Autonomous and non-cell autonomous role of cilia in structural birth defects in mice.

Ciliopathies are associated with wide spectrum of structural birth defects (SBDs), indicating important roles for cilia in development. Here, we provide novel insights into the temporospatial requirement for cilia in SBDs arising from deficiency in Ift140, an intraflagellar transport (IFT) protein regulating ciliogenesis. Ift140-deficient mice exhibit cilia defects accompanied by wide spectrum of SBDs including macrostomia (craniofacial defects), exencephaly, body wall defects, tracheoesophageal fistula (TEF), randomized heart looping, congenital heart defects (CHDs), lung hypoplasia, renal anomalies, and polydactyly. Tamoxifen inducible CAGGCre-ER deletion of a floxed Ift140 allele between E5.5 to 9.5 revealed early requirement for Ift140 in left-right heart looping regulation, mid to late requirement for cardiac outflow septation and alignment, and late requirement for craniofacial development and body wall closure. Surprisingly, CHD were not observed with 4 Cre drivers targeting different lineages essential for heart development, but craniofacial defects and omphalocele were observed with Wnt1-Cre targeting neural crest and Tbx18-Cre targeting epicardial lineage and rostral sclerotome through which trunk neural crest cells migrate. These findings revealed cell autonomous role of cilia in cranial/trunk neural crest-mediated craniofacial and body wall closure defects, while non-cell autonomous multi-lineage interactions underlie CHD pathogenesis, revealing unexpected developmental complexity for CHD associated with ciliopathies.

Discovery of a Photoaffinity Probe that Captures the Active Conformation of the Cannabinoid CB2 Receptor.

The cannabinoid receptor type 2 (CB2R) is a G protein-coupled receptor with therapeutic potential for the treatment of inflammatory disorders. Fluorescent probes are desirable to study its receptor localization, expression and occupancy. Previously, we have reported a photoaffinity probe LEI-121 that stabilized the inactive conformation of the CB2R. Here, we report the structure-based design of a novel bifunctional probe that captures the active conformation of the CB2R upon irradiation with light. An alkyne handle was incorporated to visualize the receptor using click-chemistry with fluorophore-azides. These probes may hold promise to study different receptor conformations in relation to their cellular localization and function.

Anal Cancers in Previously Screened Versus Unscreened Patients: Tumor Stage and Treatment Outcomes.

BACKGROUND: Targeted screening programs for patients at high risk for anal squamous-cell carcinoma have been proposed; however, the evidence in support of screening remains unclear. OBJECTIVE: This study aimed to determine whether screening high-risk patients (predominantly those living with HIV) detected squamous-cell carcinoma at an earlier stage compared to the routine practice of not screening. DESIGN: This is a cohort study. SETTINGS: This study was conducted at a quaternary care center in Canada. PATIENTS: Included patients were at least 18 years old with a pathologic diagnosis of invasive anal squamous-cell carcinoma between 2002 and 2022. INTERVENTIONS: Patients diagnosed through a high-risk screening program were compared to those who did not undergo screening. MAIN OUTCOME MEASURES: The primary outcome was clinical stage at presentation, categorized as T1N0M0 vs other. Secondary outcomes included treatments received, treatment failure, and overall survival. RESULTS: A total of 612 patients with anal squamous-cell carcinoma were included, with 26 of those patients diagnosed through a screening program. Patients with screen-detected cancers had greater odds of presenting with T1N0M0 tumors compared to unscreened patients (18 [69.2%] vs 84 [14.3%]; adjusted OR 9.95; 95% CI, 3.95-25.08). A propensity score-matched sensitivity analysis found similar results (OR 11.13; 95% CI, 4.67-26.52). Screened patients had greater odds of treatment with wide local excision alone, as opposed to any combination of chemotherapy, radiation therapy, and surgery (3 [12.5%] vs 18 [3.2%]; OR 4.38; 95% CI, 1.20-16.04). There were no statistically significant differences in treatment failure or overall survival between groups. LIMITATIONS: The small number of screened patients limits the power of the analysis. CONCLUSIONS: Screening for anal squamous-cell carcinoma among high-risk populations detects cancers at an earlier stage. Patients with screen-detected cancers also had a greater likelihood of being candidates for wide local excision alone, which may have spared them the morbidity associated with chemoradiotherapy or abdominoperineal resection. See Video Abstract. CNCERES DE ANO EN PACIENTES PREVIAMENTE DETECTADOS POR CRIBADO VERSUS NO DETECTADOS ESTADIO DEL TUMOR Y RESULTADOS DEL TRATAMIENTO: ANTECEDENTES:Se han propuesto programas de cribado dirigidos a pacientes con alto riesgo de carcinoma anal de células escamosas; sin embargo, la evidencia a favor de la detección sigue sin estar clara.OBJETIVO:Este estudio tuvo como objetivo determinar si el cribado de pacientes de alto riesgo (predominantemente aquellos que viven con el VIH) detectó el carcinoma de células escamosas en una etapa más temprana en comparación con la práctica habitual de no cribado.DISEÑO:Este es un estudio de cohortes.CONFIGURACIÓN:Este estudio se realizó en un centro de atención cuaternaria en Canadá.PACIENTES:Los pacientes incluidos tenían al menos 18 años con un diagnóstico patológico de carcinoma de células escamosas anal invasivo entre 2002 y 2022.INTERVENCIONES:Los pacientes diagnosticados mediante un programa de cribado de alto riesgo se compararon con aquellos que no se sometieron a cribado.PRINCIPALES MEDIDAS DE RESULTADO:El resultado primario fue el estadio clínico en la presentación, categorizado como T1N0M0 versus otro. Los resultados secundarios incluyeron los tratamientos recibidos, el fracaso del tratamiento y la supervivencia general.RESULTADOS:Se incluyeron un total de 612 pacientes con carcinoma anal de células escamosas, con 26 de esos pacientes diagnosticados a través de un programa de cribado. Los pacientes con cánceres detectados mediante cribado tenían mayores probabilidades de presentar tumores T1N0M0 en comparación con los pacientes no cribados (18 [69.2%] frente a 84 [14.3%]; razón de probabilidad ajustada 9.95; intervalo de confianza del 95 % 3.95 -25.08). Un análisis de sensibilidad emparejado por puntaje de propensión encontró resultados similares (odds ratio 11.13; intervalo de confianza del 95% 4.67 -26.52; p < 0.001). Los pacientes examinados tenían mayores probabilidades de recibir tratamiento con escisión local amplia sola, en comparación con cualquier combinación de quimioterapia, radiación y cirugía (3 [12.5%] frente a 18 [3.2%]; razón de probabilidad 4.38; intervalo de confianza del 95 % 1.20 -16.04). No hubo diferencias estadísticamente significativas en el fracaso del tratamiento o la supervivencia global entre los grupos.LIMITACIONES:El pequeño número de pacientes evaluados limita el poder del análisis.CONCLUSIONES:La detección del carcinoma anal de células escamosas entre las poblaciones de alto riesgo detecta los cánceres en una etapa más temprana. Los pacientes con cánceres detectados mediante cribado también tenían una mayor probabilidad de ser candidatos para una escisión local amplia sola, lo que puede haberles evitado la morbilidad asociada con la quimiorradioterapia o la resección abdominoperineal. (Traducción --Dr. Aurian Garcia Gonzalez ).

Factors Associated With Loss to Follow-Up in Surgical Trials: A Systematic Review and Meta-Analysis.

INTRODUCTION: Loss to follow-up (LTFU) distorts results of randomized controlled trials (RCTs). Understanding trial characteristics that contribute to LTFU may enable investigators to anticipate the extent of LTFU and plan retention strategies. The objective of this systematic review and meta-analysis was to investigate the extent of LTFU in surgical RCTs and evaluate associations between trial characteristics and LTFU. METHODS: MEDLINE, Embase, and PubMed Central were searched for surgical RCTs published between January 2002 and December 2021 in the 30 highest impact factor surgical journals. Two-hundred eligible RCTs were randomly selected. The pooled LTFU rate was estimated using random intercept Poisson regression. Associations between trial characteristics and LTFU were assessed using metaregression. RESULTS: The 200 RCTs included 37,914 participants and 1307 LTFU events. The pooled LTFU rate was 3.10 participants per 100 patient-years (95% confidence interval [CI] 1.85-5.17). Trial characteristics associated with reduced LTFU were standard-of-care outcome assessments (rate ratio [RR] 0.17; 95% CI 0.06-0.48), surgery for transplantation (RR 0.08; 95% CI 0.01-0.43), and surgery for cancer (RR 0.10; 95% CI 0.02-0.53). Increased LTFU was associated with patient-reported outcomes (RR 14.21; 95% CI 4.82-41.91) and follow-up duration ≥ three months (odds ratio 10.09; 95% CI 4.79-21.28). CONCLUSIONS: LTFU in surgical RCTs is uncommon. Participants may be at increased risk of LTFU in trials with outcomes assessed beyond the standard of care, surgical indications other than cancer or transplant, patient-reported outcomes, and longer follow-up. Investigators should consider the impact of design on LTFU and plan retention strategies accordingly.

Hospital volume-outcome relationships for robot-assisted surgeries: a population-based analysis.

BACKGROUND: Associations between procedure volumes and outcomes can inform minimum volume standards and the regionalization of health services. Robot-assisted surgery continues to expand globally; however, data are limited regarding which hospitals should be using the technology. STUDY DESIGN: Using administrative health data for all residents of Ontario, Canada, this retrospective cohort study included adult patients who underwent a robot-assisted radical prostatectomy (RARP), total robotic hysterectomy (TRH), robot-assisted partial nephrectomy (RAPN), or robotic portal lobectomy using 4 arms (RPL-4) between January 2010 and September 2021. Associations between yearly hospital volumes and 90-day major complications were evaluated using multivariable logistic regression models adjusted for patient characteristics and clustering at the level of the hospital. RESULTS: A total of 10,879 patients were included, with 7567, 1776, 724, and 812 undergoing a RARP, TRH, RAPN, and RPL-4, respectively. Yearly hospital volume was not associated with 90-day complications for any procedure. Doubling of yearly volume was associated with a 17-min decrease in operative time for RARP (95% confidence interval [CI] - 23 to - 10), 8-min decrease for RAPN (95% CI - 14 to - 2), 24-min decrease for RPL-4 (95% CI - 29 to - 19), and no significant change for TRH (- 7 min; 95% CI - 17 to 3). CONCLUSION: The risk of 90-day major complications does not appear to be higher in low volume hospitals; however, they may not be as efficient with operating room utilization. Careful case selection may have contributed to the lack of an observed association between volumes and complications.

Hospital learning curves for robot-assisted surgeries: a population-based analysis.

BACKGROUND: Robot-assisted surgery has been rapidly adopted. It is important to define the learning curve to inform credentialling requirements, training programs, identify fast and slow learners, and protect patients. This study aimed to characterize the hospital learning curve for common robot-assisted procedures. STUDY DESIGN: This cohort study, using administrative health data for Ontario, Canada, included adult patients who underwent a robot-assisted radical prostatectomy (RARP), total robotic hysterectomy (TRH), robot-assisted partial nephrectomy (RAPN), or robotic portal lobectomy using four arms (RPL-4) between 2010 and 2021. The association between cumulative hospital volume of a robot-assisted procedure and major complications was evaluated using multivariable logistic models adjusted for patient characteristics and clustering at the hospital level. RESULTS: A total of 6814 patients were included, with 5230, 543, 465, and 576 patients in the RARP, TRH, RAPN, and RPL-4 cohorts, respectively. There was no association between cumulative hospital volume and major complications. Visual inspection of learning curves demonstrated a transient worsening of outcomes followed by subsequent improvements with experience. Operative time decreased for all procedures with increasing volume and reached plateaus after approximately 300 RARPs, 75 TRHs, and 150 RPL-4s. The odds of a prolonged length of stay decreased with increasing volume for patients undergoing a RARP (OR 0.87; 95% CI 0.82-0.92) or RPL-4 (OR 0.77; 95% CI 0.68-0.87). CONCLUSION: Hospitals may adopt robot-assisted surgery without significantly increasing the risk of major complications for patients early in the learning curve and with an expectation of increasing efficiency.

GEOexplorer: a webserver for gene expression analysis and visualisation.

Gene Expression Omnibus (GEO) is a database repository hosting a substantial proportion of publicly available high throughput gene expression data. Gene expression analysis is a powerful tool to gain insight into the mechanisms and processes underlying the biological and phenotypic differences between sample groups. Despite the wide availability of gene expression datasets, their access, analysis, and integration are not trivial and require specific expertise and programming proficiency. We developed the GEOexplorer webserver to allow scientists to access, integrate and analyse gene expression datasets without requiring programming proficiency. Via its user-friendly graphic interface, users can easily apply GEOexplorer to perform interactive and reproducible gene expression analysis of microarray and RNA-seq datasets, while producing a wealth of interactive visualisations to facilitate data exploration and interpretation, and generating a range of publication ready figures. The webserver allows users to search and retrieve datasets from GEO as well as to upload user-generated data and combine and harmonise two datasets to perform joint analyses. GEOexplorer, available at https://geoexplorer.rosalind.kcl.ac.uk, provides a solution for performing interactive and reproducible analyses of microarray and RNA-seq gene expression data, empowering life scientists to perform exploratory data analysis and differential gene expression analysis on-the-fly without informatics proficiency.

Early evaluation of a natural language processing tool to improve access to educational resources for surgical patients.

PURPOSE: Accessible patient information sources are vital in educating patients about the benefits and risks of spinal surgery, which is crucial for obtaining informed consent. We aim to assess the effectiveness of a natural language processing (NLP) pipeline in recognizing surgical procedures from clinic letters and linking this with educational resources. METHODS: Retrospective examination of letters from patients seeking surgery for degenerative spinal disease at a single neurosurgical center. We utilized MedCAT, a named entity recognition and linking NLP, integrated into the electronic health record (EHR), which extracts concepts and links them to systematized nomenclature of medicine-clinical terms (SNOMED-CT). Investigators reviewed clinic letters, identifying words or phrases that described or identified operations and recording the SNOMED-CT terms as ground truth. This was compared to SNOMED-CT terms identified by the model, untrained on our dataset. A pipeline linking clinic letters to patient-specific educational resources was established, and precision, recall, and F1 scores were calculated. RESULTS: Across 199 letters the model identified 582 surgical procedures, and the overall pipeline after adding rules a total of 784 procedures (precision = 0.94, recall = 0.86, F1 = 0.91). Across 187 letters with identified SNOMED-CT terms the integrated pipeline linking education resources directly to the EHR was successful in 157 (78%) patients (precision = 0.99, recall = 0.87, F1 = 0.92). CONCLUSIONS: NLP accurately identifies surgical procedures in pre-operative clinic letters within an untrained subspecialty. Performance varies among letter authors and depends on the language used by clinicians. The identified procedures can be linked to patient education resources, potentially improving patients' understanding of surgical procedures.

RADAR-IoT: An Open-Source, Interoperable, and Extensible IoT Gateway Framework for Health Research.

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user's health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.

Chemical Proteomics Reveals Antibiotic Targets of Oxadiazolones in MRSA.

Phenotypic screening is a powerful approach to identify novel antibiotics, but elucidation of the targets responsible for the antimicrobial activity is often challenging in the case of compounds with a polypharmacological mode of action. Here, we show that activity-based protein profiling maps the target interaction landscape of a series of 1,3,4-oxadiazole-3-ones identified in a phenotypic screen to have high antibacterial potency against multidrug-resistant Staphylococcus aureus. In situ competitive and comparative chemical proteomics with a tailor-made activity-based probe, in combination with transposon and resistance studies, revealed several cysteine and serine hydrolases as relevant targets. Our data showcase oxadiazolones as a novel antibacterial chemotype with a polypharmacological mode of action, in which FabH, FphC, and AdhE play a central role.

Discharge summary hospital course summarisation of in patient Electronic Health Record text with clinical concept guided deep pre-trained Transformer models.

Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the overall care of a patient. Methods to automatically produce summaries from inpatient documentation would be invaluable in reducing clinician manual burden of summarising documents under high time-pressure to admit and discharge patients. Automatically producing these summaries from the inpatient course, is a complex, multi-document summarisation task, as source notes are written from various perspectives (e.g. nursing, doctor, radiology), during the course of the hospitalisation. We demonstrate a range of methods for BHC summarisation demonstrating the performance of deep learning summarisation models across extractive and abstractive summarisation scenarios. We also test a novel ensemble extractive and abstractive summarisation model that incorporates a medical concept ontology (SNOMED) as a clinical guidance signal and shows superior performance in 2 real-world clinical data sets.

DGLinker: flexible knowledge-graph prediction of disease-gene associations.

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.

Charting a Course for Smartphones and Wearables to Transform Population Health Research.

The use of data from smartphones and wearable devices has huge potential for population health research, given the high level of device ownership; the range of novel health-relevant data types available from consumer devices; and the frequency and duration with which data are, or could be, collected. Yet, the uptake and success of large-scale mobile health research in the last decade have not met this intensely promoted opportunity. We make the argument that digital person-generated health data are required and necessary to answer many top priority research questions, using illustrative examples taken from the James Lind Alliance Priority Setting Partnerships. We then summarize the findings from 2 UK initiatives that considered the challenges and possible solutions for what needs to be done and how such solutions can be implemented to realize the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas that must be addressed to advance the field include digital inequality and possible selection bias; easy access for researchers to the appropriate data collection tools, including how best to harmonize data items; analysis methodologies for time series data; patient and public involvement and engagement methods for optimizing recruitment, retention, and public trust; and methods for providing research participants with greater control over their data. There is also a major opportunity, provided through the linkage of digital person-generated health data to routinely collected data, to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognize that well-conducted studies need a wide range of diverse challenges to be skillfully addressed in unison (eg, challenges regarding epidemiology, data science and biostatistics, psychometrics, behavioral and social science, software engineering, user interface design, information governance, data management, and patient and public involvement and engagement). Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow for excellence throughout the life cycle of a research study. This will require a partnership of diverse people, methods, and technologies. If done right, the synergy of such a partnership has the potential to transform many millions of people's lives for the better.

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis.

BACKGROUND: Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. OBJECTIVE: We aimed to address these 3 challenges to inform future work in stratified analyses. METHODS: Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. RESULTS: We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. CONCLUSIONS: This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses.

Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis.

The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes.

Assessing the feasibility of a web-based outcome measurement system in child and adolescent mental health services - myHealthE a randomised controlled feasibility pilot study.

BACKGROUND: Interest in internet-based patient reported outcome measure (PROM) collection is increasing. The NHS myHealthE (MHE) web-based monitoring system was developed to address the limitations of paper-based PROM completion. MHE provides a simple and secure way for families accessing Child and Adolescent Mental Health Services to report clinical information and track their child's progress. This study aimed to assess whether MHE improves the completion of the Strengths and Difficulties Questionnaire (SDQ) compared with paper collection. Secondary objectives were to explore caregiver satisfaction and application acceptability. METHODS: A 12-week single-blinded randomised controlled feasibility pilot trial of MHE was conducted with 196 families accessing neurodevelopmental services in south London to examine whether electronic questionnaires are completed more readily than paper-based questionnaires over a 3-month period. Follow up process evaluation phone calls with a subset (n = 8) of caregivers explored system satisfaction and usability. RESULTS: MHE group assignment was significantly associated with an increased probability of completing an SDQ-P in the study period (adjusted hazard ratio (HR) 12.1, 95% CI 4.7-31.0; p = <.001). Of those caregivers' who received the MHE invitation (n = 68) 69.1% completed an SDQ using the platform compared to 8.8% in the control group (n = 68). The system was well received by caregivers, who cited numerous benefits of using MHE, for example, real-time feedback and ease of completion. CONCLUSIONS: MHE holds promise for improving PROM completion rates. Research is needed to refine MHE, evaluate large-scale MHE implementation, cost effectiveness and explore factors associated with differences in electronic questionnaire uptake.

Predictors of Sentinel Lymph Node Metastasis in Patients with Thin Melanoma: An International Multi-institutional Collaboration.

BACKGROUND: Consideration of sentinel lymph node biopsy (SLNB) is recommended for patients with T1b melanomas and T1a melanomas with high-risk features; however, the proportion of patients with actionable results is low. We aimed to identify factors predicting SLNB positivity in T1 melanomas by examining a multi-institutional international population. METHODS: Data were extracted on patients with T1 cutaneous melanoma who underwent SLNB between 2005 and 2018 at five tertiary centers in Europe and Canada. Univariable and multivariable logistic regression analyses were performed to identify predictors of SLNB positivity. RESULTS: Overall, 676 patients were analyzed. Most patients had one or more high-risk features: Breslow thickness 0.8-1 mm in 78.1% of patients, ulceration in 8.3%, mitotic rate > 1/mm2 in 42.5%, Clark's level ≥ 4 in 34.3%, lymphovascular invasion in 1.4%, nodular histology in 2.9%, and absence of tumor-infiltrating lymphocytes in 14.4%. Fifty-three patients (7.8%) had a positive SLNB. Breslow thickness and mitotic rate independently predicted SLNB positivity. The odds of positive SLNB increased by 50% for each 0.1 mm increase in thickness past 0.7 mm (95% confidence interval [CI] 1.05-2.13) and by 22% for each mitosis per mm2 (95% CI 1.06-1.41). Patients who had one excised node (vs. two or more) were three times less likely to have a positive SLNB (3.6% vs. 9.6%; odds ratio 2.9 [1.3-7.7]). CONCLUSIONS: Our international multi-institutional data confirm that Breslow thickness and mitotic rate independently predict SLNB positivity in patients with T1 melanoma. Even within this highly selected population, the number needed to diagnose is 13:1 (7.8%), indicating that more work is required to identify additional predictors of sentinel node positivity.

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study.

BACKGROUND: Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. METHODS: Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. RESULTS: Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. CONCLUSIONS: RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.

Cognition in informal caregivers: evidence from an English population study.

BACKGROUND AND OBJECTIVES: The relationship between caregiving and cognition remains unclear. We investigate this association comparing four cognitive tasks and exploring the role of potential explanatory pathways such as healthy behaviours (healthy caregiver hypothesis) and depression (stress process model). RESEARCH DESIGN AND METHODS: Respondents were from English Longitudinal Study of Ageing (ELSA) (N = 8910). Cognitive tasks included immediate and delayed word recall, verbal fluency and serial 7 subtraction. Series of hierarchical linear regressions were performed. Adjustments included socio-demographics, health related variables, health behaviours and depression. RESULTS: Being a caregiver was positively associated with immediate and delayed recall, verbal fluency but not with serial 7. For immediate and delayed recall, these associations were partially attenuated when adjusting for health behaviours, and depression. For verbal fluency, associations were partially attenuated when adjusting for depression but fully attenuated when adjusting for health behaviours. No associations were found for serial 7. DISCUSSION AND IMPLICATIONS: Our findings show that caregivers have higher level of memory and executive function compared to non-caregivers. For memory, we found that although health behaviours and depression can have a role in this association, they do not fully explain it. However, health behaviours seem to have a clear role in the association with executive function. Public health and policy do not need to target specifically cognitive function but other areas as the promotion of healthy behaviours and psychological adjustment such as preventing depression and promoting physical activity in caregivers.

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study.

BACKGROUND: The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. METHODS: Training cohorts comprised 1276 patients admitted to King's College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy's and St Thomas' Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. RESULTS: A baseline model of 'NEWS2 + age' had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. CONCLUSIONS: NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.