Background
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.
Background Despite the high mortality associated with bloodstream infection (BSI), early detection of this condition is challenging in critical settings. The objective of this study was to create a machine learning tool for rapid recognition of BSI in critically ill children.
Methods Data were extracted from a derivative cohort comprising patients who underwent at least one blood culture during hospitalization in the pediatric intensive care unit (PICU) of a tertiary hospital from January 2020 to June 2023 for model development. Data from another tertiary hospital were utilized for external validation. Variables selected for model development were age, white blood cell count with segmented neutrophil count, C-reactive protein, bilirubin, liver enzymes, glucose, body temperature, heart rate, and respiratory rate. Algorithms compared were extra trees, random forest, light gradient boosting, extreme gradient boosting, and CatBoost.
Results We gathered 1,806 measurements and recorded 290 hospitalizations from 263 patients in the derivative cohort. Median age on admission was 43 months, with an interquartile range of 10–118.75 months, and a male predominance was observed (n=160, 55.2%). Candida albicans was the most prevalent pathogen, and median duration to confirm BSI was 3 days (range, 3–4). Patients with BSI experienced significantly higher in-hospital mortality and prolonged stays in the PICU than patients without BSI. Random forest classifier achieved the highest area under the receiver operating characteristic curve of 0.874 (0.762 for the validation set).
Conclusions We developed a machine learning model that predicts BSI with acceptable performance. Further research is necessary to validate its effectiveness.
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.
Background Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality.
Methods This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing.
Results Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700–1.000).
Conclusions The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.
Background In this study, we reviewed the outcomes of pediatric patients with malignancies who underwent hematopoietic stem cell transplantation (HSCT) and extracorporeal membrane oxygenation (ECMO).
Methods We retrospectively analyzed the records of pediatric hemato-oncology patients treated with chemotherapy or HSCT and who received ECMO in the pediatric intensive care unit (PICU) at Seoul National University Children’s Hospital from January 2012 to December 2020.
Results Over a 9-year period, 21 patients (14 males and 7 females) received ECMO at a single pediatric institute; 10 patients (48%) received veno-arterial (VA) ECMO for septic shock (n=5), acute respiratory distress syndrome (ARDS) (n=3), stress-induced myopathy (n=1), or hepatopulmonary syndrome (n=1); and 11 patients (52%) received veno-venous (VV) ECMO for ARDS due to pneumocystis pneumonia (n=1), air leak (n=3), influenza (n=1), pulmonary hemorrhage (n=1), or unknown etiology (n=5). All patients received chemotherapy; 9 received anthracycline drugs and 14 (67%) underwent HSCT. Thirteen patients (62%) were diagnosed with malignancies and 8 (38%) were diagnosed with non-malignant disease. Among the 21 patients, 6 (29%) survived ECMO in the PICU and 5 (24%) survived to hospital discharge. Among patients treated for septic shock, 3 of 5 patients (60%) who underwent ECMO and 5 of 10 patients (50%) who underwent VA ECMO survived. However, all the patients who underwent VA ECMO or VV ECMO for ARDS died.
Conclusions ECMO is a feasible treatment option for respiratory or heart failure in pediatric patients receiving chemotherapy or undergoing HSCT.
Background Various rapid response systems have been developed to detect clinical deterioration in patients. Few studies have evaluated single-parameter systems in children compared to scoring systems. Therefore, in this study we evaluated a single-parameter system called the acute response system (ARS).
Methods This retrospective study was performed at a tertiary children’s hospital. Patients under 18 years old admitted from January 2012 to August 2023 were enrolled. ARS parameters such as systolic blood pressure, heart rate, respiratory rate, oxygen saturation, and whether the ARS was activated were collected. We divided patients into two groups according to activation status and then compared the occurrence of critical events (cardiopulmonary resuscitation or unexpected intensive care unit admission). We evaluated the ability of ARS to predict critical events and calculated compliance. We also analyzed the correlation between each parameter that activates ARS and critical events.
Results The critical events prediction performance of ARS has a specificity of 98.5%, a sensitivity of 24.0%, a negative predictive value of 99.6%, and a positive predictive value of 8.1%. The compliance rate was 15.6%. Statistically significant increases in the risk of critical events were observed for all abnormal criteria except low heart rate. There was no significant difference in the incidence of critical events.
Conclusions ARS, a single parameter system, had good specificity and negative predictive value for predicting critical events; however, sensitivity and positive predictive value were not good, and medical staff compliance was poor.
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BACKGROUND Various tools for the acute response system (ARS) predict and prevent acute deterioration in pediatric patients. However, detailed criteria have not been clarified. Thus we evaluated the effectiveness of bradycardia as a single parameter in pediatric ARS. METHODS This retrospective study included patients who had visited a tertiary care children's hospital from January 2012 to June 2013, in whom ARS was activated because of bradycardia. Patient's medical records were reviewed for clinical characteristics, cardiologic evaluations, and reversible causes that affect heart rate. RESULTS Of 271 cases, 261 (96%) had ARS activation by bradycardia alone with favorable outcomes. Evaluations and interventions were performed in 165 (64.5%) and 13 cases (6.6%) respectively. All patients in whom ARS was activated owing to bradycardia and another criteria underwent evaluation, unlike those with bradycardia alone (100.0% vs.
63.2%, p = 0.016). Electrocardiograms were evaluated in 233 (86%) cases: arrhythmias were due to borderline QT prolongation and atrioventricular block (1st and 2nd-degree) in 25 cases (9.2%). Bradycardia-related causes were reversible in 202 patients (74.5%). Specific causes were different in departments at admission. Patients admitted to the hemato-oncology department required ARS activation during the night (69.3%, p = 0.03), those to the endocrinology department required ARS activation because of medication (72.4%, p < 0.001), and those to the gastroenterology department had low body mass indexes (32%, p = 0.01). CONCLUSIONS Using bradycardia alone in pediatric ARS is not useful, because of its low specificity and poor predictive ability for deterioration. However, bradycardia can be applied to ARS concurrently with other parameters.
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