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The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19
Background: The COVID-GRAM is a clinical risk rating score for predicting the prognosis of hospitalized COVID-19 infected patients. Aim: Our study aimed to evaluate the use of the COVID-GRAM score in patients with COVID-19 based on the data from the COronavirus in the LOwer Silesia (COLOS) registry....
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9566437/ https://www.ncbi.nlm.nih.gov/pubmed/36231836 http://dx.doi.org/10.3390/ijerph191912537 |
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author | Sebastian, Agata Madziarski, Marcin Madej, Marta Proc, Krzysztof Szymala-Pędzik, Małgorzata Żórawska, Joanna Gronek, Michał Morgiel, Ewa Kujawa, Krzysztof Skarupski, Marek Trocha, Małgorzata Rola, Piotr Gawryś, Jakub Letachowicz, Krzysztof Doroszko, Adrian Adamik, Barbara Kaliszewski, Krzysztof Kiliś-Pstrusińska, Katarzyna Matera-Witkiewicz, Agnieszka Pomorski, Michał Protasiewicz, Marcin Sokołowski, Janusz Jankowska, Ewa Anita Madziarska, Katarzyna |
author_facet | Sebastian, Agata Madziarski, Marcin Madej, Marta Proc, Krzysztof Szymala-Pędzik, Małgorzata Żórawska, Joanna Gronek, Michał Morgiel, Ewa Kujawa, Krzysztof Skarupski, Marek Trocha, Małgorzata Rola, Piotr Gawryś, Jakub Letachowicz, Krzysztof Doroszko, Adrian Adamik, Barbara Kaliszewski, Krzysztof Kiliś-Pstrusińska, Katarzyna Matera-Witkiewicz, Agnieszka Pomorski, Michał Protasiewicz, Marcin Sokołowski, Janusz Jankowska, Ewa Anita Madziarska, Katarzyna |
author_sort | Sebastian, Agata |
collection | PubMed |
description | Background: The COVID-GRAM is a clinical risk rating score for predicting the prognosis of hospitalized COVID-19 infected patients. Aim: Our study aimed to evaluate the use of the COVID-GRAM score in patients with COVID-19 based on the data from the COronavirus in the LOwer Silesia (COLOS) registry. Material and methods: The study group (834 patients of Caucasian patients) was retrospectively divided into three arms according to the risk achieved on the COVID-GRAM score calculated at the time of hospital admission (between February 2020 and July 2021): low, medium, and high risk. The Omnibus chi-square test, Fisher test, and Welch ANOVA were used in the statistical analysis. Post-hoc analysis for continuous variables was performed using Tukey’s correction with the Games–Howell test. Additionally, the ROC analysis was performed over time using inverse probability of censorship (IPCW) estimation. The GRAM-COVID score was estimated from the time-dependent area under the curve (AUC). Results: Most patients (65%) had a low risk of complications on the COVID-GRAM scale. There were 113 patients in the high-risk group (13%). In the medium- and high-risk groups, comorbidities occurred statistically significantly more often, e.g., hypertension, diabetes, atrial fibrillation and flutter, heart failure, valvular disease, chronic kidney disease, and obstructive pulmonary disease (COPD), compared to low-risk tier subjects. These individuals were also patients with a higher incidence of neurological and cardiac complications in the past. Low saturation of oxygen values on admission, changes in C-reactive protein, leukocytosis, hyperglycemia, and procalcitonin level were associated with an increased risk of death during hospitalization. The troponin level was an independent mortality factor. A change from low to medium category reduced the overall survival probability by more than 8 times and from low to high by 25 times. The factor with the strongest impact on survival was the absence of other diseases. The medium-risk patient group was more likely to require dialysis during hospitalization. The need for antibiotics was more significant in the high-risk group on the GRAM score. Conclusion: The COVID-GRAM score corresponds well with total mortality. The factor with the strongest impact on survival was the absence of other diseases. The worst prognosis was for patients who were unconscious during admission. Patients with higher COVID-GRAM score were significantly less likely to return to full health during follow-up. There is a continuing need to develop reliable, easy-to-adopt tools for stratifying the course of SARS-CoV-2 infection. |
format | Online Article Text |
id | pubmed-9566437 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95664372022-10-15 The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19 Sebastian, Agata Madziarski, Marcin Madej, Marta Proc, Krzysztof Szymala-Pędzik, Małgorzata Żórawska, Joanna Gronek, Michał Morgiel, Ewa Kujawa, Krzysztof Skarupski, Marek Trocha, Małgorzata Rola, Piotr Gawryś, Jakub Letachowicz, Krzysztof Doroszko, Adrian Adamik, Barbara Kaliszewski, Krzysztof Kiliś-Pstrusińska, Katarzyna Matera-Witkiewicz, Agnieszka Pomorski, Michał Protasiewicz, Marcin Sokołowski, Janusz Jankowska, Ewa Anita Madziarska, Katarzyna Int J Environ Res Public Health Article Background: The COVID-GRAM is a clinical risk rating score for predicting the prognosis of hospitalized COVID-19 infected patients. Aim: Our study aimed to evaluate the use of the COVID-GRAM score in patients with COVID-19 based on the data from the COronavirus in the LOwer Silesia (COLOS) registry. Material and methods: The study group (834 patients of Caucasian patients) was retrospectively divided into three arms according to the risk achieved on the COVID-GRAM score calculated at the time of hospital admission (between February 2020 and July 2021): low, medium, and high risk. The Omnibus chi-square test, Fisher test, and Welch ANOVA were used in the statistical analysis. Post-hoc analysis for continuous variables was performed using Tukey’s correction with the Games–Howell test. Additionally, the ROC analysis was performed over time using inverse probability of censorship (IPCW) estimation. The GRAM-COVID score was estimated from the time-dependent area under the curve (AUC). Results: Most patients (65%) had a low risk of complications on the COVID-GRAM scale. There were 113 patients in the high-risk group (13%). In the medium- and high-risk groups, comorbidities occurred statistically significantly more often, e.g., hypertension, diabetes, atrial fibrillation and flutter, heart failure, valvular disease, chronic kidney disease, and obstructive pulmonary disease (COPD), compared to low-risk tier subjects. These individuals were also patients with a higher incidence of neurological and cardiac complications in the past. Low saturation of oxygen values on admission, changes in C-reactive protein, leukocytosis, hyperglycemia, and procalcitonin level were associated with an increased risk of death during hospitalization. The troponin level was an independent mortality factor. A change from low to medium category reduced the overall survival probability by more than 8 times and from low to high by 25 times. The factor with the strongest impact on survival was the absence of other diseases. The medium-risk patient group was more likely to require dialysis during hospitalization. The need for antibiotics was more significant in the high-risk group on the GRAM score. Conclusion: The COVID-GRAM score corresponds well with total mortality. The factor with the strongest impact on survival was the absence of other diseases. The worst prognosis was for patients who were unconscious during admission. Patients with higher COVID-GRAM score were significantly less likely to return to full health during follow-up. There is a continuing need to develop reliable, easy-to-adopt tools for stratifying the course of SARS-CoV-2 infection. MDPI 2022-10-01 /pmc/articles/PMC9566437/ /pubmed/36231836 http://dx.doi.org/10.3390/ijerph191912537 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sebastian, Agata Madziarski, Marcin Madej, Marta Proc, Krzysztof Szymala-Pędzik, Małgorzata Żórawska, Joanna Gronek, Michał Morgiel, Ewa Kujawa, Krzysztof Skarupski, Marek Trocha, Małgorzata Rola, Piotr Gawryś, Jakub Letachowicz, Krzysztof Doroszko, Adrian Adamik, Barbara Kaliszewski, Krzysztof Kiliś-Pstrusińska, Katarzyna Matera-Witkiewicz, Agnieszka Pomorski, Michał Protasiewicz, Marcin Sokołowski, Janusz Jankowska, Ewa Anita Madziarska, Katarzyna The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19 |
title | The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19 |
title_full | The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19 |
title_fullStr | The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19 |
title_full_unstemmed | The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19 |
title_short | The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19 |
title_sort | usefulness of the covid-gram score in predicting the outcomes of study population with covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9566437/ https://www.ncbi.nlm.nih.gov/pubmed/36231836 http://dx.doi.org/10.3390/ijerph191912537 |
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