Objective To investigate the influence of electrocardiogram(ECG)-controlled dose modulation on exposure dosage and image quality of cardiac CT imaging in a cardiac phantom with simulated fluctuant heart rate.Methods The basal heart rate of the cardiac pulsating phantom was set as 60 bpm.the experimental situations were divided as 6 groups according to different heart rates.The cardiac imaging was performed on the cardiac phantom when the ECG-controlled dose modulation was firstly turned off.The exposure dosage of each scan sequence was documented.The standard deviation of the CT values of the phantom was measured on the central slice after coronal reformation of the raw data.The quality of 2D and 3D images were scored.Thell cardiac imaging was performed when ECG modulation was on and set as four groups according to different modulation parameters.All the data were documented as before.The results from the five groups with and without ECG modulation current were analyzed bv F test and comparative rank sum test using the statistical software SPSS 10.0.Results Statistical analysis showed no significant difference(P＞0.05)between the SNR of images(SD value was 27.78 and 26.30)from the groups that full mA output at wide reconstruction phase(69%～99%)when the heart rate was fluctuant(≥7.5 bpm).There was also no significant difference(P＞0.05)between the quality of the 2D and 3D images.But there was a significant difference(P＜0.01)between the SNR of images(SD value was 26.78 and 29.90)that full mA was only used at 85%reconstruction phase when the heart rate Was fluctuant(≥7.5 bpm).The exposure dosage was remarkably reduced when the ECG modulated current was on than when it Was off under fluctuant heart rate.Furthermore.there were significant difierence(P＜0.01)among the difierent ECG modulated current parameter groups.The exposure dosage can be reduced by 44.7%under the situation that the heart rate was fluctuant.Whell the fluctuation of the heart rate was≤12.5 bpm,there wag no obvious relationship between the fluctuation of the heart rate and the exposure dosage (the variation was from 0.1 to 1.1 mSv),but if the heart rate fluctuation was＞12.5 bpm,the exposure dosage would increase obviously (from 0.6 to 1.7 mSv).Conclusion For cardiac imaging with 16-slice row CT,the application of ECG modulated current can effectively reduce the exposure dosage without compromising the image quality even if heart rate was fluctuant.

Humans ; Pulmonary Disease, Chronic Obstructive ; Smoke/adverse effects* ; Tobacco ; Tobacco Smoke Pollution

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.