Development of an external control arm using electronic health record-based real-world data to evaluate the efficacy of COVID-19 treatment.
Ji-Young JeonMin Gul KimYong-Jin ImEun-Young KimJi Sun KimKi Tae KwonJeong-Hwan HwangJong Seung KimMin-Gul KimPublished in: Clinical pharmacology and therapeutics (2023)
To protect people from SARS-CoV-2 infection, tremendous research efforts have been made toward coronavirus disease 19 (COVID-19) treatments development. Externally controlled trial (ECT) may help reduce their development time. To evaluate whether ECT using real-world data (RWD) of COVID-19 patients is feasible enough to be used for regulatory decision-making, we built an external control arm (ECA) based on RWD as a control arm of a previously conducted randomized controlled trial (RCT), and compare it to the control arm of the RCT. The electronic health record (EHR)-based COVID-19 cohort dataset was used as RWD, and three Adaptive COVID-19 Treatment Trial (ACTT) dataset was used as RCT. Among the RWD dataset, eligible patients were evaluated as a pool of external control subjects of the ACTT-1, ACTT-2, and ACTT-3 trials, respectively. The ECAs were built using propensity score matching, and the balance of age, sex, and baseline clinical status ordinal scale as covariates between the treatment arms of Asian patients in each ACTT and the pools of external control subjects was assessed before and after 1:1 matching. There was no statistically significant difference in time to recovery between ECAs and the control arms of each ACTT. Among the covariates, the baseline status ordinal score had the greatest influence on the building of ECA. This study demonstrates that ECA based on EHR data of COVID-19 patients could sufficiently replace the control arm of RCT, and it is expected to help develop new treatments faster in emergency situations such as the COVID-19 pandemic.
Keyphrases
- electronic health record
- coronavirus disease
- sars cov
- randomized controlled trial
- clinical decision support
- end stage renal disease
- respiratory syndrome coronavirus
- ejection fraction
- emergency department
- clinical trial
- decision making
- public health
- systematic review
- adverse drug
- prognostic factors
- combination therapy
- deep learning
- replacement therapy