and availability.
2.2. Primary Outcome
Decisional conflict, as measured by the Decisional Conflict Scale (DCS), served as the primary outcome for both the primary analysis, which investigates if decisional conflict changed after receiving a renal mass biopsy, as well as this pre-planned sub-analysis, which examines factors associated with decisional conflict. The DCS is a validated, 16-item tool used to measure a person’s perceptions of uncertainty in making healthcare decisions, the factors that contribute to that uncertainty, and the factors that lead to effective decision-making.22 The scale yields a total score from 0–100 (higher scores indicate more decisional conflict) with <25 associated with implementing decisions. Additionally, 5 subdomains can be calculated: uncertainty (3 items), uninformed (3 items), lack of clarity of values (3 items), lack of support (3 items), and ineffective decision-making (4 items).22
2.3. Additional and Exploratory Covariates
In addition to DCS, the baseline survey queried participants on their age, gender identity, race/ethnicity, marital status, education level, and income level. Participants also provided information about cancer-related symptoms and smoking status. Performance status was ascertained by the evaluating urologic oncologist using the ECOG performance status scale. Research staff extracted data from medical records for the Charlson Comorbidity Index (CCI), antiplatelet/anticoagulation medications, and body mass index (BMI). Radiology reports and images were used to record tumor characteristics (i.e., size, cystic, location, multifocality) and calculate nephrometry scores as a measure of tumor complexity (i.e., low [4–6], moderate [7–9], high [10–12]).23
Following a protocol amendment effective July 2019, participants completed a more detailed inventory of decision-making traits split across timepoint 1 and timepoint 2. The expanded survey included the single-item Control Preferences Scale (CPS) and the Single Item Literacy Screener (SILS) to assess patient involvement in decision-making and identify those with limited reading ability, respectively.24,25 Participants completed the PROMIS instrument on Self-Efficacy, which measures the respondent’s belief in managing specific problems with higher scores reflecting greater general self-efficacy.26 To assess numeracy, participants received the preference subscale of the subjective numeracy scale, with higher scores indicating greater perceptions of quantitative ability.27 Information-seeking behavior was measured using the 8-item subscale from the validated Autonomy Preference Index looking at the acquisition of information.28 All participants received the Maximizer-Minimizer scale, which is a validated survey that measures the extent to which individuals are medical “minimizers” (those who avoid medical invention unless necessary) versus medical “maximizers” (those who seek healthcare for even minor problems).29 Finally, the expanded questionnaire included Global PROMIS questions on physical and mental health (T-score with SD of 10) and quality of life (5-categories).30
All participants also received the short-form Patient-Centered Communication in Cancer scale (PCC), which is a validated instrument that measures the patient perspective on communication in cancer care.31 The short-form includes 6 questions scored 1 to 5 on topics related to exchanging information, fostering relationships, making decisions, responding to emotions, enabling self-efficacy, and managing uncertainty. Higher average score indicates better communication.
2.4. Statistical Analysis
We used descriptive statistics to summarize the patient, clinical, and decision-making factors. Because the goal of this analysis was to understand factors associated with decisional conflict at the time of decision-making, we used all available DCS scores from participants at both timepoints and accounted for repeated measures per participant. We evaluated the association of DCS scores with measured patient, clinical, and decision-making factors utilizing multivariable repeated measures linear regression analyses, which were modeled using generalized estimating equations (GEE) with an exchangeable correlation matrix. Model-based means for DCS scores were estimated from models that only included an intercept term. To evaluate the combined association of patient and clinical characteristics with DCS score, we fitted the first model for factors that may contribute to how the decision for treatment may be made, including age, race/ethnicity, gender identity, marital status, education, income, CCI score, ECOG status, nephrometry score, mass type (i.e., cystic vs. solid), multifocality, smoking history, urologic oncologist, potential kidney cancer-related symptoms, anticoagulant use, and BMI. For the second model focused on decision-making traits, we included CPS, SILS, numeracy, self-efficacy, information-seeking behavior, and maximizer/minimizer tendency with further adjustment for age, physical health, mental health, and quality of life. Because PCC may serve as a more extrinsic factor that depends on the communication delivered by a patient’s medical team and could affect the impact of intrinsic factors, we refitted the two models by including PCC as an additional covariate.
The study received approval from the UNC LCCC Protocol Review Committee and IRB (#18–1794). Statistical analyses were performed using SAS v9.4 (Cary, NC) with significance set at the 0.05 level.
Results
3.1. Baseline Patient and Clinical Factors and DCS Scores
GRADE-SRM enrolled 274 participants with 250 completing the DCS at least once pre-treatment (244 at timepoint 1, 206 at timepoint 2). As noted in Table 1, participants had a mean age of 62.4 (SD 11.3), 60% were male, 68% were non-Hispanic White. Most had masses ≤4 cm in size (74%) while 11% had high complexity tumors, 5.6% had bilateral masses, and 6.0% had multifocal masses. Overall, the estimated mean DCS score across both timepoints was 17.6 (95% CI 16.0–19.3). Figure 1 displays the maximum DCS score per participant at either timepoint 1 or timepoint 2 with 50% reporting DCS scores ≥25 at some point pre-treatment, indicating higher decisional conflict. For the subdomains, approximately half or more of participants reported scores at or above the designated threshold: uncertainty (64%), lack of clarity (65%), uninformed (60%), lack of support (50%), and ineffective decision-making (57%).
Table 1.
Patient and Clinical Characteristics
| Covariate | Level | N (%) |
|---|---|---|
| Enrollment Age, mean (SD) | 62.4 (11) | |
| Gender Identity | Female | 99 (40) |
| Male | 151 (60) | |
| Race/Ethnicity | All other | 80 (32) |
| Non-Hispanic White | 170 (68) | |
| Marital Status | No Current Partner | 79 (32) |
| Current Partner | 171 (68) | |
| Education Level | High school graduate or less | 73 (30) |
| Master’s or Doctorate degree | 36 (15) | |
| Some college or college graduate | 136 (56) | |
| Income level | Less than $50,000 | 115 (49) |
| $50,000 - $99,999 | 66 (28) | |
| $100,000 - $149,999 | 24 (10) | |
| $150,000 - $199,999 | 17 (7.2) | |
| $200,000 or more | 13 (5.5) | |
| Insurance | Private self-paid | 9 (3.6) |
| Employer purchased | 76 (30) | |
| Medicare | 104 (42) | |
| Medicaid | 19 (7.6) | |
| Other or I don’t know | 30 (12) | |
| None | 12 (4.8) | |
| Smoking Status | Current smoker | 43 (17) |
| Former smoker/never smoker/unknown | 207 (83) | |
| Charlson Comorbidity Index, mean (SD) | 2.26 (1.9) | |
| ECOG Status | 0 | 168 (67) |
| 1 | 26 (10) | |
| 2 or more | 6 (2.4) | |
| Missing | 50 (20) | |
| Anticoagulant/ Antiplatelet Use | No | 223 (89) |
| Yes | 27 (11) | |
| Body Mass Index, mean (SD) | 32.2 (8.6) | |
| Urologic Oncologist | 1 | 42 (17) |
| 2 | 144 (58) | |
| 3 | 21 (8.4) | |
| 4^ | 12 (4.8) | |
| 5 | 31 (12) | |
| Previous Abdominal Surgery | Yes | 145 (58) |
| No | 105 (42) | |
| Mass Diameter | >4 cm | 65 (26) |
| ≤4 cm | 185 (74) | |
| Multifocal | Yes | 15 (6.0) |
| No | 235 (94) | |
| Bilateral | Yes | 14 (5.6) |
| No | 236 (94) | |
| Mass Type | Cystic Component | 47 (19) |
| Solid | 203 (81) | |
| Nephrometry Score | High (10 to 12) | 26 (11) |
| Moderate (7 to 9) | 115 (47) | |
| Low (4 to 6) | 105 (43) | |
Due to small numbers, provider 4 represents 2 different urologists.
Missing observations: income (15), education (5), nephrometry (4).
Figure 1. Maximum Decisional Conflict Scale Scores.
Violin plot depicts distribution of the maximum DCS score per participant during either timepoint 1 or 2 for Total DCS and each subdomain. Scores ≥ 25, as indicated by the red line, signify higher decisional conflict. Individual density curves are built around first quartile, median, and third quartile.
3.2. Decisional Conflict by Patient and Clinical Factors
On multivariable analysis without PCC, a 10-year increase in age was associated with a +2.64 (95% CI 1.04–4.23, p=0.001) point increase in DCS score, high nephrometry score had +6.50 (95% CI 0.35–12.65, p=0.038) greater DCS score compared to low nephrometry score, and cystic mass had a +9.78 (95% CI 5.27–14.28, p<0.001) higher DCS score compared to solid mass (Table 3). When adding in the extrinsic factor of PCC, these factors remained significant. Additionally, a 1-point increase in PCC was associated with a −8.89 (95% CI −11.85– −5.94, p<0.001) decrease in DCS score.
Table 3.
Multivariable model for DCS score based on patient and clinical factors
| Covariate | Level | Without PCC | With PCC | ||
|---|---|---|---|---|---|
| Estimate [CI] | P-Value | Estimate [CI] | P-Value | ||
| Enrollment Age* | 2.64 [1.04, 4.23] | <0.001 | 2.24 [0.73, 3.74] | 0.004 | |
| Race/Ethnicity | All other | 2.83 [−1.58, 7.24] | 0.2 | 2.53 [−1.15, 6.20] | 0.18 |
| Non-Hispanic White | - | - | - | - | |
| Gender Identity | Female | −2.58 [−6.20, 1.04] | 0.16 | −1.84 [−5.32, 1.65] | 0.3 |
| Male | - | - | - | - | |
| Marital Status | No current partner | 1.63 [−2.18, 5.44] | 0.4 | 1.56 [−2.03, 5.16] | 0.4 |
| Current partner | - | - | - | - | |
| Education Level | High school graduate or less | - | - | - | - |
| Some college or college graduate | 0.34 [−3.56, 4.24] | 0.9 | 0.29 [−3.20, 3.79] | 0.9 | |
| Master’s or Doctorate degree | −2.14 [−7.77, 3.50] | 0.5 | −0.47 [−5.63, 4.69] | 0.9 | |
| Income Level | Less than $50,000 | - | - | - | - |
| $50,000–99,999 | −1.01 [−5.38, 3.36] | 0.7 | −0.16 [−4.12, 3.80] | 0.9 | |
| $100,000–149,000 | 0.38 [−5.77, 6.54] | 0.9 | 1.67 [−3.97, 7.31] | 0.6 | |
| $150,000–199,000 | −5.03 [−11.49, 1.44] | 0.13 | −3.41 [−9.71, 2.89] | 0.3 | |
| $200,000 or more | 0.81 [−6.97, 8.60] | 0.8 | −1.50 [−8.57, 5.57] | 0.7 | |
| Charlson Comorbidity Score | 0.20 [−0.76, 1.15] | 0.7 | 0.34 [−0.54, 1.21] | 0.5 | |
| ECOG Status | 0 | - | - | - | - |
| 1 | −1.30 [−6.33, 3.72] | 0.6 | 0.73 [−3.90, 5.36] | 0.8 | |
| 2 or more | −0.12 [−12.21, 11.96] | >0.9 | 2.25 [−9.37, 13.86] | 0.7 | |
| Missing | −1.33 [−5.90, 3.24] | 0.6 | 0.94 [−3.00, 4.87] | 0.6 | |
| Nephrometry Score | High (10–12) | 6.50 [0.35, 12.65] | 0.038 | 6.02 [0.66, 11.38] | 0.028 |
| Moderate (7–9) | 1.74 [−1.54, 5.02] | 0.3 | 1.00 [−2.04, 4.04] | 0.5 | |
| Low (4–6) | - | - | - | - | |
| Multifocal | Yes | −0.27 [−9.60, 9.07] | >0.9 | 1.93 [−5.71, 9.57] | 0.6 |
| No | - | - | - | - | |
| Mass Type | Cystic component | 9.78 [5.27, 14.28] | <0.001 | 7.72 [3.55, 11.90] | <0.001 |
| Solid | - | - | - | - | |
| Smoking Status | Current smoker | 2.00 [−2.59, 6.58] | 0.4 | −0.05 [−4.25, 4.14] | >0.9 |
| Former/never smoker/unknown | - | - | - | - | |
| Urologic Oncologist | 1 | 1.25 [−3.11, 5.60] | 0.6 | 2.32 [−1.94, 6.58] | 0.3 |
| 2 | - | - | - | - | |
| 3 | 4.11 [−1.94, 10.17] | 0.18 | 2.57 [−3.12, 8.26] | 0.4 | |
| 4^ | 0.65 [−4.88, 6.18] | 0.8 | 1.12 [−3.17, 5.42] | 0.6 | |
| 5 | 1.26 [−4.65, 7.18] | 0.7 | 2.92 [−2.44, 8.28] | 0.3 | |
| Previous Abdominal Surgery | Yes | 0.45 [−2.69, 3.58] | 0.8 | 1.81 [−1.22, 4.85] | 0.2 |
| No | - | - | - | - | |
| Anticoagulant/ Antiplatelet Use | Yes | 0.47 [−5.66, 6.61] | 0.9 | 1.07 [−4.03, 6.16] | 0.7 |
| No | - | - | - | - | |
| Body Mass Index | −0.06 [−0.24, 0.11] | 0.5 | −0.08 [−0.24, 0.08] | 0.3 | |
| Patient-Centered Communication | - | - | −8.89 [−11.85, −5.94] | <0.001 | |
Estimates are for 10-unit increase.
Due to small numbers, provider 4 represents 2 different urologists.
3.3. Decisional Conflict by Decision-Making Traits
Table 2 reports the responses to the expanded decision-making survey (n=182/250). For the decision-making multivariable model (Table 4), DCS score was significantly higher with age (+3.39 per 10-year increase, 95% CI 1.01–5.78, p=0.005) but significantly lower with better mental health (−3.25 per 10-point increase, 95% CI −6.15– −0.36, p=0.028), self-efficacy (−3.31 per 1-point increase, 95% CI −5.77– −0.86, p=0.008), and information-seeking behavior (−4.44 per 10-point increase, 95% CI −7.32– −1.56, p=0.003). When including PCC, DCS score was −7.87 (95% CI −10.78– −4.97, p<0.001) lower with each 1-point increase in PCC (p<0.001), and DCS score no longer differed by mental health score (p=0.2) but was greater with higher numeracy (p=0.012). Self-efficacy (p=0.041) and information-seeking (p=0.008) remained significant factors associated with DCS score.
Table 2.
Decision-Making Traits
| Covariate | Level | N | Mean (SD) or Proportion |
|---|---|---|---|
| Global Physical T-score, mean (SD) | 169 | 46.5 (8.6) | |
| Global Mental T-score, mean (SD) | 169 | 50.1 (8.3) | |
| Global Quality of Life | Excellent | 31 | 18% |
| Very Good | 61 | 36% | |
| Good | 55 | 33% | |
| Fair | 17 | 10% | |
| Poor | 5 | 3.0% | |
| Single Item Literacy Screen | Always | 4 | 2.8% |
| Often | 10 | 6.9% | |
| Sometimes | 31 | 22% | |
| Rarely | 51 | 35% | |
| Never | 48 | 33% | |
| Control Preferences Scale | I prefer to make the final treatment decision | 9 | 5.4% |
| I prefer to make the final treatment decision after seriously considering my doctor’s opinion | 52 | 31% | |
| I prefer that my doctor and I share responsibility for deciding which treatment is best | 84 | 51% | |
| I prefer that my doctor makes the final treatment decision, but seriously considers my opinion | 15 | 9.0% | |
| I prefer to leave all treatment decisions to my doctor | 6 | 3.6% | |
| Self-Efficacy, mean (SD) | 144 | 4.0 (0.75) | |
| Information-Seeking, mean (SD) | 157 | 84.0 (7.7) | |
| Numeracy, mean (SD) | 136 | 4.3 (1.0) | |
| Maximizer-Minimizer, mean (SD) | 204 | 45.8 (9.9) | |
| Patient-Centered Communication, mean (SD) | 237 | 4.4 (0.61) | |
Table 4.
Multivariable model for DCS score based on decision-making factors
| Covariate | Level | Without PCC | With PCC | ||
|---|---|---|---|---|---|
| Estimate [CI] | P-Value | Estimate [CI] | P-Value | ||
| Enrollment Age* | 3.39 [1.01, 5.78] | 0.005 | 2.47 [0.26, 4.69] | 0.029 | |
| Global Physical T-score | −2.44 [−6.06, 1.17] | 0.19 | −1.76 [−4.88, 1.36] | 0.3 | |
| Global Mental T-score | −3.25 [−6.15, −0.36] | 0.028 | −1.97 [−5.03, 1.09] | 0.2 | |
| Global Quality of Life | Poor | −5.02 [−17.56, 7.51] | 0.4 | 0.39 [−10.80, 11.58] | 0.9 |
| Fair | 2.80 [−8.00, 13.59] | 0.6 | 6.17 [−3.61, 15.95] | 0.2 | |
| Good | −2.19 [−10.20, 5.82] | 0.6 | 1.24 [−6.27, 8.75] | 0.7 | |
| Very good | −1.85 [−8.20, 4.51] | 0.6 | 1.69 [−4.56, 7.94] | 0.6 | |
| Excellent | - | - | - | - | |
| Single Item Literacy Screen | Always | −1.55 [−9.89, 6.78] | 0.7 | 1.41 [−5.48, 8.29] | 0.7 |
| Often | −2.91 [−10.61, 4.78] | 0.5 | −1.99 [−9.37, 5.38] | 0.6 | |
| Sometimes | 1.07 [−4.04, 6.18] | 0.7 | 1.09 [−3.64, 5.82] | 0.7 | |
| Rarely | 5.11 [0.19, 10.04] | 0.042 | 4.88 [0.56, 9.19] | 0.027 | |
| Never | - | - | - | - | |
| Control Preferences Scale | I prefer to make the final treatment decision | −3.19 [−10.11, 3.73] | 0.4 | −1.16 [−7.11, 4.79] | 0.7 |
| I prefer to make the final treatment decision after seriously considering my doctor’s opinion | 0.25 [−4.93, 5.42] | 0.9 | 0.60 [−4.03, 5.23] | 0.8 | |
| I prefer that my doctor makes the final treatment decision, but seriously considers my opinion | 2.42 [−4.92, 9.77] | 0.5 | 5.10 [−2.23, 12.43] | 0.17 | |
| I prefer to leave all treatment decisions to my doctor | −2.47 [−10.45, 5.50] | 0.5 | 0.33 [−5.31, 5.98] | 0.9 | |
| I prefer that my doctor and I share responsibility for deciding which treatment is best | - | - | - | - | |
| Self-Efficacy | −3.31 [−5.77, −0.86] | 0.008 | −2.50 [−4.89, −0.10] | 0.041 | |
| Information-Seeking* | −4.44 [−7.32, −1.56] | 0.003 | −3.51[−6.09, −0.94] | 0.008 | |
| Numeracy | 1.45 [−0.81, 3.70] | 0.2 | 2.60 [0.58, 4.62] | 0.012 | |
| Maximizer-Minimizer Score | 0.57 [−1.61, 2.75] | 0.6 | 0.90 [−1.20, 3.00] | 0.4 | |
| Patient-Centered Communication | - | - | −7.87 [−10.78, −4.97] | <0.001 | |
Estimates are for 10-unit increase.
Discussion
While having multiple effective approaches to the management of clinical T1 renal masses enables patients to pursue options in line with their medical condition and personal preferences, it can also lead to confusion and decisional conflict.3 In this prospective trial, despite an overall low DCS score, 50% of patients with newly diagnosed T1 renal masses experienced elevated decisional conflict at some point during the decision-making process. This arises from both patient and clinical factors (e.g., age, tumor complexity, cystic masses) as well as decision-making factors (e.g., self-efficacy, information-seeking behavior), which may be modifiable through improved communication.
The decision to undergo surgery for clinical T1 renal masses is often framed in terms of potential risks and benefits, and the factors of decisional conflict found in this study mirror known considerations in this determination.3,7,12,18–21 Age is the strongest predictor of mortality and whether patients may benefit from treatment while nephrometry score correlates with postoperative complications and the choice between radical nephrectomy and nephron-sparing approaches.7,12,18–21 For cystic masses, decision-making must consider the Bosniak classification system and the greater uncertainty surrounding malignant potential and treatment benefits.32 While it is possible that patients could be interpreting their own risks with regards to these factors, this more likely reflects physician uncertainty as its conveyed through their counseling and recommendations, which ultimately inform patient decision-making.33 Of note, DCS score did not significantly differ by urologic oncologist, suggesting that decisional conflict may be more influenced by technical and surgical considerations than the particulars of the evaluating provider.
Our findings also highlight the impact of patients’ internal processing on the decision-making experience. Prior research in SRMs showed that patients with lower decisional conflict had better knowledge and more shared decision-making.12 Our study further reveals that self-efficacy and information-seeking behavior are associated with lower decisional conflict whereas factors more related to comprehension (e.g., literacy, numeracy) are not, underscoring the importance of higher-level cognitive functions to improving knowledge and decision-making for patients with SRMs. As a result, simply providing more information may not yield the desired effect, especially when considering that self-efficacy changes with case complexity and offering more choices can paradoxically lead to more emotional distress and anxiety for those with lower self-efficacy.28,34 Rather, tailoring the communication approach based on patient decision-making factors may be key to reducing decisional conflict and supporting shared decision-making given the relationship between perceived communication quality and decisional conflict. In this setting, patients with high or low self-efficacy and information-seeking behavior can receive and process information in a manner that helps them make decisions more effectively, ultimately increasing satisfaction with their treatment choices.
These findings must be interpreted in the context of several limitations. First, this study was conducted at a single institution, potentially limiting generalizability to larger populations. It is worth noting, however, the cohort’s diversity, with over 30% non-White patients, addressing historical disparities in cancer trial participation. Second, some differences in DCS appear small. However, nearly all the factors found to be statistically different meet the one-half standard deviation threshold for meaningful difference when compared to the reference group or comparing bottom to top quartile.35 Third, some patients saw a referring urologist prior to their visit at UNC and/or have a previous history with cancer that may result in different perspective and feelings though DCS scores did not differ by these factors. Fourth, while we administered an expanded survey on decision-making, shared decision-making and trust were not specifically assessed as contributing factors. Fifth, the exploratory analysis into decision-making traits was among a smaller cohort and may limit the power to detect differences in DCS scores. Finally, other aspects of decisional conflict outside the scope of this analysis might relate to renal mass biopsy, treatment choice, and risk perception.
These limitations notwithstanding, our findings offer new insights into the decision-making experience for patients with clinical T1 renal masses. Shared decision-making has gained emphasis in urology, including prostate cancer and now SRMs.36 Shared decision-making involves collaborative evidence-based decisions aligned with patient preferences and priorities, and decision aids (DAs) have been designed to facilitate the process.16 Yet, DAs for other diseases have produced mixed results with respect to decisional conflict and regret, not to mention treatment selection and outcomes.16,17,37 In practice, shared decision-making requires physicians to draw upon their clinical acumen to tailor evidence and communicate information in a manner that patients can digest. This study helps elucidate the potential areas in which effective communicators have intuitively homed in on and DAs could purposely act upon to facilitate information processing for patients. Adapting communication based on patient self-efficacy, information-seeking behavior, and potentially other decision-making factors may be the key missing piece in designing effective DAs and more consistently achieving shared decision-making.
Conclusion
Approximately half of patients with newly diagnosed T1 renal masses experience decisional conflict as they deliberate over their management options. While certain factors are patient and clinical in nature, other factors like self-efficacy and information-seeking behavior relate to how patients process and make decisions. In this context, patient-physician communication may be the key lever to reduce decisional conflict and improve the overall experience for patients presenting with clinical T1 renal masses.
Funding Statement:
This work was supported by funding from the National Institutes of Health (UNC Integrated Translational Oncology Program T32-CA244125 to UNC/khg) and UNC Lineberger Comprehensive Cancer Center UNCseq v2. UL1TR002489 from the Clinical and Translational Science Award program of the National Center for Advancing Translational Sciences, National Institutes of Health.
Dr. Mathew C. Raynor: receives consulting fees from Intuitive Surgical
Dr. Marc Bjurlin reports funding as a clinical investigator for Urogen, ImmunityBio, Janssen Research & Development, LLC, and Anchiano Therapeutics. He is also a paid consultant for Urogen; Paid Proctor: Intuitive
Dr. Matthew Milowsky reports research funding paid to the institution: Company: Merck, Roche/Genentech, Bristol-Myers Squibb, Mirati Therapeutics, Seagen, G1 Therapeutics, Alliance Foundation Trials, Alliance for Clinical Trials in Oncology, Clovis Oncology, Arvinas, ALX Oncology, Loxo, Hoosier Cancer Research Network. Consulting or advisory role: Loxo/Lilly. Other relationships include Elsevier for which Dr. Milowsky is Co-Editor-in-Chief of the journal Clinical Genitourinary Cancer; Medscape and Research to Practice for which he provides continuing medical education (CME) presentations.
Footnotes
All conflicts of interest are as follows:
All other authors have no conflicts of interest to be reported.
Data Availability Statement:
The dataset generated during and/or analyzed during the study are available from the corresponding author upon reasonable request, pending institutional approval.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The dataset generated during and/or analyzed during the study are available from the corresponding author upon reasonable request, pending institutional approval.