Four Biomarkers-Based Artificial Neural Network Model for Accurate Early Prediction of Bacteremia with Low-level Procalcitonin
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Published on: August 16, 2020
Mingkuan Su1,2, Hongbin Chen3,2, Jie Qiu3,2
1Department of Laboratory Medicine, Mindong Hospital of Ningde City, Fuan City, China mingkuansu@qq.com 22539160@qq.com.
Objective:
Procalcitonin levels above 2.0 ng/mL are associated with a higher risk of severe sepsis. Bacteremia with procalcitonin levels lower than 2.0 ng/mL has not received much attention, and relevant prediction models are lacking. Herein, a panel of biomarkers was used to establish an early predictive model for bacteremia using machine learning approaches.
Methods:
Retrospective evaluation of 487 non-bacteremia controls and 444 bacteremia patients with low-level procalcitonin was performed. Clinical data, including procalcitonin, interleukin-6, C-reactive protein, D-dimer, and lactic acid levels, as well as leukocyte, neutrophil, and platelet counts, were used to identify a panel of relevant variables to build a machine learning model.
Results:
By comparing six prediction models, the performance of an artificial neural network (ANN) was found to be superior to that of other designed models, with a sensitivity of 0.82, a specificity of 0.85, and an accuracy rate of 83.5%. Furthermore, interleukin-6, procalcitonin, D-dimer, and lactic acid were found to be the most influential biomarkers with the potential to predict bacteremia.
Conclusion:
The ANN model described herein holds outstanding predictive performance, with the potential to provide real-time, data-driven predictions of bacteremia.
