A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring
Purpose: The aim of this research is to develop an accurate and interpretable aggregated score not only for hospitalization outcome prediction (death/discharge) but also for the daily assessment of the COVID-19 patient's condition.Patients and Methods: In this single-center cohort study, real-w...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2021.744652/full |
| _version_ | 1818834247376437248 |
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| author | Evgeny A. Bakin Evgeny A. Bakin Oksana V. Stanevich Oksana V. Stanevich Mikhail P. Chmelevsky Mikhail P. Chmelevsky Vasily A. Belash Anastasia A. Belash Galina A. Savateeva Veronika A. Bokinova Natalia A. Arsentieva Ludmila F. Sayenko Evgeny A. Korobenkov Dmitry A. Lioznov Dmitry A. Lioznov Areg A. Totolian Yury S. Polushin Alexander N. Kulikov |
| author_facet | Evgeny A. Bakin Evgeny A. Bakin Oksana V. Stanevich Oksana V. Stanevich Mikhail P. Chmelevsky Mikhail P. Chmelevsky Vasily A. Belash Anastasia A. Belash Galina A. Savateeva Veronika A. Bokinova Natalia A. Arsentieva Ludmila F. Sayenko Evgeny A. Korobenkov Dmitry A. Lioznov Dmitry A. Lioznov Areg A. Totolian Yury S. Polushin Alexander N. Kulikov |
| author_sort | Evgeny A. Bakin |
| collection | DOAJ |
| description | Purpose: The aim of this research is to develop an accurate and interpretable aggregated score not only for hospitalization outcome prediction (death/discharge) but also for the daily assessment of the COVID-19 patient's condition.Patients and Methods: In this single-center cohort study, real-world data collected within the first two waves of the COVID-19 pandemic was used (27.04.2020–03.08.2020 and 01.11.2020–19.01.2021, respectively). The first wave data (1,349 cases) was used as a training set for the score development, while the second wave data (1,453 cases) was used as a validation set. No overlapping cases were presented in the study. For all the available patients' features, we tested their association with an outcome. Significant features were taken for further analysis, and their partial sensitivity, specificity, and promptness were estimated. Sensitivity and specificity were further combined into a feature informativeness index. The developed score was derived as a weighted sum of nine features that showed the best trade-off between informativeness and promptness.Results: Based on the training cohort (median age ± median absolute deviation 58 ± 13.3, females 55.7%), the following resulting score was derived: APTT (4 points), CRP (3 points), D-dimer (4 points), glucose (4 points), hemoglobin (3 points), lymphocytes (3 points), total protein (6 points), urea (5 points), and WBC (4 points). Internal and temporal validation based on the second wave cohort (age 60 ± 14.8, females 51.8%) showed that a sensitivity and a specificity over 90% may be achieved with an expected prediction range of more than 7 days. Moreover, we demonstrated high robustness of the score to the varying peculiarities of the pandemic.Conclusions: An extensive application of the score during the pandemic showed its potential for optimization of patient management as well as improvement of medical staff attentiveness in a high workload stress. The transparent structure of the score, as well as tractable cutoff bounds, simplified its implementation into clinical practice. High cumulative informativeness of the nine score components suggests that these are the indicators that need to be monitored regularly during the follow-up of a patient with COVID-19. |
| first_indexed | 2024-12-19T02:31:47Z |
| format | Article |
| id | doaj.art-b58aca961d514e3b8fc44cc42902b2e9 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-12-19T02:31:47Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-b58aca961d514e3b8fc44cc42902b2e92022-12-21T20:39:36ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2021-12-01810.3389/fmed.2021.744652744652A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular MonitoringEvgeny A. Bakin0Evgeny A. Bakin1Oksana V. Stanevich2Oksana V. Stanevich3Mikhail P. Chmelevsky4Mikhail P. Chmelevsky5Vasily A. Belash6Anastasia A. Belash7Galina A. Savateeva8Veronika A. Bokinova9Natalia A. Arsentieva10Ludmila F. Sayenko11Evgeny A. Korobenkov12Dmitry A. Lioznov13Dmitry A. Lioznov14Areg A. Totolian15Yury S. Polushin16Alexander N. Kulikov17Raisa Gorbacheva Memorial Research Institute for Pediatric Oncology, Hematology and Transplantation, First Pavlov State Medical University, St. Petersburg, RussiaResearch Department, Bioinformatics Institute, St. Petersburg, RussiaDepartment of Infectious Diseases and Epidemiology, First Pavlov State Medical University, St. Petersburg, RussiaResearch Department, Smorodintsev Research Institute of Influenza, St. Petersburg, RussiaDepartment of Functional Diagnostics, First Pavlov State Medical University, St. Petersburg, RussiaWorld-Class Scientific Center, Saint Petersburg Electrotechnical University “LETI”, St. Petersburg, RussiaCenter for COVID-19 Treatment, First Pavlov State Medical University, St. Petersburg, RussiaCenter for COVID-19 Treatment, First Pavlov State Medical University, St. Petersburg, RussiaCenter for COVID-19 Treatment, First Pavlov State Medical University, St. Petersburg, RussiaCenter for COVID-19 Treatment, First Pavlov State Medical University, St. Petersburg, RussiaDepartment of Molecular Immunology, Saint Petersburg Pasteur Institute, St. Petersburg, RussiaInformation Technology Department, First Pavlov State Medical University, St. Petersburg, RussiaInformation Technology Department, First Pavlov State Medical University, St. Petersburg, RussiaDepartment of Infectious Diseases and Epidemiology, First Pavlov State Medical University, St. Petersburg, RussiaResearch Department, Smorodintsev Research Institute of Influenza, St. Petersburg, RussiaDepartment of Molecular Immunology, Saint Petersburg Pasteur Institute, St. Petersburg, Russia0Research Department, First Pavlov State Medical University, St. Petersburg, Russia1Clinic Management Department, First Pavlov State Medical University, St. Petersburg, RussiaPurpose: The aim of this research is to develop an accurate and interpretable aggregated score not only for hospitalization outcome prediction (death/discharge) but also for the daily assessment of the COVID-19 patient's condition.Patients and Methods: In this single-center cohort study, real-world data collected within the first two waves of the COVID-19 pandemic was used (27.04.2020–03.08.2020 and 01.11.2020–19.01.2021, respectively). The first wave data (1,349 cases) was used as a training set for the score development, while the second wave data (1,453 cases) was used as a validation set. No overlapping cases were presented in the study. For all the available patients' features, we tested their association with an outcome. Significant features were taken for further analysis, and their partial sensitivity, specificity, and promptness were estimated. Sensitivity and specificity were further combined into a feature informativeness index. The developed score was derived as a weighted sum of nine features that showed the best trade-off between informativeness and promptness.Results: Based on the training cohort (median age ± median absolute deviation 58 ± 13.3, females 55.7%), the following resulting score was derived: APTT (4 points), CRP (3 points), D-dimer (4 points), glucose (4 points), hemoglobin (3 points), lymphocytes (3 points), total protein (6 points), urea (5 points), and WBC (4 points). Internal and temporal validation based on the second wave cohort (age 60 ± 14.8, females 51.8%) showed that a sensitivity and a specificity over 90% may be achieved with an expected prediction range of more than 7 days. Moreover, we demonstrated high robustness of the score to the varying peculiarities of the pandemic.Conclusions: An extensive application of the score during the pandemic showed its potential for optimization of patient management as well as improvement of medical staff attentiveness in a high workload stress. The transparent structure of the score, as well as tractable cutoff bounds, simplified its implementation into clinical practice. High cumulative informativeness of the nine score components suggests that these are the indicators that need to be monitored regularly during the follow-up of a patient with COVID-19.https://www.frontiersin.org/articles/10.3389/fmed.2021.744652/fulldecision support systemsprognostic scoreregular monitoringCOVID-19SARS-CoV-2 |
| spellingShingle | Evgeny A. Bakin Evgeny A. Bakin Oksana V. Stanevich Oksana V. Stanevich Mikhail P. Chmelevsky Mikhail P. Chmelevsky Vasily A. Belash Anastasia A. Belash Galina A. Savateeva Veronika A. Bokinova Natalia A. Arsentieva Ludmila F. Sayenko Evgeny A. Korobenkov Dmitry A. Lioznov Dmitry A. Lioznov Areg A. Totolian Yury S. Polushin Alexander N. Kulikov A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring Frontiers in Medicine decision support systems prognostic score regular monitoring COVID-19 SARS-CoV-2 |
| title | A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring |
| title_full | A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring |
| title_fullStr | A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring |
| title_full_unstemmed | A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring |
| title_short | A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring |
| title_sort | novel approach for covid 19 patient condition tracking from instant prediction to regular monitoring |
| topic | decision support systems prognostic score regular monitoring COVID-19 SARS-CoV-2 |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2021.744652/full |
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