A risk prediction score to identify patients at low risk for COVID-19 infection
Introduction: Singapore’s enhanced surveillance programme for COVID-19 identifies and isolates hospitalised patients with acute respiratory symptoms to prevent nosocomial spread. We developed risk prediction models to identify patients with low risk for COVID-19 from this cohort of hospitalised pati...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer – Medknow Publications
2022-08-01
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| Series: | Singapore Medical Journal |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.11622/smedj.2021019 |
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| _version_ | 1823861017438846976 |
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| author | Wui Mei Chew Chee Hong Loh Aditi Jalali Grace Shi En Fong Loshini Senthil Kumar Rachel Hui Zhen Sim Russell Pinxue Tan Sunil Ravinder Gill Trilene Ruiting Liang Jansen Meng Kwang Koh Tunn Ren Tay |
| author_facet | Wui Mei Chew Chee Hong Loh Aditi Jalali Grace Shi En Fong Loshini Senthil Kumar Rachel Hui Zhen Sim Russell Pinxue Tan Sunil Ravinder Gill Trilene Ruiting Liang Jansen Meng Kwang Koh Tunn Ren Tay |
| author_sort | Wui Mei Chew |
| collection | DOAJ |
| description | Introduction:
Singapore’s enhanced surveillance programme for COVID-19 identifies and isolates hospitalised patients with acute respiratory symptoms to prevent nosocomial spread. We developed risk prediction models to identify patients with low risk for COVID-19 from this cohort of hospitalised patients with acute respiratory symptoms.
Methods:
This was a single-centre retrospective observational study. Patients admitted to our institution’s respiratory surveillance wards from 10 February to 30 April 2020 contributed data for analysis. Prediction models for COVID-19 were derived from a training cohort using variables based on demographics, clinical symptoms, exposure risks and blood investigations fitted into logistic regression models. The derived prediction models were subsequently validated on a test cohort.
Results:
Of the 1,228 patients analysed, 52 (4.2%) were diagnosed with COVID-19. Two prediction models were derived, the first based on age, presence of sore throat, dormitory residence, blood haemoglobin level (Hb), and total white blood cell counts (TW), and the second based on presence of headache, contact with infective patients, Hb and TW. Both models had good diagnostic performance with areas under the receiver operating characteristic curve of 0.934 and 0.866, respectively. Risk score cut-offs of 0.6 for Model 1 and 0.2 for Model 2 had 100% sensitivity, allowing identification of patients with low risk for COVID-19. Limiting COVID-19 screening to only elevated-risk patients reduced the number of isolation days for surveillance patients by up to 41.7% and COVID-19 swab testing by up to 41.0%.
Conclusion:
Prediction models derived from our study were able to identify patients at low risk for COVID-19 and rationalise resource utilisation. |
| format | Article |
| id | doaj-art-2f8a0663632c4a3bac47a16ef956fa6f |
| institution | Kabale University |
| issn | 0037-5675 2737-5935 |
| language | English |
| publishDate | 2022-08-01 |
| publisher | Wolters Kluwer – Medknow Publications |
| record_format | Article |
| series | Singapore Medical Journal |
| spelling | doaj-art-2f8a0663632c4a3bac47a16ef956fa6f2025-02-10T05:24:03ZengWolters Kluwer – Medknow PublicationsSingapore Medical Journal0037-56752737-59352022-08-0163842643210.11622/smedj.2021019A risk prediction score to identify patients at low risk for COVID-19 infectionWui Mei ChewChee Hong LohAditi JalaliGrace Shi En FongLoshini Senthil KumarRachel Hui Zhen SimRussell Pinxue TanSunil Ravinder GillTrilene Ruiting LiangJansen Meng Kwang KohTunn Ren TayIntroduction: Singapore’s enhanced surveillance programme for COVID-19 identifies and isolates hospitalised patients with acute respiratory symptoms to prevent nosocomial spread. We developed risk prediction models to identify patients with low risk for COVID-19 from this cohort of hospitalised patients with acute respiratory symptoms. Methods: This was a single-centre retrospective observational study. Patients admitted to our institution’s respiratory surveillance wards from 10 February to 30 April 2020 contributed data for analysis. Prediction models for COVID-19 were derived from a training cohort using variables based on demographics, clinical symptoms, exposure risks and blood investigations fitted into logistic regression models. The derived prediction models were subsequently validated on a test cohort. Results: Of the 1,228 patients analysed, 52 (4.2%) were diagnosed with COVID-19. Two prediction models were derived, the first based on age, presence of sore throat, dormitory residence, blood haemoglobin level (Hb), and total white blood cell counts (TW), and the second based on presence of headache, contact with infective patients, Hb and TW. Both models had good diagnostic performance with areas under the receiver operating characteristic curve of 0.934 and 0.866, respectively. Risk score cut-offs of 0.6 for Model 1 and 0.2 for Model 2 had 100% sensitivity, allowing identification of patients with low risk for COVID-19. Limiting COVID-19 screening to only elevated-risk patients reduced the number of isolation days for surveillance patients by up to 41.7% and COVID-19 swab testing by up to 41.0%. Conclusion: Prediction models derived from our study were able to identify patients at low risk for COVID-19 and rationalise resource utilisation.https://journals.lww.com/10.11622/smedj.2021019covid-19 infectioninfection controlrespiratory infections |
| spellingShingle | Wui Mei Chew Chee Hong Loh Aditi Jalali Grace Shi En Fong Loshini Senthil Kumar Rachel Hui Zhen Sim Russell Pinxue Tan Sunil Ravinder Gill Trilene Ruiting Liang Jansen Meng Kwang Koh Tunn Ren Tay A risk prediction score to identify patients at low risk for COVID-19 infection Singapore Medical Journal covid-19 infection infection control respiratory infections |
| title | A risk prediction score to identify patients at low risk for COVID-19 infection |
| title_full | A risk prediction score to identify patients at low risk for COVID-19 infection |
| title_fullStr | A risk prediction score to identify patients at low risk for COVID-19 infection |
| title_full_unstemmed | A risk prediction score to identify patients at low risk for COVID-19 infection |
| title_short | A risk prediction score to identify patients at low risk for COVID-19 infection |
| title_sort | risk prediction score to identify patients at low risk for covid 19 infection |
| topic | covid-19 infection infection control respiratory infections |
| url | https://journals.lww.com/10.11622/smedj.2021019 |
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