Risk Factors for Carbapenem Resistant Gram Negative Bacteria (CR-GNB) Carriage Upon Admission to the Gastroenterology Department in a Tertiary First Class Hospital of China: Development and Assessment of a New Predictive Nomogram

Abstract

Hongchen Zhang,1– 3,* Shanshan Hu,1– 3,* Dongchao Xu,1– 3 Hongzhang Shen,1– 3 Hangbin Jin,1– 3 Jianfeng Yang,1– 3 Xiaofeng Zhang1– 3 1The Department of Gastroenterology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Zhejiang, People’s Republic of China; 2Key Laboratory of Integrated Traditional Chinese and Western Medicine for Biliary and Pancreatic Diseases of Zhejiang Province, Zhejiang, People’s Republic of China; 3Hangzhou Institute of Digestive Disease, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaofeng Zhang, Department of Gastroenterology, Hangzhou First People’s Hospital, NO. 261 HuanSha Road, Hangzhou, 310006, People’s Republic of China, Tel [phone], Fax [phone], Email [email protected]: With the increasing number of critically ill patients in the gastroenterology department (GED), infections associated with Carbapenem resistant gram-negative bacteria (CR-GNB) are of great concern in GED. As the turn-around time (TAT) for a positive screening culture result is slow, contact precaution and pre-emptive isolation, cohorting methods should be undertaken immediately on admission for high-risk patients. Accurate prediction tools for CR-GNB colonization in GED can help determine target populations upon admission. And thus, clinicians and nurses can implement preventive measures more timely and effectively.Objective: The purpose of the current study was to develop and internally validate a CR-GNB carrier risk predictive nomogram for a Chinese population in GED.Methods: Based on a training dataset of 400 GED patients collected between January 2020 and December 2021, we developed a model to predict CR-GNB carrier risk. A rectal swab was used to evaluate the patients’ CR-GNB colonization status microbiologically. We optimized features selection using the least absolute shrinkage and selection operator regression model (LASSO). In order to develop a predicting model, multivariable logistic regression analysis was then undertaken. Various aspects of the predicting model were evaluated, including discrimination, calibration, and clinical utility. We assessed internal validation using bootstrapping.Results: The prediction nomogram includes the following predictors: Transfer from another hospital (Odds ratio [OR] 3.48), High Eastern Cooperative Oncology Group (ECOG) performance status (OR 2.61), Longterm in healthcare facility (OR 10.94), ICU admission history (OR 9.03), Blood stream infection history (OR 3.31), Liver cirrhosis (OR 4.05) and Carbapenem usage history within 3 month (OR 2.71). The model demonstrated good discrimination and good calibration.Conclusion: With an estimate of individual risk using the nomogram developed in this study, clinicians and nurses can take more timely infection preventive measures on isolation, cohorting and medical interventions.Keywords: carbapenem resistant gram-negative bacteria, carrier risk, gastroenterology department, predictive nomogram, screen culture

Keywords

• carbapenem resistant gram-negative bacteria
• carrier risk
• gastroenterology department
• predictive nomogram
• screen culture