Risk assessment of nosocomial infection in patients with multiple myeloma in the first hospitalization / 公共卫生与预防医学

Objective To assess the risk of nosocomial infection in patients with multiple myeloma during their first hospitalization. Methods Totally 480 patients with multiple myeloma who were hospitalized for the first time in department of hematology of West China Hospital, Sichuan University from August 2021 to August 2022 were included, and the nosocomial infection during treatment was statistically analyzed. The patients were divided into infected group and uninfected group. The independent influencing factors of nosocomial infection were analyzed and a prediction model was established. The reliability of the prediction model was analyzed by receiver operating characteristic curve (ROC). Results The incidence rate of nosocomial infection was 31.2% among 480 patients hospitalized for the first time. There were statistically significant differences in age, ISS staging, controlling nutritional status (CONUT) score, agranulocytosis, hemoglobin, and albumin between the infected group and the uninfected group (P<0.05). Logistic multivariate regression analysis showed that age, ISS staging, CONUT score, agranulocytosis, hemoglobin level, and albumin level were all independent correlated factors of nosocomial infection in patients with multiple myeloma hospitalized for the first time (P<0.05). The area under the ROC curve (AUC), sensitivity and specificity of multivariate logistic regression prediction model were 0.88 (95%CI: 0.840-0.920), 85.00% and 76.36%, respectively. Conclusion The incidence rate of nosocomial infection is high among patients with multiple myeloma in the first hospitalization. The prediction model established according to independent correlated factors of nosocomial infection has high predictive value on the occurrence of nosocomial infection.

A postoperative in-hospital mortality risk model for elderly patients undergoing cardiac valvular surgery based on LASSO-logistic regression / 中国胸心血管外科临床杂志

@#Objective     To evaluate the risk factors for postoperative in-hospital mortality in elderly patients receiving cardiac valvular surgery, and develop a new prediction models using the least absolute shrinkage and selection operator (LASSO)-logistic regression. Methods     The patients≥65 years who underwent cardiac valvular surgery from 2016 to 2018 were collected from the Chinese Cardiac Surgery Registry (CCSR). The patients who received the surgery from January 2016 to June 2018 were allocated to a training set, and the patients who received the surgery from July to December 2018 were allocated to a testing set. The risk factors for postoperative mortality were analyzed and a LASSO-logistic regression prediction model was developed and compared with the EuroSCOREⅡ. Results     A total of 7 163 patients were collected in this study, including 3 939 males and 3 224 females, with a mean age of 69.8±4.5 years. There were 5 774 patients in the training set and 1 389 patients in the testing set. Overall, the in-hospital mortality was 4.0% (290/7 163). The final LASSO-logistic regression model included 7 risk factors: age, preoperative left ventricular ejection fraction, combined coronary artery bypass grafting, creatinine clearance rate, cardiopulmonary bypass time, New York Heart Association cardiac classification. LASSO-logistic regression had a satisfying discrimination and calibration in both training [area under the curve (AUC)=0.785, 0.627] and testing cohorts (AUC=0.739, 0.642), which was superior to EuroSCOREⅡ. Conclusion     The mortality rate for elderly patients undergoing cardiac valvular surgery is relatively high. LASSO-logistic regression model can predict the risk of in-hospital mortality in elderly patients receiving cardiac valvular surgery.

Prediction model of therapeutic effect on children with idiopathic short stature based on vitamin K2,IGFBP-3 and Omentin-1 / 国际检验医学杂志

Objective To investigate the relationship between vitamin K2,insulin-like growth factor bind-ing protein 3(IGFBP-3),Omentin-1 and the therapeutic effect on children with idiopathic short stature(ISS),and to build a prediction model.Methods A total of 242 ISS children in Jinan Second Maternal and Child Health Hospital from 2019 to 2021 were selected.All of them received recombinant human growth hormone(rhGH)treatment and were divided into effective group and ineffective group according to the therapeutic effect after 12 months of treatment.The general data,vitamin K2,IGFBP-3 and Omentin-1 in the two groups were analyzed.The influencing factors of ISS children's therapeutic effect were analyzed by Logistic regression model and decision tree model.The predictive performance of two models was analyzed by using receiver oper-ating characteristic(ROC)curve.Results There were statistically significant differences in 25-hydroxy vita-min D[25(OH)D],parathyroid hormone(PTH),thyroid stimulating hormone(TSH),vitamin K2,IGFBP-3,Omentin-1,rhGH dosage and weekly outdoor exercise time between the two groups(P＜0.05).Logistic re-gression showed that PTH(OR=7.011,95％CI:2.456-20.014),vitamin K2(OR=0.605,95％CI:.0.465-0.788),IGFBP-3(OR=0.458,95％CI:0.321-0.654),Omentin-1(OR=0.514,95％CI:0.389-0.679)and rhGH dose(OR=0.563,95％CI:0.445-0.712)]were the influential factors for treatment ineffectiveness in ISS children(P＜0.05).The decision tree model showed that vitamin K2,IGFBP-3 and Omentin-1 were the factors influencing the therapeutic effect of ISS,and IGFBP-3 had the most significant impact.ROC curve re-sults showed that the area under the curve of decision tree model and Logistic regression model were 0.922 and 0.908,respectively,with good classification effect.Conclusion The therapeutic effect of ISS children is in-fluenced by factors such as vitamin K2,IGFBP-3,Omentin-1,and so on,and IGFBP-3 has the most significant impact.Logistic regression model and decision tree model could complement each other so as to provide refer-ence for improving the therapeutic effect of ISS children from different aspects.

Research hotspots and frontiers of functional magnetic resonance imaging in treatment of ischemic stroke by traditional Chinese medicine / 中国组织工程研究

BACKGROUND:This review explores the current research status and frontier hot spots of functional magnetic resonance imaging(fMRI)in the field of traditional Chinese medicine(TCM)for the treatment of ischemic stroke,and attempts to grasp future research trends,with a view to providing a reference for subsequent research in this field. OBJECTIVE:To visualize and analyze the hotspots and frontiers in the TCM treatment of ischemic stroke based on fMRI using CiteSpace knowledge mapping combined with binary logistic regression equations,in order to grasp the future research trends and further explore the distribution of brain regions with abnormal neural activity related to the types of post-stroke dysfunction. METHODS:CNKI,WanFang,VIP,Chinese Biomedical Literature Database and Web of Science core set database were searched.CiteSpace was used to plot keyword co-occurrence,keyword clustering timeline,burst term detection,co-cited literature mapping to analyze hotpots and frontiers in this field.Binary logistic regression analysis fitted the distribution of brain regions with abnormal neural activity associated with different dysfunction after ischemic stroke. RESULTS AND CONCLUSION:A total of 354 articles were included for CiteSpace knowledge mapping analysis.The number of annual publications showed that the research popularity has been raised from 2000 to 2022 with a good development prospect,but the core strength is mainly concentrated in China.Keywords co-occurrence and clustering time line analysis showed that aphasia,hemiplegia and cognitive impairment are the hot poststroke dysfunction types.Electroacupuncture,acupuncture and head acupuncture are hotspot intervention measures.Functional connectivity is a hotspot analysis method,and resting fMRI is a hotspot scanning technology.The time span of each research hotspot is long,indicating that it has a certain research value and the relevant research is gradually deepening,promoting the research progress in this field.However,acupuncture is the main intervention measure,and there is a lack of research on traditional Chinese medicine,Chinese patent medicine,acupuncture and medicine combination and other TCM therapy.Burst term detection results showed that functional connectivity,graph theory,degree centrality,default mode network,randomized controlled trials have great influence and strong explosive power.They are the current and future frontier hot spots in this field,suggesting that future research should focus on the brain network information integration and strengthen the scientific and rigorous clinical trial design.The results of co-cited literature analysis showed that the epidemiological investigation of ischemic stroke,the safety and effectiveness of acupuncture in the treatment of stroke,the brain activation patterns under different tasks,and the neuropathological mechanism of brain network dysfunction after stroke are the theoretical basis of this field.Future research direction in this field is to explore TCM-targeted brain regions and neural networks to reveal the brain effect mechanism of TCM promoting neural remodeling after stroke.A total of 255 articles were included for binary Logistic regression analysis.The results showed that sensorimotor cortex and premotor area dysfunction are positively correlated with the incidence of motor dysfunction after stroke;hippocampus,cerebellum posterior lobe,precuneus,inferior temporal gyrus and anterior cingulate nerve dysfunction are positively correlated with the incidence of cognitive impairment after stroke;cuneus,angular gyrus and prefrontal lobe neural dysfunction were positively correlated with the incidence of affective disorder after stroke;anterior cingulate,cerebellum posterior lobe neural dysfunction are positively correlated with the incidence of swallowing disorder after stroke.The above brain regions are the core brain regions of the sensorimotor network,default mode network and reward loop,suggesting that functional abnormalities within or between brain networks related to dysfunction may be potential target areas for TCM intervention,but the specific changes in neural activity activation or inhibition still need to be refined.

Relationship between blood indicators and course of nontraumatic osteonecrosis of femoral head in different stages:multiple logistic regression analysis / 中国组织工程研究

BACKGROUND:Up to now,there is no literature on the relationship between blood laboratory tests and the course of nontraumatic osteonecrosis of femoral head in different stages.It is necessary to further explore and analyze so as to better clarify the influencing factors of nontraumatic osteonecrosis of femoral head. OBJECTIVE:To analyze the relationship between blood laboratory indicators and the course of nontraumatic osteonecrosis of the femoral head by the Association Research Circulation Osseous(ARCO),thus exploring the influencing factors of blood laboratory indicators on the course of nontraumatic osteonecrosis of the femoral head. METHODS:This study used a retrospective study design.A total of 2 103 patients with osteonecrosis of the femoral head were retrieved from Wangjing Hospital of China Academy of Chinese Medical Sciences database,and 1 075 patients with nontraumatic osteonecrosis of the femoral head were ultimately included based on inclusion and exclusion criteria.Patient age,gender,body mass index,and blood laboratory test results were collected.Blood laboratory tests included low-density lipoprotein,total cholesterol,triglycerides,high-density lipoprotein,apolipoprotein β,apolipoprotein α1,uric acid,total protein quantitative,alkaline phosphatase,activated partial thromboplastin time,prothrombin time,prothrombin time International Normalized Ratio,prothrombin time activity,fibrinogen quantitative,coagulation time of thrombin,D-dimer,total iron binding capacity,and platelet count.The indicators of patients with different age groups and different ARCO stages were compared,and multiple Logistic regression analysis was applied to explore the influencing factors of ARCO stages in osteonecrosis of the femoral head. RESULTS AND CONCLUSION:(1)There were statistical differences in total cholesterol,uric acid,prothrombin time,prothrombin time International Normalized Ratio,and D-dimer among ARCO stages in the young group(P<0.05).Among young patients in ARCO stage II,total cholesterol levels were higher than those in ARCO stage III(P<0.05).Uric acid levels in ARCO stage IV were higher than those in ARCO stage II and III(P<0.05).Prothrombin time and prothrombin time International Normalized Ratio were shorter in ARCO stage IV and II than in ARCO stage III(P<0.05).D-dimer levels were higher in ARCO stage III and IV than in ARCO stage II(P<0.05).(2)There were statistically significant differences in high-density lipoprotein,coagulation time of thrombin,and D-dimer among ARCO stages in the middle-aged group(P<0.05).Among middle-aged patients in ARCO stage IV,high-density lipoprotein levels were higher than those in ARCO stages II and III(P<0.05).Coagulation time of thrombin was shorter in ARCO stage IV than in ARCO stage III(P<0.05).D-dimer levels were higher in ARCO stages IV than in ARCO stages II and III(P<0.05).(3)The uric acid,activated partial thromboplastin time,D-dimer,and platelet count in the elderly group showed statistically significant differences(P<0.05).The uric acid level in ARCO stage IV was higher than that in ARCO stage II and III patients in the elderly group(P<0.05),while the activated prothrombin time in ARCO stage II patients was shorter than that in ARCO stage III patients in the elderly group(P<0.05).The D-dimer level in ARCO stage III and IV patients was higher than that in ARCO stage II patients in the elderly group(P<0.05).The platelet count in ARCO stage IV was lower than that in ARCO stage III patients in the elderly group(P<0.05).(4)Multiple logistic regression analysis showed that total cholesterol and platelet count may be protective factors for course of nontraumatic osteonecrosis of the femoral head,while D-dimer,uric acid,overweight,and young and middle age may be risk factors for course of nontraumatic osteonecrosis of the femoral head.(5)It is indicated that total cholesterol,high-density lipoprotein,uric acid,prothrombin time,prothrombin time International Normalized Ratio,and D-dimer are statistically significant among patients with different ARCO stages.Total cholesterol and platelet count may be protective factors for the course of nontraumatic osteonecrosis of the femoral head,while D-dimer,uric acid,overweight,and middle-aged and young age groups may be hazard factors for the course of nontraumatic osteonecrosis of the femoral head.

Logistic regression analysis and risk model construction of choroidopathy sec-ondary to high myopia / 眼科新进展

Objective To analyze the influencing factors of choroidopathy(choroidal atrophy and choroidal neovas-cularization)secondary to high myopia based on Logistic regression analysis and to construct a Nomogram risk prediction model based on the related factors,so as to provide guidance for clinical treatment.Methods A total of 340 patients(680 eyes)with high myopia admitted to Beijing Jishuitan Hospital from January 2021 to January 2023 were selected and di-vided into group A(170 patients,340 eyes)and group B(170 patients,340 eyes).The incidence of choroidopathy in the two groups was compared.The groups A and B were divided into two subgroups,subgroup a and subgroup b,according to whether choroidopathy occurred or not.Multivariate Logistic regression analysis was carried out to explore the influencing factors of choroidopathy secondary to high myopia.A Nomogram risk prediction model for choroidopathy secondary to high myopia was constructed based on the influencing factors and externally validated.Results In groups A and B,the age,proportion of diabetes mellitus,axial length,and level of seruim transforming growth factor β1(TGF-β1)of patients in subgroup a were higher than those in the subgroup b,and the diopter was lower than that in the subgroup b(all P＜0.05).The Logistic regression analysis showed that age,diabetes mellitus,axial length and serum TGF-β1 level were independent risk factors for choroidopathy secondary to high myopia,and diopter was a protective factor(all P＜0.05).Age,diabetes mellitus,axial length and serum TGF-β1 level were positively correlated risk factors for choroidopathy secondary to high myopia,and diopter was a negatively correlated risk factor(all P＜0.05).The area under the curve of the Nomogram risk prediction model for predicting choroidopathy secondary to high myopia was 0.818,and the calibration was good.Con-clusion Age,diabetes mellitus,axial length,diopter and serum TGF-β1 level are the influential factors for choroidopa-thy secondary to high myopia.The Nomogram risk prediction model established based on these factors has a certain value for predicting choroidopathy secondary to high myopia.The clinical therapeutic schedules should be made based on this model to reduce the risk of secondary choroidopathy.

Risk factors for 1-year death after surgery in elderly patients with hip fractures and accuracy of prediction model: based on LASSO-logistic regression / 中华麻醉学杂志

Objective:To identify the risk factors for 1-year death after surgery in elderly patients with hip fractures and evaluate the accuracy of the prediction model based on LASSO-logistic regression analysis.Methods:A case-control study was conducted on elderly patients (age ≥65 yr) who underwent surgical treatment for hip fractures in the Second Affiliated Hospital of Wenzhou Medical University from January to December 2019. Patients were divided into death group and survival group according to their survival status at 1-year after surgery. General data and preoperative laboratory indicators were obtained. The variables were selected by utilizing LASSO regression and incorporated into multivariate logistic regression analysis to identify the risk factors for 1-year death after surgery in elderly patients with hip fractures. Then a prediction model was established based on the results and evaluated.Results:There were 63 patients in death group and 564 in survival group. The results of LASSO regression and multivariate logistic regression analysis showed that age, preoperative cognitive dysfunction, Chalson comorbidity index ≥3 points and preoperative serum prealbumin level were the independent risk factors for 1-year death after surgery in elderly patients with hip fractures ( P<0.05). The area under the receiver operating characteristic curve of the prediction model was 0.788 (95% confidence interval [0.731-0.846]), with the sensitivity and specificity of 76.2% and 68.6% respectively. The average absolute error of the calibration curve was 0.007. The results of Hosmer-Lemeshow goodness-of-fit test showed that there was no significant difference between the predicted value and actual observed value ( χ2=5.065, P=0.751). Decision curve analysis showed that patients had a high net benefit rate when the threshold probability range was 0-0.7. Conclusions:Age, preoperative cognitive dysfunction, Chalson comorbidity index ≥3 points and preoperative serum prealbumin level are the independent risk factors for 1-year death after surgery in elderly patients with hip fractures, and the prediction model developed based on LASSO-logistic regression has high accuracy.

Correlation between tongue and pulse indicators and the outcome of live birth in frozen-thawed embryo transfer / 数字中医药（英文）

Objective @#To investigate the correlation between tongue and pulse indicators and the outcome of live birth in patients undergoing frozen-thawed embryo transfer (FET), as well as the association between these indicators and patients’ endocrine parameters.@*Methods@#This study was conducted at Reproductive Medicine Center, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China, from March 8, 2021 to January 5, 2022. Patients undergoing FET were divided into live birth and non-live birth groups according to their live birth outcome. The differences between the endocrine parameters [basic follicle stimulating hormone (b FSH), basic luteinizing hormone (b LH), basic estradiol (b E2), basic progesterone (b P), basal endometrial thickness, follicle stimulating hormone (FSH) on endometrial transition day, luteinizing hormone (LH) on endometrial transition day, estradiol (E2) on endometrial transition day, progesterone (P) on endometrial transition day, and endometrial thickness on endometrial transition day] and the tongue and pulse indicators [tongue body (TB)-L, TB-a, TB-b, tongue coating (TC)-L, TC-a, TC-b, perAll, perPart, h1, h4, h5, t1, h1/t1, and h4/h1] of patients in the two groups were analyzed, with the correlation between these variables analyzed as well using Spearman’s correlation coefficient. Multivariate logistic regression was employed to identify the influential factors in the live birth prediction models across various datasets, including Model 1 consisting of endocrine indicators only, Model 2 solely consisting of tongue and pulse indicators, and Model 3 consisting of both tongue, pulse, and endocrine indicators, as well as to evaluate efficacy of the models derived from different datasets.@*Result@#This study included 78 patients in live birth group and 144 patients in non-live birth group. Compared with non-live birth group, live birth group exhibited higher levels of TB-L (P = 0.01) and TB-a (P = 0.04), while demonstrated lower levels of b FSH (P = 0.01), perAll (P = 0.04), and h4/h1 (P = 0.03). The Spearman’s correlation coefficient analysis revealed statistically significant correlation (P < 0.05) between TB-L, TB-b, TC-L, TC-b, perAll, perPart, h4, h5, t1, h1/t1 and b FSH, b LH, basal endometrial thickness, LH on endometrial transition day, E2 on endometrial transition day, P on endometrial transition day, and endometrial thickness on endometrial transition day in live birth group. The multivariate logistic regression analysis showed that the prediction Model 3 for live birth outcome [area under the curve (AUC): 0.917,95% confidence interval (CI): 0.863 − 0.971, P < 0.001] surpassed the Model 1 (AUC: 0.698,95% CI: 0.593 − 0.803, P = 0.001), or the Model 2 (AUC: 0.790, 95% CI: 0.699 − 0.880, P < 0.001). The regression equations for the live birth outcomes, integrating tongue and pulse indicators with endocrine parameters, included the following measures: FSH on endometrial transition day [odds ratio (OR): 0.523, P = 0.025], LH on endometrial transition day (OR: 1.277, P = 0.029), TB-L (OR: 2.401, P = 0.001), perPart (OR: 1.018, P = 0.013), h1(OR: 0.065, P = 0.021), t1 (OR: 4.354, P = 0.024), and h4/h1 (OR: 0.018, P = 0.016).@*Conclusion@#In infertility patients undergoing FET, there exists a correlation between tongue and pulse indicators and endocrine parameters. The corporation of tongue and pulse indicators significantly improved the predictive capability of the model for live birth outcomes. Specifically, tongue and pulse indicators such as TB-L, perPart, h1, t1, and h4/h1 exhibited a discernible correlation with the ultimate live birth outcomes.

Analysis of Depression and Influencing Factors in HIV/AIDS Patients Receiving Antiviral Therapy / 中山大学学报(医学科学版)

ObjectiveTo investigate the depression status and its influencing factors in HIV/AIDS patients receiving antiviral therapy. MethodsFrom July 2022 to September 2022， successive sampling method was used to recruit HIV/AIDS patients receiving antiviral therapy from antiviral treatment institutions in Nanjing， and they were instructed to fill out anonymous questionnaires. The questionnaire collected the basic information of patients， and depression， HIV stigma score and social support level were investigated by Patient Health Questionnaire-9 （PHQ-9）， Berger HIV stigma scale （BHSS） and Multidimensional Scale of Perceived Social Support （MSPSS）. Multivariate Logistic regression was used to analyze the influencing factors of depression. ResultsA total of 1879 valid questionnaires were collected in this study， and the detection rate of depression was 50.1%. The results of multivariate logistic analysis showed that compared with patients with middle school or below， the risk of depression was lower for those with postgraduate or above ［OR=0.534， 95%CI （0.341， 0.835）， P=0.006］. Compared with antiviral therapy duration<1 year， antiviral therapy duration for 1 to 5 years ［OR=0.729， 95%CI （0.536， 0.991）］， >5 to 10 years ［OR=0.516， 95%CI （0.379， 0.702）］， >10 years ［OR=0.603， 95%CI （0.375， 0.969）］ was associated with a lower risk of depression. High level of social support was a protective factor for depression in HIV/AIDS patients compared with middle and low level of social support ［OR=0.430， 95% CI（0.349， 0.530）， P < 0.001］. There was a higher risk of depression with side effects than without side effects ［OR=2.260， 95%CI （1.833， 2.786）， P < 0.001］. The higher the score on the HIV stigma scale， the higher the possibility of depression was. ConclusionThe detection rate of depression of patients receiving antiviral therapy in Nanjing is high. After starting antiviral therapy， we should strengthen the monitoring of side effects and psychological status of patients， carry out psychological intervention， alleviate psychological problems， and improve the quality of life of patients receiving antiviral therapy.

Association between urinary metal levels and kidney stones in metal smelter workers / 环境与职业医学

Background Arsenic, cobalt, barium, and other individual metal exposure have been confirmed to be associated with the incidence of kidney stones. However, there are few studies on the association between mixed metal exposure and kidney stones, especially in occupational groups. Objective To investigate the association between mixed metal exposure and kidney stones in an occupational population from a metal smelting plant. Methods A questionnaire survey was conducted to collect sociodemographic characteristics, medical history, and lifestyle information of 1158 mixed metal-exposed workers in a metal smelting plant in Guangdong Province from July 2021 to January 2022. Midstream morning urine samples were collected from the workers, the concentrations of 18 metals including lithium, vanadium, chromium, manganese, cobalt, nickel, copper, zinc, arsenic, selenium, strontium, molybdenum, cadmium, cesium, barium, tungsten, titanium, and lead were measured by inductively coupled plasma mass spectrometry, and the urinary mercury levels were measured by cold atomic absorption spectroscopy. Based on predetermined inclusion criteria, a total of 919 mixed metal-exposed workers were included in the study, including 117 workers in the kidney stone group and 802 workers in the non-kidney stone group. With a detection rate of urinary metals greater than 80% as entry criterion, 16 eligible metals were finally included for further analysis. Parametric or non-parametric methods were used to compare the differences between continuous or categorical variables of the non-kidney stone group and the kidney stone group. Logistic regression models were constructed to explore the association between individual metal exposures and kidney stones. Weighted quantile sum (WQS) regression models were used to evaluate the association between mixed metal exposure and kidney stones, as well as the weights of each metal on kidney stones. Then Bayesian kernel machine regression (BKMR) models were used to explore the overall effect of mixed metal exposure on renal calculi and the potential interactions between metals. Results We found that there were significant differences in sex, age, length of service, and body mass Index (BMI) between the non-kidney stone group and the kidney stone group (P<0.05). The urinary concentrations of molybdenum and barium in the kidney stone group were higher than those in the non-kidney stone group, and the differences were statistically significant (P<0.05). The logistic regression models demonstrated that urinary cobalt, arsenic, molybdenum, and barium were positively correlated with the risk of kidney stones (Ptrend<0.05). The WQS regression models showed that the mixed exposure to vanadium, cobalt, arsenic, molybdenum, and barium was positively associated with the risk of kidney stones (P<0.05). Among them, molybdenum, arsenic, and barium accounted for 0.391, 0.337, and 0.154, respectively. The BKMR results revealed a positive association between metal mixture exposure and the risk of kidney stones (P<0.05). When other metals were fixed at the 25th, 50th, or 75th percentile, arsenic, molybdenum, cobalt, and barium exhibited significant positive effects on the risk of kidney stones (P<0.05), while vanadium showed a significant negative effect (P<0.05). The interaction analysis demonstrated interactions between barium and cobalt, as well as between vanadium and cobalt (P<0.05). Conclusion In the occupational population of this smelter, occupational mixed metal exposure could increase the risk of kidney stones, and the main metals are molybdenum, arsenic, barium, and cobalt.

Analysis of clinical characteristics and risk factors for adverse outcomes in type 2 diabetic mellitus patients with COVID-19 / 中华临床营养杂志

Objective:The purpose of this study is to explore the clinical characteristics of Coronavirus Disease 2019 (COVID-19) in patients with type 2 diabetes mellitus (T2DM), and analyze the risk factors for adverse outcomes.Methods:2 052 patients diagnosed with COVID-19 who were hospitalized in Shanxi Bethune Hospital between December 1, 2022 and March 20, 2023 were included. They were divided into diabetes group ( n=70) and non-diabetes group ( n=1 982) according to the presence or absence of comorbid T2DM. The two groups were matched at 1:1 via propensity score matching. Clinical characteristics and laboratory examination results of the two groups were compared. According to the outcomes during hospitalization, the two groups were further divided into two subgroups respectively. Univariate analysis and subsequent binary Logistic regression was used to analyze the risk factors of adverse outcomes in patients with COVID-19 and type 2 diabetes. Results:After the propensity score matching, the most common comorbid condition in diabetes group and non-diabetes group was hypertension. The proportion of patients with severe or critical disease in diabetes group was higher compared with non-diabetes group. The levels of hemoglobin A1c (HbA1c), fasting blood glucose (FBG), blood urea, IL-4, IL-6, IL-10, IFN-γ and TNF-α were significantly higher in the diabetes group ( P<0.05). Logistic regression analysis within the diabetes group showed that hypertension ( OR=3.640, 95% CI: 3.156 to 4.290), FBG>11 mmol/L ( OR=3.283, 95% CI: 1.416 to 7.611), HbA1c>10% ( OR=2.718, 95% CI: 1.024 to 7.213) were independent risk factors for adverse outcomes in patients with COVID-19 and type 2 diabetes(all P<0.05). Conclusions:Compared with the non-diabetes group, patients with COVID-19 and T2DM have worse inflammatory response and higher levels of inflammatory cytokines. The elevated levels of FBG and HbA1c are related to the adverse outcome in patients with COVID-19 and T2DM.

Effects of three rehydration methods on prevention of on-site and delayed blood donation-related vasovagal responses: a cluster-randomized trial / 中国输血杂志

【Objective】 To compare the effects of 3 rehydration methods before blood donation on the prevention of on-site and delayed blood donation-related vasovagal response (VVR) . 【Methods】 From January to June 2021, 6 250 whole blood donors in 6 fixed blood donation sites signed informed consent and were divided into 198 clusters according to donor sites and dates, then they were randomly assigned to receive either oral rehydration salts (ORS), sugar water, or water group, and each drank 500 mL of ORS, sugar water or water within 20 minutes before blood donation. The researchers recorded the actual intervention accepted on site, and recorded the immediate VVR and related information. At rest after blood donation, donors submitted an electronic questionnaire containing socio-demographic information. At 48 hours after blood donation, the researchers called back every donor to record delayed VVR and related information. Logistic regression based on intention to treat (ITT) was used to analyze the difference of the incidence of VVR among the three groups, and the average treatment effect on treated (ATT) was calculated. PASS 2021was used to estimate the sample size and R (4.2.0) for statistical analysis. 【Results】 The cumulative incidence of blood donation-related VVR was 2.67% (2.29%-3.11%) among street whole blood donors under the 3 rehydration methods, in which, the incidence of immediate and delayed VVR was 1.02% (0.79%-1.31%) and 1.65% (1.36%-2.01%) respectively. ITT analysis found that ORS were more effective than water in reducing the incidence of delayed VVR【OR=0.59,95% CI[0.37,0.94]】.There was no significant difference in the incidence of immediate VVR between any two groups (P > 0.05), and there was no significant difference in the incidence of delayed VVR in the sugar water group compared with the water group (P > 0.05). There was a difference of -0.013 (【95% CI[-0.022, -0.004]】or -0.008【95% CI[-0.017, -0.000]】in the incidence of delayed VVR in the ORS group compared with water group or sugar water group, the difference was significant (P<0.05). The cumulative VVR of the three groups showed similar results to the delayed VVR. 【Conclusion】 Drinking ORS before blood donation is the most effective rehydration method to prevent delayed VVR. The next step is to establish the predictive model of delayed VVR to screen the susceptible population and provide them with ORS before blood donation, while other population can choose any liquid they like, thus achieving personalized blood donation-related VVR prevention and control.

Study on the Risk Prediction Models of Overweight and Obesity in Medical Students / 中国卫生统计

Objective To construct logistic regression,random forest and SVM models to predict the influencing factors of overweight and obesity in medical students,and the prediction performance of the three models was compared,so as to obtain the optimal model for the risk assessment of overweight and obesity.Methods Participants included 1 866 medical students from a city in Hebei Province from May to December 2020.The relevant data of overweight and obesity screening were collected through self-test questionnaire;three models of logistic regression,random forest and SVM are constructed by python.Results The test set showed that the accuracy of logistic regression,random forest and SVM models were 96.26%,98.66%and 98.13%respectively;the specificity were 99.77%,100%and 99.00%,respectively;and the AUC were 0.88,0.99 and 0.88 respectively.Random forest is the optimal prediction model;according to the random forest model results,subjective well-being,negative events and students'economic status are more than 10%of weight in the model.Conclusion Subjective well-being,negative events and students'economic status are the main factors affecting the incidence of overweight and obesity in medical students;the prediction performance of random forest model was better than logistic regression model and SVM model.

Current status and influencing factors of cognitive function among elderly residents in Qingdao city / 中华行为医学与脑科学杂志

Objective:To investigate the cognitive function and its influencing factors among residents in Qingdao city.Methods:The 5 311 research subjects over 65 years old were selected from Qingdao by simple random sampling and stratified sampling. All subjects were investigated by AD8 dementia early screening questionnaire and community screening instrument dementia (CSI-D) to evaluate the prevalence of cognitive decline in this study.The score of AD8 dementia early screening questionnaire ≥2 points or the score of CSI-D ≤7 points was considered to be cognitive decline. Mann-Whitney U test, Chi-square test, Fisher exact probability method, univariate and multivariate Logistic regression analysis and Bootstrap verification were performed using SPSS 26.0 software. Results:Among 5 311 subjects, 1 899 subjects had normal cognitive function (35.76%) and 3 412 subjects had cognitive decline (64.24%). The age of the cognitive decline group was significantly higher than that of the normal group ( P<0.01). There were significant differences in gender, educational level, rural residents, marital status, chronic medical history, hypertension, rheumatoid arthritis, cerebrovascular disease, intervertebral disc disease, ischemic heart disease, monthly household income and alcohol consumption between the two groups (all P<0.05). Univariate Logistic regression analysis showed that female ( β=0.313, OR=1.367, 95% CI=1.221-1.530), age ( β=0.052, OR=1.053, 95% CI=1.043-1.063), rural residents ( β=0.850, OR=2.340, 95% CI=2.042-2.682), widowed ( β=0.557, OR=1.745, 95% CI=1.500-2.029), chronic medical history ( β=0.290, OR=1.336, 95% CI=1.191-1.498), hypertension ( β=0.134, OR=1.143, 95% CI=1.020-1.281), rheumatoid arthritis ( β=0.458, OR=1.581, 95% CI=1.222-2.046), cerebrovascular disease ( β=0.584, OR=1.794, 95% CI=1.352-2.380), intervertebral disc disease ( β=0.578, OR=1.782, 95% CI=1.370-2.319), ischemic heart disease ( β=0.501, OR=1.651, 95% CI=1.272-2.143) were the risk factors for cognitive decline. Higher education level, higher monthly household income and abstinence ( β=-0.244, OR=0.783, 95% CI=0.619-0.992) were protective factors for cognitive decline. Multivariate logistic regression analysis showed that age ( β=0.035, OR=1.036, 95% CI=1.025-1.047), rural residents ( β=0.215, OR=1.239, 95% CI=1.047-1.468), chronic medical history ( β=0.191, OR=1.210, 95% CI=1.067-1.372), cerebrovascular disease ( β=0.480, OR=1.616, 95% CI=1.195-2.187), intervertebral disc disease ( β=0.456, OR=1.578, 95% CI=1.190-2.094) were risk factors for Alzheimer's disease. Higher education level and higher monthly household income were protective factors for Alzheimer's disease. Conclusion:The elderly with chronic diseases, low income and low education level may be at the high risk of cognitive function decline, which should be paid attention to in early screening and intervention.

Establishment of prognostic model for severe primary graft dysfunction in patients with idiopathic pulmonary fibrosis after lung transplantation / 器官移植

Objective To explore the establishment of a prognostic model based on machine learning algorithm to predict primary graft dysfunction (PGD) in patients with idiopathic pulmonary fibrosis (IPF) after lung transplantation. Methods Clinical data of 226 IPF patients who underwent lung transplantation were retrospectively analyzed. All patients were randomly divided into the training and test sets at a ratio of 7:3. Using regularized logistic regression, random forest, support vector machine and artificial neural network, the prognostic model was established through variable screening, model establishment and model optimization. The performance of this prognostic model was assessed by the area under the receiver operating characteristic curve (AUC), positive predictive value, negative predictive value and accuracy. Results Sixteen key features were selected for model establishment. The AUC of the four prognostic models all exceeded 0.7. DeLong and McNemar tests found no significant difference in the performance among different models (both P>0.05). Conclusions Based on four machine learning algorithms, the prognostic model for grade 3 PGD after lung transplantation is preliminarily established. The overall prediction performance of each model is similar, which may predict the risk of grade 3 PGD in IPF patients after lung transplantation.

DIGITAL SEARCH ADVERTISING – THE ROLE OF POST-CLICK FACTORS IN GENERATING PURCHASE CONVERSIONS

A lot of research is available on the effectiveness of search as an advertising channel. Most of these studies tend to treat a click on a search ad as a binary event. All of them study the events leading to the click. This paper goes beyond this to study the post click actions taken by a user subsequent to clicking on a search ad, and refers to those actions as depth of interaction, and testing the variables that have an effect on the nal outcome. We use a prescriptive research design employing binary logistic regression analysis. Results indicate that the duration of time spent, device used, and recency of visit have a very high positive effect on the nal outcome.

Factores predictivos de rotura de aneurismas intracraneales / Rupture Predictive Factors for Intracranial Aneurysms

Introducción: Un aneurisma intracraneal roto provoca una hemorragia subaracnoidea. La enfermedad presenta una alta mortalidad y morbilidad. Sin embargo, no todos se rompen. Mejorar la predicción de rotura permitirá un tratamiento quirúrgico preventivo en un grupo de pacientes y evitará una intervención quirúrgica con riesgos en otro grupo de enfermos. Es necesario identificar factores predictivos para mejorar la estratificación del riesgo de rotura y optimizar el tratamiento de los aneurismas intracraneales incidentales. Objetivo: Identificar factores predictivos de rotura de aneurismas intracraneales. Métodos: En una muestra de 152 pacientes espirituanos con aneurismas intracraneales saculares rotos (n = 138) y no rotos (n = 22) y 160 imágenes de angiografía por tomografía computarizada, se realizaron mensuraciones de los índices o factores morfológicos, los cuales se combinaron mediante análisis de regresión logística con variables demográficas y clínicas. Resultados: El grupo de edad con mayor frecuencia de presentación de aneurismas fue el de mayor de 65 años. La muestra estuvo representada, en su gran mayoría, por el sexo femenino. Se identificaron tres factores clínicos y cuatro factores morfológicos estadísticamente significativos, asociados con la rotura. El índice de no esfericidad (p = 0,002 y el sexo femenino (p = 0,02) fueron los de mayor significación estadística. Conclusiones: Se detectaron siete factores predictivos de rotura de aneurismas intracraneales estadísticamente significativos, de los cuales el índice de no esfericidad resultó el de mayor significación(AU)

Introduction: A ruptured intracranial aneurysm causes a subarachnoid hemorrhage. The disease has high mortality and morbidity. However, not all of them break. Improving the rupture prediction will allow preventive surgical treatment in a group of patients and it will avoid risky surgical intervention in another group of patients. It is necessary to identify predictive factors to improve rupture risk stratification and to optimize treatment of incidental intracranial aneurysms. Objective: To identify rupture predictive factors for intracranial aneurysms. Methods: Measurements of the morphological indices or factors were performed in a sample of 152 patients from Sancti Spiritus with ruptured (n = 138) and unruptured (n = 22) saccular intracranial aneurysms and 160 computed tomography angiography images. They were combined using logistic regression analysis with demographic and clinical variables. Results: The age group with the highest frequency of aneurysm presentation was older than 65. The sample was represented, in its vast majority, by the female sex. Three clinical factors and four statistically significant morphological factors associated with rupture were identified. The non-sphericity index (p = 0.002) and the female sex (p = 0.02) were the most statistically significant. Conclusions: Seven statistically significant predictors of intracranial aneurysm rupture were detected, the non-sphericity index being the most significant(AU)

A cross-sectional survey on burden of caregivers of pediatric leukemia patients attending a tertiary care hospital in Kolkata, India

Background: Care giving of children with leukemia involves considerable stress and anxiety on the part of family caregivers. Although caregivers’ burden is a crucial predictor of the health of both the child and the caregiver, it is often overlooked. Aim and Objectives: The present study aimed to assess the burden faced by caregivers of pediatric leukemia patients attending a tertiary care hospital in West Bengal, to elicit their sociodemographic characteristics and patients’ profile, and to find out relationship among these, if any. Materials and Methods: The study was descriptive observational type with cross-sectional design. It was conducted among caregivers of pediatric leukemia patients. Data were collected from 38 caregivers using predesigned, pretested, semi-structured schedule, and patients’ records. Burden was measured using Zarit Burden Interview, which is a 22 item 5-point Likert scale. Data were compiled and analyzed in Microsoft Excel and Statistical Software for the Social Sciences 20.0 for statistical analysis. Sociodemographic and clinical variables were expressed as number, percentages, mean, and standard deviations. To find out the association between different factors and caregiver burden, a logistic regression model was used. P < 0.05 was considered as statistically significant. Results: Majority of the caregivers were the mothers of the patients (68.42%), and most of the families of caregivers belonged to lower middle class according to modified BG Prasad Scale. Half of the caregivers (50%) experienced moderate–to-severe burden according to Zarit Burden Interview. Association was found between burden experienced and duration of disease and treatment. However, socioeconomic status was found to be the most significant determinant of burden as per multiple logistic regression by ENTER method. Conclusions: Majority of the caregivers were having moderate to severe and severe burden, which was significantly more among people coming from lower socioeconomic status. Prolonged disease duration and treatment were also found to be associated with increased burden of the caregivers.

Risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on the machine learning / 中国血吸虫病防治杂志

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

Death-Related Factors in Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease Treated by Sequential Mechanical Ventilation / 中国医学科学院学报

Objective To analyze the death-related factors of elderly patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) treated by sequential mechanical ventilation,so as to provide evidence for clinical practice. Methods The clinical data of 1204 elderly patients (≥60 years old) with AECOPD treated by sequential mechanical ventilation from June 2015 to June 2021 were retrospectively analyzed.The probability and influencing factors of death were analyzed. Results Among the 1204 elderly patients with AECOPD treated by sequential mechanical ventilation,167 (13.87%) died.Multivariate analysis showed that plasma procalcitonin ≥0.5 μg/L (OR=2.762, 95%CI=1.920-3.972, P<0.001),daily invasive ventilation time ≥12 h (OR=2.202, 95%CI=1.487-3.262,P<0.001),multi-drug resistant bacterial infection (OR=1.790,95%CI=1.237-2.591,P=0.002),oxygenation index<39.90 kPa (OR=2.447,95%CI=1.625-3.685,P<0.001),glycosylated hemoglobin >6% (OR=2.288,95%CI=1.509-3.470,P<0.001),and acute physiology and chronic health evaluation Ⅱ score ≥25 points (OR=2.126,95%CI=1.432-3.156,P<0.001) were independent risk factors for death in patients with AECOPD treated by sequential mechanical ventilation.Oral care>twice/d (OR=0.676,95%CI=0.457-1.000,P=0.048) and sputum excretion>twice/d (OR=0.492, 95%CI=0.311-0.776, P=0.002) were independent protective factors for death in elderly patients with AECOPD treated by sequential mechanical ventilation. Conclusions The outcomes of sequential mechanical ventilation in the treatment of elderly patients with AECOPD are affected by a variety of factors.To reduce the mortality,we put forward the following measures:attaching great importance to severe patients,restoring oxygenation function,shortening unnecessary invasive ventilation time,controlling blood glucose,preventing multidrug resistant bacterial infection,oral care twice a day,and sputum excretion twice a day.