Scenario analysis and multi-criteria decision analysis to explore alternative reimbursement pathways for whole genome sequencing for blood cancer patients.

BACKGROUND: Whole genome sequencing (WGS) has transformative potential for blood cancer management, but reimbursement is hindered by uncertain benefits relative to added costs. This study employed scenario planning and multi-criteria decision analysis (MCDA) to evaluate stakeholders' preferences for alternative reimbursement pathways, informing future health technology assessment (HTA) submission of WGS in blood cancer. METHODS: Key factors influencing WGS reimbursement in blood cancers were identified through a literature search. Hypothetical scenarios describing various evidential characteristics of WGS for HTA were developed using the morphological approach. An online survey, incorporating MCDA weights, was designed to gather stakeholder preferences (consumers/patients, clinicians/health professionals, industry representatives, health economists, and HTA committee members) for these scenarios. The survey assessed participants' approval of WGS reimbursement for each scenario, and scenario preferences were determined using the geometric mean method, applying an algorithm to improve reliability and precision by addressing inconsistent responses. RESULTS: Nineteen participants provided complete survey responses, primarily clinicians or health professionals (n = 6; 32 %), consumers/patients and industry representatives (both at n = 5; 26 %). "Clinical impact of WGS results on patient care" was the most critical criterion (criteria weight of 0.25), followed by "diagnostic accuracy of WGS" (0.21), "cost-effectiveness of WGS" (0.19), "availability of reimbursed treatment after WGS" (0.16), and "eligibility criteria for reimbursed treatment based on actionable WGS results" and "cost comparison of WGS" (both at 0.09). Participants preferred a scenario with substantial clinical evidence, high access to reimbursed targeted treatment, cost-effectiveness below $50,000 per quality-adjusted life year (QALY) gained, and affordability relative to standard molecular tests. Reimbursement was initially opposed until criteria such as equal cost to standard tests and better treatment accessibility were met. CONCLUSION: Payers commonly emphasize acceptable cost-effectiveness, but strong clinical evidence for many variants and comparable costs to standard tests are likely to drive positive reimbursement decisions for WGS.

Cognitive biases in pediatric cardiac care.

Medical practitioners are entrusted with the pivotal task of making optimal decisions in healthcare delivery. Despite rigorous training, our confidence in reasoning can fail when faced with pressures, uncertainties, urgencies, difficulties, and occasional errors. Day-to-day decisions rely on swift, intuitive cognitive processes known as heuristic or type 1 decision-making, which, while efficient in most scenarios, harbor inherent vulnerabilities leading to systematic errors. Cognitive biases receive limited explicit discussion during our training as junior doctors in the domain of paediatric cardiology. As pediatric cardiologists, we frequently confront emergencies necessitating rapid decision-making, while contending with the pressures of stress, fatigue, an earnest interest in "doing the right thing" and the impact of parental involvement. This article aims to describe cognitive biases in pediatric cardiology, highlighting their influence on therapeutic interventions for congenital heart disease. Whether future pediatric cardiologists or experienced professionals, understanding and actively combating cognitive biases are essential components of our ongoing medical education. Furthermore, it is our responsibility to thoroughly examine our own practices in our unwavering commitment to providing high-quality care.

Cost-benefit considerations of the biased diagnostician.

OBJECTIVES: In the cognitive process of establishing a diagnosis, the performance of a diagnostician can be characterized in terms of sensitivity and specificity. The aims of the present study are to analyze in quantitative terms how cognitive bias affects the performance of a diagnostician, and how a diagnostician's biased decision making is further influenced by personal cost-benefit considerations. METHODS: The test matrices of two sequential diagnostic tests are manipulated according to the rules of linear algebra, using multiplication of the second with the first test matrix to calculate their joint test characteristics. The decision tree and receiver operating characteristic (ROC) of a biased and unbiased diagnostician are used to calculate which combination of test characteristics maximizes the expected utility value. RESULTS: Biased diagnosticians cannot establish a diagnosis beyond their own limited or distorted level of understanding. An unbiased and a biased diagnostician alike adjust their choice of test characteristics according to their different cost-benefit estimation of the various test outcomes. From the perspective of an unbiased diagnostician, the choices made by a biased diagnostician appear to invert reality. However, the same appearance of inverted reality is perceived by the biased diagnostician, judging the choices made by the unbiased diagnostician. CONCLUSIONS: As a general principle, human testers cannot test beyond their own level of understanding. They only see what they know. As they base their judgment on preconceived notions about the utilities associated with different test outcomes, human testers also tend to only know what they want to know.

The role of the radiologist in the prostate cancer multidisciplinary conference.

The broad range of disease aggressiveness together with imperfect screening, diagnostic, and treatment options in prostate cancer (PCa) makes medical decision-making complex. The primary goal of a multidisciplinary conference is to improve patient outcomes by combining evidence-based data and expert opinion to discuss optimal management, including for those patients with challenging presentations. The primary purpose of the genitourinary imaging specialist in the prostate cancer multidisciplinary conference is to use imaging findings to reduce uncertainty by answering clinical questions. In this review, we discuss the role and the opportunities for an imaging specialist to add value in the care of men with prostate cancer discussed at multidisciplinary conferences.

Attitudes toward and preparedness for lung transplantation among individuals with cystic fibrosis in the era of highly effective modulators.

BACKGROUND: Outcomes for individuals with cystic fibrosis (CF) have improved due to highly effective modulator therapy (HEMT). However, lung transplant (LTx) remains an important treatment for people with advanced lung disease. This study assessed attitudes and knowledge about LTx in the HEMT era. METHODS: All patients from the University of Washington CF clinic were surveyed March 25-May 30, 2020. Questions addressed self-rated LTx preparedness and knowledge, as well as barriers and facilitators to discussing LTx. Demographic and clinical data were extracted from the electronic health record. RESULTS: There were 159/224 (71%) responses. Respondents had a median forced expiratory volume in one second (FEV1) of 70%, and 142 (89%) were on modulatory therapy. One hundred thirteen (71%) respondents felt that it was moderately or very important to be prepared to make decisions about LTx, though only 56 (35%) felt moderately or very prepared. Only 83 (30%) and 47 (52%) participants correctly answered questions about life expectancy and improved quality of life after LTx, respectively. Respondents with Medicaid insurance less frequently answered questions correctly. The most common barriers to discussing LTx were fear of being a burden on loved ones for 58 respondents (36%) and cost of LTx for 46 (29%). Most participants (94%) trusted their CF doctor, and 75% of participants selected trust as a facilitator for LTx discussions. CONCLUSIONS: Many individuals with CF, especially those with lower socioeconomic status, lacked knowledge and did not feel very prepared for decisions about LTx. Earlier education and discussions about LTx represent an area for improvement in CF care.

A Community-Guided Approach to Bronchiolitis: A Needs Assessment and Illness Perception Study.

The factors influencing caregivers' understanding of pediatric respiratory diseases, such as bronchiolitis, can guide patient care and the acceptability of treatment methods within the healthcare system. This study aims to identify illness perceptions and perform a needs assessment among caregivers of children diagnosed with respiratory diseases. This is a prospective, cross-sectional, questionnaire-driven study of a representative sample of caregivers whose children had an acute respiratory illness. The telephone-administered questionnaire was comprised of (1) demographic items; (2) illness perception questionnaire-revised (IPQ-R); and (3) items about personal barriers, the latter 2 of which employed a 5-point Likert response. Cronbach's alpha (α) was used to measure the internal consistency reliability for each item within the IPQ-R. The Pearson 2-tailed correlation coefficient was used to correlate questionnaire items. We included 75 caregivers whose children have been diagnosed with bronchiolitis (51%), reactive airway disease (RAD) (35%), asthma (33%), and wheezing (44%). We found no significance between the child's diagnosis and the site of recruitment. The most important components of the illness perception were illness coherence (α=0.849), psychological attributions (α=0.903), and barriers to diagnosis (α=0.633). Understanding caregivers' perceptions of respiratory diseases will lead to better treatment acceptance. We must clarify the terms used to define bronchiolitis from viral-induced wheezing, RAD, and the first asthma episode in older infants. Identifying caregivers' gaps in knowledge will help establish a cohesive approach to personalized treatment of respiratory diseases in children and their diagnosis.

Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures.

BACKGROUND: The frequency of hip and knee arthroplasty surgeries has been rising steadily in recent decades. This trend is attributed to an aging population, leading to increased demands on healthcare systems. Fast Track (FT) surgical protocols, perioperative procedures designed to expedite patient recovery and early mobilization, have demonstrated efficacy in reducing hospital stays, convalescence periods, and associated costs. However, the criteria for selecting patients for FT procedures have not fully capitalized on the available patient data, including patient-reported outcome measures (PROMs). METHODS: Our study focused on developing machine learning (ML) models to support decision making in assigning patients to FT procedures, utilizing data from patients' self-reported health status. These models are specifically designed to predict the potential health status improvement in patients initially selected for FT. Our approach focused on techniques inspired by the concept of controllable AI. This includes eXplainable AI (XAI), which aims to make the model's recommendations comprehensible to clinicians, and cautious prediction, a method used to alert clinicians about potential control losses, thereby enhancing the models' trustworthiness and reliability. RESULTS: Our models were trained and tested using a dataset comprising 899 records from individual patients admitted to the FT program at IRCCS Ospedale Galeazzi-Sant'Ambrogio. After training and selecting hyper-parameters, the models were assessed using a separate internal test set. The interpretable models demonstrated performance on par or even better than the most effective 'black-box' model (Random Forest). These models achieved sensitivity, specificity, and positive predictive value (PPV) exceeding 70%, with an area under the curve (AUC) greater than 80%. The cautious prediction models exhibited enhanced performance while maintaining satisfactory coverage (over 50%). Further, when externally validated on a separate cohort from the same hospital-comprising patients from a subsequent time period-the models showed no pragmatically notable decline in performance. CONCLUSIONS: Our results demonstrate the effectiveness of utilizing PROMs as basis to develop ML models for planning assignments to FT procedures. Notably, the application of controllable AI techniques, particularly those based on XAI and cautious prediction, emerges as a promising approach. These techniques provide reliable and interpretable support, essential for informed decision-making in clinical processes.

It's a spiral staircase, not just two steps: An iterative approach to assessing patient capacity for medical decision-making.

The assessment of medical decision-making capacity as part of the process of clinical informed consent has been considered a bioethical housekeeping matter for decades. Yet in practice, the reality bears little resemblance to what is described in the medical literature and professed in medical education. Most literature on informed consent refers to medical decision-making capacity as a precondition to the consent process. That is, a clinician must first determine if a patient has capacity, and only then may the clinician engage with the patient for the rest of informed consent. The problem with this two-step approach is that it makes no sense in actual practice. We see the assessment of medical decision-making capacity within the process of informed consent as a spiral staircase, not just two steps, requiring clinicians to keep circling up and around, making progress, until they get to where they need to be: 1. Clinicians start with a general presumption of capacity for most adults, sometimes having a provisional appraisal of capacity based on prior patient contact. 2. Then, they begin performing informed consent for the current situation and intervention options. 3. Next, they must reassess capacity during this process. 4. After that, they continue with informed consent. 5. If capacity is not yet clear, they repeat 1-4.

Risk-Adapted Breast Screening for Women at Low Predicted Risk of Breast Cancer: An Online Discrete Choice Experiment.

BACKGROUND: A risk-stratified breast screening program could offer low-risk women less screening than is currently offered by the National Health Service. The acceptability of this approach may be enhanced if it corresponds to UK women's screening preferences and values. OBJECTIVES: To elicit and quantify preferences for low-risk screening options. METHODS: Women aged 40 to 70 y with no history of breast cancer took part in an online discrete choice experiment. We generated 32 hypothetical low-risk screening programs defined by 5 attributes (start age, end age, screening interval, risk of dying from breast cancer, and risk of overdiagnosis), the levels of which were systematically varied between the programs. Respondents were presented with 8 choice sets and asked to choose between 2 screening alternatives or no screening. Preference data were analyzed using conditional logit regression models. The relative importance of attributes and the mean predicted probability of choosing each program were estimated. RESULTS: Participants (N = 502) preferred all screening programs over no screening. An older starting age of screening, younger end age of screening, longer intervals between screening, and increased risk of dying had a negative impact on support for screening programs (P < 0.01). Although the risk of overdiagnosis was of low relative importance, a decreased risk of this harm had a small positive impact on screening choices. The mean predicted probabilities that risk-adapted screening programs would be supported relative to current guidelines were low (range, 0.18 to 0.52). CONCLUSIONS: A deintensified screening pathway for women at low risk of breast cancer, especially one that recommends a later screening start age, would run counter to women's breast screening preferences. Further research is needed to enhance the acceptability of offering less screening to those at low risk of breast cancer. HIGHLIGHTS: Risk-based breast screening may involve the deintensification of screening for women at low risk of breast cancer.Low-risk screening pathways run counter to women's screening preferences and values.Longer screening intervals may be preferable to a later start age.Work is needed to enhance the acceptability of a low-risk screening pathway.

Is Suffering a Useless Concept?

"Suffering" is a central concept within bioethics and often a crucial consideration in medical decision making. As used in practice, however, the concept risks being uninformative, ambiguous, or even misleading. In this paper, we consider a series of cases in which "suffering" is invoked and analyze them in light of prominent theories of suffering. We then outline ethical hazards that arise as a result of imprecise usage of the concept and offer practical recommendations for avoiding them. Appeals to suffering are often getting at something ethically important. But this is where the work of ethics begins, not where it ends.

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis.

Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires an early diagnosis with high uncertainty from the clinician. Our aim is to explore the design requirements for AI systems that can support clinical experts in making better decisions for the early diagnosis of sepsis. The study begins with a formative study investigating why clinical experts abandon an existing AI-powered Sepsis predictive module in their electrical health record (EHR) system. We argue that a human-centered AI system needs to support human experts in the intermediate stages of a medical decision-making process (e.g., generating hypotheses or gathering data), instead of focusing only on the final decision. Therefore, we build SepsisLab based on a state-of-the-art AI algorithm and extend it to predict the future projection of sepsis development, visualize the prediction uncertainty, and propose actionable suggestions (i.e., which additional laboratory tests can be collected) to reduce such uncertainty. Through heuristic evaluation with six clinicians using our prototype system, we demonstrate that SepsisLab enables a promising human-AI collaboration paradigm for the future of AI-assisted sepsis diagnosis and other high-stakes medical decision making.

A Mixed-Methods Comparison of Interventions to Increase Advance Care Planning.

PURPOSE: Although interventions can increase advance care planning (ACP) engagement, it remains unclear which interventions to choose in primary care settings. This study compares a passive intervention (mailed materials) to an interactive intervention (group visits) on participant ACP engagement and experiences. METHODS: We used mixed methods to examine ACP engagement at baseline and six months following two ACP interventions. Eligible patients were randomized to receive mailed materials or participate in two ACP group visits. We administered the 4-item ACP Engagement survey (n = 110) and conducted interviews (n = 23). We compared mean scores and percent change in ACP engagement, analyzed interviews with directed content analysis to understand participants' ACP experiences, and integrated the findings based on mailed materials or group visits intervention. RESULTS: All participants demonstrated increased ACP engagement scores. At six months, group visit participants reported higher percent change in mean overall score compared with mailed materials participants (+8% vs +3%, P < .0001). Group visits participants reported that being prompted to think about end-of-life preferences, gaining knowledge about ACP, and understanding the value of completing ACP documentation influenced their ACP readiness. While both interventions encouraged patients to start considering and refining their end-of-life preferences, group visits made patients feel more knowledgeable about ACP, highlighted the importance of completing ACP documentation early, and sparked further ACP discussions with others. CONCLUSIONS: While primary care patients may benefit from mailed ACP materials, patients reported increased readiness after ACP group visits. Group visits emphasized the value of upstream preparation, ongoing conversations, and increased knowledge about ACP.

Patient-clinician collaboration in making care fit: A qualitative analysis of clinical consultations in diabetes care.

OBJECTIVE: To confirm described dimensions of making care fit and explore how patients and clinicians collaborate to make care fit in clinical practice. METHODS: As part of an ongoing study, we audiotaped and transcribed patient-clinician consultations in diabetes care. We purposively selected consultations based on participants' demographical, biomedical and biographical characteristics. We analysed transcripts using reflexive thematic analysis. We combined a deductive and inductive approach, using the pre-described dimensions of making care fit and adding new (sub-)dimensions when pertinent. RESULTS: We analysed 24 clinical consultations. Our data confirmed eight previously described dimensions and provided new sub-dimensions of making care fit with examples from clinical practice (problematic situation, influence of devices, sense of options, shared agenda setting, clinician context, adapting to changing organization of care, and possibility to reconsider). CONCLUSION: Our study confirmed, specified and enriched the conceptualization of making care fit through practice examples. We observed patient-clinician collaboration in exploration of patients' context, and by responsively changing, adapting or maintaining care plans. PRACTICE IMPLICATIONS: Our findings support clinicians and researchers with insights in important aspects of patient-clinician collaboration. Ultimately, this would lead to optimal design of care plans that fit well in each patient life.

Lowering the Age of Consent for Vaccination to Promote Pediatric Vaccination: It's Worth a Shot.

This paper challenges historically preconceived notions surrounding a minor's ability to make medical decisions, arguing that federal health law should be reformed to allow minors with capacity as young as age 12 to consent to their own Centers for Diseases Control and Prevention (CDC)-approved COVID-19 vaccinations. This proposal aligns with and expands upon current exceptions to limitations on adolescent decision-making. This analysis reviews the historic and current anti-vaccination sentiment, examines legal precedence and rationale, outlines supporting ethical arguments regarding adolescent decision-making, and offers rebuttals to anticipated ethical counterarguments.

Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm.

OBJECTIVE: To explore the factors affecting delayed medical decision-making in older patients with acute ischemic stroke (AIS) using logistic regression analysis and the Light Gradient Boosting Machine (LightGBM) algorithm, and compare the two predictive models. METHODS: A cross-sectional study was conducted among 309 older patients aged ≥ 60 who underwent AIS. Demographic characteristics, stroke onset characteristics, previous stroke knowledge level, health literacy, and social network were recorded. These data were separately inputted into logistic regression analysis and the LightGBM algorithm to build the predictive models for delay in medical decision-making among older patients with AIS. Five parameters of Accuracy, Recall, F1 Score, AUC and Precision were compared between the two models. RESULTS: The medical decision-making delay rate in older patients with AIS was 74.76%. The factors affecting medical decision-making delay, identified through logistic regression and LightGBM algorithm, were as follows: stroke severity, stroke recognition, previous stroke knowledge, health literacy, social network (common factors), mode of onset (logistic regression model only), and reaction from others (LightGBM algorithm only). The LightGBM model demonstrated the more superior performance, achieving the higher AUC of 0.909. CONCLUSIONS: This study used advanced LightGBM algorithm to enable early identification of delay in medical decision-making groups in the older patients with AIS. The identified influencing factors can provide critical insights for the development of early prevention and intervention strategies to reduce delay in medical decisions-making among older patients with AIS and promote patients' health. The LightGBM algorithm is the optimal model for predicting the delay in medical decision-making among older patients with AIS.

The voices of breast cancer survivors with chronic pain: A qualitative thematic analysis of patients' challenges to pain management.

OBJECTIVES: Recognizing the limitations of the current pain therapies, the study aimed to explore the unique needs and obstacles related to pain management in Breast Cancer Survivors (BCs) with Chronic Pain (CP). METHODS: 4 focus groups were conducted involving 17 BCs with CP (Mage = 51, SD = 7.99) with varying pain intensities. Thematic analysis was applied to transcribed discussions. FINDINGS: Three key themes emerged: (1) Challenges to pain management, including "Doctor-patients communications barriers" and "Contextual and societal barriers"; (2) Self-management needs, encompassing "Psycho-social support," "Care-related needs," and "Shared decision-making"; (3) Treatment preferences and perceptions of pain management, with subthemes like "Treatment preferences," "Institution preference," and "Decision role perception." CONCLUSIONS: This study emphasizes tailored support systems targeting patient hesitancy, countering pain normalization, and addressing healthcare providers' attitudes. It underscores the importance of integrating caregiver and peer support. Findings advocate refining healthcare provider education, adopting a comprehensive multidisciplinary approach, and strategically incorporating eHealth tools into such care.

When facts become feelings.

I have long maintained that equipoise between empathy and the rational, decisive nature of obstetric care is central to good doctoring. I had exacting standards for how to communicate facts with feeling while shielding my own. Then, after experiencing my own obstetric emergency and preterm birth, this changed. In this reflection, I explore how recognizing the intersections between facts and feelings has made me a better physician.

Verbal Probability Terms for Communicating Clinical Risk - a Systematic Review.

Verbal probability expressions such as 'likely' and 'possible' are commonly used to communicate uncertainty in diagnosis, treatment effectiveness as well as the risk of adverse events. Probability terms that are interpreted consistently can be used to standardize risk communication. A systematic review was conducted. Research studies that evaluated numeric meanings of probability terms were reviewed. Terms with consistent numeric interpretation across studies were selected and were used to construct a Visual Risk Scale. Five probability terms showed reliable interpretation by laypersons and healthcare professionals in empirical studies. 'Very Likely' was interpreted as 90% chance (range 80 to 95%); 'Likely/Probable,' 70% (60 to 80%); 'Possible,' 40% (30 to 60%); 'Unlikely,' 20% (10 to 30%); and 'Very Unlikely' with 10% chance (5% to 15%). The corresponding frequency terms were: Very Frequently, Frequently, Often, Infrequently, and Rarely, respectively. Probability terms should be presented with their corresponding numeric ranges during discussions with patients. Numeric values should be presented as X-in-100 natural frequency statements, even for low values; and not as percentages, X-in-1000, X-in-Y, odds, fractions, 1-in-X, or as number needed to treat (NNT). A Visual Risk Scale was developed for use in clinical shared decision making.

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

PURPOSE: Because multiple management options exist for clinical T1 renal masses, patients may experience a state of uncertainty about the course of action to pursue (ie, decisional conflict). To better support patients, we examined patient, clinical, and decision-making factors associated with decisional conflict among patients newly diagnosed with clinical T1 renal masses suspicious for kidney cancer. MATERIALS AND METHODS: From a prospective clinical trial, participants completed the Decisional Conflict Scale (DCS), scored 0 to 100 with < 25 associated with implementing decisions, at 2 time points during the initial decision-making period. The trial further characterized patient demographics, health status, tumor burden, and patient-centered communication, while a subcohort completed additional questionnaires on decision-making. Associations of patient, clinical, and decision-making factors with DCS scores were evaluated using generalized estimating equations to account for repeated measures per patient. RESULTS: Of 274 enrollees, 250 completed a DCS survey; 74% had masses ≤ 4 cm in size, while 11% had high-complexity tumors. Model-based estimated mean DCS score across both time points was 17.6 (95% CI 16.0-19.3), though 50% reported a DCS score ≥ 25 at least once. On multivariable analysis, DCS scores increased with age (+2.64, 95% CI 1.04-4.23), high- vs low-complexity tumors (+6.50, 95% CI 0.35-12.65), and cystic vs solid masses (+9.78, 95% CI 5.27-14.28). Among decision-making factors, DCS scores decreased with higher self-efficacy (-3.31, 95% CI -5.77 to -0.86]) and information-seeking behavior (-4.44, 95% CI -7.32 to -1.56). DCS scores decreased with higher patient-centered communication scores (-8.89, 95% CI -11.85 to -5.94). CONCLUSIONS: In addition to patient and clinical factors, decision-making factors and patient-centered communication relate with decisional conflict, highlighting potential avenues to better support patient decision-making for clinical T1 renal masses.

Euthanasia and Physician-Assisted Suicide in People With an Accumulation of Health Problems Related to Old Age: A Cross-Sectional Questionnaire Study Among Physicians in the Netherlands.

Objectives: We explored characteristics of people with an accumulation of health problems related to old age requesting euthanasia or physician-assisted suicide (EAS) and identified characteristics associated with granting EAS requests. Methods: We conducted a cross-sectional questionnaire study among Dutch physicians on characteristics of these people requesting EAS (n = 123). Associations between characteristics and granting a request were assessed using logistic regression analyses. Results: People requesting EAS were predominantly >80 years old (82.4%), female (70.0%), widow/widower (71.7%), (partially) care-dependent (76.7%), and had a life expectancy >12 months (68.6%). The most prevalent health problems were osteoarthritis (70.4%) and impaired vision and hearing (53.0% and 40.9%). The most cited reasons to request EAS were physical deterioration (68.6%) and dependence (61.2%). 44.7% of requests were granted. Granting a request was positively associated with care dependence, disability/immobility, impaired vision, osteoporosis, loss of control, suffering without prospect of improvement and a treatment relationship with the physician >12 months. Conclusion: Enhanced understanding of people with an accumulation of health problems related to old age requesting EAS can contribute to the ongoing debate on the permissibility of EAS in people without life-threatening conditions.