Patient Care Strategies Research Articles

Integrating psychiatry and family medicine in the management of somatic symptom disorders: Diagnosis, collaboration, and communication strategies

AbstractSomatic symptom disorders (SSDs) present a complex interplay of physical and psychological factors, necessitating an integrative approach to diagnosis and management. This article explores the collaborative efforts between family medicine and psychiatry in addressing SSDs, emphasizing the importance of a multidisciplinary strategy for comprehensive patient care. Effective diagnosis involves recognizing the significance of both somatic symptoms and the patient's psychological response, with tools like structured clinical interviews and self‐report questionnaires playing crucial roles. Management strategies include psychotherapeutic interventions such as cognitive behavioral therapy (CBT), pharmacological treatments, and lifestyle modifications, all tailored to the patient's needs. Communication strategies are vital in validating patients' experiences while addressing underlying psychiatric issues. Techniques such as active listening, biopsychosocial framing, and the teach‐back method foster trust and improve treatment adherence. Cultural considerations and the use of interpreters enhance communication with diverse patient populations. Training programs for healthcare providers further improve competency in managing SSDs. This integrative approach aims to enhance patient outcomes by addressing the multifaceted nature of SSDs through collaborative care, effective communication, and comprehensive treatment planning.

The Role of Quantitative PCR in Evaluating the Clinical Significance of Human Bocavirus Detection in Children.

Human bocavirus (HBoV) has emerged as a significant pathogen primarily associated with respiratory infections in children. This study aimed to evaluate the clinical relevance of HBoV infection by quantifying viral loads in nasopharyngeal swabs from hospitalized children with acute respiratory infections (ARIs) and investigating correlations with clinical outcomes. A total of 957 children were tested, with HBoV detected in 73 cases (7.6%), either as a sole infection or co-infection with other respiratory viruses. Quantitative polymerase chain reaction (qPCR) was employed to measure viral load, and a threshold of 104 copies/mL was used to differentiate high and low viral concentrations. Results have shown that children with lower respiratory tract infections (LRTIs) had significantly higher viral loads, most notably in cases where HBoV was the sole pathogen. Additionally, children with pre-existing health conditions were more likely to have elevated HBoV concentrations compared to those who were previously healthy. Children with higher viral loads were more likely to require oxygen supplementation and receive empirical antibiotic therapy, indicating a more severe clinical course. This study underscores the importance of considering HBoV viral load in clinical diagnostics, as higher concentrations were associated with more severe presentations, including the need for oxygen support. Future research should focus on refining diagnostic thresholds and exploring HBoV's role in co-infections to enhance patient care strategies.

Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing

Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).Our findings demonstrate that setting PCA components to 16 optimally enhanced predictive accuracy, achieving a remarkable 98.6% accuracy in stroke prediction. Evaluation metrics underscored the robustness of our approach in handling class imbalance and improving model performance, also comparative analyses against traditional machine learning algorithms such as SVM, logistic regression, and Naive Bayes highlighted the superiority of our proposed method.

A Simulation-based Analysis Using Machine Learning Models to Optimize Patient Flow and Treatment Costs

This study investigates the application of discrete event simulation in analyzing patient management and cost dynamics within a hospital system. A simulation that integrates machine learning models, specifically Decision Trees, Random Forests, Support Vector Machines, and Gradient Boosting methods, to predict treatment costs and appointment availability was developed. Conducted over 30 days, the simulation generates synthetic data for training the models. The results are assessed in terms of the total number of patients treated, cumulative costs incurred, and the cost-effectiveness of each predictive model. The findings reveal significant variations in the performance of different machine learning techniques, demonstrating that adopting advanced analytics can substantially improve hospital resource management. This research aims to develop more efficient patient care strategies, contributing to optimizing hospital operations and enhancing patient experiences.

The validity and reliability of the Arabic version of the EQ-5D in atrial fibrillation patients in a conflict country: a study from Syria

BackgroundThe EQ-5D is one of the most commonly used tools to establish health-related quality of life (QoL). EQ-5D data in atrial fibrillation (AF) patients in the Middle East are lacking.ObjectivesThis study aims to evaluate the reliability and validity of the Arabic version of the EQ-5D in AF inpatients in Syria.MethodsThe study involved patients admitted to the emergency department of Tishreen’s University Hospital in Latakia with AF as the primary diagnosis between the 1st of June 2021 and the 1st of June 2023. Arabic versions of the EQ-5D, EQ-VAS and SF36 questionnaires were administered to patients. Validation was done using convergent, discriminant, and known-groups validity, while reliability was conducted using EQ-5D retesting within 2–4 weeks.Results432 participants were included in the study with a mean ± standard deviation of 63 ± 15. Males represented 242 (56%) of the participants. All hypotheses relating EQ-5D responses to external variables were satisfied. All three validation hypotheses demonstrated that the EQ-5D had the convergent, discriminant and known group validity to assess QoL in this cohort. The intraclass correlation coefficient (ICC) for test-retest reliability ranged between 0.74 and 0.88, while Cohen’s κ ranged between 0.72 and 0.86. Cronbach’s α value for internal consistency was 0.73.ConclusionThe Arabic version of EQ-5D was valid and reliable in measuring QoL in AF inpatients in Syria. This validation opens the door for more widespread use of the EQ-5D in Arabic-speaking regions, facilitating better-informed healthcare decisions and improving patient care strategies in Syria and other Middle Eastern countries.

7513 Presence Of Hispanic/Latino Physicians In Academic Research. Are We There Yet?

Abstract Disclosure: C.E. Guillen Lopez: None. A. Patel: None. A. Rocha: None. C. Arima: None. A. Molkina: None. L. Robles: None. A. Arce Gastelum: None. Hispanic/Latino endocrinologists' presence and active involvement in diabetes research play a pivotal role in shaping the trajectory of healthcare initiatives in the United States. Diabetes, a prevalent and complex health condition, disproportionately affects diverse communities, with Hispanics/Latinos being among the groups facing higher incidence rates. In this context, the significance of Hispanic/Latino endocrinologists cannot be overstated, as their unique perspectives and cultural insights contribute to a more nuanced understanding of the multifaceted factors influencing diabetes prevalence, management, and outcomes in the U.S. By actively involving Hispanic/Latino endocrinologists in diabetes research, we not only enhance the representation of diverse patient populations but also foster a more comprehensive approach to addressing the intricate intersections of genetics, lifestyle, and socio-cultural factors contributing to diabetes disparities in the nation. In the 2021 Physician Specialty Data Report, the Association of American Medical Colleges (AAMC) incorporated physician race and ethnicity information for the first time. Within the 7,354 actively practicing endocrinologists surveyed, only 636 individuals identified as Hispanic/Latino. The majority of these endocrinologists were predominantly dedicated to clinical practice. A descriptive analysis of 1011 abstracts on diabetes and glucose metabolism presented at the Endo Society from 2021 to 2023 revealed that 549 were authored by women and 458 by men. The majority (705 posters, 69.7%) originated from the U.S., and 306 (30.26%) were classified as international posters. Among these, 152 posters (15%) were associated with the Hispanic or Latino authors, but only 91 (9%) were published in the U.S. In conclusion, the engagement of Hispanic/Latino physicians in endocrinology is indispensable for advancing healthcare. Their participation brings diverse perspectives, facilitates a better understanding of health disparities, and promotes innovative research and patient care strategies. By actively involving Hispanic/Latino physicians, we contribute to a more comprehensive understanding of endocrine disorders and work towards a healthcare system that prioritizes inclusivity, cultural sensitivity, and collaboration, ultimately benefiting the well-being of individuals across diverse communities. Presentation: 6/2/2024

Sentiment analysis in medication adherence: using ruled-based and artificial intelligence-driven algorithms to understand patient medication experiences.

Studies are exploring ways to improve medication adherence, with sentiment analysis (SA) being an underutilized innovation in pharmacy. This technique uses artificial intelligence (AI) and natural language processing to assess text for underlying feelings and emotions. This study aimed to evaluate the use of two SA models, Valence Aware Dictionary for Sentiment Reasoning (VADER) and Emotion English DistilRoBERTa-base (DistilRoBERTa), for the identification of patients' sentiments and emotions towards their pharmacotherapy. A dataset containing 320,095 anonymized patients' reports of experiences with their medication was used. VADER assessed sentiment polarity on a scale from - 1 (negative) to + 1 (positive). DistilRoBERTa classified emotions into seven categories: anger, disgust, fear, joy, neutral, sadness, and surprise. Performance metrics for the models were obtained using the sklearn.metrics module of scikit-learn in Python. VADER demonstrated an overall accuracy of 0.70. For negative sentiments, it achieved a precision of 0.68, recall of 0.80, and an F1-score of 0.73, while for positive sentiments, it had a precision of 0.73, recall of 0.59, and an F1-score of 0.65. The AUC for the ROC curve was 0.90. DistilRoBERTa analysis showed that higher ratings for medication effectiveness, ease of use, and satisfaction corresponded with more positive emotional responses. These results were consistent with VADER's sentiment analysis, confirming the reliability of both models. VADER and DistilRoBERTa effectively analyzed patients' sentiments towards pharmacotherapy, providing valuable information. These findings encourage studies of SA in clinical pharmacy practice, paving the way for more personalized and effective patient care strategies.

Whole-blood RNA biomarkers for predicting survival in non-human primates following thoracic radiation

Radiation injury, either from radiotherapy or a mass-casualty event requires a health care system that can efficiently allocate resources to patients. We conducted a comprehensive transcriptome analysis of whole blood from a nonhuman primate model that received upper thoracic radiation (9.8–10.7 Gy). Blood samples were collected at multiple time points, extending up to 270 days post-irradiation with a minimum n = 6 for initial time points (Day 3-Day 40) and a total number of n = 28 primates. No males receiving the higher dose survived to Day 270. Using the Elastic Net model in R we found that pooling biomarkers from Day 3–21 increased our accuracy in discerning survival time, pleural effusion or dose compared to using biomarkers specific to a single day. For survival data, in predicting short term (less than 90 day), medium term (Day 91–269) or long-term survival (Day 270), prediction accuracy using only Day 3 data was 0.14 (95% Confidence Interval (CI) 0.1, 0.19) while pooled data for Male and Female was 0.76 (CI 0.69, 0.82). When pooled data was divided by biological sex, accuracy was 0.7 (CI 0.58, 0.8) for pooled data from Males and 0.84 (CI 0.76, 0.91) for Females. The development of RNA biomarkers as a tool to aid in clinical decision-making could significantly improve patient care in cases of radiation injury, whether from radiotherapy or mass-casualty events. Further validation and clinical translation of these findings could lead to improved patient care and management strategies in cases of radiation exposure.

Exploration of the potential association between newer antiseizure medications and arrhythmias: Integrating pharmacovigilance and bioinformatics evidence

IntroductionArrhythmias resulting from newer antiseizure medications (ASMs) may significantly impact the safety and quality of life of patients with epilepsy. This study investigated the potential association between new first-line or second-line ASMs and arrhythmias. MethodsPharmacovigilance analysis was conducted using data from the Food and Drug Administration Adverse Event Reporting System (FAERS) from 2004 to 2023. A disproportionality analysis was performed to compare newer ASMs with other drugs, using carbamazepine and valproate as positive controls. Newer ASMs were categorized into sodium channel (SCN) main mechanism, SCN possible mechanism, and non-SCN group. The bioinformatics analysis involved retrieving therapeutic gene targets for ASMs from the DrugBank and OMIM databases, as well as identifying arrhythmia disease targets from the GeneCards database. Additionally, enrichment analysis of gene ontology functions and KEGG pathways was conducted. ResultsA total of 3,457 cases of arrhythmias associated with newer ASMs were identified in the FAERS database. Disproportionality analysis indicates that brivaracetam (IC025 = 0.08), zonisamide (IC025 = 0.13), eslicarbazepine (IC025 = 0.39), and lacosamide (IC025 = 0.84) exhibited a positive signal for arrhythmias, with signals predominantly observed in the SCN main mechanism group. Furthermore, bioinformatics analysis revealed the involvement of adrenergic signaling in cardiac myocytes, as well as the participation of sodium channel genes in ASM-induced arrhythmias. ConclusionOur findings suggest a potential association between SCN-ASMs and arrhythmias, highlighting the importance of monitoring and evaluating the safety profiles of newer ASMs in clinical practice. Further research is necessary to elucidate the underlying mechanisms and inform patient care strategies.

Investigating the pathophysiology and evolution of internal carotid dissection: a fluid-structure interaction simulation study.

Arterial dissection, a condition marked by the tearing of the carotid artery's inner layers, can result in varied clinical outcomes, including progression, stability, or spontaneous regression. Understanding these outcomes' underlying mechanisms is crucial for enhancing patient care, particularly with the increasing use of computer simulations in medical diagnostics and treatment planning. The aim of this study is to utilize computational analysis of blood flow and vascular wall to: (1) understand the pathophysiology of stroke-like episodes in patients with carotid artery dissection; and (2) assess the effectiveness of this method in predicting the evolution of carotid dissection. Utilizing contrast-enhanced magnetic resonance angiography (MRA), we segmented images of the patient's right internal carotid artery. These images were transformed into 3D solids for simulation in Ansys multifisic software, employing a two-way fluid structure interaction (FSI) analysis. Simulations were conducted across two wall conditions (atherosclerotic and normal) and three pressure states (hypotension, normotension, hypertension). The simulations indicated a significant pressure discrepancy between the true and false lumens of the artery. This suggests that flap motion and functional occlusion under hypertensive conditions could be the cause of the clinical episodes. Thrombotic risk and potential for dissection extension were not found to be critical concerns. However, a non-negligible risk of vessel dilation was assessed, aligning with the patient's clinical follow-up data. This study highlights specific hemodynamic parameters that could elucidate carotid artery dissection's mechanisms, offering a potential predictive tool for assessing dissection progression and informing personalized patient care strategies.

Explainability of CNN-based Alzheimer's disease detection from online handwriting.

With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer's disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer's disease detection, the widespread adoption of deep learning models raises concerns about their explainability. The lack of explainability in deep learning models for online handwriting analysis is a critical gap in the literature in the context of Alzheimer's disease detection. This paper addresses this challenge by interpreting predictions from a Convolutional Neural Network applied to multivariate time series data, generated by online handwriting data associated with continuous loop series handwritten on a graphical tablet. Our explainability methods reveal distinct motor behavior characteristics for healthy individuals and those diagnosed with Alzheimer's. Healthy subjects exhibited consistent, smooth movements, while Alzheimer's patients demonstrated erratic patterns marked by abrupt stops and direction changes. This emphasizes the critical role of explainability in translating complex models into clinically relevant insights. Our research contributes to the enhancement of early diagnosis, providing significant and reliable insights to stakeholders involved in patient care and intervention strategies. Our work bridges the gap between machine learning predictions and clinical insights, fostering a more effective and understandable application of advanced models for Alzheimer's disease assessment.

The Impact of Pain, Threat Perception and Emotional Distress on Suicide Risk in Individuals with Colorectal Cancer.

Colorectal cancer (CRC) can significantly impact mental health, increasing suicide risk. Variables such as pain and threat perception may be crucial. This study aims to identify predictors of suicide risk in individuals with CRC. A total of 71 participants (76.06% men) aged 27 to 88 years (M = 65.18, SD = 12.02) were assessed using the SF-36 for pain, the Brief Illness Perception Questionnaire (B-IPQ) for threat perception, the Hospital Anxiety and Depression Scale (HADS) for emotional distress, and the Plutchik suicide risk scale for suicide risk. Descriptive, comparative, and correlational analyses were performed, followed by a linear regression analysis. Nearly 20% of participants exhibited high suicide risk. There was a moderate-to-high association between suicide risk and the perception of threat, pain, and emotional distress. The linear regression model explained 39% of the variance in suicide risk, with threat perception, pain, and emotional distress as significant predictors. These results indicate the need for multidisciplinary care for individuals with CRC, including emotional support from health psychologists, and holistic, human-centered care from nursing and medical professionals. Future research is necessary to further explore these relationships and improve patient care strategies.

Patient Experiences and Perceptions with Infections Due to Multidrug-Resistant Organisms: A Systematic Review.

Infections by multidrug-resistant organisms (MDROs) pose significant public health challenges, including increased mortality rates, healthcare costs, and significant impacts on the quality of life for patients. Utilizing a systematic review methodology adhering to PRISMA guidelines, we performed a comprehensive search across three databases, identifying 20 relevant studies that investigated the psychological effects of infections due to MDROs on hospitalized adults. The primary outcomes examined included depression, anxiety, and other psychosocial impacts, while secondary outcomes included patient and caregiver understanding of the infection. Findings revealed consistent associations between contact isolation due to MDRO infections and heightened levels of depression and anxiety among patients, although evidence regarding the impact on anger was mixed. Other psychological aspects, such as feelings of stigmatization and reduced healthcare provider interactions, were also recorded. The current systematic review highlights the importance of addressing these psychological effects through holistic, patient-centered care approaches, emphasizing the need for better communication and comprehensive education for both patients and healthcare providers. Our findings suggest that mitigating the psychological burden of MDROs can enhance overall patient care and outcomes and call for further research to optimize care strategies for patients hospitalized for infections due to MDROs.

Navigating Ankle Fracture Surgery in the Shadow of COVID-19

Background This case control study aimed to evaluate the impact of preoperative COVID-19 diagnosis on postoperative complications in patients undergoing ankle fracture surgery using the National COVID-19 Cohort Collaborative (N3C) database. The investigation focused on the interplay between COVID-19 diagnosis timing, patient characteristics, and clinical outcomes, particularly considering the potential mechanisms by which COVID-19 may contribute to increased complications. Methods This case control study included patients who underwent ankle fracture surgery using the N3C database. The cohort was divided into two groups: patients without COVID-19 diagnosis within 12 weeks of surgery (n = 16 806) and those with a positive COVID-19 diagnosis within 12 weeks of surgery (n = 369). Demographic factors were analyzed. Clinical outcomes encompassed deep vein thrombosis (DVT), sepsis, surgical site infection, bleeding, acute kidney injury, 30-day mortality, and 365-day mortality. Multivariate logistic regression analyses were conducted. Results The COVID-19–positive cohort displayed a slightly higher mean age (52.95 ± 18.43 vs 51.62 ± 18.36, P = .169) and body mass index (34.88 ± 9.99 vs 33.86 ± 8.80, P = .028) compared to the negative cohort. Although some outcomes, such as DVT and sepsis, demonstrated slightly higher frequencies in the COVID-19–positive group, these differences were not statistically significant. Adjusted odds ratios (AORs) for various COVID-19 diagnosis periods were generally not significant, except for a heightened risk of 30-day all-cause mortality associated with COVID-19 positivity within 0 to 2 weeks of surgery (AOR = 6.29, P = .003). Conclusions Preoperative COVID-19 diagnosis within 12 weeks did not exhibit a significant association with most postoperative complications. While this study did not unveil substantial COVID-19–related effects, acknowledging the broader context of the pandemic remains essential in guiding comprehensive patient care strategies. Level of Evidence: Level III

A 37-year follow-up after Skoog lip repair of unilateral cleft lip and palate: what is the long-term verdict on lip appearance by different panels?

BackgroundThe study examined long-term lip appearance after Skoog’s lip repair in unilateral cleft, lip, and palate (UCLP) patients. To include different views, three different panels respectively, rated level of lip appearance and desire for improvement. The secondary aim was to investigate the correlation between the self-reported satisfaction of UCLP patients and controls with the panel assessments.MethodsCropped frontal photographs of the lips of 74 UCLP-treated individuals and 61 non-cleft individuals were assessed twice by three panels of five cleft surgeons (Professional panel), nine laymen (Layman panel), and five individuals with CLP (Cleft panel) using a web-based platform, at a mean follow-up of 37 years post-lip surgery.ResultsAll three panels rated the lip appearance of UCLP patients worse than that of the controls (p < 0.001). The Professional panel reported higher satisfaction levels and less desire to change lip appearance than the Layman and Cleft panels. Intra-rater agreements were, in general, moderate in the Professional panel ICC (0.57–0.82) and Laymen panel ICC (0.63–0.73) and poor in the Cleft panel ICC (0.21–0.36). The inter-rater agreement was, in general, poor in all panels. No significant correlation was found between the patient’s Self-assessments and any of the panel assessments.ConclusionsAll panels’ rated lip appearance of adults born with UCLP treated with Skoog’s technique as worse than that of non-cleft controls. The study highlights a significant discrepancy between the Professional panel’s more favorable assessment and the more critical views of the laymen and cleft panels, contradicting the initial hypothesis of uniform perception across panels. The lack of correlation between patient self-assessment and panel assessments underscores the subjective nature of lip satisfaction, emphasizing the need for personalized patient care strategies in CLP treatment outcomes.Level of evidenceNot gradable.

Light gradient boost tree classifier predictions on appendicitis with periodontal disease from biochemical and clinical parameters.

Untreated periodontitis significantly increases the risk of tooth loss, often delaying treatment due to asymptomatic phases. Recent studies have increasingly associated poor dental health with conditions such as rheumatoid arthritis, diabetes, obesity, pneumonia, cardiovascular disease, and renal illness. Despite these connections, limited research has investigated the relationship between appendicitis and periodontal disease. This study aims to predict appendicitis in patients with periodontal disease using biochemical and clinical parameters through the application of a light gradient boost tree classifier. Data from 125 patient records at Saveetha Institute of Dental College and Medical College were pre-processed and analyzed. We utilized data preprocessing techniques, feature selection methods, and model development approaches to estimate the risk of appendicitis in patients with periodontitis. Both Random Forest and Light Gradient Boosting algorithms were evaluated for accuracy using confusion matrices to assess their predictive performance. The Random Forest model achieved an accuracy of 94%, demonstrating robust predictive capability in this context. In contrast, the Light Gradient Boost algorithms achieved a significantly higher accuracy of 98%, underscoring their superior predictive efficiency. This substantial difference highlights the importance of algorithm selection and optimization in developing reliable predictive models. The higher accuracy of Light Gradient Boost algorithms suggests effective minimization of prediction errors and improved differentiation between appendicitis with periodontitis and healthy states. Our study identifies age, white blood cell count, and symptom duration as pivotal predictors for detecting concurrent periodontitis in acute appendicitis cases. The newly developed prediction model introduces a novel and promising approach, providing valuable insights into distinguishing between periodontitis and acute appendicitis. These findings highlight the potential to improve diagnostic accuracy and support informed clinical decision-making in patients presenting with both conditions, offering new avenues for optimizing patient care strategies.

Why Symptoms Linger in Quiescent Crohn's Disease: Investigating the Impact of Sulfidogenic Microbes and Sulfur Metabolic Pathways.

Even in the absence of inflammation, persistent symptoms in patients with Crohn's disease (CD) are prevalent and worsen quality of life. We previously demonstrated enrichment in sulfidogenic microbes in quiescent Crohn's disease patients with ( qCD+S ) vs. without persistent GI symptoms ( qCD-S ). Thus, we hypothesized that sulfur metabolic pathways would be enriched in stool while differentially abundant microbes would be associated with important sulfur-metabolic pathways in qCD+S. We performed a multi-center observational study nested within SPARC IBD. Quiescent inflammation was defined by fecal calprotectin level <150 mcg/g. Persistent symptoms were defined by CD-PRO2. Active CD ( aCD ) and non-IBD diarrhea-predominant irritable bowel syndrome ( IBS-D ) were included as controls. Thirty-nine patients with qCD+S, 274 qCD-S, 21 aCD, and 40 IBS-D underwent paired shotgun metagenomic sequencing and untargeted metabolomic profiling. The fecal metabolome in qCD+S was significantly different relative to qCD-S and IBS-D but not aCD. Patients with qCD+S were enriched in sulfur-containing amino acid pathways, including cysteine and methionine, as well as serine, glycine, and threonine. Glutathione and nicotinate/nicotinamide pathways were also enriched in qCD+S relative to qCD-S, suggestive of mitochondrial dysfunction, a downstream target of H 2 S signaling. Multi-omic integration demonstrated that enriched microbes in qCD+S were associated with important sulfur-metabolic pathways. Bacterial sulfur-metabolic genes, including CTH , isfD , sarD , and asrC , were dysregulated in qCD+S. Finally, sulfur metabolites with and without sulfidogenic microbes showed good accuracy in predicting presence of qCD+S. Microbial-derived sulfur pathways and downstream mitochondrial function are perturbed in qCD+S, which implicate H 2 S signaling in the pathogenesis of this condition. Future studies will determine whether targeting H 2 S pathways results in improved quality of life in qCD+S. What is Already Known Even in the absence of inflammation, persistent gastrointestinal symptoms are common in Crohn's disease.The microbiome is altered in quiescent Crohn's disease patients with persistent symptoms, but the functional significance of these changes is unknown. What is New Here Sulfur metabolites and sulfur metabolic pathways were enriched in stool in quiescent Crohn's disease patients with persistent symptoms.Multi-omic integration showed enriched microbes were associated with important sulfur metabolic pathways in quiescent Crohn's disease patients with persistent symptoms. How Can This Study Help Patient Care Strategies to decrease sulfidogenic microbes and associated sulfur metabolic pathways could represent a novel strategy to improve quality of life in quiescent Crohn's disease with persistent GI symptoms.

Effects of pain, hospitalization, and medication on depressive symptom in Syrian patients with chronic diseases

Chronic diseases and pain exacerbate depressive symptom in Syria. Limited research on hospital-induced depressive symptom among Syrian patients with chronic diseases warrants further study. A cross-sectional study in four Damascus hospitals revealed high rates of pain and depressive symptom. This study aims to investigate the prevalence and severity of chronic pain and hospital-induced depressive symptom in Syrian patients, as well as the relationship between pain, depressive symptom, and medication behavior. This study analyzes the impact of pain, hospitalization, and medication on patients with chronic diseases. The four Damascus hospitals included 453 patients from various departments. Data were collected through structured interviews and internationally recognized scales such as the PSEQ, HADS, and MMAS. These findings offer insights into pain management and psychological well-being, with implications for patient care and support strategies. The study involved 453 patients with chronic diseases, with gender distribution showing 46.6% females and 53.4% males. The age range was from 7 to 87 years, with an average of 46.87 years. Chi-square tests revealed a significant connection between gender and HADS-A scores, where 48.3% of females had abnormalities (χ2 (1, N = 453) = 7.125, p = 0.028). Marital status was significantly associated with anxiety and depressive symptom levels, particularly among widowed and divorced patients. Employment status, education, and comorbidity were linked to abnormal HADS-A scores, while education level showed a positive correlation with HADS-D scores. ANOVA tests showed significant differences in MMAS scores across income groups (F (3, 449) = 3.167, p = 0.024), with a notable difference between low-income and lower-middle-income groups (mean difference = 0.389, p = 0.031. Chronic pain and HID are prevalent among Syrian patients with chronic diseases and influenced by socio-demographic factors. Personalized interventions are needed to address psychological symptoms and medication behavior.

Visualizing Nursing Narratives: An Evaluation of Latent Dirichlet Allocation Topic Modeling for Care Reports.

The increasing volume of unstructured textual data in healthcare, particularly in nursing care reports, presents both challenges and opportunities for enhancing patient care and operational efficiency. This study explores the application of Latent Dirichlet Allocation (LDA) topic modeling to analyze free-text nursing narratives from inpatient stays in three different clinics, aiming to uncover the latent thematic structures within. Utilizing the R programming environment and the visualization tool LDAvis, we identified three main themes: "Patient Well-being," "Patient Mobility and Care Activities," and "Treatment and Pain Management," the latter combining two closely related but initially distinct topics due to their overlapping content. Our findings demonstrate the potential of LDA topic modeling in extracting meaningful insights from nursing narratives, which could inform patient care strategies and healthcare practices. However, the study also highlights significant challenges associated with the method, including the sensitivity to parameter settings, the lack of updates for key software packages, and concerns about reproducibility. These issues highlight the need for meticulous parameter validation and the exploration of alternative text analysis methodologies for future research. By addressing these methodological challenges and emphasizing the importance of comparative method analysis, this study contributes to the advancement of text analytics in healthcare. It opens avenues for further research aimed at developing more robust, efficient, and accessible tools for analyzing free-text data, thereby enhancing the ability of healthcare professionals to use unstructured data to improve decision making and patient outcomes.

Determining the prognostic value of CRP and neutrophil lymphocyte ratio in patients hospitalized for deep neck infection

ObjectivesThis study aims to assess the impact of the Neutrophil/Lymphocyte Ratio (NLR) and C-Reactive Protein (CRP), both markers of systemic inflammation, on the duration of hospitalization for Deep Neck Infections (DNIs). MethodsThe research encompassed patients of all age groups admitted between January 2016 and January 2021 due to DNIs. Patient data, including demographic details, etiology, comorbidities, radiological findings, treatment specifics, laboratory results (CRP values, leukocyte counts, neutrophil counts, lymphocyte counts, NLR), culture outcomes, length of stay, complications, mortalities were retrospectively evaluated. Patients were categorized into two groups based on their hospitalization duration: less than 7-days and 7-days or more. Univariate and multivariate analyses were conducted to examine the association between age, NLR, CRP, and hospital stay length. ResultsThe study encompassed 275 patients, with a mean age of 36 ± 20.2 years. The mean hospital stay was 9.6 ± 6.6 days. Tonsillopharyngeal infections were the most common etiology (34%). Notably, in both univariate and multivariate analyses, age, NLR, and CRP values demonstrated significant (p < 0.05) predictive influence on hospitalization duration. ConclusionAge emerges as a determinant that affects hospital stay duration in DNIs. Moreover, NLR is proven to be comparable to CRP in predicting hospitalization length for these patients. NLR's feasibility as a cost-effective predictive marker, being conveniently derived from routine complete blood count assessments, adds to its clinical significance. This study underscores the potential value of NLR and CRP in informing patient management and care strategies for DNIs.