Development and application of a nomogram model for the prediction of carbapenem-resistant Klebsiella pneumoniae infection in neuro-ICU patients

Abstract

ABSTRACT This study aimed to develop and validate a simple-to-use nomogram model to assess the risk of infection caused by carbapenem-resistant Klebsiella pneumoniae (CRKP) in neurocritically ill patients. The clinical data of 544 patients with CRKP infection admitted to a neurointensive care unit (neuro-ICU) were retrospectively analyzed. The demographic data, laboratory test results, and clinical characteristics of patients in the neuro-ICU were collected. Subsequently, multivariate regression analysis was used to construct a nomogram to predict the risk of CRKP infection in these patients. The calibration ability, clinical effectiveness, and discriminative ability of the nomogram were evaluated. The incidence of CRKP infection was estimated to be 6.43%, and a majority of bacterial isolates causing the infection were found in sputum (74.3%). Multivariate regression analysis showed that the number of antibiotics of ≥2 [odds ratio (OR): 9.08, 95% confidence interval (CI): 2.78–29.71], undergoing surgery (OR: 3.84, 95% CI: 1.09–13.54), and long neuro-ICU stay (OR: 1.08, 95% CI: 1.01–1.14) were associated with CRKP infection in neurocritically ill patients. The nomogram model demonstrated good calibration and discrimination in both the training and validation sets, with area under the curve values of 0.860 and 0.907, respectively. This study developed and validated a nomogram that combines three easily accessed variables during clinical practice to predict the risk of nosocomial CRKP infection in neuro-ICU patients. The tool demonstrated a good predictive performance and discrimination, which might serve as a useful tool to facilitate early detection and reduction of the CRKP infection risk in neurocritically ill patients. IMPORTANCE Patients in neuro-ICU are at a high risk of developing nosocomial CRKP infection owing to complex conditions, critical illness, and frequent invasive procedures. However, studies focused on constructing prediction models for assessing the risk of CRKP infection in neurocritically ill patients are lacking at present. Therefore, this study aims to establish a simple-to-use nomogram for predicting the risk of CRKP infection in patients admitted to the neuro-ICU. Three easily accessed variables were included in the model, including the number of antibiotics used, surgery, and the length of neuro-ICU stay. This nomogram might serve as a useful tool to facilitate early detection and reduction of the CRKP infection risk of neurocritically ill patients.