Bongjin Lee

Fat embolism syndrome (FES) is rare in children and not routinely considered as a differential diagnosis even in patients with established risk factors. We report a 17-year-old male with Duchenne muscular dystrophy who presented with severe hypoxemia, shock, and altered mental status 4 hours after a minor fall from a wheelchair. Initial evaluation, including chest computed tomography, lower-extremity radiography, and echocardiography, suggested aspiration pneumonia without evidence of pulmonary thromboembolism, fracture, or cardiomyopathy. Despite mechanical ventilation and vasopressor support, respiratory failure and shock progressed. Repeat physical examination identified petechial lesions. Fundoscopy revealed Purtscher flecken while brain magnetic resonance imaging showed a starfield pattern of restricted diffusion. The formal radiology report read diffuse alveolar hemorrhage compatible with FES on the initial computed tomography and a subtle fracture of the right distal femur on the radiography. Follow-up echocardiography showed pulmonary hypertension. Collectively, the final diagnosis was established as FES. This case illustrates that low-energy trauma in Duchenne muscular dystrophy can cause occult fracture and FES, requiring targeted reassessment when clinical deterioration is disproportionate to the initial diagnostic impression.

Early identification of clinical deterioration in hospitalized children is essential to improve outcomes and prevent critical events. Over the past two decades, structured approaches such as pediatric early warning scores and rapid response systems have provided a framework for systematic risk detection in general wards. More recently, artificial intelligence and continuous monitoring technologies have begun to transform this field, offering the potential for more timely and accurate recognition of subtle changes in patient status. Despite these advances, challenges remain before seamless integration of these technologies into routine clinical decision-making can be achieved. This review explores the evolution of pediatric early warning systems and examines how emerging innovations may shape the future of predictive monitoring and clinical decision support in pediatric care.

Choe YJ, et al. J Korean Med Sci. 2026 Sep;41:e229. https://doi.org/10.3346/jkms.2026.41.e229

Background: Pediatric patients with hematological and oncological (HO) diseases who are admitted to the pediatric intensive care unit (PICU) are at particularly high risk of mortality. Prior studies have predominantly relied on conventional regression methods to identify prognostic factors in this population; however, machine learning (ML) approaches may offer incremental value by enhancing prognostic modeling beyond traditional techniques. Methods: This retrospective cohort study included children with HO diseases admitted to the PICU between September 2020 and August 2025. Clinical and laboratory predictors obtained within 24 hours of PICU admission were analyzed. Univariate and multivariable logistic regression analyses were conducted to identify significant predictors and to construct a multivariable regression model. In parallel, benchmark ML models were developed, and their performance was compared with that of the regression model using the area under the receiver operating characteristic curve (AUROC). Results: A total of 180 admissions involving 124 patients were included, with an overall mortality rate of 16.1%. In multivariable logistic regression analysis, lactate (odds ratio [OR], 2.058; 95% CI, 1.440-2.941), C-reactive protein (OR, 1.095; 95% CI, 1.034-1.160), and invasive mechanical ventilation (OR, 7.498; 95% CI, 1.823-30.834) were independently associated with mortality. The final regression model demonstrated an AUROC of 0.883 (95% CI, 0.787-0.936). The ML models achieved comparable AUROCs (range, 0.882-0.919) and similarly identified lactate and C-reactive protein among the most influential predictors, with procalcitonin emerging as an additional variable of high prognostic relevance. Conclusions: Both regression and ML approaches consistently identified markers of physiological compromise as being strongly associated with mortality, outweighing baseline HO characteristics. Although performance gains over logistic regression were modest, ML approaches provided complementary perspectives that may improve risk stratification in this high-risk population.

Nafamostat mesylate (NM) is increasingly used as an alternative anticoagulant during continuous venovenous hemodiafiltration (CVVHDF) in pediatric intensive care, especially when unfractionated heparin or regional citrate is contraindicated. However, evidence regarding optimal monitoring of its anticoagulant effects remains limited. This study aimed to investigate the association between NM infusion rate and coagulation parameters—specifically activated clotting time (ACT) and activated partial thromboplastin time (aPTT)—in critically ill pediatric patients receiving CVVHDF, and to examine how hepatic function and transfusion status may influence this relationship. In this retrospective study of 99 patients, we analyzed 340 matched data pairs of NM infusion orders and coagulation test results. Linear mixed-effects models revealed a significant positive correlation between NM infusion rate and aPTT (β = 8.54, P < 0.001), while no significant association was found with ACT. Subgroup analyses stratified by liver function and transfusion status consistently supported the association between NM and aPTT. Non-linear regression further suggested a dose–response pattern for aPTT, but not for ACT. These findings indicate that aPTT may be a more sensitive and reliable parameter than ACT for monitoring NM’s anticoagulant effects in pediatric CVVHDF. The results challenge current ACT-based monitoring practices and highlight the need for prospective validation to refine anticoagulation strategies using NM in critically ill children.

Background: Extracorporeal membrane oxygenation (ECMO) is a critical intervention for pediatric patients with cardiac-related medical indications. However, data on its outcomes and associated risk factors, particularly in Korea, are limited. This study aimed to evaluate these outcomes and identify predictors of mortality. Methods: A multicenter retrospective study was conducted on 152 pediatric patients who underwent ECMO for cardiac-related medical conditions between 2012 and 2021. Data on patient demographics, clinical features, laboratory findings, and outcomes were collected and analyzed. Statistical methods included descriptive analysis, group comparisons, and regression analysis to evaluate risk factors for mortality. Results: Cardiomyopathy was the most common indication for ECMO, and peripheral cannulation was used in 57.2% of cases. Acute kidney injury requiring dialysis was observed in 57.2% of patients. Non-extracorporeal cardiopulmonary resuscitation (ECPR) patients showed better outcomes, with a 68.4% survival-to-discharge rate compared to 33.3% in the ECPR group. Significant predictors of mortality included elevated lactate levels before ECMO, reduced 24-hour urine output post-ECMO, dialysis use, and pulseless electrical activity/ asystole. While pre-ECMO left ventricular ejection fraction showed no overall association with outcomes, it was significantly higher in survivors with acute myocarditis. Conclusion: This study provides valuable insights into the outcomes and risk factors of pediatric ECMO for cardiac-related medical indications in Korea. Lactate levels, urine output, and pre-ECMO rhythm were identified as prognostic factors. These results are expected to aid clinicians in managing this patient population and lay the groundwork for future prospective research.

Background: Pediatric cardiac arrest is linked to poor outcomes, such as low survival rates and significant neurological disabilities. Effective cardiopulmonary resuscitation and high-quality post-resuscitation care are crucial, with targeted temperature management (TTM) being a key intervention. However, the evidence supporting TTM in pediatric populations, particularly in South Korea, is limited. Methods: We organized a multicenter initiative to establish a Pediatric TTM Registry, which collects comprehensive data on pediatric patients (aged 0-18 years) who underwent TTM following cardiac arrest. We are using the REDCap platform to gather information on patient demographics, clinical interventions, TTM protocols, and neurological outcomes. The development of the case report form involved regular meetings with representatives from the four participating centers. Results: The established case report form is divided into four groups: patient demographics, resuscitation-related parameters, post-resuscitation care-related parameters, and patient outcomes. As of October 2024, data from 41 pediatric patients have been collected. Conclusion: The Pediatric TTM Registry is dedicated to improving the management of pediatric cardiac arrest in Korea by collecting and analyzing data. This initiative will enable improvements in clinical practices, assist in the development of evidence-based TTM protocols specifically designed for pediatric patients, and ultimately improve outcomes for this vulnerable group.

Background: Pediatric extracorporeal cardiopulmonary resuscitation (ECPR) is increasingly utilized for cardiac arrests that do not respond to standard cardiopulmonary resuscitation (CPR), although multicenter data on outcomes remain scarce. This study evaluated trends, outcomes, and factors associated with survival in pediatric ECPR over a 10-year period. Methods: This multicenter retrospective study was conducted across 12 centers from 2012 to 2021. It included pediatric patients (<18 years) who underwent ECPR. Data were analyzed for period 1 (2012-2016) and period 2 (2017-2021), focusing on survival outcomes and factors associated with mortality. Results: Out of 754 extracorporeal membrane oxygenation (ECMO) cases, 206 (27.3%) involved ECPR. The number of annual ECPR cases decreased from 114 in period 1 to 92 in period 2. The survival rates to hospital discharge improved from 28.1% in period 1 to 34.8% in period 2, with notable improvements in post-cardiotomy survival (23.9% to 43.2%, p=0.032). Cardiac indications were the most common (93.2%), with 53.9% categorized as post-cardiotomy ECMO and 39.3% as cardiac-medical ECMO, the latter showing an increase over time. Among non-survivors, 62.7% died from incurable primary diseases, and 17.6% from hypoxic brain injury or cerebral edema. The median CPR duration before ECMO initiation was 60 minutes. Higher survival rates were observed for CPR durations ≤30 minutes (47.4% vs. 27.4%, p=0.016) and ≤60 minutes (38.0% vs. 23.4%, p=0.025). Dialysis for acute kidney injury, especially in post-cardiotomy cases, significantly increased the risk of mortality (p<0.001). Conclusion: Over the past decade, pediatric ECPR has demonstrated improved survival trends, particularly in post-cardiotomy cases. However, outcomes remain challenging. Early initiation of ECMO and optimized management are essential for increasing survival rates.

SeBackground:Optimal sedation assessment in critically ill children remains challenging due to the subjective nature of behavioral scales and intermittent evaluation schedules. This study aimed to develop a deep learning model based on heart rate variability (HRV) parameters and vital signs to predict effective and safe sedation levels in pediatric patients.Methods: This retrospective cross-sectional study was conducted in a pediatric intensive care unit at a tertiary children’s hospital. We developed deep learning models incorporating HRV parameters extracted from electrocardiogram waveforms and vital signs to predict Richmond Agitation-Sedation Scale (RASS) scores. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). The data were split into training, validation, and test sets (6:2:2), and the models were developed using a 1D ResNet architecture.Results: Analysis of 4,193 feature sets from 324 patients achieved excellent discrimination ability, with AUROC values of 0.867, 0.868, 0.858, 0.851, and 0.811 for whole number RASS thresholds of −5 to −1, respectively. AUPRC values ranged from 0.928 to 0.623, showing superior performance in deeper sedation levels. The HRV metric SDANN2 showed the highest feature importance, followed by systolic blood pressure and heart rate.Conclusions: A combination of HRV parameters and vital signs can effectively predict sedation levels in pediatric patients, offering the potential for automated and continuous sedation monitoring in pediatric intensive care settings. Future multi-center validation studies are needed to establish broader applicability.

Accurate assessment of pediatric vital signs is critical for detecting abnormalities and guiding medical interventions, but interpretation is challenging due to age-dependent physiological variations. Therefore, this study aimed to develop age-specific centile curves for blood pressure, heart rate, and respiratory rate in pediatric patients and create a user-friendly web-based application for easy access to these data. We conducted a retrospective cross-sectional observational study analyzing 3,779,482 records from the National Emergency Department Information System of Korea, focusing on patients under 15 years old admitted between January 2016 and December 2017. After applying exclusion criteria to minimize the impact of patients’ symptoms on vital signs, 1,369,608 records were used for final analysis. The box–cox power exponential distribution and Lambda–Mu–Sigma (LMS) method were used to generate blood pressure centile charts, while heart rate and respiratory rate values were drawn from previously collected LMS values. We developed comprehensive age-specific centile curves for systolic, diastolic, and mean blood pressure, heart rate, and respiratory rate. These were integrated into a web-based application (http://centile.research.or.kr), allowing users to input patient data and promptly obtain centile and z-score information for vital signs. Our study provides an accessible system for pediatric vital sign evaluation, addressing previous limitations and offering a practical solution for clinical assessment. Future research should validate these centile curves in diverse populations.