Taking a byte out of APRICOT to predict which children are at low risk for critical perioperative events

Allan F Simpao^{1

2}, Fu-Chiang Rich Tsui^{1

2}, Clyde T Matava^{3

4}

Affiliations

¹ Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
² Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
³ Department of Anesthesia and Pain Medicine, Hospital for Sick Children, Toronto, Ontario, Canada.
⁴ Department of Anesthesiology and Pain Medicine, Termerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.

Editorial

Allan F Simpao et al. Paediatr Anaesth. 2023 Sep.

. 2023 Sep;33(9):687-688.

doi: 10.1111/pan.14707. Epub 2023 Jun 13.

Allan F Simpao^{1

2}, Fu-Chiang Rich Tsui^{1

2}, Clyde T Matava^{3

4}

¹ Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
² Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
³ Department of Anesthesia and Pain Medicine, Hospital for Sick Children, Toronto, Ontario, Canada.
⁴ Department of Anesthesiology and Pain Medicine, Termerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.

No abstract available

Comment on

A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset.
Gray GM, Ahumada LM, Rehman MA, Varughese A, Fernandez AM, Fackler J, Yates HM, Habre W, Disma N, Lonsdale H. Gray GM, et al. Paediatr Anaesth. 2023 Sep;33(9):710-719. doi: 10.1111/pan.14694. Epub 2023 May 21. Paediatr Anaesth. 2023. PMID: 37211981 Free PMC article.

1. Simpao AF, Ahumada LM, Rehman MA. Big data and visual analytics in anaesthesia and health care. Br J Anaesth. 2015;115:350-356.
1. Lonsdale H, Jalali A, Ahumada L, Matava C. Machine learning and artificial intelligence in pediatric research: current state, future prospects, and examples in perioperative and critical care. J Pediatr. 2020;221S:S3-S10.
1. Gray GM, Ahumada LM, Rehman MA, et al. A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset. Pediatr Anesth. 2023. Online ahead of print. https://orcid.org/10.1111/pan.14694
1. Habre W, Disma N, Virag K, et al. APRICOT Group of the European Society of Anaesthesiology Clinical Trial Network. Lancet Respir Med. 2017;5:412-435.
1. Engelhardt T, Virag K, Veyckemans F, Habre W. APRICOT Group of the European Society of Anaesthesiology clinical trial network. Airway management in paediatric anaesthesia in Europe-insights from APRICOT (Anaesthesia practice In children observational trial): a prospective multicentre observational study in 261 hospitals in Europe. Br J Anaesth. 2018;121:66-75.