Development and validation of risk prediction models for COVID-19 positivity in a hospital setting
Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved..
OBJECTIVES: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.
METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
RESULTS: A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV.
CONCLUSION: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:101 |
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| Enthalten in: |
International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases - 101(2020) vom: 16. Dez., Seite 74-82 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ng, Ming-Yen [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 31.12.2020 Date Revised 12.11.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ijid.2020.09.022 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM315185147 |
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| 100 | 1 | |a Ng, Ming-Yen |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Development and validation of risk prediction models for COVID-19 positivity in a hospital setting |
| 264 | 1 | |c 2020 | |
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| 500 | |a Date Completed 31.12.2020 | ||
| 500 | |a Date Revised 12.11.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved. | ||
| 520 | |a OBJECTIVES: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation | ||
| 520 | |a METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) | ||
| 520 | |a RESULTS: A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV | ||
| 520 | |a CONCLUSION: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Chest x-ray | |
| 650 | 4 | |a Nomogram | |
| 650 | 4 | |a Prediction model | |
| 650 | 4 | |a White cell count | |
| 700 | 1 | |a Wan, Eric Yuk Fai |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Ho Yuen Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Leung, Siu Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Jonan Chun Yin |e verfasserin |4 aut | |
| 700 | 1 | |a Chin, Thomas Wing-Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Lo, Christine Shing Yen |e verfasserin |4 aut | |
| 700 | 1 | |a Lui, Macy Mei-Sze |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Edward Hung Tat |e verfasserin |4 aut | |
| 700 | 1 | |a Fong, Ambrose Ho-Tung |e verfasserin |4 aut | |
| 700 | 1 | |a Fung, Sau Yung |e verfasserin |4 aut | |
| 700 | 1 | |a Ching, On Hang |e verfasserin |4 aut | |
| 700 | 1 | |a Chiu, Keith Wan-Hang |e verfasserin |4 aut | |
| 700 | 1 | |a Chung, Tom Wai Hin |e verfasserin |4 aut | |
| 700 | 1 | |a Vardhanbhuti, Varut |e verfasserin |4 aut | |
| 700 | 1 | |a Lam, Hiu Yin Sonia |e verfasserin |4 aut | |
| 700 | 1 | |a To, Kelvin Kai Wang |e verfasserin |4 aut | |
| 700 | 1 | |a Chiu, Jeffrey Long Fung |e verfasserin |4 aut | |
| 700 | 1 | |a Lam, Tina Poy Wing |e verfasserin |4 aut | |
| 700 | 1 | |a Khong, Pek Lan |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Raymond Wai To |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Johnny Wai Man |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Alan Ka Lun |e verfasserin |4 aut | |
| 700 | 1 | |a Lung, Kwok-Cheung |e verfasserin |4 aut | |
| 700 | 1 | |a Hung, Ivan Fan Ngai |e verfasserin |4 aut | |
| 700 | 1 | |a Lau, Chak Sing |e verfasserin |4 aut | |
| 700 | 1 | |a Kuo, Michael D |e verfasserin |4 aut | |
| 700 | 1 | |a Ip, Mary Sau-Man |e verfasserin |4 aut | |
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