Literature Collection

General practitioners play an essential role in identifying depression and are often the first point of contact for patients. Current diagnostic tools, such as the Patient Health Questionnaire-9, provide initial screening but might lead to false positives. To address this, we developed a two-step machine learning model called Clinical 15, trained on a cohort of 581 participants using a nested cross-validation framework. The model integrates self-reported data from validated questionnaires within a study sample of patients presenting to general practitioners. Clinical 15 demonstrated a balanced accuracy of 88.2% and incorporates a traffic light system: green for healthy, red for depression, and yellow for uncertain cases. Gaussian mixture model clustering identified four depression subtypes, including an Immuno-Metabolic cluster characterized by obesity, low-grade inflammation, autonomic nervous system dysregulation, and reduced physical activity. The Clinical 15 algorithm identified all patients within the immuno-metabolic cluster as depressed, although 22.2% (30.8% across the whole dataset) were categorized as uncertain, leading to a yellow traffic light. The biological characterization of patients and monitoring of their clinical course may be used for differential risk stratification in the future. In conclusion, the Clinical 15 model provides a highly sensitive and specific tool to support GPs in diagnosing depression. Future algorithm improvements may integrate further biological markers and longitudinal data. The tool's clinical utility needs further evaluation through a randomized controlled trial, which is currently being planned. Additionally, assessing whether GPs actively integrate the algorithm's predictions into their diagnostic and treatment decisions will be critical for its practical adoption.

BACKGROUND: Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, influenced by lifestyle, socioeconomic status, and genetic factors. Emerging innovations, including wearable health technologies, telemedicine, and CRISPR-Cas9 gene editing, provide new possibilities for rapid prevention and personalized management. METHODS: This narrative review collected evidence from Scopus, PubMed, and Google Scholar, using keywords such as cardiovascular (CV) prevention, lifestyle determinants, digital health, telemedicine, CRISPR-Cas9, and public health ethics. Eligible peer-reviewed studies, clinical guidelines, and policy documents were included to assess behavioral, technological, and genomic strategies for CVD care. RESULTS: Modifications in lifestyle, such as quitting smoking, regular physical activity, following a heart-healthy diet plan, and getting adequate sleep, can significantly reduce the risk of CVD. Additionally, telemedicine and wearable devices facilitate early detection, better self-management, and treatment adherence, especially in underserved communities. CRISPR-Cas9 holds a significant potential for correcting genetic variants related to lipid disorders and inherited cardiomyopathies, but its clinical translation remains in early stages. However, existing evidence is limited by heterogeneity in study design, brief follow-up, particularly for digital health and CRISPR applications. Additional challenges, such as health inequities, digital access, data privacy, and ethical oversight, further influence their real-world implementation. CONCLUSION: Effective integration of behavioral, digital and genomic innovations requires policy frameworks that ensure equity, ethical governance, and long-term sustainability. Combining precision medicine with efforts to address social determinants of health will be crucial in reducing the global burden of CVD and shaping the future of CV care.

Chronic obstructive pulmonary disease (COPD) refers to a group of lung diseases that are distinct in underlying aetiology but share a common disease course of persistent and progressive airflow restriction. People living with COPD, as well as the people who care for them, frequently have severe and unmet physical and psychosocial needs, including breathlessness, fatigue, cough, anxiety and depression. Early proactive palliative care is well placed to address these needs, yet it is frequently under-utilised in this group. This narrative review aimed to identify core components of palliative care and examine how existing models of care are implemented to better understand which models can best serve the needs of people with COPD. Symptom palliation, advance care planning, and support for caregivers emerged as the common components underpinning both generalist and specialist models of palliative care. Models of proactive palliative care were diverse in terms of where and how care was delivered as well as which health professionals were involved. Five key models of palliative care were identified: (1) multi-disciplinary integrated services, (2) nurse-led care, (3) hospice and residential aged care, (4) home-based care, and (5) telemonitoring and telehealth. Each model describes a diverse set of interventions and many of these share common elements, including the normalisation of palliative principles within routine care and the provision of diverse delivery settings to accommodate individual preferences and needs. Successful palliative care models must be practical, accessible and innovative to respond to individuals' complex and evolving needs, foster multi-disciplinary collaboration and input and optimally utilise local healthcare resources.

BACKGROUND: Health services globally are struggling to manage the impact of COVID-19. The existing global disease burden related to opioid use is significant. Particularly challenging groups include older drug users who are more vulnerable to the effects of COVID-19. Increasing access to safe and effective opioid agonist treatment (OAT) and other harm reduction services during this pandemic is critical to reduce risk. In response to COVID-19, healthcare is increasingly being delivered by telephone and video consultation, and this report describes the development of a national model of remote care to eliminate waiting lists and increase access to OAT in Ireland. PURPOSE AND FINDINGS: The purpose of this initiative is to provide easy access to OAT by developing a model of remote assessment and ongoing care and eliminate existing national waiting lists. The Irish College of General Practitioners in conjunction with the National Health Service Executive office for Social Inclusion agreed a set of protocols to enable a system of remote consultation but still delivering OAT locally to people who use drugs. This model was targeted at OAT services with existing waiting lists due to a shortage of specialist medical staff. The model involves an initial telephone assessment with COVID-risk triage, a single-patient visit to local services to provide a point of care drug screen and complete necessary documentation and remote video assessment and ongoing management by a GP addiction specialist. A secure national electronic health link system allows for the safe and timely delivery of scripts to a designated local community pharmacy. CONCLUSION: The development of a remote model of healthcare delivery allows for the reduction in transmission risks associated with COVID-19, increases access to OAT, reduces waiting times and minimises barriers to services. An evaluation of this model is ongoing and will be reported once completed. Fast adaptation of OAT delivery is critical to ensure access to and continuity of service delivery and minimise risk to our staff, patients and community. Innovative models of remote healthcare delivery adapted during the COVID-19 crisis may inform and have important benefits to our health system into the future.

BACKGROUND: Homelessness is an important social determinant of health (SDOH), impacting health outcomes for many medical conditions. Although homelessness is common among people with opioid use disorder (OUD), few studies systematically evaluate homelessness and other SDOH among people enrolled in standard of care treatment for OUD, medication for opioid use disorder (MOUD), or examine whether homelessness affects treatment engagement. METHODS: Using data from the 2016 to 2018 U.S. Treatment Episode Dataset Discharges (TEDS-D), patient demographic, social, and clinical characteristics were compared between episodes of outpatient MOUD where homelessness was reported at treatment enrollment versus independent housing using pairwise tests adjusted for multiple testing. A logistic regression model examined the relationship between homelessness and treatment length and treatment completion while accounting for covariates. RESULTS: There were 188 238 eligible treatment episodes. Homelessness was reported in 17 158 episodes (8.7%). In pairwise analysis, episodes involving homelessness were significantly different from those involving independent living on most demographic, social, and clinical characteristics, with significantly greater social vulnerability in most SDOH variables (P's < .05). Homelessness was significantly and negatively associated with treatment completion (coefficient = -0.0853, P < 0.001, 95% CI = [-0.114, -0.056], OR = 0.918) and remaining in treatment for greater than 180 days (coefficient = -0.3435, P < 0.001, 95% CI = [-0.371, -0.316], OR = 0.709) after accounting for covariates. CONCLUSIONS: Patients reporting homelessness at treatment entry in outpatient MOUD in the U.S. represent a clinically distinct and socially vulnerable population from those not reporting homelessness. Homelessness independently predicts poorer engagement in MOUD confirming that homelessness may be an independent predictor for MOUD treatment discontinuation nationally.