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Migraine is a disabling primary headache disorder that directly affects more than one billion people worldwide. Despite its widespread prevalence, migraine remains under-diagnosed and under-treated. To support clinical decision-making, we convened a European panel of experts to develop a ten-step approach to the diagnosis and management of migraine. Each step was established by expert consensus and supported by a review of current literature, and the Consensus Statement is endorsed by the European Headache Federation and the European Academy of Neurology. In this Consensus Statement, we introduce typical clinical features, diagnostic criteria and differential diagnoses of migraine. We then emphasize the value of patient centricity and patient education to ensure treatment adherence and satisfaction with care provision. Further, we outline best practices for acute and preventive treatment of migraine in various patient populations, including adults, children and adolescents, pregnant and breastfeeding women, and older people. In addition, we provide recommendations for evaluating treatment response and managing treatment failure. Lastly, we discuss the management of complications and comorbidities as well as the importance of planning long-term follow-up.In this Consensus Statement, which is endorsed by the European Headache Federation and the European Academy of Neurology, an expert panel provides recommendations for the diagnosis and management of migraine to support clinical decision-making by general practitioners, neurologists and headache specialists.

To maximise the clinical benefits of machine learning algorithms, we need to rethink our approach to explanation, argue David Watson and colleagues

Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field. This study aimed to explore the role of large language models (LLMs) in mitigating these biases through the use of the multi-agent framework. We simulate the clinical decision-making processes through multi-agent conversation and evaluate its efficacy in improving diagnostic accuracy compared with humans. A total of 16 published and unpublished case reports where cognitive biases have resulted in misdiagnoses were identified from the literature. In the multi-agent framework, we leveraged GPT-4 (OpenAI) to facilitate interactions among different simulated agents to replicate clinical team dynamics. Each agent was assigned a distinct role: (1) making the final diagnosis after considering the discussions, (2) acting as a devil's advocate to correct confirmation and anchoring biases, (3) serving as a field expert in the required medical subspecialty, (4) facilitating discussions to mitigate premature closure bias, and (5) recording and summarizing findings. We tested varying combinations of these agents within the framework to determine which configuration yielded the highest rate of correct final diagnoses. Each scenario was repeated 5 times for consistency. The accuracy of the initial diagnoses and the final differential diagnoses were evaluated, and comparisons with human-generated answers were made using the Fisher exact test. A total of 240 responses were evaluated (3 different multi-agent frameworks). The initial diagnosis had an accuracy of 0% (0/80). However, following multi-agent discussions, the accuracy for the top 2 differential diagnoses increased to 76% (61/80) for the best-performing multi-agent framework (Framework 4-C). This was significantly higher compared with the accuracy achieved by human evaluators (odds ratio 3.49; P=.002). The multi-agent framework demonstrated an ability to re-evaluate and correct misconceptions, even in scenarios with misleading initial investigations. In addition, the LLM-driven, multi-agent conversation framework shows promise in enhancing diagnostic accuracy in diagnostically challenging medical scenarios.

PurposeThe aim of this study was to assess whether coaching doctors to enhance ethical decision-making in teams improves (1) goal-oriented care operationalized via written do-not-intubate and do-not attempt cardiopulmonary resuscitation (DNI-DNACPR) orders in adult patients potentially receiving excessive treatment (PET) during their first hospital stay and (2) the quality of the ethical climate.MethodsWe carried out a stepped-wedge cluster randomized controlled trial in the medical intensive care unit (ICU) and 9 referring internal medicine departments of Ghent University Hospital between February 2022 and February 2023. Doctors and nurses in charge of hospitalized patients filled out the ethical decision-making climate questionnaire (ethical decision-making climate questionnaire, EDMCQ) before and after the study, and anonymously identified PET via an electronic alert during the entire study period. All departments were randomly assigned to a 4-month coaching. At least one month of coaching was compared to less than one month coaching and usual care. The first primary endpoint was the incidence of written DNI-DNACPR decisions. The second primary endpoint was the EDMCQ before and after the study period. Because clinicians identified less PET than required to detect a difference in written DNI-DNACPR decisions, a post-hoc analysis on the overall population was performed. To reduce type I errors, we further restricted the analysis to one of our predefined secondary endpoints (mortality up to 1 year).ResultsOf the 442 and 423 clinicians working before and after the study period, respectively 270 (61%) and 261 (61.7%) filled out the EDMCQ. Fifty of the 93 (53.7%) doctors participated in the coaching for a mean (standard deviation [SD]) of 4.36 (2.55) sessions. Of the 7254 patients, 125 (1.7%) were identified as PET, with 16 missing outcome data. Twenty-six of the PET and 624 of the overall population already had a written DNI-DNACPR decision at study entry, resulting in 83 and 6614 patients who were included in the main and post hoc analysis, respectively. The estimated incidence of written DNI-DNACPR decisions in the intervention vs. control arm was, respectively, 29.7% vs. 19.6% (odds ratio 4.24, 95% confidence interval 4.21–4.27; P < 0.001) in PET and 3.4% vs. 1.9% (1.65, 1.12–2.43; P = 0.011) in the overall study population. The estimated mortality at one year was respectively 85% vs. 83.7% (hazard ratio 2.76, 1.26–6.04; P = 0.011) and 14.5% vs. 15.1% (0.89, 0.72–1.09; P = 0.251). The mean difference in EDMCQ before and after the study period was 0.02 points (− 0.18 to 0.23; P = 0.815).ConclusionThis study suggests that coaching doctors regarding ethical decision-making in teams safely improves goal-oriented care operationalized via written DNI-DNACPR decisions in hospitalized patients, however without concomitantly improving the quality of the ethical climate.

The therapeutic landscape of chronic myeloid leukemia (CML) has profoundly changed over the past 7 years. Most patients with chronic phase (CP) now have a normal life expectancy. Another goal is achieving a stable deep molecular response (DMR) and discontinuing medication for treatment-free remission (TFR). The European LeukemiaNet convened an expert panel to critically evaluate and update the evidence to achieve these goals since its previous recommendations. First-line treatment is a tyrosine kinase inhibitor (TKI; imatinib brand or generic, dasatinib, nilotinib, and bosutinib are available first-line). Generic imatinib is the cost-effective initial treatment in CP. Various contraindications and side-effects of all TKIs should be considered. Patient risk status at diagnosis should be assessed with the new EUTOS long-term survival (ELTS)-score. Monitoring of response should be done by quantitative polymerase chain reaction whenever possible. A change of treatment is recommended when intolerance cannot be ameliorated or when molecular milestones are not reached. Greater than 10% BCR-ABL1 at 3 months indicates treatment failure when confirmed. Allogeneic transplantation continues to be a therapeutic option particularly for advanced phase CML. TKI treatment should be withheld during pregnancy. Treatment discontinuation may be considered in patients with durable DMR with the goal of achieving TFR.

Cancer progression is driven in part by genomic alterations 1 . The genomic characterization of cancers has shown interpatient heterogeneity regarding driver alterations 2 , leading to the concept that generation of genomic profiling in patients with cancer could allow the selection of effective therapies 3 , 4 . Although DNA sequencing has been implemented in practice, it remains unclear how to use its results. A total of 1,462 patients with HER2-non-overexpressing metastatic breast cancer were enroled to receive genomic profiling in the SAFIR02-BREAST trial. Two hundred and thirty-eight of these patients were randomized in two trials (nos. NCT02299999 and NCT03386162) comparing the efficacy of maintenance treatment 5 with a targeted therapy matched to genomic alteration. Targeted therapies matched to genomics improves progression-free survival when genomic alterations are classified as level I/II according to the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT) 6 (adjusted hazards ratio (HR): 0.41, 90% confidence interval (CI): 0.27–0.61, P  < 0.001), but not when alterations are unselected using ESCAT (adjusted HR: 0.77, 95% CI: 0.56–1.06, P  = 0.109). No improvement in progression-free survival was observed in the targeted therapies arm (unadjusted HR: 1.15, 95% CI: 0.76–1.75) for patients presenting with ESCAT alteration beyond level I/II. Patients with germline BRCA1/2 mutations ( n  = 49) derived high benefit from olaparib (g BRCA1 : HR = 0.36, 90% CI: 0.14–0.89; g BRCA2 : HR = 0.37, 90% CI: 0.17–0.78). This trial provides evidence that the treatment decision led by genomics should be driven by a framework of target actionability in patients with metastatic breast cancer. Targeted therapies matched to genomics improved progression-free survival when genomic alterations were classified as level I/II (according to ESCAT), and genomics should thus be driven by target actionability in patients with metastatic breast cancer.

The health care industry must align with new digital technologies to respond to existing and new challenges. Digital twins (DTs) are an emerging technology for digital transformation and applied intelligence that is rapidly attracting attention. DTs are virtual representations of products, systems, or processes that interact bidirectionally in real time with their actual counterparts. Although DTs have diverse applications from personalized care to treatment optimization, misconceptions persist regarding their definition and the extent of their implementation within health systems. This study aimed to review DT applications in health care, particularly for clinical decision-making (CDM) and operational decision-making (ODM). It provides a definition and framework for DTs by exploring their unique elements and characteristics. Then, it assesses the current advances and extent of DT applications to support CDM and ODM using the defined DT characteristics. We conducted a scoping review following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) protocol. We searched multiple databases, including PubMed, MEDLINE, and Scopus, for original research articles describing DT technologies applied to CDM and ODM in health systems. Papers proposing only ideas or frameworks or describing DT capabilities without experimental data were excluded. We collated several available types of information, for example, DT characteristics, the environment that DTs were tested within, and the main underlying method, and used descriptive statistics to analyze the synthesized data. Out of 5537 relevant papers, 1.55% (86/5537) met the predefined inclusion criteria, all published after 2017. The majority focused on CDM (75/86, 87%). Mathematical modeling (24/86, 28%) and simulation techniques (17/86, 20%) were the most frequently used methods. Using International Classification of Diseases, 10th Revision coding, we identified 3 key areas of DT applications as follows: factors influencing diseases of the circulatory system (14/86, 16%); health status and contact with health services (12/86, 14%); and endocrine, nutritional, and metabolic diseases (10/86, 12%). Only 16 (19%) of 86 studies tested the developed system in a real environment, while the remainder were evaluated in simulated settings. Assessing the studies against defined DT characteristics reveals that the developed systems have yet to materialize the full capabilities of DTs. This study provides a comprehensive review of DT applications in health care, focusing on CDM and ODM. A key contribution is the development of a framework that defines important elements and characteristics of DTs in the context of related literature. The DT applications studied in this paper reveal encouraging results that allow us to envision that, in the near future, they will play an important role not only in the diagnosis and prevention of diseases but also in other areas, such as efficient clinical trial design, as well as personalized and optimized treatments.

Axillary lymph node metastasis (ALNM) status, typically estimated using an invasive procedure with a high false-negative rate, strongly affects the prognosis of recurrence in breast cancer. However, preoperative noninvasive tools to accurately predict ALNM status and disease-free survival (DFS) are lacking. To develop and validate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic signatures for preoperative identification of ALNM and to assess individual DFS in patients with early-stage breast cancer. This retrospective prognostic study included patients with histologically confirmed early-stage breast cancer diagnosed at 4 hospitals in China from July 3, 2007, to September 21, 2019, randomly divided (7:3) into development and vaidation cohorts. All patients underwent preoperative MRI scans, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALNM status. Data analysis was conducted from February 15, 2019, to March 20, 2020. Clinical and DCE-MRI radiomic signatures. The primary end points were ALNM and DFS. This study included 1214 women (median [IQR] age, 47 [42-55] years), split into development (849 [69.9%]) and validation (365 [30.1%]) cohorts. The radiomic signature identified ALNM in the development and validation cohorts with areas under the curve (AUCs) of 0.88 and 0.85, respectively, and the clinical-radiomic nomogram accurately predicted ALNM in the development and validation cohorts (AUC, 0.92 and 0.90, respectively) based on a least absolute shrinkage and selection operator (LASSO)-logistic regression model. The radiomic signature predicted 3-year DFS in the development and validation cohorts (AUC, 0.81 and 0.73, respectively), and the clinical-radiomic nomogram could discriminate high-risk from low-risk patients in the development cohort (hazard ratio [HR], 0.04; 95% CI, 0.01-0.11; P < .001) and the validation cohort (HR, 0.04; 95% CI, 0.004-0.32; P < .001) based on a random forest-Cox regression model. The clinical-radiomic nomogram was associated with 3-year DFS in the development and validation cohorts (AUC, 0.89 and 0.90, respectively). The decision curve analysis demonstrated that the clinical-radiomic nomogram displayed better clinical predictive usefulness than the clinical or radiomic signature alone. This study described the application of MRI-based machine learning in patients with breast cancer, presenting novel individualized clinical decision nomograms that could be used to predict ALNM status and DFS. The clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.

BackgroundArtificial intelligence (AI) tools could assist emergency doctors interpreting chest X-rays to inform urgent care. However, the impact of AI assistance on clinical decision-making, a precursor to enhanced care and patient outcomes, remains understudied. This study evaluates the effect of AI assistance on clinical decisions of emergency doctors interpreting chest X-rays.MethodJunior and senior residents, emergency registrars and consultants working in Australian emergency departments were eligible. Doctors completed 18 clinical vignettes involving chest X-ray interpretation, representative of typical patient presentations. Vignettes were randomly selected from a bank of 49 based on the emergency medicine curriculum and contained a chest X-ray, presenting complaint, relevant symptoms and observations. Of the 18 vignettes, each doctor was randomly assigned to have half assisted by a commercial AI tool capable of detecting 124 different chest X-ray findings. Four vignettes contained X-rays known to produce incorrect AI findings. Primary outcomes were correct diagnosis and patient management. X-ray interpretation time, confidence of diagnosis, perceptions about the AI tool and the differential impact of AI assistance by seniority were also examined.Results200 doctors participated. AI assistance increased correct diagnosis by 5.9% (95% CI 2.7 to 9.2%) compared with unassisted vignettes, with the largest increase among senior residents (11.8%; 95% CI 5.2% to 18.3%). Patient management increased by 3.2% (95% CI 0.1% to 6.4%). Confidence in diagnosis increased by 5% (95% CI 3.4% to 6.6%; p<0.001) and interpretation time increased by 4.9 s (p=0.08). Incorrect AI findings decreased correct diagnosis by 1% for false-positive (p=0.9) and 9% for false-negative findings (p=0.1). Participants found the AI tool helpful for interpreting chest X-rays, highlighting missed findings, but were neutral on its accuracy.ConclusionImprovements in diagnosis and patient management without meaningful increases in interpretation time suggest AI assistance could benefit clinical decisions involving chest X-ray interpretation. Further studies are required to ascertain if such improvements translate to improved patient care.