Abstract
PurposeWhile more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.MethodsWe developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).ResultsThe LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.ConclusionWe present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.
Highlights
In December 2019, a cluster of severe pneumonia occurred in the city of Wuhan, China
We present a predictor and score (SACOV-19, Score for the prediction of an Advanced disease stage of COVID19) resulting from a robust risk-stratification algorithm to assess if a patient is at risk of developing the advanced COVID-19 disease stage, based on data available at the day of the first positive SARS-CoV-2 test
To reduce the risk of reidentification, the data was anonymized using the principles used for the LEOSS Public Use File (PUF) we described earlier [22]
Summary
In December 2019, a cluster of severe pneumonia occurred in the city of Wuhan, China. The causative pathogen was identified as a new betacoronavirus [1]. It was later named the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and the infectious disease was termed coronavirus disease 2019 (COVID-19) [2]. As of September 2020, more than 32 million infections were reported worldwide and over 970,000 people had died [3]. Course and outcome of patients with COVID-19 are heterogeneous. While most SARS-CoV-2 infected patients are asymptomatic or
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
