BACKGROUND: Purpose: High-flow nasal cannula (HFNC) has many benefits in various clinical conditions. The original hypothesis suggests that the high and constant fraction of inspired oxygen (FiO2) is one of the main physiological effects. However, increasing evidence shows that there is a gap between the actual FiO2 and administered FiO2. We aimed to determine the actual FiO2 under different respiratory conditions and develop a regression model using a spontaneous breathing lung model.
METHODS: A spontaneous breathing simulation model was built using an airway manikin and a model lung. The FiO2 was measured under different respiratory conditions with varying tidal volumes and respiratory and HFNC flow rates. The relationships between the respiratory parameters and actual FiO2 were determined and used to build the predictive model.
RESULTS: The actual FiO2 was negatively correlated with respiratory rate and tidal volume and positively correlated with HFNC flow. The regression model could not be developed using simple respiratory parameters. Therefore, we introduced a new variable, defined as flow ratio, which equaled the HFNC flow divided by inspiratory flow. Our equation demonstrated that the actual FiO2 was mainly determined by the flow ratio in a non-linear relationship. Accordingly, a flow ratio greater than 1 did not ensure a constant high FiO2, whereas a flow ratio >1.435 could produce FiO2 >0.9.
CONCLUSIONS: The FiO2 during HFNC was not constant even at sufficiently high oxygen flow compared with inspiratory flow. The predictive model showed that the actual FiO2 was mainly determined by the flow ratio.

UNASSIGNED: To describe and explore predictors of bystander defibrillation in Ireland during the period 2012 to 2020. To examine the relationship between bystander defibrillation and health system developments.
UNASSIGNED: National level Out of Hospital Cardiac Arrest (OHCA) registry data were interrogated, focusing on patients who had defibrillation performed. Bystander defibrillation (as compared to EMS initiated defibrillation) was the key outcome of concern. Logistic regression models were built and refined by fitting predictors, performing stepwise variable selection and by adding pairwise interactions that improved fit.
UNASSIGNED: The data included 5,751 cases of OHCA where defibrillation was performed. Increasing year over time (OR 1.17, 95% CI 1.13, 1.21) was associated with increased adjusted odds of bystander defibrillation. Non-cardiac aetiology was associated with reduced adjusted odds of bystander defibrillation (OR 0.30, 95% CI 0.21, 0.42), as were increasing age in years (OR 0.99, 95% CI 0.987, 0.996) and night-time occurrence of OHCA (OR 0.67, 95% CI 0.53, 0.83). Six further variables in the final model (sex, call response interval, incident location (home or other), who witnessed collapse (bystander or not witnessed), urban or rural location, and the COVID period) were involved in significant interactions. Bystander defibrillation was in general less likely in urban settings and at home locations. Whilst women were less likely to receive bystander defibrillation overall, in witnessed OHCAs, occurring outside the home, in urban areas and outside of the COVID-19 period women were more likely, to receive bystander defibrillation.
UNASSIGNED: Defibrillation by bystanders has increased incrementally over time in Ireland. Interventions to address sex and age-based disparities, alongside interventions to increase bystander defibrillation at night, in urban settings and at home locations are required.

Objectives: In holistic medicine, developing personalized treatment plans is challenging due to the multitude of possible therapy combinations. This study introduces the use of a statistical approach to identify the most effective herbal medicines prescribed in Persian medicine (PM) in a small-scale sample of patients with type 2 diabetes mellitus (T2DM). Methods: This prospective observational cohort study was conducted with 47 patients with T2DM referred to Behesht Clinic in Tehran, Iran. A physician prescribed individualized PM treatment for T2DM and related systemic issues. The fasting blood sugar (FBS) level of each patient was recorded at initial and two follow-up visits, with visit intervals and treatment modifications determined by patient health status. Patients who completed two follow-up visits were included in the final analysis (n = 27). Data were analyzed using R software. A general linear model was assumed for the mean response, along with an exponential covariance pattern model, to manage irregularly timed measurements. Results: Two fitted models showed that, after adjusting for confounders, the use of the \"Diabetes Capsule\" significantly reduced the average FBS by 17.14 mmol/L (p = 0.046). For each unit increase in the consumption of \"Diabetes Capsule\" or \"Hab-e-Amber Momiai,\" the average FBS decreased by 15.22 mmol/L (p = 0.015) and 14.14 mmol/L (p = 0.047), respectively. Conclusion: It is possible to observe which medications are most effective, even when treatments are applied in a holistic and personalized fashion. Preliminary studies such as these may identify promising products for testing in clinical trials conducted under standardized conditions, to inform initial choices for future personalized treatments.

During the 2022-2023 unprecedented mpox epidemic, near real-time short-term forecasts of the epidemic\'s trajectory were essential in intervention implementation and guiding policy. However, as case levels have significantly decreased, evaluating model performance is vital to advancing the field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention and Our World in Data teams, we generated retrospective sequential weekly forecasts for Brazil, Canada, France, Germany, Spain, the United Kingdom, the United States and at the global scale using an auto-regressive integrated moving average (ARIMA) model, generalized additive model, simple linear regression, Facebook\'s Prophet model, as well as the sub-epidemic wave and n-sub-epidemic modelling frameworks. We assessed forecast performance using average mean squared error, mean absolute error, weighted interval scores, 95% prediction interval coverage, skill scores and Winkler scores. Overall, the n-sub-epidemic modelling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best most frequently. The n-sub-epidemic and spatial-wave frameworks considerably improved in average forecasting performance relative to the ARIMA model (greater than 10%) for all performance metrics. Findings further support sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases.

BACKGROUND: Interpretability and intuitive visualization facilitate medical knowledge generation through big data. In addition, robustness to high-dimensional and missing data is a requirement for statistical approaches in the medical domain. A method tailored to the needs of physicians must meet all the abovementioned criteria.
OBJECTIVE: This study aims to develop an accessible tool for visual data exploration without the need for programming knowledge, adjusting complex parameterizations, or handling missing data. We sought to use statistical analysis using the setting of disease and control cohorts familiar to clinical researchers. We aimed to guide the user by identifying and highlighting data patterns associated with disease and reveal relations between attributes within the data set.
METHODS: We introduce the attribute association graph, a novel graph structure designed for visual data exploration using robust statistical metrics. The nodes capture frequencies of participant attributes in disease and control cohorts as well as deviations between groups. The edges represent conditional relations between attributes. The graph is visualized using the Neo4j (Neo4j, Inc) data platform and can be interactively explored without the need for technical knowledge. Nodes with high deviations between cohorts and edges of noticeable conditional relationship are highlighted to guide the user during the exploration. The graph is accompanied by a dashboard visualizing variable distributions. For evaluation, we applied the graph and dashboard to the Hamburg City Health Study data set, a large cohort study conducted in the city of Hamburg, Germany. All data structures can be accessed freely by researchers, physicians, and patients. In addition, we developed a user test conducted with physicians incorporating the System Usability Scale, individual questions, and user tasks.
RESULTS: We evaluated the attribute association graph and dashboard through an exemplary data analysis of participants with a general cardiovascular disease in the Hamburg City Health Study data set. All results extracted from the graph structure and dashboard are in accordance with findings from the literature, except for unusually low cholesterol levels in participants with cardiovascular disease, which could be induced by medication. In addition, 95% CIs of Pearson correlation coefficients were calculated for all associations identified during the data analysis, confirming the results. In addition, a user test with 10 physicians assessing the usability of the proposed methods was conducted. A System Usability Scale score of 70.5% and average successful task completion of 81.4% were reported.
CONCLUSIONS: The proposed attribute association graph and dashboard enable intuitive visual data exploration. They are robust to high-dimensional as well as missing data and require no parameterization. The usability for clinicians was confirmed via a user test, and the validity of the statistical results was confirmed by associations known from literature and standard statistical inference.

UNASSIGNED: To explore predictors of bystander CPR (i.e. any CPR performed prior to EMS arrival) in Ireland over the period 2012-2020. To examine the relationship between bystander CPR and key health system developments during this period.
UNASSIGNED: National level out-of-hospital cardiac arrest (OHCA) registry data relating to unwitnessed, and bystander witnessed OHCA were interrogated. Logistic regression models were built, then refined by fitting predictors, performing stepwise variable selection and by adding pairwise interactions that improved fit. Missing data sensitivity analyses were conducted using multiple imputation.
UNASSIGNED: The data included 18,177 OHCA resuscitation attempts of whom 77% had bystander CPR. The final model included ten variables. Four variables (aetiology, incident location, time of day, and who witnessed collapse) were involved in interactions. The COVID-19 period was associated with reduced adjusted odds of bystander CPR (OR 0.77, 95% CI 0.65, 0.92), as were increasing age in years (OR 0.992, 95% CI 0.989, 0.994) and urban location (OR 0.52, 95% CI 0.47, 0.57). Increasing year over time (OR 1.23, 95% CI 1.16, 1.29), and an increased call response interval in minutes (OR 1.017, 95% CI 1.012, 1.022) were associated with increased adjusted odds of bystander CPR.
UNASSIGNED: Bystander CPR increased over the study period, and it is likely that health system developments contributed to the yearly increases observed. However, COVID-19 appeared to disrupt this positive trend. Urban OHCA location was associated with markedly decreased odds of bystander CPR compared to rural location. Given its importance bystander CPR in urban areas should be an immediate target for intervention.

Postpartum haemorrhage (PPH) is a significant cause of maternal morbidity and mortality worldwide, particularly in low-resource settings. This study aimed to develop a predictive model for PPH using early risk factors and rank their importance in terms of predictive ability. The dataset was obtained from an observational case-control study in northern Rwanda. Various statistical models and machine learning techniques were evaluated, including logistic regression, logistic regression with elastic-net regularisation, Random Forests, Extremely Randomised Trees, and gradient-boosted trees with XGBoost. The Random Forest model, with an average sensitivity of 80.7%, specificity of 71.3%, and a misclassification rate of 12.19%, outperformed the other models, demonstrating its potential as a reliable tool for predicting PPH. The important predictors identified in this study were haemoglobin level during labour and maternal age. However, there were differences in PPH risk factor importance in different data partitions, highlighting the need for further investigation. These findings contribute to understanding PPH risk factors, highlight the importance of considering different data partitions and implementing cross-validation in predictive modelling, and emphasise the value of identifying the appropriate prediction model for the application. Effective PPH prediction models are essential for improving maternal health outcomes on a global scale. This study provides valuable insights for healthcare providers to develop predictive models for PPH to identify high-risk women and implement targeted interventions.

UNASSIGNED: Alzheimer\'s disease and related dementias (ADRD) incidence varies based on demographics, but mid-life risk factor contribution to this variability requires more research.
UNASSIGNED: The purpose of this study is to forecast the 20-year incidence of dementia in the U.S. overall and stratified by race/ethnicity, socioeconomic status (SES), and U.S. geographic region given prior mid-life risk factor prevalence and to examine the extent to which risk factor differences 20 years ago may explain current SES, race/ethnicity, or regional disparities in dementia incidence.
UNASSIGNED: We applied the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) prediction model to the 2006 wave of the Health and Retirement Study (HRS) in participants aged 45 to 64 to estimate the 20-year risk of incident ADRD.
UNASSIGNED: The 20-year risk of dementia among middle-aged Americans was 3.3% (95% CI: 3.2%, 3.4%). Dementia incidence was forecast to be 1.51 (95% CI: 1.32, 1.71) and 1.27 (95% CI: 1.14, 1.44) times that in Hispanic and Non-Hispanic Black individuals respectively compared statistically to Non-Hispanic White individuals given mid-life risk factors. There was a progressive increase in dementia risk from the lowest versus highest SES quintile. For geographic region, dementia incidence was forecast to be 1.17 (95% CI: 1.06, 1.30) and 1.27 (95% CI: 1.14, 1.43) times that in Midwestern and Southern individuals respectively compared statistically to Western individuals.
UNASSIGNED: Some disparities in dementia incidence could be explained by differences in mid-life risk factors and may point toward policy interventions designed to lessen the ADRD disease burden through early prevention.

UNASSIGNED: Accurate real-time prediction of intraoperative duration can contribute to improved perioperative outcomes. We implemented a data pipeline for extraction of real-time data from nascent anaesthesia records and silently deployed a predictive machine learning (ML) algorithm.
UNASSIGNED: Clinical variables were retrieved from the electronic health record via a third-party clinical decision support platform and contemporaneously ingested into a previously developed ML model. The model was trained using 3 months data, and performance was subsequently evaluated over 10 months using continuous ranked probability score.
UNASSIGNED: The ML model made 6 173 435 predictions on 62 142 procedures. Mean continuous ranked probability score for the ML model was 27.19 (standard error 0.016) min compared with 51.66 (standard error 0.029) min for the bias-corrected scheduled duration. Linear regression did not demonstrate performance drift over the testing period.
UNASSIGNED: We implemented and silently deployed a real-time ML algorithm for predicting surgery duration. Prospective evaluation showed that model performance was preserved over a 10-month testing period.

UNASSIGNED: Reinfection of coronavirus disease 2019 (COVID-19) has raised concerns about how reliable immunity from infection and vaccination is. With mass testing for the virus halted, understanding the current prevalence of COVID-19 is crucial. This study investigated 1,191 public health workers at the Xiamen Center for Disease Control, focusing on changes in antibody titers and their relationship with individual characteristics.
UNASSIGNED: The study began by describing the epidemiological characteristics of the study participants. Multilinear regression (MLR) models were employed to explore the associations between individual attributes and antibody titers. Additionally, group-based trajectory models (GBTMs) were utilized to identify trajectories in antibody titer changes. To predict and simulate future epidemic trends and examine the correlation of antibody decay with epidemics, a high-dimensional transmission dynamics model was constructed.
UNASSIGNED: Analysis of epidemiological characteristics revealed significant differences in vaccination status between infected and non-infected groups (χ2=376.706, P<0.05). However, the distribution of antibody titers among the infected and vaccinated populations was not significantly different. The MLR model identified age as a common factor affecting titers of immunoglobulin G (IgG), immunoglobulin M (IgM), and neutralizing antibody (NAb), while other factors showed varying impacts. History of pulmonary disease and hospitalization influenced IgG titer, and factors such as gender, smoking, family history of pulmonary diseases, and hospitalization impacted NAb titers. Age was the sole determinant of IgM titers in this study. GBTM analysis indicated a \"gradual decline type\" trajectory for IgG (95.65%), while IgM and NAb titers remained stable over the study period. The high-dimensional transmission dynamics model predicted and simulated peak epidemic periods in Xiamen City, which correlated with IgG decay. Age-group-specific simulations revealed a higher incidence and infection rate among individuals aged 30-39 years during both the second and third peaks, followed by those aged 40-49, 50-59, 18-29, and 70-79 years.
UNASSIGNED: Our study shows that antibody titer could be influenced by age, previous pulmonary diseases as well as smoking. Furthermore, the decline in IgG titers is consistent with epidemic trends. These findings emphasize the need for further exploration of these factors and the development of optimized self-protection countermeasures against reinfection.