Optimizing ChatGPT's Interpretation and Reporting of Delirium Assessment Outcomes: Exploratory Study.

BACKGROUND: Generative artificial intelligence (AI) and large language models, such as OpenAI's ChatGPT, have shown promising potential in supporting medical education and clinical decision-making, given their vast knowledge base and natural language processing capabilities. As a general purpose AI system, ChatGPT can complete a wide range of tasks, including differential diagnosis without additional training. However, the specific application of ChatGPT in learning and applying a series of specialized, context-specific tasks mimicking the workflow of a human assessor, such as administering a standardized assessment questionnaire, followed by inputting assessment results in a standardized form, and interpretating assessment results strictly following credible, published scoring criteria, have not been thoroughly studied. OBJECTIVE: This exploratory study aims to evaluate and optimize ChatGPT's capabilities in administering and interpreting the Sour Seven Questionnaire, an informant-based delirium assessment tool. Specifically, the objectives were to train ChatGPT-3.5 and ChatGPT-4 to understand and correctly apply the Sour Seven Questionnaire to clinical vignettes using prompt engineering, assess the performance of these AI models in identifying and scoring delirium symptoms against scores from human experts, and refine and enhance the models' interpretation and reporting accuracy through iterative prompt optimization. METHODS: We used prompt engineering to train ChatGPT-3.5 and ChatGPT-4 models on the Sour Seven Questionnaire, a tool for assessing delirium through caregiver input. Prompt engineering is a methodology used to enhance the AI's processing of inputs by meticulously structuring the prompts to improve accuracy and consistency in outputs. In this study, prompt engineering involved creating specific, structured commands that guided the AI models in understanding and applying the assessment tool's criteria accurately to clinical vignettes. This approach also included designing prompts to explicitly instruct the AI on how to format its responses, ensuring they were consistent with clinical documentation standards. RESULTS: Both ChatGPT models demonstrated promising proficiency in applying the Sour Seven Questionnaire to the vignettes, despite initial inconsistencies and errors. Performance notably improved through iterative prompt engineering, enhancing the models' capacity to detect delirium symptoms and assign scores. Prompt optimizations included adjusting the scoring methodology to accept only definitive "Yes" or "No" responses, revising the evaluation prompt to mandate responses in a tabular format, and guiding the models to adhere to the 2 recommended actions specified in the Sour Seven Questionnaire. CONCLUSIONS: Our findings provide preliminary evidence supporting the potential utility of AI models such as ChatGPT in administering standardized clinical assessment tools. The results highlight the significance of context-specific training and prompt engineering in harnessing the full potential of these AI models for health care applications. Despite the encouraging results, broader generalizability and further validation in real-world settings warrant additional research.

Will Artificial Intelligence Be "Better" Than Humans in the Management of Syncope?

Clinical decision-making regarding syncope poses challenges, with risk of physician error due to the elusive nature of syncope pathophysiology, diverse presentations, heterogeneity of risk factors, and limited therapeutic options. Artificial intelligence (AI)-based techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), can uncover hidden and nonlinear connections among syncope risk factors, disease features, and clinical outcomes. ML, DL, and NLP models can analyze vast amounts of data effectively and assist physicians to help distinguish true syncope from other types of transient loss of consciousness. Additionally, short-term adverse events and length of hospital stay can be predicted by these models. In syncope research, AI-based models shift the focus from causality to correlation analysis between entities. This prompts the search for patterns rather than defining a hypothesis to be tested a priori. Furthermore, education of students, doctors, and health care providers engaged in continuing medical education may benefit from clinical cases of syncope interacting with NLP-based virtual patient simulators. Education may be of benefit to patients. This article explores potential strengths, weaknesses, and proposed solutions associated with utilization of ML and DL in syncope diagnosis and management. Three main topics regarding syncope are addressed: 1) clinical decision-making; 2) clinical research; and 3) education. Within each domain, we question whether "AI will be better than humans," seeking evidence to support our objective inquiry.

Effect of abatacept versus conventional synthetic disease modifying anti-rheumatic drugs on rheumatoid arthritis-associated interstitial lung disease.

BACKGROUND/AIMS: To compare the effects of abatacept and conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs) on the progression and development of rheumatoid arthritis-associated interstitial lung disease (RA-ILD). METHODS: This multi-center retrospective study included RA patients receiving abatacept or csDMARDs who underwent at least two pulmonary function tests and/or chest high-resolution computed tomography (HRCT). We compared the following outcomes between the groups: progression of RA-ILD, development of new ILD in RA patients without ILD at baseline, 28-joint Disease Activity Score with the erythrocyte sedimentation rate (DAS28-ESR), and safety. Longitudinal changes were compared between the groups by using a generalized estimating equation. RESULTS: The study included 123 patients who were treated with abatacept (n = 59) or csDMARDs (n = 64). Nineteen (32.2%) and 38 (59.4%) patients treated with abatacept and csDMARDs, respectively, presented with RA-ILD at baseline. Newly developed ILD occurred in one patient receiving triple csDMARDs for 32 months. Among patients with RA-ILD at baseline, ILD progressed in 21.1% of cases treated with abatacept and 34.2% of cases treated with csDMARDs during a median 21-month follow-up. Longitudinal changes in forced vital capacity and diffusing capacity for carbon monoxide were comparable between the two groups. However, the abatacept group showed a more significant decrease in DAS28-ESR and glucocorticoid doses than csDMARDs group during the follow-up. The safety of both regimens was comparable. CONCLUSION: Abatacept and csDMARDs showed comparable effects on the development and stabilization of RA-ILD. Nevertheless, compared to csDMARDs, abatacept demonstrated a significant improvement in disease activity and led to reduced glucocorticoid use.

Low-Dose Radiotherapy Attenuates Experimental Autoimmune Arthritis by Inducing Apoptosis of Lymphocytes and Fibroblast-Like Synoviocytes.

Low-dose radiotherapy (LDRT) has been explored as a treatment option for various inflammatory diseases; however, its application in the context of rheumatoid arthritis (RA) is lacking. This study aimed to elucidate the mechanism underlying LDRT-based treatment for RA and standardize it. LDRT reduced the total numbers of immune cells, but increased the apoptotic CD4+ T and B220+ B cells, in the draining lymph nodes of collagen induced arthritis and K/BxN models. In addition, it significantly reduced the severity of various pathological manifestations, including bone destruction, cartilage erosion, and swelling of hind limb ankle. Post-LDRT, the proportion of apoptotic CD4+ T and CD19+ B cells increased significantly in the PBMCs derived from human patients with RA. LDRT showed a similar effect in fibroblast-like synoviocytes as well. In conclusion, we report that LDRT induces apoptosis in immune cells and fibro-blast-like synoviocytes, contributing to attenuation of arthritis.

Machine learning approaches to identify spatial factors and their influential distances for heavy metal contamination in downstream sediment.

Contaminated sediments can adversely affect aquatic ecosystems, making the identification and management of pollutant sources extremely important. In this study, we proposed machine learning approaches to reveal sources and their influential distances for heavy metal contamination of downstream sediment. We employed classification models with artificial neural networks (ANN) and random forest (RF), respectively, to predict the heavy metal contamination of stream sediments using upland environmental variables as input features. A comprehensive Korean nationwide monitoring database containing 1546 datasets was used to train and test the models. These datasets encompass the concentrations of eight heavy metals (Ar, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in sediment samples collected from 160 stream sites across the nation from 2014 to 2018. Model's prediction accuracy was evaluated for input feature sets from different influential upland areas defined by different buffer radii and the watershed boundary for each site. Although both ANN and RF models were unsatisfactory in predicting heavy metal quartile classes, RF-classifiers with adaptive synthetic oversampling (ORFC) showed reasonably well-predicted classes of the sediment samples based on the Canada's Sediment Quality Guidelines (accuracy ranged from 0.67 to 0.94). The best influential distance (i.e., buffer radius) was determined for each heavy metal based on the accuracy of ORFC. The results indicated that Cd, Cu and Pb had shorter influential distances (1.5-2.0 km) than the other heavy metals with little difference in accuracy for different influential distances. Feature importance calculation revealed that upland soil contamination was the primary factor for Hg and Ni, while residential areas and roads were significant features associated with Pb and Zn contamination. This approach offers information on major contamination sources and their influential areas to be prioritized for managing contaminated stream sediments.

Multivariate analysis of multimodal brain structure predicts individual differences in risk and intertemporal preference.

Large changes to brain structure (e.g., from damage or disease) can explain alterations in behavior. It is therefore plausible that smaller structural differences in healthy samples can be used to better understand and predict individual differences in behavior. Despite the brain's multivariate and distributed structure-to-function mapping, most studies have used univariate analyses of individual structural brain measures. Here we used a multivariate approach in a multimodal data set composed of volumetric, surface-based, diffusion-based, and functional resting-state MRI measures to predict reliable individual differences in risk and intertemporal preferences. We show that combining twelve brain structure measures led to better predictions across tasks than using any individual measure, and by examining model coefficients, we visualize the relative contribution of different brain measures from different brain regions. Using a multivariate approach to brain structure-to-function mapping that combines across many brain structure properties, along with reliably measured behavior phenotypes, may increase out-of-sample prediction accuracies and insight into neural underpinnings. Furthermore, this methodological approach may be useful to improve predictions and neural insight across basic, translational, and clinical research fields.

Risk factors and risk stratification approaches for delirium screening: A Geriatric Emergency Department Guidelines 2.0 systematic review.

OBJECTIVE: As part of the Geriatric Emergency Department (ED) Guidelines 2.0 project, we conducted a systematic review to find risk factors or risk stratification approaches that can be used to identify subsets of older adults who may benefit from targeted ED delirium screening. METHODS: An electronic search strategy was developed with a medical librarian, conducted in April 2021 and November 2022. Full-text studies of patients ≥65 years assessed for prevalent delirium in the ED were included. Risk of bias was assessed using the McMaster University Clarity Group tool. Outcomes measures pertained to the risk stratification method used. Due to heterogeneity of patient populations, risk stratification methods, and outcomes, a meta-analysis was not conducted. RESULTS: Our search yielded 1878 unique citations, of which 13 were included. Six studies developed a novel delirium risk score with or without evaluation of specific risk factors, six studies evaluated specific risk factors only, and one study evaluated an existing nondelirium risk score for association with delirium. The most common risk factor was history of dementia, with odds ratios ranging from 3.3 (95% confidence interval [CI] 1.2-8.9) to 18.33 (95% CI 8.08-43.64). Other risk factors that were consistently associated with increased risk of delirium included older age, use of certain medications (such as antipsychotics, antidepressants, and opioids, among others), and functional impairments. Of the studies that developed novel risk scores, the reported area under the curve ranged from 0.77 to 0.90. Only two studies reported potential impact of the risk stratification tool on screening burden. CONCLUSIONS: There is significant heterogeneity, but results suggest that factors such as dementia, age over 75, and functional impairments should be used to identify older adults who are at highest risk for ED delirium. No studies evaluated implementation of a risk stratification method for delirium screening or evaluated patient-oriented outcomes.

Evaluation of medications used for opioid use disorder in emergency departments: A cross-sectional analysis of the 2020 National Hospital Ambulatory Medical Care Survey.

INTRODUCTION: Opioid use disorder (OUD) is a significant health issue impacting millions in the United States (US). Medications used for OUD (MOUD) (e.g., buprenorphine, methadone, naltrexone) and medications for overdose and symptom management (e.g., naloxone, clonidine) have been shown to be safe and effective tools in clinical management. MOUD therapy in Emergency Departments (EDs) improves patient outcomes and enhances engagement with formal addiction treatment; however, provider factors and institutional barriers have created hurdles to ED-based MOUD treatment and heterogeneity in ED based OUD care. We used a nationally representative dataset, the National Hospital Ambulatory Medical Care Survey (NHAMCS) to characterize MOUD prescribing practices across patient demographics, geographic regions, payers, providers, and comorbidities in EDs. METHODS: NHAMCS is a survey conducted by the US Census Bureau assessing utilization of ambulatory healthcare services nationally. Survey staff compile encounter records from a nationally representative sample of EDs. We conducted a cross-sectional study using this data to assess visits in 2020 among patients aged 18-64 presenting with an opioid overdose or OUD. We estimated the proportion of patients who had any MOUD, clonidine, or naloxone treatment and 95% confidence intervals (CI). We modeled the association between patient demographic, location, comorbidities, and provider characteristics with receipt of MOUD treatment as unadjusted odds ratios (OR) and 95% CI. RESULTS: There was a weighted frequency of 469,434 patients who were discharged from EDs after being seen for OUD or overdose. Naloxone, clonidine, and buprenorphine were the most frequent treatments administered and/or prescribed for OUDs or overdose. Overall, 54,123 (11.5%, 95%CI 0-128,977) patients who were discharged from the ED for OUDs or overdose received at least one type of MOUD. Hispanic race, (OR 17.9, 95%CI 1.33-241.90) and Western region (OR43.77, 95%CI 2.97-645.27) were associated with increased odds of receiving MOUDs, while arrival by ambulance was associated with decreased odds of receiving MOUDs (OR0.01, 95%CI 0.001-0.19). Being seen by an APP or physician assistant was associated with MOUD treatment (OR 16.68, 95%CI: 1.41-152.33; OR: 13.84, 95%CI: 3.58-53.51, respectively). CONCLUSION: Our study findings suggest that MOUD and other medications for opioid overdose are infrequently used in the ED setting. This finding was especially notable in race, geographic region, mode of arrival, and those seen by APP, underscoring the need for further study into the root causes of these disparities. Our study provides a foundational understanding of MOUD patterns, guiding future research as the landscape of OUD treatment continues to shift.

A Nationwide Emergency Department Data Analysis to Predict Beers List Medications Use Among Older Adults.

BACKGROUND: The use of potentially inappropriate medications (PIMs) is considered an important quality indicator for older adults seen in the ambulatory care setting. STUDY OBJECTIVES: To evaluate the pattern of potentially inappropriate medication (PIMs) use as specified in the Beers Criteria, for older adults during emergency department (ED) visits in the United States. METHODS: Using data from the National Hospital Ambulatory Care Survey (NHAMCS) we identified older adults (age 65 or older) discharged home from an ED visit in 2019. We defined PIMs as those with an 'avoid' recommendation under the American Geriatrics Society (AGS) 2019 Beers Criteria in older adults. Logistic regression models were used to assess demographic, clinical, and hospital factors associated with the use of any PIMs upon ED discharge. RESULTS: Overall, 5.9% of visits by older adults discharged from the ED included administration or prescriptions for PIMs. Among those who received any PIMs, 25.5% received benzodiazepines, 42.5 % received anticholinergics, 1.4% received nonbenzodiazepine hypnotics, and 0.5% received barbiturates. A multivariable model showed statistically significant associations for age 65 to 74 (OR 1.91, 95% CI 1.39-2.62 vs. age >=75), dementia (OR 0.45, 95% CI 0.21-0.95), lower immediacy (OR 2.45, 95% CI 1.56-3.84 vs. higher immediacy), and Northeastern rural region (OR 0.34, 95% CI 0.21-0.55 vs. Midwestern rural). CONCLUSION: We found that younger age and lower immediacy were associated with increased prescriptions of PIMs for older adults seen, while dementia and Northeastern rural region was associated with reduced use of PIMs seen and discharged from EDs in United States.

Enhancing healthcare access for an older population: The age-friendly emergency department.

Healthcare systems face significant challenges in meeting the unique needs of older adults, particularly in the acute setting. Age-friendly healthcare is a comprehensive approach using the 4Ms framework-what matters, medications, mentation, and mobility-to ensure that healthcare settings are responsive to the needs of older patients. The Age-Friendly Emergency Department (AFED) is a crucial component of a holistic age-friendly health system. Our objective is to provide an overview of the AFED model, its core principles, and the benefits to older adults and healthcare clinicians. The AFED optimizes the delivery of emergency care by integrating age-specific considerations into various aspects of (1) ED physical infrastructure, (2) clinical care policies, and (3) care transitions. Physical infrastructure incorporates environmental modifications to enhance patient safety, including adequate lighting, nonslip flooring, and devices for sensory and ambulatory impairment. Clinical care policies address the physiological, cognitive, and psychosocial needs of older adults while preserving focus on emergency issues. Care transitions include communication and involving community partners and case management services. The AFED prioritizes collaboration between interdisciplinary team members (ED clinicians, geriatric specialists, nurses, physical/occupational therapists, and social workers). By adopting an age-friendly approach, EDs have the potential to improve patient-centered outcomes, reduce adverse events and hospitalizations, and enhance functional recovery. Moreover, healthcare clinicians benefit from the AFED model through increased satisfaction, multidisciplinary support, and enhanced training in geriatric care. Policymakers, healthcare administrators, and clinicians must collaborate to standardize guidelines, address barriers to AFEDs, and promote the adoption of age-friendly practices in the ED.

Delirium detection in the emergency department: A diagnostic accuracy meta-analysis of history, physical examination, laboratory tests, and screening instruments.

INTRODUCTION: Geriatric emergency department (ED) guidelines emphasize timely identification of delirium. This article updates previous diagnostic accuracy systematic reviews of history, physical examination, laboratory testing, and ED screening instruments for the diagnosis of delirium as well as test-treatment thresholds for ED delirium screening. METHODS: We conducted a systematic review to quantify the diagnostic accuracy of approaches to identify delirium. Studies were included if they described adults aged 60 or older evaluated in the ED setting with an index test for delirium compared with an acceptable criterion standard for delirium. Data were extracted and studies were reviewed for risk of bias. When appropriate, we conducted a meta-analysis and estimated delirium screening thresholds. RESULTS: Full-text review was performed on 55 studies and 27 were included in the current analysis. No studies were identified exploring the accuracy of findings on history or laboratory analysis. While two studies reported clinicians accurately rule in delirium, clinician gestalt is inadequate to rule out delirium. We report meta-analysis on three studies that quantified the accuracy of the 4 A's Test (4AT) to rule in (pooled positive likelihood ratio [LR+] 7.5, 95% confidence interval [CI] 2.7-20.7) and rule out (pooled negative likelihood ratio [LR-] 0.18, 95% CI 0.09-0.34) delirium. We also conducted meta-analysis of two studies that quantified the accuracy of the Abbreviated Mental Test-4 (AMT-4) and found that the pooled LR+ (4.3, 95% CI 2.4-7.8) was lower than that observed for the 4AT, but the pooled LR- (0.22, 95% CI 0.05-1) was similar. Based on one study the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) is the superior instrument to rule in delirium. The calculated test threshold is 2% and the treatment threshold is 11%. CONCLUSIONS: The quantitative accuracy of history and physical examination to identify ED delirium is virtually unexplored. The 4AT has the largest quantity of ED-based research. Other screening instruments may more accurately rule in or rule out delirium. If the goal is to rule in delirium then the CAM-ICU or brief CAM or modified CAM for the ED are superior instruments, although the accuracy of these screening tools are based on single-center studies. To rule out delirium, the Delirium Triage Screen is superior based on one single-center study.

Real-world effectiveness of a single conventional disease-modifying anti-rheumatic drug (cDMARD) plus an anti-TNF agent versus multiple cDMARDs in rheumatoid arthritis: a prospective observational study.

Objective: The objective of this prospective, observational multicenter study (NCT03264703) was to compare the effectiveness of single conventional disease-modifying anti-rheumatic drug (cDMARD) plus anti-tumor necrosis factor (TNF) therapy versus multiple cDMARD treatments in patients with moderate-to-severe rheumatoid arthritis (RA) following cDMARD failure in the real-world setting in South Korea. Methods: At the treating physicians' discretion, patients received single cDMARD plus anti-TNF therapy or multiple cDMARDs. Changes from baseline in disease activity score 28-joint count with erythrocyte sedimentation rate (DAS28-ESR), corticosteroid use, and Korean Health Assessment Questionnaire (KHAQ-20) scores were evaluated at 3, 6, and 12 months. Results: Of 207 enrollees, the final analysis included 45 of 73 cDMARD plus anti-TNF and 91 of 134 multiple-cDMARD recipients. There were no significant between-group differences (BGDs) in ANCOVA-adjusted changes from baseline in DAS28-ESR at 3, 6 (primary endpoint), and 12 months (BGDs -0.18, -0.38, and -0.03, respectively). More cDMARD plus anti-TNF than multiple-cDMARD recipients achieved a >50% reduction from baseline in corticosteroid dosage at 12 months (35.7% vs 14.6%; p=0.007). Changes from baseline in KHAQ-20 scores at 3, 6, and 12 months were significantly better with cDMARD plus anti-TNF therapy than with multiple cDMARDs (BGD -0.18, -0.19, and -0.19 points, respectively; all p≤0.024). Conclusion: In the real-world setting, relative to multiple cDMARDs, single cDMARD plus anti-TNF therapy significantly improved quality-of-life scores and reduced corticosteroid use, with no significant BGD in disease activity, in RA patients in whom previous cDMARD therapy had failed.

Usability of the 4Ms Worksheet in the Emergency Department for Older Patients: A Qualitative Study.

Introduction: Older adults often have multiple comorbidities; therefore, they are at high risk for adverse events after discharge. The 4Ms framework-what matters, medications, mentation, mobility-has been used in acute and ambulatory care settings to identify risk factors for adverse events in older adults, although it has not been used in the emergency department (ED). We aimed to determine whether 1) use of the 4Ms worksheet would help emergency clinicians understand older adult patients' goals of care and 2) use of the worksheet was feasible in the ED. Methods: We conducted a qualitative, descriptive study among patients aged ≥60 years and emergency clinicians from January-June 2022. Patients were asked to fill out a 4Ms worksheet; following this, semi-structured interviews were conducted with patients and clinicians separately. We analysed data to create codes, which were divided into categories and sub-categories. Results: A total of 20 older patients and 19 emergency clinicians were interviewed. We identified two categories based on our aims: understanding patient goals of care (sub-categories: clinician/ patient concordance; understanding underlying goals of care; underlying goals of care discrepancy) and use of 4Ms Worksheet (sub-categories: worksheet to discussion discrepancy; challenges using worksheet; challenge completing worksheet before discharge). Rates of concordance between patient and clinician on main concern/goal of care and underlying goals of care were 82.4% and 15.4%, respectively. Conclusion: We found that most patients and emergency clinicians agreed on the main goal of care, although clinicians often failed to elicit patients' underlying goal(s) of care. Additionally, many patients preferred to have the interviewer fill out the worksheet for them. There was often discrepancy between what was written and what was discussed with the interviewer. More research is needed to determine the best way to integrate the 4Ms framework within emergency care.

Very long-lasting spinal anesthesia with dexmedetomidine: A report of two cases.

Spinal anesthesia usually lasts up to two hours, but an infusion of IV dexmedetomidine can prolong it to three to four hours. We report two cases where single spinal anesthesia with IV dexmedetomidine was maintained for more than six hours during tibia fracture surgery. The spinal anesthesia was maintained for 350 and 390 minutes without another medication, and the sensory level confirmed after the surgery was T10 and L1. Dexmedetomidine can very-prolong the duration of spinal anesthesia beyond what has been reported. However, longer infusion times can also result in longer recovery times.

Comparison of diagnosis-based risk adjustment methods for episode-based costs to apply in efficiency measurement.

BACKGROUND: The recent rising health spending intrigued efficiency and cost-based performance measures. However, mortality risk adjustment methods are still under consideration in cost estimation, though methods specific to cost estimate have been developed. Therefore, we aimed to compare the performance of diagnosis-based risk adjustment methods based on the episode-based cost to utilize in efficiency measurement. METHODS: We used the Health Insurance Review and Assessment Service-National Patient Sample as the data source. A separate linear regression model was constructed within each Major Diagnostic Category (MDC). Individual models included explanatory (demographics, insurance type, institutional type, Adjacent Diagnosis Related Group [ADRG], diagnosis-based risk adjustment methods) and response variables (episode-based costs). The following risk adjustment methods were used: Refined Diagnosis Related Group (RDRG), Charlson Comorbidity Index (CCI), National Health Insurance Service Hierarchical Condition Categories (NHIS-HCC), and Department of Health and Human Service-HCC (HHS-HCC). The model accuracy was compared using R-squared (R2), mean absolute error, and predictive ratio. For external validity, we used the 2017 dataset. RESULTS: The model including RDRG improved the mean adjusted R2 from 40.8% to 45.8% compared to the adjacent DRG. RDRG was inferior to both HCCs (RDRG adjusted R2 45.8%, NHIS-HCC adjusted R2 46.3%, HHS-HCC adjusted R2 45.9%) but superior to CCI (adjusted R2 42.7%). Model performance varied depending on the MDC groups. While both HCCs had the highest explanatory power in 12 MDCs, including MDC P (Newborns), RDRG showed the highest adjusted R2 in 6 MDCs, such as MDC O (pregnancy, childbirth, and puerperium). The overall mean absolute errors were the lowest in the model with RDRG ($1,099). The predictive ratios showed similar patterns among the models regardless of the  subgroups according to age, sex, insurance type, institutional type, and the upper and lower 10th percentiles of actual costs. External validity also showed a similar pattern in the model performance. CONCLUSIONS: Our research showed that either NHIS-HCC or HHS-HCC can be useful in adjusting comorbidities for episode-based costs in the process of efficiency measurement.

Insensitive Effects of Inflammatory Cytokines on the Reference Genes of Synovial Fluid Resident-Mesenchymal Stem Cells Derived from Rheumatoid Arthritis Patients.

Mesenchymal stem cells derived from rheumatoid arthritis patients (RA-MSCs) provide an understanding of a variety of cellular and immunological responses within the inflammatory milieu. Sustained exposure of MSCs to inflammatory cytokines is likely to exert an influence on genetic variations, including reference genes (RGs). The sensitive effect of cytokines on the reference genes of RA-SF-MSCs may be a variation factor affecting patient-derived MSCs as well as the accuracy and reliability of data. Here, we comparatively evaluated the stability levels of nine RG candidates, namely GAPDH, ACTB, B2M, EEF1A1, TBP, RPLP0, PPIA, YWHAZ, and HPRT1, to find the most stable ones. Alteration of the RG expression was evaluated in MSCs derived from the SF of healthy donors (H-SF-MSCs) and in RA-SF-MSCs using the geNorm and NormFinder software programs. The results showed that TBP, PPIA, and YWHAZ were the most stable RGs for the normalization of H-SF-MSCs and RA-SF-MSCs using RT-qPCR, whereas ACTB, the most commonly used RG, was less stable and performed poorly. Additionally, the sensitivity of RG expression upon exposure to proinflammatory cytokines (TNF-α and IL-1ß) was evaluated. RG stability was sensitive in the H-SF-MSCs exposed to TNF-α and IL-1ß but insensitive in the RA-SF-MSCs. Furthermore, the normalization of IDO expression using ACTB falsely diminished the magnitude of biological significance, which was further confirmed with a functional analysis and an IDO activity assay. In conclusion, the results suggest that TBP, PPIA, and YWHAZ can be used in SF-MSCs, regardless of their exposure to inflammatory cytokines.

Association of lncRNA SOX2OT rs9839776 polymorphism with gastric cancer risk in Korean: Case-control study.

Aberrant regulation of the long non-coding RNA SRY-box transcription factor 2 overlapping transcript (SOX2OT) has been reported in various diseases including gastric cancer (GC). However, an association between the well-studied rs9839776 single nucleotide polymorphism in SOX2OT and GC susceptibility has not been reported. This study aimed to evaluate the association between the rs9839776 single nucleotide polymorphism in SOX2OT and GC risk. Genotyping of rs9839776 was conducted using TaqMan genotyping assay for 460 patients with GC and 386 controls. We found that the dominant model (CT+TT) and rs9839776 T allele were significantly associated with decreased GC risk (P = .046, adjusted odds ratio [AOR] = 0.72, 95% confidence interval [CI] = 0.52-1.00 and P = .044, AOR = 0.74, 95% CI = 0.56-0.99, respectively). In addition, stratified analysis revealed that the dominant model (CT+TT) and rs9839776 T allele were significantly associated with decreased risk of lymph node metastasis-negative (P = .039, AOR = 0.67, 95% CI = 0.46-0.98 and P = .049, AOR = 0.71, 95% CI = 0.51-1.00, respectively) and tumor stage I (A+B)/II (A+B+C) (P = .028, AOR = 0.66, 95% CI = 0.50-0.96 and P = .041, AOR = 0.71, 95% CI = 0.52-0.99, respectively) GC. Our findings suggest that the rs9839776 T allele may be a protective factor against GC susceptibility. Further research is needed to clarify whether rs9839776 affects SOX2OT expression.

Delirium and Delirium Prevention in the Emergency Department.

This article covers the epidemiology of delirium and the overlapping condition of altered mental status and encephalopathy that is relevant to those who practice in the emergency department.