Cohort profile: creation of the SAIL MELD-B e-cohort (SMC) and SAIL MELD-B children and young adult e-cohort (SMYC) to investigate the lived experience of the 'burdensomeness' of multimorbidity.

Purpose: We have established the SAIL MELD-B electronic cohort (e-cohort SMC) and the SAIL MELD-B children and Young adults e-cohort (SMYC) as a part of the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) project. Each cohort has been created to investigate and develop a deeper understanding of the lived experience of the 'burdensomeness' of multimorbidity by identifying new clusters of burdensomeness concepts, exploring early life risk factors of multimorbidity and modelling hypothetical prevention scenarios.

Participants: The SMC and SMYC are longitudinal e-cohorts created from routinely collected individual-level population-scale anonymised data sources available within the Secure Anonymised Information Linkage (SAIL) Databank.

Sonographic Mimic of Ovarian Ectopic Pregnancy by Point-of-Care Ultrasound.

The detection of ectopic pregnancy (EP) by point-of-care ultrasound has become an integral competency of emergency medicine practice. Clinical algorithms usually include a female of childbearing age, positive urine or serum human chorionic gonadotropin (hCG) test, and an ultrasound evaluation to assess the presence or absence of intrauterine pregnancy. This case report illustrates incidental findings observed during focused assessment with sonography in trauma (FAST examination), which initially suggested an intra-ovarian EP.

Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance.

Mobile Health (mHealth) has the potential to be transformative in the management of chronic conditions. Machine learning can leverage self-reported data collected with apps to predict periods of increased health risk, alert users, and signpost interventions. Despite this, mHealth must balance the treatment burden of frequent self-reporting and predictive performance and safety.

Personal data store ecosystems in health and social care.

This paper considers how the development of personal data store ecosystems in health and social care may offer one person-centered approach to improving the ways in which individual generated and gathered data-e.g., from wearables and other personal monitoring and tracking devices-can be used for wellbeing, individual care, and research.

Validation of oxygen saturations measured in the community by emergency medical services as a marker of clinical deterioration in patients with confirmed COVID-19: a retrospective cohort study.

Objectives: To evaluate oxygen saturation and vital signs measured in the community by emergency medical services (EMS) as clinical markers of COVID-19-positive patient deterioration.

Design: A retrospective data analysis.

Setting: Patients were conveyed by EMS to two hospitals in Hampshire, UK, between 1 March 2020 and 31 July 2020.

Exploring the Validity of GOLD 2023 Guidelines: Should GOLD C and D Be Combined?

Introduction: The GOLD (Global Initiative for Chronic Obstructive Lung Disease) 2023 guidelines proposed important changes to the stratification of disease severity using the "ABCD" assessment tool. The highest risk groups "C" and "D" were combined into a single category "E" based on exacerbation history, no longer considering symptomology.

Purpose: We quantify the differential disease progression of individuals initially stratified by the GOLD 2022 "ABCD" scheme to evaluate these proposed changes.

Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) - protocol for a research collaboration.

Background: Most people living with multiple long-term condition multimorbidity (MLTC-M) are under 65 (defined as 'early onset'). Earlier and greater accrual of long-term conditions (LTCs) may be influenced by the timing and nature of exposure to key risk factors, wider determinants or other LTCs at different life stages. We have established a research collaboration titled 'MELD-B' to understand how wider determinants, sentinel conditions (the first LTC in the lifecourse) and LTC accrual sequence affect risk of early-onset, burdensome MLTC-M, and to inform prevention interventions.

Eliciting and prioritising determinants of improved care in multimorbidity: A modified online Delphi study.

Background: Multimorbidity is a major challenge to health and social care systems around the world. There is limited research exploring the wider contextual determinants that are important to improving care for this cohort. In this study, we aimed to elicit and prioritise determinants of improved care in people with multiple conditions.

A conceptual framework for characterising lifecourse determinants of multiple long-term condition multimorbidity.

Objective: Social, biological and environmental factors in early-life, defined as the period from preconception until age 18, play a role in shaping the risk of multiple long-term condition multimorbidity. However, there is a need to conceptualise these early-life factors, how they relate to each other, and provide conceptual framing for future research on aetiology and modelling prevention scenarios of multimorbidity. We develop a conceptual framework to characterise the population-level domains of early-life determinants of future multimorbidity.

Early-onset burdensome multimorbidity: an exploratory analysis of sentinel conditions, condition accrual sequence and duration of three long-term conditions using the 1970 British Cohort Study.

Objectives: The prevalence of multiple long-term condition (LTC) multimorbidity is increasing with younger onset among socioeconomically deprived populations. Research on life course trajectories towards multimorbidity is limited and early-onset multimorbidity poorly characterised. Understanding sentinel conditions (the first LTC occurring in the life course), the sequence of LTC accrual and the permanency of the reporting of LTCs may help identify time points for prevention efforts.

Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions).

Background: Multiple long-term health conditions (multimorbidity) (MLTC-M) are increasingly prevalent and associated with high rates of morbidity, mortality, and health care expenditure. Strategies to address this have primarily focused on the biological aspects of disease, but MLTC-M also result from and are associated with additional psychosocial, economic, and environmental barriers. A shift toward more personalized, holistic, and integrated care could be effective.

Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations.

Background: The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual's longer term control.

COVID-19 Oximetry @home: evaluation of patient outcomes.

Background: COVID-19 has placed unprecedented demands on hospitals. A clinical service, COVID-19 Oximetry @home (CO@h) was launched in November 2020 to support remote monitoring of COVID-19 patients in the community. Remote monitoring through CO@h aims to identify early patient deterioration and provide timely escalation for cases of silent hypoxia, while reducing the burden on secondary care.

Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach.

Background: Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging the data collected by these apps in prognostic models could provide increased personalization of care and reduce the burden of care for people who live with chronic conditions. This study evaluated the predictive ability of prognostic models for the prediction of acute exacerbation events in people with chronic obstructive pulmonary disease by using data self-reported to a digital health app.

Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19.

A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of clinical outcomes.

Delayed Presentation of Basal Cell Carcinoma: A Case Report.

Basal cell carcinoma (BCC) is the most common skin cancer in the United States. Although BCC has a low metastatic potential, it can be locally invasive and destructive, especially when there is a delay in diagnosis or treatment. This can affect not only the surrounding skin, but deeper tissues including muscle, cartilage, and even bone.

Differences Between Esophageal and Tracheal Intubation Ultrasound View Proficiency: An Educational Study of Novice Prehospital Providers.

Objectives Airway ultrasound is now possible in the prehospital setting due to advances in ultrasound equipment portability. We questioned how well prehospital providers without prior experience could determine both esophageal and tracheal placement of an endotracheal tube in cadavers after a brief training course in ultrasound.  Methods This educational prospective study at the Simulation Center in Mayo Clinic Jacksonville Florida enrolled 50 prehospital providers.

Acid-base disturbances: an emergency department approach.

Acid-base disturbances are physiological responses to a wide variety of underlying conditions and critical illnesses. Homeostasis of acid-base physiology is complex and interdependent with the function of the lungs, kidneys, and endogenous buffer systems. Traditionally, these disturbances have been classified in terms of being caused by either a primary respiratory or a metabolic insult and by chronicity and compensation.

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

Introduction: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet.

A Longitudinal Curriculum In Point-Of-Care Ultrasonography Improves Medical Knowledge And Psychomotor Skills Among Internal Medicine Residents.

Purpose: Despite its growing popularity and clinical utility among hospital-based physicians, there are no formal competency requirements nor training standards for United States based Internal Medicine Residencies for learning point-of-care ultrasonography (POCUS). The purpose of this investigation was to study the impact and effectiveness of a novel POCUS curriculum for an Internal Medicine (IM) residency program.

Patients And Methods: This was a Single-Group Educational Quasi-Experiment involving Categorical and Preliminary Internal Medicine Residents in Post-Graduate Years 1 through 3 at a single United States academic tertiary center.

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors.