Elke Wynberg

Chapter 6 196 Clinical severity groups defined as: mild as having an RR <20/min and SpO2 on room air >94% at both D0 and D7; moderate disease as having a RR 20–30/minutes, SpO2 90–94% and/or receiving oxygen therapy at D0 or D7; severe disease as having a RR >30/minutes or SpO2 <90% at D0 or D7; critical disease as requiring ICU admission. COVID-related comorbidities are based on WHO Clinical Management Guidelines and include: cardiovascular disease (including hypertension), chronic pulmonary disease (excluding asthma), renal disease, liver disease, cancer, immunosuppression (excluding HIV, including previous organ transplantation), previous psychiatric illness and dementia. Physical measurements at D0 and D7 study visits. Oxygen saturation measured on room air if possible or retrieved from ambulance records for hospitalised participants admitted on oxygen on day of enrollment. Time-dependent outcomes not compared between groups (NA) due to bias resulting from differing follow-up lengths. Supplementary Table S2. Bayesian Information Criteria and entropy values for select unadjusted GBTM of total numbers of long COVID symptoms over time, according to numbers of groups and trajectory shapes Number of groups Trajectory shapes BIC (N=275) % of participants in smallest trajectory group Entropy 3 1 1 1 -4412 16.1% 0.690 3 2 2 2 -4418 16.1% 0.690 3 3 3 3 -4420 16.1% 0.692 3 1 1 0 -4410 16.1% 0.690 3 1 1 2 -4415 16.1% 0.690 3 1 1 3 -4417 16.1% 0.689 4 0 0 0 1 -4306 8.3% 0.652 4 0 0 0 2 * -4303 8.2% 0.651 4 0 0 0 3 -4306 8.2% 0.651 4 0 0 1 1 -4309 8.2% 0.653 4 0 0 2 2 -4306 8.2% 0.654 4 0 0 3 3 -4310 7.8% 0.656 GBTM = group-based trajectory modelling. Trajectory shapes classified as: 0 (intercept-only), 1 (linear), 2 (quadratic), 3 (cubic). BIC denotes the Bayesian Information Criterion which denotes the posterior probability of a model given the data. Entropy measures how accurately the model classifies participants into different trajectories. Limited selection of all possible combinations shown. * Chosen model due to least negative BIC

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