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Original Articles

CPR/Resuscitation
Prognostic value of initial hemoglobin levels for neurological outcomes in patients with out-of-hospital cardiac arrest in South Korea: Jihun Keum, Kyung Hun Yoo, Yongil Cho, Tae Ho Lim, Hyunggoo Kang, Jaehoon Oh, Byuk Sung Ko, Juncheol Lee; Received October 21, 2025 Accepted March 2, 2026 Published online May 19, 2026; DOI: https://doi.org/10.4266/acc.005075 [Epub ahead of print]

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Abstract PDF Supplementary Material: Background
Previous studies suggest that lower hemoglobin (Hb) levels are associated with adverse outcomes after out-of-hospital cardiac arrest (OHCA). However, most of these were limited by small sample sizes and single-center designs. We aimed to evaluate the association between initial Hb levels and clinical outcomes after OHCA using a large multicenter registry.
Methods
This retrospective observational study analyzed prospectively collected multicenter registry data. Hb levels measured at emergency department arrival were analyzed as continuous variables with restricted cubic spline (RCS) models, which flexibly characterize dose–response relationships with unfavorable neurological outcomes and in-hospital mortality. Generalized estimating equations with a logit link were used to account for within-hospital clustering and to adjust for clinically relevant covariates.
Results
Lower Hb levels were independently associated with higher adjusted odds of unfavorable neurological outcomes and in-hospital mortality. RCS analyses showed a statistically significant overall association between Hb levels and both outcomes after multivariable adjustment (overall P<0.001). The adjusted odds of adverse outcomes increased progressively with decreasing Hb levels. Cluster-adjusted analyses using generalized estimating equations yielded consistent results.
Conclusions
Initial Hb levels were independently associated with neurological outcomes and in-hospital mortality after OHCA. Modeling Hb as a continuous variable showed a graded association across the Hb spectrum. These findings highlight the prognostic relevance of baseline Hb levels.

Rapid response system
Impact of the National Early Warning Score-based sepsis response system on hospital-onset sepsis in a tertiary hospital in South Korea: Dong-gon Hyun, Sohyeon Lee, Sunhui Choi, Jeongsuk Son, So-Hee Park, Sang-Bum Hong, Chae-Man Lim; Acute Crit Care. 2025;40(2):186-196. Published online May 20, 2025; DOI: https://doi.org/10.4266/acc.000625

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Abstract PDF Supplementary Material

Background
The effectiveness of electronic medical record-based alert systems, response protocols for sepsis diagnosis, and treatment in hospitalized patients remains unclear. This study aimed to determine whether the introduction of an electronic medical record-based sepsis response protocol (SRP) along with a 24/7 operating rapid response system affects the prognosis for patients with hospital-onset sepsis.
Methods
In August 2022, a SRP based on the National Early Warning Score was implemented in the electronic medical record system at Asan Medical Center. We retrospectively analyzed patients screened by the detection system for 1 year after the SRP implementation. Patients of the first 6 months (preliminary group) and those of the second 6 months (SRP group) were matched 1:1 based on propensity scores. The primary outcome was 30-day mortality.
Results
Of the 608 hospitalized patients screened by the system, 176 were assigned to each group after 1:1 propensity score matching. Patients in the SRP group were significantly more likely to receive blood cultures (58.5%) compared with the preliminary group (45.5%) (P=0.019). The SRP group showed a lower 30-day mortality risk (hazard ratio, 0.56; 95% CI, 0.36–0.86; P=0.017) compared to the preliminary group. A restricted cubic spline curve showed that SRP survival benefit began to manifest after the first 4 months (P=0.036).
Conclusions
Alongside an existing rapid response system, the National Early Warning Score-based SRP in the electronic medical record reduced mortality for hospital-onset sepsis within 1 year.

Citations

Citations to this article as recorded by

Sepsis precoz detectada mediante el National Early Warning Score (NEWS) por enfermería
Narcisa de Jesus Marcillo Peralta, Doris Grace Alvario Pillajo, Wilmer Mauricio Arguello Pazmiño , Cecilia Maribel Fiallos Miranda, Elsa Patricia Guerrero Romero
Arandu UTIC.2026; 13(1): 949. CrossRef
Collective agency in the implementation of computerized clinical decision support: an interpretive description study
Manasha Fernando, Sundresan Naicker, Bridget Abell, Steven M. McPhail, Zephanie Tyack
JBI Evidence Implementation.2026; 24(3): 574. CrossRef
Prioritizing the patient through a continuum of personalized care: insights from the 2026 Surviving Sepsis Campaign guidelines
Gyungah Kim, Won-Young Kim
Acute and Critical Care.2026; 41(2): 419. CrossRef
Characteristics and management of mechanically ventilated patients in South Korea compared with other high-income Asian countries and regions
Kyung Hun Nam, Kyeongman Jeon, Suk-Kyung Hong, Ah Young Leem, Jee Hwan Ahn, Hang Jea Jang, Ki Sup Byun, So Hee Park, Sojung Park, Yoon Mi Shin, Jisoo Park, Sung Wook Kang, Jin Hyoung Kim, Jinkyeong Park, Deokkyu Kim, Bo young Lee, Woo Hyun Cho, Kwangha Le
Acute and Critical Care.2025; 40(3): 413. 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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2 Web of Science
2 Crossref

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

The S100B Protein Could Be Used as Adjuvant Diagnostic Tool in Acute Ischemic Stroke: Min Hee Jung, Dong Hoon Lee, Chan Woong Kim; Korean J Crit Care Med. 2011;26(4):217-220.; DOI: https://doi.org/10.4266/kjccm.2011.26.4.217

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Abstract PDF: BACKGROUND
In the emergency department, the diagnosis of ischemic stroke is difficult because the diagnostic modalities are limited to non-contrast brain CT and neurologic examination. Serum S100B protein, a bio-marker for ischemic stroke, is needed as an additional diagnostic aid in acute ischemic stroke.
METHODS
We retrospectively reviewed 50 patients diagnosed with ischemic stroke between August 2007 and December 2008 by brain MRI after brain CT and serum S100B measurement in the emergency department. The serum levels of S100B protein were analyzed and the diagnostic sensitivity of non-contrast brain CT combined with abnormal elevation of S100B protein was compared with that of non-contrast brain CT alone.
RESULTS
The overall sensitivity of non-contrast brain CT in the diagnosis of ischemia was 54%. S100B protein in early ischemia had a sensitivity of 58%. However, combining non-contrast brain CT and S100B increased the sensitivity to 74%.
CONCLUSIONS
A biomarker-based diagnostic test would not replace the necessity for CT or other early imaging studies, and before contemplating any reperfusion strategy, neuro-imaging must be performed to rule out intracranial hemorrhage. However, S100B protein, a serum bio-marker, is able to help emergency physicians evaluate patients with suspected ischemic stroke and decide on treatment.

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