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BACKGROUND: There is a need for activation criteria that reflect the different factors affecting rural trauma patients. OBJECTIVE: To develop effective activation criteria for a rural trauma center among adults, incorporating variables specific to the geography, mechanisms of injury, and population served. METHODS: This is a single-center, retrospective cohort study conducted from (23 years) January 1, 2000, to July 31, 2023. The data collected patient demographics, injury details, morbidity, and preexisting comorbidity. This research included all adult (≥15 years) true Level I trauma activations defined as an injury severity score > 25 and met the need for trauma intervention criteria. The patients were grouped into adult and elderly categories. The analysis utilized a logistic regression model with the outcome of a true Level I trauma activation. RESULTS: A total of 19,480 patients were included in the sample; 2,858 (14.6%) met the Level I activation criteria. Elderly Level I activation included assault, pedestrian struck, multiple pelvic fractures, traumatic pneumo/hemothorax, mediastinal fracture, sternum fracture, and flail rib fracture. CONCLUSION: Using the findings of the logistic regression model, this center has made more robust activation guidelines adapted to its rural population.

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INTRODUCTION: Vehicles play an important role in pedestrian injury risk in crashes. This study examined the association between vehicle front-end geometry and the risk of fatal pedestrian injuries in motor vehicle crashes. METHOD: A total of 17,897 police-reported crashes involving a single passenger vehicle and a single pedestrian in seven states were used in the analysis. Front-end profile parameters of vehicles (2,958 vehicle makes, series, and model years) involved in these crashes were measured from vehicle profile photos, including hood leading edge height, bumper lead angle, hood length, hood angle, and windshield angle. We defined a front-end-shape indicator based on the hood leading edge height and bumper lead angle. Logistic regression analysis evaluated the effects of these parameters on the risk that a pedestrian was fatally injured in a single-vehicle crash. RESULTS: Vehicles with tall and blunt, tall and sloped, and medium-height and blunt front ends were associated with significant increases of 43.6%, 45.4%, and 25.6% in pedestrian fatality risk, respectively, when compared with low and sloped front ends. There was a significant 25.1% increase in the risk if a hood was relatively flat as defined in this study. A relatively long hood and a relatively large windshield angle were associated with 5.9% and 10.7% increases in the risk, respectively, but the increases were not significant. CONCLUSIONS: Vehicle front-end profiles that were significantly associated with increased pedestrian fatal injury risk were identified. PRACTICAL APPLICATIONS: Automakers can make vehicles more pedestrian friendly by designing vehicle front ends that are lower and more sloped. The National Highway Traffic Safety Administration (NHTSA) can consider evaluations that account for the growing hood heights and blunt front ends of the vehicle fleet in the New Car Assessment Program or regulation.

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In this study, we applied a multivariate logistic regression model to identify deforested areas and evaluate the current effects on environmental variables in the Brazilian state of Rondônia, located in the southwestern Amazon region using data from the MODIS/Terra sensor. The variables albedo, temperature, evapotranspiration, vegetation index, and gross primary productivity were analyzed from 2000 to 2022, with surface type data from the PRODES project as the dependent variable. The accuracy of the models was evaluated by the parameters area under the curve (AUC), pseudo R2, and Akaike information criterion, in addition to statistical tests. The results indicated that deforested areas had higher albedo (25%) and higher surface temperatures (3.2 °C) compared to forested areas. There was a significant reduction of the EVI (16%), indicating water stress, and a decrease in GPP (18%) and ETr (23%) due to the loss of plant biomass. The most precise model (91.6%) included only surface temperature and albedo, providing important information about the environmental impacts of deforestation in humid tropical regions.

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OBJECTIVE: To explore the optimal pulse oxygen saturation (SpO2) range during hospitalization for patients with sepsis. METHODS: A case-control study design was employed. Demographic information, vital signs, comorbidities, laboratory parameters, critical illness scores, clinical treatment information, and clinical outcomes of sepsis patients were extracted from the Medical Information Mart for Intensive Care- IV (MIMIC- IV). A generalized additive model (GAM) combined with a Loess smoothing function was employed to analyze and visualize the nonlinear relationship between SpO2 levels during hospitalization and in-hospital all-cause mortality. The optimal range of SpO2 was determined, and Logistic regression model along with Kaplan-Meier curve were utilized to validate the association between the determined range of SpO2 and in-hospital all-cause mortality. RESULTS: A total of 5 937 patients met the inclusion criteria, among whom 1 191 (20.1%) died during hospitalization. GAM analysis revealed a nonlinear and U-shaped relationship between SpO2 levels and in-hospital all-cause mortality among sepsis patients during hospitalization. Multivariable Logistic regression analysis further confirmed that patients with SpO2 levels between 0.96 and 0.98 during hospitalization had a decreased mortality compared to those with SpO2 < 0.96 [hypoxia group; odds ratio (OR) = 2.659, 95% confidence interval (95%CI) was 2.190-3.229, P < 0.001] and SpO2 > 0.98 (hyperoxia group; OR = 1.594, 95%CI was 1.337-1.900, P < 0.001). Kaplan-Meier survival curve showed that patients with SpO2 between 0.96 and 0.98 during hospitalization had a higher probability of survival than those patient with SpO2 < 0.96 and SpO2 > 0.98 (Log-Rank test: χ 2 = 113.400, P < 0.001). Sensitivity analyses demonstrated that, with the exception of subgroups with smaller sample sizes, across the strata of age, gender, body mass index (BMI), admission type, race, heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, respiratory rate, body temperature, myocardial infarction, congestive heart failure, cerebrovascular disease, chronic liver disease, diabetes mellitus, sequential organ failure assessment (SOFA), simplified acute physiology score II (SAPS II), systemic inflammatory response syndrome score (SIRS), and Glasgow coma score (GCS), the mortality of patients with SpO2 between 0.96 and 0.98 was significantly lower than those of patients with SpO2 < 0.96 and SpO2 > 0.98. CONCLUSIONS: During hospitalization, the level of SpO2 among sepsis patients exhibits a U-shaped relationship with in-hospital all-cause mortality, indicating that heightened and diminished oxygen levels are both associated with increased mortality risk. The optimal SpO2 range is determined to be between 0.96 and 0.98.

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OBJECTIVE: To construct and validate a nomogram model for predicting sepsis-associated acute kidney injury (SA-AKI) risk in intensive care unit (ICU) patients. METHODS: A retrospective cohort study was conducted. Adult sepsis patients admitted to the department of ICU of the 940th Hospital of Joint Logistic Support Force of PLA from January 2017 to December 2022 were enrolled. Demographic characteristics, clinical data within 24 hours after admission to ICU diagnosis, and clinical outcomes were collected. Patients were divided into training set and validation set according to a 7 : 3 ratio. According to the consensus report of the 28th Acute Disease Quality Initiative Working Group (ADQI 28), the data were analyzed with serum creatinine as the parameter and AKI occurrence 7 days after sepsis diagnosis as the outcome. Lasso regression analysis and univariate and multivariate Logistic regression analysis were performed to construct the nomogram prediction model for SA-AKI. The discrimination and accuracy of the model were evaluated by the Hosmer-Lemeshow test, receiver operator characteristic curve (ROC curve), decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: A total of 247 sepsis patients were enrolled, 184 patients developed SA-AKI (74.49%). The number of AKI patients in the training and validation sets were 130 (75.58%) and 54 (72.00%), respectively. After Lasso regression analysis and univariate and multivariate Logistic regression analysis, four independent predictive factors related to the occurrence of SA-AKI were selected, namely procalcitonin (PCT), prothrombin activity (PTA), platelet distribution width (PDW), and uric acid (UA) were significantly associated with the onset of SA-AKI, the odds ratio (OR) and 95% confidence interval (95%CI) was 1.03 (1.01-1.05), 0.97 (0.55-0.99), 2.68 (1.21-5.96), 1.01 (1.00-1.01), all P < 0.05, respectively. A nomogram model was constructed using the above four variables. ROC curve analysis showed that the area under the curve (AUC) was 0.869 (95%CI was 0.870-0.930) in the training set and 0.710 (95%CI was 0.588-0.832) in the validation set. The P-values of the Hosmer-Lemeshow test were 0.384 and 0.294, respectively. In the training set, with an optimal cut-off value of 0.760, a sensitivity of 77.5% and specificity of 88.1% were achieved. Both DCA and CIC plots demonstrated the model's good clinical utility. CONCLUSIONS: A nomogram model based on clinical indicators of sepsis patients admitted to the ICU within 24 hours could be used to predict the risk of SA-AKI, which would be beneficial for early identification and treatment on SA-AKI.

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OBJECTIVE: To estimate the prevalence of inappropriate use of prophylaxis to prevent venous thromboembolism (VTE) in patients with medical diseases admitted to hospital from the emergency department. To explore variables associated with inappropriate thromboprophylaxis. METHODS: Prospective multicenter cohort study in 15 hospital emergency departments. We included patients admitted for a medical condition during 7 days in the first quarter of 2022. We assessed risk for VTE with the Padua Prediction Score (PPS). Inappropriate thromboprophylaxis was defined by failure to prescribe prophylaxis in patients with a high-risk PPS assessment unless there were absolute contraindications (active bleeding or severe thrombopenia) or, alternatively, the prescription of prophylaxis in patients with a low-risk PPS assessment or absolute contraindications. A logistic regression model was adjusted for risk level to identify variables associated with inappropriate use of thromboprophylaxis. RESULTS: Of a total of 630 patients included, 450 (71.4%) had PPS scores indicating high risk for VTE; 180 patients were at low risk. Thromboprophylaxis was inappropriate in 248 patients (39.4%): 165 high-risk patients who received no prophylaxis, 82 low-risk patients who were nonetheless treated, and 1 patient who was treated in spite of severe thrombopenia. Odds ratios (ORs) revealed that the variables associated with inappropriate use of thromboprophylaxis were trauma or recent surgery (OR, 5.53; 95% CI, 1.58-19.34), presence of factors indicating risk for bleeding (OR, 2.61; 95% CI, 1.44-4.73), and hospital admission for either urinary tract infection (OR, 2.29; 95% CI, 1.07-4.87) or gastrointestinal disease (OR, 4.30; 95% CI, 1.71-10.85). CONCLUSIONS: The inappropriate use of thromboprophylaxis in Spanish emergency departments is high and associated with certain clinical characteristics.

OBJETIVO: Evaluar la inadecuación de la tromboprofilaxis farmacológica, según la escala Padua (PPS), para prevenir la enfermedad tromboembólica venosa (ETV) entre los pacientes que ingresan desde el servicio de urgencias hospitalario (SUH) por patología médica, así como las variables asociadas a su uso inadecuado. METODO: Estudio de cohortes, prospectivo, multicéntrico donde participaron 15 SUH. Se incluyeron los pacientes atendidos que requirieron ingreso por enfermedad médica durante 7 días del primer trimestre de 2022. La inadecuación de la tromboprofilaxis farmacológica se definió como la no utilización en pacientes clasificados por PPS de alto riesgo sin contraindicaciones absolutas para su uso (hemorragia activa o trombopenia grave) o su utilización en pacientes de riesgo bajo o con contraindicaciones absolutas. Se ajustó, para cada grupo de riesgo, un modelo de regresión logística para identificar las variables asociadas a la inadecuación. RESULTADOS: Se incluyeron 630 pacientes, 450 (71,4%) tenían riesgo alto y 180 (28,6%) riesgo bajo para ETV según la PPS. De ellos, la tromboprofilaxis fue inadecuada en 248 pacientes (39,4%) (165 tenían riesgo alto pero no recibieron tromboprofilaxis, 1 la recibió teniendo trombopenia grave y 82 tenían riesgo bajo pero recibieron tromboprofilaxis). Las variables asociadas con la inadecuación en pacientes de alto riesgo fueron trauma o cirugía recientes con odds ratio (OR) de OR 5,53 (IC 95%: 1,58-19,34), presencia de factores de riesgo hemorrágico con OR de 2,61 (IC 95%: 1,44-4,73), e infección del tracto urinario con OR de 2,29 (IC 95%: 1,07-4,87) y enfermedad gastrointestinal con OR de 4,30 (IC 95%: 1,71-10,85) como motivos de ingreso. CONCLUSIONES: En los SUH españoles, el uso inadecuado de la tromboprofilaxis farmacológica es elevado. Algunas características clínicas se asocian al uso inadecuado de dicha tromboprofilaxis.

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INTRODUCTION: It is unclear whether the risk of suicide differs among individuals with only physical health condition, those with only mental health conditions, and those with both types of conditions (multimorbidity) and how emotional social support modifies these associations. This study aimed to examine differences in the association of suicidal ideation with the presence of only physical health conditions, only mental health conditions, and multimorbidity and the modifying role of emotional social support in these associations. METHODS: A cross-sectional survey was conducted between August and September 2023 in a Japanese rural town to collect data. The exposure variable was the health condition, and it was classified into 4 groups: disease-free, only physical health conditions, only mental health conditions, and multimorbidity. The outcome variable was suicidal ideation. The data collected were analyzed using multivariate logistic regression analysis and stratified analysis. RESULTS: Suicidal ideation was found to have a significant positive association with the presence of only mental health conditions and multimorbidity. These associations remained unchanged in the absence of emotional social support. However, the odds ratio for the only mental health conditions group decreased in the presence of emotional social support, while the odds ratio for the multimorbidity group remained significantly higher. CONCLUSIONS: Suicidal ideation is positively associated with the presence of only mental health conditions and multimorbidity, but emotional social support modifies only the association between suicidal ideation and the presence of only mental health conditions. These results suggest that it may be important to identify the type of social support one needs based on one's health condition to prevent suicide.

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Accurate prediction of coronary artery disease (CAD) is crucial for enabling early clinical diagnosis and tailoring personalized treatment options. This study attempts to construct a machine learning (ML) model for predicting CAD risk and further elucidate the complex nonlinear interactions between the disease and its risk factors. Employing the Z-Alizadeh Sani dataset, which includes records of 303 patients, univariate analysis and the Boruta algorithm were applied for feature selection, and nine different ML techniques were subsequently deployed to produce predictive models. To elucidate the intricate pathogenesis of CAD, this study harnessed the analytical capabilities of Shapley values, alongside the use of generalized additive models for curve fitting, to probe into the nonlinear interactions between the disease and its associated risk factors. Furthermore, we implemented a piecewise linear regression model to precisely pinpoint inflection points within these complex nonlinear dynamics. The findings of this investigation reveal that logistic regression (LR) stands out as the preeminent predictive model, demonstrating remarkable efficacy, it achieved an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.981 (95% CI: 0.952-1), and an Area Under the Precision-Recall Curve (AUPRC) of 0.993. The utilization of the 14 most pivotal features in constructing a dynamic nomogram. Analysis of the Shapley smoothing curves uncovered distinctive "S"-shaped and "C"-shaped relationships linking age and triglycerides to CAD, respectively. In summary, machine learning models could provide valuable insights for the early diagnosis of CAD. The SHAP method may provide a personalized risk assessment of the relationship between CAD and its risk factors.

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BACKGROUND: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction. METHODS: We explore four imputation methods to address missing data and ME: single imputation (SI), multiple imputation under missing at random (MI-MAR), multiple imputation with calibration procedure (MI-ME), and multiple imputation under missing not at random (MI-MNAR). We compare four machine-learning models (Decision Tree, Naive Bayes, Random Forest, and Xgboost) and three statistical models (logistic regression, stepwise logistic regression, and least absolute shrinkage and selection operator logistic regression) in myopia risk prediction. We apply these models to the Shanghai Jinshan Myopia Cohort Study and also conduct a simulation study to investigate the impact of missing mechanisms, the degree of ME, and the importance of predictors on model performance. Model performance is evaluated using the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). RESULTS: Our findings indicate that in scenarios with missing data and ME, using MI-ME in combination with logistic regression yields the best prediction results. In scenarios without ME, employing MI-MAR to handle missing data outperforms SI regardless of the missing mechanisms. When ME has a greater impact on prediction than missing data, the relative advantage of MI-MAR diminishes, and MI-ME becomes more superior. Furthermore, our results demonstrate that statistical models exhibit better prediction performance than machine-learning models. CONCLUSION: MI-ME emerges as a reliable method for handling missing data and ME in important predictors for early-onset myopia risk prediction.

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Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min-Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635-0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC.

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BACKGROUND: Uptake of HIV early infant diagnosis (HEID) among HIV-exposed infants is the key to timely initiation of Antiretroviral Treatment (ART). However, despite the availability of HEID services in Tanzania, its uptake is low. We aimed to determine predictors of mothers living with HIV' with HIV-exposed infants' uptake of HEID services in Iringa District, Tanzania. METHODS: A health facility-based cross-sectional study was conducted in Iringa District from May to June 2023. Mothers with HIV-exposed infants were recruited in the study through a multistage sampling technique and interviewed using pre-tested structured questions. Logistic regression analysis was employed to determine potential predictors of HEID uptake. RESULTS: A total of 309 mothers with HIV-exposed infants participated in the study. About 78.3% of the HIV-exposed infants had initial DNA PCR for HEID within 6 weeks of age and 86.1% within 8 weeks. Most mothers had high perceived benefits on uptake of HEID with a mean score of 4.3, high perceived self-efficacy with a mean score of 3.8 and 2.7 perceived risk of HIV infection on their HIV-exposed infants on the 5 scale Likert scale with 5 showing the highest perceived benefit, self-efficacy and risk. High perceived self-efficacy and being a businesswoman were the predictors of uptake of HEID. The odds of self-efficacy on the uptake of HEID by 2.4 times (aOR 2.4 95% CI 1.6-3.2) within 6 weeks of age and 1.9 (aOR 1.9 95% CI 1.3-2.7) within 8 weeks. The odds of being a businesswoman were 0.4 for 6 weeks and 0.3 for 8 weeks (aOR 0.4 95% CI 0.2-0.8) and (aOR 0.3 95% CI 0.1-0.8) respectively. CONCLUSION: Over three-quarters of the HIV-exposed infants had initial DNA PCR for HEID testing as recommended. Perceived self-efficacy was the main factor influencing HEID uptake. These findings highlight the need for strengthening HIV-positive mother's self-efficacy for improved uptake of HEID services.

Predictors of mothers living with HIV' uptake of HIV early infant diagnosis services in Iringa District, TanzaniaThis study aimed to find out the factors associated with the uptake of HIV early infant diagnosis (HEID) services among mothers living with HIV in Iringa District, Tanzania. The uptake of HEID in Tanzania is still below the 95% national and global target of ending AIDS as a public health by 2030 We employed a cross-sectional study design and collected data from May to June 2023 to determine predictors of mothers with HIV-exposed infants' uptake of HEID in Iringa District, Tanzania. The analysis was done by descriptive statistics and logistic regression analysis. A total of 309 mothers with HIV-exposed infants participated in the study. About 78.3% of the HIV-exposed infants had initial DNA PCR for HEID within 6 weeks of age and 86.1% within 8 weeks. Most mothers had high perceived benefits on uptake of HEID with a mean score of 4.3, high perceived self-efficacy with a mean score of 3.8 and 2.7 perceived risk of HIV infection on their HIV-exposed infants. High perceived self-efficacy was positively associated These findings highlight the need for strengthening HIV-positive mother's self-efficacy for improved uptake of HEID services.

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Objective To identify the risk factors of patients with frequent acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and construct a prediction model based on the clinical data,providing a theoretical basis for the clinical prevention and treatment. Methods A total of 25 638 COPD patients admitted to the Department of Respiratory and Critical Care Medicine,the Third People's Hospital of Chengdu from January 1,2013 to May 1,2023 were selected.Among them,11 315 patients were included according to the inclusion and exclusion criteria,and their clinical characteristics were analyzed.Multivariate Logistic regression was carried out to identify the risk factors for frequent AECOPD.A nomogram model was utilized to quantify the risk of acute exacerbation,and the performance of the prediction model was assessed based on the area under the receiver operating characteristic (ROC) curve. Results In the patients with frequent AECOPD,male percentage (P<0.001),age (P<0.001),urban residence (P<0.001),smoking (P<0.001),length of stay (P<0.001),total cost (P<0.001),antibiotic cost (P<0.001),diabetes (P=0.003),respiratory failure (P<0.001),heart disease (P<0.001),application of systemic glucocorticoids (P<0.001),white blood cell count (P<0.001),neutrophil percentage (P<0.001),C-reactive protein (P<0.001),total cholesterol (P<0.001),and brain natriuretic peptide (BNP) (P<0.001) were all higher than those in the patients with infrequent AECOPD.Multivariate Logistic regression analysis revealed that age,urban residence,smoking,diabetes,heart disease,Pseudomonas aeruginosa infection,application of systemic glucocorticoids,antibiotics,respiratory failure,and elevated white blood cell count,total cholesterol,and BNP were independent risk factors for hospitalization due to frequent AECOPD.A nomogram model of hospitalization due to frequent AECOPD was constructed according to risk factors.The ROC curve was established to evaluate the performance of the model,which showed the area under the ROC curve of 0.899 (95%CI=0.892-0.905),the sensitivity of 85.30%,and the specificity of 79.80%. Conclusion Frequent AECOPD is associated with smoking,heart disease,application of systemic glucocorticoids,Pseudomonas aeruginosa infection,age,low body mass index,and elevated BNP.Predicting the risks of hospitalization due to frequent AECOPD by the established model can provide theoretical support for the treatment and risk factor management of the patients.

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Objective: To explore the risk factors of neck work-related musculoskeletal disorders (WMSDs) among automobile manufacturing enterprise workers, and construct the risk prediction model. Methods: In May 2022, a cluster convenience sampling method was used to selet all front-line workers from an automobile manufacturing factory in Xiangyang City as the research objects. And a questionnaire survey was conducted using the modified Musculoskeletal Disorders Questionnaire to analyze the occurrence and exposure to risk factors of neck WMSDs. Logistic regression was used to analyze the influencing factors of workers' neck WMSDs symptoms, and Nomogram column charts was used to construct the risk prediction model. The accuracy of the model was evaluated by the receiver operating characteristic (ROC) curve, the Bootstrap resampling method was used to verify the model, Hosmer-Lemeshow goodness of fit test was used to evaluate the model, and the Calibration curve was drawn. Results: A total of 1783 workers were surveyed, and the incidence of neck WMSDs symptoms was 24.8% (442/1783). Univariate logistic regression showed that age, female, smoking, working in uncomfortable postures, repetitive head movement, feeling constantly stressed at work, and completing conflicting tasks in work could increase the risk of neck WMSDs symptoms in automobile manufacturing enterprise workers (OR=1.37, 95%CI: 1.16-1.62; OR=2.85, 95%CI: 1.56-5.20; OR=1.50, 95%CI: 1.18-1.91; OR=1.18, 95%CI: 1.02-1.37; OR=1.34, 95%CI: 1.04-1.72; OR=1.62, 95%CI: 1.21-2.17; OR=1.48, 95%CI: 1.13-1.92; P<0.05). While adequate rest time could reduce the risk of neck WMSDs symptoms (OR=0.56, 95%CI: 0.52-0.86, P<0.05). The risk prediction model of neck WMSDs of workers in automobile manutacturing factory had good prediction efficiency, and the area under the ROC curve was 0.72 (95%CI: 0.70-0.75, P<0.001) . Conclusion: The occurrence of neck WMSDs symptoms of workers in automobile manufacturing factory is relatively high. The risk prediction model constructed in this study can play a certain auxiliary role in predicting neck WMSDs symptoms of workers in automobile manufacturing enterprise workers.

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Objective: To understand the occupational stress and mental health status of hospital infection prevention and control practitioner (HIPCPs) in medical institutions, and analyze their main influencing factors. Methods: In November 2021, 550 nosocomial infection managers in Tianjin were randomly selected to conduct a questionnaire survey using the Concise Occupational Stress Questionnaire, Depression Screening Scale (PHQ-9) and Self-Rating Anxiety Scale (SAS). 497 valid questionnaires were obtained, and the total recovery efficiency was 90.36%. Single factor analysis and multivariate logistic regression method were used to analyze the main influencing factors of occupational stress and mental health status of psychiatric managers. Results: The detection rate of anxiety and depression among 497 HIPCPs was 22.73% (113/497) and 58.95% (293/497), respectively. Gender and major were the influencing factors of depression (P=0.000, 0.001). Average working hours>52 hours per week and night shift days>1 days per week were the influencing factors of anxiety (P=0.035, 0.014). Average working hours>52 h per week, night shift days >1 d per week and different majors were the influencing factors of occupational stress (P=0.000, 0.025, 0.010). Multivariate logistic regression results showed that the risk of anxiety in those who worked more than 52 hours per week was 1.753 times that of those who worked less than 52 hours per week (P=0.038), and the risk of depression in women was 3.071 times that of men (P=0.006) . Conclusion: Working hours are an important influencing factor for occupational stress and anxiety among HIPCPs. In order to reduce the occurrence of occupational stress and mental health problems, it is necessary to strengthen psychological counseling for HIPCPs and balance work and rest.

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OBJECTIVE: To establish a nomogram model for predicting the risk of sepsis in diabetic foot patients, and to provide reference for clinical prevention and treatment. METHODS: The clinical data of 430 patients with diabetic foot who were hospitalized in Chu Hsien-I Memorial Hospital of Tianjin Medical University from January 2022 to March 2023 were reviewed and collected, including age, gender, past medical history, smoking and drinking history, family history, diabetes course, Texas grade of diabetic foot and laboratory indicators within 24 hours after admission. Patients were divided into sepsis group and non-sepsis group according to the presence or absence of sepsis during hospitalization. The differences in clinical data between the two groups were compared. Multivariate Logistic regression analysis was used to screen the influencing factors of sepsis in patients with diabetic foot during hospitalization, and a nomogram predictive model was established. The performance of the prediction model was evaluated by receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA). Internal validation was performed by using Bootstrap method. RESULTS: A total of 430 patients were enrolled, among which 90 patients developed sepsis during hospitalization and 340 patients did not. There were statistically significant differences in diabetes course, Texas grade of diabetic foot, white blood cell count (WBC), neutrophil count (NEU), lymphocyte count (LYM), neutrophil to lymphocyte ratio (NLR), hemoglobin (Hb), albumin (Alb), glycosylated hemoglobin (HbA1c), C-reactive protein (CRP), and blood urea nitrogen (BUN) between the two groups. Multivariate Logistic regression analysis showed that diabetes course [odds ratio (OR) = 2.774, 95% confidence interval (95%CI) was 1.053-7.308, P = 0.039], Texas grade of diabetic foot (OR = 2.312, 95%CI was 1.014-5.273, P = 0.046), WBC (OR = 1.160, 95%CI was 1.042-1.291, P = 0.007), HbA1c (OR = 1.510, 95%CI was 1.278-1.784, P < 0.001), CRP (OR = 1.007, 95%CI was 1.000-1.014, P = 0.036) were independent risk factors for sepsis in patients with diabetic foot during hospitalization, while Alb was a protective factor (OR = 0.885, 95%CI was 0.805-0.972, P = 0.011). A nomogram predictive model was constructed based on the above 6 indicators. The ROC curve showed that the area under ROC curve (AUC) of the nomogram predictive model for identifying the sepsis patients was 0.919 (95%CI was 0.889-0.948). The AUC of the nomogram predictive model after internal verification was 0.918 (95%CI was 0.887-0.946). Hosmer-Lemeshow test showed χ 2 = 2.978, P = 0.936, indicating that the calibration degree of the predictive model was good. Calibration curve showed that the predicted probability of sepsis was in good agreement with the actual probability. DCA curve showed that the nomogram predictive model had good clinical usefulness. CONCLUSIONS: The nomogram predictive model based on the risk factors of diabetes course, Texas grade of diabetic foot, WBC, HbA1c, CRP and Alb has good predictive value for the occurrence of sepsis in patients with diabetic foot during hospitalization, which is helpful for clinical screening of the possibility of diabetic foot patients progressing to sepsis, and timely personalized intervention for different patients.

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OBJECTIVE: To explore the correlation between serum nitric oxide synthase (NOS) levels and readmission due to acute exacerbation within 30 days in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). METHODS: A prospective cohort study was conducted. The AECOPD patients admitted to the First Affiliated Hospital of Hebei North University from January 2020 to December 2022 were enrolled as the research subjects. The general data such as gender, age, body mass index (BMI), chronic obstructive pulmonary disease (COPD) course, smoking history, and basic diseases were collected. The laboratory indicators, serum NOS level [inducible nitric oxide synthase (iNOS), endothelial nitric oxide synthase (eNOS), neuronal nitric oxide synthase (nNOS)] and acute physiology and chronic health evaluation II (APACHE II) score within 24 hours after admission and total length of hospital stay were also collected, and whether patients were readmitted due to acute exacerbation within 30 days after discharge were recorded. The differences in the above clinical indexes between the readmitted and non-readmitted patients within 30 days were compared. Multivariate Logistic regression analysis was used to screen the influencing factors of readmission within 30 days after discharge in AECOPD patients. Receiver operator characteristic curve (ROC curve) was drawn to analyze the predictive value of various influencing factors on readmission. RESULTS: A total of 168 patients were enrolled, 38 patients were readmitted due to acute aggravation within 30 days after discharge, and 130 were not readmitted. Compared with the non-readmission group, the levels of white blood cell count (WBC), C-reactive protein (CRP), APACHE II score, and serum iNOS and eNOS levels within 24 hours after admission in the readmission group were significantly increased [WBC (×109/L): 14.19 (12.88, 16.12) vs. 11.81 (10.63, 14.11), CRP (mg/L): 51.41±12.35 vs. 40.12±7.79, APACHE II score: 22.0 (19.0, 25.0) vs. 18.0 (14.0,20.5), iNOS (µg/L): 5.87±1.36 vs. 4.52±0.89, eNOS (µg/L): 4.40±1.00 vs. 3.51±1.08, all P < 0.01], and the levels of hemoglobin (Hb) and albumin (Alb) were significantly decreased [Hb (g/L): 108.82±22.06 vs. 123.98±24.26, Alb (g/L): 30.28±3.27 vs. 33.68±2.76, both P < 0.01]. There were no significant differences in gender, age, BMI, COPD course, smoking history, basic diseases, total length of hospital stay and serum nNOS level between the two groups. Multivariate Logistic regression analysis showed that CRP [odds ratio (OR) = 1.201, 95% confidence interval (95%CI) was 1.075-1.341], APACHE II score (OR = 1.335, 95%CI was 1.120-1.590), and serum iNOS (OR = 5.496, 95%CI was 2.143-14.095) and eNOS (OR = 3.366, 95%CI was 1.272-8.090) were the independent risk factors for readmission within 30 days after discharge in AECOPD patients (all P < 0.05), and Hb (OR = 0.965, 95%CI was 0.933-0.997) and Alb (OR = 0.551, 95%CI was 0.380-0.799) were protective factors (both P < 0.05). ROC curve analysis showed that serum iNOS and eNOS levels had predictive value for readmission within 30 days after discharge in AECOPD patients, and the area under the ROC curve (AUC) was 0.791 (95%CI was 0.694-0.887) and 0.742 (95%CI was 0.660-0.823), respectively. When the optimal cut-off value was 5.22 µg/L and 3.82 µg/L, the sensitivity was 81.54% and 69.23%, and the specificity was 71.05% and 81.58%, respectively. The AUC of serum iNOS and eNOS levels combined with Hb, Alb, CRP and APACHE II score for predicting the readmission was 0.979 (95%CI was 0.958-1.000), the sensitivity was 91.54%, and the specificity was 97.37%. CONCLUSIONS: The increased serum iNOS and eNOS levels of AECOPD patients correlate with the readmission due to acute exacerbation within 30 days after discharge. Combined detection of Hb, Alb, CRP, serum iNOS and eNOS levels, and evaluation of APACHE II score within 24 hours after admission can effectively predict readmission.

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OBJECTIVE: To investigate the predictive value of plasma exosome count for the prognosis of patients with sepsis. METHODS: A prospective observational study was conducted. The patients with sepsis admitted to intensive care unit (ICU) of Zhejiang Hospital from November 2020 to December 2021 were enrolled as the study subjects. On the 1st day of admission to the ICU, the patient's gender, age, underlying disease, infection site, mean arterial pressure (MAP) and severity scores were recorded, and venous blood was taken for detecting the blood routine, blood biochemistry, and procalcitonin (PCT), and arterial blood was taken for blood gas analysis, simultaneously, the patient's noradrenaline (NA) dosage was recorded. On the 1st, 3rd, 5th, and 7th day of ICU admission, plasma exosomes were extracted, and the number of exosomes was detected by nanoparticle tracking analyzer. The endpoint of observation was the death of the patient 28 days after admission to the ICU. The differences in baseline data and plasma exosome counts of patients with different 28-day prognosis were analyzed and compared. The Spearman correlation method was used to analyze the correlation between plasma exosome counts and other clinical indicators. Binary multivariate Logistic regression analysis was used to screen the 28-day death risk factors of septic patients. The receiver operator characteristic curve (ROC curve) was plotted to analyze the predictive value of each index on the 28-day death of septic patients. The Kaplan-Meier method was used to analyze the 28-day survival curve. RESULTS: A total of 26 patients with sepsis were enrolled, of whom 21 survived and 5 died on the 28th day. Compared with the survival group, the patients in the death group had lower MAP, higher sequential organ failure assessment (SOFA) score, acute physiology and chronic health evaluation II (APACHE II) score, white blood cell count (WBC), cardiac troponin I (cTnI), and worse oxygenation. The plasma exosome count on the 1st day of ICU admission in the death group was significantly higher than that in the survival group (×1015/L: 16.96±9.11 vs. 5.20±2.42, P < 0.05). Subsequently, the plasma exosome counts in both groups continued to decrease, and there was no statistically significant difference between the two groups. Spearman correlation analysis showed that the plasma exosome count on the 1st day of ICU admission in septic patients was significantly positively correlated with SOFA score, APACHE II score, blood lactic acid (Lac), alanine aminotransferase (ALT) and NA dosage (r values were 0.572, 0.585, 0.463, 0.411, 0.696, all P < 0.05), and it significantly negatively correlated with MAP and oxygenation index (PaO2/FiO2; r values were -0.392 and -0.496, both P < 0.05). Multivariate Logistic regression analysis showed that plasma exosome count on the 1st day of ICU admission was an independent risk factor for 28-day death in septic patients [odds ratio (OR) = 1.385, 95% confidence interval (95%CI) was 1.075-1.785, P = 0.012]. ROC curve analysis showed that the area under the ROC curve (AUC) of plasma exosome count on the 1st day of ICU admission for predicting 28-day death in septic patients was 0.800 (95%CI was 0.449-1.000); when the optimal cut-off value was 14.50×1015/L, the sensitivity was 80.0% and the specificity was 100%. According to the optimal cut-off value of 1-day plasma exosome count, the patients were divided into two groups for Kaplan-Meier survival curve analysis, and the results showed that the cumulative survival rate of patients with plasma exosome count < 14.50×1015/L was significantly higher than that of patients with plasma exosome count ≥ 14.50×1015/L (Log-Rank test: χ 2 = 19.100, P < 0.001). CONCLUSIONS: The plasma exosome count of septic patients is significantly increased on the 1st day of admission to the ICU, which is related to the severity, and can predict the risk of death at 28 days.

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OBJECTIVE: To explore the predictive value of leukocyte derived markers for postoperative delirium (POD) in patients undergoing cardiac valve surgery. METHODS: A prospective cohort study was conducted. The patients who underwent cardiac valve surgery admitted to Beijing Anzhen Hospital of Capital Medical University from October 2021 to March 2023 were enrolled. The demographic, baseline and perioperative data were collected, and the neutrophil to lymphocyte ratio (NLR) and platelet to white blood cell ratio (PWR) were calculated before operation and within 24 hours after operation. Delirium assessment was conducted twice a day for patients within 1-5 days after surgery or discharged within 5 days. According to the evaluation results, the patients were divided into delirium group and non-delirium group. The clinical indexes between the two groups were compared. Multivariate Logistic regression analysis was used to screen the independent risk factors of POD, and the POD predictive model was constructed. The predictive value of POD predictive model was evaluated by receiver operator characteristic curve (ROC curve). RESULTS: A total of 235 patients were enrolled in the analysis, of which 83 patients had POD (35.32%) and 152 patients did not have POD (64.68%). Compared with the non-delirious group, the patients in the delirious group had higher Charlson comorbidity index (CCI) score and lower mini-mental state examination (MMSE) score. In terms of perioperative data, compared with the non-delirium group, the patients in the delirium group had longer operative time, duration of cardiopulmonary bypass, length of intensive care unit (ICU) stay, duration of mechanical ventilation, and postoperative hospital stay, higher incidence of perioperative atrial fibrillation, and lower discharge life score. In terms of leukocyte derived markers, NLR within 24 hours after surgery in both groups were significantly higher than those before surgery, and PWR were significantly lower than those before surgery. The NLR within 24 hours after surgery, PWR difference and NLR difference in the delirium group were significantly higher than those in the non-delirium group. Multivariate Logistic regression analysis showed that CCI score [odds ratio (OR) = 1.394, 95% confidence interval (95%CI) was 1.038-1.872, P = 0.027], perioperative atrial fibrillation (OR = 3.697, 95%CI was 1.711-7.990, P < 0.001), duration of cardiopulmonary bypass (OR = 1.008, 95%CI was 1.002-1.015, P = 0.016), length of ICU stay (OR = 1.006, 95%CI was 1.002-1.010, P = 0.002), NLR difference (OR = 1.029, 95%CI was 1.009-1.050, P = 0.005) and PWR difference (OR = 1.044, 95%CI was 1.009-1.080, P = 0.013) were independently correlated with POD. POD predictive model was constructed by multivariate Logistic regression analysis result: POD predictive model index = -4.970+0.336×CCI score+1.317×perioperative atrial fibrillation+0.009×duration of cardiopulmonary bypass+0.006×length of ICU stay+0.030×NLR difference+0.044×PWR difference. ROC curve analysis showed that the area under the ROC curve (AUC) of NLR difference for predicting POD was 0.659 (95%CI was 0.583-0.735), the optimal critical value was 16.62, the sensitivity was 60.2%, and the specificity was 70.4% (P < 0.05). The AUC of PWR difference for predicting POD was 0.608 (95%CI was 0.528-0.688), the optimal critical value was 25.68, the sensitivity was 51.8%, and the specificity was 75.7% (P < 0.05). The AUC of POD predictive model for predicting POD was 0.805 (95%CI was 0.745-0.865), the optimal critical value was 0.39, the sensitivity was 74.7%, and the specificity was 79.6% (P < 0.05). CONCLUSIONS: The differences of NLR and PWR are independently related to POD, which has potential value in predicting POD after cardiac valve surgery.

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We develop a methodology for valid inference after variable selection in logistic regression when the responses are partially observed, that is, when one observes a set of error-prone testing outcomes instead of the true values of the responses. Aiming at selecting important covariates while accounting for missing information in the response data, we apply the expectation-maximization algorithm to compute maximum likelihood estimators subject to LASSO penalization. Subsequent to variable selection, we make inferences on the selected covariate effects by extending post-selection inference methodology based on the polyhedral lemma. Empirical evidence from our extensive simulation study suggests that our post-selection inference results are more reliable than those from naive inference methods that use the same data to perform variable selection and inference without adjusting for variable selection.

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INTRODUCTION: Traffic-related air and noise pollution are important public health issues. The aim of this study was to estimate their effects on allergic/respiratory outcomes in adult and elderly subjects. MATERIALS AND METHODS: Six hundred and forty-five subjects living in Pisa (Tuscany, Italy) were investigated through a questionnaire on allergic/respiratory symptoms and diseases. Traffic-related air pollution and noise exposures were assessed at residential address by questionnaire, modelled annual mean NO2 concentrations (1 km and 200 m resolution), and noise level over a 24-h period (Lden). Exposure effects were assessed through logistic regression models stratified by age group (18-64 years, ≥65 years), and adjusted for sex, educational level, occupational exposure, and smoking habits. RESULTS: 63.6% of the subjects reported traffic exposure near home. Mean exposure levels were: 28.24 (±3.26 SD) and 27.23 (±3.16 SD) µg/m3 for NO2 at 200 m and 1 km of resolution, respectively; 57.79 dB(A) (±6.12 SD) for Lden. Exposure to vehicular traffic (by questionnaire) and to high noise levels [Lden ≥ 60 dB(A)] were significantly associated with higher odds of allergic rhinitis (OR 2.01, 95%CI 1.09-3.70, and OR 1.99, 95%CI 1.18-3.36, respectively) and borderline with rhino-conjunctivitis (OR 2.20, 95%CI 0.95-5.10, and OR 1.76, 95%CI 0.91-3.42, respectively) only in the elderly. No significant result emerged for NO2. CONCLUSIONS: Our findings highlighted the need to better assess the effect of traffic-related exposure in the elderly, considering the increasing trend in the future global population's ageing.

Global population is ageing.Allergic diseases are globally widespread even on adult population.The susceptibility due to ageing may increase the impact of air pollution on the elderly.Traffic-related air and noise pollution affects allergic status of the elderly.

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