A nomogram to predict long COVID risk based on pre- and post-infection factors: Results from a cross-sectional study...

Long COVID has received much attention as a complex multi-system disease due to its serious impact on quality of life. However, there remains inconsistent results in terms of risk factors, and a prediction model for the accurate prediction of long COVID is still lacking.

Cross-sectional study.

In this retrospective study, a community population from the Futian District of Shenzhen, Guangdong Province, China, were included. Data were collected from September to December 2023 using an electronic questionnaire. Logistic regression analyses were used to identify predictors of long COVID. Pre-infection and post-infection prediction models (with/without post-infection characteristics) were developed, and the C-index was used to evaluate accuracy.

In total, 420 patients infected COVID-19 were included. The prevalence of long COVID was 32.9 %. The most common symptoms of long COVID were weakness/fatigue, persistent cough and cognitive dysfunction. Independent predictors of long COVID included in the pre-infection model were age, long-term medication, and psychological problems such as stress and doing things without enthusiasm/interest before COVID-19 infection (C-index: 0.721). Independent predictors included in the post-infection model were age, inability to concentrate before COVID-19 infection, and symptoms of weakness/fatigue, abnormal smell/taste, diarrhoea, eye conjunctivitis and headache/dizziness during the acute-phase (C-index: 0.857).

Age, psychological problems before COVID-19 infection and acute-phase symptoms were important risk factors of long COVID. Results from the pre-infection model provide guidance for non-infected individuals on how to prevent long COVID. Results from the post-infection model can be used to accurately predict individuals who are at high risk of long COVID and help design treatment plans for patients in the acute phase.