Akaike Information Criterion Research Articles

Serum Immunoglobulin G Levels Are Associated with Risk for Exacerbations: An Analysis of SPIROMICS.

Serum Immunoglobulin G (IgG) deficiency is associated with morbidity in chronic obstructive pulmonary disease (COPD) but it is unclear whether concentrations in the lower end of the normal range still confer risk. To determine if levels above traditional cutoffs for serum IgG deficiency are associated with exacerbations among current and former smokers with or at risk for COPD. Former and current smokers in SPIROMICS (n=1,497) were studied, n=1,026 with and n=471 at risk for COPD. In a subset (n=1,031), IgG subclasses were measured. Associations between total IgG or subclasses and prospective exacerbations were evaluated with multivariable models adjusting for demographics, current smoking, smoking history, FEV1% predicted, inhaled corticosteroids, and serum IgA. The 35th percentile (1225 mg/dL in this cohort) of IgG was the best cutoff by Akaike Information Criterion (AIC). Below this, there was increased exacerbation risk (IRR 1.28, 95% CI 1.08-1.51). Among subclasses, IgG1 and IgG2 below 35th percentile (354 and 105 mg/dL, respectively) were both associated with increased risk of severe exacerbation (IgG1: IRR 1.39, 95% CI 1.06-1.84; IgG2: IRR 1.50, 95% CI 1.14-1.1.97). These associations remained significant when additionally adjusting for history of exacerbations. Lower serum IgG is prospectively associated with exacerbations in individuals with or at risk for COPD. Among subclasses, lower IgG1 and IgG2 are prospectively associated with severe exacerbations. The optimal IgG cutoff was substantially higher than traditional cutoffs for deficiency, suggesting subtle impairment of humoral immunity may be associated with exacerbations.

Prognostic prediction and treatment options for gastric signet ring cell carcinoma: a SEER database analysis.

In recent years, the overall incidence of gastric cancer has decreased. However, the incidence of gastric signet ring cell carcinoma (SRCC) is still increasing year by year. Compared with other subtypes (non-SRCC) such as adenocarcinoma, SRCC usually exhibits a more aggressive biological behavior. Therefore, studying the prognostic differences and factors associated with SRCC is essential to improve the accuracy of diagnosis and prognosis. The purpose of this study was to investigate the prognostic factors influencing the prognosis of patients with SRCC and to develop personalized treatments for different subgroups of patients. The data on gastric SRCC patients and gastric adenocarcinoma (AC) patients from 1992 to 2020 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The data of gastric SRCC as the external validation group was reviewed from the First Affiliated Hospital of Soochow University. The overall survival (OS) and cancer specific survival (CSS) at 1 and 2 years were predicted for SRCC patients by constructing prognostic nomograms. A series of validation methods, including Akaike information criterion (AIC), decision curve analysis (DCA), calibration curve analysis, the concordance index (C-index) and the area under the receiver operating characteristic (AUC) curve, were used to verify the accuracy and reliability of the models. In all, 549 patients with SRCC were included after propensity score matching (PSM). Multivariate Cox regression analysis showed that T stage, N stage, M stage and surgical approach were independent risk factors affecting the prognosis of SRCC patients. A prognostic nomogram was constructed and validated as an accurate model for SRCC patients after scoring by receiver operating characteristic curve (ROC) curves and calibration plots. The patients were further divided into high-risk and low-risk groups, and the Kaplan-Meier curves showed that SRCC patients in the low-risk group could receive only surgery without chemotherapy, while chemotherapy plus surgery was a better option for SRCC patients in the high-risk group. The prognosis for SRCC was less favorable than that of AC in terms of CSS. The nomograms were developed and validated to predict OS and CSS in patients with SRCC, helping in developing appropriate individualized treatment schedules.

Impact of ADA Guidelines and Medication Shortage on GLP-1 Receptor Agonists Prescribing Trends in the UK: A Time-Series Analysis with Country-Specific Insights

Background: Several GLP-1 receptor agonists (GLP-1 RAs) are used to treat type 2 diabetes (T2DM). Their cardio- and renal-protective effects and their association with substantial weight loss have been evident and progressively expanded their role in the American Diabetes Association (ADA) guidelines, which are endorsed by the European Association for the Study of Diabetes (EASD). The increased demand led to a global shortage. Methods: We utilized a repeated cross-sectional design, drawing data from national prescribing databases, to analyze six GLP-1 RAs: Dulaglutide, Exenatide, Liraglutide, Lixisenatide, Semaglutide, and Tirzepatide. AutoRegressive Integrated Moving Average (ARIMA) models with exogenous variables were applied to assess the trends over time and in different regions. Results: The prescription rates significantly differ between regions. Wales shows the highest prescribing rate for most GLP-1 RAs. The ARIMA models indicated a significant increase in their prescribing rates after the release of the 2022 ADA guidelines (e.g., Dulaglutide: Post-ADA effect of 15.22, 95% CI: [12.97, 17.47]). Following the GLP-1 RA shortages in July 2023, the prescribing rates, particularly for Semaglutide, increased (Shortage effect: 74.36, 95% CI: [71.92, 76.80]). Model diagnostics, including the Akaike Information Criterion (AIC) and Durbin–Watson statistics, confirmed the robustness of these trends. Conclusions: Informed decisions should be made by considering the prescribing trends before and after important events such as the issuing of new guidelines or safety alerts.

Predictors of CD4 cell count progression over time among adolescents and young adults transitioning to adult-oriented HIV care in Southern Ethiopia from 2017 to 2021: a retrospective cohort study

BackgroundHuman immunodeficiency virus (HIV), a viral infection impacting CD4 cells, requires an understanding of factors influencing CD4 cell counts to reduce acquired immunodeficiency syndrome(AIDS)-related morbidity and mortality. Despite its importance, no studies have examined predictors of CD4 cell count progression post-transition to adult-oriented HIV care in Ethiopia. This study aimed to identify predictors affecting CD4 cell count trends among adolescents and young adults transitioning to adult-oriented HIV care. Therefore, this study aimed to identify the predictors that affect the trends of CD4 cell count over time among adolescents and young adults who transitioned to adult-oriented HIV care.MethodsA retrospective cohort study was conducted among 206 adolescents and young adults who transitioned to adult-oriented HIV care and had at least two CD4 cell counts in eight selected health facilities in Southern Ethiopia. These facilities were chosen due to their high volume of people living with HIV. A multivariable linear mixed effect model was fitted to identify the predictors of CD4 cell count progression through time. Multi-collinearity between variables was checked using the variance inflation factor. Model comparisons were done using Akaike and Bayesian information criteria. Regression coefficients with 95% confidence intervals (CI) and P-value ≤ 0.05 were used to measure the strength of association and significance level.ResultsThe average CD4 cell count was raised by 6.7 cells/mm3 every six months among the study participants. Transitioning at the age of 18 or older (β = 204.71; 95% CI = 81.86347, 327.5574; P < 0.001), being rural residents (β= -119.776; 95% CI = -215.362, -24.189; P < 0.014), having World Health Organization (WHO) stage III & IV HIV (β= -182.161; 95% CI = -318.475, -45.847; P < 0.009), were the predictors for CD4 cell count progression over time.ConclusionBased on the identified predictors of CD4 cell count progression over time among HIV-positive adolescents and young adults transitioning to adult-oriented HIV care in southern Ethiopia, such as observation time, age at transition, residence, and WHO staging, the study highlights the need for interventions focused on tailored support. Specifically, these interventions should target individuals transitioning with less ART time, those younger than 18, those residing in rural areas, and those with advanced WHO staging, to improve their CD4 cell counts over time.

Prognostic value of the advanced lung cancer inflammation index in intrahepatic cholangiocarcinoma

IntroductionThe advanced lung cancer inflammation index (ALI), which combines inflammation and nutrition data, was recently proposed as a prognostic biomarker. We assessed the impact of ALI on overall survival (OS) among patients undergoing surgery for intrahepatic cholangiocarcinoma (ICC). MethodsPatients who underwent surgery for ICC were identified from an international cohort. ALI was calculated as body-mass index (BMI)∗albumin/neutrophil-to-lymphocyte ratio; patients were categorized into “low-” and “high-ALI” using log-rank statistics. The impact of ALI on OS was compared against other inflammatory markers (i.e., neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], systemic immune inflammation index [SII = platelets∗NLR]) using Harrell's Concordance index (C-index) and the Akaike Information Criterion (AIC). To minimize intergroup differences, propensity score matching was employed. ResultsAmong 1045 patients, more than one-half of individuals underwent major hepatectomy (n = 582, 55.7 %), median tumor size was 5.5 cm (IQR, 3.8–7.8), and median ALI was 38.9 (IQR 26.5–57.2). On multivariate analysis, low ALI was an independent risk factor for worse OS (HR 1.21, 95 % CI 1.01–1.46; p = 0.04). Patients with low ALI had worse 5-year OS (36.9 % vs. 49.9 %; p < 0.001), which remained significant after PSM (36.9 % vs. 41.3 %; p = 0.039). ALI had a comparable discriminatory ability compared with NLR, PLR, and SII (C-index: 0.646 vs. 0.644 vs. 0.640 vs. 0.641, respectively), yet had a lower AIC (5475.31 vs. 5546.80 vs. 5550.45 vs. 5548.62, respectively) suggesting slightly better model fit and accuracy. ConclusionsALI was an independent predictor of OS among patients undergoing surgery for ICC. Nutritional and inflammatory markers should be incorporated into predictive models to improve prognostic stratification.

Hospital Mortality in Acute Decompensation of Alcoholic Liver Cirrhosis: Can Novel Survival Markers Outperform Traditional Ones?

Background: There is a strong correlation between systemic inflammation intensity and clinical presentation, disease progression, and survival during liver cirrhosis decompensation. This study aimed to evaluate the prognostic performance of blood-based biomarkers as meta-inflammation markers, including NLR, PLR, LMR, INPR, MPR, ALBI, FIB4, and APRI, in predicting hospital mortality in patients with acute decompensation of alcohol-related liver cirrhosis. Methods: Data from 411 patients with their first onset of acute decompensation were analyzed, forming two groups: deceased and survived during hospitalization. Generalized partial least squares regression analysis was applied to explore the effects of surrogate indicators on mortality rates, using mortality rate as the dependent variable. Root Mean Square Error, Akaike's, and Bayesian information criteria determined that four components accounted for most of the variance. Results: Variables with significant negative contributions to the outcome prediction (ranked by standardized regression coefficients) were encephalopathy grade, total bilirubin, Child-Turcotte-Pugh score, MELD, NLR, MPV, FIB4, INR, PLR, and ALT. Coefficient sizes ranged from -0.63 to -0.09, with p-values from 0 to 0.018. Conclusions: NLR, PLR, and FIB4 significantly contribute to hospital mortality prediction in patients with acute decompensation of alcohol-related liver cirrhosis. Conversely, some variables used to predict liver disease severity, including INPR, APRI, LMR, and ALBI score, did not significantly contribute to hospital mortality prediction in this patient population.

Evaluation of the relationships between physico-chemical parameters and the abundance of Vibrio spp. in blue crabs (Callinectes sapidus) and seawater from the Maryland Coastal Bays.

Fluctuations in water quality characteristics influence the productivity of blue crabs (Callinectes sapidus), and the risk of human exposure to pathogenic Vibrio species. Thus, this study assessed the prevalence of total and pathogenic/clinical markers of Vibrio parahaemolyticus and Vibrio vulnificus in blue crabs and seawater from the Maryland Coastal Bays (MCBs) and the correlation between Vibrio levels and physicochemical parameters. Three to five crabs and 1 L of seawater were collected monthly for 3 years (May 2018 to December 2020) from six sites within the MCBs. Hemolymph and crab tissue were extracted and pooled for each site. Extracted hemolymph, crab tissue, and seawater were analyzed for V. parahaemolyticus and V. vulnificus using the Most Probable Number (MPN) and real-time PCR methods. A one-way Analysis of Variance (ANOVA), correlations, and linear models were used to analyze the data. Akaike Information Criterion (AICc) was evaluated to determine the model that provides the best fit to the data relating to Vibrio concentrations and environmental factors. Results suggested that environmental factors could influence the growth of Vibrio spp. Both V. parahaemolyticus and V. vulnificus were more prevalent during the warmer months than colder months. Vibrio was more prevalent in crab samples compared to seawater. Vibrio vulnificus concentrations in seawater and hemolymph were positively correlated with temperature (p = 0.0143 seawater) and pH (p = 0.006 hemolymph). A negative correlation was observed between the concentration of V. vulnificus in whole crab (tissue) and dissolved oxygen level (p = 0.0256). The concentration of V. parahaemolyticus in seawater was positively correlated with temperature (p = 0.009) and negatively correlated with dissolved oxygen (p = 0.012). These results provide current information on the spatial and temporal distributions of Vibrio spp. in the MCBs that are useful for implementing more efficient processing and handling procedures of seafood products.

Analyzing Rear-End Crash Counts on Ohio Interstate Freeways Using Advanced Multilevel Modeling

This study presents a new modeling approach for rear-end crash counts on Ohio’s interstate freeways based on a dataset for 2021 that contains 2745 rear-end crashes. The analysis encompasses 20 interstate freeways, comprising 1833 homogeneous segments and extending over approximately 1313 miles. These interstate freeways exhibit varying safety performances, indicating a significant degree of heterogeneity. A unique rear-end crash risk rate was devised for each interstate, capturing diverse risk profiles. Three distinct models were developed: a standard negative binomial model, an uncorrelated two-level negative binomial model, and a correlated two-level negative binomial model. The correlated two-level negative binomial model demonstrated superior fit, as evidenced by the likelihood ratio test, Akaike information criterion, and Bayesian information criterion. The correlated two-level negative binomial model exhibited enhanced forecasting precision, as measured by the Root Mean Square Error. A significant finding is that the rear-end crash risk rate significantly improves the fit of the models. The study also reveals that rear-end crashes are expected to occur more frequently in urban segments of interstate freeways with high rear-end risk rates. However, rural segments experience no such significant variations in the rear-end crash risk rate. However, an increase in the inner shoulder width is associated with a decrease in expected rear-end crashes. This research offers a valuable methodology for modeling rear-end crashes on interstate freeways, providing insights into the contributing variables that could inform targeted safety improvements.

Adherence to IMCI guidelines for key symptoms in Ethiopian children: A 2021-2022 national service provision survey.

The Health Services Provision Assessment in Ethiopia (SPA-ET) is a survey that generates data on the availability and quality of health services in Ethiopia. Despite the presence of integrated management of childhood illness guidelines in healthcare settings, there has been inadequate exploration or assessment of how effectively and consistently health professionals follow the guidelines. This study aims to identify factors influencing healthcare worker adherence to the integrated management of childhood illness guidelines to identify spatial clusters. The data for this study were gathered from the Service Provision Assessment (SPA) survey in Ethiopia, which was conducted nationwide from August 11, 2021, to February 4, 2022. It included a total of 788 health professionals who assessed sick children experiencing at least one of the three main childhood illness symptoms: fever, cough, or diarrhea. We employed STATA version 16 for data analysis, utilizing cross-tabulations to explore relationships between variables and logistic regression modeling to identify factors influencing adherence. To account for the hierarchical structure of the health survey data, we employed multilevel logistic regression. Model selection was based on comparison parameters including the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC). We computed adjusted odds ratios with 95% confidence intervals, and statistical significance was determined at a significance level of p < 0.05. The rate of adherence to the integrated management of childhood illness guideline was 33% (95% CI: 29.70%, 36.26%). The analysis revealed several factors influencing adherence to IMCI protocols. child's age (being ≥24 months) [aOR = 0.66, 95% CI: (0.45, 0.87)], facility type (health center) [aOR = 2.61, 95% CI: (1.84, 3.37)], place of residency (being rural) [aOR = 0.54, 95% CI: (0.38, 0.77)], and care provider's qualification (health officer) [aOR = 1.71, 95% CI: (1.18, 2.48)] were all statistically significant. Moreover, the primary cluster is situated in the west Oromia region, with a central focus at coordinates (7.982108 N, 36.203355 E) and extends to a radius of 78.28 km. This study confirms a low adherence rate (33%) among health professionals in Ethiopia to the IMCI guideline for assessing the three main symptoms of sick children. The study identified child's age, facility type, academic qualification, and place of residence as crucial factors correlated with adherence rate. Furthermore, 5 secondary clusters (hotspot areas) were identified using SaTScan software. To address the higher protocol refusal, interventional plan needs to be based on academic qualification of care provider, facility type, age of child and place of residency. Moreover, interventions to reduce non-adherence to IMCI guidelines should be location-tailored based on identified hotspot areas to restore guidelines adherence equality.

Effect of Microplastic Exposure to the Reproductive Energy and Fecundity of Female Wami Tilapia (Oreochromis urolepis, Norman 1922) Fish

ABSTRACTThere is mounting evidence indicating that microplastics (MPs, &lt; 5 mm) cause reproductive dysfunction in fish, yet information on the long‐term effects of MP exposure remains scarce. In this study, Wami tilapia fries were exposed to 38–45 µm polyethylene (PE) MPs for their first 2 months in treatment groups of control (0 PE MPs/mL), 1, 10, and 100 PE MPs/mL (with 60 individuals per group in triplicates), and subsequently maintained in a ratio of 3 females to 1 male for an additional 5 months. Reproductive proxies and parameters of female fish health were calculated, and models were developed according to the Akaike information criterion (AIC) and no significant differences in fecundity, relative fecundity, hepatosomatic index (HSI), weight, length, gonadosomatic index, and oocyte packing density were found between treatment groups (one‐Way ANOVA, p &gt; 0.05). However, condition factors varied significantly between treatment groups (Kruskal–Wallis Test, p = 0.006), with the control group differing significantly from the groups exposed to 1 PE MPs/mL (p = 0.007), 10 PE MPs/mL (p = 0.03), and 100 PE MPs/mL (p = 0.001). Fecundity showed strong correlations with weight and length in all treatment groups except the group exposed to 10 PE MPs/mL (weight: r = 0.471, p = 0.346; length: r = 0.688, p = 0.131) and showed insignificant correlations with condition factors and HSI. Multiple regression models revealed that weight significantly contributed to fecundity in all treatment groups except the group exposed to 10 PE MPs/mL. Histopathological analysis indicated damage to livers and small intestines proportional to the dose of PE MPs. This study demonstrates that long‐term exposure of fish to MPs has no significant effect on fecundity but impairs fish health, which could potentially jeopardize the quality of fish eggs and recruitment.

Summer roost site selection of a declining bat species

AbstractSummer habitats are critical to bat population persistence as they support multiple life history stages, including maternity colonies, nursery sites, and foraging locations. The tricolored bat (Perimyotis subflavus) is a hibernating North American bat species that uses forested landscapes during summer months; however, information on the summer habitat requirements is limited. The objective of this work was to quantify the characteristics of roost sites selected by tricolored bats during summer months. We captured, tagged, and tracked 15 bats using radio-telemetry to 55 roost locations. At each roost, we recorded roost habitat characteristics and other forest characteristics within a 0.1 ha circular plot surrounding the roost tree and a 1 km buffer at the landscape scale. We repeated these measurements for three random trees per roost tree to characterize available habitat for selection. We used a suite of mixed conditional logistic regression models to test multiple factors known to influence roost-site selection for various bat species and compared using Akaike information criterion to select the best model. The top model at the roost scale demonstrated that roost selection was influenced by roost tree height, while the landscape scale was influenced by deciduous forest and distance to roads. There is a critical information gap for the ongoing recovery of tricolored bats; better understanding of summer habitat and proper forest management implications, as well as information on scale-specific habitat selection, is needed to better understand tricolored bat management needs.

Are information criteria good enough to choose the right the number of regimes in hidden Markov models?

Selecting the number of regimes in Hidden Markov models is an important problem. Many criteria are used to do so, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), integrated completed likelihood (ICL), deviance information criterion (DIC), and Watanabe-Akaike information criterion (WAIC), to name a few. In this article, we introduced goodness-of-fit tests for general Hidden Markov models with covariates, where the distribution of the observations is arbitrary, i.e. continuous, discrete, or a mixture of both. A selection procedure is proposed based on this goodness-of-fit test. The main aim of this article is to compare the classical information criterion with the new criterion when the outcome is either continuous, discrete or zero-inflated. Numerical experiments assess the finite sample performance of the goodness-of-fit tests, and comparisons between the different criteria are made.

Time-series forecasting of particulate organic carbon on the Sunda Shelf: Comparative performance of the SARIMA and SARIMAX models

Conducting in-depth studies of carbon dynamics in the Southeast Asia’s Sunda Shelf waters is challenging. Particulate organic carbon (POC) is a key component that can be monitored in the region, but ocean assessment requires advanced research and POC monitoring, especially in relation to ocean dynamics and forecasts. Accurate forecasting of POC in marine environments is difficult due to the complexity of oceanic processes and the influence of various environmental factors. Statistical forecasting models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) or SARIMA with Exogenous Factors (SARIMAX) are useful for this purpose. This study compares these two time-series forecasting models to assess the role of exogenous factors in predicting POC variability in Indonesian seas. The models were specified as SARIMA (3, 1, 3)x(2, 0, 0, 60) and SARIMAX (3, 1, 3)x(2, 0, 0, 60). We obtained Akaike Information Criterion (AIC) values of 1237.767 for the SARIMA model and 1207.341 for SARIMAX[exog=SST]. The SARIMA model's validation metrics were 6.94 % for MAPE, 10.80 for RMSE, and 0.65 for the correlation coefficient, outperforming the SARIMAX model. While SARIMAX incorporates additional environmental variables, SARIMA outperforms it based on MAPE, RMSE, and correlation coefficients. Our findings reveal that only sea surface temperature can significantly influence POC forecasts, thus providing a new perspective on oceanic carbon dynamics. From 2022–2030, POC levels are expected to range between 108.3 and 135.9 mg/m³, with a mean value of 120.9±5.4 mg/m³, lower than that observed from 2002 to 2022, which was 118.8±11.9 mg/m³. The highest peak in POC is predicted for the end of 2026 and 2027. The annual trend shows that the highest POC values correspond to the northwest monsoon season, with the lowest during the intermonsoon period.

Modelling of the time to death of breast cancer patients at Hiwot Fana Specialized University Hospital

Breast cancer is the most common cause of cancer death and is a frequently diagnosed cancer among women worldwide. It is becoming a challenging health condition in Ethiopia with a high rate of morbidity and mortality. The main aim of this study was to model the time to death in breast cancer patients at Hiwot Fana Specialized University Hospital. A retrospective cohort study was carried out from April 1st, 2020, to April 1st, 2023, and 296 women were included in the study. We used nonparametric methods and Bayesian accelerated failure time models (with Laplace approximation) to identify risk factors and choose a model fitting breast cancer patient data. Model comparison was performed using the marginal likelihood, deviance information criterion and Watanabe Akaike information criterion. From the total of 296 patients in the study, 56 (18.9%) died. The estimated median survival time was 33 months. The log-rank test showed that age group, stage, alcohol consumption, smoking habit, and comorbidity were potential risk factors associated with the time to death in breast cancer patients at the 5% level of significance. The Bayesian Weibull accelerated failure time model was found to be the best fitted model for predicting the survival time of patients with minimum DIC (520.39) and WAIC (521.59) values. The final Bayesian Weibull AFT model with the integrated nested Laplace approximation estimation technique revealed that age group, stage, alcohol consumption, smoking habit, and comorbidity were significantly associated with the time to death in breast cancer patients. Individuals older than 65 years, with stage IV disease, drinking alcohol, smoking cigarettes and having comorbidities had shortened survival times in patients with breast cancer. Hence, Hiwot Fana Specialized University Hospital and related bodies should work on awareness creation to reduce smoking habits and alcohol use as well as give due attention to elderly and stage IV breast cancer patients during intervention.

Response-Aided Score-Matching Representative Approaches for Big Data Analysis and Model Selection under Generalized Linear Models

In this paper, we propose an efficient method called the response-aided score-matching representative (RASMR) approach to facilitate massive data model selection and data analysis with generalized linear models (GLMs) and a predetermined data partition due to data localization. Similar to the original score-matching representative (SMR) approach, RASMR constructs an artificial data point, called the representative, for each data block. It then fits a GLM on the representative dataset, which provides not only an efficient approach for massive data analysis but also an ideal solution in response to privacy concerns by avoiding the transfer of sensitive data. By further splitting the data blocks according to the values of the response variables, RASMR can obtain more accurate parameter estimates than SMR. Furthermore, by theoretical justifications and simulation studies, we show that RASMR can be more efficiently utilized for model selection and variable selection for a massive dataset by approximating the Akaike information criterion (AIC) and the aggregated prediction errors for cross-validation, which are commonly used for choosing the most appropriate statistical model and drawing reliable conclusions. We also apply the proposed RASMR approach to the airline on-time performance data, which consists of 371 data files labeled by month, and show that RASMR can be successfully used for selecting the most appropriate model for real massive data analysis.

Prediction of Fetal Death in Preterm Preeclampsia Using Fetal Sex, Placental Growth Factor and Gestational Age.

Preeclampsia (PE) is a systemic disease that affects 4.6% of pregnancies. Despite the existence of a first-trimester screening for the prediction of preterm PE, no consensus exists regarding neither the right moment to end the pregnancy nor the appropriate variables to estimate the prognosis. The objective of this study was to obtain a prediction model for perinatal death in patients with preterm PE, useful for clinical practice. Singleton pregnant women with PE and preterm delivery were included in an observational retrospective study. Multiple maternal and fetal variables were collected, and several multivariable logistic regression analyses were applied to construct models to predict perinatal death, selecting the most accurate and reproducible according to the highest area under the curve (AUC) and the lowest Akaike Information Criteria (AIC). A group of 148 pregnant women were included, and 18 perinatal deaths were registered. Univariable logistic regression selected as statistically significant variables the following: gestational age (GA) at admission, fetal sex, poor response to antihypertensive drugs, PlGF, umbilical artery (UA) pulsatility index (PI), cerebroplacental ratio (CPR), and absent/reversed ductus venosus (DV). The multivariable model, including all these parameters, presented an AUC of 0.95 and an AIC of 76.5. However, a model including only GA and fetal sex presented a similar accuracy with the highest simplicity (AUC 0.93, AIC 67.6). Finally, in fetuses with a similar GA, fetal death became dependent on PlGF and fetal sex, underlying the role of fetal sex in all circumstances. Female fetal sex and low PlGF are notorious predictors of perinatal death in preterm PE, only surpassed by early GA at birth.

Comparative Performance of ARIMA and ARCH Models On Time Series of Monthly Libyan Brent Oil Price

Many economic and financial time series exhibit a phenomenon known as heteroscedastic, where the variance of the series changes over time. This research study focuses on financial time series modelling, with special application to modelling the price of Libyan Brent Oil. In particular, the theory of univariate nonlinear time series analysis is explored and applied to the price Libyan of Brent Oil, spanning from January 2000 to December 2010. The data was obtained from Bullent of the Platts Market Wire of Statistics. This study aims to evaluate the performance of ARIMA as a linear model and ARCH as a nonlinear modelling data. Multiple time series models were considered for fitting the data, and the best ARIMA models were selected based on the Akaike Information Criteria (AIC). ARIMA (0, 1, 1), and ARIMA (1, 1, 0) were identified as the best models. After estimating the parameters of the selected models, model checking revealed that these models were not suitable for modeling the data, as they lacked validity according to the test of squared residuals. The goodness of fit was assessed using the AIC, and based on minimum AIC values, the best fit ARCH models were found to be ARIMA (0, 1, 1) - ARCH (1). After estimating the parameters of the selected model, a series of diagnostic and forecast accuracy tests were performed. Based on this model, a twelve-month forecast of the price of Libyan Brent crude was made.

Technology Company Stock Trend Prediction Based on ARIMA Model

With the development of finance, more and more people are paying attention to stock price prediction, hoping to gain economic benefits from it. From the literature review, due to the complexity of stock trends, it is necessary to study the analysis of industry stocks. This paper studies the effectiveness of the autoregressive moving average model (ARIMA) for 5 stocks in the technology industry. Then, we will compare and parameterize the ARIMA model by using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). After the data processing, we find that the development prospects of four head technology companies have steadily increased due to years of economic accumulation and stable operation. However, as a technology company that has emerged in recent years, OpenAI is extremely susceptible to external factors such as academic fraud and AI replacing manual labor. Its stock trend has fluctuated greatly and its prospects are sluggish.

Assessment of heart rate measurements by commercial wearable fitness trackers for early identification of metabolic syndrome risk

Metabolic syndrome increases the risks of cardiovascular diseases, type 2 diabetes, and certain cancers. The early detection of metabolic syndrome is clinically relevant, as it enables timely and targeted interventions. In the current study, we aimed to investigate the association between metabolic syndrome and heart rate measured using wearable devices in a real-world setting and compare this association with that for clinical resting heart rate. Data from 564 middle-aged adults who wore wearable devices for at least 7 days with a minimum daily wear time of 20 h were analyzed. The results showed significantly elevated all-day, sleeping, minimum, and inactive heart rates in men with pre-metabolic or metabolic syndrome compared with those in normal individuals, whereas sleeping heart rate and heart rate dips were significantly increased and decreased, respectively, in women with metabolic syndrome. After adjusting for confounders, every 10-beats-per-minute increment in all-day, sleeping, minimum, and inactive heart rates in men corresponded to odds ratios of 2.80 (95% confidence interval 1.53–5.44), 3.06 (1.57–6.40), 4.21 (1.87–10.47), and 3.09 (1.64–6.29), respectively, for the presence of pre-metabolic or metabolic syndrome. In women, the association was significant only for heart rate dips (odds ratio = 0.49 [95% confidence interval 0.25–0.96] for every 10% increment). Models incorporating inactive or minimum heart rate in men and heart rate dip in women demonstrated better fits, as indicated by lower Akaike information criterion values (170.3 in men and 364.9 in women), compared with models that included clinical resting heart rate (173.4 in men and 369.1 in women). These findings suggest that the heart rate indices obtained from wearable devices may facilitate early identification of metabolic syndrome.

Including educational status may improve cardiovascular risk calculations such as SCORE2.

The association between education and atherosclerotic cardiovascular disease (ASCVD) has been well described for decades. Nevertheless, most cardiovascular risk models, including SCORE2, still do not take educational status into account even if this factor is easily assessed and costs nothing to acquire. Using carotid plaques as a proxy for ASCVD, we analysed educational status as associated with carotid plaque development, to determine if the relationship remains, how it relates to traditional risk factors and, how it impacts the European cardiovascular risk model, SCORE2. Our study also provides further data on plaque development in a well-characterised population nearly equally weighted by gender. 9,083 subjects (51% female, 49% male) from the Paracelsus 10,000 cohort, underwent a carotid doppler duplex as part of thorough screening for subclinical ASCVD. Well over 90% of carotid doppler duplex examinations were performed by the same experienced clinician. Subjects were then classified by educational status using the Generalized International Standard Classification of Education. Plaque absence or presence was dichotomised and variables analysed using regression modelling to examine educational status relative to cardiovascular risk factors and with respect to the SCORE2 model. Using medium educational status as a reference, subjects in our cohort with low educational status had higher odds, while subjects with high educational status had lower odds for carotid plaques compared to subjects with medium education (aOR 1.76 95%CI 1.50-2.06; and 0.0.63 95%CI 0.57-0.70, respectively). Even after adjusting for common risk factors including metabolic syndrome and SCORE2, the relationship was maintained. Furthermore, when comparing the potential predictive power of SCORE2 alone and plus educational status using the Akaike information criterion, we showed a 'better fit' when educational status was added. Measuring educational status is cost-free and easy for clinicians to obtain. We believe cardiovascular risk prediction models such as SCORE2 may more accurately reflect individual risk if educational status is also taken into account. Additionally, we believe clinicians need to understand and appropriately address educational status as a risk factor, to better quantify individual risk and take appropriate measures to reduce risk so that the association may finally be broken.