UNASSIGNED: Serious illness conversations (SICs) that elicit patients\' values, goals, and care preferences reduce anxiety and depression and improve quality of life, but occur infrequently for patients with cancer. Behavioral economic implementation strategies (nudges) directed at clinicians and/or patients may increase SIC completion.
UNASSIGNED: To test the independent and combined effects of clinician and patient nudges on SIC completion.
UNASSIGNED: A 2 × 2 factorial, cluster randomized trial was conducted from September 7, 2021, to March 11, 2022, at oncology clinics across 4 hospitals and 6 community sites within a large academic health system in Pennsylvania and New Jersey among 163 medical and gynecologic oncology clinicians and 4450 patients with cancer at high risk of mortality (≥10% risk of 180-day mortality).
UNASSIGNED: Clinician clusters and patients were independently randomized to receive usual care vs nudges, resulting in 4 arms: (1) active control, operating for 2 years prior to trial start, consisting of clinician text message reminders to complete SICs for patients at high mortality risk; (2) clinician nudge only, consisting of active control plus weekly peer comparisons of clinician-level SIC completion rates; (3) patient nudge only, consisting of active control plus a preclinic electronic communication designed to prime patients for SICs; and (4) combined clinician and patient nudges.
UNASSIGNED: The primary outcome was a documented SIC in the electronic health record within 6 months of a participant\'s first clinic visit after randomization. Analysis was performed on an intent-to-treat basis at the patient level.
UNASSIGNED: The study accrued 4450 patients (median age, 67 years [IQR, 59-75 years]; 2352 women [52.9%]) seen by 163 clinicians, randomized to active control (n = 1004), clinician nudge (n = 1179), patient nudge (n = 997), or combined nudges (n = 1270). Overall patient-level rates of 6-month SIC completion were 11.2% for the active control arm (112 of 1004), 11.5% for the clinician nudge arm (136 of 1179), 11.5% for the patient nudge arm (115 of 997), and 14.1% for the combined nudge arm (179 of 1270). Compared with active control, the combined nudges were associated with an increase in SIC rates (ratio of hazard ratios [rHR], 1.55 [95% CI, 1.00-2.40]; P = .049), whereas the clinician nudge (HR, 0.95 [95% CI, 0.64-1.41; P = .79) and patient nudge (HR, 0.99 [95% CI, 0.73-1.33]; P = .93) were not.
UNASSIGNED: In this cluster randomized trial, nudges combining clinician peer comparisons with patient priming questionnaires were associated with a marginal increase in documented SICs compared with an active control. Combining clinician- and patient-directed nudges may help to promote SICs in routine cancer care.
UNASSIGNED: ClinicalTrials.gov Identifier: NCT04867850.

UNASSIGNED: Atrial fibrillation (AF) is a disease of high heterogeneity, and the association between AF phenotypes and the outcome of different catheter ablation strategies remains unclear. Conventional classification of AF (e.g. according to duration, atrial size, and thromboembolism risk) fails to provide reference for the optimal stratification of the prognostic risks or to guide individualized treatment plan. In recent years, research on machine learning has found that cluster analysis, an unsupervised data-driven approach, can uncover the intrinsic structure of data and identify clusters of patients with pathophysiological similarity. It has been demonstrated that cluster analysis helps improve the characterization of AF phenotypes and provide valuable prognostic information. In our cohort of AF inpatients undergoing radiofrequency catheter ablation, we used unsupervised cluster analysis to identify patient subgroups, to compare them with previous studies, and to evaluate their association with different suitable ablation patterns and outcomes.
UNASSIGNED: The participants were AF patients undergoing radiofrequency catheter ablation at West China Hospital between October 2015 and December 2017. All participants were aged 18 years or older. They underwent radiofrequency catheter ablation during their hospitalization. They completed the follow-up process under explicit informed consent. Patients with AF of a reversible cause, severe mitral stenosis or prosthetic heart valve, congenital heart disease, new-onset acute coronary syndrome within three months prior to the surgery, or a life expectancy less than 12 months were excluded according to the exclusion criteria. The cohort consisted of 1102 participants with paroxysmal or persistent/long-standing persistent AF. Data on 59 variables representing demographics, AF type, comorbidities, therapeutic history, vital signs, electrocardiographic and echocardiographic findings, and laboratory findings were collected. Overall, data for the variables were rarely missing (<5%), and multiple imputation was used for correction of missing data. Follow-up surveys were conducted through outpatient clinic visits or by telephone. Patients were scheduled for follow-up with 12-lead resting electrocardiography and 24-hours Holter monitoring at 3 months and 6 months after the ablation procedure. Early ablation success was defined as the absence of documented AF, atrial flutter, or atrial tachycardia >30 seconds at 6-month follow-up. Hierarchical clustering was performed on the 59 baseline variables. All characteristic variables were standardized to have a mean of zero and a standard deviation of one. Initially, each patient was regarded as a separate cluster, and the distance between these clusters was calculated. Then, the Ward minimum variance method of clustering was used to merge the pair of clusters with the minimum total variance. This process continued until all patients formed one whole cluster. The \"NbClust\" package in R software, capable of calculating various statistical indices, including pseudo t2 index, cubic clustering criterion, silhouette index etc, was applied to determine the optimal number of clusters. The most frequently chosen number of clusters by these indices was selected. A heatmap was generated to illustrate the clinical features of clusters, while a tree diagram was used to depict the clustering process and the heterogeneity among clusters. Ablation strategies were compared within each cluster regarding ablation efficacy.
UNASSIGNED: Five statistically driven clusters were identified: 1) the younger age cluster (n=404), characterized by the lowest prevalence of cardiovascular and cerebrovascular comorbidities but the highest prevalence of obstructive sleep apnea syndrome (14.4%); 2) a cluster of elderly adults with chronic diseases (n=438), the largest cluster, showing relatively higher rates of hypertension, diabetes, stroke, and chronic obstructive pulmonary disease; 3) a cluster with high prevalence of sinus node dysfunction (n=160), with patients showing the highest prevalence of sick sinus syndrome and pacemaker implantation; 4) the heart failure cluster (n=80), with the highest prevalence of heart failure (58.8%) and persistent/long-standing persistent AF (73.7%); 5) prior coronary artery revascularization cluster (n=20), with patients of the most advanced age (median: 69.0 years old) and predominantly male patients, all of whom had prior myocardial infarction and coronary artery revascularization. Patients in cluster 2 achieved higher early ablation success with pulmonary veins isolation alone compared to extensive ablation strategies (79.6% vs. 66.5%; odds ratio [OR]=1.97, 95% confidence interval [CI]: 1.28-3.03). Although extensive ablation strategies had a slightly higher success rate in the heart failure group, the difference was not statistically significant.
UNASSIGNED: This study provided a unique classification of AF patients undergoing catheter ablation by cluster analysis. Age, chronic disease, sinus node dysfunction, heart failure and history of coronary artery revascularization contributed to the formation of the five clinically relevant subtypes. These subtypes showed differences in ablation success rates, highlighting the potential of cluster analysis in guiding individualized risk stratification and treatment decisions for AF patients.

OBJECTIVE: We aim to integrate the parameters of two-dimensional (2D) echocardiography and identify the high-risk population for all-cause mortality in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI).
METHODS: The study involved a retrospective cohort population with STEMI who were admitted to Yongchuan Hospital of Chongqing Medical University between January 2016 and January 2019. Baseline data were collected, including 2D echocardiography parameters and left ventricular ejection fraction (LVEF). The parameters of 2D echocardiography were subjected to cluster analysis. Logistic regression models were employed to assess univariate and multivariate adjusted odds ratios (ORs) of cluster information in relation to all-cause mortality. Four logistic regression models were generated, utilizing cluster information, clinical variables, clinical variables in conjunction with LVEF, and clinical variables in conjunction with LVEF and cluster information as predictive variables, respectively. The area under the curve (AUC) were utilized to evaluate the incremental risk stratification value of cluster information.
RESULTS: The study included 633 participants with 28.8% female, a mean age of 65.68 ± 11.98 years. Over the course of a 3-year follow-up period, 108 (17.1%) patients experienced all-cause mortality. Utilizing cluster analysis of 2D echocardiography parameters, the patients were categorized into two distinct clusters, with statistically significant differences observed in most clinical variables, echocardiography, and survival outcomes between the clusters. Multivariate regression analysis revealed that cluster information was independently associated with the risk of all-cause mortality with adjusted OR 7.33 (95% confidence interval [CI] 3.99-14.06, P < 0.001). The inclusion of LVEF enhanced the predictive capacity of the model utilized with clinical variables with AUC 0.848 (95% CI 0.809-0.888) versus AUC 0.872 (95% CI 0.836-0.908) (P < 0.001), and the addition of cluster information further improved its predictive performance with AUC 0.906 (95% CI 0.878-0.934, P < 0.001). This cluster analysis was translated into a free available online calculator (https://app-for-mortality-prediction-cluster.streamlit.app/).
CONCLUSIONS: The 2D echocardiographic diagnostic information based on cluster analysis had good prognostic value for STEMI population, which was helpful for risk stratification and individualized intervention.

In a phase 3 trial (PANAMO, NCT04333420), vilobelimab, a complement 5a (C5a) inhibitor, reduced 28-day mortality in mechanically ventilated COVID-19 patients. This post hoc analysis of 368 patients aimed to explore treatment heterogeneity through unsupervised learning. All available clinical variables at baseline were used as input. Treatment heterogeneity was assessed using latent class analysis (LCA), Ward\'s hierarchical clustering (HC) and the adjudication to previously described clinical sepsis phenotypes. The primary outcome was 28-day mortality. For LCA, a 2-class latent model was deemed most suitable. In the LCA model, 82 (22%) patients were assigned to class 1 and 286 (78%) to class 2. Class 1 was defined by more severely ill patients with significantly higher mortality. In an adjusted logistic regression, no heterogeneity of treatment effect (HTE) between classes was observed (p = 0.998). For HC, no significant classes were found (p = 0.669). Using the previously described clinical sepsis subtypes, 41 patients (11%) were adjudicated subtype alpha (α), 17 (5%) beta (β), 112 (30%) delta (δ) and 198 (54%) gamma (γ). HTE was observed between clinical subtypes (p = 0.001) with improved 28-day mortality after treatment with vilobelimab for the δ subtype (OR = 0.17, 95% CI 0.07-0.40, p < 0.001). No signal for harm of treatment with vilobelimab was observed in any class or clinical subtype. Overall, treatment effect with vilobelimab was consistent across different classes and subtypes, except for the δ subtype, suggesting potential additional benefit for the most severely ill patients.

We aimed to identify the clinical subtypes in individuals starting long-term care in Japan and examined their association with prognoses. Using linked medical insurance claims data and survey data for care-need certification in a large city, we identified participants who started long-term care. Grouping them based on 22 diseases recorded in the past 6 months using fuzzy c-means clustering, we examined the longitudinal association between clusters and death or care-need level deterioration within 2 years. We analyzed 4,648 participants (median age 83 [interquartile range 78-88] years, female 60.4%) between October 2014 and March 2019 and categorized them into (i) musculoskeletal and sensory, (ii) cardiac, (iii) neurological, (iv) respiratory and cancer, (v) insulin-dependent diabetes, and (vi) unspecified subtypes. The results of clustering were replicated in another city. Compared with the musculoskeletal and sensory subtype, the adjusted hazard ratio (95% confidence interval) for death was 1.22 (1.05-1.42), 1.81 (1.54-2.13), and 1.21 (1.00-1.46) for the cardiac, respiratory and cancer, and insulin-dependent diabetes subtypes, respectively. The care-need levels more likely worsened in the cardiac, respiratory and cancer, and unspecified subtypes than in the musculoskeletal and sensory subtype. In conclusion, distinct clinical subtypes exist among individuals initiating long-term care.

OBJECTIVE: Identifying whether experienced symptom burden in individuals with medical predisposition indicates somatic symptom disorder (SSD) is challenging, given the high overlap in the phenomenology of symptoms within this group. This study aimed to enhance understanding SSD in individuals at risk for heart failure.
METHODS: Cross-sectional data from the Hamburg City Health Study was analyzed including randomly selected individuals from the general population of Hamburg, Germany recruited from February 2016 to November 2018. SSD symptoms assessed with the Somatic Symptom Scale-8 and the Somatic Symptom Disorder-12 scale were categorized by applying cluster analysis including 412 individuals having at least 5% risk for heart failure-related hospitalization within the next ten years. Clusters were compared for biomedical and psychological factors using ANOVA and chi-square tests. Linear regressions, adjusting for sociodemographic, biomedical, and psychological factors, explored associations between clusters with general practitioner visits and quality of life.
RESULTS: Three clusters emerged: none (n = 215; 43% female), moderate (n = 151; 48% female), and severe (n = 46; 54% female) SSD symptom burden. The SSS-8 mean sum scores were 3.4 (SD = 2.7) for no, 6.4 (SD = 3.4) for moderate, and 12.4 (SD = 3.7) for severe SSD symptom burden. The SSD-12 mean sum scores were 3.1 (SD = 2.6) for no, 12.2 (SD = 4.2) for moderate, and 23.5 (SD = 6.7) for severe SSD symptom burden. Higher SSD symptom burden correlated with biomedical factors (having diabetes: p = .005 and dyspnea: p ≤ .001) and increased psychological burden (depression severity: p ≤ .001; anxiety severity: p ≤ .001), irrespective of heart failure risk (p = .202). Increased SSD symptoms were associated with more general practitioner visits (β = 0.172; p = .002) and decreased physical quality of life (β = -0.417; p ≤ .001).
CONCLUSIONS: Biomedical factors appear relevant in characterizing individuals at risk for heart failure, while psychological factors affect SSD symptom experience. Understanding SSD symptom diversity and addressing subgroup needs could prove beneficial.

UNASSIGNED: Stronger social and emotional well-being during primary school is positively associated with the health and educational outcomes of young people. However, there is little evidence on which programmes are the most effective for improving social and emotional well-being.
UNASSIGNED: The objective was to rigorously evaluate the Social and Emotional Education and Development (SEED) intervention process for improving pupils\' social and emotional well-being.
UNASSIGNED: This was a stratified cluster randomised controlled trial with embedded process and economic evaluations. Thirty-eight primary schools were randomly assigned to the SEED intervention or to the control group. Hierarchical regression analysis allowing for clustering at school learning community level was conducted in R (statistical package).
UNASSIGNED: The SEED intervention is a whole-school intervention; it involved all school staff and two cohorts of pupils, one starting at 4 or 5 years of age and the second starting at 8 or 9 years of age, across all 38 schools.
UNASSIGNED: A total of 2639 pupils in Scotland.
UNASSIGNED: The SEED intervention used an iterative process that involved three components to facilitate selection and implementation of school-based actions: (1) questionnaire completion, (2) benchmarked feedback to all staff and (3) reflective discussions (all staff and an educational psychologist).
UNASSIGNED: The primary outcome was pupils\' Strengths and Difficulties Questionnaire-Total Difficulties Score when pupils were 4 years older than at baseline.
UNASSIGNED: The primary outcome, pupils\' Strengths and Difficulties Questionnaire-Total Difficulties Score at follow-up 3, showed improvements for intervention arm pupils, compared with those in the control arm [relative risk -1.30 (95% confidence interval -1.87 to -0.73), standardised effect size -0.27 (95% confidence interval -0.39 to -0.15)]. There was no evidence of intervention effects according to deprivation: the results were significant for both affluent and deprived pupils. Subgroup analysis showed that all effect sizes were larger for the older cohort, particularly boys [relative risk -2.36 (95% confidence interval -3.62 to -1.11), standardised effect size -0.42 (95% confidence interval -0.64 to -0.20)]. Although there was no statistically significant difference in incremental cost and quality-adjusted life-years, the probability that the intervention is cost-effective at a willingness-to-pay threshold of £20,000 per quality-adjusted life-year was high, at 88%. Particularly valued mechanisms of the SEED intervention were its provision of time to reflect on and discuss social and emotional well-being and its contribution to a culture of evaluating practice.
UNASSIGNED: It was a challenge to retain schools over five waves of data collection.
UNASSIGNED: This trial demonstrated that the SEED intervention is an acceptable, cost-effective way to modestly improve pupil well-being and improve school climate, particularly for older boys and those with greater levels of psychological difficulties. It was beneficial during the transition from primary to secondary school, but this diminished after 6 years. The SEED intervention can be implemented alongside existing systems for addressing pupil well-being and can be complementary to other interventions.
UNASSIGNED: Assess whether or not the SEED intervention has a beneficial impact on academic attainment, is transferable to other countries and other organisational settings, would be strengthened by adding core training elements to the intervention process and is transferable to secondary schools. Understand the gender differences illustrated by the outcomes of this trial. Conduct further statistical research on how to handle missing data in longitudinal studies of complex social interventions.
UNASSIGNED: This trial is registered as ISRCTN51707384.
UNASSIGNED: This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 10/3006/13) and is published in full in Public Health Research; Vol. 12, No. 6. See the NIHR Funding and Awards website for further award information.
We studied the Social and Emotional Education and Development (SEED) primary school intervention to see if it could improve the social and emotional well-being of pupils in Scotland. The SEED intervention is a process with several elements. We collected information from school pupils, staff and parents, and assessed if the schools involved were happy, safe and caring environments. We sought to highlight any strengths or weaknesses in how each school approaches social and emotional well-being. The SEED intervention also measures the social and emotional well-being of pupils. This includes pupils’ strengths and difficulties, confidence, understanding of emotions and quality of relationships. We gave the information back to each school to help them decide what they can do to improve the social and emotional well-being of their pupils. We gave schools a guide to available resources, reviewed according to how well they are known to work elsewhere. The same social and emotional well-being measurements were repeated every 1 or 2 years, to see if any improvements had been made, and to guide any further adaptions of activities. The study ran in 38 schools over 7 years; half of the schools were randomly selected to receive the SEED intervention and half carried on as normal. Two age groups of pupils were recruited; the younger group was aged 4 or 5 years and the older group was aged 8 or 9 years at the start of the study. We found that the SEED intervention did slightly improve social and emotional well-being. Improvements were greater for older pupils, in particular for boys, and lasted beyond their transition from primary to secondary school. We also found that it was cost-effective for schools to run the SEED intervention. Schools valued the structure and shared ownership associated with the process. We concluded that the SEED intervention is an acceptable way to modestly improve pupil well-being and school ethos.

UNASSIGNED: Understanding chronic disease prevalence, patterns, and co-occurrence is pivotal for effective health care planning and disease prevention strategies. In this paper, we aimed to identify the clustering of major non-communicable diseases among Indian adults aged ≥50 years based on their self-reported diagnosed non-communicable disease status and to find the risk factors that heighten the risk of developing the identified disease clusters.
UNASSIGNED: We utilised data from the nationally representative survey Study on Global AGEing and Adult Health (SAGE Wave-2). The eligible sample size was 6298 adults aged ≥50 years. We conducted the latent class analysis to uncover latent subgroups of multimorbidity and the multinomial logistic regression to identify the factors linked to observed latent class membership.
UNASSIGNED: The latent class analysis grouped our sample of men and women >49 years old into three groups - mild multimorbidity risk (41%), moderate multimorbidity risk (30%), and severe multimorbidity risk (29%). In the mild multimorbidity risk group, the most prevalent diseases were asthma and arthritis, and the major prevalent disease in the moderate multimorbidity risk group was low near/distance vision, followed by depression, asthma, and lung disease. Angina, diabetes, hypertension, and stroke were the major diseases in the severe multimorbidity risk category. Individuals with higher ages had an 18% and 15% higher risk of having moderate multimorbidity and severe multimorbidity compared to those in the mild multimorbidity category. Females were more likely to have a moderate risk (3.36 times) and 2.82 times more likely to have severe multimorbidity risk.
UNASSIGNED: The clustering of diseases highlights the importance of integrated disease management in primary care settings and improving the health care system to accommodate the individual\'s needs. Implementing preventive measures and tailored interventions, strengthening the health and wellness centres, and delivering comprehensive primary health care services for secondary and tertiary level hospitalisation may cater to the needs of multimorbid patients.

BACKGROUND: Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient\'s tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis.
RESULTS: Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees.
METHODS: https://github.com/cbg-ethz/oncotree2vec.

Remarkable progress has been made in infection prevention and control (IPC) in many countries, but some gaps emerged in the context of the coronavirus disease 2019 (COVID-19) pandemic. Core capabilities such as standard clinical precautions and tracing the source of infection were the focus of IPC in medical institutions during the pandemic. Therefore, the core competences of IPC professionals during the pandemic, and how these contributed to successful prevention and control of the epidemic, should be studied. To investigate, using a systematic review and cluster analysis, fundamental improvements in the competences of infection control and prevention professionals that may be emphasized in light of the COVID-19 pandemic. We searched the PubMed, Embase, Cochrane Library, Web of Science, CNKI, WanFang Data, and CBM databases for original articles exploring core competencies of IPC professionals during the COVID-19 pandemic (from January 1, 2020 to February 7, 2023). Weiciyun software was used for data extraction and the Donohue formula was followed to distinguish high-frequency technical terms. Cluster analysis was performed using the within-group linkage method and squared Euclidean distance as the metric to determine the priority competencies for development. We identified 46 studies with 29 high-frequency technical terms. The most common term was \"infection prevention and control training\" (184 times, 17.3%), followed by \"hand hygiene\" (172 times, 16.2%). \"Infection prevention and control in clinical practice\" was the most-reported core competency (367 times, 34.5%), followed by \"microbiology and surveillance\" (292 times, 27.5%). Cluster analysis showed two key areas of competence: Category 1 (program management and leadership, patient safety and occupational health, education and microbiology and surveillance) and Category 2 (IPC in clinical practice). During the COVID-19 pandemic, IPC program management and leadership, microbiology and surveillance, education, patient safety, and occupational health were the most important focus of development and should be given due consideration by IPC professionals.