Predictors of transition to schizophrenia and other long-lasting non-affective psychoses in first-episode patients with acute and transient psychotic disorders: A validation study

Background and objectives Almost half of the individuals with a first-episode of psychosis who initially meet criteria for acute and transient psychotic disorder (ATPD) will have had a diagnostic revision during their follow-up, mostly toward schizophrenia. This study aimed to determine the proportion of diagnostic transitions to schizophrenia and other long-lasting non-affective psychoses in patients with first-episode ATPD, and to examine the validity of the existing predictors for diagnostic shift in this population. Methods We designed a prospective two-year follow-up study for subjects with first-episode ATPD. A multivariate logistic regression analysis was performed to identify independent variables associated with diagnostic transition to persistent non-affective psychoses. This prediction model was built by selecting variables on the basis of clinical knowledge. Results Sixty-eight patients with a first-episode ATPD completed the study and a diagnostic revision was necessary in 30 subjects at the end of follow-up, of whom 46.7% transited to long-lasting non-affective psychotic disorders. Poor premorbid adjustment and the presence of schizophreniform symptoms at onset of psychosis were the only variables independently significantly associated with diagnostic transition to persistent non-affective psychoses. Conclusion Our findings would enable early identification of those inidividuals with ATPD at most risk for developing long-lasting non-affective psychotic disorders, and who therefore should be targeted for intensive preventive interventions. (AU)

Determinants of self-reported chronic disease diagnoses among older persons in South Africa.

BACKGROUND:  Chronic diseases tend to affect the quality of life for older persons worldwide, especially in resource-constrained developing countries. Chronic diseases contribute to a large number of deaths among the population of South Africa. AIM:  This study examines the determinants of self-reported chronic disease diagnoses among older persons in South Africa. SETTING:  The study setting was South Africa. METHODS:  Cross-sectional data from the 2019 South Africa General Household Survey were analysed (n [weighted] = 4 887 334). We fitted a binary logistic regression model to determine the relationship between socio-demographic factors and being diagnosed with self-reported chronic diseases. RESULTS:  We found that at least 5 in 10 older persons were diagnosed with self-reported chronic disease. The bivariate findings showed that age, population group, sex, marital status, level of education, disability status, household composition and province were significantly associated with self-reported chronic disease diagnoses. At the multivariate level, we found that age, sex, population group, marital status, educational level, disability status, household wealth status, household composition and province were key predictors of self-reported chronic disease diagnoses. CONCLUSION:  We found that various factors were key determinants of being diagnosed with self-reported chronic diseases. This study offers important insights into the main correlations between older adults and self-reported chronic illness diagnoses. More study is required on the health of the elderly as it will help direct policy discussions and improve the development of health policies about the elderly.Contribution: This study highlights the need for a better understanding of, and continued research into, the determinants health among older populations to guide future healthcare strategies.

Predictors of excess fluid volume in hemodialysis patients: an observational study.

OBJECTIVES: to assess risk factors for excess fluid volume in hemodialysis patients. METHODS: a retrospective case-control study was conducted. A total of 392 patients (196 cases and 196 controls) from two hemodialysis centers were included. Sociodemographic data and 23 risk factors for excess fluid volume were assessed using a data collection form. Data were analyzed using a multivariate logistic regression model. RESULTS: the insufficient knowledge (OR=2.06), excessive fluid intake (OR=2.33), inadequate fluid removal during hemodialysis (OR=2.62) and excessive sodium intake (OR=1.91) risk factors may increase the chance of occurrence of excess fluid volume in hemodialysis patients by approximately two times. Education level (OR=0.95) and age (OR=0.97) are protective factors for excessive fluid volume. CONCLUSIONS: knowing these risk factors may help nurses with accurate and rapid diagnostic inference of the risk of excessive fluid volume.

Living well with diabetes in Alaska.

Many people with diabetes mellitus experience minimal or no complications. Our objective was to determine the proportion of Alaska Native people who experienced four major complications or mortality and to identify factors that may be associated with these outcomes. We used records in a diabetes registry and clinical and demographic variables in our analyses. We used logistic regression and Cox Proportional Hazards models to evaluate associations of these parameters with death and complications that occurred prior to 2013. The study included 591 Alaska Native people with non-type 1 diabetes mellitus, diagnosed between 1986 and 1992. Over 60% of people in this study remained free of four major diabetes-related complications for the remainder of life or throughout the approximately 20-year study period. Lower BMI, higher age at diagnosis of diabetes, and use of at least one diabetes medication were associated with death and a composite of four complications. A majority of Alaska Native people with DM had none of four major complications over a 20-year period. Lower BMI and use of diabetes medications were associated with higher hazard for some deleterious outcomes. This suggests that goals in care of elders should be carefully individualised. In addition, we discuss several programme factors that we believe contributed to favourable outcomes.

Serum iron level is independently associated with sarcopenia: a retrospective study.

Sarcopenia greatly reduces the quality of life of the elderly, and iron metabolism plays an important role in muscle loss. This study aimed to investigate the association between iron status and sarcopenia. A total of 286 adult patients hospitalized between 2019 and 2021 were included in this study, of which 117 were diagnosed with sarcopenia. Serum iron, total iron binding capacity (TIBC), transferrin, and transferrin saturation levels were compared between groups with and without sarcopenia and were included in the logistic analyses, with significant variables further included in the logistic regression model for the prediction of sarcopenia. Serum iron, TIBC, and transferrin levels decreased significantly in the sarcopenia group (p < 0.05), and were negatively associated with handgrip strength, relative skeletal muscle index, and multiple test performances (p < 0.05). Multivariate logistic analysis showed that sex, age, body mass index (BMI), and serum iron level were independent risk factors for sarcopenia. In the final logistic regression model, male sex (odds ratio [OR] 3.65, 95% confidence interval [CI] 1.67-7.98), age > 65 years (OR 5.40, 95% CI 2.25-12.95), BMI < 24 kg/m2 (OR 0.17, 95% CI 0.08-0.36), and serum iron < 10.95 µmol/L (OR 0.39, 95% CI 0.16-0.93) were included. Our study supported the impact of iron metabolism on muscle strength and performance.

Application of machine learning algorithms to identify people with low bone density.

Background: Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis. Objective: This study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data. Methods: Using NHANES 2017-2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013-2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance. Results: A total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase. Conclusion: The machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.

The magnitude of stunting and its determinants among late adolescent girls in East Africa: Multilevel binary logistics regression analysis.

BACKGROUND: Stunting poses a significant health risk to adolescent girls aged 15-19 in low- and middle-income countries, leading to lower education levels, reduced productivity, increased disease vulnerability, and intergenerational malnutrition. Despite the inclusion of adolescent nutrition services in the Sustainable Development Goals, little progress has been made in addressing malnutrition among adolescent girls in several African nations. Limited evidence exists in East Africa due to small sample sizes and methodological limitations. To overcome these constraints, this study utilizes the latest Demographic and Health Survey data to estimate the prevalence and factors influencing stunting among late adolescent girls in ten East African countries. METHODS: This study utilized the most recent Demographic and Health Survey (DHS) data from 10 East African countries, including a total sample weight of 22,504 late-adolescent girls. A multilevel mixed-effect binary logistic regression model with cluster-level random effects was employed to identify factors associated with stunting among these girls. The odds ratio, along with the 95% confidence interval, was calculated to determine individual and community-level factors related to stunting. A p-value less than 0.05 was considered statistically significant in determining the factors influencing stunting among late-adolescent girls. RESULTS: The prevalence of stunting among late adolescent girls in East Africa was found to be 13.90% (95% CI: 0.13-0.14). Religion, relationship to the head, presence of under-five children in the household, lactating adolescent, marital status, Time to get water source, and country of residence were significantly associated with Stunting. CONCLUSION: This study highlights the complexity of stunting in East Africa and identifies key factors that need attention to reduce its prevalence. Interventions should focus on improving water access, supporting lactating girls, addressing socioeconomic disparities, promoting optimal care practices, and implementing country-specific interventions to combat stunting and improve adolescent girls' nutrition.

Relationship between dietary live microbe intake and the prevalence of COPD in adults: a cross-sectional study of NHANES 2013-2018.

OBJECTIVE: To explore the potential association between dietary live microbes and the prevalence of Chronic Obstructive Pulmonary Diseases (COPD). METHODS: In this cross-sectional study, data of 9791 participants aged 20 years or older in this study were collected from the National Health and Nutrition Examination Survey (NHANES) between 2013 and 2018. Participants in this study were classified into three groups according to the Sanders' dietary live microbe classification system: low, medium, and high dietary live microbe groups. COPD was defined by a combination of self-reported physician diagnoses and standardized medical status questionnaires. Logistic regression and subgroup analysis were used to assess whether dietary live microbes were associated with the risk of COPD. RESULTS: Through full adjustment for confounders, participants in the high dietary live microbe group had a low prevalence of COPD in contrast to those in low dietary live microbe group (OR: 0.614, 95% CI: 0.474-0.795, and p < 0.001), but no significant association with COPD was detected in the medium and the low dietary live microbe groups. This inverse relationship between dietary live microbe intake and COPD prevalence was more inclined to occur in smokers, females, participants aged from 40 to 59 years old and non-obese participants. CONCLUSION: A high dietary live microbe intake was associated with a low prevalence of COPD, and this negative correlation was detected especially in smokers, females, participants aged from 40 to 59 years old and non-obese participants.

Machine learning approach for the development of a crucial tool in suicide prevention: The Suicide Crisis Inventory-2 (SCI-2) Short Form.

The Suicide Crisis Syndrome (SCS) describes a suicidal mental state marked by entrapment, affective disturbance, loss of cognitive control, hyperarousal, and social withdrawal that has predictive capacity for near-term suicidal behavior. The Suicide Crisis Inventory-2 (SCI-2), a reliable clinical tool that assesses SCS, lacks a short form for use in clinical settings which we sought to address with statistical analysis. To address this need, a community sample of 10,357 participants responded to an anonymous survey after which predictive performance for suicidal ideation (SI) and SI with preparatory behavior (SI-P) was measured using logistic regression, random forest, and gradient boosting algorithms. Four-fold cross-validation was used to split the dataset in 1,000 iterations. We compared rankings to the SCI-Short Form to inform the short form of the SCI-2. Logistic regression performed best in every analysis. The SI results were used to build the SCI-2-Short Form (SCI-2-SF) utilizing the two top ranking items from each SCS criterion. SHAP analysis of the SCI-2 resulted in meaningful rankings of its items. The SCI-2-SF, derived from these rankings, will be tested for predictive validity and utility in future studies.

Logistic regression analysis of the value of biomarkers, clinical symptoms, and imaging examinations in COVID-19 for SARS-CoV-2 nucleic acid detection.

The detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) nucleic acid detection provides a direct basis for diagnosing Coronavirus Disease 2019. However, nucleic acid test false-negative results are common in practice and may lead to missed diagnosis. Certain biomarkers, clinical symptoms, and imaging examinations are related to SARS-CoV-2 nucleic acid detection and potential predictors. We examined nucleic acid test results, biomarkers, clinical symptoms, and imaging examination data for 116 confirmed cases and asymptomatic infections in Zhuhai, China. Patients were divided into nucleic acid-positive and -false-negative groups. Predictive values of biomarkers, symptoms, and imaging for the nucleic acid-positive rate were calculated by Least Absolute Shrinkage and Selection Operators regression analysis and binary logistic regression analysis, and areas under the curve of these indicators were calculated. Hemoglobin (OR = 1.018, 95% CI: 1.006-1.030; P = .004) was higher in the respiratory tract-positive group than the nucleic acid-negative group, but platelets (OR = 0.996, 95% CI: 0.993-0.999; P = .021) and eosinophils (OR = 0.013, 95% CI: 0.001-0.253; P = .004) were lower; areas under the curve were 0.563, 0.614, and 0.642, respectively. Some biomarkers can predict SARS-CoV-2 viral nucleic acid detection rates in Coronavirus Disease 2019 and are potential auxiliary diagnostic tests.

[HBeAg-positive patients hepatic tissue inflammatory activity and influencing factors during normal ALT and indeterminate phases].

Objective: To analyze the hepatic tissue inflammatory activity and influencing factors in HBeAg-positive patients during normal alanine aminotransferase (ALT) and indeterminate phases so as to provide a basis for evaluating the disease condition. Methods: Patients with HBeAg-positive with normal ALT and HBV DNA levels below 2 × 10(7) IU/ml from January 2017 to December 2021 were selected as the study subjects. A histopathologic liver test was performed on these patients. Age, gender, time of HBV infection, liver function, HBsAg level, HBV DNA load, genotype, portal vein inner diameter, splenic vein inner diameter, splenic thickness, and others of the patients were collected. Significant influencing factors of inflammation were analyzed in patients using logistic regression analysis, and its effectiveness was evaluated using receiver operating characteristic (ROC) curves. Results: Of the 178 cases, there were 0 cases of inflammation in G0, 52 cases in G1, 101 cases in G2, 24 cases in G3, and one case in G4. 126 cases (70.8%) had inflammatory activity ≥ G2. Infection time (Z=-7.138, P<0.001), γ-glutamyltransferase (t =-2.940, P=0.004), aspartate aminotransferase (t =-2.749, P=0.007), ALT (t =-2.153, P=0.033), HBV DNA level (t =-4.771, P=0.010) and portal vein inner diameter (t =-4.771, P<0.001) between the ≥G2 group and < G2 group were statistically significantly different. A logistic regression analysis showed that significant inflammation in liver tissue was independently correlated with infection time [odds ratio (OR)=1.437, 95% confidence interval (CI): 1.267-1.630; P<0.001)] and portal vein inner diameter (OR=2.738, 95% CI: 1.641, 4.570; P<0.001). The area under the curve (AUROC), specificity, and sensitivity for infection time and portal vein inner diameter were 0.84, 0.71, 0.87, 0.72, 0.40, and 0.95, respectively. Conclusion: A considerable proportion of HBeAg-positive patients have inflammation grade ≥G2 during normal ALT and indeterminate phases, pointing to the need for antiviral therapy. Additionally, inflammatory activity has a close association with the time of infection and portal vein inner diameter.

Recommended homemade fluid utilization for the treatment of diarrhea and associated factors among children under five in sub-Saharan African countries: a multilevel analysis of the recent demographic and health survey.

INTRODUCTION: Diarrhea is a common public health problem and the third leading cause of death in the world among children under the age of five years. An estimated 2 billion cases and 1.9 million deaths are recorded among children under the age of five years every year. It causes body fluid loss and electrolyte imbalance. Even though, early initiation of recommended homemade fluid is a simple and effective approach to prevent diarrhea-related complications and mortality of children, recommended homemade fluid utilization for the treatment of diarrhea is still low in sub-Saharan African countries. Therefore, this study aimed to assess the magnitude of recommended homemade fluid utilization for the treatment of diarrhea and associated factors among children under five in sub-Saharan African countries. METHOD: The most recent Demographic and Health Survey dataset of 21 sub-Saharan African countries from 2015 to 2022 was used for data analysis. A total of 33,341 participants were included in this study as a weighted sample. Associated factors were determined using a multilevel mixed-effects logistic regression model. Significant factors in the multilevel mixed-effect logistic regression model were declared significant at p-values < 0.05. The adjusted odds ratio (AOR) and confidence interval (CI) were used to interpret the results. RESULT: The overall recommended homemade fluid utilization for the treatment of diarrhea among children under five in sub-Saharan African countries was 19.08% (95% CI = 18.66, 19.51), which ranged from 4.34% in Burundi to 72.53% in South Africa. In the multivariable analysis, being an educated mother/caregiver (primary and secondary level) (AOR = 1.15, 95% CI: 1.04, 1.27) and (AOR = 1.30, 95% CI: 1.15, 1.1.47), the primary and secondary level of fathers education (AOR = 1.53, 95% CI: 1.37, 1.71) and (AOR = 1.41, 95% CI: 1.19, 1.1.68), having antenatal care follow-up (AOR = 1.16, 95% CI: 1.01, 1.33), having multiple children (AOR = 1.17, 95% CI: 1.07, 1.28), and being an urban dweller (AOR = 1.15, 95% CI: 1.04, 1.27) were factors associated with recommended homemade fluid utilization. CONCLUSION: The overall recommended homemade fluid utilization for the treatment of diarrhea was low. Individual and community-level variables were associated with recommended homemade fluid utilization for the treatment of diarrhea. Therefore, special consideration should be given to rural dwellers and caregivers who have three and below children. Furthermore, better to strengthen the antenatal care service, mother/caregiver education, and father's education to enhance recommended homemade fluid utilization for the treatment of diarrhea.

Tobacco policies and changes in the tendency of smoking cessation in cigarette users in Chile: a longitudinal cross-sectional study.

OBJECTIVE: To assess the impact of tobacco control regulations and policy implementation on smoking cessation tendencies in cigarette users born between 1982 and 1991 in Chile. DESIGN: Longitudinal cross-sectional study. SETTING: National level. PARTICIPANTS: Data from the National Survey of Drug Consumption (Service of Prevention and Rehabilitation for Drug and Alcohol Consumption). A pseudo-cohort of smokers born between 1982 and 1991 (N=17 905) was tracked from 2002 to 2016. PRIMARY AND SECONDARY OUTCOMES MEASURES: Primary outcome was the tendency to cease smoking conceptualised as the report of using cigarettes 1 month or more ago relative to using cigarettes in the last 30 days. The main exposure variable was the Tobacco Policy Index-tracking tobacco policy changes over time. Logistic regression, controlling for various factors, was applied. RESULTS: Models suggested a 14% increase in the smoking cessation tendency of individuals using cigarettes 1 month or more ago relative to those using cigarettes in the last 30 days (OR 1.14, CI 95% CI 1.10 to 1.19) for each point increment in the Tobacco Policy index. CONCLUSIONS: Our study contributes to documenting a positive impact of the implementation of interventions considered in the MPOWER strategy in the progression of smoking cessation tendencies in smokers born between 1982 and 1991 in Chile.

Determinants of unmet need for modern contraceptives in Ethiopia.

OBJECTIVE: To identify the determinants of the unmet need for modern contraceptives in Ethiopia. DESIGN: Community-based cross-sectional study. SETTING: Ethiopia. PARTICIPANTS: A group of 6636 women of reproductive age (15-49 years) who were sexually active were included in the study. OUTCOME: Unmet need for modern contraceptives METHOD: The study used data from the 2019 Performance Monitoring for Action-Ethiopia survey, which was community-based and cross-sectional. The sample consisted of women aged 15-49 from households randomly selected to be nationally representative. Multinomial logistic regression and spatial analysis were performed to determine the factors influencing unmet needs for modern contraceptives. The descriptive analysis incorporated svy commands to account for clustering. RESULTS: The proportion of unmet need for modern contraceptives was 19.7% (95% CI: 18% to 21.5%). Women with supportive norms towards family planning had a lower risk of unmet need for spacing (relative risk ratio (RRR)=0.92, 95% CI: 0.86 to 0.99). Older age lowered the risk of unmet need for spacing 40-44 (RRR=0.28, 95% CI: 0.13 to 0.59) and 45-49 (RRR=0.11, 95% CI: 0.04 to 0.31). Being married increased the unmet need for spacing (RRR=1.9, 95% CI: 1.36 to 2.7) and limiting (RRR=3.7, 95% CI: 1.86 to 7.4). Increasing parity increases the risk of unmet need for spacing (RRR=1.27, 95% CI: 1.16 to 1.38) and limiting (RRR=1.26, 95% CI: 1.15 to 1.4). Contrarily, older age increased the risk of unmet need for limiting 40-44 (RRR=10.2, 95% CI: 1.29 to 79.5), 45-49 (RRR=8.4, 95% CI: 1.03 to 67.4). A clustered spatial unmet need for modern contraceptives was observed (Global Moran's I=0.715: Z-Score=3.8496, p<0.000118). The SaTScan identified 102 significant hotspot clusters located in Harari (relative risk (RR)=2.82, log-likelihood ratio (LLR)=28.2, p value<0.001), South Nations Nationalities and People, Oromia, Gambella and Addis Ababa (RR=1.33, LLR=15.6, p value<0.001). CONCLUSIONS: High levels of unmet need for modern contraceptives were observed in Ethiopia, showing geographical variations. It is essential to address the key factors affecting women and work towards reducing disparities in modern contraceptive unmet needs among different regions.

Prevalence and associated factors of neurocognitive disorder among people living with HIV/AIDS in the South Gondar zone primary hospitals, North-West Ethiopia: an institution-based cross-sectional study.

OBJECTIVE: To assess the prevalence and associated factors of neurocognitive disorder among people living with HIV/AIDS in South Gondar primary hospitals, North-West Ethiopia, 2023. DESIGN: Institution-based cross-sectional study design. SETTING: South Gondar primary hospitals, North-West Ethiopia. PARTICIPANTS: 608 participants were recruited using the systematic random sampling technique. MEASUREMENT: Data were collected using an interviewer-administered questionnaire and medical chart reviews. The International HIV Dementia Scale was used to screen for neurocognitive disorder. The data were entered through EPI-DATA V.4.6 and exported to SPSS V.21 statistical software for analysis. In the bivariable logistic regression analyses, variables with a value of p<0.25 were entered into a multivariable logistic regression analysis to identify factors independently associated with neurocognitive disorder. Statistical significance was declared at a value of p<0.05. RESULTS: The prevalence of neurocognitive disorder among HIV-positive participants was 39.1%. In multivariable logistic regression, lower level of education (adjusted OR (AOR)=2.94; 95% CI 1.29 to 6.82), unemployment (AOR=2.74; 95% CI 1.29 to 6.84) and comorbid medical illness (AOR=1.80; 95% CI 1.03 to 3.14) were significantly associated with neurocognitive disorder. CONCLUSION: HIV-associated neurocognitive problems affected over a third of the participants. According to the current study, comorbid medical conditions, unemployment and low educational attainment are associated with an increased risk of neurocognitive disorder. Therefore, early detection and treatment are essential.

Analyzing the transition from two-vehicle collisions to chain reaction crashes: A hybrid approach using random parameters logit model, interpretable machine learning, and clustering.

Chain reaction crashes (CRC) begin with a two-vehicle collision and rapidly intensify as more vehicles get directly involved. CRCs result in more extensive damage compared to two-vehicle crashes and understanding the progression of a two-vehicle collision into a CRC can unveil preventive strategies that have received less attention. In this study, to align with recent research direction and overcome the limitations of econometric and machine learning (ML) modelling, a hybrid approach is adopted. Moreover, to tackle the existing challenges in crash analysis, addressing unobserved heterogeneity in ML, and exploring random parameter effects and interactions more precisely, a new approach is proposed. To achieve this, a hybrid random parameter logit model and interpretable ML, joint with prior latent class clustering is implemented. Notably, this is the first attempt at using a clustering with hybrid modeling. The significant risk factors, their critical values, distinct effects, and interactions are interpreted using both marginal effects and the SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes crash, traffic, and geometric data from eleven suburban freeways in Iran collected over a 5-year period. The overall results indicate an increased risk of CRC in congested traffic, higher traffic variation, and on horizontal curves combined with longitudinal slopes. Some parameters exhibit distinct or fluctuating effects, which are discussed across different conditions or considering interactions. For instance, during nighttime, heightened congestion on 2-lane freeways, increased traffic variation in less congested conditions, and adverse weather combined with horizontal curves and slopes pose risks. During daytime, increased traffic variation within highly congested sections, higher proportion of heavy vehicle traffic in moderately congested sections, and two lanes in each direction coupled with curves, elevate the levels of risk. The results of this study provide a better understanding of risk factors impact across different conditions, which are usable for policy makers.

Factors influencing optimal diabetes care and clinical outcomes in Thai patients with type 2 diabetes mellitus: a multilevel modelling analysis.

BACKGROUND: Increasing levels of poor glycaemic control among Thai patients with type 2 diabetes mellitus (T2DM) motivated us to compare T2DM care between urban and suburban primary care units (PCUs), to identify gaps in care, and to identify significant factors that may influence strategies to enhance the quality of care and clinical outcomes in this population. METHODS: We conducted a cross-sectional study involving 2160 patients with T2DM treated at four Thai PCUs from 2019 to 2021, comprising one urban and three suburban facilities. Using mixed effects logistic regression, we compared care factors between urban and suburban PCUs. RESULTS: Patients attending suburban PCUs were significantly more likely to undergo eye (adjusted OR (AOR): 1.83, 95% CI 1.35 to 1.72), foot (AOR: 1.61, 95% CI 0.65 to 4.59) and HbA1c (AOR: 1.66, 95% CI 1.09 to 2.30) exams and achieved all ABC (HbA1c, blood pressure (BP) and low-density lipoprotein cholesterol (LDL-C)) goals (AOR: 2.23, 95% CI 1.30 to 3.83). Conversely, those at an urban PCU were more likely to undergo albuminuria exams. Variables significantly associated with good glycaemic control included age (AOR: 1.51, 95% CI 1.31 to 1.79), T2DM duration (AOR: 0.59, 95% CI 0.41 to 0.88), FAACE (foot, HbA1c, albuminuria, LDL-C and eye) goals (AOR: 1.23, 95% CI 1.12 to 1.36) and All8Q (AOR: 1.20, 95% CI 1.05 to 1.41). Chronic kidney disease (CKD) was significantly linked with high triglyceride and HbA1c levels (AOR: 5.23, 95% CI 1.21 to 7.61). Elevated HbA1c levels, longer T2DM duration, insulin use, high systolic BP and high lipid profile levels correlated strongly with diabetic retinopathy (DR) and CKD progression. CONCLUSION: This highlights the necessity for targeted interventions to bridge urban-suburban care gaps, optimise drug prescriptions and implement comprehensive care strategies for improved glycaemic control, DR prevention and CKD progression mitigation among in Thai patients with T2DM. The value of the clinical target aggregate (ABC) and the process of care aggregate (FAACE) was also conclusively demonstrated.

Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study.

BACKGROUND: Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet. METHODS: aSAH patients from 2 prospective cohorts (Zurich-derivation cohort, Aachen-validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1-4 vs. 5-8) or ordinal outcome (GOSE-extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination. RESULTS: A total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity. CONCLUSIONS: MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

Impact of Preoperative Neutrophil to Prealbumin Ratio Index (NPRI) on Short-Term Complications and Long-Term Prognosis in Patients Undergoing Laparoscopic Radical Surgery for Colorectal Cancer.

Objective: This study aims to evaluate the impact and predictive value of the preoperative NPRI on short-term complications and long-term prognosis in patients undergoing laparoscopic radical surgery for colorectal cCancer (CRC). Methods: A total of 302 eligible CRC patients were included, assessing five inflammation-and nutrition-related markers and various clinical features for their predictive impact on postoperative outcomes. Emphasis was on the novel indicator NPRI to elucidate its prognostic and predictive value for perioperative risks. Results: Multivariate logistic regression analysis identified a history of abdominal surgery, prolonged surgical duration, CEA levels ≥5 ng/mL, and NPRI ≥ 3.94 × 10-2 as independent risk factors for postoperative complications in CRC patients. The Clavien--Dindo complication grading system highlighted the close association between preoperative NPRI and both common and severe complications. Multivariate analysis also identified a history of abdominal surgery, tumor diameter ≥5 cm, poorly differentiated or undifferentiated tumors, and NPRI ≥ 2.87 × 10-2 as independent risk factors for shortened overall survival (OS). Additionally, a history of abdominal surgery, tumor maximum diameter ≥5 cm, tumor differentiation as poor/undifferentiated, NPRI ≥ 2.87 × 10-2, and TNM Stage III were determined as independent risk factors for shortened disease-free survival (DFS). Survival curve results showed significantly higher 5-year OS and DFS in the low NPRI group compared to the high NPRI group. The incorporation of NPRI into nomograms for OS and DFS, validated through calibration and decision curve analyses, attested to the excellent accuracy and practicality of these models. Conclusion: Preoperative NPRI independently predicts short-term complications and long-term prognosis in patients undergoing laparoscopic colorectal cancer surgery, enhancing predictive accuracy when incorporated into nomograms for patient survival.

Risk factors and early prediction of cardiorenal syndrome type 3 among acute kidney injury patients: a cohort study.

BACKGROUND: Type 3 cardiorenal syndrome (CRS type 3) triggers acute cardiac injury from acute kidney injury (AKI), raising mortality in AKI patients. We aimed to identify risk factors for CRS type 3 and develop a predictive nomogram. METHODS: In this retrospective study, 805 AKI patients admitted at the Department of Nephrology, Second Hospital of Shanxi Medical University from 1 January 2017, to 31 December 2021, were categorized into a study cohort (406 patients from 2017.1.1-2021.6.30, with 63 CRS type 3 cases) and a validation cohort (126 patients from 1 July 2021 to 31 Dec 2021, with 22 CRS type 3 cases). Risk factors for CRS type 3, identified by logistic regression, informed the construction of a predictive nomogram. Its performance and accuracy were evaluated by the area under the curve (AUC), calibration curve and decision curve analysis, with further validation through a validation cohort. RESULTS: The nomogram included 6 risk factors: age (OR = 1.03; 95%CI = 1.009-1.052; p = 0.006), cardiovascular disease (CVD) history (OR = 2.802; 95%CI = 1.193-6.582; p = 0.018), mean artery pressure (MAP) (OR = 1.033; 95%CI = 1.012-1.054; p = 0.002), hemoglobin (OR = 0.973; 95%CI = 0.96--0.987; p < 0.001), homocysteine (OR = 1.05; 95%CI = 1.03-1.069; p < 0.001), AKI stage [(stage 1: reference), (stage 2: OR = 5.427; 95%CI = 1.781-16.534; p = 0.003), (stage 3: OR = 5.554; 95%CI = 2.234-13.805; p < 0.001)]. The nomogram exhibited excellent predictive performance with an AUC of 0.907 in the study cohort and 0.892 in the validation cohort. Calibration and decision curve analyses upheld its accuracy and clinical utility. CONCLUSIONS: We developed a nomogram predicting CRS type 3 in AKI patients, incorporating 6 risk factors: age, CVD history, MAP, hemoglobin, homocysteine, and AKI stage, enhancing early risk identification and patient management.