2nd Affiliated Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, 310009, China
NCT Number: NCT04688216
1. A retrospective analysis was performed to determine the prevalence of multidrug- resistant organisms infection in ICU from October 2017 to October 2019. 2. Non-MDRO patients were selected by random sampling in a ratio of 1:1 to the final MDRO group during the same period , and select the risk factors of infection with multi-drug resistant bacteria by comparing the two groups. 3. Randomly select 30% of the sample size as the validation set, and the remaining 70% for the training set to establish a model. Using multi-factor Logistic regression, decision tree classification, artificial neural network, support vector machine, Bayesian network Method to establish risk assessment system for multidrug-resistant organisms infection respectively.Using validation set data to calculate the area under the ROC curve (AUC) and sensitivity, specificity of models and comparing the prediction accuracy of several models. Finally, choose a more suitable risk assessment system for multidrug-resistant organisms infection. 4. Predict the patient's infection risk level according to the best risk assessment system and develop a low-to-high intervention plan.
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Notify Me18 year and older
All sexes
Observational
Hangzhou, Zhejiang, 310009, China
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
:
Exclusion criteria
Time frame: From date of ICU admissions until the date of ICU discharge or date of diagnosis of multidrug-resistant organisms infection , whichever came first, assessed up to 24 months
Ratio of the number of multi-drug resistant bacterial infections to the total number of patients
Second Affiliated Hospital, School of Medicine, Zhejiang University
Other
Developing the Best Risk Assessment System of Multidrug-resistant Organisms Infection in Critically Ill Patients Based on Big Data Analysis Technology
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