Open AccessPredicting the duration of inpatient treatment for COVID-19 patients
Author(s) -
V. V Tsvetkov,
И. И. Токин,
Dmitry Lioznov,
Evgeny V. Venev,
A. N. Kulikov
Publication year - 2020
Publication title -
medicinskij sovet
Language(s) - English
Resource type - Journals
eISSN - 2658-5790
pISSN - 2079-701X
DOI - 10.21518/2079-701x-2020-17-82-90
Subject(s) - medicine , covid-19 , triage , context (archaeology) , duration (music) , body mass index , emergency medicine , medical diagnosis , intensive care medicine , art , paleontology , literature , disease , pathology , infectious disease (medical specialty) , biology
. 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, SpO 2 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.