A deep learning model for estimating sedation levels using heart rate variability and vital signs: a retrospective cross-sectional study at a center in South Korea
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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.
Abstract
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.
참고문헌 (25)
- Pomeranz B, "Assessment of autonomic function in humans by heart rate spectral analysis", Am J Physiol, 248(1 Pt 2), pp.H151~H153 (1985)
- Anand KJ, "Tolerance and withdrawal from prolonged opioid use in critically ill children", Pediatrics, 125, pp.e1208~e1225 (2010)
- Best KM, "Risk factors associated with iatrogenic opioid and benzodiazepine withdrawal in critically ill pediatric patients : a systematic review and conceptual model", Pediatr Crit Care Med, 16, pp.175~183 (2015)
- de Wit M, "Observational study of patient-ventilator asynchrony and relationship to sedation level", J Crit Care, 24, pp.74~80 (2009)
- Devlin JW, "Clinical practice guidelines for the prevention and management of pain, agitation", sedation, delirium, immobility, and sleep disruption in adult patients in the ICU, 2018, pp.46, e825~e873
- Thille AW, "Patient-ventilator asynchrony during assisted mechanical ventilation", Intensive Care Med, 32, pp.1515~1522 (2006)
- Balit CR, "Sedation protocols in the pediatric intensive care unit : fact or fiction?", Transl Pediatr, 10, pp.2814~2824 (2021)
- Nagaraj SB, "Automatic classification of sedation levels in ICU patients using heart rate variability", Crit Care Med, 44, pp.e782~e789 (2016)
- Spencer EM, "Continuous monitoring of depth of sedation by EEG spectral analysis in patients requiring mechanical ventilation", Br J Anaesth, 73, pp.649~654 (1994)
- Shaffer F, "An overview of heart rate variability metrics and norms", Front Public Health, 5, pp.258~ (2017)
- Bradley BD, "Impact of sedation and organ failure on continuous heart and respiratory rate variability monitoring in critically ill patients : a pilot study", Crit Care Med, 41, pp.433~444 (2013)
- Bae W, "Distribution of pediatric vital signs in the emergency department : a nationwide study", Children(Basel), 7, pp.89~ (2020)
- Goo S, "Streamlining pediatric vital sign assessment : innovations and insights", Sci Rep, 14, pp.22542~ (2024)
- Kim L, "Effect of diurnal variation of heart rate and respiratory rate on activation of rapid response system and clinical outcome in hospitalized children", Children(Basel), 10, pp.167~ (2023)
- Lee HC, "Vital Recorder : a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices", Sci Rep, 8, pp.1527~ (2018)
- Tapia R, "Validity and reliability of the Richmond agitation-sedation scale in pediatric intensive care patients : a multicenter study", Front Pediatr, 9, pp.795487~ (2022)
- Smith HA, "2022 Society of Critical Care Medicine clinical practice guidelines on prevention and management of pain, agitation, neuromuscular blockade, and delirium in critically Ill pediatric patients with consideration of the ICU environment and early mobility", Pediatr Crit Care Med, 23, pp.e74~e110 (2022)
- Boerlage AA, "The COMFORT behavior scale : is a shorter observation period feasible?", Pediatr Crit Care Med, 13, pp.e124~e125 (2012)
- Kerson AG, "Validity of the Richmond Agitation-Sedation Scale(RASS)in critically ill children", J Intensive Care, 4, pp.65~ (2016)
- Nagaraj SB, "Heart rate variability as a biomarker for sedation depth estimation in ICU patients", Annu Int Conf IEEE Eng Med Biol Soc, 2016, pp.6397~6400 (2016)
- Nagaraj SB, "Patient-specific classification of ICU sedation levels from heart rate variability", Crit Care Med, 45, pp.e683~e690 (2017)
- Toyofuku A, "Heart rate variability and cognitive functions in adolescents with complex congenital heart disease", Pediatr Res (2024)[DOI]
- Goldenberg I, "Heart rate variability for risk assessment of myocardial ischemia in patients without known coronary artery disease : the HRV-DETECT(Heart Rate Variability for the Detection of Myocardial Ischemia)study", J Am Heart Assoc, 8, pp.e014540~ (2019)
- Kong SD, "Heart rate variability during slow wave sleep is linked to functional connectivity in the central autonomic network", Brain Commun, 5, pp.fcad129~ (2023)
- Ernst G, "Hidden signals-the history and methods of heart rate variability", Front Public Health, 5, pp.265~ (2017)