Research on early warning model of the hearing loss of workers exposed to noise / 中华劳动卫生职业病杂志

Objective: To explore the change of hearing threshold of workers exposed to noise, establish an individual-based hearing loss early warning model, accurately and differentiated the health of workers exposed to noise. Methods: In September 2019, all physical examination data of 561 workers exposed to noise from an enterprise were collected since their employment. Three indicators of average hearing threshold of the better ear, namely, at high frequency, 4000 Hz and speech frequency, were constructed. The generalized estimating equation (GEE) was used to adjust gender and age and establish the warning model of each indicator. Finally, sensitive indicators and warning models were screened according to AUC and Yoden index. Results: Among the 561 workers exposed to noise, 26 (4.6%) workers had hearing loss. The sensitivity indicators were the average hearing threshold at speech frequency ≥20 dB, high frequency ≥30 dB and 4000 Hz ≥25 dB. The AUC of each index was 0.602, 0.794 and 0.804, and the Youden indexes were 0.204, 0.588 and 0.608, respectively. In GEE of hearing loss warning models, high-frequency hearing threshold ≥20 dB and 4000 Hz hearing threshold ≥25 dB were the optimal models, with AUC of 0.862. Conclusion: Combined with the changes of individual hearing threshold over the years, can accurately assess the risk of individual hearing loss of workers exposed to noise.

A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network / 生物医学与环境科学（英文）

Objectives@#Hand, foot and mouth disease (HFMD) is a widespread infectious disease that causes a significant disease burden on society. To achieve early intervention and to prevent outbreaks of disease, we propose a novel warning model that can accurately predict the incidence of HFMD.@*Methods@#We propose a spatial-temporal graph convolutional network (STGCN) that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019. The 2011-2018 data served as the training and verification set, while data from 2019 served as the prediction set. Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.@*Results@#As the first application using a STGCN for disease forecasting, we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level, especially for cities of significant concern.@*Conclusions@#This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance, which may significantly reduce the morbidity associated with HFMD in the future.

Early Warning Model of Fall Risk for the Elderly Based on Gait Characteristics / 医用生物力学

Objective To construct an early warning model of fall risk for the elderly based on six kinds gait parameters. Methods A digital field was used to collect parameters from six kinds of gait for the elderly with or without the history of falls, and the binomial logistic regression analysis was used to establish a regression equation for predicting the fall risks in the elderly, and an early warning model was constructed. Results The regression equations constructed according to the parameters from six kinds of gait were statistically significant. The overall correct rate was predicted from high to low: walking forward with closed eyes (97.1%), walking backward with open eyes (92.9%), walking backward with closed eyes (88.6%), walking forward with open eyes (87.1%), turning head up and down with open eyes (85.7%), turning head left and right with open eyes(82.9%). The constructed early warning model for fall risk of the elderly mainly included five steps, namely, judgment, test, extraction, calculation and early warning, which was suitable for gait testing and evaluation of the elderly in the laboratory. Conclusions Parameters from six kinds of gait could predict the fall risk of the elderly. Among them, walking forward with closed eyes was best to predict the fall risk in the elderly. The established early warning model of fall risk for the elderly could be used to predict the fall risk of 65-75 year old people within one year, which could provide early warning based on the probability of falling, playing a positive effect on preventing falls in the elderly.

Risk index and early-warning model of H7N9 human infections / 预防医学

Objective To establish the risk index of early-warning on the human infections with avian influenza A (H7N9) virus. Methods The risk index (X) was calculated by using Principal Component Analysis based on the surveillance results (including the positive rates of environmental specimens and premises) during the period from April 2013 to March 2017 in Zhejiang Province. Then, the method of Classification and Regression Trees was used to establish the early-warning model for forewarning the epidemic situation of H7N9 human infections. Results The weights of two rates (the positive rates of specimens and premises) used to establish the risk index were 0.0545 and 0.0230 respectively. In the model of Classification and Regression Trees, risk index was divided into 4 grades: X ≤0.140, 0.140<X ≤0.757, 0.757<X ≤3.285 and X>3.285. Compared to the 1st grade, the risk ratios of the 2nd, 3rd and 4th grades were 7.4, 21.7 and 29.9 respectively. The accuracy, sensitivity and specificity of prediction were 86.1％ , 80.8％ and 87.3％ respectively, and the Kappa value was 0.592 . Conclusion The established risk index can be used to forewarn the H7N9 human infections, which is helpful for emergency preparedness and disease control.

Construction of death early-warning model for patients with septic myocardial depression: a retrospective analysis of 129 patients / 中华危重病急救医学

Objective To explore the death risk factors of septic myocardial depression (SMD) and their predictive effect, and to set up a death early-warning model. Methods A retrospective analysis was conducted. The patients with SMD admitted to emergency department and rescue room of Beilun Branch of the First Affiliated Hospital of Zhejiang University Medical College from January 2015 to November 2017 were enrolled. The patients were divided into survival group and non-survival group according to 28-day outcome, and the gender, age, and the initial examination parameters [white blood cell (WBC) count, neutrophil (Neut) count, activated partial thromboplastin time (APTT), procalcitonin (PCT), D-dimer, C-reactive protein (CRP), cardiac troponin I (cTnI), N-terminal pro-brain natriuretic peptide (NT-proBNP), left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDD), and left atrium diameter (LAD)] of both groups were compared. Binary logistic regression analysis was conducted on the factors with statistically significant difference analyzed in univariate analysis, and death early-warning model was set up subsequently. For parameters in early-warning model after variable screening, receiver operating characteristic curve (ROC) was applied to evaluate the predictive effect of death. Results A total of 129 patients were enrolled, 34 patients died within 28 days with the mortality of 26.4%. Univariate analysis showed that the PCT, cTnI and NT-proBNP in non-survival group were significantly higher than those of the survival group. However, there was no statistical difference in gender, age, WBC, Neut, APTT, D-dimer, CRP, LVEF, LVEDD or LAD between the two groups. Logistic stepwise regression analysis showed that PCT and cTnI were the independent factors influencing the death of patients with SMD [PCT: odds ratio (OR) =1.495, 95% confidence interval (95%CI) = 1.192-1.876, P = 0.001; cTnI: OR = 11.154, 95%CI = 5.709-17.264, P = 0.004], and the death early-warning model was logP =-3.737+0.402×PCT+2.412×cTnI. According to the statistics of Homser-Lemeshow, the effect of this model was good (χ2= 6.258, P = 0.617). The analysis of ROC displayed that the area under ROC curve (AUC) of the combination of PCT and cTnI for predicting the prognosis of SMD patients was 0.851, and it was significantly higher than that of PCT and cTnI alone (0.738 and 0.719, respectively, both P < 0.05). When the combination of PCT and cTnI was 0.26, the sensitivity was 79.97%, the specificity was 87.01%, the positive predictive value was 71.3%, and the negative predictive value was 91.7%. Conclusions PCT and cTnI are independent factors influencing the death of SMD patients. The combination of PCT and cTnI has predictive value for the prognosis of SMD patients. The death early-warning model of SMD patients can be used to predict the prognosis of SMD patients.

Early warning model for vulnerable plaque biomarker / 实用医学杂志

Objective To analyse the risk factors of vulnerable plaque biomarker and to construct an early warning system. Methods Ninety patients with suspected acute coronary syndrome (ACS) hospitalized during December 2012 and December 2013 were selected. The coronary artery lesions were divided into type I, II and III plaque groups by the morphology of atherosclerotic plaque. Serum SAA, PLGF, sCD40L and Npt were measured. The results of SAA, PLGF, sCD40L and Npt were compared. Logistic regression model was fitted to explore the main influencing factors of the vulnerable plaque. Results SAA, PLGF, sCD40L, and Npt were main influencing factors of the vulnerable plaques, and the ORs were 1.61, 1.88, 1.96 and 1.79 respectively. Conclusion The detection of SAA, PLGF, sCD40L and Npt biochemical markers in patients with chest pain is important for predicting the vulnerable plaque and guiding clinical treatment.