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European Renal Association 2025

Development and Validation of Deep-Learning Model for Diagnosing Systemic Acidemia From the Electrocardiograms

  • Jun. 2025

Background and Aims:
Systemic acidemia critically impairs cardiovascular function and is typically diagnosed through arterial blood gas analysis, which may delay timely intervention. This study aimed to develop a deep learning model using electrocardiogram (ECG) data for rapid, non-invasive diagnosis of systemic acidemia.

Method:
We developed and validated deep learning models to detect systemic acidemia, categorized by severity (mild: pH < 7>

Results:
The models achieved high performance in detecting systemic acidemia, with the area under the receiver operating characteristic curves (AUCs) of 0.69, 0.73, and 0.82 for mild, moderate, and severe acidemia, respectively, in the testing cohort. No statistically significant differences in AUC were observed between the group with pCO₂ ≤ 45 mmHg and HCO₃⁻ < 22> 45 mmHg and HCO₃⁻ ≥ 22 mEq/L, with p-values (DeLong’s method) of 0.07, 0.07, and 0.25 for mild, moderate, and severe acidemia, respectively. The group predicted to be positive by the severe academia model showed a lower 30-day survival rate compared to the negative group (p<0>

Conclusion:
Deep learning offers a non-invasive method for diagnosing systemic acidemia in critically ill patients, enabling earlier intervention and improved patient care.

Link

https://era-distribution.m-anage.com/from.storage?image=NRyIzIOLNUsiY-tRxCdd2tHB-e9pcIorpX7u1Pf3TeV5-LOuf6Y7IYKI9BpcSgLG0

Author

Won Ho Park, Minje Park, Giae Yun, Jung Nam An, Do Hyoung Kim, Hee Jung Jeon, Sung Gyun Kim, Eun Jung Kim, Jang Won Seo, Ja-Ryong Koo, Seon Ha Baek

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