Concordance between vessel-specific and vascular territory coronary functional assessment: A comparison of quantitative flow ratio and myocardial perfusion scintigraphy.

BACKGROUND: Quantitative flow ratio (QFR) and myocardial perfusion scintigraphy (MPS) are utilized for assessing coronary artery disease (CAD) significance. We aimed to analyze their concordance and prognostic impact. AIMS: We aimed to analyze the concordance between QFR and MPS and their risk stratification. METHODS: Patients with invasive coronary angiography and MPS were categorized as concordant if QFR ≤ 0.80 and summed difference score (SDS) ≥ 4 or if QFR > 0.80 and SDS < 4; otherwise, they were discordant. Concordance was classified by coronary territory involvement: total (three territories), partial (two territories), poor (one territory), and total discordance (zero territories). Leaman score assessed coronary atherosclerotic burden. RESULTS: 2010 coronary territories (670 patients) underwent joint QFR and MPS analysis. MPS area under the curve for QFR ≤ 0.80 was 0.637. Concordance rates were total (52.5%), partial (29.1%), poor (15.8%), and total discordance (2.6%). Most concordance occurred in patients without significant CAD or with single-vessel disease (89.5%), particularly without MPS perfusion defects (91.5%). Leaman score (odds ratio [OR]: 0.839, 95% confidence interval [CI]: 0.805-0.875, p < 0.001) and MPS perfusion defect (summed stress score [SSS] ≥ 4) (OR: 0.355, 95% CI: 0.211-0.596, p < 0.001) were independent predictors for discordance. After 1400 days, no significant difference in death/myocardial infarction was observed based on MPS assessment, but Leaman score, functional Leaman score, and average QFR identified higher risk patients. CONCLUSIONS: MPS showed good overall accuracy in assessing QFR significance but substantial discordance existed. Predictors for discordance included higher atherosclerotic burden and MPS perfusion defects (SSS ≥ 4). Leaman score, QFR-based functional Leaman score, and average QFR provided better risk stratification for all-cause death and myocardial infarction than MPS.

Effect of intra-arrest transport, extracorporeal cardiopulmonary resuscitation and immediate invasive assessment in refractory out-of-hospital cardiac arrest: a long-term follow-up of the Prague OHCA trial.

BACKGROUND: Randomized data evaluating the impact of the extracorporeal cardiopulmonary resuscitation (ECPR) approach on long-term clinical outcomes in patients with refractory out-of-hospital cardiac arrest (OHCA) are lacking. The objective of this follow-up study was to assess the long-term clinical outcomes of the ECPR-based versus CCPR approach. METHODS: The Prague OHCA trial was a single-center, randomized, open-label trial. Patients with witnessed refractory OHCA of presumed cardiac origin, without return of spontaneous circulation, were randomized during ongoing resuscitation on scene to conventional CPR (CCPR) or an ECPR-based approach (intra-arrest transport, ECPR if ROSC is not achieved prehospital and immediate invasive assessment). RESULTS: From March 2013 to October 2020, 264 patients were randomized during ongoing resuscitation on scene, and 256 patients were enrolled. Long-term follow-up was performed 5.3 (interquartile range 3.8-7.2) years after initial randomization and was completed in 255 of 256 patients (99.6%). In total, 34/123 (27.6%) patients in the ECPR-based group and 26/132 (19.7%) in the CCPR group were alive (log-rank P = 0.01). There were no significant differences between the treatment groups in the neurological outcome, survival after hospital discharge, risk of hospitalization, major cardiovascular events and quality of life. Of long-term survivors, 1/34 (2.9%) in the ECPR-based arm and 1/26 (3.8%) in the CCPR arm had poor neurological outcome (both patients had a cerebral performance category score of 3). CONCLUSIONS: Among patients with refractory OHCA, the ECPR-based approach significantly improved long-term survival. There were no differences in the neurological outcome, major cardiovascular events and quality of life between the groups, but the trial was possibly underpowered to detect a clinically relevant difference in these outcomes. Trial registration ClinicalTrials.gov Identifier: NCT01511666, Registered 19 January 2012.

Analysis of China's carbon market price fluctuation and international carbon credit financing mechanism using random forest model.

This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established. Additionally, a multivariate dynamic regression Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX) model is utilized. In tandem with the modeling, a data index system is developed, encompassing various factors that influence carbon market trading prices. The random forest algorithm is then applied for feature selection, effectively identifying features with high scores and eliminating low-score features. The research findings reveal that the ARIMAX Least Absolute Shrinkage and Selection Operator (LASSO) model exhibits high forecasting accuracy for time series data. The model's Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are reported as 0.022, 0.1344, and 0.1543, respectively, approaching zero and surpassing other evaluation models in predictive accuracy. The goodness of fit for the national carbon market price forecasting model is calculated as 0.9567, indicating that the selected features strongly explain the trading prices of the carbon emission rights market. This study introduces innovation by conducting a comprehensive analysis of multi-dimensional data and leveraging the random forest model to explore non-linear relationships among data. This approach offers a novel solution for investigating the complex relationship between the carbon market and the carbon credit financing mechanism.

Influence of gastric residual assessment in preterm neonates on time to achieve enteral feeding (the GRASS trial)-Multi-centre, assessor-blinded randomised clinical trial.

PURPOSE: Gastric residual measurement is routinely performed in premature infants prior to feeding despite a lack of evidence of benefit. We aimed to evaluate if the exclusion of routine gastric residual measurement and evaluation has an impact on the time taken to achieve full enteral feeding in preterm neonates. METHODS: International multi-centre randomised controlled trial. Clinically stable, appropriate for gestational age infants between 26+0 and 30+6 weeks of gestation and less than 1.5 kg birth weight were eligible. Infants were randomised to the intervention arm (no monitoring of gastric aspirates) or control arm (routine care). Primary outcome was the achievement of enteral feeds of 100 ml/kg/day by day 5 of life. RESULTS: Ninety-five infants were recruited with 88 included in an intention-to-treat analysis, 45 in the intervention arm and 43 in the control arm. There was no imbalance in baseline characteristics. Thirty-three (73.3%) infants in the intervention group and 32 infants (74.4%) in the control group reached full feeds by day 5 of life (p = 0.91) with no difference in median time to full feeds. There were no statistically significant differences in survival or the major morbidities of prematurity. CONCLUSION:  There was no difference in time to attainment of enteral feeds of 100 ml/kg/day in premature infants when gastric residuals were not monitored. In the absence of a clinical benefit to routine monitoring, it may be appropriate to discontinue this practice and only monitor residuals when clinical concern of feeding intolerance or gastrointestinal pathology arises in this group of patients. TRIAL REGISTRATION:  NCT03111329- https://clinicaltrials.gov/ . Registered 06/04/2017. WHAT IS KNOWN: • Previous randomized trials have shown little benefit to the performance of routine assessment of gastric residuals in preterm infants. Despite this, they continue to be performed due to concerns from observational data regarding development of NEC. Meta-analysis to date has failed to answer the question regarding NEC. WHAT IS NEW: • In very low birth weight infants who are fed using modern feeding practice of faster feed advancement, to minimize use of central access and parenteral nutrition, exclusion of routine checks of gastric residuals did not increase the proportion of infants reaching full enteral feeds by day 5. No harm was seen when residual checks were not performed. • In the absence of a clinical benefit to the routine performance of gastric residuals in very low birth weight infants, it may be appropriate to discontinue their use and instead check residuals when clinical concern of pathology arises.

Analysis and prediction of ground deformation in Yinxi Industrial Park based on time-series InSAR technology.

Monitoring ground deformation in industrial parks is of great importance for the economic development of urban areas. However, limited research has been conducted on the deformation mechanism in industrial parks, and there is a lack of integrated monitoring and prediction models. Therefore, this study proposes a comprehensive monitoring and prediction model for industrial parks, utilizing time-series Interferometry Synthetic Aperture Radar (InSAR) technology and the Whale Optimization Algorithm-Back Propagation (WOA-BP) neural network algorithm. Taking Yinxi Industrial Park in Baiyin District as a case study, we used 68 scenes of Sentinel-1A ascending and descending orbit data from June 2018 to April 2021. The Stanford Method for Persistent Scatterers-Permanent Scatterers (StaMPS-PS) and the Small Baseline Subsets-Interferometry Synthetic Aperture Radar (SBAS-InSAR) technologies were employed to obtain the surface deformation information of the park. The deformation information obtained by the two technologies was cross-validated in terms of temporal and spatial distribution, and the vertical and east-west deformation of the park was obtained by combining the ascending and descending orbit data. The results show that the deformation feature points in the line of sight (LOS) direction obtained by the two technologies have a high consistency in spatial distribution, using the ascending orbit data as an example. Additionally, the SBAS-InSAR technology was used to obtain the east-west and vertical deformation results of the park after merging the ascending and descending orbit data for the same period. It was found that the park is mainly affected by vertical deformation, with a maximum subsidence rate of 14.67 mm/yr. The subsidence areas correspond to the deformation positions observed in field survey photos. Based on the ascending orbit deformation data, the two technologies were validated with 585 points of the same latitude and longitude, and the coefficient of determination R2 was found to be 0.82, with a root mean square error (RMSE) of 2.20 mm/a. The deformation rates were also highly consistent. Due to the 47% increase in the number of sampling points provided by the StaMPS-PS technique compared to the SBAS-InSAR technique, the former was found to be more applicable in the industrial park. Based on the ground deformation mechanism in the park, we combined the StaMPS-PS technique with the WOA-BP neural network to construct a deformation zone prediction model. We conducted predictive studies on the deformation zones of buildings and roads within the park, and the results showed that the WOA-optimized BP neural network achieved higher accuracy and lower overall error compared to the unoptimized network. Finally, we analyzed and discussed the geological conditions and inducing factors of ground deformation in the park, providing a reference for a better understanding of the deformation mechanism and early warning of disasters in the industrial park.

Analyzing entropy features in time-series data for pattern recognition in neurological conditions.

In the field of medical diagnosis and patient monitoring, effective pattern recognition in neurological time-series data is essential. Traditional methods predominantly based on statistical or probabilistic learning and inference often struggle with multivariate, multi-source, state-varying, and noisy data while also posing privacy risks due to excessive information collection and modeling. Furthermore, these methods often overlook critical statistical information, such as the distribution of data points and inherent uncertainties. To address these challenges, we introduce an information theory-based pipeline that leverages specialized features to identify patterns in neurological time-series data while minimizing privacy risks. We incorporate various entropy methods based on the characteristics of different scenarios and entropy. For stochastic state transition applications, we incorporate Shannon's entropy, entropy rates, entropy production, and the von Neumann entropy of Markov chains. When state modeling is impractical, we select and employ approximate entropy, increment entropy, dispersion entropy, phase entropy, and slope entropy. The pipeline's effectiveness and scalability are demonstrated through pattern analysis in a dementia care dataset and also an epileptic and a myocardial infarction dataset. The results indicate that our information theory-based pipeline can achieve average performance improvements across various models on the recall rate, F1 score, and accuracy by up to 13.08 percentage points, while enhancing inference efficiency by reducing the number of model parameters by an average of 3.10 times. Thus, our approach opens a promising avenue for improved, efficient, and critical statistical information-considered pattern recognition in medical time-series data.

Measuring Costs of Cardiovascular Disease Prevention for Patients with Familial Hypercholesterolemia in Administrative Claims Data.

INTRODUCTION: Familial hypercholesterolemia is a common genetic condition that significantly increases an individual's risk of cardiovascular events such as heart attack, stroke, and cardiac death and is a candidate for population-wide screening programs. Economic analyses of strategies to identify and treat familial hypercholesterolemia are limited by a lack of real-world cost estimates for screening services and medications for reducing cardiovascular risk in this population. METHODS: We estimated the cost of lipid panel testing in patients with hyperlipidemia and the cost of statins, ezetimibe, and PCKS9 inhibitors in patients with familial hypercholesterolemia from a commercial claims database and report costs and charges per panel and prescription by days' supply. RESULTS: The mean cost for a 90-day supply for statins was $183.33, 2.3 times the mean cost for a 30-day supply at $79.35. PCSK9 inhibitors generated the highest mean costs among medications used by patients with familial hypercholesterolemia. CONCLUSIONS: Lipid testing and lipid-lowering medications for cardiovascular disease prevention generate substantial real-world costs which can be used to improve cost-effectiveness models of familial hypercholesterolemia screening and care management.

Racial inequities in homicide rates and homicide methods among Black and White women aged 25-44 years in the USA, 1999-2020: a cross-sectional time series study.

BACKGROUND: In the USA, Black women aged 25-44 years are disproportionately murdered compared with their White counterparts. Despite ongoing efforts to reduce racial and structural inequities, the result of these efforts remains unclear, particularly in light of the COVID-19 pandemic. METHODS: This study examined a cross-sectional time series of homicide death rates, by race, from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research system. We included data for women aged 25-44 years between 1999 and 2020 among 30 states in the USA. Homicide death was classified using underlying cause and multiple cause of death codes; mortality rates were calculated per 100 000 based on US Census Bureau population sizes. Homicide methods were classified as firearm, cutting or piercing, and other. Firearm homicides were compared with other homicides with logistic regression including covariates of race, time, and their interaction. We report odds ratios and 95% CIs. FINDINGS: In 2020, the homicide rate among Black women was 11·6 per 100 000, compared with 3 per 100 000 among White women. This inequity has persisted over time and is virtually unchanged since 1999. Homicide inequities vary across US states; in 11 states, racial inequities have increased since 1999. The racial inequity was greatest in Wisconsin, where in 2019-20, Black women aged 25-44 years were 20 times more likely to die by homicide than White women. Homicide by firearm is increasing in frequency; women in the USA had 2·44 (95% CI 2·14-2·78) times the odds of homicide involving firearms in 2019-20 compared with 1999-2003. Firearm homicide deaths are disproportionately concentrated among Black women in every region in the USA. INTERPRETATION: Our findings suggest that there is an urgent need to address homicide inequities among Black and White women in the USA. Enacting federal legislation that reduces gun access is a crucial step. Policy makers must address long-standing structural factors that underpin elevated gun violence by implementing sustainable wealth-building opportunities; developing desegregated, mixed income and affordable housing; and increasing green spaces in communities where Black women largely reside. FUNDING: National Institute of Mental Health of the National Institutes of Health.

Evaluating the effects of the 2017 National Health Insurance coverage expansion on amenable mortality and its disparities between areas in South Korea using Bayesian structural time-series models.

To improve the low coverage rate of the National Health Insurance (NHI), South Korea implemented the NHI coverage expansion plan in 2017 to cover medically essential non-covered services and reduce copayment rates. This study aimed to estimate the effects of the 2017 NHI coverage expansion on amenable mortality and its disparities between areas in South Korea under a controlled interrupted time-series design using Bayesian structural time-series models. Age-standardized amenable mortality rates and rate differences (RDs) and rate ratios (RRs) between areas for amenable mortality were calculated monthly between July 2012 and December 2021 and used as the response series. The non-equivalent control series were monthly non-avoidable mortality rates and their regional disparities. After the coverage expansion, amenable mortality rates decreased for both males (-8.8%, 95% credible interval [CrI] -13.4% to -3.9%) and females (-8.3%, 95% CrI -13.4% to -2.4%), with the largest decline in the non-Seoul-Capital metropolitan area (-11.6%, 95% CrI -16.5% to -6.3%) rather than the Seoul Capital Area (-7.5%, 95% CrI -11.9% to -2.5%) and a non-significant reduction in the non-Seoul-Capital non-metropolitan area in females. RDs and RRs between areas for amenable mortality decreased non-significantly (-16.2%, 95% CrI -31.3% to 2.6% for RD and -1.2%, 95% CrI -3.7% to 1.5% for RR), except for a significant decrease in RD in males (-21.8%, 95% CrI -38.0% to -1.5%), and decreased less in females than in males. The coverage expansion was generally effective in reducing amenable mortality rates by area, but had limited effects in closing amenable mortality disparities between areas, favoring males and the non-Seoul-Capital metropolitan area. These results implied that additional measures are necessary to improve access to quality health care for females and underserved areas to enhance the effectiveness of the coverage expansion.

Value of information dynamics in Disease X vaccine clinical trials.

BACKGROUND: Solutions have been proposed to accelerate the development and rollout of vaccines against a hypothetical disease with epidemic or pandemic potential called Disease X. This may involve resolving uncertainties regarding the disease and the new vaccine. However the value for public health of collecting this information will depend on the time needed to perform research, but also on the time needed to produce vaccine doses. We explore this interplay, and its effect on the decision on whether or not to perform research. METHOD: We simulate numerically the emergence and transmission of a disease in a population using a susceptible-infected-recovered (SIR) compartmental model with vaccination. Uncertainties regarding the disease and the vaccine are represented by parameter prior distributions. We vary the date at which vaccine doses are available, and the date at which information about parameters becomes available. We use the expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI) to measure the value of information. RESULTS: As expected, information has less or no value if it comes too late, or (equivalently) if it can only be used too late. However we also find non trivial dynamics for shorter durations of vaccine development. In this parameter area, it can be optimal to implement vaccination without waiting for information depending on the respective durations of dose production and of clinical research. CONCLUSION: We illustrate the value of information dynamics in a Disease X outbreak scenario, and present a general approach to properly take into account uncertainties and transmission dynamics when planning clinical research in this scenario. Our method is based on numerical simulation and allows us to highlight non trivial effects that cannot otherwise be investigated.

Short term exposure to low level ambient fine particulate matter and natural cause, cardiovascular, and respiratory morbidity among US adults with health insurance: case time series study.

OBJECTIVE: To estimate the excess relative and absolute risks of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease associated with daily exposure to fine particulate matter (PM2.5) at concentrations below the new World Health Organization air quality guideline limit among adults with health insurance in the contiguous US. DESIGN: Case time series study. SETTING: US national administrative healthcare claims database. PARTICIPANTS: 50.1 million commercial and Medicare Advantage beneficiaries aged ≥18 years between 1 January 2010 and 31 December 2016. MAIN OUTCOME MEASURES: Daily counts of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease based on the primary diagnosis code. RESULTS: During the study period, 10.3 million hospital admissions and 24.1 million emergency department visits occurred for natural causes among 50.1 million adult enrollees across 2939 US counties. The daily PM2.5 levels were below the new WHO guideline limit of 15 µg/m3 for 92.6% of county days (7 360 725 out of 7 949 713). On days when daily PM2.5 levels were below the new WHO air quality guideline limit of 15 µg/m3, an increase of 10 µg/m3 in PM2.5 during the current and previous day was associated with higher risk of hospital admissions for natural causes, with an excess relative risk of 0.91% (95% confidence interval 0.55% to 1.26%), or 1.87 (95% confidence interval 1.14 to 2.59) excess hospital admissions per million enrollees per day. The increased risk of hospital admissions for natural causes was observed exclusively among adults aged ≥65 years and was not evident in younger adults. PM2.5 levels were also statistically significantly associated with relative risk of hospital admissions for cardiovascular and respiratory diseases. For emergency department visits, a 10 µg/m3 increase in PM2.5 during the current and previous day was associated with respiratory disease, with an excess relative risk of 1.34% (0.73% to 1.94%), or 0.93 (0.52 to 1.35) excess emergency department visits per million enrollees per day. This association was not found for natural causes or cardiovascular disease. The higher risk of emergency department visits for respiratory disease was strongest among middle aged and young adults. CONCLUSIONS: Among US adults with health insurance, exposure to ambient PM2.5 at concentrations below the new WHO air quality guideline limit is statistically significantly associated with higher rates of hospital admissions for natural causes, cardiovascular disease, and respiratory disease, and with emergency department visits for respiratory diseases. These findings constitute an important contribution to the debate about the revision of air quality limits, guidelines, and standards.

Robust control chart for nonlinear conditionally heteroscedastic time series based on Huber support vector regression.

This study proposes a control chart that monitors conditionally heteroscedastic time series by integrating the Huber support vector regression (HSVR) and the one-class classification (OCC) method. For this task, we consider the model that incorporates nonlinearity to the generalized autoregressive conditionally heteroscedastic (GARCH) time series, named HSVR-GARCH, to robustly estimate the conditional volatility when the structure of time series is not specified with parameters. Using the squared residuals, we construct the OCC-based control chart that does not require any posterior modifications of residuals unlike previous studies. Monte Carlo simulations reveal that deploying squared residuals from the HSVR-GARCH model to control charts can be immensely beneficial when the underlying model becomes more complicated and contaminated with noises. Moreover, a real data analysis with the Nasdaq composite index and Korea Composite Stock Price Index (KOSPI) datasets further disclose the validity of using the bootstrap method in constructing control charts.

Modelling the GDP of KSA using linear and non-linear NNAR and hybrid stochastic time series models.

BACKGROUND: Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series approach for modeling and forecasting the GDP annual growth rate (%) of Saudi Arabia, a key financial indicator of the country. METHODOLOGY: Stochastic linear and non-linear time series modeling, along with hybrid approaches, are employed and their results are compared. Initially, conventional linear and nonlinear methods such as ARIMA, Exponential smoothing, TBATS, and NNAR are applied. Subsequently, hybrid models combining these individual time series approaches are utilized. Model diagnostics, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), are employed as criteria for model selection to identify the best-performing model. RESULTS: The findings demonstrated that the neural network autoregressive (NNAR) model, as a non-linear approach, outperformed all other models, exhibiting the lowest values of MAE, RMSE and MAPE. The NNAR(5,3) projected the GDP of 1.3% which is close to the projection of IMF benchmark (1.9) for the year 2023. CONCLUSION: The selected model can be employed by economists and policymakers to formulate appropriate policies and plans. This quantitative study provides policymakers with a basis for monitoring fluctuations in GDP growth from 2022 to 2029 and ensuring the sustained progression of GDP beyond 2029. Additionally, this study serves as a guide for researchers to test these approaches in different economic dynamics.

Socio-environmental modifiers of heat-related mortality in eight Swiss cities: A case time series analysis.

In the light of growing urbanization and projected temperature increases due to climate change, heat-related mortality in urban areas is a pressing public health concern. Heat exposure and vulnerability to heat may vary within cities depending on structural features and socioeconomic factors. This study examined the effect modification of the temperature-mortality association of three socio-environmental factors in eight Swiss cities and population subgroups (<75 and ≥ 75 years, males, females): urban heat islands (UHI) based on within-city temperature contrasts, residential greenness measured as normalized difference vegetation index (NDVI) and neighborhood socioeconomic position (SEP). We used individual death records from the Swiss National Cohort occurring during the warm season (May to September) in the years 2003-2016. We performed a case time series analysis using conditional quasi-Poisson and distributed lag non-linear models with a lag of 0-3 days. As exposure variables, we used daily maximum temperatures (Tmax) and a binary indicator for warm nights (Tmin ≥20 °C). In total, 53,593 deaths occurred during the study period. Overall across the eight cities, the mortality risk increased by 31% (1.31 relative risk (95% confidence interval: 1.20-1.42)) between 22.5 °C (the minimum mortality temperature) and 35 °C (the 99th percentile) for warm-season Tmax. Stratified analysis suggested that the heat-related risk at 35 °C is 26% (95%CI: -4%, 67%) higher in UHI compared to non-UHI areas. Indications of smaller risk differences were observed between the low vs. high greenness strata (Relative risk difference = 13% (95%CI: -11%; 44%)). Living in low SEP neighborhoods was associated with an increased heat related risk in the non-elderly population (<75 years). Our results indicate that UHI are associated with increased heat-related mortality risk within Swiss cities, and that features beyond greenness are responsible for such spatial risk differences.

Assessment of the cardiac output at rest and during exercise stress using real-time cardiovascular magnetic resonance imaging in HFpEF-patients.

This methodological study aimed to validate the cardiac output (CO) measured by exercise-stress real-time phase-contrast cardiovascular magnetic resonance imaging (CMR) in patients with heart failure and preserved ejection fraction (HFpEF). 68 patients with dyspnea on exertion (NYHA ≥ II) and echocardiographic signs of diastolic dysfunction underwent rest and exercise stress right heart catheterization (RHC) and CMR within 24 h. Patients were diagnosed as overt HFpEF (pulmonary capillary wedge pressure (PCWP) ≥ 15mmHg at rest), masked HFpEF (PCWP ≥ 25mmHg during exercise stress but < 15mmHg at rest) and non-cardiac dyspnea. CO was calculated using RHC as the reference standard, and in CMR by the volumetric stroke volume, conventional phase-contrast and rest and stress real-time phase-contrast imaging. At rest, the CMR based CO showed good agreement with RHC with an ICC of 0.772 for conventional phase-contrast, and 0.872 for real-time phase-contrast measurements. During exercise stress, the agreement of real-time CMR and RHC was good with an ICC of 0.805. Real-time measurements underestimated the CO at rest (Bias:0.71 L/min) and during exercise stress (Bias:1.4 L/min). Patients with overt HFpEF had a significantly lower cardiac index compared to patients with masked HFpEF and with non-cardiac dyspnea during exercise stress, but not at rest. Real-time phase-contrast CO can be assessed with good agreement with the invasive reference standard at rest and during exercise stress. While moderate underestimation of the CO needs to be considered with non-invasive testing, the CO using real-time CMR provides useful clinical information and could help to avoid unnecessary invasive procedures in HFpEF patients.

Comparative assessment of synthetic time series generation approaches in healthcare: leveraging patient metadata for accurate data synthesis.

BACKGROUND: Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with other privacy enhancing technologies. Generating uncompromised synthetic data could significantly benefit external researchers performing secondary analyses by providing unlimited access to information while fulfilling pertinent regulations. However, the original data to be synthesized (e.g., data acquired in Living Labs) may consist of subjects' metadata (static) and a longitudinal component (set of time-dependent measurements), making it challenging to produce coherent synthetic counterparts. METHODS: Three synthetic time series generation approaches were defined and compared in this work: only generating the metadata and coupling it with the real time series from the original data (A1), generating both metadata and time series separately to join them afterwards (A2), and jointly generating both metadata and time series (A3). The comparative assessment of the three approaches was carried out using two different synthetic data generation models: the Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). The experiments were performed with three different healthcare-related longitudinal datasets: Treadmill Maximal Effort Test (TMET) measurements from the University of Malaga (1), a hypotension subset derived from the MIMIC-III v1.4 database (2), and a lifelogging dataset named PMData (3). RESULTS: Three pivotal dimensions were assessed on the generated synthetic data: resemblance to the original data (1), utility (2), and privacy level (3). The optimal approach fluctuates based on the assessed dimension and metric. CONCLUSION: The initial characteristics of the datasets to be synthesized play a crucial role in determining the best approach. Coupling synthetic metadata with real time series (A1), as well as jointly generating synthetic time series and metadata (A3), are both competitive methods, while separately generating time series and metadata (A2) appears to perform more poorly overall.

[Determinants of use of rehabilitation services before entering disability pension: Time series analysis of insured members of the German Pension Insurance Federation in Berlin and Brandenburg]. / Determinanten zur Inanspruchnahme von Rehabilitationsleistungen vor Eintritt in die Erwerbsminderung: Zeitreihenanalyse von Versicherten der Deutschen Rentenversicherung Bund in Berlin und Brandenburg.

BACKGROUND: The age-standardized application rate for medical rehabilitation services of the German Pension Insurance Association has been declining since 2009. Two of the most frequent reasons for applying for rehabilitation services represent musculoskeletal disorders and mental disorders. The aim of this analysis was to identify factors influencing the utilization of rehabilitation services in the federal states of Berlin and Brandenburg. METHODS: The explorative time series analysis is based on a research dataset of the German Pension Insurance Federation. Insured persons of the German Pension Insurance Federation from the federal states of Berlin and Brandenburg with a diagnosis of musculoskeletal disorders or mental disorders were included. Descriptive differences in targeted and prognostic parameters were calculated using chi-square and t-test statistics. Predictive parameters for the utilization of medical rehabilitation services were calculated using binary, logistic regression analyses. RESULTS: A total of 11,257 insured cases were examined. For the population of insured persons from Berlin, the use of medical rehabilitation services showed significant prognostic variables for gender, age at retirement, marital status, level of education, occupational requirement level, total accumulated earning points, gross pension level, status of a temporary pension, health insurance status, and diagnosis group. For Brandenburg, significant prognostic variables were found for age at retirement, marital status, nationality, education level, occupational requirement level, total accumulated earning points, status of a temporary pension, occupational sector, and diagnosis group. CONCLUSION: In Berlin and Brandenburg mental disorders and sociodemographic parameters are associated with an increased probability of not claiming medical rehabilitation services before the onset of reduced earning capacity. It remains to be investigated which mechanisms cause people with mental disorders to make no use of medical rehabilitation services. Future analyses should examine isolated interaction mechanisms for the utilization of medical rehabilitation services, especially in the case of existing F-diagnoses. In addition, explanations for different influencing variables between states remain to be explored.

Trial of Selective Early Treatment of Patent Ductus Arteriosus with Ibuprofen.

BACKGROUND: The cyclooxygenase inhibitor ibuprofen may be used to treat patent ductus arteriosus (PDA) in preterm infants. Whether selective early treatment of large PDAs with ibuprofen would improve short-term outcomes is not known. METHODS: We conducted a multicenter, randomized, double-blind, placebo-controlled trial evaluating early treatment (≤72 hours after birth) with ibuprofen for a large PDA (diameter of ≥1.5 mm with pulsatile flow) in extremely preterm infants (born between 23 weeks 0 days' and 28 weeks 6 days' gestation). The primary outcome was a composite of death or moderate or severe bronchopulmonary dysplasia evaluated at 36 weeks of postmenstrual age. RESULTS: A total of 326 infants were assigned to receive ibuprofen and 327 to receive placebo; 324 and 322, respectively, had data available for outcome analyses. A primary-outcome event occurred in 220 of 318 infants (69.2%) in the ibuprofen group and 202 of 318 infants (63.5%) in the placebo group (adjusted risk ratio, 1.09; 95% confidence interval [CI], 0.98 to 1.20; P = 0.10). A total of 44 of 323 infants (13.6%) in the ibuprofen group and 33 of 321 infants (10.3%) in the placebo group died (adjusted risk ratio, 1.32; 95% CI, 0.92 to 1.90). Among the infants who survived to 36 weeks of postmenstrual age, moderate or severe bronchopulmonary dysplasia occurred in 176 of 274 (64.2%) in the ibuprofen group and 169 of 285 (59.3%) in the placebo group (adjusted risk ratio, 1.09; 95% CI, 0.96 to 1.23). Two unforeseeable serious adverse events occurred that were possibly related to ibuprofen. CONCLUSIONS: The risk of death or moderate or severe bronchopulmonary dysplasia at 36 weeks of postmenstrual age was not significantly lower among infants who received early treatment with ibuprofen than among those who received placebo. (Funded by the National Institute for Health Research Health Technology Assessment Programme; Baby-OSCAR ISRCTN Registry number, ISRCTN84264977.).