Related Experiment Video
Updated: Jul 13, 2025
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Development and Validation of a Nomogram Prediction Model for Multidrug-Resistant Organisms Infection in a
Ya Wang1, Jiajia Zhang2, Xiaoyan Chen1
1Neurosurgical Intensive Care Unit, Department of Neurosurgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, People's Republic of China.
Objective:
To develop a predictive model for assessing the risk of multidrug-resistant organisms (MDROs) infection and validate its effectiveness.
Abstract:
We conducted a study on a total of 2516 patients admitted to the neurosurgery intensive care unit (NICU) of a Grade-III hospital in Nantong City, Jiangsu Province, China, between January 2014 and February 2022. Patients meeting the inclusion criteria were selected using convenience sampling. The patients were randomly divided into modeling and validation groups in a 7:3 ratio. To address the category imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to adjust the MDROs infection ratio from 203:1558 to 812:609 in the training set. Univariate analysis and logistic regression analysis were performed to identify risk factors associated with MDROs infection in the NICU. A risk prediction model was developed, and a nomogram was created. Receiver operating characteristic (ROC) analysis was used to assess the predictive performance of the model.
Results:
Logistic regression analysis revealed that sex, hospitalization time, febrile time, invasive operations, postoperative prophylactic use of antibiotics, mechanical ventilator time, central venous catheter indwelling time, urethral catheter indwelling time, ALB, PLT, WBC, and L% were independent predictors of MDROs infection in the NICU. The area under the ROC curve for the training set and validation set were 0.880 (95% CI: 0.857-0.904) and 0.831 (95% CI: 0.786-0.876), respectively. The model's prediction curve closely matched the ideal curve, indicating excellent predictive performance.
Conclusion:
The prediction model developed in this study demonstrates good accuracy in assessing the risk of MDROs infection. It serves as a valuable tool for neurosurgical intensive care practitioners, providing an objective means to effectively evaluate and target the risk of MDROs infection.
Insights
A new model accurately predicts multidrug-resistant organism (MDRO) infections in neurosurgery intensive care units. This tool helps clinicians identify high-risk patients for targeted interventions, improving patient outcomes and infection control.
Area of Science:
- Medical Microbiology
- Infectious Diseases
- Intensive Care Medicine
Background:
- Multidrug-resistant organisms (MDROs) pose a significant threat in intensive care settings.
- Accurate risk assessment is crucial for timely intervention and infection control.
Purpose of the Study:
- To develop and validate a predictive model for MDRO infection risk in neurosurgery intensive care unit (NICU) patients.
- To identify key risk factors associated with MDRO infections in this population.
Main Methods:
- A cohort of 2516 patients from a Chinese Grade-III hospital NICU was analyzed.
- Synthetic Minority Over-sampling Technique (SMOTE) was used to address class imbalance.
- Logistic regression and ROC analysis were employed to build and evaluate the predictive model.
Main Results:
- Independent predictors of MDRO infection included sex, hospitalization duration, fever duration, invasive procedures, and catheter indwelling times.
- The developed model demonstrated strong predictive performance with an AUC of 0.880 in the training set and 0.831 in the validation set.
Conclusions:
- The developed predictive model offers high accuracy for assessing MDRO infection risk in NICU patients.
- This tool can aid neurosurgical intensive care practitioners in objective risk evaluation and targeted management of MDRO infections.
