Acute and Critical Care

Original Articles

Pediatrics
Multicenter validation of a deep-learning-based pediatric early-warning system for prediction of deterioration events: Yunseob Shin, Kyung-Jae Cho, Yeha Lee, Yu Hyeon Choi, Jae Hwa Jung, Soo Yeon Kim, Yeo Hyang Kim, Young A Kim, Joongbum Cho, Seong Jong Park, Won Kyoung Jhang; Acute Crit Care. 2022;37(4):654-666. Published online October 26, 2022; DOI: https://doi.org/10.4266/acc.2022.00976

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Background
Early recognition of deterioration events is crucial to improve clinical outcomes. For this purpose, we developed a deep-learning-based pediatric early-warning system (pDEWS) and aimed to validate its clinical performance. Methods: This is a retrospective multicenter cohort study including five tertiary-care academic children’s hospitals. All pediatric patients younger than 19 years admitted to the general ward from January 2019 to December 2019 were included. Using patient electronic medical records, we evaluated the clinical performance of the pDEWS for identifying deterioration events defined as in-hospital cardiac arrest (IHCA) and unexpected general ward-to-pediatric intensive care unit transfer (UIT) within 24 hours before event occurrence. We also compared pDEWS performance to those of the modified pediatric early-warning score (PEWS) and prediction models using logistic regression (LR) and random forest (RF). Results: The study population consisted of 28,758 patients with 34 cases of IHCA and 291 cases of UIT. pDEWS showed better performance for predicting deterioration events with a larger area under the receiver operating characteristic curve, fewer false alarms, a lower mean alarm count per day, and a smaller number of cases needed to examine than the modified PEWS, LR, or RF models regardless of site, event occurrence time, age group, or sex. Conclusions: The pDEWS outperformed modified PEWS, LR, and RF models for early and accurate prediction of deterioration events regardless of clinical situation. This study demonstrated the potential of pDEWS as an efficient screening tool for efferent operation of rapid response teams.

Citations

Citations to this article as recorded by

Predicting cardiac arrest after neonatal cardiac surgery
Alexis L. Benscoter, Mark A. Law, Santiago Borasino, A. K. M. Fazlur Rahman, Jeffrey A. Alten, Mihir R. Atreya
Intensive Care Medicine – Paediatric and Neonatal.2024;[Epub] CrossRef
Volumetric regional MRI and neuropsychological predictors of motor task variability in cognitively unimpaired, Mild Cognitive Impairment, and probable Alzheimer's disease older adults
Michael Malek-Ahmadi, Kevin Duff, Kewei Chen, Yi Su, Jace B. King, Vincent Koppelmans, Sydney Y. Schaefer
Experimental Gerontology.2023; 173: 112087. CrossRef
Predicting sepsis using deep learning across international sites: a retrospective development and validation study
Michael Moor, Nicolas Bennett, Drago Plečko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten Borgwardt
eClinicalMedicine.2023; 62: 102124. CrossRef
A model study for the classification of high-risk groups for cardiac arrest in general ward patients using simulation techniques
Seok Young Song, Won-Kee Choi, Sanggyu Kwak
Medicine.2023; 102(37): e35057. CrossRef
An advanced pediatric early warning system: a reliable sentinel, not annoying extra work
Young Joo Han
Acute and Critical Care.2022; 37(4): 667. CrossRef

Pediatric
Oxygenation Index in the First 24 Hours after the Diagnosis of Acute Respiratory Distress Syndrome as a Surrogate Metric for Risk Stratification in Children: Soo Yeon Kim, Byuhree Kim, Sun Ha Choi, Jong Deok Kim, In Suk Sol, Min Jung Kim, Yoon Hee Kim, Kyung Won Kim, Myung Hyun Sohn, Kyu-Earn Kim; Acute Crit Care. 2018;33(4):222-229. Published online November 29, 2018; DOI: https://doi.org/10.4266/acc.2018.00136

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Abstract PDF Supplementary Material

Background
The diagnosis of pediatric acute respiratory distress syndrome (PARDS) is a pragmatic decision based on the degree of hypoxia at the time of onset. We aimed to determine whether reclassification using oxygenation metrics 24 hours after diagnosis could provide prognostic ability for outcomes in PARDS.
Methods
Two hundred and eighty-eight pediatric patients admitted between January 1, 2010 and January 30, 2017, who met the inclusion criteria for PARDS were retrospectively analyzed. Reclassification based on data measured 24 hours after diagnosis was compared with the initial classification, and changes in pressure parameters and oxygenation were investigated for their prognostic value with respect to mortality.
Results
PARDS severity varied widely in the first 24 hours; 52.4% of patients showed an improvement, 35.4% showed no change, and 12.2% either showed progression of PARDS or died. Multivariate analysis revealed that mortality risk significantly increased for the severe group, based on classification using metrics collected 24 hours after diagnosis (adjusted odds ratio, 26.84; 95% confidence interval [CI], 3.43 to 209.89; P=0.002). Compared to changes in pressure variables (peak inspiratory pressure and driving pressure), changes in oxygenation (arterial partial pressure of oxygen to fraction of inspired oxygen) over the first 24 hours showed statistically better discriminative power for mortality (area under the receiver operating characteristic curve, 0.701; 95% CI, 0.636 to 0.766; P<0.001).
Conclusions
Implementation of reclassification based on oxygenation metrics 24 hours after diagnosis effectively stratified outcomes in PARDS. Progress within the first 24 hours was significantly associated with outcomes in PARDS, and oxygenation response was the most discernable surrogate metric for mortality.

Citations

Citations to this article as recorded by

A single‐center PICU present status survey of pediatric sepsis‐related acute respiratory distress syndrome
Liang Zhou, Shaojun Li, Tian Tang, Xiu Yuan, Liping Tan
Pediatric Pulmonology.2022; 57(9): 2003. CrossRef

Pediatric
Serum Albumin as a Biomarker of Poor Prognosis in the Pediatric Patients in Intensive Care Unit: Young Suh Kim, In Suk Sol, Min Jung Kim, Soo Yeon Kim, Jong Deok Kim, Yoon Hee Kim, Kyung Won Kim, Myung Hyun Sohn, Kyu-Earn Kim; Korean J Crit Care Med. 2017;32(4):347-355. Published online November 30, 2017; DOI: https://doi.org/10.4266/kjccm.2017.00437

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Abstract PDF Supplementary Material

Background
Serum albumin as an indicator of the disease severity and mortality is suggested in adult patients, but its role in pediatric patients has not been established. The objectives of this study are to investigate the albumin level as a biomarker of poor prognosis and to compare it with other mortality predictive indices in children in intensive care unit (ICU).
Methods
Medical records of 431 children admitted to the ICU at Severance Hospital from January 1, 2012 to December 31, 2015 were retrospectively analyzed. Children who expired within 24 hours after ICU admission, children with hepatic or renal failure, and those who received albumin replacement before ICU admission were excluded.
Results
The children with hypoalbuminemia had higher 28-day mortality rate (24.60% vs. 9.28%, P < 0.001), Pediatric Index of Mortality (PIM) 3 score (9.23 vs. 8.36, P < 0.001), Pediatric Risk of Mortality (PRISM) III score (7.0 vs. 5.0, P < 0.001), incidence of septic shock (12% vs. 3%, P < 0.001), C-reactive protein (33.0 mg/L vs. 5.8 mg/L, P < 0.001), delta neutrophil index (2.0% vs. 0.6%, P < 0.001), lactate level (1.6 mmol/L vs. 1.2 mmol/L, P < 0.001) and lower platelet level (206,000/μl vs. 341,000/μl, P < 0.001) compared to the children with normal albumin level. PIM 3 (r = 0.219, P < 0.001) and PRISM III (r = 0.375, P < 0.001) were negatively correlated with serum albumin level, respectively.
Conclusions
Our results highlight that hypoalbuminemia can be a biomarker of poor prognosis including mortality in the children in ICU.

Citations

Citations to this article as recorded by

Prognostic factors and models to predict pediatric sepsis mortality: A scoping review
Irene Yuniar, Cut Nurul Hafifah, Sharfina Fulki Adilla, Arifah Nur Shadrina, Anthony Christian Darmawan, Kholisah Nasution, Respati W. Ranakusuma, Eka Dian Safitri
Frontiers in Pediatrics.2023;[Epub] CrossRef
The association between serum albumin and long length of stay of patients with acute heart failure: A retrospective study based on the MIMIC-IV database
Tao Liu, Haochen Xuan, Lili Wang, Xiaoqun Li, Zhihao Lu, Zhaoxuan Tian, Junhong Chen, Chaofan Wang, Dongye Li, Tongda Xu, Chiara Lazzeri
PLOS ONE.2023; 18(2): e0282289. CrossRef
Pediatric Inflammatory Multisystem Syndrome Temporally Associated With SARS-CoV-2 (PIMS-TS) and Serous Effusions in a Child With Severe Hypoalbuminemia: A Case Report
Zohair El Haddar, Aziza Elouali, Ilham Belga, Maria Rkain, Abdeladim Babakhouya
Cureus.2023;[Epub] CrossRef
Inappropriate empirical antibiotic therapy was an independent risk factor of pediatric persistent S. aureus bloodstream infection
Xingmei Wang, Ziyao Guo, Xi Zhang, Guangli Zhang, Qinyuan Li, Xiaoyin Tian, Dapeng Chen, Zhengxiu Luo
European Journal of Pediatrics.2022; 182(2): 719. CrossRef
Evaluation of models for predicting pediatric fraction unbound in plasma for human health risk assessment
Yejin Esther Yun, Andrea N. Edginton
Journal of Toxicology and Environmental Health, Part A.2021; 84(2): 67. CrossRef
Diabetes Mellitus and Hypertension Increase Risk of Death in Novel Corona Virus Patients Irrespective of Age: a Prospective Observational Study of Co-morbidities and COVID-19 from India
Anirban Gupta, Neelabh Nayan, Ranjith Nair, Krishna Kumar, Aditya Joshi, Shivangi Sharma, Jasdeep Singh, Rajan Kapoor
SN Comprehensive Clinical Medicine.2021; 3(4): 937. CrossRef
Overview of Albumin Physiology and its Role in Pediatric Diseases
Charles B. Chen, Bilasan Hammo, Jessica Barry, Kadakkal Radhakrishnan
Current Gastroenterology Reports.2021;[Epub] CrossRef
The effect of nutritional status on post-operative outcomes in pediatric otolaryngology-head and neck surgery
Jordan Luttrell, Matthew Spence, Hiba Al-Zubeidi, Michael J. Herr, Madhu Mamidala, Anthony Sheyn
International Journal of Pediatric Otorhinolaryngology.2021; 150: 110875. CrossRef

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