Latent Transition Analysis for Longitudinal Studies of Post-Acute Infection Syndromes: A Multinational Investigation of Post-COVID-19 Condition

Post-Acute Infectious Syndromes (PAISs) refer to the symptoms persisting for months after an initial infection. PAISs are multi-systemic conditions, and thus clinical research studies often collect rich, multi-modal datasets - including demographic information, patients comorbidities, acute-phase presentation, and observations over extended periods that can include clinical data, patient-reported outcomes (PROMs) or experimental data. Yet, the complexity of the datasets and lack of the precise clinical case definition pose difficulties in comprehensive and meaningful analyses of such datasets. Studies on Post-COVID-19 condition (PCC) represent the most prominent example of this challenge. In this work, we outline a flexible and interpretable framework for modelling and analysing data from longitudinal studies of PAIS using Latent Transition Analysis (LTA). The framework enables the identification of distinct disease phenotypes and the patient-level analysis of transitions between them, without relying on predefined clinical categorisations. Furthermore, we introduce a method for incorporating covariate information, which enables exploration of how patient characteristics influence disease trajectories. We apply this methodology to the ORCHESTRA dataset, composed of individuals affected by SARS-CoV-2 infection from multiple European centres. Longitudinal data were collected at SARS-CoV-2 infection, and at 6, 12, 18, and 24 months of follow-up from patients in France, Italy, Spain, and the Netherlands. In total, 5,094 patients were clinically assessed, reporting symptoms and quality-of-life information. Our model identifies distinct PCC phenotypes and uncovers complex transition patterns among them. We found that advanced age and pre-existing chronic respiratory conditions significantly increased the likelihood of transitioning to more severe PCC states in later timepoints. Conversely, male sex and exposure to later SARS-CoV-2 variants were associated with a higher probability of complete recovery. Our study highlights how LTA can enhance the interpretability of complex, time-resolved clinical data, support personalized patient monitoring and management, and accelerate therapeutic development -- not only for PCC but also for other PAISs in both pandemic and inter-pandemic settings.