Acute and Critical Care

Original Articles

Rapid response system
Prospective external validation of a deep-learning-based early-warning system for major adverse events in general wards in South Korea: Taeyong Sim, Eun Young Cho, Ji-hyun Kim, Kyung Hyun Lee, Kwang Joon Kim, Sangchul Hahn, Eun Yeong Ha, Eunkyeong Yun, In-Cheol Kim, Sun Hyo Park, Chi-Heum Cho, Gyeong Im Yu, Byung Eun Ahn, Yeeun Jeong, Joo-Yun Won, Hochan Cho, Ki-Byung Lee; Acute Crit Care. 2025;40(2):197-208. Published online May 30, 2025; DOI: https://doi.org/10.4266/acc.000525

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Background
Acute deterioration of patients in general wards often leads to major adverse events (MAEs), including unplanned intensive care unit transfers, cardiac arrest, or death. Traditional early warning scores (EWSs) have shown limited predictive accuracy, with frequent false positives. We conducted a prospective observational external validation study of an artificial intelligence (AI)-based EWS, the VitalCare - Major Adverse Event Score (VC-MAES), at a tertiary medical center in the Republic of Korea.
Methods
Adult patients from general wards, including internal medicine (IM) and obstetrics and gynecology (OBGYN)—the latter were rarely investigated in prior AI-based EWS studies—were included. The VC-MAES predictions were compared with National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) predictions using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and logistic regression for baseline EWS values. False-positives per true positive (FPpTP) were assessed based on the power threshold.
Results
Of 6,039 encounters, 217 (3.6%) had MAEs (IM: 9.5%, OBGYN: 0.26%). Six hours prior to MAEs, the VC-MAES achieved an AUROC of 0.918 and an AUPRC of 0.352, including the OBGYN subgroup (AUROC, 0.964; AUPRC, 0.388), outperforming the NEWS (0.797 and 0.124) and MEWS (0.722 and 0.079). The FPpTP was reduced by up to 71%. Baseline VC-MAES was strongly associated with MAEs (P<0.001).
Conclusions
The VC-MAES significantly outperformed traditional EWSs in predicting adverse events in general ward patients. The robust performance and lower FPpTP suggest that broader adoption of the VC-MAES may improve clinical efficiency and resource allocation in general wards.

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Clinical Context Is More Important than Data Quantity to the Performance of an Artificial Intelligence-Based Early Warning System
Taeyong Sim, Eunyoung Cho, Jihyun Kim, Ho Gwan Kim, Soo-Jeong Kim
Journal of Clinical Medicine.2025; 14(13): 4444. CrossRef
Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review
Mohamed Mustaf Ahmed, Zhinya Kawa Othman, Uthman Okikiola Adebayo, Omar Kasimieh, Olalekan John Okesanya, Shuaibu Saidu Musa, Francesco Branda, Victor C. Cañezo , Edgar G. Cue, Don Eliseo Lucero Prisno III
Patient Safety in Surgery.2025;[Epub] CrossRef

Epidemiology
Pediatric septic shock estimation using deep learning and electronic medical records: Ji Weon Lee, Bongjin Lee, June Dong Park; Acute Crit Care. 2024;39(3):400-407. Published online August 1, 2024; DOI: https://doi.org/10.4266/acc.2024.00031

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Abstract PDF

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.

Citations

Citations to this article as recorded by

Comparison of Pediatric Risk of Mortality-III, Phoenix Sepsis, and pediatric Sequential Organ Failure Assessment scores for predicting septic shock in Vietnamese children with sepsis
Khai Quang Tran, Ngan Tuong Thien Pham, Tri Duc Nguyen, Quan Minh Pham
The Brazilian Journal of Infectious Diseases.2026; 30(1): 104612. CrossRef
Aligning prediction models with clinical information needs: infant sepsis case study
Lusha Cao, Aaron J Masino, Mary Catherine Harris, Lyle H Ungar, Gerald Shaeffer, Alexander Fidel, Elease McLaurin, Lakshmi Srinivasan, Dean J Karavite, Robert W Grundmeier
JAMIA Open.2025;[Epub] CrossRef

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