A standing cohort to understand the characteristics of patients with COVID-19 and contextualize the COVID-19 complication and safety events of interests using US OPTUM EHR data

Study type

• Study topic:
  Disease /health condition
• Study type:
  Non-interventional study
• Scope of the study:
  Disease epidemiology

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Name of medicine:
  PAXLOVID
• Medical condition to be studied:
  SARS-CoV-2 test positive

Population studied

• Age groups:
  Children (2 to < 12 years)

  Adolescents (12 to < 18 years)

  Adults (18 to < 46 years)

  Adults (46 to < 65 years)

  Adults (65 to < 75 years)

  Adults (75 to < 85 years)

  Adults (85 years and over)

Study design details

• Main study objective:
  This study aims to understand the characteristics of patients with COVID-19 and contextualize the COVID-19 complication and safety events of interests using real world data.
• Outcomes:
  Distribution of demographics, medical history, biomarkers, health-equity, healthcare utilization and time to clinical events at baseline for COVID-19 patients. Background incidence of COVID-19 manifestations and safety outcomes for COVID-19 patients. Incidence of long COVID-19, Incidence of oxygen supplementation among COVID-19 patients hospitalized with mechanical ventilation/ECMO Predictors of COVID-19 severe outcomes/manifestations and long COVID Real world effectiveness of Paxlovid in reducing the COVID-19 related outcomes.
• Data analysis plan:
  Baseline characteristics will be summarized for COVID 19 patients in the study. Means with standard deviations, medians with interquartile will be provided for continuous variables. Numbers and percentages will be provided for dichotomous variables or categorical variables. For dichotomous endpoints such as COVID-19 complications and outcomes, the crude cumulative incidence and incidence rate of each endpoint will be estimated. To assess the feasibility of real-world effectiveness of Paxlovid, we will apply propensity score matching (PSM) method to control confounding and prescription time distribution method (PTDM) to handle immortal bias. For exploratory analysis, machine learning models (eg, LASSO Cox regression, Random Survival Forest (RSF), and XGBoosting (XGB) models) will be used to identify important predictors associated with progression to COVID-19 severe outcomes/manifestations as well as long COVID.