A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset
© 2023 John Wiley & Sons Ltd..
BACKGROUND: Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at-risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA-PS) score, despite reported inconsistencies with this method.
AIMS: The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day.
METHODS: Our dataset was derived from APRICOT, a prospective observational cohort study conducted by 261 European institutions in 2014 and 2015. We included only the first procedure, ASA-PS classification I to III, and perioperative adverse events not classified as drug errors, reducing the total number of records to 30 325 with an adverse event rate of 4.43%. From this dataset, a stratified train:test split of 70:30 was used to develop predictive machine learning algorithms that could identify children in ASA-PS class I to III at low risk for severe perioperative critical events that included respiratory, cardiac, allergic, and neurological complications.
RESULTS: Our selected models achieved accuracies of >0.9, areas under the receiver operating curve of 0.6-0.7, and negative predictive values >95%. Gradient boosting models were the best performing for both the booking phase and the day-of-surgery phase.
CONCLUSIONS: This work demonstrates that prediction of patients at low risk of critical PAEs can be made on an individual, rather than population-based, level by using machine learning. Our approach yielded two models that accommodate wide clinical variability and, with further development, are potentially generalizable to many surgical centers.
| Errataetall: |
CommentIn: Paediatr Anaesth. 2023 Sep;33(9):687-688. - PMID 37309606 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:33 |
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| Enthalten in: |
Paediatric anaesthesia - 33(2023), 9 vom: 02. Sept., Seite 710-719 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gray, Geoffrey M [VerfasserIn] |
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| Links: |
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| Themen: |
124155-78-8 |
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| Anmerkungen: |
Date Completed 03.08.2023 Date Revised 06.09.2023 published: Print-Electronic CommentIn: Paediatr Anaesth. 2023 Sep;33(9):687-688. - PMID 37309606 Citation Status MEDLINE |
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| doi: |
10.1111/pan.14694 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM357144678 |
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| 100 | 1 | |a Gray, Geoffrey M |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset |
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| 500 | |a CommentIn: Paediatr Anaesth. 2023 Sep;33(9):687-688. - PMID 37309606 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 John Wiley & Sons Ltd. | ||
| 520 | |a BACKGROUND: Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at-risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA-PS) score, despite reported inconsistencies with this method | ||
| 520 | |a AIMS: The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day | ||
| 520 | |a METHODS: Our dataset was derived from APRICOT, a prospective observational cohort study conducted by 261 European institutions in 2014 and 2015. We included only the first procedure, ASA-PS classification I to III, and perioperative adverse events not classified as drug errors, reducing the total number of records to 30 325 with an adverse event rate of 4.43%. From this dataset, a stratified train:test split of 70:30 was used to develop predictive machine learning algorithms that could identify children in ASA-PS class I to III at low risk for severe perioperative critical events that included respiratory, cardiac, allergic, and neurological complications | ||
| 520 | |a RESULTS: Our selected models achieved accuracies of >0.9, areas under the receiver operating curve of 0.6-0.7, and negative predictive values >95%. Gradient boosting models were the best performing for both the booking phase and the day-of-surgery phase | ||
| 520 | |a CONCLUSIONS: This work demonstrates that prediction of patients at low risk of critical PAEs can be made on an individual, rather than population-based, level by using machine learning. Our approach yielded two models that accommodate wide clinical variability and, with further development, are potentially generalizable to many surgical centers | ||
| 650 | 4 | |a Observational Study | |
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Rehman, Mohamed A |e verfasserin |4 aut | |
| 700 | 1 | |a Varughese, Anna |e verfasserin |4 aut | |
| 700 | 1 | |a Fernandez, Allison M |e verfasserin |4 aut | |
| 700 | 1 | |a Fackler, James |e verfasserin |4 aut | |
| 700 | 1 | |a Yates, Hannah M |e verfasserin |4 aut | |
| 700 | 1 | |a Habre, Walid |e verfasserin |4 aut | |
| 700 | 1 | |a Disma, Nicola |e verfasserin |4 aut | |
| 700 | 1 | |a Lonsdale, Hannah |e verfasserin |4 aut | |
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