Electronic health record data for assessing risk of hospitalization for COVID-19: Methodological considerations applied to multiple sclerosis.

INTRODUCTION

During the COVID-19 pandemic, electronic health record (EHR) data has been used to investigate disease severity and risk factors for severe COVID-19 in people with multiple sclerosis (pwMS). Methodological challenges including sampling bias, and residual confounding should be considered when conducting EHR-based studies. We aimed to address these limitations related to the use of EHR data in order to identify risk factors, including the use of disease modifying therapies (DMTs), associated with hospitalization for COVID-19 amongst pwMS.

METHODS

We performed a retrospective cohort study including a sample of 47,051 pwMS using a large US-based EHR and claims linked database. Follow-up started at the beginning of the pandemic, February 20th 2020, and continued until September 30th 2020. COVID-19 diagnosis was determined by the presence of ICD-10 diagnostic code for COVID-19, or a positive diagnostic laboratory test, or an ICD-10 diagnostic code for coronaviruses. We used Cox regression modeling to assess the impact of baseline demographics, MS disease history and pre-existing comorbidities on the risk of hospitalization for COVID-19. Then, we identified 5,169 pwMS using ocrelizumab (OCR) and 3,351 pwMS using dimethyl fumarate (DMF) at baseline, and evaluated the distribution of the identified COVID-19 risk factors between the two groups. Finally, we used Cox regression models, adjusted for the identified confounders, to estimate the risk of hospitalization for COVID-19 in pwMS treated with OCR compared to DMF.

RESULTS

Among the pwMS cohort, we identified 799 COVID-19 cases (1.7%) which resulted in 182 hospitalizations for COVID-19 (0.4%). Population differences between the pwMS and COVID-19 cohorts were observed. Statistical modeling identified older age, male gender, African-American race, walking with assistance, non-ambulatory status, severe relapse requiring hospitalization in year prior to baseline, and specific comorbidities to be associated with a higher risk of COVID-19 related-hospitalization. Comparing the COVID-19 risk factors between OCR users and DMF users, MS characteristics including ambulatory status and MS subtype were highly imbalanced, likely arising from key differences in the labelled indications for these therapies. Compared to DMF use, in unadjusted (HR 1.58, 95% CI 0.73 - 3.44), adjusted (HR 1.28, 95% CI 0.58 - 2.83), propensity score weighted (HR 1.25, 95% CI 0.56 - 2.80), and doubly robust models (HR 1.29, 95% CI 0.57 - 2.89), no significantly increased risk of hospitalization for COVID-19 was associated with OCR use.

CONCLUSION

We observed significant population differences when comparing all pwMS to COVID-19 cases, as well as significant differences in key confounders between OCR and DMF treated patients. In unadjusted analyses we did not observe a statistically significant higher risk of COVID-19 hospitalization in pwMS treated with OCR compared to DMF, with further attenuation of risk when adjusting for the key confounders. This study re-emphasises the importance to appropriately consider both sampling and confounding bias in EHR-based MS research.