Systematic review and meta-analysis of prediction models for multidrug-resistant organism infections in comprehensive intensive care units - Zai-Chun Pu; Xiao-Li Wei; Yan Zhou; Xiao-Li Liu; Zi-Ji Fang; Le-Le Li; Ping Jia (Journal of Global Antimicrobial Resistance, 2025)
Objective: To explore the predictive factors for infections caused by multidrug-resistant bacteria and to systematically evaluate risk prediction models for multidrug-resistant bacterial infections in comprehensive intensive care units (ICUs), with the aim of providing references for clinical medical personnel to establish and improve risk prediction models for such infections. Methods: A computer search was conducted in Chinese and English database for studies on the construction of risk prediction models for multidrug-resistant bacterial infections in comprehensive ICUs, with the search timeframe from the establishment of the database to 26 December 2024. The quality of the literature was assessed via the Prediction Model Risk Of Bias ASsessment Tool, and meta-analysis was performed via RevMan 5.4 and MedCalc software. Results: Among the 27 articles, 37 risk prediction models were constructed, with area under the receiver operating characteristic curve (AUC) values ranging from 0.718 to 0.992. A quality assessment of the literature indicated a high risk of bias and good applicability. A meta-analysis using MedCalc on AUC values revealed a combined modelling group AUC of 0.867. The meta-analysis revealed 12 risk factors that could predict multidrug-resistant infections. Conclusions: Current risk prediction models for multidrug-resistant bacterial infections in the ICU are still in the developmental stage. Most prediction models lack calibration methods and external validation, and only univariate analysis is used to select variables, resulting in a high risk of bias. Future efforts should focus on improving model construction methods and continuing to develop risk prediction models with higher accuracy.