Prediction of fatal outcome in patients with confirm COVID-19
SARS-CoV-2, the new coronavirus underlying the development of the COVID-19 pandemic, has led to a sharp increase in the burden on healthcare systems, high mortality and significant difficulties in organizing medical care. The aim of the study was to conduct a systematic analysis of factors affecti...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
2022-10-01
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| Series: | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| Subjects: | |
| Online Access: | https://ntv.ifmo.ru/file/article/21525.pdf |
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| author | Igor N. Korsakov Tatiana L. Karonova Alexandra O. Konradi Arkadii D. Rubin Dmitry I. Kurapeev Alena T. Chernikova Arina A. Mikhaylova Evgeny V. Shlyakhto |
| author_facet | Igor N. Korsakov Tatiana L. Karonova Alexandra O. Konradi Arkadii D. Rubin Dmitry I. Kurapeev Alena T. Chernikova Arina A. Mikhaylova Evgeny V. Shlyakhto |
| author_sort | Igor N. Korsakov |
| collection | DOAJ |
| description | SARS-CoV-2, the new coronavirus underlying the development of the COVID-19 pandemic, has led to a sharp increase
in the burden on healthcare systems, high mortality and significant difficulties in organizing medical care. The aim
of the study was to conduct a systematic analysis of factors affecting the course of infectious disease in patients with
diagnosed COVID-19 hospitalized. In order to predict the course of the disease and determine the indications for more
aggressive treatment, many different clinical and biological markers have been proposed, however, clinical and laboratory
assessment of the condition is not always simple and can clearly predict the development of a severe course. Technologies
based on artificial intelligence (AI) have played a significant role in predicting the development of the disease. One of
the main requirements during a pandemic is an accurate prediction of the required resources and likely outcomes. In the
present study, a machine learning (ML) approach is proposed to predict the fatal outcome in patients with an established
diagnosis of COVID-19 based on the patient’s medical history and clinical, laboratory and instrumental data obtained
in the first 72 hours of the patient’s stay in the hospital. A machine learning algorithm for predicting the lethal outcome
in patients with COVID-19 during 72 hours of hospitalization demonstrated high sensitivity (0.816) and specificity
(0.865). Given the serious concerns about limited resources, including ventilators, during the COVID-19 pandemic,
accurately predicting patients who are likely to require artificial ventilation can help provide important recommendations
regarding patient triage and resource allocation among hospitalized patients. In addition, early detection of such persons
may allow for routine ventilation procedures, reducing some of the known risks associated with emergency intubation.
Thus, this algorithm can help improve patient care, reduce patient mortality and minimize the burden on doctors during
the COVID-19 pandemic.
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| first_indexed | 2024-04-11T08:23:00Z |
| format | Article |
| id | doaj.art-f85fde44224f44ea9935c33d5a7cb4e1 |
| institution | Directory Open Access Journal |
| issn | 2226-1494 2500-0373 |
| language | English |
| last_indexed | 2024-04-11T08:23:00Z |
| publishDate | 2022-10-01 |
| publisher | Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University) |
| record_format | Article |
| series | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| spelling | doaj.art-f85fde44224f44ea9935c33d5a7cb4e12022-12-22T04:34:52ZengSaint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki2226-14942500-03732022-10-0122597098110.17586/2226-1494-2022-22-5-970-981Prediction of fatal outcome in patients with confirm COVID-19Igor N. Korsakov0https://orcid.org/0000-0003-2343-9641Tatiana L. Karonova1https://orcid.org/0000-0002-1547-0123Alexandra O. Konradi2https://orcid.org/0000-0001-8169-7812Arkadii D. Rubin3https://orcid.org/0000-0001-5469-5635Dmitry I. Kurapeev4https://orcid.org/0000-0002-2190-1495Alena T. Chernikova5https://orcid.org/0000-0002-4878-6909Arina A. Mikhaylova6https://orcid.org/0000-0001-6066-3525Evgeny V. Shlyakhto7https://orcid.org/0000-0003-2929-0980PhD (Physics & Mathematics), IT Specialist, Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation, sc 57189603967D. Sc. (Medicine), Associate Professor, Chief Researcher, Head of Laboratory, Chair Professor, Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation, sc 55812730000D. Sc. (Medicine), Academician of the RAS, Deputy Director General for Research, Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation, sc 7004144504D. Sc. (Medicine), Associate Professor, Medical Director of the Treatment and Rehabilitation Facility, Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian FederationPhD (Medicine), Deputy CEO for Information Technology, Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation, sc 57225231263Junior Researcher, Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian FederationDepartment Resident, Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian FederationD. Sc. (Medicine), Academician of the RAS, Director General, Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation, sc 16317213100SARS-CoV-2, the new coronavirus underlying the development of the COVID-19 pandemic, has led to a sharp increase in the burden on healthcare systems, high mortality and significant difficulties in organizing medical care. The aim of the study was to conduct a systematic analysis of factors affecting the course of infectious disease in patients with diagnosed COVID-19 hospitalized. In order to predict the course of the disease and determine the indications for more aggressive treatment, many different clinical and biological markers have been proposed, however, clinical and laboratory assessment of the condition is not always simple and can clearly predict the development of a severe course. Technologies based on artificial intelligence (AI) have played a significant role in predicting the development of the disease. One of the main requirements during a pandemic is an accurate prediction of the required resources and likely outcomes. In the present study, a machine learning (ML) approach is proposed to predict the fatal outcome in patients with an established diagnosis of COVID-19 based on the patient’s medical history and clinical, laboratory and instrumental data obtained in the first 72 hours of the patient’s stay in the hospital. A machine learning algorithm for predicting the lethal outcome in patients with COVID-19 during 72 hours of hospitalization demonstrated high sensitivity (0.816) and specificity (0.865). Given the serious concerns about limited resources, including ventilators, during the COVID-19 pandemic, accurately predicting patients who are likely to require artificial ventilation can help provide important recommendations regarding patient triage and resource allocation among hospitalized patients. In addition, early detection of such persons may allow for routine ventilation procedures, reducing some of the known risks associated with emergency intubation. Thus, this algorithm can help improve patient care, reduce patient mortality and minimize the burden on doctors during the COVID-19 pandemic. https://ntv.ifmo.ru/file/article/21525.pdfmachine learningmathematical modelclassificationmodel metricsroc analysisrisk factorcovid-19 |
| spellingShingle | Igor N. Korsakov Tatiana L. Karonova Alexandra O. Konradi Arkadii D. Rubin Dmitry I. Kurapeev Alena T. Chernikova Arina A. Mikhaylova Evgeny V. Shlyakhto Prediction of fatal outcome in patients with confirm COVID-19 Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki machine learning mathematical model classification model metrics roc analysis risk factor covid-19 |
| title | Prediction of fatal outcome in patients with confirm COVID-19 |
| title_full | Prediction of fatal outcome in patients with confirm COVID-19 |
| title_fullStr | Prediction of fatal outcome in patients with confirm COVID-19 |
| title_full_unstemmed | Prediction of fatal outcome in patients with confirm COVID-19 |
| title_short | Prediction of fatal outcome in patients with confirm COVID-19 |
| title_sort | prediction of fatal outcome in patients with confirm covid 19 |
| topic | machine learning mathematical model classification model metrics roc analysis risk factor covid-19 |
| url | https://ntv.ifmo.ru/file/article/21525.pdf |
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