Construction and validation of a predictive model for the risk of multidrug-resistant Klebsiella pneumoniae infection
Tao Sun1, Pei-Pei Wang2, Jun-Ji Liu3
1Department of Hematology and Oncology Laboratory, The Central Hospital of Shaoyang, Shaoyang, Hunan Province, China.
Objective:
The development of a reliable predictive model for Multi-drug-resistant Klebsiella pneumoniae (MDR-KP) infections is imperative for the timely identification of at-risk individuals.
Methods:
This study analyzed data from 3,554 patients with KP infection at multiple hospitals. By comparing six machine learning algorithms (Logistic Regression(LR), Efficient Neural Network (ENet), Decision Tree(DT), MultiLayer Perceptron(MLP), Support Vector Machine(SVM), and Extreme Gradient Boosting(XGBoost)), we constructed and validated the prediction model. Furthermore, the model interpretation was conducted through SHapley Additive exPlanations (SHAP) analysis. Subsequently, nomograms were developed to estimate the risk of MDR-KP infection in hospitalized individuals. Finally, the association between independent variables and the risk of MDR-KP acquisition was elucidated through Restricted Cubic Spline (RCS) analysis.
Results:
The results of the multivariable logistic regression analysis indicated that C-reactive protein (CRP), Uric Acid (UA), Urea, Platelet (PLT), Hemoglobin (HB), Red blood cell counts (RBC), Age, and Gender were identified as independent risk factors. We incorporated these independent risk factors into six machine learning, and found that the XGBoost-based model exhibited superior performance compared to other machine learning algorithms, achieving a recall of 0.732, an F1 score of 0.707, and an AUC of 0.777. Furthermore, the SHAP method highlighted Urea, UA, and PLT as the primary decision factors predicted by the machine learning model, and the RCS analysis revealed a nonlinear relationship between Age, CRP, RBC, HB, UA, UREA, and the risk of MDR-KP infection.
Conclusion:
This study has developed an effective risk prediction model for MDR-KP infection. The model has the potential to assist healthcare providers in early identification of high-risk patients, enabling timely implementation of preventive and therapeutic interventions.
Insights
This study developed an effective prediction model for multi-drug-resistant Klebsiella pneumoniae (MDR-KP) infections. The model identifies high-risk patients early, enabling timely interventions for better patient outcomes.
Area of Science:
- Infectious Diseases
- Medical Informatics
- Computational Biology
Background:
- Multi-drug-resistant Klebsiella pneumoniae (MDR-KP) poses a significant threat in healthcare settings.
- Timely identification of patients at risk for MDR-KP infections is crucial for effective management.
Purpose of the Study:
- To develop and validate a reliable predictive model for MDR-KP infections.
- To identify key risk factors associated with MDR-KP acquisition.
- To create nomograms for estimating MDR-KP infection risk in hospitalized patients.
Main Methods:
- Analysis of data from 3,554 patients with KP infection.
- Comparison of six machine learning algorithms (LR, ENet, DT, MLP, SVM, XGBoost).
- Model interpretation using SHapley Additive exPlanations (SHAP) and Restricted Cubic Spline (RCS) analysis.
Main Results:
- XGBoost model demonstrated superior performance (AUC=0.777, Recall=0.732, F1=0.707).
- Identified independent risk factors: C-reactive protein (CRP), Uric Acid (UA), Urea, Platelet (PLT), Hemoglobin (HB), Red blood cell counts (RBC), Age, and Gender.
- SHAP analysis highlighted Urea, UA, and PLT as primary predictors; RCS revealed nonlinear relationships for several factors.
Conclusions:
- An effective risk prediction model for MDR-KP infection was successfully developed.
- The model can aid healthcare providers in early detection of high-risk patients.
- Facilitates timely implementation of preventive and therapeutic strategies against MDR-KP.
Related Concept Videos
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
01:26Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
01:15Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions