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Development and Validation of a Model for Predicting the Risk of Death in Patients with Acinetobacter baumannii  Infection: A Retrospective Study

 

Authors Zhang H, Zhao Y, Zheng Y, Kong Q, Lv N, Liu Y, Zhao D, Li J, Ye Y

Received 9 March 2020

Accepted for publication 10 July 2020

Published 10 August 2020 Volume 2020:13 Pages 2761—2772

DOI https://doi.org/10.2147/IDR.S253143

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Sahil Khanna

Purpose: This study aimed to develop and validate a personalized prediction model of death risk in patients with Acinetobacter baumannii  (A. baumannii ) infection and thus guide clinical research and support clinical decision-making.
Patients and Methods: The development group is comprised of 350 patients with A. baumannii  infection admitted between January 2013 and December 2015 in The First Affiliated Hospital of Anhui Medical University. Further, 272 patients in the validation group were admitted between January 2016 and December 2018. The univariate and multivariate logistic regression analyses were used to determine the independent risk factors for death with A. baumannii  infection. The nomogram prediction model was established based on the regression coefficients. The discrimination of the proposed prediction model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves and decision curve analysis (DCA). The calibration diagram was used to evaluate the calibration degree of this model.
Results: The infectious source, carbapenem-resistant A. baumannii  (CRAB), hypoalbuminemia, Charlson comorbidity index (CCI), and mechanical ventilation (MV) were independent risk factors for death. The AUC of the ROC curve of the two groups was 0.768 and 0.792, respectively. The net income was higher when the probability was between 30% and 80%, showing a strong discrimination capacity of the proposed model. The calibration curve swung around the 45° oblique line, indicating a high degree of calibration.
Conclusion: The proposed model helped predict the risk of death from A. baumannii  infection, improve the early identification of patients with a higher risk of death, and guide clinical treatment.
Keywords: Acinetobacter baumannii , carbapenem resistance, prediction model, risk factors