Predicting the duration of inpatient treatment for COVID-19 patients
Introduction. In the context of a high load on all links in the structure of providing medical care to patients with COVID-19, solving the issue of effective triage of patients seems to be extremely urgent. The duration of inpatient treatment is one of the most objective and unambiguously interprete...
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| Format: | Article |
| Language: | Russian |
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Remedium Group LLC
2020-11-01
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| Series: | Медицинский совет |
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| Online Access: | https://www.med-sovet.pro/jour/article/view/5880 |
| _version_ | 1797841529680691200 |
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| author | V. V. Tsvetkov I. I. Tokin D. A. Lioznov E. V. Venev A. N. Kulikov |
| author_facet | V. V. Tsvetkov I. I. Tokin D. A. Lioznov E. V. Venev A. N. Kulikov |
| author_sort | V. V. Tsvetkov |
| collection | DOAJ |
| description | Introduction. In the context of a high load on all links in the structure of providing medical care to patients with COVID-19, solving the issue of effective triage of patients seems to be extremely urgent. The duration of inpatient treatment is one of the most objective and unambiguously interpreted indicators that can be used to indirectly assess the severity of the patient’s condition.Objective. Develop a machine learning model to predict the duration of inpatient care for patients with COVID-19 based on routine clinical indicators assessed at the prehospital stage.Materials and methods. A total of 564 patients were examined with diagnoses: U07.1 COVID-19, virus identified (n = 367) and U07.2 COVID-19, virus not identified (n = 197). The study included 270 patients, of whom in 50.37% of patients the duration of inpatient treatment did not exceed 7 days, in 49.63% of patients the duration of inpatient treatment was more than 10 days. Eleven clinical parameters were chosen as the most important predictors for predicting the duration of inpatient treatment: age, height and weight of the patient, SpO2 level, body temperature, body mass index, pulse rate, number of days from the onset of illness, respiratory rate, systolic and diastolic arterial pressure.Results. The accuracy of our machine learning model for predicting the duration of inpatient treatment more than 10 days was 83.75% (95% CI: 73.82–91.05%), sensitivity — 82.50%, specificity — 85.00%. AUC = 0.86.Conclusion. The method developed by us based on machine learning is characterized by high accuracy in predicting the duration of inpatient treatment of patients with COVID-19, which makes it possible to consider it as a promising new tool to support medical decisions on further tactics of patient management and to resolve the issue of the need for hospitalization. |
| first_indexed | 2024-04-09T16:32:18Z |
| format | Article |
| id | doaj.art-d53298b8a13d494784a481a6447316db |
| institution | Directory Open Access Journal |
| issn | 2079-701X 2658-5790 |
| language | Russian |
| last_indexed | 2024-04-09T16:32:18Z |
| publishDate | 2020-11-01 |
| publisher | Remedium Group LLC |
| record_format | Article |
| series | Медицинский совет |
| spelling | doaj.art-d53298b8a13d494784a481a6447316db2023-04-23T06:57:03ZrusRemedium Group LLCМедицинский совет2079-701X2658-57902020-11-01017829010.21518/2079-701X-2020-17-82-905347Predicting the duration of inpatient treatment for COVID-19 patientsV. V. Tsvetkov0I. I. Tokin1D. A. Lioznov2E. V. Venev3A. N. Kulikov4Smorodintsev Research Institute of InfluenzaSmorodintsev Research Institute of Influenza; North-Western State Medical University named after I.I. MechnikovSmorodintsev Research Institute of Influenza; Pavlov First Saint Petersburg State Medical UniversitySmorodintsev Research Institute of Influenza; Clinical Infectious Disease Hospital named after S.P. BotkinPavlov First Saint Petersburg State Medical UniversityIntroduction. In the context of a high load on all links in the structure of providing medical care to patients with COVID-19, solving the issue of effective triage of patients seems to be extremely urgent. The duration of inpatient treatment is one of the most objective and unambiguously interpreted indicators that can be used to indirectly assess the severity of the patient’s condition.Objective. Develop a machine learning model to predict the duration of inpatient care for patients with COVID-19 based on routine clinical indicators assessed at the prehospital stage.Materials and methods. A total of 564 patients were examined with diagnoses: U07.1 COVID-19, virus identified (n = 367) and U07.2 COVID-19, virus not identified (n = 197). The study included 270 patients, of whom in 50.37% of patients the duration of inpatient treatment did not exceed 7 days, in 49.63% of patients the duration of inpatient treatment was more than 10 days. Eleven clinical parameters were chosen as the most important predictors for predicting the duration of inpatient treatment: age, height and weight of the patient, SpO2 level, body temperature, body mass index, pulse rate, number of days from the onset of illness, respiratory rate, systolic and diastolic arterial pressure.Results. The accuracy of our machine learning model for predicting the duration of inpatient treatment more than 10 days was 83.75% (95% CI: 73.82–91.05%), sensitivity — 82.50%, specificity — 85.00%. AUC = 0.86.Conclusion. The method developed by us based on machine learning is characterized by high accuracy in predicting the duration of inpatient treatment of patients with COVID-19, which makes it possible to consider it as a promising new tool to support medical decisions on further tactics of patient management and to resolve the issue of the need for hospitalization.https://www.med-sovet.pro/jour/article/view/5880covid-19clinical scorespredictionduration of treatmentmachine learning |
| spellingShingle | V. V. Tsvetkov I. I. Tokin D. A. Lioznov E. V. Venev A. N. Kulikov Predicting the duration of inpatient treatment for COVID-19 patients Медицинский совет covid-19 clinical scores prediction duration of treatment machine learning |
| title | Predicting the duration of inpatient treatment for COVID-19 patients |
| title_full | Predicting the duration of inpatient treatment for COVID-19 patients |
| title_fullStr | Predicting the duration of inpatient treatment for COVID-19 patients |
| title_full_unstemmed | Predicting the duration of inpatient treatment for COVID-19 patients |
| title_short | Predicting the duration of inpatient treatment for COVID-19 patients |
| title_sort | predicting the duration of inpatient treatment for covid 19 patients |
| topic | covid-19 clinical scores prediction duration of treatment machine learning |
| url | https://www.med-sovet.pro/jour/article/view/5880 |
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