Health-related quality of life by veterans RAND 12 and healthcare resource utilization in cancer patients with sleep disorders: insights from the Medical Expenditure Panel Survey.

PURPOSE: This study aims to investigate the joint effects of cancer and sleep disorders on the health-related quality of life (HRQoL), healthcare resource utilization, and expenditures among US adults. METHODS: Utilizing the 2018-2019 Medical Expenditure Panel Survey (MEPS) database, a sample of 25,274 participants was categorized into four groups based on cancer and sleep disorder status. HRQoL was assessed using the VR-12 questionnaire. Generalized linear model (GLM) with a log-linear regression model combined gamma distribution was applied for the analysis of healthcare expenditure data. RESULTS: Individuals with both cancer and sleep disorders (C+/S+) exhibited notably lower physical health (PCS) and mental health (MCS) scores-1.45 and 1.87 points lower, respectively. They also showed significantly increased clinic visits (2.12 times), outpatient visits (3.59 times), emergency visits (1.69 times), and total medical expenditures (2.08 times) compared to those without cancer or sleep disorders (C-/S-). In contrast, individuals with sleep disorders alone (C-/S+) had the highest number of prescription drug usage (2.26 times) and home health care days (1.76 times) compared to the reference group (C-/S-). CONCLUSIONS: Regardless of cancer presence, individuals with sleep disorders consistently reported compromised HRQoL. Furthermore, those with cancer and sleep disorders experienced heightened healthcare resource utilization, underscoring the considerable impact of sleep disorders on overall quality of life. IMPLICATIONS FOR CANCER SURVIVORS: The findings of this study address the importance of sleep disorders among cancer patients and their potential implications for cancer care. Healthcare professionals should prioritize screening, education, and tailored interventions to support sleep health in this population.

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.

BACKGROUND: Hyperglycemic crises are associated with high morbidity and mortality. Previous studies have proposed methods to predict adverse outcomes of patients in hyperglycemic crises; however, artificial intelligence (AI) has never been used to predict adverse outcomes. We implemented an AI model integrated with the hospital information system (HIS) to clarify whether AI could predict adverse outcomes. METHODS: We included 2,666 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. The patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from the electronic medical records were collected. The performance of the multilayer perceptron (MLP), logistic regression, random forest, Light Gradient Boosting Machine (LightGBM), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms was compared. We selected the best algorithm to construct an AI model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. The outcomes between the non-AI and AI groups were compared after implementing the HIS and predicting the hyperglycemic crisis death (PHD) score. RESULTS: The MLP had the best performance in predicting the three adverse outcomes, compared with the random forest, logistic regression, SVM, KNN, and LightGBM models. The areas under the curves (AUCs) using the MLP model were 0.852 for sepsis or septic shock, 0.743 for ICU admission, and 0.796 for all-cause mortality. Furthermore, we integrated the AI predictive model with the HIS to assist decision making in real time. No significant differences in ICU admission or all-cause mortality were detected between the non-AI and AI groups. The AI model performed better than the PHD score for predicting all-cause mortality (AUC 0.796 vs. 0.693). CONCLUSIONS: A real-time AI predictive model is a promising method for predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies recruiting more patients are warranted.

Cyclic Stretching Triggers Cell Orientation and Extracellular Matrix Remodeling in a Periodontal Ligament 3D In Vitro Model.

During orthodontic tooth movement (OTM), the periodontal ligament (PDL) plays a crucial role in regulating the tissue remodeling process. To decipher the cellular and molecular mechanisms underlying this process in vitro, suitable 3D models are needed that more closely approximate the situation in vivo. Here, a customized bioreactor is developed that allows dynamic loading of PDL-derived fibroblasts (PDLF). A collagen-based hydrogel mixture is optimized to maintain structural integrity and constant cell growth during stretching. Numerical simulations show a uniform stress distribution in the hydrogel construct under stretching. Compared to static conditions, controlled cyclic stretching results in directional alignment of collagen fibers and enhances proliferation and spreading ability of the embedded PDLF cells. Effective force transmission to the embedded cells is demonstrated by a more than threefold increase in Periostin protein expression. The cyclic stretch conditions also promote extensive remodeling of the extracellular matrix, as confirmed by increased glycosaminoglycan production. These results highlight the importance of dynamic loading over an extended period of time to determine the behavior of PDLF and to identify in vitro mechanobiological cues triggered during OTM-like stimulus. The introduced dynamic bioreactor is therefore a useful in vitro tool to study these mechanisms.

National situation, trends, and predictions of disease burden of atopic dermatitis in Chinese children and adolescents.

Background: Atopic dermatitis (AD) is an important global health problem affecting children and adolescents and detailed national information of disease burden in China is lacking. We aimed to evaluate the national disease burden of AD in Chinese children and adolescent, to provide the temporal trends over the past 30 years and to predict the burden for the next 10 years. Methods: The data of AD in China, including incidence, prevalence, and DALY, and population data were obtained from the Global Burden of Disease Study 2019 (GBD study 2019), which were estimated using the DisMod-MR 2.1. We analyzed the three measures by age and sex; the age groups were <5 years, 5-9 years, 10-14 years, and 15-19 years. The joinpoint regression analyses was conducted to assess the temporal trends from 1990 to 2019. The Bayesian age-period cohort (BAPC) model was used to predict measures from 2020 to 2030. Results: In 2019, the highest incidence case and rate were observed in <5 years group; for prevalence and disability adjusted life year (DALY), the groups of <5 years and 5-9 years showed similar higher levels and the groups of 10-14 years and 15-19 years had similar relatively lower levels. Overall, the male-to-female ratios were >1 in <5 years group and <1 in 10-14 and 15-19 age groups. The trend analyses found an overall trend of decrease in cases of the three measures; in recent about 3 years, slight increase trends were shown in cases and rates of the three measures in <5 years group. The prediction analyses found a slight decreasing trend for cases of these measures and a slight increasing trend for rates of these measures in the <5 years group in the next 10 years; the 5-9 years group was predicted to increase slightly in rates of the three measures. Conclusion: In conclusion, the groups of <5 years and 5-9 years are two important populations that need targeted measures to reduce disease burden of AD in China. Regarding sex disparity, we should pay more attention to males in <5 years group and to females in 10-19 years group.

External validation of geriatric influenza death score: A multicenter study.

The Geriatric Influenza Death (GID) score was developed to help decision making in older patients with influenza in the emergency department (ED), but external validation is unavailable. Thus, we conducted a study was to fill the data gap. We recruited all older patients (≥65 years) who visited the ED of three hospitals between 2009 and 2018. Demographic data and clinical characteristics were retrospectively collected. Discrimination, goodness of fit, and performance of the GID score were evaluated. Of the 5,508 patients (121 died) with influenza, the mean age was 76.6±7.4 (standard deviation) years, and 49.3% were males. The GID score was higher in the mortality group (1.7±1.1 vs. 0.8±0.8, p <0.01). With 0 as the reference, the odds ratio for morality with score of 1, 2 and ≥3 was 3.08 (95% confidence interval [CI]: 1.66-5.71), 6.69 (95% CI: 3.52-12.71), and 23.68 (95% CI: 11.95-46.93), respectively. The area under the curve was 0.722 (95% CI: 0.677-0.766), and the Hosmer-Lemeshow goodness of fit test was 1.000. The GID score had excellent negative predictive values with different cut-offs. The GID score had good external validity, and further studies are warranted for wider application.

Associations between dietary patterns and stages of chronic kidney disease.

BACKGROUND: Studies have revealed that patients with chronic kidney disease (CKD) have dietary patterns different from those of the general population. However, no studies have compared the dietary patterns of between patients with early-stages (stages 1-3a) and late-stages (stages 3b-5) of CKD. Our objective was to investigate the associations between dietary patterns in early and late-stage CKD. METHODS: We analyzed 4480 participants with CKD at various stages based on the data recorded between 2007 and 2016 from the database of the American National Health and Nutrition Examination Survey. RESULTS: In total, 3683 and 797 participants had early and late-stage CKD, respectively. Through principal components analysis, the dietary intake dimension was reduced from 63 variables to 3 dietary patterns. We adopted logistic regression for analysis. The three dietary patterns are as follows: (1) saturated fatty acids and mono-unsaturated fatty acids (MUFA); (2) vitamins and minerals; and (3) cholesterols and polyunsaturated fatty acids (PUFA). These 3 patterns explained > 50% of dietary nutrient intake. Results indicated that among participants with dietary patterns 2 (vitamins and minerals) and 3 (cholesterols and PUFA), those with low intakes were more likely to have late-stage CKD. The odds ratios for patterns 2 and 3 were 1.74 (95% CI: 1.21-2.50) and 1.66 (95% CI: 1.13-2.43), respectively. CONCLUSIONS: This study revealed that intakes of vitamins and minerals and cholesterols and PUFA were associated with the stages of CKD.

Short- and long-term recurrence of early-stage invasive ductal carcinoma in middle-aged and old women with different treatments.

Most new cases and the highest mortality rates of breast cancer occur among middle-aged and old women. The recurrence rate of early-stage invasive ductal carcinoma (IDC) among women aged ≥ 50 years and receiving different treatments remains unclear. Therefore, this study was conducted to determine these rates. We used Surveillance, Epidemiology, and End Results (SEER) data for this nationwide population-based cohort study. All women aged ≥ 50 years and diagnosed with early-stage IDC between 2000 and 2015 were identified and divided into three treatment groups, namely, breast conservation therapy (BCT), mastectomy alone (MAS), and mastectomy with radiation therapy (MAS + RT). The recurrence rates of IDC among these groups were then compared. The BCT group had a lower short-term recurrence risk than the MAS and MAS + RT groups (hazard ratio [HR]: 1.00 vs. 2.90 [95% CI 1.36-2.66] vs. 2.07 [95% CI 0.97-4.44]); however, the BCT group also had a higher long-term recurrence risk than MAS and MAS + RT groups (HR 1.00 vs. 0.30 [95% CI 0.26-0.35] vs. 0.43 [95% CI 0.30-0.63]). The high long-term recurrence rate of the BCT group was especially prominent at the 10- and 15-year follow-ups. The results provide valuable evidence of the most reliable treatment strategy for this population. Further studies including more variables and validation in other countries are warranted to confirm our findings.

Effect of treatments on skin microbiota in patients with atopic dermatitis: a protocol for systematic review.

INTRODUCTION: Atopic dermatitis (AD) is a chronic inflammatory skin disease and skin microbiota dysbiosis shows an important role in the pathogenesis of AD. Effects of treatment on skin microbiota for patients with AD have been evaluated in recent years; however, the results remained controversial across studies. This systematic review will summarise studies evaluating the effect of treatments on skin microbiota among patients with AD. METHODS AND ANALYSIS: We will search PubMed, EMBASE, Web of Science, ClinicalTrials.gov and Chinese Clinical Trial Registry in November 2021; other data sources will also be considered, including searching specific authors and screening references cited in the enrolled articles. Interventional studies, which enrolled patients with AD receiving treatments and reported treatment-related skin microbiota changes, will be included. Our primary outcomes include skin microbiota diversity and treatment-related differential microbes; the secondary outcomes include microbiota functions and microbial interactions. Risk of bias assessment will be performed using Cochrane risk-of-bias tool for randomised trials, risk of bias in non-randomised studies of interventions and methodological index for non-randomised studies. Two researchers will independently perform study selection, data extraction and risk of bias assessment, with disagreements resolved by group discussions. Subgroup analyses will be performed according to different types of treatment for AD. ETHICS AND DISSEMINATION: Ethics approval is not required for this systematic review. Findings will be disseminated via peer-reviewed publication or conference proceedings. PROSPERO REGISTRATION NUMBER: CRD42021246566.

Real-time interactive artificial intelligence of things-based prediction for adverse outcomes in adult patients with pneumonia in the emergency department.

BACKGROUND: Artificial intelligence of things (AIoT) may be a solution for predicting adverse outcomes in emergency department (ED) patients with pneumonia; however, this issue remains unclear. Therefore, we conducted this study to clarify it. METHODS: We identified 52,626 adult ED patients with pneumonia from three hospitals between 2010 and 2019 for this study. Thirty-three feature variables from electronic medical records were used to construct an artificial intelligence (AI) model to predict sepsis or septic shock, respiratory failure, and mortality. After comparisons of the predictive accuracies among logistic regression, random forest, support-vector machine (SVM), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost), we selected the best one to build the model. We further combined the AI model with the Internet of things as AIoT, added an interactive mode, and implemented it in the hospital information system to assist clinicians with decision making in real time. We also compared the AIoT-based model with the confusion-urea-respiratory rate-blood pressure-65 (CURB-65) and pneumonia severity index (PSI) for predicting mortality. RESULTS: The best AI algorithms were random forest for sepsis or septic shock (area under the curve [AUC] = 0.781), LightGBM for respiratory failure (AUC = 0.847), and mortality (AUC = 0.835). The AIoT-based model represented better performance than CURB-65 and PSI indicators for predicting mortality (0.835 vs. 0.681 and 0.835 vs. 0.728). CONCLUSIONS: A real-time interactive AIoT-based model might be a better tool for predicting adverse outcomes in ED patients with pneumonia. Further validation in other populations is warranted.

Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain.

BACKGROUND: A big-data-driven and artificial intelligence (AI) with machine learning (ML) approach has never been integrated with the hospital information system (HIS) for predicting major adverse cardiac events (MACE) in patients with chest pain in the emergency department (ED). Therefore, we conducted the present study to clarify it. METHODS: In total, 85,254 ED patients with chest pain in three hospitals between 2009 and 2018 were identified. We randomized the patients into a 70%/30% split for ML model training and testing. We used 14 clinical variables from their electronic health records to construct a random forest model with the synthetic minority oversampling technique preprocessing algorithm to predict acute myocardial infarction (AMI) < 1 month and all-cause mortality < 1 month. Comparisons of the predictive accuracies among random forest, logistic regression, support-vector clustering (SVC), and K-nearest neighbor (KNN) models were also performed. RESULTS: Predicting MACE using the random forest model produced areas under the curves (AUC) of 0.915 for AMI < 1 month and 0.999 for all-cause mortality < 1 month. The random forest model had better predictive accuracy than logistic regression, SVC, and KNN. We further integrated the AI prediction model with the HIS to assist physicians with decision-making in real time. Validation of the AI prediction model by new patients showed AUCs of 0.907 for AMI < 1 month and 0.888 for all-cause mortality < 1 month. CONCLUSIONS: An AI real-time prediction model is a promising method for assisting physicians in predicting MACE in ED patients with chest pain. Further studies to evaluate the impact on clinical practice are warranted.