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PurposeDespite promising achievements in precision cancer medicine (PCM), participating patients are still faced with manifold uncertainties, especially regarding a potential treatment benefit of molecular diagnostics (MD). Hence, MD poses considerable challenges for patient information and communication. To meet these challenges, healthcare professionals need to gain deeper insight into patients’ subjective experiences. Therefore, this qualitative study examined information aspects of MD programs in cancer patients.MethodsIn two German Comprehensive Cancer Centers, 30 cancer patients undergoing MD participated in semi-structured interviews on information transfer and information needs regarding MD. Additionally, patients provided sociodemographic and medical data and indicated their subjective level of information (visual analogue scale, VAS, 0–10).ResultsOn average patients had high levels of information (mean = 7, median = 8); nevertheless 20% (n = 6) showed an information level below 5 points. Qualitative analysis revealed that patients show limited understanding of the complex background of MD and have uncertainties regarding their personal benefit. Further, patients described unmet information needs. Existential threat in awaiting the results was experienced as burdensome. To withstand the strains of their situation, patients emphasized the importance of trusting their physician.ConclusionThe challenges in PCM consist in providing unambiguous information, especially concerning treatment benefit, and providing guidance and support. Therefore, psycho-oncology needs to develop guidelines for adequate patient communication in order to help healthcare providers and cancer patients to handle these challenges in the developing field of PCM.

Background The optical accessibility of the eye and technological advances in ophthalmic diagnostics have put ophthalmology at the forefront of data-driven medicine. The focus of this study is rare eye disorders, a group of conditions whose clinical heterogeneity and geographic dispersion make data-driven, evidence-based practice particularly challenging. Inter-institutional collaboration and information sharing is crucial but the lack of standardised terminology poses an important barrier. Ontologies are computational tools that include sets of vocabulary terms arranged in hierarchical structures. They can be used to provide robust terminology standards and to enhance data interoperability. Here, we discuss the development of the ophthalmology-related component of two well-established biomedical ontologies, the Human Phenotype Ontology (HPO; includes signs, symptoms and investigation findings) and the Orphanet Rare Disease Ontology (ORDO; includes rare disease nomenclature/nosology). Methods A variety of approaches were used including automated matching to existing resources and extensive manual curation. To achieve the latter, a study group including clinicians, patient representatives and ontology developers from 17 countries was formed. A broad range of terms was discussed and validated during a dedicated workshop attended by 60 members of the group. Results A comprehensive, structured and well-defined set of terms has been agreed on including 1106 terms relating to ocular phenotypes (HPO) and 1202 terms relating to rare eye disease nomenclature (ORDO). These terms and their relevant annotations can be accessed in http://www.human-phenotype-ontology.org/ and http://www.orpha.net/ ; comments, corrections, suggestions and requests for new terms can be made through these websites. This is an ongoing, community-driven endeavour and both HPO and ORDO are regularly updated. Conclusions To our knowledge, this is the first effort of such scale to provide terminology standards for the rare eye disease community. We hope that this work will not only improve coding and standardise information exchange in clinical care and research, but also it will catalyse the transition to an evidence-based precision ophthalmology paradigm.

Although pulmonary vein isolation (PVI) has become the cornerstone ablation procedure for atrial fibrillation (AF), the optimal ablation procedure for persistent and long-standing persistent AF remains elusive. Targeting spatio-temporal electrogram dispersion in a tailored procedure has been suggested as a potentially beneficial alternative to a conventional PVI-only procedure. In this multicenter, randomized, controlled, double-blind, superiority trial, patients with drug-refractory persistent AF were randomly assigned to a tailored ablation procedure targeting areas of spatio-temporal dispersion, as detected by an artificial intelligence (AI) algorithm, in addition to PVI (tailored arm, n  = 187, 23% women) or to a conventional PVI-only procedure (anatomical arm, n  = 183, 19% women). The primary efficacy endpoint was freedom from documented AF with or without antiarrhythmic drugs at 12 months after a single ablation procedure. Secondary endpoints included freedom from any atrial arrhythmic events, and the secondary composite safety endpoint consisted of death, cerebrovascular events, or treatment-related serious adverse events. One year post-procedure, the trial met its primary efficacy endpoint, which was achieved in 88% of patients in the tailored arm compared with 70% of patients in the anatomical arm (log-rank P  < 0.0001 for superiority). However, no significant difference between arms was observed for the freedom from any atrial arrhythmia endpoint after one ablation. The safety endpoint did not differ between arms, with procedure and ablation times being twice as long in the tailored arm. These results show that AI-guided ablation of spatio-temporal dispersion areas in addition to PVI is superior to PVI alone in eliminating AF at 1-year follow-up in patients with persistent and long-standing persistent AF. Ablation of subsequent organized atrial tachycardias may be needed to maintain sinus rhythm long term. ClinicalTrials.gov identifier: NCT04702451 . In a randomized controlled trial in individuals with persistent atrial fibrillation, an individualized ablation procedure, in which areas with abnormal electrophysiological characteristics—as detected by an AI algorithm—were targeted for ablation, led to improved efficacy for reducing arrhythmia recurrence at 12 months following the ablation procedure.

Large variability exists in people’s responses to foods. However, the efficacy of personalized dietary advice for health remains understudied. We compared a personalized dietary program (PDP) versus general advice (control) on cardiometabolic health using a randomized clinical trial. The PDP used food characteristics, individual postprandial glucose and triglyceride (TG) responses to foods, microbiomes and health history, to produce personalized food scores in an 18-week app-based program. The control group received standard care dietary advice (US Department of Agriculture Guidelines for Americans, 2020–2025) using online resources, check-ins, video lessons and a leaflet. Primary outcomes were serum low-density lipoprotein cholesterol and TG concentrations at baseline and at 18 weeks. Participants ( n  = 347), aged 41–70 years and generally representative of the average US population, were randomized to the PDP ( n  = 177) or control ( n  = 170). Intention-to-treat analysis ( n  = 347) between groups showed significant reduction in TGs (mean difference = −0.13 mmol l −1 ; log-transformed 95% confidence interval = −0.07 to −0.01, P  = 0.016). Changes in low-density lipoprotein cholesterol were not significant. There were improvements in secondary outcomes, including body weight, waist circumference, HbA1c, diet quality and microbiome (beta-diversity) ( P  < 0.05), particularly in highly adherent PDP participants. However, blood pressure, insulin, glucose, C-peptide, apolipoprotein A1 and B, and postprandial TGs did not differ between groups. No serious intervention-related adverse events were reported. Following a personalized diet led to some improvements in cardiometabolic health compared to standard dietary advice. ClinicalTrials.gov registration: NCT05273268 . A randomized controlled trial showed that following a personalized dietary program led to significant improvements in cardiometabolic and gut health as well as reductions in body weight compared to following standard dietary advice according to US Department of Agriculture guidelines.

Background The experience sampling method (ESM) builds an intensive time series of experiences and contexts in the flow of daily life, typically consisting of around 70 reports, collected at 8–10 random time points per day over a period of up to 10 days. Methods With the advent of widespread smartphone use, ESM can be used in routine clinical practice. Multiple examples of ESM data collections across different patient groups and settings are shown and discussed, varying from an ESM evaluation of a 6‐week randomized trial of mindfulness, to a twin study on emotion dynamics in daily life. Results Research shows that ESM‐based self‐monitoring and feedback can enhance resilience by strengthening the capacity to use natural rewards. Personalized trajectories of starting or stopping medication can be more easily initiated and predicted if sensitive feedback data are available in real time. In addition, personalized trajectories of symptoms, cognitive abilities, symptoms impacting on other symptoms, the capacity of the dynamic system of mental health to “bounce back” from disturbance, and patterns of environmental reactivity yield uniquely personal data to support shared decision making and prediction in clinical practice. Finally, ESM makes it possible to develop insight into previous implicit patterns of thought, experience, and behavior, particularly if rapid personalized feedback is available. Conclusions ESM enhances clinical practice and research. It is empowering, providing co‐ownership of the process of diagnosis, treatment evaluation, and routine outcome measurement. Blended care, based on a mix of face‐to‐face and ESM‐based outside‐the‐office treatment, may reduce costs and improve outcomes.

Patients with glioblastoma currently do not sufficiently benefit from recent breakthroughs in cancer treatment that use checkpoint inhibitors 1 , 2 . For treatments using checkpoint inhibitors to be successful, a high mutational load and responses to neoepitopes are thought to be essential 3 . There is limited intratumoural infiltration of immune cells 4 in glioblastoma and these tumours contain only 30–50 non-synonymous mutations 5 . Exploitation of the full repertoire of tumour antigens—that is, both unmutated antigens and neoepitopes—may offer more effective immunotherapies, especially for tumours with a low mutational load. Here, in the phase I trial GAPVAC-101 of the Glioma Actively Personalized Vaccine Consortium (GAPVAC), we integrated highly individualized vaccinations with both types of tumour antigens into standard care to optimally exploit the limited target space for patients with newly diagnosed glioblastoma. Fifteen patients with glioblastomas positive for human leukocyte antigen (HLA)-A*02:01 or HLA-A*24:02 were treated with a vaccine (APVAC1) derived from a premanufactured library of unmutated antigens followed by treatment with APVAC2, which preferentially targeted neoepitopes. Personalization was based on mutations and analyses of the transcriptomes and immunopeptidomes of the individual tumours. The GAPVAC approach was feasible and vaccines that had poly-ICLC (polyriboinosinic-polyribocytidylic acid-poly- l -lysine carboxymethylcellulose) and granulocyte–macrophage colony-stimulating factor as adjuvants displayed favourable safety and strong immunogenicity. Unmutated APVAC1 antigens elicited sustained responses of central memory CD8 + T cells. APVAC2 induced predominantly CD4 + T cell responses of T helper 1 type against predicted neoepitopes. In a phase I trial, highly individualized peptide vaccines against unmutated tumour antigens and neoepitopes elicited sustained responses in CD8 + and CD4 + T cells, respectively, in patients with newly diagnosed glioblastoma.

Single-cell technologies have contributed to unravelling tumour heterogeneity, now considered a hallmark of cancer and one of the main causes of tumour resistance to cancer therapies. Liquid biopsy (LB), defined as the detection and analysis of cells or cell products released by tumours into the blood, offers an appealing minimally invasive approach that allows the characterization and monitoring of tumour heterogeneity in individual patients. Here, we will review and discuss how circulating tumour cell (CTC) analysis at single-cell resolution provides unique insights into tumour heterogeneity that are not revealed by analysis of circulating tumour DNA (ctDNA) derived from LBs. The molecular analysis of CTCs provides complementary information to that of genomic aberrations determined using ctDNA to fully describe many different cellular components (for example, DNA, RNA, proteins and metabolites) that can influence tumour heterogeneity.

ObjectiveTo compare individually tailored, based on trimestrial biological parameter monitoring, to fixed-schedule rituximab reinfusion for remission maintenance of antineutrophil cytoplasm antibody (ANCA)-associated vasculitides (AAVs).MethodsPatients with newly diagnosed or relapsing granulomatosis with polyangiitis (GPA) or microscopic polyangiitis (MPA) in complete remission after induction therapy were included in an open-label, multicentre, randomised controlled trial. All tailored-arm patients received a 500 mg rituximab infusion at randomisation, with rituximab reinfusion only when CD19+B lymphocytes or ANCA had reappeared or ANCA titre rose markedly based on trimestrial testing until month 18. Controls received a fixed 500 mg rituximab infusion on days 0 and 14 postrandomisation, then 6, 12 and 18 months after the first infusion. The primary endpoint was the number of relapses (new or reappearing symptom(s) or worsening disease with Birmingham Vasculitis Activity Score (BVAS)>0) at month 28 evaluated by an independent Adjudication Committee blinded to treatment group.ResultsAmong the 162 patients (mean age: 60 years; 42% women) included, 117 (72.2%) had GPA and 45 (27.8%) had MPA. Preinclusion induction therapy included cyclophosphamide for 100 (61.7%), rituximab for 61 (37.6%) and methotrexate for 1 (0.6%). At month 28, 21 patients had suffered 22 relapses: 14/81 (17.3%) in 13 tailored-infusion recipients and 8/81 (9.9%) in 8 fixed-schedule patients (p=0.22). The tailored-infusion versus fixed-schedule group, respectively, received 248 vs 381 infusions, with medians (IQR) of 3 (2–4) vs 5 (5–5) administrations.ConclusionAAV relapse rates did not differ significantly between individually tailored and fixed-schedule rituximab regimens. Individually tailored-arm patients received fewer rituximab infusions.Trial registration numberNCT01731561; Results.

Pelvic organ prolapse is common and is strongly associated with childbirth and increasing age. Women with prolapse are often advised to do pelvic floor muscle exercises, but evidence supporting the benefits of such exercises is scarce. We aimed to establish the effectiveness of one-to-one individualised pelvic floor muscle training for reducing prolapse symptoms. We did a parallel-group, multicentre, randomised controlled trial at 23 centres in the UK, one in New Zealand, and one in Australia, between June 22, 2007, and April 9, 2010. Female outpatients with newly-diagnosed, symptomatic stage I, II, or III prolapse were randomly assigned (1:1), by remote computer allocation with minimsation, to receive an individualised programme of pelvic floor muscle training or a prolapse lifestyle advice leaflet and no muscle training (control group). Outcome assessors, and investigators who were gynaecologists at trial sites, were masked to group allocation; the statistician was masked until after data analysis. Our primary endpoint was participants' self-report of prolapse symptoms at 12 months. Analysis was by intention-to-treat analysis. This trial is registered, number ISRCTN35911035. 447 eligible patients were randomised to the intervention group (n=225) or the control group (n=222). 377 (84%) participants completed follow-up for questionnaires at 6 months and 295 (66%) for questionnaires at 12 months. Women in the intervention group reported fewer prolapse symptoms (ie, a significantly greater reduction in the pelvic organ prolapse symptom score [POP-SS]) at 12 months than those in the control group (mean reduction in POP-SS from baseline 3·77 [SD 5·62] vs 2·09 [5·39]; adjusted difference 1·52, 95% CI 0·46–2·59; p=0·0053). Findings were robust to missing data. Eight adverse events (six vaginal symptoms, one case of back pain, and one case of abdominal pain) and one unexpected serious adverse event, all in women from the intervention group, were regarded as unrelated to the intervention or to participation in the study. One-to-one pelvic floor muscle training for prolapse is effective for improvement of prolapse symptoms. Long-term benefits should be investigated, as should the effects in specific subgroups. Chief Scientist Office of the Scottish Government Health and Social Care Directorates, New Zealand Lottery Board, and National Health and Medical Research Council (Australia).

Background Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminatory accuracy (0.53–0.64). Machine learning (ML) offers an alternative approach to standard prediction modeling that may address current limitations and improve accuracy of those tools. The purpose of this study was to compare the discriminatory accuracy of ML-based estimates against a pair of established methods—the Breast Cancer Risk Assessment Tool (BCRAT) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models. Methods We quantified and compared the performance of eight different ML methods to the performance of BCRAT and BOADICEA using eight simulated datasets and two retrospective samples: a random population-based sample of U.S. breast cancer patients and their cancer-free female relatives ( N  = 1143), and a clinical sample of Swiss breast cancer patients and cancer-free women seeking genetic evaluation and/or testing ( N  = 2481). Results Predictive accuracy (AU-ROC curve) reached 88.28% using ML-Adaptive Boosting and 88.89% using ML-random forest versus 62.40% with BCRAT for the U.S. population-based sample. Predictive accuracy reached 90.17% using ML-adaptive boosting and 89.32% using ML-Markov chain Monte Carlo generalized linear mixed model versus 59.31% with BOADICEA for the Swiss clinic-based sample. Conclusions There was a striking improvement in the accuracy of classification of women with and without breast cancer achieved with ML algorithms compared to the state-of-the-art model-based approaches. High-accuracy prediction techniques are important in personalized medicine because they facilitate stratification of prevention strategies and individualized clinical management.