Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts

Zang et al. use RECOVER EHR data to study Long COVID risk factors and apply mathematical modeling to predict the development of long COVID conditions.

They find that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities are likely associated with increased risk of having Long COVID.

Most people who develop COVID-19 make a full recovery, but some go on to develop post-acute sequelae of SARS-CoV-2 infection, commonly known as Long COVID.

Up to now, we did not know why some people are affected by Long COVID whilst others are not.

We conducted a study to identify risk factors for Long COVID and developed a mathematical modeling approach to predict those at risk.

We find that Long COVID is associated with some factors such as experiencing severe acute COVID-19, being underweight, and having conditions including cancer or cirrhosis.

Due to the wide variety of symptoms defined as Long COVID, it may be challenging to come up with a set of risk factors that can predict the whole spectrum of Long COVID.

However, our approach could be used to predict a variety of Long COVID conditions.

Background SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection.

These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems.

Limited studies have investigated the predictability of Long COVID development and its associated risk factors.

Methods In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development.

Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021.

Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests.

Results We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID.

Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8).

Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7–0.8).

Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6).

Conclusions This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies.

However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions.