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Establishment and Validation of a Risk Prediction Model for Mortality in Patients with Acinetobacter baumannii
Haiyan Song1,2, Hui Zhang1, Ding Zhang1
1Department of Infectious Disease, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People's Republic of China.
Purpose:
This study aims to establish a valuable risk prediction model for mortality in patients with Acinetobacter baumannii (A. baumannii).
Patients And Methods:
The 622 patients with A. baumannii infection from the First Affiliated Hospital of Anhui Medical University were enrolled as the study cohort. Univariate and multivariate logistic regression analysis was used to preliminarily screen the independent risk factors of death caused by A. baumannii infection, followed by LASSO regression analysis to determine the risk factors. According to the calculated regression coefficient, the Nomogram death prediction model is established. The area under the curve (AUC) and decision curve analysis (DCA) of the operating characteristic (ROC) curve of the subjects are used to evaluate the discrimination of the established prediction model. The calibration degree of the prediction model is represented by a calibration chart. A validation cohort that consisted of 477 patients admitted to the 901st Hospital was also included.
Results:
Our results revealed that the source of infection, carbapenem-resistant A. baumannii, mechanical ventilation, serum albumin value, and Charlson comorbidity index were independent risk factors for death caused by A. baumannii infection. The AUC value of ROC curves of study cohort and validation cohort were 0.76 and 0.69, respectively. The probability range (30-80%) indicated a high net income of the modified model and strong capacity of discrimination. The calibration curve obtained by analysis swings up and down around the 45 diagonal line, which shows that the calibration degree of the prediction model is very high.
Conclusion:
In this study, we have reconstructed a risk prediction model for mortality in patients with A. baumannii infections. This model provides useful information to predict the risk of death in patients with A. baumannii infection, but the specificity is not optimistic. If this prediction model is wanted to be applied to clinical practice, more analysis and research are necessary.
Insights
This study developed a risk prediction model for mortality in Acinetobacter baumannii infections. Key factors include infection source and mechanical ventilation, aiding clinical risk assessment.
Area of Science:
- Infectious Diseases
- Clinical Epidemiology
- Medical Informatics
Background:
- Acinetobacter baumannii (A. baumannii) poses a significant threat due to its increasing resistance.
- Accurate mortality prediction is crucial for managing A. baumannii infections.
Purpose of the Study:
- To establish a reliable risk prediction model for mortality in patients with A. baumannii infections.
- To identify independent risk factors associated with death in A. baumannii cases.
Main Methods:
- Logistic regression and LASSO analysis were employed to identify risk factors.
- A Nomogram prediction model was constructed based on regression coefficients.
- Model performance was evaluated using ROC curves (AUC) and decision curve analysis (DCA).
- A validation cohort was used to assess model generalizability.
Main Results:
- Independent risk factors for mortality included infection source, carbapenem-resistant A. baumannii, mechanical ventilation, serum albumin, and Charlson comorbidity index.
- The prediction model demonstrated good discrimination with AUC values of 0.76 (study cohort) and 0.69 (validation cohort).
- Calibration analysis indicated a high degree of accuracy for the model.
Conclusions:
- A novel risk prediction model for A. baumannii infection mortality was developed.
- The model offers valuable insights for predicting patient outcomes.
- Further research is needed to optimize specificity and facilitate clinical application.
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