JMIR Publications

ABSTRACT

Background:

Post-acute Sequelae of COVID-19 (PASC), also known as Long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19 infection. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts. However, current knowledge regarding PASC risk factors is limited.

Objective:

We sought to predict individual risk of PASC diagnosis from a curated set of clinically informed covariates available in electronic health records.

Methods:

We predicted individual PASC status, given covariate information, using Super Learner (an ensemble machine learning algorithm also known as stacking) to learn the optimal, AUC-maximizing combination of gradient boosting and random forest algorithms. We evaluated variable importance via Shapley values. We included data from the National COVID Cohort Collaborative, and these efforts were part of the NIH Long COVID Computational Challenge.

Results:

Using a sample of 55,257 participants, we were able to accurately predict individual PASC diagnoses (AUC 0.947). Temporally, we found that baseline characteristics were most predictive of future PASC diagnosis, compared with characteristics immediately before, during, or after COVID-19 infection. In terms of clinical domains of predictors, we found that medical utilization, demographics, anthropometry, and respiratory factors were most predictive of PASC diagnosis.

Conclusions:

These findings support the hypothesis that clinicians may be able to accurately assess the risk of PASC in patients prior to acute COVID diagnosis, which could improve early interventions and preventive care. In addition, these results highlight the importance of respiratory characteristics in PASC risk assessment. The methods outlined here provide an open-source, applied example of using Super Learner to predict PASC status using electronic health record data, which can be replicated across a variety of settings.