ABSTRACT
Objective:To investigate urinary microalbumin to creatinine ratio (ACR) and α1-microglobulin to creatinine ratio (MCR) of people aged 40 years old and above in Shanxi province, and analyze the influencing factors of abnormal ACR and MCR, and to provide evidence for the prevention and control of chronic kidney diseases.Methods:It was a cross-sectional study. The data came from a screening study of chronic kidney diseases conducted by Shanxi Provincial People's Hospital from April to November 2019, involving aged 40 years old and above from 10 counties (Ningwu county, Yu county, Yangqu county, Lin county, Shouyang county, Zezhou county, Huozhou city, Hejin city, Linyi county and Ruicheng county) in Shanxi province. The related data were collected through questionnaire surveys, physical examinations, and blood and urine sample collection. Urinary α1-microglobulin, creatinine, and microalbuminuria were measured. Urinary ACR and MCR were calculated using urinary creatinine correction. ACR abnormality was defined as ≥30 mg/g, and MCR abnormality was defined as >23 mg/g. Covariate analysis was used to control confounding factors, and adjusted urinary ACR and MCR of 10 counties were calculated. Spearman correlation analysis and chi-square test were performed to analyze the factors associated with abnormal urinary ACR and MCR. Logistic regression analysis model was used to identify the influencing factors of abnormal urinary ACR and MCR.Results:A total of 12 285 residents were enrolled in the study, including 5 206 males (42.4%) and 7 079 females (57.6%). The median age was 58.0 (51.0, 66.0) years old. The median urinary ACR was 7.5 (4.5, 15.7) mg/g, and the median urinary MCR was 10.2 (6.4, 16.2) mg/g. A total of 1 572 individuals (12.80%) had urinary ACR abnormality and 1 450 individuals (11.80%) had urinary MCR abnormality. Yangqu county, Yuxian county, and Ningwu county had higher urinary ACR with (35.58±3.04) mg/g, (34.08±4.50) mg/g and (32.09±3.19) mg/g, respectively. The urinary MCR was generally similar among the 10 counties and Yangqu county had higher urinary MCR with (13.86±0.41) mg/g. In addition to Yu county, female individuals had higher urinary ACR compared to males in other counties, whereas female individuals had lower urinary MCR compared to males in 10 counties. Multivariate logistic regression analysis results showed that elevated triglyceride, fasting blood glucose, glycated hemoglobin, systolic blood pressure, diastolic blood pressure, age, body mass index and gender were independent influencing factors of abnormal urinary ACR and MCR (all P<0.05). Elevated blood homocysteine and low educational level were independent influencing factors of urinary MCR abnormality (both P<0.05). Conclusions:There are differences of gender and region in urinary ACR and MCR among individuals aged 40 years old and above in the 10 counties of Shanxi province. Triglyceride, fasting blood glucose, glycated hemoglobin, systolic blood pressure, diastolic blood pressure, age, gender, and body mass index are independent related factors of abnormal urinary ACR and MCR. Blood homocysteine and education level are independent related factors of abnormal urinary MCR.
ABSTRACT
Objective:To construct a Bayesian network risk prediction model for delirium during recovery from general anesthesia. To explore the network relationship between awakening delirium of general anesthesia and its related factors, and to reflect the influence intensity of each factor on awakening delirium of general anesthesia through network reasoning.Methods:This is a cross-sectional study. From February to May 2022, the Chinese version of the four rapid delirium diagnosis protocols for general anesthesia patients admitted to the department of Anesthesia, the First Hospital of Shanxi Medical University were adopted as research subjects through convenience sampling method to carry out the delirium screening program during awakening, and general information and blood sample laboratory test results of the subjects were collected. The single factor analysis was used to screen the correlative factors of awakening delirium and a Bayesian network model based on the maximum minimum climb method (MMHC) was constructed.Results:A total of 480 patients were included in the study, and the delirium rate during the recovery period of general anesthesia was 12.9%(62/480). The Bayesian network of awakening delirium consisted of 11 nodes and 18 directed edges. The Bayesian network showed that age, sodium, cerebral infarction and hypoproteinemia were the direct factors related to awakening delirium, while ASA grade, hematocele and hemoglobin were the indirect factors related to awakening delirium. The area under its ROC curve was 0.80(0.78-0.83).Conclusions:Bayesian networks can well reveal the complex network connections between awakening delirium and its related factors, and then prevent and control awakening delirium accordingly.