Diverse Clinical Settings Research Articles

Using Machine Learning and Electronic Health Records to Identify Neuropsychiatric Risk Scores for Delirium in ICU and General Hospital Settings.

Delirium is a common and acute neuropsychiatric syndrome that requires timely intervention to prevent its associated morbidity and mortality. Yet, its diagnosis and symptoms are often overlooked due to its variable clinical presentation and fluctuating nature. Thus, in this study, we address the barriers to delirium diagnosis by utilizing a machine learning-based predictive algorithm for incident delirium that relies on archived electronic health records (EHRs) data. We used the Medical Information Mart for Intensive Care (MIMIC) database to create a detailed dataset for identifying delirium in intensive care unit (ICU) patients. Our approach involved training machine learning models on this dataset to pinpoint critical clinical features for delirium detection. These features were then refined and applied to non-ICU patients using EHRs from the American University of Beirut Medical Center (AUBMC). Our study assessed machine learning models like Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Classification and Regression Trees (CART), Random Forest (RF), Neural Oblivious Decision Ensembles (NODE), and Logistic Regression (LR), highlighting superior delirium detection in diverse clinical settings. The CatBoost model excelled in ICU environments with an F1 Score of 89.2%, while XGBoost performed best in general hospital settings with a 75.4% F1 Score. Interpretations using Tabular Local Interpretable Model-agnostic Explanations (LIME) revealed critical indicators such as prothrombin time and hematocrit levels, enhancing model transparency and clinical applicability. These clinical insights help differentiate the delirium predictors between ICU patients, who are often sensitive to various factors. The proposed predictive algorithm improves delirium detection rates and streamlines efficiency in hospital electronic systems, thereby enabling prompt interventions to prevent delirium progression and associated complications. The clinical indicators for delirium that we identified in general hospital settings and ICU can greatly help healthcare professionals identify potential causes of delirium and reduce misdiagnosis.

The role of single case experimental designs in evidence creation in rehabilitation.

Randomized controlled trials (RCTs) are considered the gold standard of evidence guiding intervention selection in rehabilitation. However, conduct of sufficiently powered RCTs in rehabilitation can be expensive, pose ethical and attrition concerns when participants are assigned to ineffective treatment as usual conditions, and are infeasible with low-incidence populations. Single-case experimental designs (SCEDs), including N-of-1 RCTs are causal inference studies for small numbers of participants and not necessarily one participant as the name implies. These designs are increasingly used to evaluate the effectiveness of rehabilitation interventions in diverse clinical settings and employ design features including but not limited to randomization and each participant serving as their own control. These and other internal validity enhancements can increase the confidence in study results coming from these designs. This manuscript discusses the expanded application of SCEDs in rehabilitation contexts to answer everyday clinical rehabilitation research questions with emphasis on strategies to use: 1) to maximize internal validity of this family of designs; 2) improve utility, effectiveness, and acceptability of these designs for rehabilitation end-users (clinicians, policymakers, and participants); 3) build evidence bases in areas of rehabilitation where RCTs are uncommonly used. Primary considerations for situating SCEDs within the continuum of experimental designs include increasing internal validity within designs, improving transparency in conduct and reporting of these studies, and increasing access to advanced research methods training for rehabilitation professionals.

Emergency cerclage: Neonatal outcomes and evaluation of prognostic scores − A decade of experience at a tertiary center

ObjectivesEmergency cervical cerclage is a high-risk surgical procedure associated with maternal and fetal risks, including preterm birth. Authors have developed scores to try to predict the probability of success following emergency cerclage, but these tools have yet to be externally validated.Our main objective was to assess the preterm birth rate before 32 weeks following emergency cerclage. Secondary objectives included assessing perinatal outcomes and the predictive validity of two published scores in our setting. We hypothesized that predictive scores, such as Fuchs and Kokia scores, may not perform optimally in our population due to variability in clinical and biological characteristics that influence eligibility for cerclage across different settings. Study designWe conducted a retrospective, monocentric study in a tertiary maternity hospital. All patients who underwent emergency cerclage between January 1st, 2010, and December 31st, 2021, were included. The main outcome was preterm birth rate before 32 weeks. Secondary outcomes were need for maternal hospitalization after initial hospitalization for cerclage, preterm premature rupture of membrane, and perinatal death. The rate of preterm birth before 32 weeks and adverse perinatal outcomes were estimated. To assess the predictive validity of Fuchs’ score in our population, we performed a multivariable logistic regression model including the score’s variables and constructed receiver operating characteristic (ROC) curve. To assess the validity of Kokia’s score in our population, we studied the correlation between the initial score and gestational age at delivery using Pearson’s correlation coefficient. ResultsOur population was composed of 64 women who underwent an emergency cerclage. The median gestational age at delivery was 30.6 weeks (IQR (24.2; 37.6)). The preterm birth rate before 32 weeks was 54.7 %. The rate of perinatal death was 30.7 %. There were no statistical differences between the patients delivered before and after 32 weeks. The multivariable logistic regression model showed that none of the variables of Fuchs’s score were significantly associated with preterm birth risk in our population. The AUC of the ROC curve using Fuchs’ score was 0.63. There was a minor yet significant correlation between Kokia’s score and gestational age at delivery (r = 0.26 – p = 0.04). ConclusionsOur study underscores the persistent risks associated with cervical insufficiency, including very preterm birth. It highlights the variability in performance of predictive scores across different populations, underscoring the need for external validation in diverse clinical settings.

Interventions to Support Resident and Fellow Well-Being During the COVID-19 Pandemic: A Scoping Review.

Background The COVID-19 pandemic led to rapid and wide-scale changes in graduate medical education and impacted the well-being of frontline physicians, including residents and fellows. While institutions and programs implemented initiatives to support the unique needs of trainees during the pandemic, there remains a gap in the literature in examining the approaches used, the domains of well-being addressed, and the effectiveness of these efforts. Objective To review the literature on interventions designed to promote resident and fellow well-being during the COVID-19 pandemic. Methods The authors conducted a scoping review of the literature published between January 1, 2020, and November 30, 2023, in PubMed, Scopus, Embase, PsycINFO, CINAHL, and ERIC to identify interventions to promote the well-being of trainees during the COVID-19 pandemic. Results Eighteen articles met inclusion criteria, mainly studies conducted in the United States (14 of 18, 77.8%). Most interventions targeted psychological well-being (16 of 18, 88.9%), with only a few studies that included interventions in the physical or social domains. Interventions entailed redeployment, schedule modifications, communication strategies, and expanded mental health support. Most interventions were limited to a few weeks' duration in the first surge phase of the pandemic. Only 11 studies (61%) reported outcome measures, and only 2 (11%) used instruments with validity evidence. Most studies did not report sufficient data to evaluate study quality. Conclusions While longer-term outcome data were often lacking, studies described a range of interventions to support resident well-being. Future research should focus on the effectiveness of well-being interventions and include cohorts from more diverse clinical settings.

Prevalence of Cardiac Amyloidosis Among Elderly Patients With Recent-Onset Atrial Fibrillation: The PREVAL-ATTR Study

BackgroundTransthyretin cardiac amyloidosis (ATTR-CA) is increasingly recognized as a treatable form of heart failure. Atrial fibrillation (AF) is common in patients with ATTR-CA. Whether recent-onset AF can be used as an early marker to identify patients with ATTR-CA has not been elucidated. MethodsThis was a prospective study conducted at 3 Spanish centres. ATTR-CA noninvasive screening was offered to patients ≥ 65 years of age recently diagnosed (< 1 year) with nonvalvular AF and who had ≥ 1 echocardiographic, electrocardiographic, or clinical sign suggestive of ATTR-CA. ResultsA total of 121 patients were included (75% male, mean age 77 ± 7 years). Ten patients (8.3%; 95% confidence interval [CI],4-14.7%), were diagnosed with cardiac amyloidosis (CA): 5 with definite wild-type ATTR-CA (ATTRwt), 4 with likely ATTRwt, and 1 with undetermined CA. Compared with patients without CA, patients with CA were older (84 ± 4 vs 76 ± 7 years; P < 0.001), more frequently men (90% vs 59%; P = 0.047), presented higher median N-terminal pro-B-type natriuretic peptide (NTproBNP) (3800 pg/L, interquartile range [IQR]:1682-6101 vs 1048 pg/mL, IQR: 427-3154; P = 0.017) and higher left ventricular hypertrophy (LVH) (14 mm, IQR: 13-17 vs 12 mm, IQR: 12-13; P = 0.003). Patients with CA also showed higher rate of permanent AF (90% vs 49.5%; P = 0.018) and a greater need for pacemaker implantation during follow-up (30% vs 7.3%; P = 0.049). No differences in mortality were observed between patients with and without CA after a median follow-up of 13 months (IQR: 11-16 months). ConclusionsRoutine DPD scanning in elderly patients with recent-onset AF, LVH and an additional red flag may help to identify patients with ATTR-CA. However, larger studies evaluating this strategy in more diverse clinical settings would be required.

Validation of the Sinhala version of the Autism Treatment Evaluation Checklist (ATEC)

Background: Autism Spectrum Disorder is a neuro developmental disorder which has a trend in its increasing prevalence globally. Measuring the outcome of intervention will have a great value for parents and clinicians. At present, there are no tools validated in the Sri Lankan setting to assess treatment outcomes in Autism Spectrum Disorder. Autism Treatment Evaluation Checklist (ATEC) is a self-administered questionnaire in Sinhala for parents/caregivers that is a feasible method of evaluating outcome in a busy clinic.Aims: This study was conducted to translate and culturally adapt the ATEC to the Sinhala language and to establish the validity and reliability of the translation to the local setting.Methods: Permission was sought from the developers of the ATEC questionnaire and ethical approval was obtained for the study. Study was conducted in two stages. In the first stage, ATEC was translated using the translation-back translation method. An expert panel produced a pre-final questionnaire which was pre-tested prior to formulating the final Sinhala version of ATEC. The second stage of the study was conducted to establish validity and reliability of Sinhala ATEC questionnaire in the Sri Lankan popu-lation. Content validity was assured in each step of the translation by the expert committee. Construct validity of the Sinhala ATEC questionnaire was assessed by comparing the sample of 119 parent/caregiver filled final version of the Sinhala ATEC score against the Consultant Child and Adolescent Psychiatrist assessment of the same children conducted independently and recorded according to DSM-5 (level 1, 2 or 3). Test-retest reliability was determined by the same parent/caregiver filling the Sinhala ATEC one week later, in a sample of 14 children. Intra-rater reliability was taken by com-paring 18 pairs of parents/caregivers Sinhala ATEC scores for the same child.Results: Sinhala ATEC showed high validity and reliability. Its concurrent validity has a strong positive Pearson correlation of 0.856 with the Consultant Psychiatrist assessment. Correlation was higher in total score and as well as in subscale II- Sociability score of the Sinhala ATEC. Internal consistency was revealed by Cronbach’s alpha 0.778. Cut off points to distinguish mild autism spectrum disorder from the rest taken as “48 or less” with sensitivity 82%, specificity 94% and area under the curve 0.968. Cut off to separate severe autism spectrum disorder from the rest were taken as “76 or more” with sensitivity 84%, specificity 90%and area under the curve 0.971. Reliability tests, intra class correlation was 0.98 and 0.96 for test-retest and intra rater reliability showed high reliability of Sinhala ATEC. For developing burnout in this study population.Conclusions: Translated version of Sinhala ATEC is suitable for the use in the Sri Lankan population as it shows high validity and reliability. However, further studies are recommended in other areas of the country to assess its usefulness across diverse subcultures and clinical settings.

Comparing Point-of-Care Technology to ELISA Testing for Infliximab and Adalimumab Levels in Adult Inflammatory Bowel Disease Patients: A Prospective Pilot Study.

Introduction: Therapeutic drug monitoring (TDM) has proven to be a valuable strategy for optimizing biologic therapies, among which are anti-tumor necrosis factor (anti-TNF) treatments in inflammatory bowel disease (IBD). In particular, reactive TDM has been shown to manage treatment failures more cost-effectively than empirical dose adjustments for anti-TNF drugs. However, several challenges currently impede the widespread adoption of TDM in clinical practice, particularly addressing the delay between sample collection and result availability. To overcome this limitation, the use of point-of-care technology tests (POCTs) is a potential solution. Point-of-care technology tests are medical diagnostic tests performed at the site of patient care to provide immediate results, allowing for quicker decision-making and treatment. The current standard of care (SOC) for drug level measurement relies on the enzyme-linked immunosorbent assay (ELISA), a method that is time-consuming and requires specialized personnel. This study aims to evaluate a novel, user-friendly, and efficient POCT method (ProciseDx Inc.) and compare its performance with the SOC ELISA in assessing infliximab and adalimumab levels in blood samples from IBD patients. Methods: In this prospective, single-center study, we collected blood samples from IBD patients, both CD and UC, receiving infliximab (87 IBD patients; 50% UC and 50% CD) or adalimumab (60 patients; 14% UC and 48% CD) and we analyzed the blood's drugs levels using both the ProciseDx Analyzer POC and the SOC ELISA. We examined the correlation between the two methods using statistical analyses, including the Deming regression test. Additionally, we assessed the ease of use, turnaround time, and overall practicality of the POCT in a clinical setting. Results: The ProciseDx test demonstrated a strong correlation with the SOC ELISA for measuring both infliximab and adalimumab levels. In particular, the overall correlation between the ProciseDx POCT and the ELISA assessments showed an r coefficient of 0.83 with an R squared value of 0.691 (95% CI 0.717-0.902) for IFX measurements, and an r coefficient of 0.85 with an R squared value of 0.739 (95% CI 0.720-0.930). Conclusions: the ProciseDx POC test offers significantly faster turnaround times and is more straightforward to use, making it a viable alternative for routine clinical monitoring. Despite its promising potential, further refinement and validation of the ProciseDx test are necessary to ensure its effectiveness across diverse patient populations and clinical settings. Future research should focus on optimizing the POC tests' performance and evaluating its long-term impact on IBD management.

Comment on, "Decoding pediatric spinal tumors: a single-center retrospective case series on etiology, presentation, therapeutic strategies, and outcomes".

The article "Decoding pediatric spinal tumors: a single-center retrospective case series on etiology, presentation, therapeutic strategies, and outcomes" by Lenga et al. (2024) provides essential insights into pediatric spinal tumors, a rare and challenging area of medical research. The authors present a thorough analysis of clinical presentations, treatment strategies, and patient outcomes, offering valuable data to refine therapeutic approaches and improve outcomes. However, the study's retrospective design, confined to a single center, introduces potential biases and limits the generalizability of findings. The lack of long-term follow-up data and a control group further restricts the study's scope. Future research should prioritize multi-center collaborations, incorporate control groups, and extend follow-up durations to better understand long-term outcomes. The establishment of standardized treatment protocols is also recommended to enhance consistency in managing pediatric spinal tumors across diverse clinical settings.

Valsalva Maneuver Versus Carotid Sinus Massage for Supraventricular Tachycardia: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Supraventricular tachycardia (SVT) is one of the most common cardiac arrhythmias, characterized by a sudden increase in heart rate. Initial management often involves vagal maneuvers, including the Valsalva maneuver (VM) and carotid sinus massage (CSM). VM can be categorized into standard VM (sVM) and modified VM (mVM). This study aimed to synthesize the first evidence from published randomized controlled trials (RCTs) comparing the efficacy of VM versus CSM. A comprehensive search across databases, including PubMed, Web of Science, Scopus, Cochrane Library, and Google Scholar, was conducted up to July 29, 2024. The primary endpoint was the success rate of converting SVT to sinus rhythm. The dichotomous outcome was analyzed using a fixed-effect model to calculate the risk ratio (RR) and 95% confidence intervals (CI). The Risk of Bias (RoB) tool, version 2, was employed to assess bias in the included RCTs. In total, three RCTs with 346 cases were analyzed. Concerns were noted regarding potential bias related to the randomization process in all three studies. The meta-analysis of these RCTs (comprising four arms) revealed that VM had a higher success rate than CSM for treating SVT, with an RR of 1.82 (95% CI: 1.29-2.57, p<0.001). Subgroup analysis showed that the rate of conversion to sinus rhythm was significantly higher in the sVM compared to CSM (RR=1.61, 95% CI (1.13-2.29), p=0.01). Additionally, subgroup analysis of one study indicated that mVM was associated with a higher rate of SVT conversion to sinus rhythm compared to CSM (RR=9.28, 95% CI (1.25-69.13), p=0.03). In conclusion, VM demonstrated a higher success rate compared to CSM in treating SVT. Specifically, mVM was more effective than CSM in both terminating SVT and restoring sinus rhythm, though this evidence was based on a single RCT; hence, the related conclusion should be interpreted with caution and requires validation using additional RCTs. Further research in diverse patient populations and clinical settings is necessary to validate these findings and potentially support the broader use of mVM in practice. Additional well-designed, multi-center studies with diverse populations are needed to confirm these observations and provide more comprehensive guidance on SVT management. This is important to enhance the generalizability of results across different demographics and clinical settings. This approach helps ensure that treatment effectiveness is applicable to a broader range of patients, accounting for variations in age, gender, comorbidities, and regional practices.

Preclinical Evaluation of Electronic Health Records (EHRs) to Predict Poor Control of Chronic Respiratory Diseases in Primary Care: A Novel Approach to Focus Our Efforts.

Background/Objectives: Managing chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) within the Spanish Sistema Nacional de Salud (SNS) presents significant challenges, particularly due to their high prevalence and poor disease control rates-approximately 45.1% for asthma and 63.2% for COPD. This study aims to develop a novel predictive model using electronic health records (EHRs) to estimate the likelihood of poor disease control in these patients, thereby enabling more efficient management in primary care settings. Methods: The Seleida project employed a bioinformatics approach to identify significant clinical variables from EHR data in primary care centers in Seville and Valencia. Statistically significant variables were incorporated into a logistic regression model to predict poor disease control in patients with asthma and COPD patients. Key variables included the number of short-acting β-agonist (SABA) and short-acting muscarinic antagonist (SAMA) canisters, prednisone courses, and antibiotic courses over the past year. Results: The developed model demonstrated high accuracy, sensitivity, and specificity in predicting poorly controlled disease in both asthma and COPD patients. These findings suggest that the model could serve as a valuable tool for the early identification of at-risk patients, allowing healthcare providers to prioritize and optimize resource allocation in primary care settings. Conclusions: Integrating this predictive model into primary care practice could enhance the proactive management of asthma and COPD, potentially improving patient outcomes and reducing the burden on healthcare systems. Further validation in diverse clinical settings is warranted to confirm the model's efficacy and generalizability.

Enhancing diabetic foot ulcer prediction with machine learning: A focus on Localized examinations

Backgrounddiabetices foot ulcer (DFU) are serious complications. It is crucial to detect and diagnose DFU early in order to provide timely treatment, improve patient quality of life, and avoid the social and economic consequences. Machine learning techniques can help identify risk factors associated with DFU development. ObjectiveThe aim of this study was to establish correlations between clinical and biochemical risk factors of DFU through local foot examinations based on the construction of predictive models using automated machine learning techniques. MethodsThe input dataset consisted of 566 diabetes cases and 50 DFU risk factors, including 9 local foot examinations. 340 patients with Class 0 labeling (low-risk DFU), 226 patients with Class 1 labeling (high-risk DFU). To divide the training group (consisting of 453 cases) and the validation group (consisting of 113 cases), as well as preprocess the data and develop a prediction model, a Monte Carlo cross-validation approach was employed. Furthermore, potential high-risk factors were analyzed using various algorithms, including Bayesian BYS, Multi-Gaussian Weighted Classifier (MGWC), Support Vector Machine (SVM), and Random Forest Classifier (RF). A three-layer machine learning training was constructed, and model performance was estimated using a Confusion Matrix. The top 30 ranking feature variables were ultimately determined. To reinforce the robustness and generalizability of the predictive model, an independent dataset comprising 248 cases was employed for external validation. This validation process evaluated the model's applicability and reliability across diverse populations and clinical settings. Importantly, the external dataset required no additional tuning or adjustment of parameters, enabling an unbiased assessment of the model's generalizability and its capacity to predict the risk of DFU. ResultsThe ensemble learning method outperformed individual classifiers in various performance evaluation metrics. Based on the ROC analysis, the AUC of the AutoML model for assessing diabetic foot risk was 88.48 % (74.44–97.83 %). Other results were found to be as follows: 87.23 % (63.33 %–100.00 %) for sensitivity, 87.43 % (70.00 %–100.00 %) for specificity, 87.33 % (76.66 %–95.00 %) for accuracy, 87.69 % (75.00 %–100.00 %) for positive predictive value, and 87.70 % (71.79 %–100.00 %) for negative predictive value. In addition to traditional DFU risk factors such as cardiovascular disorders, peripheral artery disease, and neurological damage, we identified new risk factors such as lower limb varicose veins, history of cerebral infarction, blood urea nitrogen, GFR (Glomerular Filtration Rate), and type of diabetes that may be related to the development of DFU. In the external validation set of 158 samples, originating from an initial 248 with exclusions due to missing labels or features, the model still exhibited strong predictive accuracy. The AUC score of 0.762 indicated a strong discriminatory capability of the model. Furthermore, the Sensitivity and Specificity values provided insights into the model's ability to correctly identify both DFU cases and non-cases, respectively. ConclusionThe predictive model, developed through AutoML and grounded in local foot examinations, has proven to be a robust and practical instrument for the screening, prediction, and diagnosis of DFU risk. This model not only aids medical practitioners in the identification of potential DFU cases but also plays a pivotal role in mitigating the progression towards adverse outcomes. And the recent successful external validation of our DFU risk prediction model marks a crucial advancement, indicating its readiness for clinical application. This validation reinforces the model's efficacy as an accessible and reliable tool for early DFU risk assessment, thereby facilitating prompt intervention strategies and enhancing overall patient outcomes.

Recovery Coach Program Implementation Across an Integrated Health System.

Peer recovery coaches (PRCs) are increasingly playing a role in helping patients with substance use disorders engage with treatment. PRCs can support and motivate patients in meeting their self-defined recovery goals, engaging in addiction treatment, navigating the health care system, and overcoming barriers to recovery. This support increases patient engagement and is cost-effective. Little has been written about integrating PRCs in health care settings. In this column, the authors describe the implementation of a PRC program with 23 coaches serving 5,662 participants in diverse clinical settings. The authors discuss key facilitators and barriers and opportunities for further research.

Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine.

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims to dissect the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic strategies can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. The main issues are ethical considerations related to patient privacy, biases, and the lack of reliability and generalizability. Furthermore, there is a need for high-quality real-world validation and the development of standardized protocols and policies to guide the implementation of these technologies in diverse clinical settings.

Unique model for pharmacist cross‐coverage in the ambulatory care setting

AbstractClinical cross‐coverage in ambulatory care pharmacy practice is currently lacking and can lead to gaps in patient care, lost revenue generation, and pharmacist burnout due to a lack of time and support for indirect patient care and non‐clinical activities. To address this need, a cross‐coverage model for a team of ambulatory care pharmacists practicing in diverse clinical settings was developed and is primarily supported by a single broadly‐trained pharmacist known as an ambulatory care generalist. Cross‐coverage from the ambulatory care generalist includes in‐person and virtual patient care as well as in‐basket management and is available to all clinic‐based pharmacists on the specialty and primary care team with a few select clinics involving a designated collaborative practice agreement. The cross‐covering pharmacist has been shown to positively impact productivity measures and components of pharmacist burnout. Widespread implementation of this cross‐coverage model has the potential to greatly impact pharmacy practice and patient care in the ambulatory care setting and increase sources of revenue.

EASIX (endothelial activation and stress index) predicts mortality in patients with coronary artery disease.

Coronary interventions reduce morbidity and mortality in patients with acute coronary syndrome. However, the risk of mortality for patients with coronary artery disease (CAD) additionally depends on their systemic endothelial health status. The 'Endothelial Activation and Stress Index' (EASIX) predicts endothelial complications and survival in diverse clinical settings. We hypothesized that EASIX may predict mortality in patients with CAD. In 1283 patients undergoing coronary catheterization (CC) and having a diagnosis of CAD, EASIX was measured within 52days (range -1year to -14days) before CC and correlated with overall survival. In an independent validation cohort of 1934 patients, EASIXval was measured within 174days (+ 28days to + 11years) after CC. EASIX predicted the risk of mortality after CC (per log2: hazard ratio (HR) 1.29, 95% confidence interval: [1.18-1.41], p < 0.001) in multivariable Cox regression analyses adjusting for age, sex, a high-grade coronary stenosis ≥ 90%, left ventricular ejection fraction, arterial hypertension and diabetes. In the independent cohort, EASIX correlated with EASIXval with rho = 0.7. The long-term predictive value of EASIXval was confirmed (per log2: HR 1.53, [1.42-1.64], p < 0.001) and could be validated by integrated Brier score and concordance index. Pre-established cut-offs (0.88-2.32) associated with increased mortality (cut-off 0.88: HR training: 1.63; HR validation: 1.67, p < 0.0001 and cut-off 2.32: HR training: 3.57; HR validation: 4.65, p < 0.0001). We validated EASIX as a potential biomarker to predict death of CAD patients, irrespective of the timing eitherbefore or after catheterization.

Identification of Pain through Actigraphy-Recorded Patient Movement: A Comprehensive Review.

Chronic pain affects over 50 million people in the United States, particularly older adults, making effective assessment and treatment essential in primary care. Actigraphy, which monitors and records limb movement to estimate wakefulness and sleep, has emerged as a valuable tool for assessing pain by providing insights into activity patterns. This review highlights the non-invasive, cost-effective nature of actigraphy in pain monitoring, along with its ability to offer continuous, detailed data on patient movement. However, actigraphy's reliance on physical activity as a proxy for pain, and its inability to directly measure pain intensity, limit its applicability to certain pain types, such as neuropathic pain. Further research is needed to overcome these limitations and to improve the effectiveness of actigraphy in diverse clinical settings.

Pulse oximetry in pediatric care: Balancing advantages and limitations.

Pulse oximetry has become a cornerstone technology in healthcare, providing non-invasive monitoring of oxygen saturation levels and pulse rate. Despite its widespread use, the technology has inherent limitations and challenges that must be addressed to ensure accurate and reliable patient care. To comprehensively evaluate the advantages, limitations, and challenges of pulse oximetry in clinical practice, as well as to propose recommendations for optimizing its use. A systematic literature review was conducted to identify studies related to pulse oximetry and its applications in various clinical settings. Relevant articles were selected based on predefined inclusion and exclusion criteria, and data were synthesized to provide a comprehensive overview of the topic. Pulse oximetry offers numerous advantages, including non-invasiveness, real-time feedback, portability, and cost-effectiveness. However, several limitations and challenges were identified, including motion artifacts, poor peripheral perfusion, ambient light interference, and patient-specific factors such as skin pigmentation and hemoglobin variants. Recommendations for optimizing pulse oximetry use include technological advancements, education and training initiatives, quality assurance protocols, and interdisciplinary collaboration. Pulse oximetry is crucial in modern healthcare, offering invaluable insights into patients' oxygenation status. Despite its limitations, pulse oximetry remains an indispensable tool for monitoring patients in diverse clinical settings. By implementing the recommendations outlined in this review, healthcare providers can enhance the effectiveness, accessibility, and safety of pulse oximetry monitoring, ultimately improving patient outcomes and quality of care.

A Comprehensive Review on Understanding Magnesium Disorders: Pathophysiology, Clinical Manifestations, and Management Strategies.

Magnesium is vital in a broad spectrum of physiological processes, including enzyme activity, energy production, and neuromuscular function. Despite its crucial role, magnesium disorders - comprising both deficiency (hypomagnesemia) and excess (hypermagnesemia) - are frequently underrecognized and inadequately managed in clinical practice. Magnesium deficiency is widespread, particularly among populations with chronic illnesses, the elderly, and those experiencing malnutrition, often leading to significant neuromuscular, cardiovascular, and metabolic complications. Conversely, hypermagnesemia, though less common, poses serious risks, especially in individuals with impaired renal function or those receiving high doses of magnesium supplements or medications. This review comprehensively examines magnesium disorders, detailing their pathophysiology, clinical manifestations, and management strategies. It highlights the essential functions of magnesium in maintaining cellular integrity, cardiovascular health, and bone structure and discusses the global prevalence and risk factors associated with magnesium imbalances. By offering insights into the current understanding of magnesium homeostasis and its disruptions, this review aims to enhance the awareness and treatment of magnesium-related conditions, ultimately improving patient outcomes across diverse clinical settings.

Experiences of podiatrists in the delivery of routine foot care with self-management

Objective: Explore the experiences of podiatrists in the provision of foot care and identify gaps in promoting self-care to their patients. Methods: This study adopted a qualitative research paradigm comprising semi-structured interviews with 16 podiatrists from diverse clinical settings and varying experiences from February to May 2023. The data was analyzed inductively to develop themes. Results: Four themes emerged from the data: 1) Provision of nail care service, 2) Perceived reasons for referral for podiatric nail care, 3) Self-management with various patient sub-groups, and 4) Effectiveness of self-management, all from our participants’ perspective. Podiatrists agree that pathological nails and high-risk feet warrant continual podiatry care. However, the majority are ambivalent about providing care for non-pathological nails due to fear, indifference, lack of social support, and physical limitations. Interviewees reported the importance of managing patients’ expectations of the role of podiatry in nail care from the first visit to avert potential over-reliance on the service. Clear explanations and equipping patients with the appropriate tools helped promote self-management. Conclusion: This paper examined the complexity of the demand for podiatry nail care services, focusing on podiatrists’ perspectives. It emphasized the importance of effective communication with patients to clarify the purpose of podiatry and to ensure sustainable podiatry care for the future. Level of evidence V; Therapeutic studies - investigating the results of treatment; Expert Opinion.

GaitKeeper: An AI-Enabled Mobile Technology to Standardize and Measure Gait Speed.

Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings.