Research
Print page Print page
Switch language
The Capital Region of Denmark - a part of Copenhagen University Hospital
Published

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

Research output: Contribution to journalJournal articleResearchpeer-review

  1. Identification of two different coagulation phenotypes in people living with HIV with undetectable viral replication

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. A meta-analysis uncovers the first sequence variant conferring risk of Bell's palsy

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Detection of biological signals from a live mammalian muscle using an early stage diamond quantum sensor

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Isolation and characterisation of novel phages infecting Lactobacillus plantarum and proposal of a new genus, "Silenusvirus"

    Research output: Contribution to journalJournal articleResearchpeer-review

  1. Lower or Higher Oxygenation Targets for Acute Hypoxemic Respiratory Failure

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Predictive Importance of Blood Pressure Characteristics With Increasing Age in Healthy Men and Women: The MORGAM Project

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. På vej mod en sundere og bedre behandlet befolkning?

    Research output: Contribution to journalComment/debateCommunication

  4. Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight

    Research output: Contribution to journalJournal articleResearchpeer-review

View graph of relations

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics-Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

Original languageEnglish
JournalScientific Reports
Volume11
Issue number1
Pages (from-to)3246
ISSN2045-2322
DOIs
Publication statusPublished - 5 Feb 2021

ID: 62084862