Development of a Cocreated Decision Aid for Patients With Depression—Combining Data-Driven Prediction With Patients’ and Clinicians’ Needs and Perspectives: Mixed Methods Study

Shared decision making for high-risk surgery

The main objective of this study was to investigate possible predictors of response to cholinesterase inhibitor (ChEI) treatment, including pre-treatment progression rates and levels of the cerebrospinal fluid (CSF) biomarkers. A secondary objective was to evaluate whether treatment with ChEI changed progression. Out-patient individuals (n = 191) with the clinical diagnosis of Alzheimer's disease received ChEI treatment and were part of the Swedish Alzheimer Treatment Study (SATS), a prospective, longitudinal, non-randomised study in a routine clinical setting. Patients were assessed with MMSE, ADAS-cog and a global rating (CIBIC) at baseline, 2 months and every 6 months for a total period of 3 years. The following potential predictors of treatment response were investigated: age, gender, APOE epsilon 4 carrier, education, duration of disease, cognitive level, pre-treatment progression rate (in MMSE) and the levels of the CSF biomarkers A beta 42, T-tau and P-tau. Fast pre-treatment progression rate was a predictor of treatment response even after adjusting for baseline severity, another positive predictor of response. Patients in the fastest quartile of pre-treatment progression rates were significantly more prone to be responders at 2 months (adjusted OR 6.6, p = 0.001) and 6 months (adjusted OR 10.4, p < 0.001) than those in the slowest progressing quartile. Moreover, the linearity of progression was significantly changed by ChEI treatment at 6 months compared to the pre-treatment period. The rate of pre-treatment progression was the most consistent positive predictor of ChEI treatment response in the routine clinical setting. The progression rate was significantly changed by ChEI treatment.

Background: Shared decision-making (SDM) may facilitate challenging discussions between patients with facial trauma and reconstructive surgeons. Objective: To determine among patients seeking surgical evaluation for facial trauma, whether patient demographics, decisional conflict (DC), or experiences of discrimination in health care are associated with patient perceptions of SDM, as measured by scored responses on the CollaboRATE-10 questionnaire. Methods: English-speaking adults who presented to the offices of five facial trauma surgeons were contacted by telephone after their visit to complete a cross-sectional survey. Results: After screening 247 patients, 131 patients were recruited (53.0%). DC and history of discrimination were associated with lower perceived SDM (p < 0.001 and p = 0.048, respectively). After adjusting for age, sex, race, education, initial emergency department presentation, DC, and past discrimination, patients of older age (odds ratio [OR] 1.1, 95% confidence interval [CI] 1.02-1.09) and non-White race (OR 3.5, 95% CI 1.1-11.4) had higher perceptions of SDM; patients with DC (OR 0.52, 95% CI 0.01-0.20) reported less SDM. Conclusions: Patients who present to clinic for surgical evaluation after facial trauma feel that their physicians involve them less when deciding on a treatment plan if they have experienced discrimination in health care settings in the past, or if they have significant difficulty deciding between treatment options.

Cognitive predictors of treatment response to bupropion and cognitive effects of bupropion in patients with major depressive disorder

Shared decision-making (SDM) plays an important role in patients with prostate cancer due to the complexities of screening and treatment. This study assessed the racial and ethnic disparities in SDM in patients diagnosed with prostate cancer in the United States. This cross-sectional analysis utilized 2014-2021 Medical Expenditure Panel Survey (MEPS) data. Male patients ≥ 18years old with a diagnosis of prostate cancer were included. The Consumer Assessment of Healthcare Providers and Systems survey was used to operationalize the SDM, with scores categorized as poor (4-8), average (9-11), and optimal SDM (12). Multinomial regression model was used to analyze the racial and ethnic disparities in SDM among prostate cancer patients after adjusting for other covariates guided by the Andersen Behavioral Model. We identified 1.39 million (95%CI: 1.3-1.49) patients with prostate cancer between 2014 and 2021. The predominant race was White (64.4%), followed by African American (20.65%) and Hispanic (10.60%). Overall, 41.56% of patients reported optimal SDM scores, 49.82% reported average, and 8.62% reported poor SDM scores. The multinomial model revealed that the Hispanic patients had significantly higher odds of poor SDM scores than White patients (adjusted odds ratio:3.23, 95% CI: 1.14-9.46). Compared to optimal SDM, African American and White patients showed no significant differences in the odds of reporting poor or average SDM (p = 0.14 and p = 0.09, respectively). This study found that one in ten patients reported poor SDM for prostate cancer, with a higher likelihood of poor SDM among Hispanics. Concerted efforts are needed to improve the cultural competency and communication skills aimed at Hispanic patients to improve patient-centered care.

Background: As the terminal stage of cardiovascular disease, heart failure (HF) has garnered significant attention due to its recurrent nature, high mortality rates, and substantial medical burden. Shared decision-making (SDM) is an innovative strategy to improve medication adherence. From positive psychology insights, the effects on spirituality, benefit-finding (BF), decision self-efficacy, and patient engagement in SDM remain unexplored. Methods: This quantitative cross-sectional study was conducted from January 2023 to September 2024 at a hospital in Jiangsu. Data on general information, spirituality, BF, decision self-efficacy, and SDM were collected from 387 patients with chronic heart failure. Results: Spirituality was significantly associated with SDM (β = 0.8839, p < 0.001). BF played a mediating role in the relationship between spirituality and SDM (β = 0.2020, 95% CI: 0.0058-0.0261), accounting for 22.9% of the total effect. Decision self-efficacy was identified as a mediator in this relationship (β = 0.2636, 95% CI: 0.0120-0.0284), accounting for 29.8%. In addition, both BF and decision self-efficacy exhibited a chain mediation effect on the association between spirituality and SDM (β = 0.1451, 95% CI: 0.0061-0.0162), and the total indirect effect accounted for 69.1%. Conclusions: This study is the first to demonstrate that spirituality has significant direct and indirect effects on SDM, and it also reveals the underlying psychological mechanisms. Spiritual support services, BF intervention, and enhancing patients' decision self-efficacy can promote their participation in SDM. These findings highlight the role of positive psychology in promoting SDM, showing potential contribution to promoting medication adherence in HF patients.

Background: Predicting treatment response with antidepressant is a challenging task for clinicians and researchers. An important limitation of an antidepressant trial is the increased time spent before an adequacy of trial can be decided. Quantitative Electroencephalography has shown some evidence in identifying early changes seen with antidepressants. No data has been reported from Indian population on its predictive capabilities. Aim: To examine whether early changes in frontal and prefrontal theta value in QEEG could predict antidepressant treatment response. Methods: Structured clinical assessments were conducted at baseline and after one week in a sample of treatment-seeking adults with major depressive disorder (n = 50). Patients were started on SSRI (Escitalopram, fluoxetine, paroxetine or sertraline) and followed for 8 weeks. QEEG recordings were carried out at baseline and week 1 and its parameters (relative theta power and cordance) were assessed to identify its predictive value for treatment response. Treatment response was assessed using Hamilton depression rating scale with 50% reduction after 8 weeks being considered as response. Results: Mean age of the sample was 39 ± 10 years and majority of them were females (64%). A significant reduction was found in relative frontal theta value (p = 0.021) from baseline to one week in responders. However, linear regression revealed that this change could not predict the treatment response (p = 0.37). Conclusions: QEEG changes are observed in initial phase of antidepressant treatment but these changes can't predict the treatment response.

BackgroundPatients after kidney transplantation eventually face the risk of graft loss with the concomitant need for dialysis or retransplantation. Choosing the right kidney replacement therapy after graft loss is an important preference-sensitive decision for kidney transplant recipients. However, the rate of conversations about treatment options after kidney graft loss has been shown to be as low as 13% in previous studies. It is unknown whether the implementation of artificial intelligence (AI)–based risk prediction models can increase the number of conversations about treatment options after graft loss and how this might influence the associated shared decision-making (SDM).ObjectiveThis study aims to explore the impact of AI-based risk prediction for the risk of graft loss on the frequency of conversations about the treatment options after graft loss, as well as the associated SDM process.MethodsThis is a 2-year, prospective, randomized, 2-armed, parallel-group, single-center trial in a German kidney transplant center. All patients will receive the same routine post–kidney transplant care that usually includes follow-up visits every 3 months at the kidney transplant center. For patients in the intervention arm, physicians will be assisted by a validated and previously published AI-based risk prediction system that estimates the risk for graft loss in the next year, starting from 3 months after randomization until 24 months after randomization. The study population will consist of 122 kidney transplant recipients >12 months after transplantation, who are at least 18 years of age, are able to communicate in German, and have an estimated glomerular filtration rate <30 mL/min/1.73 m2. Patients with multi-organ transplantation, or who are not able to communicate in German, as well as underage patients, cannot participate. For the primary end point, the proportion of patients who have had a conversation about their treatment options after graft loss is compared at 12 months after randomization. Additionally, 2 different assessment tools for SDM, the CollaboRATE mean score and the Control Preference Scale, are compared between the 2 groups at 12 months and 24 months after randomization. Furthermore, recordings of patient-physician conversations, as well as semistructured interviews with patients, support persons, and physicians, are performed to support the quantitative results.ResultsThe enrollment for the study is ongoing. The first results are expected to be submitted for publication in 2025.ConclusionsThis is the first study to examine the influence of AI-based risk prediction on physician-patient interaction in the context of kidney transplantation. We use a mixed methods approach by combining a randomized design with a simple quantitative end point (frequency of conversations), different quantitative measurements for SDM, and several qualitative research methods (eg, records of physician-patient conversations and semistructured interviews) to examine the implementation of AI-based risk prediction in the clinic.Trial RegistrationClinicalTrials.gov NCT06056518; https://clinicaltrials.gov/study/NCT06056518International Registered Report Identifier (IRRID)PRR1-10.2196/54857

420 Background: To align BC treatment with patient goals, it is vital that healthcare providers (HCPs) engage their patients (pts) in SDM for treatment planning. We assessed alignment and discordances on aspects of SDM among BC pts and their urology and oncology teams. Methods: Between 05/2020 and 06/2020, surveys were administered to 53 pts with BC (48% female, mean age 68 years) and 23 HCPs, as part of in-clinic and virtual collaborative patient education sessions across 5 US-based practices. Surveys were designed to assess perceptions, preferences, and experiences with regard to SDM during BC care. Results: Survey findings indicated key alignments and discordances in pts’ reported experience and HCPs’ perceptions of the use of SDM in BC care. HCPs and pts identified the same top 2 patient goals for BC care: 1) preventing progression/recurrence (61% pts, 48% HCPs) and 2) maintaining quality of life (35% pts, 78% HCPs). When asked to identify patient’s top challenges for pts in BC care, both pts and HCPs indicated post-treatment aspects as the top challenge, though pts indicated managing side effects/serious worry about side effects from treatment as the top challenge (22%); whereas, HCPs were split evenly between managing side effects from treatment (26%) and managing life changes as a result of urinary diversion (26%). HCPs overestimated the effect that fatigue and worry had on pts capacity for SDM: only 9% of pts indicated worry or fatigue as a barrier to SDM, but 65% of HCPs indicated this as a likely barrier. Furthermore, the patient experience of SDM differed from HCP perception of SDM (Table); for some aspects of SDM, such as explaining different treatment options, explaining pros/cons of treatment options, and overall involvement in treatment decisions, fewer HCPs indicated that these aspects of SDM always or usually occurred as compared to pts. Conclusions: These findings reveal important alignments and discordances between pts and HCPs with regard to BC care and SDM, which may inform future bladder cancer and SDM initiatives. [Table: see text]

The Two-Edged Sword of Shared Clinical Decision-Making in the Post-ISCHEMIA World of Stable Coronary Artery Disease Management

Coronary heart disease (CHD) is the leading cause of death in the world. There are some decision-making conflicts in the management of chest pain, treatment methods, stent selection, and other aspects due to the unstable condition of CHD in the treatment stage. Although using decision aids to facilitate shared decision-making (SDM) contributes to high-quality decision-making, it has not been evaluated in the field of CHD. This review systematically assessed the effects of SDM in patients with CHD. We conducted a systematic review and meta-analysis of randomized controlled trials of SDM interventions in patients with CHD from database inception to 1 June 2022 (PROSPERO [Unique identifier: CRD42022338938]). We searched for relevant studies in the PubMed, Embase, Cochrane Library, Web of Science, CNKI, and Wan Fang databases. The primary outcomes were knowledge and decision conflict. The secondary outcomes were satisfaction, patient participation, trust, acceptance, quality of life, and psychological condition. A total of 8244 studies were retrieved. After screening, ten studies were included in the analysis. Compared with the control group, SDM intervention with patient decision aids obviously improved patients' knowledge, decision satisfaction, participation, and medical outcomes and reduced decision-making conflict. There was no significant effect of SDM on trust. This study showed that SDM intervention in the form of decision aids was beneficial to decision-making quality and treatment outcomes among patients with CHD. The results of SDM interventions need to be evaluated in different environments.

Brain-derived neurotrophic factor (BDNF) has been studied as a biomarker of major depressive disorder (MDD). Besides diagnostic biomarkers, clinically useful biomarkers can inform response to treatment. We aimed to review all studies that sought to relate BDNF baseline levels, or BDNF polymorphisms, with response to treatment in MDD. In order to achieve this, we performed a systematic review of studies that explored the relation of BDNF with both pharmacological and non-pharmacological treatment. Finally, we reviewed the evidence that relates peripheral levels of BDNF and BDNF polymorphisms with the development and management of treatment-resistant depression.

The care of critically ill patients involves communication and shared decision-making with families and determination of goals of care. Analyzing these aspects through electronic health records (EHRs) can support research in ICUs, associating them with outcomes. This review aims to explore studies that examine these topics. A scoping review was conducted through a systematic literature search of articles in PubMed, Web of Science, and Embase databases using MESH terms up to 2024, conducted in ICU settings, focusing on communication with families, shared decision-making, goals, and end-of-life care. A total of 10 articles were included, divided into themes: Records and family, and records in quality improvement projects. Variables based on records with common characteristics were identified. Outcome analysis was performed through questionnaires to family members, healthcare professionals or by analyzing care processes. The studies revealed associations between family members' perceptions and mental health symptoms and documented elements such as communication, therapeutic limitations, social and spiritual support. Studies evaluating quality communication improvement projects did not show significant impact on documented care, except for those that assessed improvements based on palliative care. The analysis of documented care for critically ill patients can be conducted from various perspectives. Processes amenable to improvement, such as communication with family members, definition of goals of care, limitations, shared decision-making, evaluated through EHRs, are associated with mental health symptoms and perceptions of families of critically ill patients. Documentation-based studies can contribute to improvements in patient- and family-centered care in the ICU. de Aquino VW, da Silveira GF, Boniatti MM, Terres MS. Communication, Shared Decision-making and Goals of Care in the ICU through Electronic Health Records: A Scoping Review. Indian J Crit Care Med 2024;28(10):977-987.

Major depressive disorder (MDD) affects millions of Americans each year and is often diagnosed and treated in primary care. Evidence shows that self-management techniques, shared decision-making (SDM), and goal setting are effective strategies for managing MDD, but the required collaboration between patients and primary care clinicians can be difficult. Primary Care Path is a program for supporting depression management in primary care that includes a patient-facing mobile app and an accompanying care team-facing web interface. Leveraging programs that provide clinician-facing software with companion patient-facing mobile technology may help patients and physicians align depression treatment and management goals, support effective SDM, alleviate barriers, and improve both clinical care and patient outcomes. To pilot-test the use of Primary Care Path for MDD management in primary care and evaluate the impact of its use on depression treatment, symptoms, goal setting and attainment, and SDM. Four primary care clinical practices in the United States were assigned to program use (2 practices; intervention) versus usual care (2 practices; control). Intervention practices used the Primary Care Path program in their clinics and engaged patient participants in app use for 18 weeks. Clinical care teams engaged with the patient-informed program portal primarily during patient encounters (in-person, virtual or calls). Patient participants were smartphone users aged 18 years and older who were being treated for MDD. Patient participants received online surveys (medication changes, Patient Health Questionnaire-9 [PHQ-9], goal setting and attainment questions, and Shared Decision-Making Questionnaire-9 [SDM-Q-9]) at baseline, 6, 12, and 18 weeks. A total of 76 patient participants (34 intervention; 42 control) were enrolled; the majority were female (27/34, 79%; 32/42, 76%), White (31/34, 91%; 40/42, 95%), non-Hispanic/Latino/a (29/34, 85%; 40/40, 100%), and employed (26/34, 77%; 34/42, 81%). Control patient participants' conversations with their medical providers increased over the study period, while intervention patient conversations with their medical providers decreased over time. At week 18, intervention participants felt more successful than control in achieving their personalized treatment goals. More intervention patient participants initiated antidepressant medication by weeks 12 (P=.03) and 18 (P=.04) and switched medications by weeks 6 (P=.009) and 12 (P=.04) versus control. All patient participants demonstrated significant improvement in PHQ-9 scores throughout the study period (P<.001), with no difference in change by group. Clinicians and patients indicated using the program to support SDM, but no significant differences were observed in SDM-Q-9 between intervention and control. Preliminarily, the use of this digital health program related to earlier medication optimization, earlier conversations between patients and medical providers, and patient attainment of goals that matter most to them, indicating that coordinated use of the program by both patients and clinical team members may enhance MDD management in primary care clinical settings.

Polygenic Scores Derived From Neuroimaging Endophenotypes Predict Outcomes To Psychotherapy And Medication Treatments For Major Depressive Disorder