Alpha-fetoprotein and APRI as predictive markers for patients with Type C hepatitis B-related acute-on-chronic liver failure: a retrospective study.

BACKGROUND: Type C hepatitis B-related acute-on-chronic liver failure (HBV-ACLF), which is based on decompensated cirrhosis, has different laboratory tests, precipitating events, organ failure and clinical outcomes. The predictors of prognosis for type C HBV-ACLF patients are different from those for other subgroups. This study aimed to construct a novel, short-term prognostic score that applied serological indicators of hepatic regeneration and noninvasive assessment of liver fibrosis to predict outcomes in patients with type C HBV-ACLF. METHOD: Patients with type C HBV-ACLF were observed for 90 days. Demographic information, clinical examination, and laboratory test results of the enrolled patients were collected. Univariate and multivariate logistic regression were performed to identify independent prognostic factors and develop a novel prognostic scoring system. A receiver operating characteristic (ROC) curve was used to analyse the performance of the model. RESULTS: A total of 224 patients with type C HBV-ACLF were finally included. The overall survival rate within 90 days was 47.77%. Age, total bilirubin (TBil), international normalized ratio (INR), alpha-fetoprotein (AFP), white blood cell (WBC), serum sodium (Na), and aspartate aminotransferase/platelet ratio index (APRI) were found to be independent prognostic factors. According to the results of the logistic regression analysis, a new prognostic model (named the A3Twin score) was established. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.851 [95% CI (0.801-0.901)], the sensitivity was 78.8%, and the specificity was 71.8%, which were significantly higher than those of the MELD, IMELD, MELD-Na, TACIA and COSSH-ACLF II scores (all P < 0.001). Patients with lower A3Twin scores (<-9.07) survived longer. CONCLUSIONS: A new prognostic scoring system for patients with type C HBV-ACLF based on seven routine indices was established in our study and can accurately predict short-term mortality and might be used to guide clinical management.

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)

[Study on the relationship between hemoglobin glycosylation index and arteriosclerosis- related blood lipids].

Objective: To study the relationship between hemoglobin glycation index (HGI) and blood lipid indices such as low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and plasma atherogenic index (AIP). Methods: This cross-sectional study included 16 049 participants from the Beijing Apple Garden community between December 2011 and August 2012. The subjects were divided into three groups based on the HGI quartile: low (n=5 388), medium (n=5 249), and high (n=5 412). The differences in blood lipid indicators between different HGI groups were compared and multivariate logistic regression model was established to analyze the association between HGI and dyslipidemia. And multivariate logistic regression model was established to analyze the relationship between HGI and blood lipid indicators in different glucose metabolism populations. Results: There were 16 049 participants in all (mean age: 56 years), including 10 452 women (65.1%). They were classified into normal glucose tolerance (9 093 cases), prediabetes (4 524 cases), and diabetes (2 432 cases) based on glucose tolerance status. In the general population, with the increase of HGI, LDL-C, non-HDL-C, and AIP gradually increased (all P values for trends were <0.05), and the proportion of abnormalities increased significantly (χ2=101.40, 42.91, 39.80; all P<0.001). A multivariate logistic regression model was established, which suggested a significant correlation between HGI and LDL-C, non-HDL-C, and AIP (all P<0.05), after adjusting for factors such as age, sex, fasting blood glucose, hypertension, body mass index, smoking, and alcohol consumption. In the overall population, normal glucose tolerance group, and diabetes group, HGI had the highest correlation with non-HDL-C (OR values of 1.325, 1.678, and 1.274, respectively); in the prediabetes group, HGI had a higher correlation with LDL-C (OR value: 1.510); and in different glucose metabolism groups, AIP and HGI were both correlated (OR: 1.208-1.250), but not superior to non-HDL-C and LDL-C. Conclusion: HGI was closely related to LDL-C, non HDL-C, and AIP in the entire population and people with different glucose metabolism, suggesting that HGI may be a predictor of atherosclerotic cardiovascular disease.

[Association between onset time of sepsis-associated acute kidney injury and clinical outcome in patients with sepsis].

Objective: To explore the relationship between the onset time of sepsis-associated acute kidney injury (SA-AKI) and adverse clinical outcomes. Methods: Data were derived from Beijing Acute Kidney Injure Trial (BAKIT) which investigated the epidemiology of acute kidney injury (AKI) in critically ill patients at 30 intensive care units (ICU) of 28 tertiary hospitals in Beijing from 1 March to 31 August 2012. Patients who were older than 18 years and diagnosed with sepsis and AKI, and expected to stay in ICU for at least 24 h were included in this study. A total of 653 patients were included in this study, 414 males and 239 females with a mean age of (68.2±17.0) years. According to the onset time of SA-AKI, patients were grouped into early AKI (E-AKI) (AKI occurred within 48 hours after ICU admission) and late AKI (L-AKI) (AKI occurred after 48 hours of ICU admission) group. The primary outcome was major adverse kidney events (MAKE), consisted of all-cause mortality, renal replacement therapy-dependence, and an inability to recover to 1.5 times of the baseline creatinine value up to 30 days. Multivariable logistic regression was used to investigate the association between the onset time of SA-AKI and clinical outcomes. Results: A total of 653 patients with SA-AKI were included, 423 (64.8%) patients developed E-AKI, 230 (35.2%) cases developed L-AKI, MAKE occurred in 405 (62.0%) cases, and 301 (46.1%) patients died in hospital. Compared with E-AKI group, L-AKI patients showed higher AKI 3 level rate [55.7%(128/230) vs 40.2%(170/423), P<0.001], incidence of MAKE [72.6%(167/230) vs 56.3%(238/423,P<0.001)] and hospital mortality [55.2%(127/230) vs 44.1%(174/423), P=0.001]. The risk of MAKE and in-hospital mortality in L-AKI group increased for 2.55-fold times (OR=3.55, 95%CI: 1.94-6.04) and 1.84-fold times (OR=2.84, 95%CI: 1.44-5.60) when compared with those in E-AKI, respectively (both P<0.05). Conclusion: Late timing onset of SA-AKI is associated with poor clinical outcomes.

Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables.

OBJECTIVE: Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario. METHODS: In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction. RESULTS: The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively. CONCLUSION: The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters.

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Alcohol and substance use in older adults with treatment-resistant depression.

INTRODUCTION: Alcohol and substance use are increasing in older adults, many of whom have depression, and treatment in this context may be more hazardous. We assessed alcohol and other substance use patterns in older adults with treatment-resistant depression (TRD). We examined patient characteristics associated with higher alcohol consumption and examined the moderating effect of alcohol on the association between clinical variables and falls during antidepressant treatment. METHODS: This secondary and exploratory analysis used baseline clinical data and data on falls during treatment from a large randomized antidepressant trial in older adults with TRD (the OPTIMUM trial). Multivariable ordinal logistic regression was used to identify variables associated with higher alcohol use. An interaction model was used to evaluate the moderating effect of alcohol on falls during treatment. RESULTS: Of 687 participants, 51% acknowledged using alcohol: 10% were hazardous drinkers (AUDIT-10 score ≥5) and 41% were low-risk drinkers (score 1-4). Benzodiazepine use was seen in 24% of all participants and in 21% of drinkers. Use of other substances (mostly cannabis) was associated with alcohol consumption: it was seen in 5%, 9%, and 15% of abstainers, low-risk drinkers, and hazardous drinkers, respectively. Unexpectedly, use of other substances predicted increased risk of falls during antidepressant treatment only in abstainers. CONCLUSIONS: One-half of older adults with TRD in this study acknowledged using alcohol. Use of alcohol concurrent with benzodiazepine and other substances was common. Risks-such as falls-of using alcohol and other substances during antidepressant treatment needs further study.

Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

Temporal relationships between blood glucose, lipids and BMI, and their impacts on atherosclerosis: a prospective cohort study.

OBJECTIVES: This study aimed to explore the temporal relationship between blood glucose, lipids and body mass index (BMI), and their impacts on atherosclerosis (AS). DESIGN: A prospective cohort study was designed. SETTING AND PARTICIPANTS: A total of 2659 subjects from Harbin Cohort Study on Diet, Nutrition and Chronic Non-communicable Diseases, and aged from 20 to 74 years were included. PRIMARY AND SECONDARY OUTCOME MEASURES: Body weight, height, fasting blood glucose (FBG) and 2-hour postprandial glucose (2-h PG), blood lipids including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-c) and high-density lipoprotein cholesterol (HDL-c) were measured at baseline and follow-up. Brachial ankle pulse wave velocity (baPWV) was examined at follow-up as a marker of AS risk. Logistic regression analysis, cross-lagged path analysis and mediation analysis were performed to explore the temporal relationships between blood glucose, lipids and BMI, and their impacts on AS risk. RESULTS: Logistic regression analysis indicated that increased FBG, 2-h PG, TC, TG, LDL-c and BMI were positively associated with AS risk, while increased HDL-c was negatively associated with AS risk. The path coefficients from baseline blood parameters to the follow-up BMI were significantly greater than those from baseline BMI to the follow-up blood parameters. Mediation analysis suggested that increased FBG, 2-h PG, TC, TG and LDL-c could increase AS risk via increasing BMI, the effect intensity from strong to weak was LDL-c>TC>TG>FBG>2 h PG, while increased HDL-c could decrease AS risk via decreasing BMI. CONCLUSIONS: Changes in blood glucose and lipids could cause change in BMI, which mediated the impacts of blood glucose and lipids on AS risk. These results highlight the importance and provide support for the early and comprehensive strategies of AS prevention and control.

[Association between exposure to PM_(2.5) and chronic kidney disease in a population with metabolic syndrome].

OBJECTIVE: To investigate the association between long-term fine particulate matter(PM_(2.5)) exposure and the risk of chronic kidney disease(CKD) in people with abnormal metabolism syndrome(MS) components. METHODS: Based on health checkup data from a hospital in Beijing, a retrospective cohort study was used to collect annual checkup data from 2013-2019. A questionnaire was used to obtain information on demographic characteristics and lifestyle habits. We measured blood pressure, height, weight, waist circumference, concentrations of triglycerides(TG), fasting glucose, and high-density lipoprotein cholesterol(HDL-C). Longitude and latitude were also extracted from the addresses of the study subjects for pollutant exposure data estimation. Logistic regression models were used to explore the estimated effect of long-term PM_(2.5) exposure on the risk of CKD prevalence in people with abnormal MS components. Two-pollutant and multi-pollutant models were developed to test the stability of these result. Subgroup analysis was conducted based on age, the presence of MS, individual MS component abnormalities, and dual-component MS abnormalities. RESULTS: The study included 1540 study subjects with abnormal MS components at baseline, 206 with CKD during the study period. The association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions was statistically significant, with a 2.26-fold increase in risk of CKD for every 10 µg/m~3 increase in PM_(2.5) exposure(OR=3.26, 95% CI 2.72-3.90). The result in the dual-pollutant models and multi-pollutant models suggested that the association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions remained stable after controlling for contemporaneous confounding by other air pollutants. The result of subgroup analysis revealed that individuals aged 45 or older, without MS, with TG<1.7 mmol/L, HDL-C≥1.04 mmol/L, without hypertension, and with central obesity and high blood sugar had a stronger association between PM_(2.5) exposure and CKD-related health effects. CONCLUSION: Long-term exposure to PM_(2.5) may increase the risk of CKD in people with abnormal MS components. More attention should be paid to middle-aged and elderly people aged ≥45 years, people with central obesity and hyperglycemia.

Cross-sectional study of the association between burnout and work overload and work-life imbalance among medical personnel in Liaoning, China: role of specialty.

OBJECTIVES: Burnout is common among medical personnel in China and may be related to excessive and persistent work-related stressors by different specialties. The aims of this study were to assess the prevalence of burnout, work overload and work-life imbalance according to different specialties and to explore the effect of specialty, work overload and work-life imbalance on burnout among medical personnel. DESIGN: A cross-sectional study. SETTING: This study was conducted in 1 tertiary general public hospital, 2 secondary general hospitals and 10 community health service stations in Liaoning, China. PARTICIPANTS: A total of 3299 medical personnel participated in the study. METHODS: We used the 15-item Chinese version of the Maslach Burnout Inventory General Survey (MBI-GS) to measure burnout. Multivariable logistic regression models were used to explore the association between medical specialty, work overload, work-life imbalance and burnout. RESULTS: 3299 medical personnel were included in this study. The prevalence of burnout, severe burnout, work overload and work-life imbalance were 88.7%, 13.6%, 23.4% and 23.2%, respectively. Compared with medical personnel in internal medicine, working in obstetrics and gynaecology (OR=0.61, 95% CI 0.38, 0.99) and management (OR=0.45, 95% CI 0.28, 0.72) was significantly associated with burnout, and working in ICU （Intensive Care Unit）(OR=2.48, 95% CI 1.07, 5.73), surgery (OR=1.66, 95% CI 1.18, 2.35) and paediatrics (OR=0.24, 95% CI 0.07, 0.81) was significantly associated with severe burnout. Work overload and work-life imbalance were associated with higher ORs for burnout (OR=1.64, 95% CI 1.16, 2.32; OR=2.79, 95% CI 1.84, 4.24) and severe burnout (OR=4.33, 95% CI 3.43, 5.46; OR=3.35, 95% CI 2.64, 4.24). CONCLUSIONS: Burnout, work overload and work-life imbalance were prevalent among Chinese medical personnel but varied considerably by clinical specialty. Burnout may be reduced by decreasing work overload and promoting work-life balance across different specialties.

Assessing the association between a sedentary lifestyle and prevalence of primary osteoporosis: a community-based cross-sectional study among Chinese population.

OBJECTIVES: To reveal the association between a sedentary lifestyle and the prevalence of primary osteoporosis (POP). DESIGN: A community-based cross-sectional study was conducted. SETTING: This study was conducted in communities in Hefei city, Anhui province, China. PARTICIPANTS: A total of 1346 residents aged 40 and above underwent POP screening via calcaneus ultrasound bone mineral density (BMD) testing and completed a questionnaire survey. OUTCOME MEASURES: The average daily sitting time was included in the study variable and used to assess sedentary behaviour. The 15 control variables included general information, dietary information and life behaviour information. Logistic regression was used to analyse the association between the POP prevalence and study or control variables in different models. RESULTS: 1346 participants were finally included in the study. According to the 15 control variables, the crude model and 4 models were established. The analysis revealed that the average daily sitting time showed a significant correlation with the prevalence of POP in the crude model (OR=2.02, 95% CI=1.74 to 2.36, p<0.001), Model 1 (OR=2.65, 95% CI=2.21 to 3.17, p<0.001), Model 2 (OR=2.63, 95% CI=2.19 to 3.15, p<0.001), Model 3 (OR=2.62, 95% CI=2.18 to 3.15, p<0.001) and Model 4 (OR=2.58, 95% CI=2.14 to 3.11, p<0.001). Besides, gender, age and body mass index showed a significant correlation with the POP prevalence in all models. CONCLUSIONS: This study suggests a potential association between a sedentary lifestyle and the prevalence of POP within the Chinese population. Modifying sedentary behaviours could contribute to a reduction in POP risk. However, longitudinal cohort studies are necessary to confirm this hypothesis in the future.

Machine learning trial to detect sex differences in simple sticker arts of 1606 preschool children.

BACKGROUND: Previous studies suggested that drawings made by preschool boys and girls show distinguishable differences. However, children's drawings on their own are too complexly determined and inherently ambiguous to be a reliable indicator. In the present study, we attempted to develop a machine learning algorithm for classification of sex of the subjects using children's artworks. METHODS: We studied three types of simple sticker artworks from 1606 Japanese preschool children aged 51-83 months (803 boys and 803 girls). Those artworks were processed into digitalized data. Simulated data based on the original data were also generated. Logistic regression approach was applied to each dataset to make a classifier, and run on each dataset in a stratified ten-fold cross-validation with hyperparameter tuning. A probability score was calculated in each sample and utilized for sex classification. Prediction performance was evaluated using accuracy, recall, and precision scores, as well as learning curves. RESULTS: Two models created from the original and simulated data showed comparably low metrics. The distributions of probability scores in the samples from boys and girls mostly overlapped and were indistinguishable. Learning curves of the models showed an extremely under-fitted pattern. CONCLUSIONS: Our machine learning algorithm was unable to distinguish simple sticker arts created by boys and girls. More complex tasks will enable to develop an accurate classifier.

Relationships between symptoms and lung function in asthma and/or chronic obstructive pulmonary disease in a real-life setting: the NOVEL observational longiTudinal studY.

BACKGROUND: The relationships between spirometric assessment of lung function and symptoms (including exacerbations) in patients with asthma and/or chronic obstructive pulmonary disease (COPD) in a real-life setting are uncertain. OBJECTIVES: To assess the relationships between baseline post-bronchodilator (post-BD) spirometry measures of lung function and symptoms and exacerbations in patients with a physician-assigned diagnosis of asthma and/or COPD. DESIGN: The NOVEL observational longiTudinal studY (NOVELTY) is a global, prospective, 3-year observational study. METHODS: Logistic regression analysis was used to evaluate relationships. Spirometry measures were assessed as percent predicted (%pred). Symptoms were assessed at baseline, and exacerbations were assessed at baseline and Year 1. RESULTS: A total of 11,181 patients in NOVELTY had spirometry data (asthma, n = 5903; COPD, n = 3881; asthma + COPD, n = 1397). A 10% lower post-BD %pred forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) - adjusted for age and sex - were significantly associated with dyspnea (modified Medical Research Council ⩾ grade 2), frequent breathlessness [St George's Respiratory Questionnaire (SGRQ)], frequent wheeze attacks (SGRQ), nocturnal awakening (Respiratory Symptoms Questionnaire; ⩾1 night/week), and frequent productive cough (SGRQ). Lower post-BD %pred FEV1 and, to a lesser extent, lower post-BD %pred FVC were significantly associated with ⩾1 physician-reported exacerbation at baseline or Year 1. This association was stronger in patients with COPD than in those with asthma. CONCLUSION: In a real-life setting, reduced lung function is consistently associated with symptoms in patients with asthma, COPD, or asthma + COPD. The relationship with exacerbations is stronger in COPD only than in asthma. TRAIL REGISTRATION: clinicaltrials.gov identifier: NCT02760329 (www.clinicaltrials.gov).

Relationships between symptoms and lung function in asthma and/or chronic obstructive pulmonary disease in a study performed in a real-life setting: the NOVELTY studyBackground: Asthma and chronic obstructive pulmonary disease (COPD) have many symptoms in common. To confirm diagnosis, doctors use spirometry, a test to measure the amount of air that can be breathed out from the lungs and how fast it can be blown out. The relationship between these measurements and symptoms in asthma and COPD is not well understood.Objectives: The aim of this research is to describe the characteristics, treatment, and impact of asthma and/or COPD in patients who are receiving their usual medical care.Methods: NOVELTY is a large study of around 12,000 patients across 19 countries. This analysis of NOVELTY looked at the relationships between two spirometry measurements and the symptoms of asthma and/or COPD experienced by patients. The spirometry measurements were: - forced expiratory volume in 1 second (FEV₁) ­ the amount of air that can be blown out of the lungs in 1 second- forced vital capacity (FVC) ­ the amount of air that can be forcibly breathed out from the lungs after taking the deepest breath possibleResults: The lower the FEV₁ and FVC, the more common the symptoms of breathlessness, wheeze attacks, night-time awakening, and coughing up of phlegm or mucus. These relationships were similar for FEV₁ and FVC. Lower FEV₁ was more strongly associated with worse symptoms in COPD than in asthma.Conclusion: These findings help to improve our understanding of the relationships between spirometry measures and symptoms in patients with asthma and/or COPD.

Self-reported gastrointestinal adverse effects of non-steroidal anti-inflammatory drugs in female students with dysmenorrhoea at Makerere University: prevalence, discontinuation and associated factors. a cross sectional study.

BACKGROUND: Primary dysmenorrhoea occurs in up to 50% of menstruating females. Non-steroidal anti-inflammatory drugs (NSAIDs) are the most commonly used therapeutic remedies for dysmenorrhoea in Uganda. However, NSAIDs are associated with a 3-5 fold increase in the risk of gastrointestinal (GI) adverse drug effects. OBJECTIVES: We aimed to determine the prevalence and associated factors of self-reported NSAID-related GI adverse effects in female students who use NSAIDs in managing dysmenorrhoea-associated pain at Makerere University. DESIGN: A cross-sectional study. SETTING: Makerere University's main campus, situated North of Kampala, Uganda. PARTICIPANTS: 314 female students pursuing an undergraduate programme at Makerere University and residing in different halls of residence and hostels. OUTCOMES: Social demographic data, menstrual history and treatment data. RESULTS: Overall, 314 valid responses were received from female students with a median age of 22 years (IQR: 18-29 years). The median age at menarche was 13 years (IQR: 9-18 years). 41% (n=129/314) of the respondents had used medication for dysmenorrhoea and 32% (n=41/129) of whom reported NSAID-associated GI adverse effects with nausea being the most frequently reported (44%, n=18/41)Factors independently associated with GI adverse effects were: age at menarche (p=0.026), duration of menstruation (p=0.030) and use of ibuprofen (p=0.005). Females taking ibuprofen for dysmenorrhoea were about four times as likely to have NSAID-associated GI adverse effects (adjusted OR 3.87, 95% CI 1.51 to 9.91) than those who did not receive ibuprofen. Logistic regression was used to determine factors associated with self-reported adverse effects of NSAIDs among the female students. A p<0.05 was considered statistically significant. CONCLUSION: We found a considerably high prevalence of NSAID-related GI adverse effects driven by factors such as age at menarche and ibuprofen use.

The receipt of health information on neonatal dangers signs during the immediate postpartum period and its determinants in Ethiopia: a multilevel mixed-effects logistic regression analysis of the 2016 Ethiopian demographic health survey report.

BACKGROUND: Identification of neonatal danger signs and immediate access to health care are two global efforts aimed at enhancing newborn and child survival by preventing 75% of neonatal deaths. Despite various small-scale studies on women's awareness of neonatal danger signs in Ethiopia, little is known about the level of receiving health information on those danger signs during the immediate postpartum period at the national level. Hence, this study aimed at assessing the level, and its determinants of the service uptake in Ethiopia. METHODS: The data for this study was taken from the Ethiopian Demographic and Health Survey (EDHS), which took place from January to June 2016 and covered all administrative regions of Ethiopia. A weighted sample of 7,589.8 women was analyzed using STATA version 16. To account for data clustering, a multivariable multilevel mixed-effect logistic regression analysis was employed to determine the effects of each predictor on the outcome variable. Adjusted odds ratio with its corresponding 95% confidence interval was used to declare the statistical significance of the explanatory variables. RESULTS: The receipt of health information on neonatal danger signs during the immediate postpartum period was 10.70% [95% CI:10.01, 11.40]. Variables namely living in Metropolitans [AOR = 2.06; 95%CI: 1.48, 2.88] and Large central [AOR = 1.83; 95%CI: 1.38, 2.42] regions, being in the highest wealth quintile [AOR = 1.87; 95% CI: 1.23, 2.84], being nulliparous [AOR = 0.27; 95% CI: 0.08, 0.87] and primiparous[AOR = 0.61;95% CI: 0.46, 0.79], getting adequate antenatal visits [AOR = 2.42; 95% CI: 1.75, 3.33], institutional delivery [AOR = 5.91; 95% CI: 4.66, 7.53], and receipt of postnatal visits [AOR = 3.52; 95% CI: 2.84, 4.38] were identified as significant determinants of receiving health information on newborn danger signs. CONCLUSION: The findings revealed that unacceptably low uptake of health information on newborn danger signs during the immediate postpartum period in Ethiopia. A concerted effort is needed from all stakeholders in the health sector to enhance the uptake of maternal health services (antenatal care, skilled delivery service, and postnatal care). Healthcare providers should pay special attention to nulliparous and primiparous women during and after delivery, and the government should also focus on women of peripheral regions, who make up a large portion of the low coverage.

Risk factors for maternal near-miss in an undeveloped province in south-central China, 2012-2022.

OBJECTIVE: To explore the risk factors for maternal near-miss (MNM) using the WHO near-miss approach. METHODS: Data were obtained from the Maternal Near-Miss Surveillance System in Hunan Province, China, 2012-2022. Multivariate logistic regression analysis (method: Forward, Wald, α = 0.05) and adjusted odds ratios (aORs) were used to identify risk factors for MNM. RESULTS: Our study included 780,359 women with 731,185 live births, a total of 2461 (0.32%) MNMs, 777,846 (99.68%) non-MNMs, and 52 (0.006%) maternal deaths were identified. The MNM ratio was 3.37‰ (95%CI: 3.23-3.50). Coagulation/hematological dysfunction was the most common cause of MNM (75.66%). Results of multivariate logistic regression analysis showed risk factors for MNM: maternal age > = 30 years old (aOR > 1, P < 0.05), unmarried women (aOR = 2.21, 95%CI: 1.71-2.85), number of pregnancies > = 2 (aOR > 1, P < 0.05), nulliparity (aOR = 1.51, 95%CI: 1.32-1.72) or parity > = 3 (aOR = 1.95, 95%CI: 1.50-2.55), prenatal examinations < 5 times (aOR = 1.13, 95%CI: 1.01-1.27), and number of cesarean sections was 1 (aOR = 1.83, 95%CI: 1.64-2.04) or > = 2 (aOR = 2.48, 95%CI: 1.99-3.09). CONCLUSION: The MNM ratio was relatively low in Hunan Province. Advanced maternal age, unmarried status, a high number of pregnancies, nulliparity or high parity, a low number of prenatal examinations, and cesarean sections were risk factors for MNM. Our study is essential for improving the quality of maternal health care and preventing MNM.

Association of longitudinal pet ownership with wheezing in 3-year-old children using the distributed lag model: the Japan Environment and Children's Study.

BACKGROUND: Time-varying exposures like pet ownership pose challenges for identifying critical windows due to multicollinearity when modeled simultaneously. The Distributed Lag Model (DLM) estimates critical windows for time-varying exposures, which are mainly continuous variables. However, applying complex functions such as high-order splines and nonlinear functions within DLMs may not be suitable for situations with limited time points or binary exposure, such as in questionnaire surveys. OBJECTIVES: (1) We examined the estimation performance of a simple DLM with fractional polynomial function for time-varying binary exposures through simulation experiments. (2) We evaluated the impact of pet ownership on childhood wheezing onset and estimate critical windows. METHODS: (1) We compared logistic regression including time-varying exposure in separate models, in one model simultaneously, and using DLM. For evaluation, we employed bias, empirical standard error (EmpSE), and mean squared error (MSE). (2) The Japan Environment and Children's Study (JECS) is a prospective birth cohort study of approximately 100,000 parent-child pairs, registered across Japan from 2011 to 2014. We applied DLM to the JECS data up to age 3. The estimated odds ratios (OR) were considered to be within critical windows when they were significant at the 5% level. RESULTS: (1) DLM and the separate model exhibited lower bias compared to the simultaneously model. Additionally, both DLM and the simultaneously model demonstrated lower EmpSEs than the separate model. In all scenarios, DLM had lower MSEs than the other methods. Specifically, where critical windows is clearly present and exposure correlation is high, DLM showed MSEs about 1/2 to 1/200 of those of other models. (2) Application of DLM to the JECS data showed that, unlike other models, a significant exposure effect was observed only between the ages of 0 and 6 months. During that periods, the highest ORs were 1.07 (95% confidence interval, 1.01 to 1.14) , observed between the ages of 2 and 5 months. CONCLUSIONS: (1) A simple DLM improves the accuracy of exposure effect and critical windows estimation. (2) 0-6 months may be the critical windows for the effect of pet ownership on the wheezing onset at 3 years.

Association between lactate dehydrogenase and ventilator-associated pneumonia risk: an analysis of the MIMIC database 2001-2019.

BACKGROUND: Serum lactate dehydrogenase (LDH) is a nonspecific inflammatory biomarker and has been reported to be associated with pneumonia prognosis. This study aimed to evaluate the relationship between LDH levels and ventilator-associated pneumonia (VAP) risk in intensive care unit (ICU) patients. METHODS: This retrospective cohort study used data from the Multiparameter Intelligent Monitoring in Intensive Care database from 2001 to 2019. ICU patients aged ≥ 18 years and receiving mechanical ventilation were included. LDH levels were analyzed as continuous and categorical variables (< 210, 210-279, 279-390, > 390 IU/L), respectively. Restricted cubic spline (RCS) curves and quartiles were used to categorize LDH levels. Logistic regression and linear regression were utilized to assess the relationship of LDH levels with VAP risk and duration of mechanical ventilation, respectively. RESULTS: A total of 9,164 patients were enrolled, of which 646 (7.05%) patients developed VAP. High levels of LDH increased the risk of VAP [odds ratio (OR) = 1.15, 95% confidence interval (CI): 1.06-1.24] and LDH levels were positively correlated with the duration of mechanical ventilation [ß = 4.49, 95%CI: (3.42, 5.56)]. Moreover, patients with LDH levels of 279-390 IU/L (OR = 1.38, 95%CI: 1.08-1.76) and > 390 IU/L (OR = 1.50, 95%CI: 1.18-1.90) had a higher risk of VAP than patients with LDH levels < 210 IU/L. Patients with LDH levels of 279-390 IU/L [ß = 3.84, 95%CI: (0.86, 6.82)] and > 390 IU/L [ß = 11.22, 95%CI: (8.21, 14.22)] (vs. <210 IU/L) had a longer duration of mechanical ventilation. CONCLUSION: Elevated serum LDH levels were related to a higher risk of VAP and longer duration of mechanical ventilation and may be useful for monitoring VAP risk.

Determining the optimal daily gonadotropin dose to maximize the oocyte yield in elective egg freezing cycles.

OBJECTIVE: Ovarian stimulation (OS) with high daily gonadotropin doses are commonly offered to patients attempting social/elective egg freezing. However, the optimal daily gonadotropin dose that would allow a higher oocyte yield in the successive IVF cycle attempt was not settled and should be determined. PATIENTS AND METHODS: Data from all women admitted to our IVF unit for social/EEF, who underwent two consecutive IVF cycle attempts, with only those who used in the first attempt a starting daily gonadotropin dose of 300IU were analyzed. Patients characteristics and OS variables were used in an attempt to build a logistic model, helping in determining the daily gonadotropin dose that should be offered to patient during their second EEF attempt, aiming to further increase their oocyte yield. RESULTS: Three hundred and thirteen consecutive women undergoing two successive IVF cycle attempts were evaluated. Using logistic regression model, two equations were developed using individual patient-level data that determine the daily gonadotropin dose needed aiming to increase the oocyte yield in the successive cycle. (a): X=-0.514 + 2.87*A1 + 1.733*A2-0.194* (E2/1000) and (b): P = EXP(X) / [1 + EXP(X)]. CONCLUSIONS: Using the aforementioned equations succeeded in determining the daily gonadotropin dose that might result in increasing oocyte yield, with an AUC of 0.85. Any additional oocyte retrieved to these EEF patients might get them closer to fulfil their desire to parenthood.