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A Data-Driven Identification of People Recovering from Post-Acute Sequelae of SARS-CoV-2 Infection (PASC)
24 Pages Posted: 25 Oct 2023
More...Abstract
Background: This study prospectively characterized subjective and objective attributes in people with post-acute sequelae COVID-19 (PASC), leveraging machine learning techniques. Subjective attributes included symptoms of PASC and were analyzed with topic modeling, while objective attributes included laboratory findings, frailty and physical function and were analyzed with clustering. PASC attributes were used to explore predictors of a pre-defined PASC recovery definition at follow-up.
Methods: This was a prospective, observational study of patients consecutively attending a multidisciplinary PASC clinic from Jun 2020 to Jun 2023. Each patient was longitudinally described by the combination of domains and phenotypes. The former were built from fine-grained subjective attributes depicting symptoms' intensity changes; whilst the latter were synthesized from objective attributes comprising clinical variables collected during visits.
Findings: A total of 1,012 people were evaluated, mean age was 60.3 years, 42.1% were females. 70.8% met the recovery definition 15.9% lost at follow-up and 13.2% were still in charge after a mean of 1.76±0.62 years after the acute infection. 368 people were visited at least twice, while 2 people died before the follow-up. Patients with at least two visits generated phenotype trajectories, enabling the identification of clinical characteristics associated with PASC recovery. Metabolic variables and lifestyle discriminated at baseline PASC recovery.
Interpretation: This study underlined the multifaceted nature of PASC using a patient-centered clinical approach which combined symptoms domains and patient phenotypes. Metabolic variables were identified as predictors for PASC recovery suggesting that future studies should explore metabolic rehabilitation programs.
Funding: This study is supported by a Gilead Sciences Inc. unrestricted grant.
Declaration of Interest: GG and CM received research grants and speaker honoraria from Gilead, ViiV, MERCK and Jansen and attended advisory boards of Gilead, ViiV and MERCK. JM received speaker honoraria from Gilead and ViiV. Other authors reported no conflict of interest.
Ethical Approval: Data were obtained from electronic health records and complied fully with Italian law on personal data protection and the ethics committee of the Area Vasta Nord Emilia Romagna who approved the study (Approval document: Prot AOU 0028729/21 dated 24/09/2021). All patients signed an informed consent.
Keywords: post-acute sequelae of SARS-CoV-2 infection, long COVID, recovery, machine learning, topic modelling, clustering
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