Long COVID diagnostic with differentiation from chronic lyme disease using machine learning and cytokine hubs

Abstract

The absence of a long COVID (LC) or post-acute sequelae of COVID-19 (PASC) diagnostic has profound implications for research and potential therapeutics given the lack of specificity with symptom-based identification of LC and the overlap of symptoms with other chronic inflammatory conditions. Here, we report a machine-learning approach to LC/PASC diagnosis on 347 individuals using cytokine hubs that are also capable of differentiating LC from chronic lyme disease (CLD). We derived decision tree, random forest, and gradient-boosting machine (GBM) classifiers and compared their diagnostic capabilities on a dataset partitioned into training (178 individuals) and evaluation (45 individuals) sets. The GBM model generated 89% sensitivity and 96% specificity for LC with no evidence of overfitting. We tested the GBM on an additional random dataset (106 LC/PASC and 18 Lyme), resulting in high sensitivity (97%) and specificity (90%) for LC. We constructed a Lyme Index confirmatory algorithm to discriminate LC and CLD.

Keywords: COVID-19; Chronic lyme disease (CLD); Cytokines; Long COVID; Machine Learning/AI; Myalgic encephalomyelitis-chronic fatigue syndrome (ME-CFS); PASC.

Fig. 1

Surrogate tree visualization of the GBM classifier’s decision paths. Nodes branching left are indicative of less than or equal ( ≤) values, while nodes branching right represent greater than ( >) values.