Predicting abnormal C-reactive protein level for improving utilization by deep neural network model.

Area of Science:

• Biomedical Informatics
• Artificial Intelligence in Healthcare
• Clinical Diagnostics

Background:

• C-reactive protein (CRP) is a key inflammatory biomarker.
• Current clinical use of CRP testing suffers from overuse and underuse due to insufficient evidence-based guidelines.
• Predictive models are needed to optimize CRP test ordering.

Purpose of the Study:

• To develop and validate deep neural network (DNN) models for predicting normal and abnormal C-reactive protein (CRP) levels.
• To enhance the appropriate and intelligent ordering of CRP tests in clinical practice.
• To leverage complete blood count (CBC) parameters for CRP level prediction.

Main Methods:

• Utilized a large dataset of 53,834 medical records for model development.
• Employed complete blood count (CBC) parameters as feature vectors.
• Compared DNN models against other machine learning algorithms including support vector classification, logistic regression, decision trees, and random forests.
• Externally validated the best performing DNN models on an independent dataset of 20,723 samples using discrimination, calibration curves, and decision curve analysis.

Main Results:

• Deep neural network (DNN) models demonstrated superior performance with the highest Area Under the Receiver Operating Characteristic Curve (AUC).
• Internal validation showed an AUC of 0.818, balanced accuracy of 0.741, and F1 score of 0.649.
• External validation yielded comparable results with an AUC of 0.817, balanced accuracy of 0.741, and F1 score of 0.641.
• The CRP-C2 model was identified as the target model due to its lowest Brier score (0.154) and excellent calibration (y=1.001x-0.010).

Conclusions:

• DNN models provide moderate performance in distinguishing binary C-reactive protein (CRP) levels, outperforming baseline methods.
• The developed models exhibit good generalization and calibration, indicating reliability.
• The CRP-C2 model can optimize CRP test utilization and support inflammatory diagnostics, particularly in primary care settings where CBC data is available but CRP testing may be limited.