ObjectiveConstruction of a risk prediction model for central line-associated bloodstream infections (CLABSI) in ICU patients based on machine learning algorithms to provide a basis for early identification of high-risk CLABSI patients. MethodsA total of 4 581 ICU patients with central venous catheters (CVC) from the MIMIC-IV database in the United States were selected as the research subjects, and their clinical data were retrospectively collected. Five machine learning algorithms, namely logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (Light GBM), and adaptive boosting (AdaBoost), were used to construct CLABSI risk prediction models for ICU patients. The predictive performance of these models was evaluated using metrics including F1 score, precision, recall, brier score, area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive Explanation (SHAP) method was used to perform interpretability analysis on the optimal model, based on which a CLABSI scoring table for ICU patients was developed. ResultsA total of 4 581 ICU patients with CVC were included, and the incidence of CLABSI was 2.82%. The AUC values of the five prediction models, including LR, RF, XGBoost, Light GBM, and AdaBoost, in the test set were 0.877, 0.824, 0.901, 0.856, and 0.790, respectively. Among them, XGBoost had the best predictive performance. The SHAP method analysis revealed that the following factors are risk factors for the occurrence of CLABSI in ICU patients: the use of ≥3 types of antibiotics, high SOFA scores, high APS Ⅲ scores, the administration of immunosuppressants, low albumin levels, and age ≥60 years. In contrast, internal jugular vein catheterization and subclavian vein catheterization were identified as protective factors. The total score of the CLABSI scoring table for ICU patients constructed based on the XGBoost model ranged from 0 to 268 points. When the optimal cut-off value was 172 points, the AUC was 0.796. ConclusionThe risk prediction model and scoring table for CLABSI in ICU patients constructed based on XGBoost demonstrate good predictive efficacy and clinical applicability.
ObjectiveTo systematically review the risk prediction model of intensive care unit (ICU) readmissions. MethodsCNKI, WanFang Data, VIP, CBM, PubMed, EMbase, Web of Science and The Cochrane Library databases were electronically searched to collect the related studies on risk prediction models of ICU readmissions from inception to June 12th, 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, the qualitative systematic review was performed. ResultsA total of 15 studies involving 23 risk prediction models were included. The area under the ROC curve of the models was 0.609-0.924. The most common five predictors of the included model were age, length of ICU hospitalization, heart rate, respiration, and admission diagnosis. ConclusionThe overall prediction performance of the risk prediction model of ICU readmissions is good; however, there are differences in research types and outcomes, and the clinical value of the model needs to be further studied.