Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort Study
Background: Risk stratification models have been developed to identify patients that are at a higher risk of COVID-19 infection and severe illness. Objectives To develop and implement a scoring tool to identify COVID-19 patients that are at risk for severe illness during the Omicron wave. Methods: T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/11/3/767 |
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| author | Miri Mizrahi Reuveni Jennifer Kertes Shirley Shapiro Ben David Arnon Shahar Naama Shamir-Stein Keren Rosen Ori Liran Mattan Bar-Yishay Limor Adler |
| author_facet | Miri Mizrahi Reuveni Jennifer Kertes Shirley Shapiro Ben David Arnon Shahar Naama Shamir-Stein Keren Rosen Ori Liran Mattan Bar-Yishay Limor Adler |
| author_sort | Miri Mizrahi Reuveni |
| collection | DOAJ |
| description | Background: Risk stratification models have been developed to identify patients that are at a higher risk of COVID-19 infection and severe illness. Objectives To develop and implement a scoring tool to identify COVID-19 patients that are at risk for severe illness during the Omicron wave. Methods: This is a retrospective cohort study that was conducted in Israel’s second-largest healthcare maintenance organization. All patients with a new episode of COVID-19 between 26 November 2021 and 18 January 2022 were included. A model was developed to predict severe illness (COVID-19-related hospitalization or death) based on one-third of the study population (the train group). The model was then applied to the remaining two-thirds of the study population (the test group). Risk score sensitivity, specificity, and positive predictive value rates, and receiver operating characteristics (ROC) were calculated to describe the performance of the model. Results: A total of 409,693 patients were diagnosed with COVID-19 over the two-month study period, of which 0.4% had severe illness. Factors that were associated with severe disease were age (age > 75, OR-70.4, 95% confidence interval [CI] 42.8–115.9), immunosuppression (OR-4.8, 95% CI 3.4–6.7), and pregnancy (5 months or more, OR-82.9, 95% CI 53–129.6). Factors that were associated with a reduced risk for severe disease were vaccination status (patients vaccinated in the previous six months OR-0.6, 95% CI 0.4–0.8) and a prior episode of COVID-19 (OR-0.3, 95% CI 0.2–0.5). According to the model, patients who were in the 10th percentile of the risk severity score were considered at an increased risk for severe disease. The model accuracy was 88.7%. Conclusions: This model has allowed us to prioritize patients requiring closer follow-up by their physicians and outreach services, as well as identify those that are most likely to benefit from anti-viral treatment during the fifth wave of infection in Israel, dominated by the Omicron variant. |
| first_indexed | 2024-03-11T06:54:06Z |
| format | Article |
| id | doaj.art-ff71f22cdf2e4c61b19a9f8b855e7030 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-11T06:54:06Z |
| publishDate | 2023-03-01 |
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| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-ff71f22cdf2e4c61b19a9f8b855e70302023-11-17T09:45:18ZengMDPI AGBiomedicines2227-90592023-03-0111376710.3390/biomedicines11030767Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort StudyMiri Mizrahi Reuveni0Jennifer Kertes1Shirley Shapiro Ben David2Arnon Shahar3Naama Shamir-Stein4Keren Rosen5Ori Liran6Mattan Bar-Yishay7Limor Adler8Health Division, Maccabi Healthcare Services, Tel Aviv 6812509, IsraelHealth Division, Maccabi Healthcare Services, Tel Aviv 6812509, IsraelHealth Division, Maccabi Healthcare Services, Tel Aviv 6812509, IsraelHealth Division, Maccabi Healthcare Services, Tel Aviv 6812509, IsraelHealth Division, Maccabi Healthcare Services, Tel Aviv 6812509, IsraelHealth Division, Maccabi Healthcare Services, Tel Aviv 6812509, IsraelHealth Division, Maccabi Healthcare Services, Tel Aviv 6812509, IsraelHealth Division, Maccabi Healthcare Services, Tel Aviv 6812509, IsraelHealth Division, Maccabi Healthcare Services, Tel Aviv 6812509, IsraelBackground: Risk stratification models have been developed to identify patients that are at a higher risk of COVID-19 infection and severe illness. Objectives To develop and implement a scoring tool to identify COVID-19 patients that are at risk for severe illness during the Omicron wave. Methods: This is a retrospective cohort study that was conducted in Israel’s second-largest healthcare maintenance organization. All patients with a new episode of COVID-19 between 26 November 2021 and 18 January 2022 were included. A model was developed to predict severe illness (COVID-19-related hospitalization or death) based on one-third of the study population (the train group). The model was then applied to the remaining two-thirds of the study population (the test group). Risk score sensitivity, specificity, and positive predictive value rates, and receiver operating characteristics (ROC) were calculated to describe the performance of the model. Results: A total of 409,693 patients were diagnosed with COVID-19 over the two-month study period, of which 0.4% had severe illness. Factors that were associated with severe disease were age (age > 75, OR-70.4, 95% confidence interval [CI] 42.8–115.9), immunosuppression (OR-4.8, 95% CI 3.4–6.7), and pregnancy (5 months or more, OR-82.9, 95% CI 53–129.6). Factors that were associated with a reduced risk for severe disease were vaccination status (patients vaccinated in the previous six months OR-0.6, 95% CI 0.4–0.8) and a prior episode of COVID-19 (OR-0.3, 95% CI 0.2–0.5). According to the model, patients who were in the 10th percentile of the risk severity score were considered at an increased risk for severe disease. The model accuracy was 88.7%. Conclusions: This model has allowed us to prioritize patients requiring closer follow-up by their physicians and outreach services, as well as identify those that are most likely to benefit from anti-viral treatment during the fifth wave of infection in Israel, dominated by the Omicron variant.https://www.mdpi.com/2227-9059/11/3/767COVID-19risk stratification modelmortalityhospitalization |
| spellingShingle | Miri Mizrahi Reuveni Jennifer Kertes Shirley Shapiro Ben David Arnon Shahar Naama Shamir-Stein Keren Rosen Ori Liran Mattan Bar-Yishay Limor Adler Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort Study Biomedicines COVID-19 risk stratification model mortality hospitalization |
| title | Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort Study |
| title_full | Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort Study |
| title_fullStr | Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort Study |
| title_full_unstemmed | Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort Study |
| title_short | Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort Study |
| title_sort | risk stratification model for severe covid 19 disease a retrospective cohort study |
| topic | COVID-19 risk stratification model mortality hospitalization |
| url | https://www.mdpi.com/2227-9059/11/3/767 |
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