Objective To study the value of CT diagnosis of atraumatic acute abdominal disease and how to select CT scan rationally.Methods The CT findings of 319 cases of atraumatic acute abdominal disease were reviewed restrospectively.Results Of 319 cases,226 cases of non-traumatic acute abdomen had positive findings on CT,the positive rate was 70.84%.Of them,the diseases included:urinary tract system in 63 cases,bile system in 62 cases,pancreas in 41 cases,gastro-intestinal system in 37 cases and others in 23 cases.The positive rate of CT findings was higher with aging in non-traumatic acute abdomen.Conclusion CT is of diagnostic value in atraumatic acute abdominal disease.

Objective To compare the cost and utility of aflibercept and conbercept for the treatment of wet age-related macular degeneration(wetAMD),in order to provide a reference for the selection of treatment regimens from the perspective of pharmacoeconomics.Methods The incremental cost-utility ratio(ICUR)was obtained by using Markov model to simulate the survival of the two treatment regimens over the 5-year period,calculating costs and health outputs separately.Univariate sensitivity analysis was used to determine the impact of the parameter on ICUR,and probability sensitivity analysis was used to determine the influence of the uncertainty of each model parameter on the research results.One times the 2022 gross domestic product(GDP)per capita of China was used as the willingness-to-pay threshold(WTP)to judge its economy.Results Over the simulation period,the compazine regimen was significantly economical against the aflibercept regimen,with an ICUR of-1 528 840 per quality-adjusted life year(QALY),which was lower than the WTP.Univariate sensitivity analysis showed that the transition probability between mild and moderate visual status between the two regimens and the number of aflibercept injections per year were significant influencing factors of ICUR.Probabilistic sensitivity analysis pointed to a significant cost-utility advantage for conbercept at a WTP of one times GDP(probability of 65.9％),which was a more robust result.Conclusion For the treatment of wetAMD,conbercept has a cost-utility advantage compared with aflibercept.

ObjectiveIn view of the problems of large errors and poor accuracy in pulmonary function testing in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), a predictive classification model of pulmonary function in patients with AECOPD was proposed by comparing the prediction performance of different machine learning models to find the optimal model. MethodsFrom January, 2018 to February, 2020, 90 patients with different degrees of COPD from the Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University were collected. Six machine learning model algorithms (K-nearest neighbor, logistic regression, support vector machine, naive Bayes, decision tree and random forest) were used to establish AECOPD predictive classification models. Their area under the curve of receiver operating characteristic (AUC-ROC) and accuracy were compared. Ten-fold cross-validation method was used to validate the data set. ResultsThe model based on random forest worked best in predicting and classifying AECOPD patients, with an accuracy rate of 0.844 and an AUC-ROC of 0.916. ConclusionRandom forest-based predictive model is a powerful tool for identifying patients with AECOPD, providing decision support when it is difficult to give a definitive diagnosis.