Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries
IntroductionEarly and accurate recognition of children at risk of progressing to critical illness could contribute to improved patient outcomes and resource allocation. In resource limited settings digital triage tools can support decision making and improve healthcare delivery. We developed a model...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2022.976870/full |
| _version_ | 1811189149915414528 |
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| author | Alishah Mawji Alishah Mawji Edmond Li Dustin Dunsmuir Dustin Dunsmuir Clare Komugisha Stefanie K. Novakowski Stefanie K. Novakowski Matthew O. Wiens Tagoola Abner Vesuvius Niranjan Kissoon J. Mark Ansermino J. Mark Ansermino |
| author_facet | Alishah Mawji Alishah Mawji Edmond Li Dustin Dunsmuir Dustin Dunsmuir Clare Komugisha Stefanie K. Novakowski Stefanie K. Novakowski Matthew O. Wiens Tagoola Abner Vesuvius Niranjan Kissoon J. Mark Ansermino J. Mark Ansermino |
| author_sort | Alishah Mawji |
| collection | DOAJ |
| description | IntroductionEarly and accurate recognition of children at risk of progressing to critical illness could contribute to improved patient outcomes and resource allocation. In resource limited settings digital triage tools can support decision making and improve healthcare delivery. We developed a model for rapid identification of critically ill children at triage.MethodsThis was a prospective cohort study of acutely ill children presenting at Jinja Regional Referral Hospital in Eastern Uganda. Variables collected in the emergency department informed the development of a logistic model based on hospital admission using bootstrap stepwise regression. Low and high-risk thresholds for 90% minimum sensitivity and specificity, respectively generated three risk level categories. Performance was assessed using receiver operating characteristic curve analysis on a held-out test set generated by an 80:20 split with 10-fold cross validation. A risk stratification table informed clinical interpretation.ResultsThe model derivation cohort included 1,612 participants, with an admission rate of approximately 23%. The majority of admitted patients were under five years old and presenting with sepsis, malaria, or pneumonia. A 9-predictor triage model was derived: logit (p) = −32.888 + (0.252, square root of age) + (0.016, heart rate) + (0.819, temperature) + (−0.022, mid-upper arm circumference) + (0.048 transformed oxygen saturation) + (1.793, parent concern) + (1.012, difficulty breathing) + (1.814, oedema) + (1.506, pallor). The model afforded good discrimination, calibration, and risk stratification at the selected thresholds of 8% and 40%.ConclusionIn a low income, pediatric population, we developed a nine variable triage model with high sensitivity and specificity to predict who should be admitted. The triage model can be integrated into any digital platform and used with minimal training to guide rapid identification of critically ill children at first contact. External validation and clinical implementation are in progress. |
| first_indexed | 2024-04-11T14:31:03Z |
| format | Article |
| id | doaj.art-8f2976ac1dfb41b480488bf512f24f8d |
| institution | Directory Open Access Journal |
| issn | 2296-2360 |
| language | English |
| last_indexed | 2024-04-11T14:31:03Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj.art-8f2976ac1dfb41b480488bf512f24f8d2022-12-22T04:18:38ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602022-11-011010.3389/fped.2022.976870976870Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countriesAlishah Mawji0Alishah Mawji1Edmond Li2Dustin Dunsmuir3Dustin Dunsmuir4Clare Komugisha5Stefanie K. Novakowski6Stefanie K. Novakowski7Matthew O. Wiens8Tagoola Abner Vesuvius9Niranjan Kissoon10J. Mark Ansermino11J. Mark Ansermino12Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, CanadaCentre for International Child Health, BC Children’s Hospital Research Institute, Vancouver, BC, CanadaDepartment of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, CanadaDepartment of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, CanadaCentre for International Child Health, BC Children’s Hospital Research Institute, Vancouver, BC, CanadaWALIMU, Kampala, UgandaDepartment of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, CanadaCentre for International Child Health, BC Children’s Hospital Research Institute, Vancouver, BC, CanadaCentre for International Child Health, BC Children’s Hospital Research Institute, Vancouver, BC, CanadaDepartment of Pediatrics, Jinja Regional Referral Hospital, Jinja, UgandaDepartment of Pediatrics, University of British Columbia, Vancouver, BC, CanadaDepartment of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, CanadaCentre for International Child Health, BC Children’s Hospital Research Institute, Vancouver, BC, CanadaIntroductionEarly and accurate recognition of children at risk of progressing to critical illness could contribute to improved patient outcomes and resource allocation. In resource limited settings digital triage tools can support decision making and improve healthcare delivery. We developed a model for rapid identification of critically ill children at triage.MethodsThis was a prospective cohort study of acutely ill children presenting at Jinja Regional Referral Hospital in Eastern Uganda. Variables collected in the emergency department informed the development of a logistic model based on hospital admission using bootstrap stepwise regression. Low and high-risk thresholds for 90% minimum sensitivity and specificity, respectively generated three risk level categories. Performance was assessed using receiver operating characteristic curve analysis on a held-out test set generated by an 80:20 split with 10-fold cross validation. A risk stratification table informed clinical interpretation.ResultsThe model derivation cohort included 1,612 participants, with an admission rate of approximately 23%. The majority of admitted patients were under five years old and presenting with sepsis, malaria, or pneumonia. A 9-predictor triage model was derived: logit (p) = −32.888 + (0.252, square root of age) + (0.016, heart rate) + (0.819, temperature) + (−0.022, mid-upper arm circumference) + (0.048 transformed oxygen saturation) + (1.793, parent concern) + (1.012, difficulty breathing) + (1.814, oedema) + (1.506, pallor). The model afforded good discrimination, calibration, and risk stratification at the selected thresholds of 8% and 40%.ConclusionIn a low income, pediatric population, we developed a nine variable triage model with high sensitivity and specificity to predict who should be admitted. The triage model can be integrated into any digital platform and used with minimal training to guide rapid identification of critically ill children at first contact. External validation and clinical implementation are in progress.https://www.frontiersin.org/articles/10.3389/fped.2022.976870/fullsepsistriagelow-and-middle income countriespredictionmodelchildren |
| spellingShingle | Alishah Mawji Alishah Mawji Edmond Li Dustin Dunsmuir Dustin Dunsmuir Clare Komugisha Stefanie K. Novakowski Stefanie K. Novakowski Matthew O. Wiens Tagoola Abner Vesuvius Niranjan Kissoon J. Mark Ansermino J. Mark Ansermino Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries Frontiers in Pediatrics sepsis triage low-and-middle income countries prediction model children |
| title | Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries |
| title_full | Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries |
| title_fullStr | Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries |
| title_full_unstemmed | Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries |
| title_short | Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries |
| title_sort | smart triage development of a rapid pediatric triage algorithm for use in low and middle income countries |
| topic | sepsis triage low-and-middle income countries prediction model children |
| url | https://www.frontiersin.org/articles/10.3389/fped.2022.976870/full |
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