Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data.

Luke DainesRachel H MulhollandEleftheria Vasileiou Victoria HammersleyDavid WeatherillVittal Srinivasa KatikireddiSteven KerrEmily MooreElisa PesentiJennifer Kathleen Quint Syed Ahmar Shah Ting ShiColin R SimpsonChris RobertsonAziz Sheikh

Published in: BMJ open (2022)

The EAVE II study has obtained approvals from the Research Ethics Committee (reference: 12/SS/0201), and the Public Benefit and Privacy Panel for Health and Social Care (reference: 1920-0279). Study findings will be published in peer-reviewed journals and presented at conferences. Understanding the predictors for long-COVID and identifying the patient groups at greatest risk of persisting symptoms will inform future treatments and preventative strategies for long-COVID.

Keyphrases

coronavirus disease
healthcare
sars cov
mental health
public health
big data
systematic review
respiratory syndrome coronavirus
emergency department
risk assessment
electronic health record
artificial intelligence
deep learning
current status
chronic pain
social media
health insurance
adverse drug