Abstract
BackgroundTo explore the long-term trajectories considering pneumonia volumes and lymphocyte counts with individual data in COVID-19.MethodsA cohort of 257 convalescent COVID-19 patients (131 male and 126 females) were included. Group-based multi-trajectory modelling was applied to identify different trajectories in terms of pneumonia lesion percentage and lymphocyte counts covering the time from onset to post-discharge follow-ups. We studied the basic characteristics and disease severity associated with the trajectories.ResultsWe characterised four distinct trajectory subgroups. (1) Group 1 (13.9%), pneumonia increased until a peak lesion percentage of 1.9% (IQR 0.7–4.4) before absorption. The slightly decreased lymphocyte rapidly recovered to the top half of the normal range. (2) Group 2 (44.7%), the peak lesion percentage was 7.2% (IQR 3.2–12.7). The abnormal lymphocyte count restored to normal soon. (3) Group 3 (26.0%), the peak lesion percentage reached 14.2% (IQR 8.5–19.8). The lymphocytes continuously dropped to 0.75 × 109/L after one day post-onset before slowly recovering. (4) Group 4 (15.4%), the peak lesion percentage reached 41.4% (IQR 34.8–47.9), much higher than other groups. Lymphopenia was aggravated until the lymphocytes declined to 0.80 × 109/L on the fourth day and slowly recovered later. Patients in the higher order groups were older and more likely to have hypertension and diabetes (all P values < 0.05), and have more severe disease.ConclusionsOur findings provide new insights to understand the heterogeneous natural courses of COVID-19 patients and the associations of distinct trajectories with disease severity, which is essential to improve the early risk assessment, patient monitoring, and follow-up schedule.
Highlights
To explore the long-term trajectories considering pneumonia volumes and lymphocyte counts with individual data in COVID-19
Our findings provide new insights to understand the heterogeneous natural courses of COVID-19 patients and the associations of distinct trajectories with disease severity, which is essential to improve the early risk assessment, patient monitoring, and follow-up schedule
Using Group-based multi-trajectory modelling (GBMTM), for the first time we identified four distinct trajectory groups based on individual time series data of the long-term pneumonia lesion percentage and lymphocyte patterns, providing a comprehensive depiction of the COVID-19 disease course from admission to post-discharge
Summary
To explore the long-term trajectories considering pneumonia volumes and lymphocyte counts with individual data in COVID-19. The population-level averages of biomarkers across a certain time interval were computed and the estimations of successive intervals over time were connected to outline the course. Such an approach mixed all patients together and could not differentiate the heterogeneity between patients who had distinct disease severity, clinical progression, and long-term prognosis [5, 8]. Group-based multi-trajectory modelling (GBMTM) is a latent class technique that takes full advantage of multivariate longitudinal data that are interrelated and complementary indicators of disease progression [9]. GBMTM decomposes the population into distinct groups, each with a different underlying trajectory in terms of the studied biomarkers, is suitable for capturing the longitudinal characteristics of COVID-19
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