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)

Pretreatment mortality risk prediction model in patients with polymyositis/dermatomyositis-associated interstitial lung disease.

OBJECTIVES: Risk prediction for patients with polymyositis/dermatomyositis-associated interstitial lung disease (PM/DM-ILD) is challenging due to heterogeneity in the disease course. We aimed to develop a mortality risk prediction model for PM/DM-ILD. METHODS: This prognostic study analysed patients with PM/DM-ILD admitted to Nanjing Drum Hospital from 2016 to 2021. The primary outcome was mortality within 1 year. We used a least absolute shrinkage and selection operator (LASSO) logistic regression model to identify predictive laboratory indicators. These indicators were used to create a laboratory risk score, and we developed a mortality risk prediction model by incorporating clinical factors. The evaluation of model performance encompassed discrimination, calibration, clinical utility and practical application for risk prediction and prognosis. RESULTS: Overall, 418 patients with PM/DM-ILD were enrolled and randomly divided into development (n=282) and validation (n=136) cohorts. LASSO logistic regression identified four optimal features in the development cohort, forming a laboratory risk score: C reactive protein, lactate dehydrogenase, CD3+CD4+ T cell counts and PO2/FiO2. The final prediction model integrated age, arthralgia, anti-melanoma differentiation-associated gene 5 antibody status, high-resolution CT pattern and the laboratory risk score. The prediction model exhibited robust discrimination (area under the receiver operating characteristic: 0.869, 95% CI 0.811 to 0.910), excellent calibration and valuable clinical utility. Patients were categorised into three risk groups with distinct mortality rates. The internal validation, sensitivity analyses and comparative assessments against previous models further confirmed the robustness of the prediction model. CONCLUSIONS: We developed and validated an evidence-based mortality risk prediction model with simple, readily accessible clinical variables in patients with PM/DM-ILD, which may inform clinical decision-making.

The association between waist circumference and adult asthma attack using nationally representative samples.

AIMS: This study aims to explore the relationship between waist circumference and asthma attack in adults. METHODS: In this cross-sectional study, we analysed data from 5,530 U.S. adults diagnosed with asthma. Participants were categorized into two groups based on their experience of asthma attacks: with or without asthma attacks. We employed adjusted weighted logistic regression models, weighted restricted cubic splines, subgroup and sensitivity analyses to assess the association between waist circumference and asthma attack. RESULTS: The median age of all participants was 43 years, and the median waist circumference was 98.9 cm, with a median BMI was 28.50 kg/m2. Participants in the asthma attack group had significantly higher waist circumferences than those in the non-attack group (P < 0.001). After full adjustment for body mass index-defined obesity, age, gender, race, education levels, poverty income ratio levels, smoking status, and metabolic syndrome, every 5 cm increase in waist circumference exhibited a 1.06 times higher likelihood of asthma attack probability. The weighted restricted cubic spline analysis demonstrated an increased risk of asthma attacks with rising waist circumference. Subgroup analyses confirmed this relationship across various groups differentiated by gender, age, and smoking status. When applying a stricter definition of asthma attack, the weighted logistic regression models showed robust association between waist circumference and asthma attack. CONCLUSION: Waist circumference is an independent predictor of asthma attacks. Our findings underscore the importance of waist circumference measurement in evaluating the risk of asthma attacks.

Multivariate logistic regression analysis of risk factors for birth defects: a study from population-based surveillance data.

OBJECTIVE: To explore risk factors for birth defects (including a broad range of specific defects). METHODS: Data were derived from the Population-based Birth Defects Surveillance System in Hunan Province, China, 2014-2020. The surveillance population included all live births, stillbirths, infant deaths, and legal termination of pregnancy between 28 weeks gestation and 42 days postpartum. The prevalence of birth defects (number of birth defects per 1000 infants) and its 95% confidence interval (CI) were calculated. Multivariate logistic regression analysis (method: Forward, Wald, α = 0.05) and adjusted odds ratios (ORs) were used to identify risk factors for birth defects. We used the presence or absence of birth defects (or specific defects) as the dependent variable, and eight variables (sex, residence, number of births, paternal age, maternal age, number of pregnancies, parity, and maternal household registration) were entered as independent variables in multivariate logistic regression analysis. RESULTS: Our study included 143,118 infants, and 2984 birth defects were identified, with a prevalence of 20.85% (95%CI: 20.10-21.60). Multivariate logistic regression analyses showed that seven variables (except for parity) were associated with birth defects (or specific defects). There were five factors associated with the overall birth defects. The risk factors included males (OR = 1.49, 95%CI: 1.39-1.61), multiple births (OR = 1.44, 95%CI: 1.18-1.76), paternal age < 20 (OR = 2.20, 95%CI: 1.19-4.09) or 20-24 (OR = 1.66, 95%CI: 1.42-1.94), maternal age 30-34 (OR = 1.16, 95%CI: 1.04-1.29) or > = 35 (OR = 1.56, 95%CI: 1.33-1.81), and maternal non-local household registration (OR = 2.96, 95%CI: 2.39-3.67). Some factors were associated with the specific defects. Males were risk factors for congenital metabolic disorders (OR = 3.86, 95%CI: 3.15-4.72), congenital limb defects (OR = 1.34, 95%CI: 1.14-1.58), and congenital kidney and urinary defects (OR = 2.35, 95%CI: 1.65-3.34). Rural areas were risk factors for congenital metabolic disorders (OR = 1.21, 95%CI: 1.01-1.44). Multiple births were risk factors for congenital heart defects (OR = 2.09, 95%CI: 1.55-2.82), congenital kidney and urinary defects (OR = 2.14, 95%CI: 1.05-4.37), and cleft lip and/or palate (OR = 2.85, 95%CI: 1.32-6.15). Paternal age < 20 was the risk factor for congenital limb defects (OR = 3.27, 95%CI: 1.10-9.71), 20-24 was the risk factor for congenital heart defects (OR = 1.64, 95%CI: 1.24-2.17), congenital metabolic disorders (OR = 1.56, 95%CI: 1.11-2.21), congenital limb defects (OR = 1.61, 95%CI: 1.14-2.29), and congenital ear defects (OR = 2.13, 95%CI: 1.17-3.89). Maternal age < 20 was the risk factor for cleft lip and/or palate (OR = 3.14, 95%CI: 1.24-7.95), 30-34 was the risk factor for congenital limb defects (OR = 1.37, 95%CI: 1.09-1.73), >=35 was the risk factor for congenital heart defects (OR = 1.51, 95%CI: 1.14-1.99), congenital limb defects (OR = 1.98, 95%CI: 1.41-2.78), and congenital ear defects (OR = 1.82, 95%CI: 1.06-3.10). Number of pregnancies = 2 was the risk factor for congenital nervous system defects (OR = 2.27, 95%CI: 1.19-4.32), >=4 was the risk factor for chromosomal abnormalities (OR = 2.03, 95%CI: 1.06-3.88) and congenital nervous system defects (OR = 3.03, 95%CI: 1.23-7.47). Maternal non-local household registration was the risk factor for congenital heart defects (OR = 3.57, 95%CI: 2.54-5.03), congenital metabolic disorders (OR = 1.89, 95%CI: 1.06-3.37), congenital limb defects (OR = 2.94, 95%CI: 1.86-4.66), and congenital ear defects (OR = 3.26, 95%CI: 1.60-6.65). CONCLUSION: In summary, several risk factors were associated with birth defects (including a broad range of specific defects). One risk factor may be associated with several defects, and one defect may be associated with several risk factors. Future studies should examine the mechanisms. Our findings have significant public health implications as some factors are modifiable or avoidable, such as promoting childbirths at the appropriate age, improving the medical and socio-economic conditions of non-local household registration residents, and devoting more resources to some specific defects in high-risk groups, which may help reducing birth defects in China.

Association between leucocyte telomere length and erectile dysfunction in US adults: a secondary study based on 2001-2002 NHANES data.

OBJECTIVE: We aimed to explore the association between the leucocyte telomere length (LTL) and erectile dysfunction (ED) among a nationally representative sample of US adults. DESIGN: Secondary population-based study. SETTING: The National Health and Nutrition Examination Survey (NHANES) (2001-2002). PARTICIPANTS: A total of 1694 male participants were extracted from the NHANES database for 2001-2002. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary focus of the study was to determine the association between the LTL and ED, using multivariate logistic regression and restricted cubic spline models for examination. The secondary outcome measures involved conducting stratified subgroup analyses to exclude interactions of different variables with the LTL. RESULTS: Participants with ED had shorter LTLs than those without ED (p<0.05). After adjusting for confounding factors, compared with the reference lowest LTL quartile, the ORs and 95% CIs for the second, third and fourth LTL quartiles were (OR 1.51; 95% CI 1.01 to 2.26), (OR 1.79; 95% CI 1.24 to 2.58) and (OR 1.25; 95% CI 0.74 to 2.11), respectively. In addition, restricted cubic splines showed an inverted J-curve relationship between the LTL and ED. At an LTL of 1.037, the curve showed an inflection point. The ORs (95% CI) of ED on the left and right sides of the inflection point were (OR 1.99; 95% CI 0.39 to 10.20; p=0.385) and (OR 0.17; 95% CI 0.03 to 0.90; p=0.039). CONCLUSION: Our results demonstrated an inverted J-curve relationship between the LTL and ED. When the LTL was ≥1.037, the incidence of ED decreased with increasing LTL.

Relationship between lipid metabolism, coagulation and other blood indices and etiology and staging of non-traumatic femoral head necrosis: a multivariate logistic regression-based analysis.

BACKGROUND: To analyze the relationship between lipid metabolism, coagulation function, and bone metabolism and the contributing factor and staging of non-traumatic femoral head necrosis, and to further investigate the factors influencing the blood indicators related to the staging of non-traumatic femoral head necrosis. METHODS: The medical records of patients with femoral head necrosis were retrieved from the inpatient medical record management system, and the lipid metabolism, bone metabolism, and coagulation indices of non-traumatic femoral head necrosis (including alcoholic, hormonal, and idiopathic group) were obtained according to the inclusion and exclusion criteria, including Low-Density Lipoprotein Cholesterol, Triglycerides, Non-High-Density Lipoprotein Cholesterol, Apolipoprotein A1, Apolipoprotein (B), Apolipoprotein (E), Uric Acid, Alkaline Phosphatase, Bone-specific Alkaline Phosphatase, Activated Partial Thromboplastin Time, Prothrombin Time, D-dimer, Platelet count. The relationship between these blood indices and the different stages under different causative factors was compared, and the factors influencing the stages of non-traumatic femoral head necrosis were analyzed using multivariate logistic regression. RESULTS: (i) Gender, Age and BMI stratification, Low-density Lipoprotein Cholesterol, Triglycerides, Non-High-density Lipoprotein Cholesterol, Apolipoprotein (B), Apolipoprotein (E), Uric Acid, Bone-specific Alkaline Phosphatase, Activated Partial Thromboplastin Time, Plasminogen Time, D-dimer, and Platelet count of the alcohol group were statistically different when compared among the different ARCO staging groups; (ii) The differences in Age and BMI stratification, Triglycerides, Non-High-density Lipoprotein Cholesterol, Apolipoprotein A1, Apolipoprotein B, Apolipoprotein E, Uric Acid, Bone-specific Alkaline Phosphatase, Activated Partial Thromboplastin Time, Plasminogen Time, D-dimer, and Platelet count were statistically significant when compared among the different phases in the hormone group (P < 0.05); (iii) The differences in Age and BMI stratification, Non-High-Density Lipoprotein Cholesterol, Apolipoprotein A1, Apolipoprotein (B), Apolipoprotein (E), Uric Acid, Activated Partial Thromboplastin Time, D-dimer, and Platelet count were statistically significant when compared among the different stages in the idiopathic group (P < 0.05); (v) Statistically significant indicators were included in the multivariate logistic regression analysis, excluding the highly correlated bone-specific alkaline phosphatase, and the results showed that Low-density lipoprotein was negatively correlated with changes in the course of ARCO, and Non-High-Density Lipoprotein cholesterol, Apo B, Activated Partial Thromboplastin Time, and Platelet count were significantly and positively correlated with disease progression. CONCLUSION: An abnormal hypercoagulable state as well as an abnormal hyperlipidemic state are risk factors for the progression of non-traumatic femoral head necrosis under various exposure factors, as indicated by Non-High-Density Lipoprotein Cholesterol, Apolipoprotein B, Activated Fractional Thromboplastin Time, and Platelet Counts.

Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data.

Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p<0.001 significance level. This performance superiority is consistent across both the model development and test phases. This study also used paired-sample t-tests for the subsets of the research datasets from disease prediction (66) and university-ranking (50) research contexts for further validation. The observed superiority of the tree-based ML algorithms remains valid for these subsets. Tree-based ML algorithms significantly outperformed non-tree-based algorithms for these two research contexts for all four performance measures. We discuss the research implications of these findings in detail in this article.

Stage at diagnosis of colorectal cancer through diagnostic route: Who should be screened?

BACKGROUND: Colorectal cancer (CRC) is a global health concern, with advanced-stage diagnoses contributing to poor prognoses. The efficacy of CRC screening has been well-established; nevertheless, a significant proportion of patients remain unscreened, with > 70% of cases diagnosed outside screening. Although identifying specific subgroups for whom CRC screening should be particularly recommended is crucial owing to limited resources, the association between the diagnostic routes and identification of these subgroups has been less appreciated. In the Japanese cancer registry, the diagnostic routes for groups discovered outside of screening are primarily categorized into those with comorbidities found during hospital visits and those with CRC-related symptoms. AIM: To clarify the stage at CRC diagnosis based on diagnostic routes. METHODS: We conducted a retrospective observational study using a cancer registry of patients with CRC between January 2016 and December 2019 at two hospitals. The diagnostic routes were primarily classified into three groups: Cancer screening, follow-up, and symptomatic. The early-stage was defined as Stages 0 or I. Multivariate and univariate logistic regressions were exploited to determine the odds of early-stage diagnosis in the symptomatic and cancer screening groups, referencing the follow-up group. The adjusted covariates were age, sex, and tumor location. RESULTS: Of the 2083 patients, 715 (34.4%), 1064 (51.1%), and 304 (14.6%) belonged to the follow-up, symptomatic, and cancer screening groups, respectively. Among the 2083 patients, CRCs diagnosed at an early stage were 57.3% (410 of 715), 23.9% (254 of 1064), and 59.5% (181 of 304) in the follow-up, symptomatic, and cancer screening groups, respectively. The symptomatic group exhibited a lower likelihood of early-stage diagnosis than the follow-up group [P < 0.001, adjusted odds ratio (aOR), 0.23; 95% confidence interval (95%CI): 0.19-0.29]. The likelihood of diagnosis at an early stage was similar between the follow-up and cancer screening groups (P = 0.493, aOR for early-stage diagnosis in the cancer screening group vs follow-up group = 1.11; 95%CI = 0.82-1.49). CONCLUSION: CRCs detected during hospital visits for comorbidities were diagnosed earlier, similar to cancer screening. CRC screening should be recommended, particularly for patients without periodical hospital visits for comorbidities.

Learning epistatic polygenic phenotypes with Boolean interactions.

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

Frailty and spatialization of older adults in the city of Uberlândia with IVCF-20.

OBJECTIVE: To describe the functional clinical profile of elderly people linked to primary health care, using the Functional Clinical Vulnerability Index (IVCF-20) and to spatialize those with the greatest functional decline by primary health care units in the municipality of Uberlândia, in the state of Minas Gerais (MG), in the year 2022. METHODS: A cross-sectional study with secondary data from the Municipal Health Department of Uberlândia-MG. The variables were compared using Student's t-test, Mann Whitney test, Pearson's chi-square, and multinomial logistic regression to obtain the independent effect of each variable. The significance level adopted was 5% (p < 0.05). The georeferenced database in ArcGIS® was used. RESULTS: 47,182 older adults were evaluated with a mean age of 70.3 years (60 to 113 years), 27,138 of whom were women (57.52%), with a clear predominance of low-risk or robust older adults (69.40%). However, 11.09% are high-risk older adults and 19.52% are at risk of frailty. Older men had independently lower odds of moderate and high risk compared to older women (OR = 0.53; p < 0.001). A high prevalence of polypharmacy was observed, 21.40% of the older adult population, particularly in frail older adults, with a prevalence of 63.08%. There was a greater distribution of frail older adults around the central region of the municipality and in health units with a larger coverage area. The IVCF-20 made it possible to screen frailty in primary health care. CONCLUSION: The instrument is capable of stratifying the risk of older adults in health care networks through primary health care, enabling the application of individualized preventive, promotional, palliative, or rehabilitative interventions, according to the clinical functional stratum of the older adult and the compromised functional domains. Risk stratification and spatial distribution of the frailest older adults can be a good strategy for qualifying health professionals with the aim of maximizing the autonomy and independence of the older adults.

Predicting the need for urgent endoscopic intervention in lower gastrointestinal bleeding: a retrospective review.

BACKGROUND: Lower gastrointestinal bleeding (LGIB) is a common reason for emergency department visits and subsequent hospitalizations. Recent data suggests that low-risk patients may be safely evaluated as an outpatient. Recommendations for healthcare systems to identify low-risk patients who can be safely discharged with timely outpatient follow-up have yet to be established. The primary objective of this study was to determine the role of patient predictors for the patients with LGIB to receive urgent endoscopic intervention. METHODS: A retrospective chart review was performed on 142 patients. Data was collected on patient demographics, clinical features, comorbidities, medications, hemodynamic parameters, laboratory values, and diagnostic imaging. Logistic regression analysis, independent samples t-testing, Mann Whitney U testing for non-parametric data, and univariate analysis of categorical variables by Chi square test was performed to determine relationships within the data. RESULTS: On logistic regression analysis, A hemoglobin drop of > 20 g/L was the only variable that predicted endoscopic intervention (p = 0.030). Tachycardia, hypotension, or presence of anticoagulation were not significantly associated with endoscopic intervention (p > 0.05). CONCLUSIONS: A hemoglobin drop of > 20 g/L was the only patient parameter that predicted the need for urgent endoscopic intervention in the emergency department.

Association of two novel systemic inflammatory biomarkers and frailty based on NHANES 2007-2018.

Background: Frailty is a significant concern in the field of public health. However, currently, there is a lack of widely recognized and reliable biological markers for frailty. This study aims to investigate the association between systemic inflammatory biomarkers and frailty in the older adult population in the United States. Methods: This study employed data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2018 and conducted a rigorous cross-sectional analysis. We constructed weighted logistic regression models to explore the correlation between the Systemic Immune-Inflammation Index (SII), Systemic Inflammatory Response Index (SIRI), and frailty in the population aged 40 to 80 years. Using restricted cubic spline (RCS), we successfully visualized the relationship between SII, SIRI, and frailty. Finally, we presented stratified analyses and interaction tests of covariates in a forest plot. Results: This study involved 11,234 participants, 45.95% male and 54.05% female, with an average age of 64.75 ± 0.13 years. After adjusting for relevant covariates, the weighted logistic regression model indicated an odds ratio (OR) and 95% confidence interval(CI) for the correlation between frailty and the natural logarithm (ln) transformed lnSII and lnSIRI as 1.38 (1.24-1.54) and 1.69 (1.53-1.88), respectively. Subsequently, we assessed different levels of lnSII and lnSIRI, finding consistent results. In the lnSII group model, the likelihood of frailty significantly increased in the fourth quartile (OR = 1.82, 95% CI: 1.55-2.12) compared to the second quartile. In the lnSIRI group model, the likelihood of frailty significantly increased in the third quartile (OR = 1.30, 95% CI: 1.10-1.53) and fourth quartile (OR = 2.29, 95% CI: 1.95-2.70) compared to the second quartile. The interaction results indicate that age and income-to-poverty ratio influence the association between lnSIRI and frailty. RCS demonstrated a nonlinear relationship between lnSII, lnSIRI, and frailty. Conclusion: The results of this cross-sectional study indicate a positive correlation between systemic inflammatory biomarkers (SII, SIRI) and frailty.

Temporal trends and the association between self-rated oral health and falls in community-dwelling older adults in South Korea: a secondary analysis of survey data.

OBJECTIVE: This study aims to examine temporal trends and the association between falls and self-rated oral health (SROH) status in community-dwelling older Korean adults. DESIGN: Secondary analysis of a serial cross-sectional study. METHODS: We analysed biennial data from 314 846 older adults in South Korea from 2011 to 2019 using data from the Korea Community Health Survey. Self-reported fall experience data came from the injury questionnaire question, 'Have you fallen in the past year (slipping, tripping, stumbling and falling)?'. For SROH, the participants were asked, 'How do you feel about your oral health, such as teeth and gums, in your own opinion?'. The association between SROH and falls was examined using a weighted multivariable logistic regression model adjusted for sociodemographic characteristics, health status and behaviours, and psychological factors. RESULTS: Those who reported poor SROH had a lower declining slope than the other groups, although all groups showed a significantly decreasing trend in both men and women from 2011 to 2019 (p for trend<0.001). Older adults who reported having poor SROH had a more significant history of fall accidents than the good SROH groups, controlling for potential confounding variables (adjusted OR 1.16 (95% CI 1.10 to 1.21) in men; adjusted OR 1.17 (95% CI 1.13 to 1.21) in women). CONCLUSIONS: Older adults with poor SROH, especially women, had more fall accidents, making them a priority target for fall prevention strategies. This study suggests considering SROH status when identifying fall risk factors in community-dwelling older adults.

Anthropometric failures and its predictors among under five children in Ethiopia: multilevel logistic regression model using 2019 Ethiopian demographic and health survey data.

BACKGROUND: Composite Index of Anthropometric Failure (CIAF) combines all three forms of anthropometric failures to assess undernutrition status of children. There is no study on CIAF to identify the real and severe form of under nutrition among Ethiopian children that addressed community level factors. So, this study determined CIAF and identified important factors which helps to design appropriate intervention strategies by using multi-level advanced statistical model. METHODS: The study included 5,530 under five children and utilized a secondary data (EMDHS 2019) which was collected through community-based and cross-sectionally from March 21 to June 28, 2019. Composite index of anthropometric failure among under five children was assessed and a two-stage sampling technique was used to select the study participants. Descriptive summary statistics was computed. A multi-level binary logistic regression model was employed to identify important predictors of CIAF in under five children. Adjusted odds ratio with its 95% CI was estimated and level of significance 0.05 was used to determine significant predictors of CIAF. RESULTS: The prevalence of composite index of anthropometric failure (CIAF) was 40.69% (95% CI: 39.41, 42.00) in Ethiopia. Both individual and community level predictors were identified for CIAF in under five children. Among individual level predictors being male sex, older age, short birth interval, from mothers who have not formal education, and from poor household wealth quintile were associated with higher odds of CIAF among under five children. Low community women literacy and being from agriculturally based regions were the community level predictors that were associated with higher odds of CIAF in under five children in Ethiopia. CONCLUSIONS: The burden of composite index of anthropometric failure in under five children was high in Ethiopia. Age of child, sex of child, preceding birth interval, mother's education, household wealth index, community women literacy and administrative regions of Ethiopia were identified as significant predictors of CIAF. Therefore, emphasis should be given for those factors to decrease the prevalence of CIAF in under five children in Ethiopia.

Outdoor artificial light at night exposure and gestational diabetes mellitus: a case-control study.

Objective: This study aims to explore the association between outdoor artificial light at night (ALAN) exposure and gestational diabetes mellitus (GDM). Methods: This study is a retrospective case-control study. According with quantiles, ALAN has been classified into three categories (Q1-Q3). GDM was diagnosed through oral glucose tolerance tests. Conditional logistic regression models were used to evaluate the association between ALAN exposure and GDM risk. The odds ratio (OR) with 95% confidence interval (CI) was used to assess the association. Restricted cubic spline analysis (RCS) was utilized to investigate the no liner association between ALAN and GDM. Results: A total of 5,720 participants were included, comprising 1,430 individuals with GDM and 4,290 matched controls. Pregnant women exposed to higher levels of ALAN during the first trimester exhibited an elevated risk of GDM compared to those with lower exposure levels (Q2 OR = 1.39, 95% CI 1.20-1.63, p < 0.001); (Q3 OR = 1.70, 95% CI 1.44-2.00, p < 0.001). Similarly, elevated ALAN exposure during the second trimester also conferred an increased risk of GDM (second trimester: Q2 OR = 1.70, 95% CI 1.45-1.98, p < 0.001; Q3 OR = 2.08, 95% CI 1.77-2.44, p < 0.001). RCS showed a nonlinear association between ALAN exposure and GDM risk in second trimester pregnancy, with a threshold value of 4.235. Conclusion: Outdoor ALAN exposure during pregnancy is associated with an increased risk of GDM.

Nomogram for predicting in-hospital mortality in trauma patients undergoing resuscitative endovascular balloon occlusion of the aorta: a retrospective multicenter study.

Recently, resuscitative endovascular balloon occlusion of the aorta (REBOA) had been introduced as an innovative procedure for severe hemorrhage in the abdomen or pelvis. We aimed to investigate risk factors associated with mortality after REBOA and construct a model for predicting mortality. This multicenter retrospective study collected data from 251 patients admitted at five regional trauma centers across South Korea from 2015 to 2022. The indications for REBOA included patients experiencing hypovolemic shock due to hemorrhage in the abdomen, pelvis, or lower extremities, and those who were non-responders (systolic blood pressure (SBP) < 90 mmHg) to initial fluid treatment. The primary and secondary outcomes were mortality due to exsanguination and overall mortality, respectively. After feature selection using the least absolute shrinkage and selection operator (LASSO) logistic regression model to minimize overfitting, a multivariate logistic regression (MLR) model and nomogram were constructed. In the MLR model using risk factors selected in the LASSO, five risk factors, including initial heart rate (adjusted odds ratio [aOR], 0.99; 95% confidence interval [CI], 0.98-1.00; p = 0.030), initial Glasgow coma scale (aOR, 0.86; 95% CI 0.80-0.93; p < 0.001), RBC transfusion within 4 h (unit, aOR, 1.12; 95% CI 1.07-1.17; p < 0.001), balloon occlusion type (reference: partial occlusion; total occlusion, aOR, 2.53; 95% CI 1.27-5.02; p = 0.008; partial + total occlusion, aOR, 2.04; 95% CI 0.71-5.86; p = 0.187), and post-REBOA systolic blood pressure (SBP) (aOR, 0.98; 95% CI 0.97-0.99; p < 0.001) were significantly associated with mortality due to exsanguination. The prediction model showed an area under curve, sensitivity, and specificity of 0.855, 73.2%, and 83.6%, respectively. Decision curve analysis showed that the predictive model had increased net benefits across a wide range of threshold probabilities. This study developed a novel intuitive nomogram for predicting mortality in patients undergoing REBOA. Our proposed model exhibited excellent performance and revealed that total occlusion was associated with poor outcomes, with post-REBOA SBP potentially being an effective surrogate measure.

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Objective: To construct nomogram models to predict the risk factors for early death in patients with metastatic melanoma (MM). Methods: The study covered 2138 cases from the Surveillance, Epidemiology, and End Results Program (SEER) database and all these patients were diagnosed with MM between 2010 and 2015. Logistic regression was performed to identify independent risk factors affecting early death in MM patients. These risk factors were then used to construct nomograms of all-cause early death and cancer-specific early death. The efficacy of the model was assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). In addition, external validation of the model was performed with clinicopathologic data of 105 patients diagnosed with MM at Sichuan Cancer Hospital between January 2015 and January 2020. Results: According to the results of logistic regression, marital status, the primary site, N staging, surgery, chemotherapy, bone metastases, liver metastases, lung metastases, and brain metastases could be defined as independent predictive factors for early death. Based on these factors, 2 nomograms were plotted to predict the risks of all-cause early death and cancer-specific early death, respectively. For the models for all-cause and cancer-specific early death, the areas under the curve (AUCs) for the training group were 0.751 (95% confidence interval [CI]: 0.726-0.776) and 0.740 (95% CI: 0.714-0.765), respectively. The AUCs for the internal validation group were 0.759 (95% CI: 0.722-0.797) and 0.757 (95% CI: 0.718-0.780), respectively, while the AUCs for the external validation group were 0.750 (95% CI: 0.649-0.850) and 0.741 (95% CI: 0.644-0.838), respectively. The calibration curves showed high agreement between the predicted and the observed probabilities. DCA analysis indicated high clinical application value of the models. Conclusion: The nomogram models demonstrated good performance in predicting early death in MM patients and can be used to help clinical oncologists develop more individualized treatment strategies.

Correlation Between Anesthesia Methods and Adverse Short-Term Postoperative Outcomes Depending on Frailty: A Prospective Cohort Study.

Purpose: This study aims to investigate how the type of anesthesia used during major orthopedic surgery may impact adverse short-term postoperative outcomes depending on frailty. Methods: To conduct this investigation, we recruited individuals aged 65 years and older who underwent major orthopedic surgery between March 2022 and April 2023 at a single institution. We utilized the FRAIL scale to evaluate frailty. The primary focus was on occurrences of death or the inability to walk 60 days after the surgery. Secondary measures included death within 60 days; inability to walk without human assistance at 60 days; death or the inability to walk without human assistance at 30 days after surgery, the first time out of bed after surgery, postoperative blood transfusion, length of hospital stay, hospital costs, and the occurrence of surgical complications such as dislocation, periprosthetic fracture, infection, reoperation, wound complications/hematoma. Results: In a study of 387 old adult patients who had undergone major orthopedic surgery, 41.3% were found to be in a frail state. Among these patients, 262 had general anesthesia and 125 had neuraxial anesthesia. Multifactorial logistic regression analyses showed that anesthesia type was not linked to complications. Instead, frailty (OR 4.04, 95% CI 1.04 to 8.57, P< 0.001), age (OR 1.05, 95% CI 1.00-1.10, P= 0.017), and aCCI scores, age-adjusted Charlson Comorbidity Index, (OR 1.36, 95% CI 1.12 to 1.66, P= 0.002) were identified as independent risk factors for death or new walking disorders in these patients 60 days after surgery. After adjusting for frailty, anesthesia methods was not associated with the development of death or new walking disorders in these patients (P > 0.05). Conclusion: In different frail populations, neuraxial anesthesia is likely to be comparable to general anesthesia in terms of the incidence of short-term postoperative adverse outcomes.

Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR) and Monocyte-to-Lymphocyte Ratio (MLR) as Biomarkers in Diagnosis Evaluation of Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Retrospective, Observational Study.

Purpose: Hierarchical management is advocated in China to effectively manage chronic obstructive pulmonary disease (COPD) patients and reduce the incidence and mortality of acute exacerbation of COPD (AE-COPD). However, primary and community hospitals often have limited access to advanced equipment and technology. Complete blood count (CBC), which is commonly used in these hospitals, offers the advantages of being cost-effective and easily accessible. This study aims to evaluate the significance of routine blood indicators in aiding of diagnosing AE-COPD. Patients and Methods: In this research, we enrolled a total of 112 patients diagnosed with AE-COPD, 92 patients with stable COPD, and a control group comprising 60 healthy individuals. Clinical characteristics, CBC parameters, and serum CRP levels were collected within two hours. To assess the associations between NLR/PLR/MLR and CRP by Spearman correlation test. The diagnostic accuracy of NLR, PLR and MLR in AE-COPD was assessed using Receiver Operating Characteristic Curve (ROC) and the area under the curve (AUC). Binary Logistic Regression analysis was conducted for the indicators of NLR, PLR and MLR. Results: We found that patients with AE-COPD had significantly higher levels of NLR, PLR and MLR in contrast to patients with stable COPD. Additionally, the study revealed a noteworthy correlation between CRP and NLR (rs=0.5319, P<0.001), PLR (rs=0.4424, P<0.001), and MLR (rs=0.4628, P<0.001). By utilizing specific cut-off values, the amalgamation of NLR, PLR and MLR augmented diagnostic sensitivity. Binary logistic regression analysis demonstrated that heightened NLR and MLR act as risk factors for the progression of AE-COPD. Conclusion: The increasing levels of NLR, PLR and MLR could function as biomarkers, akin to CRP, for diagnosis and assessment of acute exacerbations among COPD patients. Further research is required to validate this concept.

Multilevel multinomial regression analysis of factors associated with birth weight in sub-Saharan Africa.

Birth weight significantly determines newborns immediate and future health. Globally, the incidence of both low birth weight (LBW) and macrosomia have increased dramatically including sub-Saharan African (SSA) countries. However, there is limited study on the magnitude and associated factors of birth weight in SSA. Thus, thus study investigated factors associated factors of birth weight in SSA using multilevel multinomial logistic regression analysis. The latest demographic and health survey (DHS) data of 36 sub-Saharan African (SSA) countries was used for this study. A total of a weighted sample of 207,548 live births for whom birth weight data were available were used. Multilevel multinomial logistic regression model was fitted to identify factors associated with birth weight. Variables with p-value < 0.2 in the bivariable analysis were considered for the multivariable analysis. In the multivariable multilevel multinomial logistic regression analysis, the adjusted Relative Risk Ratio (aRRR) with the 95% confidence interval (CI) was reported to declare the statistical significance and strength of association. The prevalence of LBW and macrosomia in SSA were 10.44% (95% CI 10.31%, 10.57%) and 8.33% (95% CI 8.21%, 8.45%), respectively. Maternal education level, household wealth status, age, and the number of pregnancies were among the individual-level variables associated with both LBW and macrosomia in the final multilevel multinomial logistic regression analysis. The community-level factors that had a significant association with both macrosomia and LBW were the place of residence and the sub-Saharan African region. The study found a significant association between LBW and distance to the health facility, while macrosomia had a significant association with parity, marital status, and desired pregnancy. In SSA, macrosomia and LBW were found to be major public health issues. Maternal education, household wealth status, age, place of residence, number of pregnancies, distance to the health facility, and parity were found to be significant factors of LBW and macrosomia in this study. Reducing the double burden (low birth weight and macrosomia) and its related short- and long-term effects, therefore, calls for improving mothers' socioeconomic status and expanding access to and availability of health care.