Construction of nosocomial multi-drug resistant bacterias infection risk model of trauma patients undergoing surgery.
Objective: To analyze the risk factors of nosocomial multi-drug resistant bacterias (MDRO) infection in trauma patients undergoing surgery by Lasso "Logistic regression analysis and classification tree(CHAID) algorithm, build a risk prediction model and compare the results. Methods: The clinical dat...
| Publicado en: | Chinese Nursing Research Vol. 39; no. 3; pp. 361 - 368 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Chinese Nursing Research Editorial Office
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183360745&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183360745 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10096493 YV6 jtl: Chinese Nursing Research issn: 10096493 maglogo: N pubinfo: dt: Feb2025 vid: 39 iid: 3 pid: 37375 pub: Chinese Nursing Research Editorial Office artinfo: ui: 183360745 183360745 183360745 10.12102/j.issn.1009-6493.2025.03.002 183360745 ppf: 361 ppct: 7 formats: tig: atl: Construction of nosocomial multi-drug resistant bacterias infection risk model of trauma patients undergoing surgery. aug: au: GUO Leilei QIN Hongying WU Zhenzhen ZHANG Yi ZHAO Zhichen affil: Zhengzhou Central Hospital Affiliated to Zhengzhou University, Henan 450007 China sug: subj: Emergency Patients Surgical Patients Cross Infection Risk Factors Drug Resistance, Multiple Risk Assessment Prediction Models Algorithms Human China Retrospective Design Logistic Regression ROC Curve Goodness of Fit Chi Square Test Descriptive Statistics Confidence Intervals Predictive Validity ab: Objective: To analyze the risk factors of nosocomial multi-drug resistant bacterias (MDRO) infection in trauma patients undergoing surgery by Lasso "Logistic regression analysis and classification tree(CHAID) algorithm, build a risk prediction model and compare the results. Methods: The clinical data of trauma inpatients in Zhengzhou University Affiliated Zhengzhou Central Hospital from January 2019 to January 2022 were retrospectively analyzed. The risk prediction models were established by CHAID algorithm and Lasso-Logistic regression, respectively. The goodness of fit test was used to evaluate the effect of the model, and the area under the receiver operating characteristic curve (ROC) curve (AUC) was used to compare the advantages and disadvantages of the two prediction models. Results: A total of 821 trauma patients were included as the modeling group, including 191 trauma patients with MDRO 23.26%; Classification tree model and logistic regression showed that APACHE II score ≥ 20 scores, fever days ≥ 3 days, hospitalization days ≥ 10 days, PCT level ≥ 0.5 ng/L on admission were independent risk factors for postoperative MDRO infection in trauma patients. The risk prediction accuracy of classification tree model was 79.2%, and the model fit effect was good. The Hosmer-Lemeshow goodness of fit test for Lasso-Logistic regression showed that the fitting effect of the model was relatively good (P=0.146). And the Bootstrap internal validation showed that the prediction ability of the model was good. The AUC of classification tree model was 0. 792 (95% CI 0.763-0.819),and the AUC of Lasso-Logistic regression model was 0. 862 (95%CI 0.836-0.885),the predictive value of the two models were medium. The difference between the two models was statistically significant (P<0.001). Net Reclassification Index(NRI) evaluation indicated that the Lasso-Logistic regression model was superior to the classification tree model (NRI=0.153 6). Conclusion: Both models could provide a more intuitive form of presentation. The complementary combination of the two models could early identify the risk factors of postoperative MDRO infection in trauma patients from different perspectives. We should take effective prevention and control measures to reduce the incidence of MDRO nosocomial infection. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: Chinese refInfo: holdings: @attributes: islocal: N |
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