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For many problems in clinical practice, multiple treatment alternatives are available. Given data from a randomized controlled trial or an observational study, an important challenge is to estimate an optimal decision rule that specifies for each client the most effective treatment alternative, given his or her pattern of pretreatment characteristics. In the present paper we will look for such a rule within the insightful family of classification trees. Unfortunately, however, there is dearth of readily accessible software tools for optimal decision tree estimation in the case of more than two treatment alternatives. Moreover, this primary tree estimation problem is also cursed with two secondary problems: a structural missingness in typical studies on treatment evaluation (because every individual is assigned to a single treatment alternative only), and a major issue of replicability. In this paper we propose solutions for both the primary and the secondary problems at stake. We evaluate the proposed solution in a simulation study, and illustrate with an application on the search for an optimal tree-based treatment regime in a randomized controlled trial on K = 3 different types of aftercare for younger women with early-stage breast cancer. We conclude by arguing that the proposed solutions may have relevance for several other classification problems inside and outside the domain of optimal treatment assignment.

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BACKGROUND: With recent advancements in the treatment of chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL), healthcare specialists may face challenges making treatment and management decisions based on latest evidence for the optimal care of patients with these conditions. This study aimed to identify specific knowledge, skills, and confidence gaps impacting the treatment of CLL and MCL, to inform future educational activities. METHODS: Hematologists and hemato-oncologists (HCPs, n = 224) from France (academic settings), Germany, and the United States (academic and community settings) responded to a 15-minute quantitative needs assessment survey that measured perceived knowledge, skills, and confidence levels regarding different aspects of treatment and management of CLL and MCL patients, as well as clinical case questions. Descriptive statistics (cross tabulations) and Chi-square tests were conducted. RESULTS: Four areas of educational need were identified: (1) sub-optimal knowledge of treatment guidelines; (2) sub-optimal knowledge of molecular testing to inform CLL/MCL treatment decisions; (3) sub-optimal skills when making treatment decisions according to patient profile (co-morbidities, molecular testing results); and (4) challenges balancing the risk of toxicities with benefits of treatment. Over one-third of the respondents reported skill gaps when selecting suitable treatment options and prescribing therapies and reported a lack in confidence to initiate and manage treatment. Larger gaps in knowledge of guidelines and skills in patient assessment were identified in MCL, compared to CLL. CONCLUSIONS: This study suggests the need for continuing medical education specifically to improve knowledge of treatment guidelines, and to assist clinicians in developing skills and confidence when faced with clinical decision-making scenarios of patients with specific comorbidities and/or molecular test results, for example, through case-based learning activities.

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ABSTRACTImmune thrombocytopenia (ITP), an autoimmune disease characterized by low platelet counts and increased bleeding risk, can impair health-related quality of life (HRQoL), impacting patients' daily lives and mental health. A number of patient-reported outcome (PRO) measures (both generic and specific to ITP) can be used to understand the impact of ITP on HRQoL and generate evidence to guide disease management. As well-developed PRO tools could help in HRQoL assessment, their optimization could help to solidify a patient-centric approach to ITP management. Shared decision-making is a collaborative process between a patient and their healthcare professional in making decisions about care. Treatment decisions based on this shared process between physician and patient are recommended by clinical guidelines. The goal of this narrative review is to discuss treatment decisions with regards to patient-centric ITP management, with a focus on the impact of PRO measures and the process of shared decision-making in practice.

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PURPOSE: Frailty influences outcomes in patients with cancer and should be considered when making decisions about treatment but concerns have been raised about possible negative consequences of doing this. Many patients with lung cancer have attributes of frailty and this study explores patient and staff perspectives on its role in decision making in older people with lung cancer. METHODS: The study adopted a two-phase qualitative approach using semi-structured telephone interviews and an in-person focus group. Data was analysed using framework techniques. RESULTS: Three key themes were identified: describing frailty, assessing frailty and perceptions of frailty. Each presented opportunities and challenges for using frailty in the context of treatment decision making. Frailty was described in relation to diverse attributes which made it difficult to define and assess. It was also associated with negative connotations, particularly by patients. CONCLUSION: Frailty has the potential to inform decision making that balances the wish to provide optimum treatment against risks to patients who may not be able to tolerate it. The challenge for healthcare staff is to incorporate frailty assessment into clinical practice in a way that is acceptable to patients and avoids potential unintended harms.

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OBJECTIVES: This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs). METHODS: CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation. RESULTS: There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76). CONCLUSIONS: Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC. ADVANCES IN KNOWLEDGE: This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.

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BACKGROUND: Heart failure guidelines have recently introduced a narrow category with mildly reduced left ventricular ejection fraction (LVEF) (heart failure with mildly reduced ejection fraction; LVEF 41%-49%) between the previous categories of reduced (heart failure with reduced ejection fraction; LVEF ≤40%) and preserved (heart failure with preserved ejection fraction; LVEF ≥50%) ejection fraction. Grouping of continuous measurements into narrow categories can be questioned if their variability is high. METHODS AND RESULTS: We constructed a cohort of all 9716 new cases of chronic heart failure with an available LVEF in Stockholm, Sweden, from January 1, 2015, until December 31, 2020. All values of LVEF were collected over time, and patients were followed up until death, moving out of Stockholm, or end of study. Mixed models were used to quantify within-person variance in LVEF, and multistate Markov models, with death as an absorbing state, to quantify the stability of LVEF categories. LVEF values followed a normal distribution. The SD of the within-person variance in LVEF over time was 7.4%. The mean time spent in any LVEF category before transition to another category was on average <1 year for heart failure with mildly reduced ejection fraction. Probabilities of transitioning between categories during the first year were substantial; patients with heart failure with mildly reduced ejection fraction had a probability of <25% of remaining in that category 1 year later. CONCLUSIONS: LVEF follows a normal distribution and has considerable variability over time, which may impose a risk for underuse of efficient treatment. The heart failure with mildly reduced ejection fraction category is especially inconstant. Assumptions of a patient's current LVEF should take this variability and the normal distribution of LVEF into account.

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BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has strained healthcare systems globally, particularly in terms of access to medicines. Lebanon has been greatly affected by the pandemic, having faced concomitant financial and economic crises. The objective of the study was to understand the experiences of patients with COVID-19 in Lebanon, as well as those of their families, and healthcare providers, with regards to their treatment decisions and accessibility to COVID-19 medicines. METHODS: For this qualitative study, we conducted 28 semi-structured interviews. We used purposive sampling to recruit participants with a diverse range of perspectives. The data collection phase spanned from August to November 2021 and was conducted virtually. After transcribing and translating the interviews, we employed thematic analysis to identify recurring themes and patterns. RESULTS: In total, 28 individuals participated in this study. Participants highlighted challenges owing to the COVID-19 pandemic and economic crisis. Accessing COVID-19 medicines posed major hurdles for physicians and patients, given limited availability, global shortages, local circumstances, community hoarding and stockpiling by pharmacies. Providers based treatment decisions on research, local and international practice guidelines, experiences and expert feedback. Patients sought information from social media, community members and physicians, as well as through word of mouth. Accessing medicines involved navigating the healthcare system, the black market, charities, personal networks and political parties and sourcing from abroad. The medicines were either free, subsidized or at inflated costs. CONCLUSIONS: This study highlights the diversity and complexity of factors influencing decision-making and accessing medicines during the COVID-19 pandemic in Lebanon. Future research should explore strategies for ensuring medicine access during crises, drawing insights from comparative studies across different countries.

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OBJECTIVE: To determine how decision making interventions for use in advanced cancer treatment consultations function and whether they increase perceptions of shared decision making (SDM) behaviours within consultations. METHODS: A systematic search of five literature databases was conducted. Evaluations of decision making interventions where participants faced active treatment decisions for stage 4 or otherwise incurable cancer were included. Intervention descriptions were coded using Behaviour Change Techniques (BCTs) to provide a narrative of how the interventions function. A narrative synthesis of interventions effect on perceptions of SDM behaviours compared to usual care was conducted. RESULTS: Four studies presenting different interventions were included. Education, training, modelling and enablement intervention functions were identified. Oncologist SDM training alone and combined with a patient communication aid demonstrated the only significant effect (p < 0.05) on SDM behaviours in advanced cancer consultations. CONCLUSION: Healthcare professional (HCP) SDM training which includes modelling and enablement functions may be effective in increasing clinician motivation, capability and opportunity to facilitate SDM in advanced cancer consultations. PRACTICE IMPLICATIONS: Implementing HCP SDM training into practice may encourage greater uptake of SDM which may lead to treatment decisions concordant with the goals of care of people with advanced cancer.

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OBJECTIVE: 'Treat-to-target principles' are advised for axial spondyloarthritis (axSpA), although a clear target is not yet defined and targets do not always reflect inflammation. Treat-to-target use and motives for treatment choices in clinics are unknown. Therefore, we studied the presence of residual disease activity according physician's opinion, patient's opinion and composite indices and compared them to the subsequent treatment decisions. METHODS: This cross-sectional multicentre study included 249 patients with a clinical diagnosis of axSpA ≥6 months. Remission and low disease activity according to the BASDAI (<1.9 and <3.5, respectively) and physician's and patient's opinion were assessed. Questionnaires included patient-reported outcomes and patients and physicians completed questions regarding treatment decisions. RESULTS: A total of 115/249 (46%) patients were in remission according to the physician and 37% (n = 43) of these patients reached remission according to the BASDAI. In 51/83 (60%) of the patients with residual disease activity according to the physician and a BASDAI >3.5 the treatment was left unchanged, either because of low disease activity as rated by the physician [n = 15 (29%)] or because of a combination of low disease activity with non-inflammatory complaints or comorbidities [n = 11 (25%)]. Retrospective treat-to-target evaluations showed that treatments were most frequently intensified in patients with arthritis or inflammatory back pain and less often in patients with other (non-inflammatory) musculoskeletal comorbidities. CONCLUSION: This study shows that physicians do not always strictly apply treat-to-target in case of residual disease activity in axSpA. Usually, they accept low disease activity as satisfactory.

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BACKGROUND AND OBJECTIVES: Discussing treatment wishes and limitations during medical consultations aims to enable patients to define goals and preferences for future care. Patients and physicians, however, face multiple barriers, resulting in postponing or avoiding the conversation. The aim of this study was to explore an internal medicine outpatient clinic population's perception on (discussing) treatment wishes and limitations. METHODS: Semi-structured interviews were conducted in two rounds with 44 internal medicine outpatient clinic patients at the University Medical Centre Utrecht, a tertiary care teaching medical centre in the Netherlands. Interviews were transcribed verbatim and thematically analysed with a phenomenological approach and inductive, data-driven coding. RESULTS: Four themes were identified, two (1-2) represent a deep conviction, two (3-4) are practically oriented: (1) patients associate treatment wishes and limitations with the end-of-life, making it sensitive and currently irrelevant, (2) patients assume this process leads to fixed choices, whilst their wishes might be situation dependent, (3) treatment wishes and limitations are about balancing whether a treatment 'is worth it', in which several subthemes carry weight, (4) the physician is assigned a key role. CONCLUSION AND PRACTICE IMPLICATIONS: The themes provide starting points for future interventions. It should be emphasized that care decisions are a continuous, dynamic process, relevant at any time in any circumstance and the physician should be aware of his/her key role.

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BACKGROUND: Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. METHODS: In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. RESULTS: The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort. CONCLUSIONS: This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

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Integrating artificial intelligence (AI) and robotics in prostate cancer (PCa) offers a game-changing breakthrough with far-reaching implications for diagnosis, treatment, and research. AI-driven algorithms have tremendous promise for assisting early diagnosis by analyzing invisible trends within medical imaging devices such as MRI and ultrasounds. In addition, by evaluating big datasets containing patient data, genetic attributes, and treatment outcomes, these AI algorithms offer the possibility of allowing individualized treatment regimens. This ability to personalize actions to specific patients might improve therapy efficacy while reducing side effects. Robotics can increase accuracy in less invasive surgery, revolutionize therapies like prostatectomies, and improve recovery time for patients. Robotic-assisted procedures provide clinicians with remarkable skills and flexibility, allowing clinicians to negotiate complicated anatomical structures more precisely. However, the symbiotic combination of AI and robotics has several drawbacks. Concerns about data privacy, algorithm biases, and the need to continually assess AI's diagnostic proficiency offer significant hurdles. To ensure patient privacy and data security, the ethical and regulatory aspects of integrating AI and robotics require proper attention. However, combining AI and robotics opens up a galaxy of possibilities. The joint use of AI and robotics can potentially speed up drug development procedures by filtering through massive databases, resulting in the identification of new medicinal compounds. Furthermore, combining AI and robotics might usher in an innovative era of personalized medicine, allowing healthcare providers to design therapies based on detailed patient profiles. The merging of AI and robotics in PCa care gives up unprecedented prospects. While limitations highlight the necessity for caution, the possibilities of better diagnostics, tailored therapies, and new research pathways highlight the transformational abilities of AI and robotics in determining the future of PCa management. This study explores the limitations and opportunities presented by using AI and robotics in the context of PCa.

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Objective: Fabry disease is a progressive disorder caused by deficiency of the α-galactosidase A enzyme (α-Gal A), leading to multisystemic organ damage with heterogenous clinical presentation. The addition of the oral chaperone therapy migalastat to the available treatment options for Fabry disease is not yet universally reflected in all treatment guidelines. These consensus recommendations are intended to provide guidance for the treatment and monitoring of patients with Fabry disease receiving migalastat. Methods: A modified Delphi process was conducted to determine consensus on treatment decisions and monitoring of patients with Fabry disease receiving migalastat. The multidisciplinary panel comprised 14 expert physicians across nine specialties and two patients with Fabry disease. Two rounds of Delphi surveys were completed and recommendations on the use of biomarkers, multidisciplinary monitoring, and treatment decisions were generated based on statements that reached consensus. Results: The expert panel reached consensus agreement on 49 of 54 statements, including 16 that reached consensus in round 1. Statements that reached consensus agreement are summarized in recommendations for migalastat treatment and monitoring, including baseline and follow-up assessments and frequency. All patients with Fabry disease and an amenable mutation may initiate migalastat treatment if they have evidence of Fabry-related symptoms and/or organ involvement. Treatment decisions should include holistic assessment of the patient, considering clinical symptoms and organ involvement as well as patient-reported outcomes and patient preference. The reliability of α-Gal A and globotriaosylsphingosine as pharmacodynamic response biomarkers remains unclear. Conclusion: These recommendations build on previously published guidelines to highlight the importance of holistic, multidisciplinary monitoring for patients with Fabry disease receiving migalastat, in addition to shared decision-making regarding treatments and monitoring throughout the patient journey. GRAPHICAL ABSTRACT.

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BACKGROUND: Various factors influence tuberculosis preventive treatment (TPT) decisions thus it is important to understand the health beliefs and concerns of patients before starting TPT to ensure treatment compliance. This study aims to explore facilitators and barriers for TPT among patients diagnosed with Latent Tuberculosis infection (LTBI) attending six primary healthcare clinics in Selangor, Malaysia. METHOD: In-depth interviews were conducted face-to-face or via telephone among patients with a clinical diagnosis of LTBI using a semi-structured topic guide developed based on the common-sense model of self-regulation and literature review. Audio recordings of interviews were transcribed verbatim and analysed thematically. RESULTS: We conducted 26 In-depth interviews; Good knowledge of active tuberculosis (TB) and its associated complications, including the perceived seriousness and transmissibility of active TB, facilitates treatment. LTBI is viewed as a concern when immune status is compromised, thus fostering TPT. However, optimal health is a barrier for TPT. Owing to the lack of knowledge, patients rely on healthcare practitioners (HCPs) to determine their treatment paths. HCPs possessing comprehensive knowledge play a role in facilitating TPT whereas barriers to TPT encompass misinterpretation of tuberculin skin test (TST), inadequate explanation of TST, and apprehensions about potential medication side effects. CONCLUSIONS: Knowledge of LTBI can influence TPT uptake and patients often entrust their HCPs for treatment decisions. Improving knowledge of LTBI both among patients and HCPs can lead to more effective doctor-patient consultation and consequently boost the acceptance of TPT. Quality assurance should be enhanced to ensure the effective usage of TST as a screening tool.

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Aim: To assess rates of no systemic treatment (NST), attrition across lines of therapy, and factors influencing treatment selection in patients with locally advanced or metastatic urothelial cancer (la/mUC). Methods: Systematic literature review to identify real-world studies reporting NST or attrition rates in la/mUC from 2017-2022 (including data reported since 2015). Results: Of 2439 publications screened, 29 reported NST rates, ranging from 40-74% in eight European-based studies, 14-60% in 12 US-based studies, and 9-63% in nine studies in other locations (meta-analysis estimate, 39%). Factors associated with NST or no second-line therapy included older age, female sex, poor performance status, poor renal function and distant metastases. Conclusion: A substantial proportion of patients with la/mUC do not receive guideline-recommended treatment.

People with advanced bladder cancer have a short survival. Bladder cancer is called advanced when it has spread outside of the urinary tract. Several drug treatments are available for people with advanced bladder cancer. However, sometimes people do not receive any drug treatment. We looked at published studies to see how many people with advanced bladder cancer did not receive any drug treatment and the reasons why. We also looked at how long people lived with or without drug treatment. We found that many people with advanced bladder cancer did not receive drug treatment. The number of people who received no drug treatment varied in studies from different countries. People who were older, were female, had poor health or kidney problems, or had cancer that had spread to other parts of the body were less likely to receive drug treatment. People who did not receive drug treatment lived for an average of 2 to 7 months, compared with 9 to 35 months for people who received drug treatment. More studies are needed to investigate the reasons why drug treatment is sometimes not used in people with advanced bladder cancer who could receive treatment, so that more people can benefit from available treatments.

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Background: Frailty is common among urology patients in general as well as among men seeking evaluation for stress urinary incontinence (SUI), with 6.1% of men undergoing artificial urinary sphincter placement considered frail. It is unclear if and how patient views on frailty and incontinence severity impact decision-making with regards to SUI treatment. Methods: We undertook a mixed methods analysis to evaluate the intersection of frailty, incontinence severity, and treatment decision-making is presented. To do so, we utilized a previously published cohort of men undergoing evaluation for SUI at the University of California, San Francisco between 2015 and 2020, selecting those who had evaluation with timed up and go test (TUGT), objective measures of incontinence, and patient-reported outcome measures (PROMs). A subset of these participants had additionally undergone semi-structured interviews, and these interviews were re-examined to thematically code them with a focus on the impact of frailty and incontinence severity on SUI treatment decision-making. Results: Among the original cohort of 130 patients, 72 had an objective measure of frailty and were included in our analysis; 18 of these individuals had corresponding qualitative interviews. Common themes identified included (I) impact of incontinence severity on decision-making; (II) the interaction between frailty and incontinence; (III) the impact of comorbidity on treatment decision-making; and (IV) age as a construct of frailty and impact on surgical choice and/or recovery. Direct quotations regarding each theme provides insight into patients' views and drivers of SUI treatment decision-making. Conclusions: The impact of frailty on treatment decision-making for patients with SUI is complex. This mixed methods study highlights the variety of patient views on frailty with regards to surgical intervention for male SUI. Urologists should make a concerted effort to personalize patient counseling for SUI management and take time to understand each patient's perspective in order to individualize SUI treatment decision-making. More research is needed to help identify factors that influence decision-making for frail male patients with SUI.

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The patient authors with over 48 years of combined real-world experience with chronic myeloid leukemia, and a healthcare professional, use their own personal experiences to describe the factors that may affect the patien-doctor relationship, patient care and outcomes. The authors believe that this podcast series and associated manuscript will aid both patients and healthcare professionals to improve their patient-doctor relationships and communication through diagnosis, treatment decision-making and learning to live with CML (or another disease). In this episode, they discuss managing financial costs and where to find resources and support groups.

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The patient authors with over 48 years of combined real-world experience with chronic myeloid leukemia, and a healthcare professional, use their own personal experiences to describe the factors that may affect the patient-doctor relationship, patient care and outcomes. The authors believe that this podcast series and associated manuscript will aid both patients and healthcare professionals to improve their patient-doctor relationships and communication through diagnosis, treatment decision-making and learning to live with CML (or another disease). In this episode, they discuss the importance of the patient-doctor relationship, the patient authors' own diagnosis experiences and considerations when selecting a health care team.

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The patient authors with over 48 years of combined real-world experience with chronic myeloid leukemia, and a healthcare professional, use their own personal experiences to describe the factors that may affect the patient-doctor relationship, patient care and outcomes. The authors believe that this podcast series and associated manuscript will aid both patients and healthcare professionals to improve their patient-doctor relationships and communication through diagnosis, treatment decision-making and learning to live with CML (or another disease). In this episode, they discuss communication between a patient and their healthcare team and establishing a treatment protocol.

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