Acute Crit Care.  2024 Aug;39(3):400-407. 10.4266/acc.2024.00031.

Pediatric septic shock estimation using deep learning and electronic medical records

• Lee JW
• Lee B
• Park JD

• KMID: 2563529
• DOI: http://doi.org/10.4266/acc.2024.00031

Abstract

Background
Diagnosing pediatric septic shock is difficult due to the complex and often impractical traditional criteria, such as systemic inflammatory response syndrome (SIRS), which result in delays and higher risks. This study aims to develop a deep learning-based model using SIRS data for early diagnosis in pediatric septic shock cases.
Methods
The study analyzed data from pediatric patients (<18 years old) admitted to a tertiary hospital from January 2010 to July 2023. Vital signs, lab tests, and clinical information were collected. Septic shock cases were identified using SIRS criteria and inotrope use. A deep learning model was trained and evaluated using the area under the receiver operating characteristics curve (AUROC) and area under the precision-recall curve (AUPRC). Variable contributions were analyzed using the Shapley additive explanation value.
Results
The analysis, involving 9,616,115 measurements, identified 34,696 septic shock cases (0.4%). Oxygen supply was crucial for 41.5% of the control group and 20.8% of the septic shock group. The final model showed strong performance, with an AUROC of 0.927 and AUPRC of 0.879. Key influencers were age, oxygen supply, sex, and partial pressure of carbon dioxide, while body temperature had minimal impact on estimation.
Conclusions
The proposed deep learning model simplifies early septic shock diagnosis in pediatric patients, reducing the diagnostic workload. Its high accuracy allows timely treatment, but external validation through prospective studies is needed.

Keyword

clinical decision support systems; early diagnosis; machine learning, pediatrics; septic shock

Figure

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  Figure 1. Flowchart of patient selection.

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  Figure 2. Septic shock estimation performance of the developed model. (A) The area under the receiver operating characteristics curve (AUROC) graph of the estimated model. (B) The area under the precision-recall curve (AUPRC) graph.

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  Figure 3. Impact on the model output for each feature of the developed model. pCO2: partial pressure of carbon dioxide; RR: respiratory rate; CRP: C-reactive protein; SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate; SpO2: pulse oxygen saturation; SHAP: Shapley additive explanation.

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  Figure 4. Impact changes on the model output according to the value of each feature of the developed model. It shows that the Shapley additive explanation (SHAP) value varies depending on whether the value of each feature is high (red) or low (blue). In the sex division, women are coded as 0 and men are coded as 1, thus blue means female and red means male. Similarly, for oxygen supply, it is coded as 1 (red) if supplied, and 0 (blue) if not supplied. pCO2: partial pressure of carbon dioxide; RR: respiratory rate; CRP: C-reactive protein; SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate; SpO2: pulse oxygen saturation.

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