Applying the OMOP Common Data Model to Facilitate Benefit-Risk Assessments of Medicinal Products Using Real-World Data from Singapore and South Korea.

OBJECTIVES: The aim of this study was to characterize the benefits of converting Electronic Medical Records (EMRs) to a common data model (CDM) and to assess the potential of CDM-converted data to rapidly generate insights for benefit-risk assessments in post-market regulatory evaluation and decisions. METHODS: EMRs from January 2013 to December 2016 were mapped onto the Observational Medical Outcomes Partnership-CDM (OMOP-CDM) schema. Vocabulary mappings were applied to convert source data values into OMOP-CDM-endorsed terminologies. Existing analytic codes used in a prior OMOP-CDM drug utilization study were modified to conduct an illustrative analysis of oral anticoagulants used for atrial fibrillation in Singapore and South Korea, resembling a typical benefit-risk assessment. A novel visualization is proposed to represent the comparative effectiveness, safety and utilization of the drugs. RESULTS: Over 90% of records were mapped onto the OMOP-CDM. The CDM data structures and analytic code templates simplified the querying of data for the analysis. In total, 2,419 patients from Singapore and South Korea fulfilled the study criteria, the majority of whom were warfarin users. After 3 months of follow-up, differences in cumulative incidence of bleeding and thromboembolic events were observable via the proposed visualization, surfacing insights as to the agent of preference in a given clinical setting, which may meaningfully inform regulatory decision-making. CONCLUSIONS: While the structure of the OMOP-CDM and its accessory tools facilitate real-world data analysis, extending them to fulfil regulatory analytic purposes in the post-market setting, such as benefit-risk assessments, may require layering on additional analytic tools and visualization techniques.

Impact of COVID-19 on Liver Transplantation in Hong Kong and Singapore: A Modelling Study.

BACKGROUND: Liver transplantation (LT) activities during the COVID-19 pandemic have been curtailed in many countries. The impact of various policies restricting LT on outcomes of potential LT candidates is unclear. METHODS: We studied all patients on the nationwide LT waitlists in Hong Kong and Singapore between January 2016 and May 2020. We used continuous time Markov chains to model the effects of different scenarios and varying durations of disruption on LT candidates. FINDINGS: With complete cessation of LT, the projected 1-year overall survival (OS) decreased by 3•6%, 10•51% and 19•21% for a 1-, 3- and 6-month disruption respectively versus no limitation to LT, while 2-year OS decreased by 4•1%, 12•55%, and 23•43% respectively. When only urgent (acute-on-chronic liver failure [ACLF] or acute liver failure) LT was allowed, the projected 1-year OS decreased by a similar proportion: 3•1%, 8•41% and 15•20% respectively. When deceased donor LT (DDLT) and urgent living donor LT (LDLT) were allowed, 1-year projected OS decreased by 1•2%, 5•1% and 8•85% for a 1-, 3- and 6-month disruption respectively. OS was similar when only DDLT was allowed. Complete cessation of LT activities for 3-months resulted in an increased projected incidence of ACLF and hepatocellular carcinoma (HCC) dropout at 1-year by 49•1% and 107•96% respectively. When only urgent LT was allowed, HCC dropout and ACLF incidence were comparable to the rates seen in the scenario of complete LT cessation. INTERPRETATION: A short and wide-ranging disruption to LT results in better outcomes compared with a longer duration of partial restrictions. FUNDING: None to disclose.

The Search for Optimal Oxygen Saturation Targets in Critically Ill Patients: Observational Data From Large ICU Databases.

BACKGROUND: Although low oxygen saturations are generally regarded as deleterious, recent studies in ICU patients have shown that a liberal oxygen strategy increases mortality. However, the optimal oxygen saturation target remains unclear. The goal of this study was to determine the optimal range by using real-world data. METHODS: Replicate retrospective analyses were conducted of two electronic medical record databases: the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care III database (MIMIC). Only patients with at least 48 h of oxygen therapy were included. Nonlinear regression was used to analyze the association between median pulse oximetry-derived oxygen saturation (Spo2) and hospital mortality. We derived an optimal range of Spo2 and analyzed the association between the percentage of time within the optimal range of Spo2 and hospital mortality. All models adjusted for age, BMI, sex, and Sequential Organ Failure Assessment score. Subgroup analyses included ICU types, main diagnosis, and comorbidities. RESULTS: The analysis identified 26,723 patients from eICU-CRD and 8,564 patients from MIMIC. The optimal range of Spo2 was 94% to 98% in both databases. The percentage of time patients were within the optimal range of Spo2 was associated with decreased hospital mortality (OR of 80% vs 40% of the measurements within the optimal range, 0.42 [95% CI, 0.40-0.43] for eICU-CRD and 0.53 [95% CI, 0.50-0.55] for MIMIC). This association was consistent across subgroup analyses. CONCLUSIONS: The optimal range of Spo2 was 94% to 98% and should inform future trials of oxygen therapy.