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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 considere...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier B.V.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831975/ https://www.ncbi.nlm.nih.gov/pubmed/36716576 http://dx.doi.org/10.1016/j.msard.2023.104512 |
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author | Dillon, Paul Siadimas, Athanasios Roumpanis, Spyros Fajardo, Otto Fitovski, Kocho Jessop, Nikki Whitley, Louise Muros-Le Rouzic, Erwan |
author_facet | Dillon, Paul Siadimas, Athanasios Roumpanis, Spyros Fajardo, Otto Fitovski, Kocho Jessop, Nikki Whitley, Louise Muros-Le Rouzic, Erwan |
author_sort | Dillon, Paul |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9831975 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Authors. Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98319752023-01-11 Electronic health record data for assessing risk of hospitalization for COVID-19: Methodological considerations applied to multiple sclerosis Dillon, Paul Siadimas, Athanasios Roumpanis, Spyros Fajardo, Otto Fitovski, Kocho Jessop, Nikki Whitley, Louise Muros-Le Rouzic, Erwan Mult Scler Relat Disord Original Article 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. The Authors. Published by Elsevier B.V. 2023-03 2023-01-11 /pmc/articles/PMC9831975/ /pubmed/36716576 http://dx.doi.org/10.1016/j.msard.2023.104512 Text en © 2023 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Original Article Dillon, Paul Siadimas, Athanasios Roumpanis, Spyros Fajardo, Otto Fitovski, Kocho Jessop, Nikki Whitley, Louise Muros-Le Rouzic, Erwan Electronic health record data for assessing risk of hospitalization for COVID-19: Methodological considerations applied to multiple sclerosis |
title | Electronic health record data for assessing risk of hospitalization for COVID-19: Methodological considerations applied to multiple sclerosis |
title_full | Electronic health record data for assessing risk of hospitalization for COVID-19: Methodological considerations applied to multiple sclerosis |
title_fullStr | Electronic health record data for assessing risk of hospitalization for COVID-19: Methodological considerations applied to multiple sclerosis |
title_full_unstemmed | Electronic health record data for assessing risk of hospitalization for COVID-19: Methodological considerations applied to multiple sclerosis |
title_short | Electronic health record data for assessing risk of hospitalization for COVID-19: Methodological considerations applied to multiple sclerosis |
title_sort | electronic health record data for assessing risk of hospitalization for covid-19: methodological considerations applied to multiple sclerosis |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831975/ https://www.ncbi.nlm.nih.gov/pubmed/36716576 http://dx.doi.org/10.1016/j.msard.2023.104512 |
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