Logistic Regression Method Research Articles

Abstract 4140120: Artificial intelligence-driven morbidity prediction in acute kidney injury after acute type A aortic dissection surgery

Background: Acute kidney injury (AKI) often complicates acute type A aortic dissection (ATAAD), with elevated comorbidity rates and a significant tie to in-hospital mortality. Identifying risk factors early can mitigate AKI severity. Research Questions: This research endeavors to develop and corroborate predictive models leveraging Machine Learning (ML) techniques from Artificial Intelligence to forecast AKI occurrences in ATAAD-afflicted individuals. Methods: The study employed various machine learning (ML) algorithms including Gradient Boosting Machine (GBM), LightGBM, Random Forest (RF), K-Nearest Neighbors (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Naive Bayes (NB), Logistic Regression (LR), and ensemble methods (combining LR&LightGBM), employing tenfold cross-validation. Model performance was evaluated using SHapley Additive exPlanations (SHAP). A web-based tool for predicting AKI incidence was developed using Streamlit, based on the most effective model. The analysis involved 1350 ATAAD patients, among whom 586 (43.4%) developed post-operative AKI. Patients were divided into two cohorts: 85% for training and 15% for testing, with 126 features included in the predictive model. Results: Incorporating top 10 features, LightGBM (AUROC=0.886, 95% CI 0.841-0.930) excelled in predictive accuracy, calibration, and clinical utility, identifying key factors such as ventilation time in ICU, hourly urine output post-surgery, diuretic use, Scr, heart rate, urea, administration of recombinant human brain natriuretic peptide and ebrantil, MCHC, and blood glucose as associated with ATAAD-AKI. Conclusion(s): These ML models are robust tools for predicting AKI in ATAAD patients, with LightGBM's superior predictive ability standing out. They offer valuable support for clinical decision-making in ATAAD management, helping optimize postoperative strategies to minimize AKI occurrence after surgery.

Abstract 4114705: Clinical Predictors of Stress Induced Cardiomyopathy

Introduction: Stress Induced Cardiomyopathy is increasingly becoming more prevalent with increasing awareness about disease condition with annual incidence of 30 cases/100000 per year and an incidence of 1-2% in the patients presenting with acute coronary syndrome.[1] Physical and emotional triggers have been linked with occurrence of Stress induced Cardiomyopathy. Methods: We have obtained the National Readmission database for the year of 2020. We have used the ICD 10 code I51.81 for stress induced cardiomyopathy and found 10450 patients in the data base. Total 494 patients had cardiac arrest and 191 patients out of this 494 had died. We have used Binary logistic regression methods to find the odd ratio for physical and emotional risk factors for stress induced cardiomyopathy. Results: Grief disorder with an odd ratio of 7.2, followed by female gender with an odd ratio of 4.1, septic shock with an odd ratio of 3.3, Hemorrhagic stroke with an odd ratio of 1.73, ischemic stroke with an odd ratio of 1.72, depression with an odd ratio of 1.5, followed by asthma exacerbation with an odd ratio of 1.35 and seizure disorder with an odd ratio of 1.34 were among the few predictors for stress induced cardiomyopathy. Incidence of Cardiac Arrest was 4.7% and mortality rate of 1.8% was observed in the patients with stress induced Cardiomyopathy. Discussion: Extreme emotional and physical triggers like stroke, septic shock are among few significant risk factors for the stress induced cardiomyopathy.

Infant Mortality among the Tea Garden Workers in Upper Assam: Insights from a Field Survey

Infant mortality rate is an important indicator that reflects not only the health status of the infant but also the well-being of society as a whole. The north-eastern state of Assam is one of the states having the highest number of maternal and infant deaths within the country. For the first time, infant mortality in Assam registered a decline to 31.9 compared to the national average of 35.2 per 1,000 live births (NFHS-5). Though the overall rank of the state in terms of infant mortality has improved (from the 34th position to the 25th position), it is not enough and is not evenly distributed across the communities within the state. Human rights violations are particularly prominent among the tea garden workers of Assam and are mostly seen against the Adivasi children and woman, which is reflected by high maternal and infant deaths. The study aims to determine the factors responsible for infant mortality among tea garden workers by using primary data collected from 300 women workers across tea gardens in the Sonitpur and Tinsukia districts of Upper Assam. The binary logistic regression method is used for the analysis. It was observed that among the six probable factors, namely educational qualifications of the mother, marriage age of the mother, daily working hours, daily wage as tea garden labour, total number of births and the source of drinking water, the following three factors—namely the total number of births, age at marriage and the source of drinking water—are found to be the significant determinants of infant mortality among the tea garden labourers of Assam. JEL Codes: I12, I15

Prevalence of intestinal parasitic helminths and its associated risk factors in Mekaneselam Town, South Wollo, Ethiopia.

Intestinal parasitic (IP) infections caused by helminths are among the most significant public health concerns that mainly affect deprived people in Sub-Sahara African countries. The current study aimed to determine the prevalence and associated risk factors of intestinal parasitic helminths among patients attending Mekaneselam hospital. In this cross-sectional study conducted from August 2022 to February 2023, stool specimens were collected and examined microscopically to detect intestinal helminths using direct wet-mount and formal-ether concentration techniques. A structured questionnaire was employed to obtain information regarding the socio-demographic characteristics and associated risk factors. Data were analyzed via appropriate univariate and multivariable logistic regression methods using SPSS version 25.0. The overall prevalence of the parasitic helminths was 51 (12.6%) out of 404 patients attending in Mekaneselam town during the study period. In this study, seven species of intestinal helminths were detected, with Ascaris lumbricoides (5.7%) being the most prevalent followed by Trichuris trichiura (2.2%). Significant positive associations were observed between intestinal helminths infection and the socio-demographic characteristics including marital status (χ2 = 7.97, p = 0.047), educational level (χ2 = 9.34, p = 0.025) and residence (χ2 = 3.90, p = 0.048). The odds of being infected with IP were three times higher among divorced patients than married individuals (AOR = 3.21, CI = 1.15-8.96, p = 0.03). Illiterate individuals were three times (AOR = 2.61, CI = 1.24-5.49, p = 0.011) and those who attended primary school were two times (AOR = 2.3, CI = 1.029-5.143, p = 0.042), more likely to have helminthic infections. Participants who did not wash their hands after using the toilet were five times more likely to acquire IP helminths than those who did (AOR = 4.36, CI = 2.83-6.72, p = 0.000). In the case of the feeding habit, study subjects who ate unwashed vegetables were more likely to acquire helminths than those who did not eat unwashed vegetables (AOR = 5. 43, CI = 4.33-23.77, p = 0.000). This study highlighted a substantial prevalence of intestinal helminths infections (12.6%) in the study area, warranting the urgent need for attention and interventions from relevant public health authorities.

Association between Hematological Parameters and Severity of COVID-19 disease

Background This study aimed to determine the relationships between hematological parameters- hemoglobin, Total Leucocyte Counts (TLC), platelet counts, Absolute Neutrophil Counts (ANC), Absolute Lymphocyte Counts (ALC), Neutrophil Lymphocyte Ratio (NLR), Systemic Immune Inflammatory Index (SII), Neutrophil Monocyte Ratio (NMR), Platelet Lymphocyte Ratio (PLR) and the severity of COVID-19 disease and their use in predicting severity of COVID-19 disease. Methods and Material This was a prospective, observational, single-center study of 573 symptomatic adult inpatients of COVID 19 admitted to our tertiary care center. Statistical analysis used The above-mentioned hematological parameter levels were noted and compared between the two categories of COVID-19 disease, namely non-severe and severe COVID-19 using logistic regression methods. Their cut-off values were detected using the ROC curve. Results The median TLC, ANC, NLR, SII, NMR, PLR were notably higher in patients with severe COVID-19 than in those with non-severe COVID-19. Logistic regression analysis showed that NMR (OR=1.029, p=0.006) and ALC (OR=0.999, p=0.002) were statistically significant independent predictors of COVID-19 severity. Conclusions The hematological parameters mentioned, can be used for predicting severe COVID-19 disease at admission. ALC and NMR levels could be used as hematological markers to predict severity of COVID-19 in adult patients with their cut off values being < 1105 cells/cubic millimeter and > 10.434 respectively.

Continuing medical education program completion and influencing factors: a cross-sectional study in Sichuan Province, China

ObjectivesThis cross-sectional study was designed to explore the completion of continuing medical education (CME) programs and identify the factors influencing their completion.MethodsThe data for this study were generated from the National CME Program Application and Information Feedback Online System and the Sichuan CME Administrative Platform. The data were processed using descriptive analysis, Chi-square test, and binary logistic regression methods. The completion of each CME program was determined by the research team members according to the criteria created by the Office of the Sichuan CME Commission.ResultsA total of 180 hospitals and 3,622 CME programs were included. Among the 3,622 CME programs, 2,936 (81.1%) were determined to be completed. Comparative analysis showed that in terms of hospital characteristics, specialist hospitals, county hospitals, hospitals with 500–1,000 beds, and hospitals in the regions with government medical expenditure input equal to or more than 3,000 million RMB displayed the highest completion rates. For program attributes, national programs, programs in the field of pharmacy, and programs with 1–3 duration days demonstrated the highest completion rates. The binary logistic regression analysis showed that hospital region with different government medical expenditure input had the strongest positive association with the completion of CME programs [OR = 2.922, 95%CI (1.642–5.198)], while the duration time showed the strongest negative association [OR = 0.235, 95%CI (0.141–0.393)].ConclusionThis is the first study in China to analyze the completion of CME programs and identify its influencing factors at the provincial level. It is recommended that the government in the region should pay great attention to the construction of measures regarding the factors affecting the completion of CME programs. This includes providing more financial support to CME providers to ensure the formal operation of their CME activities, formulating guidelines on the application of CME programs to reasonably allocate and control the distribution of accredited CME programs across different hospital scales and disciplines, especially offering more training support to county hospitals, promulgating administrative documents to raise attention to the completion of CME programs, and special scrutiny on CME programs with longer durations to provide and protect training opportunities for those in need.

Predictive modeling of preoperative acute heart failure in older adults with hypertension: a dual perspective of SHAP values and interaction analysis

BackgroundIn older adults with hypertension, hip fractures accompanied by preoperative acute heart failure significantly elevate surgical risks and adverse outcomes, necessitating timely identification and management to improve patient outcomes.Research objectiveThis study aims to enhance the early recognition of acute heart failure in older hypertensive adults prior to hip fracture surgery by developing a predictive model using logistic regression (LR) and machine learning methods, optimizing preoperative assessment and management.MethodsEmploying a retrospective study design, we analyzed hypertensive older adults who underwent hip fracture surgery at Hebei Medical University Third Hospital from January 2018 to December 2022. Predictive models were constructed using LASSO regression and multivariable logistic regression, evaluated via nomogram charts. Five additional machine learning methods were utilized, with variable importance assessed using SHAP values and the impact of key variables evaluated through multivariate correlation analysis and interaction effects.ResultsThe study included 1,370 patients. LASSO regression selected 18 key variables, including sex, age, coronary heart disease, pulmonary infection, ventricular arrhythmias, acute myocardial infarction, and anemia. The logistic regression model demonstrated robust performance with an AUC of 0.753. Although other models outperformed it in sensitivity and F1 score, logistic regression’s discriminative ability was significant for clinical decision-making. The Gradient Boosting Machine model, notable for a sensitivity of 95.2%, indicated substantial capability in identifying patients at risk, crucial for reducing missed diagnoses.ConclusionWe developed and compared efficacy of predictive models using logistic regression and machine learning, interpreting them with SHAP values and analyzing key variable interactions. This offers a scientific basis for assessing preoperative heart failure risk in older adults with hypertension and hip fractures, providing significant guidance for individualized treatment strategies and underscoring the value of applying machine learning in clinical settings.

The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration.

Machine learning methods are increasingly being used to predict clinical outcomes. Optimism is the difference in model performance between derivation and validation samples. The term "data hungriness" refers to the sample size needed for a modelling technique to generate a prediction model with minimal optimism. Our objective was to compare the relative data hungriness of different statistical and machine learning methods when assessed using calibration. We used Monte Carlo simulations to assess the effect of number of events per variable (EPV) on the optimism of six learning methods when assessing model calibration: unpenalized logistic regression, ridge regression, lasso regression, bagged classification trees, random forests, and stochastic gradient boosting machines using trees as the base learners. We performed simulations in two large cardiovascular datasets each of which comprised an independent derivation and validation sample: patients hospitalized with acute myocardial infarction and patients hospitalized with heart failure. We used six data-generating processes, each based on one of the six learning methods. We allowed the sample sizes to be such that the number of EPV ranged from 10 to 200 in increments of 10. We applied six prediction methods in each of the simulated derivation samples and evaluated calibration in the simulated validation samples using the integrated calibration index, the calibration intercept, and the calibration slope. We also examined Nagelkerke's R2, the scaled Brier score, and the c-statistic. Across all 12 scenarios (2 diseases × 6 data-generating processes), penalized logistic regression displayed very low optimism even when the number of EPV was very low. Random forests and bagged trees tended to be the most data hungry and displayed the greatest optimism. When assessed using calibration, penalized logistic regression was substantially less data hungry than methods from the machine learning literature.

Establishment and Validation of a Dynamic Nomogram for Persistent Organ Failure in Acute Biliary Pancreatitis: A Retrospective Study.

The objective of this study was to create a predictive model for the onset of persistent organ failure (POF) in individuals suffering from acute biliary pancreatitis (ABP) by utilizing indicators observed within 24hours of hospital admission. Early detection of high-risk POF patients is crucial for clinical decision-making. Clinical data and laboratory indicators within 24hours of admission from ABP patients diagnosed at The First Affiliated Hospital of Wenzhou Medical University between January 1, 2016, and January 1, 2024 were collected and retrospectively analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression (stepwise regression) methods were employed to identify variables for constructing the prediction model. The prediction model's performance was evaluated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). It was compared with other scoring systems such as SIRS, BISAP, APACHE II, CTSI, and MCTSI. Additionally, a web-based calculator was created to simplify the calculation process. Out of 324 ABP patients, 25 developed POF. Initial screening identified 18 variables; through LASSO regression and multivariable logistic regression analysis, five variables including BMI, Hb, ALB, Ca, and LIP were determined as independent predictors of POF. According to these factors to build prediction model, draw the nomogram. The AUC's receiver operating characteristic curve analysis demonstrated a significantly higher value in comparison to other scoring systems. Calibration curve and DCA show that the established model to predict the accuracy of POF is higher, clinical decision of net benefit is also higher. A network calculator utilizing this predictive model was developed. A predictive model incorporating five risk indicators has been established exhibiting high discriminatory power and accuracy which aids in early identification of ABP patients at risk for developing POF. This holds significant value in guiding clinical decision-making.

Association between dietary iron intake and hyperuricemia in U.S. adults: a cross-sectional study

BackgroundElevating blood iron levels may increase the risk of hyperuricemia. However, the association between dietary iron intake and hyperuricemia remains unclear. This study examines the association between dietary iron and hyperuricemia.MethodsThe data for this cross-sectional study came from the 2011–2020 National Health and Nutrition Examination Survey (NHANES), which included adults aged 20 and above in the United States. Detailed information on their demographics, dietary iron intake, serum uric acid, and other variables were gathered. This study employs a multifactorial logistic regression method to assess the relationship between dietary iron intake and hyperuricemia. Additionally, it utilizes Restricted Cubic Spline analysis to determine whether there is a non-linear relationship between dietary iron and hyperuricemia. Furthermore, sub-group analyses are conducted to further validate the robustness of this correlation.ResultsThis study included 11,441 patients, with a prevalence of hyperuricemia at 14.96%. Regardless of whether confounding factors were adjusted for, there was a positive correlation between dietary iron intake and hyperuricemia. In Model III, after adjusting for age, gender, race, educational background, body mass index, hypertension, carbohydrate intake, dietary fiber intake, total saturated fatty acid intake, vitamin K intake, vitamin C intake, marital status, poverty income ratio, smoking status, drinking status, work activity, diabetes, sodium intake, energy intake, protein intake, zinc intake, copper intake, selenium intake and total sugars intake, we found that compared to the lowest quintile of dietary iron intake (Q1, ≤ 8.03 mg/day), the odds ratios (ORs) for hyperuricemia in the second (Q2, 8.04–11.07 mg/day), third (Q3, 11.08–14.27 mg/day), fourth (Q4, 14.28–19.33 mg/day), and fifth quintiles (Q5, ≥ 19.34 mg/day) were 1.05 (95% confidence interval: 0.79–1.40), 1.30 (95% confidence interval: 0.99–1.73), 1.39 (95% confidence interval: 1.02–1.89), and 1.36 (95% confidence interval: 0.95–1.97), respectively. The study results indicate that a higher dietary iron intake is significantly positively associated with the likelihood of hyperuricemia.ConclusionIn the adult population of the United States, there is a positive correlation between dietary iron intake and hyperuricemia, with higher dietary iron intake associated with an increased likelihood of hyperuricemia.

Novel tools for evaluating COVID-19 at the emergency department: Surfactant protein D level and CHARISMA score

ObjectivesTo investigate the serum surfactant protein D (SP-D) level required to determine the diagnosis and prognosis of coronavirus disease 2019 (COVID-19), and create a new scale for disease prognosis prediction. MethodsThis study was conducted among 64 patients with COVID-19 symptoms and 16 healthy volunteers. The participants were assessed by comparing “Controls/Patients”, “PCR-negative/PCR-positive”, “Simple COVID-19/Acute respiratory distress syndrome (ARDS)-accompanied COVID-19”, “Mild ARDS/Moderate-severe ARDS”, and “Survived/Dead” subgroups. Serum SP-D levels and pulmonary infiltration area (volume and percentage) measurements on CT were compared between the groups. A new scale, the “CHARISMA Score”, was created by logistic regression method for a complete prognosis assessment. This includes confusion, heart rate, age, respiratory rate, percentage of infiltration on CT, serum SP-D level, mean arterial pressure and SaO2. ResultsSerum SP-D levels differed significantly across the groups. There was a strong correlation between SP-D levels and infiltration volumes. CHARISMA scores were higher in the severe than in the mild ARDS group and in patients who died than in survivors. In the receiver operating characteristic curve analysis of the CHARISMA scores, a cutoff value of 4 indicated mortality. ConclusionSerum SP-D levels can be used to determine COVID-19 diagnosis and prognosis, and the CHARISMA score can be used to predict prognosis and mortality risk in patients with COVID-19 pneumonia.

RELATIONSHIP BETWEEN BENIGN PROSTATIC HYPERPLASIA AND METABOLIC SYNDROME

Introduction: Factors that may increase the risk of Benign Prostatic Hyperplasia (BPH), and one theory suggests a possible connection between metabolic syndrome and the development of BPH. This study aims to investigate the potential relationship between metabolic syndrome and the development of BPH. Methods: This study analyzed medical records from Saiful Anwar Hospital in Malang from 2015 to 2020, using a case-control research design. It applied Chi-Square and Logistic Regression methods for data analysis. Results: The study, based on data from 90 patients, found that the highest number of BPH cases (75.56%) occurred in those with hypertension. Factors like body mass index and blood pressure were significantly associated with BPH (p-values = 0.046; OR = 2.473). Logistic regression confirmed the impact of fasting blood glucose, blood pressure, and body mass index categories on BPH. Conclusion: This research illustrates that there is an association between type 2 diabetes, hypertension, and obesity with the occurrence of BPH. However, there is no statistically significant relationship between the components of metabolic syndrome and the occurrence of BPH.

A goodness-of-fit measure for logistic regression under separation

Logistic regression models have a severe problem called separation. The maximum likelihood estimator does not exist in logistic regression models for data structures under separation. Under separation, the forcibly estimated maximum likelihood estimate may have an extremely large value. Separation often occurs when the size of dataset is small. Consequently, goodness-of-fit measures based on the likelihood ratio and those based on covariance functions using the maximum likelihood estimate indicate that the model is excessively good regardless of the cause of the separation. The Firth and exact logistic regression methods are valid estimation methods for separation problems. Therefore, we propose methods to reasonably evaluate the goodness-of-fit measures of statistical models under separation with dataset of a small sample size with the abovementioned methods. The goodness-of-fit measures based on covariance functions which are a generalization of the multiple correlation coefficient, referred to as the regression correlation coefficient and the entropy coefficient of determination are then used combined with the abovementioned methods for the separation data. In addition, we conducted a data analysis using the definition of the non separation ratio based on the regression depth.

PROFITABILITAS, FINANCIAL LEVERAGE, DAN CASH HOLDING TERHADAP PERATAAN LABA DENGAN GOOD CORPORATE GOVERNANCE SEBAGAI VARIABEL MODERASI

Income smoothing is one of the earnings management techniques to manage company profits. There are several phenomena regarding income smoothing carried out by several companies in Indonesia. This research develops from previous research by adding one variable, namely profitability. This research used binary logistic regression methods and Moderated Regression Analysis (MRA). Data is taken from annual reports in food and beverage companies. The results of this study indicate that profitability has a positive effect on income smoothing, financial leverage has a negative effect on income smoothing, cash holding has no effect on income smoothing. The moderation results show that good corporate governance moderates the effect of profitability and financial leverage on income smoothing, but good corporate governance does not moderate the effect of cash holding on income smoothing. The results of this study are expected to be useful for further research and as a consideration for decision making for investors.

Digitalization of cotton farming in the municipality of Banikoara in Northwestern Benin

The widespread use of mobile telephony and the internet constitutes an asset for digitalization in the agricultural sectors in Africa. Cotton being the leading export crop in Benin, this study aims to understand the determinants of the adoption of digitalization (use of digital machines, membership of a social network for information exchange, use of a mobile money account) by cotton producers in the municipality of Banikoara. In this context, a socio-economic survey was carried out with a sample of 314 producers, obtained by purposive sampling. The binary logistic regression method made it possible to identify the factors affecting the adoption of digitalization. Thus, the level of banking, the use of labor, the area of cotton sown and knowledge of agricultural information and exchange platforms had a significant impact on the use of digital machines in agriculture. Membership of a social network for information exchange between producers was influenced by the level of banking, the type of cotton grown, the strengthening of relationships with others, the risk linked to the use of digitalization and the assessment of the level of security of digitization by the producers. Finally, the level of banking, the exercise of a secondary activity and the use of labor were the significant variables in the adoption of mobile money by cotton farmers in Banikoara. However, the use of more advanced technologies such as drones and sensors was not yet a reality for these producers. This information is very useful for any project to popularize and promote these new technologies in the modernization of agriculture.

Does inflammation matter in cardiogenic shock?

Abstract Background The current Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification does not include the systemic inflammatory response to risk stratify patients with cardiogenic shock. Purpose To evaluate the prognostic impact of the inflammatory response, on top of the SCAI shock classification, during cardiogenic shock using C-Reactive protein (CRP). Methods We analyzed CRP levels and SCAI shock classification in consecutive patients admitted for cardiogenic shock between January 2010 and December 2020, included in the ACTION-SHOCK registry of our tertiary center. Patients with ongoing sepsis or other extracardiac causes for elevated CRP were excluded. Using logistic regression method, we assessed both the association between CRP levels at admission and all-cause mortality at 90 days, and the value of the addition of baseline CRP level to SCAI stage of shock for the prediction of 90-day mortality. Results Among 755 patients admitted with cardiogenic shock (median age 61 years, interquartile range [IQR] 50-72, 27% of female), only 128 (17%) had normal values of CRP (< 5mg/L) at admission. Median CRP at admission was 19mg/l (IQR 6-55). There was an association between 90-day mortality and baseline CRP (Odds Ratio [OR] = 1.02, 95% Confidence Interval [95%CI] 1.01-1.03 per 5mg/L of CRP, p=0.004), still significant after adjustment on clinical and biological variables associated with 90-day mortality in univariate analysis, including the SCAI shock stage (adjusted OR 1.02, 95%CI 1.01 - 1.03 per 5 mg/L, p=0.042). The addition of CRP increased the accuracy of the SCAI shock stages for the prediction of 90-day mortality (Area under the Curve increased from 0.59, 95% CI 0.50 - 0.66 to 0.65, 95% CI 0.61 - 0.72). Interestingly, the association of CRP with mortality was driven by patients in SCAI shock classes A and B (figure). Conclusions CRP elevation was frequently observed in this population of patients with any cause of cardiogenic shock and was associated with 90-day mortality. This was particularly evident for patients in lower SCAI shock stages on admission.Figure 1

Toothbrushing habits are associated with self perceived stress

Abstract Background Personal self-care is one of the most important factors in maintaining and improving oral health. Public health guidelines are recommending tooth brushing at least twice daily, once before bed time and at least on one occasion. On the other hand, our regular routines and caring for our own health is influenced by different factors including stress. We wanted to research how much does daily stress influencing tooth brushing habits. Methods We analyzed data from 2020 CINDI nationwide cross-sectional survey in Slovenia. Tooth brushing once a day or less was assessed as inadequate. Stress was assessed with question >>How often in the last 14 days have you felt tense, stressed or under a lot of pressure? << Participants who chose often or always as an answer were assessed as persons under stress. Method of binary logistic regression was used for statistical analysis of association between tooth brushing frequency and self-perceived stress, modified for some socioeconomic factors. Results Prevalence of inadequate frequency of tooth brushing was present in 31,6 % of sample, 31,5% in participants who were not feeling tense and 31,8% in participants who often felt tense or stressed. At first it seemed stress is not associated with tooth brushing habits. We adjusted this association for gender, education level and age and this association became clearer. Participants who felt stress and/or under pressure had higher odds for poor tooth brushing habits, their odds ratio was 1,283 (p < 0,001; 95% CI 1,124; 1,464) when adjusted for aforementioned factors. Conclusions Oral health is integral part of general health. Association between oral and mental health is often overlooked even though psycho-social aspects are part of modern oral health definition. As personal self-care is cornerstone of maintaining and improving oral health, implementation of mental health aspects should be incorporated in public health advises and programmes for promoting oral health. Key messages • Self-perceived stress is associated with toothbrushing habits. • People who are under stress less regularly brush their teeth.

The intra-tumoral microbiome as a potential biomarker of response to external beam radiation therapy in cervical cancer

BackgroundWe aimed to determine the potential predictive value of the intra-tumoral microbiome as a marker of the response to external beam radiation therapy (EBRT) in cervical cancer (CC).MethodsA prospective longitudinal trial of 36 CC patients receiving pelvic radiotherapy was designed to investigate microbial characteristic signatures and diversity (alpha and beta) of multiple sites (tumor, vaginal, gut, urethral, and oral) in the superior response (SR) and inferior response (IR) groups of CC patients by 16S rRNA sequencing. Utilized the least absolute shrinkage and selection operator (LASSO) logistic regression method to analyze clinicopathological factors that potentially influenced the efficacy of EBRT. LEfSe analysis highlighted the microbiome features that best distinguished the categorized patient samples. Selected parameters were validated with Spearman correlation analysis, receiver operating characteristic (ROC) area under the curve (AUC) analysis and Kaplan-Meier survival analysis.ResultsFirstly, in our cohort, LASSO logistic regression analysis revealed no association between clinicopathological factors and EBRT efficacy. Subsequently, we employed 16S rRNA sequencing to compare microbiome differences across multiple sites and their correlations with major clinicopathological factors. We discovered that the intra-tumoral microbiome was independent of clinicopathologic features and represented the most direct and reliable reflection of the microbial differences between the SR and IR groups. We found lower alpha diversity in the tumor microbiome of SR group and identified the most relevant microbiome taxa (Bifidobacteriaceae, Beijerinckiaceae, and Orbaceae) associated with the efficacy of the response to EBRT in CC patients. We then conducted ROC analysis, finding that specific microbial taxa had an AUC of 0.831 (95% CI, 0.667–0.995), indicating the potential of these taxa as biomarkers for predicting EBRT efficacy. Kaplan-Meier survival analysis showed a better prognosis for patients with lower alpha diversity and higher relative abundance of Bifidobacteriaceae.ConclusionsOur data suggested that intra-tumoral specific microbiome taxa and lower alpha diversity may play an important role in the CC patient sensitivity to EBRT and offer novel potential biomarkers for predicting the response to EBRT efficacy.

Relationship Between Gross Motor Function and Nutritional Status of Children with Cerebral Palsy-A cross-sectional study in a Tertiary Care Hospital of Bangladesh

Background: Cerebral Palsy (CP) is one of the most common causes of chronic disability in children. Childhood malnutrition is a major public health issue in low- and middle-income countries. The gross motor function of children with CP can easily be assessed by the tool Gross Motor Function Classification System- Expanded &Revised (GMFCS-E&R). This study aimed to determine the relationship between GMFCS and nutritional status in children with cerebral palsy. Method: This cross-sectional observational study was conducted in the Department of Pediatric Neurology, IPNA, BSMMU from October 2019 to August 2020. All the patients aged 2-12 years were diagnosed as CP by clinical criteria and classified by GMFCS. Nutritional status (weight for age, height for age, weight for height, BMI) is assessed and classified according to the World Health Organization growth charts. Then linear and multinomial logistic regression methods to determine the associations between GMFCS and nutritional status. The correlation between motor function and nutritional status was assessed. Results: Among the 50 children 68% were male and 32% were female. The mean age of the CP children was 4.90±3.05 years. Most patients (66.0%) came from poor families followed by 24% from the middle class. Functionally majority of patients were in GMFCS level IV (28.0% ) and level V (20.0%). Maximum patients in spastic quadriplegia 42%, spastic hemiplegia 16%. The level of GMFCS was associated with nutritional status, as determined by anthropometry. Moderate and severe underweight significantly related with GMFC level IV and V. Moderate and severe stunted was significantly related with IV and V GMFC level. Moderate wasted was significantly related to GMFC levels III and IV. Mild malnutrition was significantly with GMFC level II and III. Severe malnutrition is significantly related to GMFC level IV and V. Conclusion: This study showed majority of cerebral palsy cases had severe functional gross motor disability and also severe grade of malnutrition. A significant association between severe grade of malnutrition and severe grade of motor disability was found. J Bangladesh Coll Phys Surg 2024; 42: 342-349

Analysis of perinatal outcomes in pregnant women with the resolution of placenta previa in the second trimester

Objective: To investigate perinatal outcomes of pregnant women with the resolution of placenta previa in the second trimester. Methods: This study was a retrospective cohort study, which included singleton pregnant women who received prenatal care and delivered at Peking University Third Hospital from January 1st 2019 to December 31st 2020. A total of 403 pregnant women diagnosed with placenta previa by ultrasound at 20-24 weeks of gestation and the placental position returned to normal before delivery were included in the study group, and 403 pregnant women with normal placental position matched 1∶1 were the control group. The primary outcome was postpartum hemorrhage rate, and secondary outcomes included postpartum bleeding volume, severe hemorrhage complications, blood transfusion, drug application, the application of instrument or surgical hemostasis measures, gestational week of delivery, and neonatal outcomes. The perinatal outcomes were analyzed by univariate and multivariate logistic regression methods. Results: (1) Compared with the control group, the incidence of postpartum hemorrhage [10.4% (42/403) vs 17.6% (71/403)], postpartum hemorrhage volume (median: 375 vs 400 ml), the proportion of postpartum hemorrhage≥500 ml [18.6% (75/403) vs 30.5% (123/403)], and the proportion of application of instrument or surgical hemostasis measures [1.7% (7/403) vs 4.5% (18/403)] in the study group were increased, and the differences were statistically significant (all P<0.05). Neonatal outcomes, including birth weight, small for gestational age, hospitalization in neonatal intensive care unit and incidence of neonatal asphyxia, were compared between the two groups, and there were no statistically significant differences (all P>0.05). (2) In pregnant women with vaginal delivery, the postpartum hemorrhage rate [31.7% (66/208) vs 17.5% (39/223)], postpartum hemorrhage volume (median: 390 vs 380 ml), the proportion of instrument or surgical hemostasis measures [3.8% (8/208) vs 0.4% (1/223)] of the study group were higher than those of the control group, and the differences were statistically significant (all P<0.05). There was no significant difference in the rate of postpartum hemorrhage between the study group and the control group who gave birth by cesarean setion (P=0.545), but the proportion of postpartum hemorrhage≥500 ml in the study group and the control group were 29.2% (57/195) and 20.0% (36/180), and the difference was statistically significant (P=0.039). (3) The results of multivariate analysis showed that compared with the control group, the risk of postpartum hemorrhage (aOR=2.042, 95%CI: 1.313-3.175), the application of drugs (aOR=1.684, 95%CI: 1.142-2.484) and the application of instruments or surgical hemostasis measures (aOR=2.696, 95%CI: 1.089-6.675) were significantly increased in the study group (all P<0.05). Among women who delivered vaginally, the risk of postpartum hemorrhage in the study group was 2.021 times greater than that in the control group (95%CI: 1.269-3.220; P=0.003). Conclusion: In women with placental previa in the second trimester of pregnancy, even if the placental position returns to normal before delivery, it is still a high risk factor for postpartum hemorrhage, especially in vaginal delivery.