OBJECTIVES

To determine if there is a difference in the prevalence of abnormal spirometry in female adult residents of OLBAC who have significant and nonsignificant exposure to biomass fuel smoke

METHODS

A convenience sample of 54 adult female residents of OLBAC in San Mateo, Rizal, will be recruited in this analytical cross-sectional study. After enrollment, they will undergo a single spirometry procedure to determine their lung function status. The primary data to be collected from the experimental groups are FEV1, FVC and FEV1/ FVC ratio. The data will undergo descriptive and inferential analysis, and the lung function variable will be analyzed with logistic regression to account for confounding variables.

EXPECTED RESULTS

The descriptive data analysis will determine the mean values of lung function parameters (FEV1 and FVC) where long exposures may lead to an abnormal FVC compared to short or no exposure. The results in the inferential analysis may indicate a negative association between length of biomass fuel exposure and percentage predicted FVC among the sample, suggesting that more prolonged exposure to biomass fuel increases the risk of impaired lung function.

CONCLUSION

 

Human ; Female ; Adult: 25-44 Yrs Old ; Young Adult: 19-24 Yrs Old ; Adult ; Biomass ; Cross-sectional Studies ; Female ; Logistic Models ; Regression (psychology) ; Smoke ; Volition ; World Health Organization ; Spirometry

INTRODUCTION

Neonatal sepsis is a significant cause of morbidity and mortality, particularly among preterm infants, and remains a pressing global health concern. Early-onset neonatal sepsis is particularly challenging to diagnose due to its nonspecific clinical presentation, necessitating effective and timely diagnostic tools to reduce adverse outcomes. Traditional methods, such as microbial cultures, are slow and often unavailable in resource-limited settings. This study aimed to develop a robust multivariable risk prediction model tailored to improve early detection of Early Onset Sepsis (EOS) among preterm infants in the Philippines.

METHODS

We conducted a retrospective analysis at a tertiary hospital in the Philippines using data from 1,354 preterm infants admitted between January 2019 and June 2024. Logistic regression models were employed, and predictors were selected through reverse stepwise elimination. Two scoring methods were developed: one based on beta coefficients divided by standard errors and another standardized to a total score of 100. The models were validated using Receiver Operator Characteristic curve analysis.

RESULTS

Version 1 of the scoring model demonstrated an Area Under the Curve (AUC) of 0.991, with a sensitivity of 90.91% and a specificity of 98.10%. Version 2 achieved an AUC of 0.999, with a sensitivity of 96.4% and a specificity of 92.44%.

CONCLUSIONS

The developed models provide a reliable, region specific tool for early detection of neonatal sepsis. Further validation across diverse populations and the integration of emerging diagnostic technologies, such as biomarkers and artificial intelligence, are warranted to enhance their applicability and accuracy.

Human ; Bacteria ; Infant: 1-23 Months ; Neonatal Sepsis ; Logistic Models ; Infant, Premature ; Philippines

OBJECTIVES: According to the World Health Organization (WHO), infertility rates have been steadily rising worldwide. Identifying risk factors for contrast agent reflux into the myometrium during hysterosalpingo-contrast sonography (HyCoSy) is of clinical significance in reducing this complication and improving infertility treatment. However, there is currently no standardized pre-evaluation method for predicting intra-myometrial contrast reflux, with clinical assessment often relying on physician experience and patient symptoms. This study aims to identify imaging risk factors for contrast agent reflux into the myometrium using four-dimensional (4D) HyCoSy and to construct a nomogram-based predictive model to assist in clinical decision-making. METHODS: A retrospective analysis was conducted on 1 274 infertile women who underwent 4D HyCoSy at the Women and Children's Hospital of Hunan and the the Third Xiangya Hospital of Central South University from January 1, 2020, to December 15, 2022. Patients were divided into a reflux group (n=234) and a non-reflux group (n=1 040) based on the presence of intra-myometrial contrast reflux. Univariate and multivariable Logistic regression analyses were used to identify significant predictors, which were then used to construct a nomogram model. Internal validation was performed using 500 bootstrap resamples. RESULTS: The age of the reflux group was significantly higher than that of the non-reflux group [(31.82±5.27) years vs (30.66±4.83) years, P=0.001 1]. Primary infertility was more common in the non-reflux group (50.96%), while secondary infertility dominated in the reflux group (76.50%), with 72.65% having a history of gynecological surgery (P<0.001). Abnormal menstrual volume and discomfort during the procedure were more common in the reflux group, while the non-reflux group tolerated higher contrast agent doses (P<0.001). Imaging differences included endometrial thickness, tubal wall smoothness, and peritoneal contrast dispersion, with the non-reflux group showing thicker endometrium and smoother, more patent tubes. The nomogram model yielded an area under the curve (AUC) of 0.854, indicating good predictive performance. The AUC of the decision curve analysis (DCA) for internal validation of the model was 0.737. When the threshold probability for contrast agent reflux into the myometrium ranged from 0.05 to 0.95, the maximum net benefit reached 0.18. The net benefit of applying the nomogram predictive model exceeded that of either full intervention or no intervention, indicating that the model demonstrates good clinical predictive performance. CONCLUSIONS The nomogram model, based on infertility type, endometrial thickness, contrast agent dose, and discomfort symptoms, effectively predicts intra-myometrial contrast agent reflux after 4D HyCoSy. It provides a valuable tool for clinicians to implement early preventive measures and reduce the risk of contrast leakage and associated complications.
Humans ; Female ; Nomograms ; Contrast Media/adverse effects* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Hysterosalpingography/methods* ; Infertility, Female/diagnostic imaging* ; Myometrium/diagnostic imaging* ; Risk Factors

OBJECTIVES: Multiple myeloma (MM) is a hematologically malignant clonal plasma cell disease. This study aims to explore the association between immunophenotypes and prognosis in patients with MM, to determine whether the expression of CD45 and CD200 is related to the prognosis of newly diagnosed MM (NDMM) patients, and to evaluate the significance of the combined expression of CD45 and CD200 in NDMM. METHODS: A total of 123 NDMM patients admitted to Shengjing Hospital of China Medical University from July 2015 to August 2019 were enrolled. Five key immunophenotypic markers (including CD38, CD138, CD45, CD56, and CD200) were screened through flow cytometry and identified using random forest analysis and univariate Cox regression analysis. Patients were divided into 3 groups: Group A, CD45 and CD200 double-positive; Group B, CD45 or CD200 single-positive; Group C, CD45 and CD200 double-negative. Kaplan-Meier curves were used to analyze overall survival (OS) and progression-free survival (PFS) across groups. Multivariate Cox regression was performed to evaluate prognostic factors, and a nomogram was constructed based on these results. RESULTS: The OS and PFS of single-positive groups for CD38, CD138, CD45, CD56, and CD200 were all shorter than those of their respective single-negative groups (all P<0.05). Significant differences were observed in OS (P<0.001) and PFS (P=0.001) among Groups A, B, and C. Group A had shorter OS and PFS (all P=0.001) compared to the Group B+C (cases from Group B and Group C were combined). CD45 and CD200 double-positive was an independent prognostic factor for NDMM [hazard ratio (HR)=2.178, 95% confidence interval (CI) 1.048 to 4.529; P=0.037]. The nomogram and calibration curves constructed from multivariate Cox regression analysis demonstrated good concordance (concordance index=0.706; 95% CI 0.661 to 0.751). CONCLUSIONS NDMM patients with double-positive expression of CD45 and CD200 have significantly shorter OS and PFS. Compared with the use of either marker alone, the combined assessment of CD45 and CD200 may provide better prognostic stratification for MM patients.
Humans ; Multiple Myeloma/metabolism* ; Male ; Female ; Middle Aged ; Antigens, CD/metabolism* ; Prognosis ; Leukocyte Common Antigens/metabolism* ; Aged ; Adult ; Immunophenotyping ; Nomograms ; Biomarkers, Tumor ; Clinical Relevance

OBJECTIVES: The Charlson comorbidity index reflects overall comorbidity burden and has been applied in cardiovascular medicine. However, its role in predicting in-hospital mortality in patients with acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VA) remains unclear. This study aims to evaluate the predictive value of the Charlson comorbidity index in this setting and to construct a nomogram model for early risk identification and individualized management to improve outcomes. METHODS: Using the open-access critical care database MIMIC-IV (Medical Information Mart for Intensive Care IV), we identified intensive care unit (ICU) patients diagnosed with AMI complicated by VA. Patients were grouped according to in-hospital survival. The predictive performance of the Charlson comorbidity index and other clinical variables for in-hospital mortality was analyzed. Key predictors were selected using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable Logistic regression. A nomogram model was constructed based on the regression results. Model performance was assessed using receiver operating characteristic (ROC) curves and calibration plots. RESULTS: A total of 1 492 patients with AMI and VA were included, of whom 340 died and 1 152 survived during hospitalization. Significant differences were observed between survivors and non-survivors in sex distribution, vital signs, comorbidity burden, organ function, and laboratory parameters (all P<0.05). The area under the curve (AUC) of the Charlson comorbidity index for predicting in-hospital mortality was 0.712 (95% CI 0.681 to 0.742), significantly higher than albumin, international normalized ratio (INR), hemoglobin, body temperature, and platelet count (all P<0.001), but comparable to Sequential Organ Failure Assessment (SOFA) score (P>0.05). LASSO regression identified seven key predictors: the Charlson comorbidity index (quartile groups: T1, <6; T2, ≥6-<7; T3, ≥7-<9; T4, ≥9), ventricular fibrillation, age, systolic blood pressure, respiratory rate, body temperature, and SOFA score. Multivariate Logistic regression showed that compared with T1, mortality risk increased significantly in T2 (OR=1.996, 95% CI 1.135 to 3.486, P=0.016), T3 (OR=3.386, 95% CI 2.192 to 5.302, P<0.001), and T4 (OR=5.679, 95% CI 3.711 to 8.842, P<0.001). Age (OR=1.056, P<0.001), respiratory rate (OR=1.069, P<0.001), SOFA score (OR=1.223, P<0.001), and ventricular fibrillation (OR=2.174, P<0.001) were independent risk factors, while systolic blood pressure (OR=0.984, P<0.001) and body temperature (OR=0.648, P<0.001) were protective factors. The nomogram incorporating these predictors achieved an AUC of 0.849 (95% CI 0.826 to 0.871) with high discrimination and good calibration (mean absolute error=0.014). CONCLUSIONS The Charlson comorbidity index is an independent predictor of in-hospital mortality in AMI patients complicated by VA, with performance comparable to the SOFA score. The nomogram model based on the Charlson comorbidity index and additional clinical variables effectively estimates mortality risk and provides a valuable reference for clinical decision-making.
Humans ; Nomograms ; Hospital Mortality ; Myocardial Infarction/complications* ; Male ; Female ; Comorbidity ; Middle Aged ; Aged ; Arrhythmias, Cardiac/complications* ; ROC Curve ; Intensive Care Units

OBJECTIVES: Global epidemiological data indicate that 20% to 30% of intensive care unit (ICU) sepsis patients progress to deep vein thrombosis (DVT) due to coagulopathy, with an associated mortality rate of 25% to 40%. Existing prognostic tools have limitations. This study aims to develop and validate nomogram and machine learning models to predict in-hospital mortality in sepsis patients with DVT and assess their clinical applicability. METHODS: This multicenter retrospective study drew on data from the Medical Information Mart for Intensive Care IV (MIMIC-IV; n=2 235), the eICU Collaborative Research Database (eICU-CRD; n=1 274), and the Patient Admission Dataset from the ICU of Third Xiangya Hospital, Central South University (CSU-XYS-ICU; n=107). MIMIC-IV was split into a training set (n=1 584) and internal validation set (n=651), with the remaining datasets used for external validation. Predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and Bayesian Information Criterion (BIC), and a nomogram model was constructed. An extreme gradient boosting (XGBoost) algorithm was used to build the machine learning model. Model performance was assessed by the concordance index (C-index), calibration curves, Brier score, decision curve analysis (DCA), and net reclassification improvement index (NRI). RESULTS: Five key predictors, age [odds ratio (OR)=1.02, 95% CI 1.01 to 1.03, P<0.001], minimum activated partial thromboplastin (APTT; OR=1.09, 95% CI 1.08 to 1.11, P<0.001), maximum APTT (OR=1.01, 95% CI 1.00 to 1.01, P<0.001), maximum lactate (OR=1.56, 95% CI 1.39 to 1.75, P<0.001), and maximum serum creatinine (OR=2.03, 95% CI 1.79 to 2.30, P<0.001), were included in the nomogram. The model showed robust performance in internal validation (C-index=0.845, 95% CI 0.811 to 0.879) and external validation (eICU-CRD: C-index=0.827, 95% CI 0.800 to 0.854; CSU-XYS-ICU: C-index=0.779, 95% CI 0.687 to 0.871). Calibration curves indicated good agreement between predicted and observed outcomes (Brier score<0.25), and DCA confirmed clinical benefit. The XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.982 (95% CI 0.969 to 0.985) in the training set, but performance declined in external validation (eICU-CRD, AUC=0.825, 95% CI 0.817 to 0.861; CSU-XYS-ICU, AUC=0.766, 95% CI 0.700 to 0.873), though it remained above clinical thresholds. Net reclassification improvement was slightly lower for XGBoost compared with the nomogram (NRI=0.58). CONCLUSIONS Both the nomogram and XGBoost models effectively predict in-hospital mortality in sepsis patients with DVT. However, the nomogram offers superior generalizability and clinical usability. Its visual scoring system provides a quantitative tool for identifying high-risk patients and implementing individualized interventions.
Humans ; Sepsis/complications* ; Machine Learning ; Nomograms ; Venous Thrombosis/complications* ; Retrospective Studies ; Hospital Mortality ; Male ; Female ; Middle Aged ; Aged ; Intensive Care Units ; Prognosis ; Bayes Theorem

OBJECTIVES: Cardiovascular disease (CVD) poses a major threat to global health. Evaluating atherosclerosis in asymptomatic individuals can help identify those at high risk of CVD. This study aims to establish an individualized nomogram prediction model to estimate the risk of vascular plaque formation in asymptomatic individuals. METHODS: A total of 5 655 participants who underwent CVD screening at the Health Management Center of The Third Xiangya Hospital, Central South University, between January 2022 and June 2024 we retrospectively enrolled. Using simple random sampling, participants were divided into a training set (n=4 524) and a validation set (n=1 131) in an 8꞉2 ratio. Demographic and clinical data were collected and compared between groups. Multivariate logistic regression analysis was used to identify independent factors associated with vascular plaques and to construct a nomogram prediction model. The predictive performance and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow goodness-of-fit test, calibration plots, and decision curve analysis (DCA). RESULTS: The mean age of participants was 52 years old. There were 3 400 males (60.12%). The overall detection rate of vascular plaque in the screening population was 49.87% (2 820/5 655). No statistically significant differences were observed in clinical indicators between the training and validation sets (all P>0.05). Multivariate Logistic regression analysis identified age, systolic blood pressure, high-density lipoprotein (HDL), low-density lipoprotein (LDL), lipoprotein(a), male sex, smoking history, hypertension history, and diabetes history as independent risk factors for vascular plaque in asymptomatic individuals (all P<0.05). The area under the curve (AUC) of the nomogram model for predicting vascular plaque risk were 0.778 (95% CI 0.765 to 0.791, P<0.001) in the training set and 0.760 (95% CI 0.732 to 0.787, P<0.001) in the validation set. The Hosmer-Lemeshow goodness-of-fit test indicated good model calibration (training set: P=0.628; validation set: P=0.561). The calibration curve plotted using the Bootstrap method demonstrated good agreement between predicted probabilities and actual probabilities. DCA showed that the nomogram provided a clinical net benefit for predicting vascular plaque risk when the threshold probability ranged from 0.02 to 0.99. CONCLUSIONS The nomogram prediction model for vascular plaque risk, constructed using readily available and cost-effective physical examination indicators, exhibited good predictive performance. This model can assist in the early identification and intervention of asymptomatic individuals at high risk for cardiovascular disease.
Humans ; Male ; Middle Aged ; Female ; Nomograms ; Retrospective Studies ; Risk Factors ; Plaque, Atherosclerotic/diagnosis* ; Aged ; Adult ; Physical Examination ; Logistic Models ; Cardiovascular Diseases/epidemiology* ; ROC Curve

Objective:Inflammation has been confirmed to play an important role in the occurrence and development of sudden sensorineural hearing loss（SSNHL）, and the neutrophil-to-lymphocyte ratio（NLR） is a biomarker positively correlated with the degree of inflammation. This study aims to identify the difference in serum NLR between patients with SSNHL and normal population, and to evaluate the predictive efficacy of NLR for the occurrence and prognosis of SSNHL, thereby guiding the clinical diagnosis and treatment of SSNHL. Methods:In this study, 96 patients diagnosed with SSNHL admitted to our department from January 2023 to March 2024 and 96 patients diagnosed with vocal cord polyps admitted to our department during the same period were recruited as a control group. Multivariate Logistic regression was used to evaluate independent related factors, and a nomogram was constructed to predict the probability of SSNHL. The receiver operating characteristic（ROC） curve and calibration curve were used to evaluate the accuracy of prediction. Results:Multivariate logistic regression analysis showed that a high level NLR（OR2.215; 95%CI1.597-3.073; P<0.001） were independently associated with the presence of SSNHL. High age（OR1.036; 95%CI1.009-1.067; P=0.012）, high FIB（OR2.35; 95%CI1.176-4.960; P=0.019） were the risk factor for SSNHL. Incorporating these 3 factors, a forest plot and a nomogram were generated. The ROC curve, nomogram and calibration curve showed that the model had good clinical practicability. A low NLR（OR0.598; 95%CI0.439-0.816; P<0.001） was significantly associated with a favorable prognosis of SSNHL. Conclusion:Elevated NLR can serve as an promising biomarker for assessing the risk of SSNHL. The nomograms calculation model may be utilized as a tool to estimate the probability of SSNHL. Low level NLR is significantly associated with a good prognosis of SSNHL.
Humans ; Neutrophils ; Female ; Male ; Lymphocytes ; Hearing Loss, Sensorineural/blood* ; Hearing Loss, Sudden/diagnosis* ; Middle Aged ; Prognosis ; Nomograms ; ROC Curve ; Adult ; Logistic Models ; Biomarkers/blood* ; Lymphocyte Count ; Inflammation/blood* ; Clinical Relevance

Objective:To summarize and analyze the clinical characteristics of patients with sudden sensorineural hearing loss（SSHL） accompanying diabetes mellitus, to explore whether diabetes affects the treatment outcomes during hospitalization, and to identify the factors influencing the efficacy of SSHL patients with diabetes. Methods:A retrospective analysis was conducted on clinical data from 939 patients with SSHL. The baseline characteristics, and onset conditions of the diabetes group（79 cases） and the non-diabetes group（860 cases） were compared. Propensity score matching（PSM） was applied in a 1︰ 2 ratio to match initial hearing levels with baseline characteristics such as age, sex, and BMI, resulting in 73 diabetes cases and 144 non-diabetes cases for treatment efficacy comparison. For the analysis of prognostic factors, a logistic regression model was established based on the treatment outcomes of 217 patients with SSHL. Results:The proportion of SSHL patients accompanying diabetes was 8.40%（79/939）. Compared to non-diabetic patients, those with diabetes were older（median age of 53 years in the diabetes group and 39 years in the non-diabetes group） and had a higher proportion of hypertension（43.04% vs 12.67%）, with significant difference observed（P<0.05）. After PSM, the treatment efficacy during hospitalization was better in the diabetes group than in the non-diabetes group（58.90% vs 47.92%）, although the difference was not statistically significant（P>0.05）. The prognosis of patients with SSNHL accompanied by diabetes was analyzed using a multivariate logistic regression model that included age, HDL-C, and INR as variables; however, no statistically significant differences were found（P>0.05）. Conclusion:Patients with SSHL accompanying diabetes are generally older with a higher incidence of hypertension. The presence of diabetes does not affect the treatment outcomes during hospitalization.
Humans ; Propensity Score ; Retrospective Studies ; Hearing Loss, Sensorineural/therapy* ; Hearing Loss, Sudden/therapy* ; Middle Aged ; Diabetes Mellitus ; Male ; Female ; Prognosis ; Adult ; Logistic Models ; Diabetes Complications ; Aged ; Treatment Outcome

Objective:To explore the factors influencing adherence to voice therapy among patients with voice disorders in China. Methods:Patients with voice disorders who visited the Voice Therapy Center at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, from February to May 2022 were enrolled in the study. Adherence was assessed using the URICA-Voice scale, while influencing factors were assessed through the Voice Handicap Index（VHI） scale and a general information questionnaire. Correlation analysis was conducted using univariate and multivariate logistic regression analysis. Results:A total of 247 patients were included in the study, comprising 57 males（23.08%） and 190 females（76.92%）. The results revealed that: ①Female patients demonstrated higher likelihood of being in the contemplation stage（OR=0.22） compared to males. ②Patients with a monthly family income per capita>6 000 yuan were more likely to be in the contemplation stage than those with<3 000 yuan with an OR = 13.94. ③High vocal-demand occupations increased contemplation stage probability（OR=7.70） compared to moderate-demand occupations. ④Residence within 30-minute commute predicted action/maintenance stages（OR=7.14） versus≥60-minute commute. ⑤Patients whose occupations had high voice demands were more likely to be in the action and maintenance stages than those with average voice demands, with an OR of 16.20. Conclusion:Gender, monthly family income per capita, occupational voice demands, and distance to the hospital significantly impact the URICA-Voice compliance stages of patients. Patients who are female, have higher family income, have occupations with high voice demands, and live closer to the hospital exhibit higher compliance with voice training.
Humans ; Male ; Female ; Voice Disorders/therapy* ; Patient Compliance ; Voice Training ; Surveys and Questionnaires ; China ; Middle Aged ; Adult ; Voice Quality ; Logistic Models ; Aged