Applying artificial neural network in predicting sepsis mortality in the emergency department based on clinical features and complete blood count parameters
© 2023. The Author(s)..
A complete blood count (CBC) is routinely ordered for emergency department (ED) patients with infections. Certain parameters, such as the neutrophil-to-lymphocyte ratio (NLR), might have prognostic value. We aimed to evaluate the prognostic value of the presenting CBC parameters combined with clinical variables in predicting 30-day mortality in adult ED patients with infections using an artificial neural network (ANN). We conducted a retrospective study of ED patients with infections between 17 December 2021 and 16 February 2022. Clinical variables and CBC parameters were collected from patient records, with NLR, monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) calculated. We determined the discriminatory performance using the area under the receiver operating characteristic curve (AUROC) and performed a 70/30 random data split and supervised ANN machine learning. We analyzed 558 patients, of whom 144 (25.8%) had sepsis and 60 (10.8%) died at 30 days. The AUROCs of NLR, MLR, PLR, and their sum were 0.644 (95% CI 0.573-0.716), 0.555 (95% CI 0.482-0.628), 0.606 (95% CI 0.529-0.682), and 0.610 (95% CI 0.534-0.686), respectively. The ANN model based on twelve variables including clinical variables, hemoglobin, red cell distribution width, NLR, and PLR achieved an AUROC of 0.811 in the testing dataset.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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| Enthalten in: |
Scientific reports - 13(2023), 1 vom: 05. Dez., Seite 21463 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wong, Beata Pui Kwan [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 07.12.2023 Date Revised 22.12.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41598-023-48797-9 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365437867 |
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| 245 | 1 | 0 | |a Applying artificial neural network in predicting sepsis mortality in the emergency department based on clinical features and complete blood count parameters |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a A complete blood count (CBC) is routinely ordered for emergency department (ED) patients with infections. Certain parameters, such as the neutrophil-to-lymphocyte ratio (NLR), might have prognostic value. We aimed to evaluate the prognostic value of the presenting CBC parameters combined with clinical variables in predicting 30-day mortality in adult ED patients with infections using an artificial neural network (ANN). We conducted a retrospective study of ED patients with infections between 17 December 2021 and 16 February 2022. Clinical variables and CBC parameters were collected from patient records, with NLR, monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) calculated. We determined the discriminatory performance using the area under the receiver operating characteristic curve (AUROC) and performed a 70/30 random data split and supervised ANN machine learning. We analyzed 558 patients, of whom 144 (25.8%) had sepsis and 60 (10.8%) died at 30 days. The AUROCs of NLR, MLR, PLR, and their sum were 0.644 (95% CI 0.573-0.716), 0.555 (95% CI 0.482-0.628), 0.606 (95% CI 0.529-0.682), and 0.610 (95% CI 0.534-0.686), respectively. The ANN model based on twelve variables including clinical variables, hemoglobin, red cell distribution width, NLR, and PLR achieved an AUROC of 0.811 in the testing dataset | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Ip, Carrie Yuen Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Ho Ching |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Lingyun |e verfasserin |4 aut | |
| 700 | 1 | |a Lau, Michael Chun Kai |e verfasserin |4 aut | |
| 700 | 1 | |a Tsang, Tat Chi |e verfasserin |4 aut | |
| 700 | 1 | |a Tsui, Matthew Sik Hon |e verfasserin |4 aut | |
| 700 | 1 | |a Rainer, Timothy Hudson |e verfasserin |4 aut | |
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