Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data.
BMJ Open
; 12(7): e059385, 2022 07 06.
Article
en En
| MEDLINE
| ID: mdl-35793922
ABSTRACT
INTRODUCTION:
COVID-19 is commonly experienced as an acute illness, yet some people continue to have symptoms that persist for weeks, or months (commonly referred to as 'long-COVID'). It remains unclear which patients are at highest risk of developing long-COVID. In this protocol, we describe plans to develop a prediction model to identify individuals at risk of developing long-COVID. METHODS ANDANALYSIS:
We will use the national Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, a population-level linked dataset of routine electronic healthcare data from 5.4 million individuals in Scotland. We will identify potential indicators for long-COVID by identifying patterns in primary care data linked to information from out-of-hours general practitioner encounters, accident and emergency visits, hospital admissions, outpatient visits, medication prescribing/dispensing and mortality. We will investigate the potential indicators of long-COVID by performing a matched analysis between those with a positive reverse transcriptase PCR (RT-PCR) test for SARS-CoV-2 infection and two control groups (1) individuals with at least one negative RT-PCR test and never tested positive; (2) the general population (everyone who did not test positive) of Scotland. Cluster analysis will then be used to determine the final definition of the outcome measure for long-COVID. We will then derive, internally and externally validate a prediction model to identify the epidemiological risk factors associated with long-COVID. ETHICS AND DISSEMINATION 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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
4_TD
/
6_ODS3_enfermedades_notrasmisibles
Problema de salud:
4_covid_19
/
4_pneumonia
/
6_other_respiratory_diseases
Asunto principal:
COVID-19
Tipo de estudio:
Etiology_studies
/
Guideline
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Aspecto:
Ethics
Límite:
Humans
Idioma:
En
Revista:
BMJ Open
Año:
2022
Tipo del documento:
Article
País de afiliación:
Reino Unido