OBJECTIVE To explore and analyze the risk factors for hospital-associated infection caused by carbapenem-resistant gram-negative organism (CRO) in patients in the intensive care unit (ICU), and to establish and validate a risk prediction model.

METHODS A retrospective collection was conducted on 338 patients with hospital-associated infection caused by gram-negative organism admitted to the adult ICU of Peking University Third Hospital from Jan. 2024 to Jun. 2024, serving as the modeling group. Additionally, 141 patients with hospital-associated infection caused by gram-negative organism who were admitted from Jul. 2024 to Dec. 2024 were included as the validation group. Based on the data from the modeling group, a Lasso logistic regression model was established. Risk factors were assigned scores based on β values, and a risk prediction model was developed. The model′s performance was verified by plotting the receiver operating characteristic (ROC) curve for predicting CRO hospital-associated infection risk in ICU patients.

RESULTS The logistic regression analysis indicated that mechanical ventilation, emergency admission, Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score ≥15, C-reactive protein level ≥10mg/L, use of enzyme inhibitor compound preparations and use of carbapenems were risk factors for CRO hospital-associated infection in the ICU patients in the modeling group (P < 0.05). In the corresponding risk prediction model, these factors were assigned scores of 2, 1, 1, 1, 1 and 2, respectively. The area under the ROC curve (AUC) of the risk model in the modeling group was 0.895 (95%CI: 0.860-0.930, P < 0.001), with a specificity of 81.02% and a sensitivity of 83.84%. In the validation group, the AUC was 0.764 (95%CI: 0.686-0.842, P < 0.001), with a specificity of 59.13% and a sensitivity of 77.30%.

CONCLUSIONS The risk assessment system constructed in this study has a good prediction effect for all ICU inpatients, including those in the modeling group and validation group. It allows for the early identification of potential high-risk groups and facilitates timely intervention.