Getting specific: participation preference in urooncological decision-making.

BACKGROUND: Shared decision-making is the gold standard for good clinical practice, and thus, psychometric instruments have been established to assess patients' generic preference for participation (e.g., the Autonomy Preference Index, API). However, patients' preferences may vary depending on the specific disease and with respect to the specific decision context. With a modified preference index (API-Uro), we assessed patients' specific participation preference in preference-sensitive decisions pertaining to urological cancer treatments and compared this with their generic participation preference. METHODS: In Study 1, we recruited (N = 469) urological outpatients (43.1% urooncological) at a large university hospital. Participation preference was assessed with generic measures (API and API case vignettes) and with the disease-specific API-Uro (urooncological case vignettes describing medical decisions of variable difficulty). A polychoric exploratory factor analysis was used to establish factorial validity and reduce items. In Study 2, we collected data from N = 204 bladder cancer patients in a multicenter study to validate the factorial structure with confirmatory factor analysis. Differences between the participation preference for different decision contexts were analyzed. RESULTS: Study 1: Scores on the specific urooncological case vignettes (API-Uro) correlated with the generic measure (r = .44) but also provided incremental information. Among the disease-specific vignettes of the API-Uro, there were two factors with good internal consistency (α ≥ .8): treatment versus diagnostic decisions. Patients desired more participation for treatment decisions (77.8%) than for diagnostic decisions (22%), χ2(1) = 245.1, p ≤ .001. Study 2: Replicated the correlation of the API-Uro with the API (r = .39) and its factorial structure (SRMR = .08; CFI = .974). Bladder cancer patients also desired more participation for treatment decisions (57.4%) than for diagnostic decisions (13.3%), χ²(1) =84, p ≤ .001. CONCLUSIONS: The desire to participate varies between treatment versus diagnostic decisions among urological patients. This underscores the importance of assessing participation preference for specific contexts. Overall, the new API-Uro has good psychometric properties and is well suited to assess patients' preferences. In routine care, measures of participation preference for specific decision contexts may provide incremental, allowing clinicians to better address their patients' individual needs.

Training machine learning algorithms for automatic facial coding: The role of emotional facial expressions' prototypicality.

Automatic facial coding (AFC) is a promising new research tool to efficiently analyze emotional facial expressions. AFC is based on machine learning procedures to infer emotion categorization from facial movements (i.e., Action Units). State-of-the-art AFC accurately classifies intense and prototypical facial expressions, whereas it is less accurate for non-prototypical and less intense facial expressions. A potential reason might be that AFC is typically trained with standardized and prototypical facial expression inventories. Because AFC would be useful to analyze less prototypical research material as well, we set out to determine the role of prototypicality in the training material. We trained established machine learning algorithms either with standardized expressions from widely used research inventories or with unstandardized emotional facial expressions obtained in a typical laboratory setting and tested them on identical or cross-over material. All machine learning models' accuracies were comparable when trained and tested with held-out dataset from the same dataset (acc. = [83.4% to 92.5%]). Strikingly, we found a substantial drop in accuracies for models trained with the highly prototypical standardized dataset when tested in the unstandardized dataset (acc. = [52.8%; 69.8%]). However, when they were trained with unstandardized expressions and tested with standardized datasets, accuracies held up (acc. = [82.7%; 92.5%]). These findings demonstrate a strong impact of the training material's prototypicality on AFC's ability to classify emotional faces. Because AFC would be useful for analyzing emotional facial expressions in research or even naturalistic scenarios, future developments should include more naturalistic facial expressions for training. This approach will improve the generalizability of AFC to encode more naturalistic facial expressions and increase robustness for future applications of this promising technology.

When attitudes and beliefs get in the way of shared decision-making: A mediation analysis of participation preference.

INTRODUCTION: Certain sociodemographic characteristics (e.g., older age) have previously been identified as barriers to patients' participation preference in shared decision-making (SDM). We aim to demonstrate that this relationship is mediated by the perceived power imbalance that manifests itself in patients' negative attitudes and beliefs about their role in decision-making. METHODS: We recruited a large sample (N = 434) of outpatients with a range of urological diagnoses (42.2% urooncological). Before the medical consultation at a university hospital, patients completed the Patients' Attitudes and Beliefs Scale and the Autonomy Preference Index. We evaluated attitudes as a mediator between sociodemographic factors and participation preference in a path model. RESULTS: We replicated associations between relevant sociodemographic factors and participation preference. Importantly, attitudes and beliefs about one's own role as a patient mediated this relationship. The mediation path model explained a substantial proportion of the variance in participation preference (27.8%). Participation preferences and attitudes did not differ for oncological and nononcological patients. CONCLUSION: Patients' attitudes and beliefs about their role determine whether they are willing to participate in medical decision-making. Thus, inviting patients to participate in SDM should encompass an assessment of their attitudes and beliefs. Importantly, negative attitudes may be accessible to change. Unlike stable sociodemographic characteristics, such values are promising targets for interventions to foster more active participation in SDM. PATIENT OR PUBLIC CONTRIBUTION: This study was part of a larger project on implementing SDM in urological practice. Several stakeholders were involved in the design, planning and conduction of this study, for example, three authors are practising urologists, and three are psychologists with experience in patient care. In addition, the survey was piloted with patients, and their feedback was integrated into the questionnaire. The data presented in this study is based on patients' responses. Results may help to empower our patients.

What the future holds: Machine learning to predict success in psychotherapy.

Machine learning (ML) may help to predict successful psychotherapy outcomes and to identify relevant predictors of success. So far, ML applications are scant in psychotherapy research and they are typically based on small samples or focused on specific diagnoses. In this study, we predict successful therapy outcomes with ML in a heterogeneous sample in routine outpatient care. We trained established ML models (decision trees and ensembles of them) with routinely collected clinical baseline information from n = 685 outpatients to predict a successful outcome of cognitive behavioral therapy. Treatment success was defined as clinically significant change (CSC) on the Brief-Symptom-Checklist (reached by 326 patients; 48%). The best performing model (Gradient Boosting Machines) achieved a balanced accuracy of 69% (p < .001) on unseen validation data. Out of 383 variables, we identified the 16 most important predictors, which were still able to predict CSC with 67% balanced accuracy. Our study demonstrates that ML models built on data, which is typically available at the outset of therapy, can predict whether an individual will substantially benefit from the intervention. Some of the predictors were theoretically expected (e.g., level of functioning), but others need further validation (e.g., somatization). From a theoretical and practical perspective, ML is clearly an attractive addition to more established psychotherapy research methodology.

What's in a face: Automatic facial coding of untrained study participants compared to standardized inventories.

Automatic facial coding (AFC) is a novel research tool to automatically analyze emotional facial expressions. AFC can classify emotional expressions with high accuracy in standardized picture inventories of intensively posed and prototypical expressions. However, classification of facial expressions of untrained study participants is more error prone. This discrepancy requires a direct comparison between these two sources of facial expressions. To this end, 70 untrained participants were asked to express joy, anger, surprise, sadness, disgust, and fear in a typical laboratory setting. Recorded videos were scored with a well-established AFC software (FaceReader, Noldus Information Technology). These were compared with AFC measures of standardized pictures from 70 trained actors (i.e., standardized inventories). We report the probability estimates of specific emotion categories and, in addition, Action Unit (AU) profiles for each emotion. Based on this, we used a novel machine learning approach to determine the relevant AUs for each emotion, separately for both datasets. First, misclassification was more frequent for some emotions of untrained participants. Second, AU intensities were generally lower in pictures of untrained participants compared to standardized pictures for all emotions. Third, although profiles of relevant AU overlapped substantially across the two data sets, there were also substantial differences in their AU profiles. This research provides evidence that the application of AFC is not limited to standardized facial expression inventories but can also be used to code facial expressions of untrained participants in a typical laboratory setting.

Different patients, different preferences: A multicenter assessment of patients' personality traits and anxiety in shared decision making.

OBJECTIVE: Patient-centered care and shared decision making (SDM) are generally recognized as the gold standard for medical consultations, especially for preference-sensitive decisions. However, little is known about psychological patient characteristics that influence patient-reported preferences. We set out to explore the role of personality and anxiety for a preference-sensitive decision in bladder cancer patients (choice of urinary diversion, UD) and to determine if anxiety predicts patients' participation preferences. METHODS: We recruited a sample of bladder cancer patients (N = 180, primarily male, retired) who awaited a medical consultation on radical cystectomy and their choice of UD. We asked patients to fill in a set of self-report questionnaires before this consultation, including measures of treatment preference, personality (BFI-10), anxiety (STAI), and participation preference (API and API-Uro), as well as sociodemographic characteristics. RESULTS: Most patients (79%) indicated a clear preference for one of the treatment options (44% continent UD, 34% incontinent UD). Patients who reported more conscientiousness were more likely to prefer more complex methods (continent UD). The majority (62%) preferred to delegate decision making to healthcare professionals. A substantial number of patients reported elevated anxiety (32%), and more anxiety was predictive of higher participation preference, specifically for uro-oncological decisions (ß = 0.207, p < 0.01). CONCLUSIONS: Our findings provide insight into the role of psychological patient characteristics for SDM. Aspects of personality such as conscientiousness influence treatment preferences. Anxiety contributes to patients' motivation to be involved in pertinent decisions. Thus, personality and negative affect should be considered to improve SDM.

Decision Aids for Shared Decision-making in Uro-oncology: A Systematic Review.

CONTEXT: Decision aids (DAs) aim to support patients in the process of shared decision-making for complex treatment decisions. To improve patient-centered care in uro-oncology, it is essential to evaluate the availability and quality of existing DAs. OBJECTIVE: To assess the quality of existing DAs for patients across the most prevalent uro-oncological entities. EVIDENCE ACQUISITION: This study was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. A systematic literature search (MedLine, Cochrane Library, Web of Science Core Collection, and CCMed) was conducted to identify DAs for treatment decisions for patients with prostate, renal, or bladder cancer. All studies reporting on the development or evaluation of DAs were included. The DAs were examined based on the International Patient Decision Aid Standards (IPDAS) and the evaluation studies were compared in accordance with Standards for Universal reporting of a patient Decision Aid Evaluations (SUNDAE). EVIDENCE SYNTHESIS: The literature search identified 1995 potentially relevant publications. Thirty-two studies reporting on 25 DAs met the inclusion criteria. Twenty-two DAs address prostate cancer, two renal tumor, and one bladder cancer. In the majority of DAs (n = 20), patients can enter individual data. A few (n = 6) DAs allow for personalization using a risk-adapted presentation of treatment options. The percentage of IPDAS criteria met in DAs ranged between 50% and 100% (median 87.5%), and the studies' adherence to the SUNDAE checklist was between 62% and 96% (median 86.6%). Evaluation studies suggest that interventions are likely efficacious. However, a preliminary meta-analysis revealed no significant difference between "DA" and "usual care" for decisional conflict or decisional regret. CONCLUSIONS: This review highlights that a number of well-developed DAs exist in urology. However, there is a need for specific instruments targeting kidney and bladder cancer. Personalization of tools and adherence to international standards of DAs should be further improved. PATIENT SUMMARY: The majority of uro-oncological decision aids target prostate cancer, whereas fewer address kidney or bladder cancer. The quality of the existing instruments is high, but can be increased further to better address specific needs of individual patients.

Patients' perspective on shared decision-making in urology: a prospective study at a university hospital.

PURPOSE: This study aims to determine the degree of shared decision-making (SDM) from urological patients' perspective and to identify possible predictors. METHODS: Overall, 469 urological patients of a university outpatient clinic were recruited for this prospective study. Before a medical consultation, clinical and sociodemographic information, and patients' emotional distress were assessed by questionnaires. After the consultation, patients completed the SDM-Questionnaire-9 (SDM-Q-9). The SDM-Q-9 scores of relevant subgroups were compared. Logistic regression was used to identify patients at risk for experiencing low involvement (SDM-Q-9 total score ≤ 66) in SDM. RESULTS: Data from 372 patients were available for statistical analyses. The SDM-Q-9 mean total score was 77.8 ± 20.6. The majority of patients (n = 271, 73%) experienced a high degree of involvement (SDM-Q-9 total score > 66). The mean score per SDM-Q-9 item was in the upper range (3.9 ± 1.4 out of 5). The most poorly rated item was "My doctor wanted to know how I want to be involved in decision-making" (3.5 ± 1.6). Immigration status (OR 3.7, p = 0.049), and nonscheduled hospital registration (OR 2.1, p = 0.047) were significant predictors for less perceived involvement. Comorbidity, oncological status, and emotional distress did not significantly predict perceived participation. CONCLUSION: In a university hospital setting, most urological patients feel adequately involved in SDM. Nevertheless, urologists should routinely ask for patients' participation preference. Patients without a scheduled appointment and patients who immigrated may need more support to feel involved in SDM.

Predicting decisional conflict: Anxiety and depression in shared decision making.

OBJECTIVES: Emotional distress can be a potential barrier to shared decision making (SDM), yet affect is typically not systematically assessed in medical consultation. We examined whether urological patients report anxiety or depression prior to a consultation and if emotional distress predicts decisional conflict thereafter. METHODS: We recruited a large sample of urological outpatients (N = 397) with a range of different diagnoses (42 % oncological). Prior to a medical consultation, patients filled in questionnaires, including the Hospital Anxiety and Depression Scale. After the consultation, patients completed the Decisional Conflict Scale. We scored the rate of anxiety and depression in our sample and conducted multiple regression analysis to examine if emotional distress before the consultation predicts decisional conflict thereafter. RESULTS: About a quarter of patients reported values at or above cut-off for clinically relevant emotional distress. Emotional distress significantly predicted a higher degree of decisional conflict. There were no differences in emotional distress between patients with and without uro-oncological diagnosis. CONCLUSIONS: Emotional distress is common in urology patients - oncological as well as non-oncological. It predicts decisional conflict after physician consultation. PRACTICE IMPLICATIONS: Emotional distress should be systematically assessed in clinical consultations. This may improve the process and outcome of SDM.