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

Published:January 10, 2023DOI:https://doi.org/10.1016/j.msard.2023.104512

      Highlights

      • Associations between DMTs and COVID-19 in pwMS have been studied using EHR data
      • Sampling and channeling biases, residual confounding must be considered to compare risk for severe COVID-19 between DMTs
      • No higher risk of COVID-19 hospitalization in OCR- vs DMF treated pwMS

      Abstract

      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 modelling 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 modelling 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.

      Keywords

      Abbreviations:

      ATT (average treatment effect in the treated), DMF (dimethyl fumarate), DMT (disease modifying therapy), EDSS (expanded disability status scale), EHR (electronic health record), ER (emergency room), IDN (integrated delivery networks), IPTW (inverse probability of treatment weighting), LASSO (least absolute shrinkage and selection operator), MDRR (minimum detectable relative risk), MS (multiple sclerosis), NLP (natural language processing), OCR (ocrelizumab), PPMS (primary-progressive MS), PS (propensity score), pwMS (people with MS), RRMS (relapsing-remitting MS), SMD (standardized mean difference)

      1. Introduction

      Several real-world studies investigating the effects of disease modifying therapies (DMTs) for multiple sclerosis (MS) on the risk of COVID-19 related-hospitalization have reported an increased risk associated with anti-CD20 agents such as ocrelizumab (OCR) (
      • Langer-Gould A
      • Smith JB
      • Li BH
      • et al.
      Multiple sclerosis, rituximab, and COVID-19.
      ,
      • Salter A
      • Fox RJ
      • Newsome SD
      • et al.
      Outcomes and risk factors associated with SARS-CoV-2 infection in a North American registry of patients with multiple sclerosis.
      ,
      • Pérez CA
      • Zhang GQ
      • Li X
      • et al.
      COVID-19 severity and outcome in multiple sclerosis: results of a national, registry-based, matched cohort study.
      ,
      • Reder AT
      • Centonze D
      • Naylor ML
      • et al.
      COVID-19 in patients with multiple sclerosis: associations with disease-modifying therapies.
      ,
      • Sormani MP
      • De Rossi N
      • Schiavetti I
      • et al.
      Disease-modifying therapies and coronavirus disease 2019 severity in multiple sclerosis.
      A,
      • Sormani MP
      • De Rossi N
      • Schiavetti I
      • et al.
      Disease-modifying therapies and coronavirus disease 2019 severity in multiple sclerosis.
      B, Simpson-Yap et al 2022,
      • Spelman T
      • Forsberg L
      • McKay K
      • et al.
      Increased rate of hospitalization for COVID-19 among rituximab-treated multiple sclerosis patients: a study of the Swedish multiple sclerosis registry.
      ). Some of these studies relied on electronic health records (EHR) data (
      • Reder AT
      • Centonze D
      • Naylor ML
      • et al.
      COVID-19 in patients with multiple sclerosis: associations with disease-modifying therapies.
      , Perez et al. 2021,
      • Langer-Gould A
      • Smith JB
      • Li BH
      • et al.
      Multiple sclerosis, rituximab, and COVID-19.
      ), however methodological challenges to the use of EHR data exist, including sampling and collider biases, and inadequate adjustment for COVID-19 related-hospitalization risk factors leading to residual confounding.
      COVID-19 testing is non-random, as is the recording of results in medical records such as the EHR, and thus sampling bias cannot be excluded in studies of COVID-19 cohorts when analyzing only COVID-19 cases (Griffiths et al 2021). The sampling bias may induce distorted associations in subsequent analyses due to collider bias (Griffiths et al 2021, Holmberg et al 2022). People with MS (pwMS) perceived to be at higher risk of infection or hospitalization, and those with more contacts with a healthcare system (e.g. I.V. administration of medication) may be more likely to be tested and included in such studies (Smith et al 2022), as are people with severe COVID-19 symptoms (Griffiths et al 2021). Analyses performed on COVID-19 infected cohorts, whereby those with severe COVID-19 and those with a perceived higher risk (e.g., anti-CD20 treatment such as OCR) are more likely to be sampled, may induce distorted associations between the outcome (COVID-19 severity) and the risk factor of interest (e.g. OCR), known as collider bias (Griffiths et al 2021).
      EHR-based studies must also control for confounding arising from differences in clinical characteristics, including risk factors for COVID-19, between groups of pwMS defined by DMT use. Channeling bias occurs when key prognostic factors for MS disease (e.g. MS subtype, Expanded Disability Status Scale (EDSS)) influence the choice of DMT (Petri et al. 1991,
      • Lobo FS
      • Wagner S
      • Gross CR
      • et al.
      Addressing the issue of channeling bias in observational studies with propensity scores analysis.
      ). Specifically, differences in COVID-19 outcomes amongst MS subtypes and EDSS levels have been reported (
      • Louapre C
      • Collongues N
      • Stankoff B
      • et al.
      Clinical characteristics and outcomes in patients with coronavirus disease 2019 and multiple sclerosis.
      ,
      • Sormani MP
      • De Rossi N
      • Schiavetti I
      • et al.
      Disease-modifying therapies and coronavirus disease 2019 severity in multiple sclerosis.
      A,
      • Salter A
      • Fox RJ
      • Newsome SD
      • et al.
      Outcomes and risk factors associated with SARS-CoV-2 infection in a North American registry of patients with multiple sclerosis.
      , Parotta et al. 2020,
      • Chaudhry F
      • Bulka H
      • Rathnam AS
      • et al.
      COVID-19 in multiple sclerosis patients and risk factors for severe infection.
      , Simpson-Yap et al. 2022). Indications and guideline recommendations for the different DMTs vary by MS disease characteristics. However, missing data on important confounders including MS subtype and EDSS have limited the interpretation of EHR-based studies (Perez et al. 2021,
      • Reder AT
      • Centonze D
      • Naylor ML
      • et al.
      COVID-19 in patients with multiple sclerosis: associations with disease-modifying therapies.
      ). Additionally, large studies in the general population have identified that cardiovascular disease, respiratory disease, diabetes, liver disease, kidney disease, cancer and obesity, are risk factors of poor COVID-19 outcomes (
      • Williamson E.J.
      • Walker A.J.
      • Bhaskaran K.
      • et al.
      Factors associated with COVID-19-related death using OpenSAFELY.
      ,
      • Docherty AB
      • Harrison EM
      • Green CA
      • et al.
      Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study.
      ,
      • Petrilli CM
      • Jones SA
      • Yang J
      • et al.
      Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study.
      ,
      • Cummings MJ
      • Baldwin MR
      • Abrams D
      • et al.
      Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study.
      ). These confounders for COVID-19 outcomes only emerged as the pandemic progressed and may have been unknown at time of data analyses.
      In this study, we investigated the challenges and limitations related to the use of EHR data when assessing risk factors for COVID-19 hospitalization amongst pwMS, looking at pwMS treated with OCR or dimethyl fumarate (DMF).

      2. Material and methods

      2.1 Study Design and data source

      We performed a retrospective cohort study using anonymized data from routinely collected EHR and administrative healthcare claims data in the US Optum® de-identified Market Clarity database during the early phase of the pandemic from February 20th (index date) until September 30th 2020.
      The Optum® Market Clarity MS database is derived from anonymized longitudinal EHR records for 63 million US patients who receive treatment in integrated delivery networks (IDN) or outpatient settings using the Optum partner EHR system. These data are linked to claims data for a subset of patients with EHR records. This data provides comprehensive information on patient demographics, medical information, procedures, outcomes, medication, as well as provider details and notes. The provider notes contain narrative text from which natural language processing (NLP) can be used to derive additional structured data.
      First, we evaluated sampling bias arising from selective sampling of pwMS based on COVID-19 diagnosis compared to sampling of pwMS. Second, baseline characteristics were evaluated for associations with COVID-19 hospitalization, in multivariate Cox regression models to identify risk factors associated with COVID-19 hospitalization amongst pwMS. The distribution of the identified risk factors were then described by current DMT use to evaluate channeling bias. Finally, we explored the association between OCR and DMF for time to COVID-19 hospitalization using a prevalent-user study design adjusted for identified confounding factors.

      2.2 Study population

      The study included individuals treated within an IDN and with linked claims data to ensure capture of both outpatient and inpatient EHR data and of outpatient pharmacy dispensing data. At least one year of EHR data prior to index date was required based on earliest EHR activity occurring prior to the baseline period (Yu et al. 2015) or continuous claims enrollment during the year prior to the index date to ensure adequate capture of baseline characteristics. Finally, MS diagnosis relied on a validated algorithm requiring a combination of ≥3 inpatient or outpatient encounters including a diagnosis for MS (ICD-10, G35; ICD-9, 340) and/or prescription/dispensation of a DMT, recorded on different dates within the EHR or claims data during the year prior to the index date (
      • Culpepper WJ
      • Marrie RA
      • Langer-Gould A
      • et al.
      Validation of an algorithm for identifying MS cases in administrative health claims datasets.
      ).

      2.3 Outcome assessment

      COVID-19 hospitalization is the primary outcome defined by ≥1 of the following criteria: (1) inpatient or emergency room (ER) visit with an overnight stay, with COVID-19 diagnosis occurring upon admission or within 7 days after hospital admission, or (2) inpatient or ER visit with overnight stay up to 21 days after COVID-19 diagnosis (
      • Incerti D
      • Rizzo S
      • Li X
      • et al.
      Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA.
      ). Positive or clinically confirmed COVID-19 diagnosis was determined by the presence of ICD-10 diagnostic code for COVID-19 (‘U07.1’ or ‘U07.2), or a positive diagnostic laboratory test, or an ICD-10 diagnostic code for coronaviruses (‘B97.29’) (Appendix A).

      2.4 COVID-19 Risk factors

      Patient characteristics, including socio-demographics (age, sex, smoking status and race), MS characteristics and comorbidities were identified at index-date looking back 12 months to classify each variable using the EHR, claims and provider notes data. The US census regions were included as a stratification factor in statistical models.
      Ambulatory status was used as a proxy indicator of MS disability which consists of a 3-level category (fully ambulatory, walks with assistance, non-ambulatory) derived from ICD-10 diagnosis codes, procedure codes and NLP terms (
      • Salter A
      • Fox RJ
      • Newsome SD
      • et al.
      Outcomes and risk factors associated with SARS-CoV-2 infection in a North American registry of patients with multiple sclerosis.
      ). In a separate analysis, we assessed that ambulatory status showed moderate correlation with recorded EDSS (0.68, p<0.001, Spearmans’ rank), and appeared to distinguish those who are fully ambulatory (EDSS<4) with 74% sensitivity and 85% specificity (
      • Dillon P
      • Roumpanis S
      • Muros-Le Rouzic E.
      Validity of an electronic health record and claims derived proxy measure of Multiple Sclerosis disability. International Conference of Pharmacoepidemiology 36.
      ). MS subtype was derived from provider notes using NLP, however only recorded for approximately 40% of pwMS, thus limiting its use to subgroup analyses with complete information available. Finally, a validated proxy measure of relapse was used (
      • Chastek BJ
      • Oleen-Burkey M
      • Lopez-Bresnahan MV.
      Medical chart validation of an algorithm for identifying multiple sclerosis relapse in healthcare claims.
      ) which required fulfillment of either (1) a claim with a MS diagnosis code in the primary position at any time during an inpatient hospitalization (i.e., severe relapse requiring hospitalization) or (2) a claim with a MS diagnosis code in the primary or secondary position in an outpatient setting in addition to a pharmacy or medical claim for a corticosteroid on the day of or within 7 days after the visits (i.e., moderate relapse requiring outpatient steroid treatment). Patients meeting neither criteria were classified as patients without relapses.
      Comorbidities were predefined based on prognostic factors for COVID-19 identified from large general population studies (Appendix B). Comorbidities were identified by presence of ≥1 inpatient and/or ≥2 outpatient diagnoses during the 12 month baseline period prior to index date. Additionally, summary measures based on simple counts of the number of comorbidities were derived as binary indicators (e.g. 1+ comorbidities, 2+ comorbidities, 3+ comorbidities).
      DMTs were identified from prescription, procedure or administration records from the EHR and/or dispensing record from the claims data during the 6 months prior to the index date to determine current DMT use. PwMS with records for more than one DMT during this 6 month period were not included in the channeling bias and comparative analyses.

      2.5 Statistical analysis

      Risk factors for COVID-19 outcomes amongst pwMS were evaluated in univariate and multivariable Cox regression models. For the purposes of statistical modelling, risk factors with less than a 1% prevalence amongst the entire sample were excluded or when appropriate combined with another categorical variable; and risk factors with ≤10 COVID-19 hospitalization events were also excluded. As per Optum® Market Clarity policy, subgroups with ≤10 people are represented as <11 to ensure the data remains anonymized. To avoid overfitting of statistical models and to assess robustness of predictions, least absolute shrinkage and selection operator (LASSO) regression was performed to finalize the set of risk factors for COVID-19 hospitalization. Subgroup analyses were performed in the subset of pwMS with MS Subtype available.
      To evaluate channeling bias, differences in the distribution of the identified COVID-19 risk factors between OCR and DMF users were quantified using the standardized mean difference (SMD).
      For the comparative analysis, we employed a prevalent-user design, assigning eligible patients to treatment groups at time zero and following patients up until the outcome event occurred, censoring or end of follow-up. Patients were censored when a non-COVID-19 related hospitalization occurred or the patient died. To overcome limitations in prior studies due to the use of multiple comparator DMTs, we choose DMF for a single comparison (Smith et al. 2022). DMF was considered a relevant comparator based on prior studies highlighting an absence of increased risk of COVID-19; due to its more recent FDA approval compared to other DMTs such as glatiramer acetate and interferons to minimize the impact of channeling bias; and due to adequate sample size to perform a sufficiently powered statistical analysis. Prior to analysis we estimated the minimum detectable relative risk (MDRR), which was 2.80 and within the ranges of relative risks reported in the literature. Thus if this estimated increased risk is true, this association will be detectable with an alpha of 0.05 (Appendix C). The hazard ratio (HR) and 95% confidence intervals for the time to COVID-19 hospitalization for OCR compared to DMF use was estimated in univariable, adjusted, propensity score (PS) weighted and doubly robust Cox models. The PS weighted analyses were adjusted using Inverse Probability of Treatment Weighting (IPTW), as defined by the average treatment effect in the treated estimand (ATT). The PS was computed as the probability of being in the OCR group rather than the DMF group, conditioned on the identified set of risk factors required for minimum confounder adjustment. The double robust Cox regression models were weighted using IPTW and also included the covariates used in the PS model. Secondary outcome analyses tested the association between OCR use compared to DMF, and COVID-19 diagnosis.

      3. Results

      3.1 Participants

      A total of 180,436 patients were considered eligible for inclusion into our study (Fig. 1). Of these, 54,628 were excluded as they were treated outside of IDNs, a further 42,037 were excluded due to absence of activity in the database during the year prior to the index date, and finally 36,720 were excluded for failing to meet the criteria for confirmed MS diagnosis, leaving 47,051 pwMS eligible for this analysis.
      Figure 1
      Figure 1Population flow diagram and COVID-19 outcomes

      3.2 Baseline characteristics

      Mean (SD) age at baseline was 53.4 (13.3) years and most pwMS were female. The majority of patients were Caucasian (80.4%), 12.2% were African-American, 0.5% Asian and 6.9% had unknown or other racial backgrounds. MS subtype was recorded for 38.3%, of whom 78.5% had relapsing-remitting MS (RRMS) and 18.8% primary-progressive MS (PPMS). EDSS was recorded for less than 1% of pwMS and thus ambulatory status was used as a proxy measure of MS disability, with 24.2% experiencing difficulties walking and 12.7% classified as non-ambulatory. Half of pwMS had ≥1 comorbid condition, including hypertension (29.8%), obesity (15.9%), other cardiovascular diseases (15.6%), and diabetes without complications (10.4%) (Table 1).
      Table 1Baseline characteristics for eligible pwMS and stratified separately by COVID-19 cases and COVID-19 hospitalization
      All PwMSCOVID-19 CasesCOVID-19 Hospitalization
      N=47051N=799N=182
      Age, mean (sd)53.4 (13.3)56.0 (14.9)61.2 (13.5)
      Female35680 (75.8%)577 (72.2%)116 (63.7%)
      Male11371 (24.2%)222 (27.8%)66 (36.3%)
      African American5755 (12.2%)168 (21.0%)52 (28.6%)
      Asian238 (0.5%)<11<11
      Caucasian37835 (80.4%)565 (70.7%)117 (64.3%)
      Other/Unknown3223 (6.9%)65 (8.1%)13 (7.1%)
      Current smoker6649 (14.1%)91 (11.4%)20 (11.0%)
      Not current smoker40402 (85.7%)708 (88.6%)162 (89.0%)
      Years since MS Diagnosis, mean (sd)6.7 (3.3)6.8 (3.3)6.7 (3.2)
      EDSS Available, n444 (0.9%)<11<11
      EDSS, median (IQR)2.5 (1.0, 6.0)2.8 (1.6, 6.9)6.5 (NR)
      Fully ambulatory29706 (63.1%)404 (50.6%)49 (26.9%)
      Walks with assistance11374 (24.2%)212 (26.5%)54 (29.7%)
      Non Ambulatory5971 (12.7%)183 (22.9%)79 (43.4%)
      MS Subtype Available18039 (38.3%)333 (41.7%)72 (39.6%)
      PPMS3391 (18.8%)89 (26.7%)31 (43.1%)
      RRMS14167 (78.5%)233 (70.0%)38 (52.8%)
      SPMS481 (2.7%)11 (3.3%)<11
      No relapse39515 (84.0%)619 (77.5%)126 (69.2%)
      Moderate relapse5518 (11.7%)107 (13.4%)24 (13.2%)
      Severe relapse2018 (4.3%)73 (9.1%)32 (17.6%)
      Arrhythmia4091 (8.7%)111 (13.9%)38 (20.9%)
      Asthma2963 (6.3%)71 (8.9%)19 (10.4%)
      Chronic Heart Failure1499 (3.2%)72 (9.0%)31 (17.0%)
      Chronic Respiratory Diseases2940 (6.2%)89 (11.1%)36 (19.8%)
      Dementia1449 (3.1%)81 (10.1%)37 (20.3%)
      Diabetes with complications2058 (4.4%)71 (8.9%)32 (17.6%)
      Diabetes without complications4882 (10.4%)135 (16.9%)52 (28.6%)
      HIV57 (0.1%)<11<11
      Hypertension14020 (29.8%)337 (42.2%)116 (63.7%)
      Kidney Disease2672 (5.7%)91 (11.4%)30 (16.5%)
      Liver Disease1444 (3.1%)24 (3.0%)<11
      Malignancy (haematological)273 (0.6%)<11<11
      Malignancy (solid tumour)2305 (4.9%)46 (5.8%)15 (8.2%)
      Obesity7479 (15.9%)166 (20.8%)56 (30.8%)
      Organ Transplant129 (0.3%)<11<11
      Other cardiovascular disease7320 (15.6%)231 (28.9%)84 (46.2%)
      Other neurological conditions605 (1.3%)21 (2.6%)<11
      Psoriasis431 (0.9%)<11<11
      Rheumatoid Arthritis792 (1.7%)19 (2.4%)<11
      Spleen Disease39 (0.1%)<11<11
      Stroke/Cerebrovascular Disease2162 (4.6%)59 (7.4%)23 (12.6%)
      Systemic Lupus Erythematosus341 (0.7%)11 (1.4%)<11
      1+ Comorbidities24941 (53.0%)523 (65.5%)152 (83.5%)
      2+ Comorbidities14641 (31.1%)379 (47.4%)130 (71.4%)
      3+ Comorbidities8597 (18.3%)259 (32.4%)104 (57.1%)
      Ocrelizumab5169 (11.0%)81 (10.1%)22 (12.1%)
      Dimethyl fumarate3351 (7.1%)43 (5.4%)<11
      EDSS=expanded disability status scale, MS=multiple sclerosis, PPMS=primary-progressive MS, RRMS=relapsing-remitting MS, SPMS=secondary progressive, NR=not reported. Note subgroups with 10 or less people are represented as <11.

      3.3 COVID-19 outcomes

      Amongst 47,051 eligible pwMS, 799 COVID-19 cases were observed (1.7%) and 182 hospitalizations for COVID-19 occurred (0.4%), representing a hospitalization rate of 22.8% amongst cases.
      Comparing, separately, the COVID-19 cases and COVID-19 hospitalised cohorts to the overall pwMS cohort, both COVID-19 cohorts were proportionally more likely to be older, male, African-American, diagnosed with PPMS, have ambulatory issues, experienced a relapse during baseline, and have a higher prevalence of comorbidities, while in contrast the prevalence of OCR use remained consistent in each sample (Table 1). As the true COVID-19 population is largely unknown, we suspect that some of these differences are due to sampling biases rather than true risk differences for COVID-19.

      3.4 Risk factor analysis

      In the fully-adjusted Cox regression model, risk factors identified for COVID-19 hospitalization were older age, male gender, African-American race, walking with assistance, non-ambulatory status, severe relapse in year prior to baseline, dementia, chronic respiratory diseases, other cardiovascular disease, hypertension, and obesity (Table 2). With the exception of age, gender, chronic respiratory diseases, and obesity these risk factors were also identified as significant in LASSO (Figure 2). In addition diabetes with complications, and chronic heart failure were highlighted as potential risk factors in LASSO, both of which trended towards being significant risk factors in the Cox regression model.
      Table 2Cox Proportional Hazard regression models testing the association between risk factors and COVID-19 hospitalization
      COVID-19 Hospitalization (Age, gender adjusted) n=47051COVID-19 Hospitalization (Age, gender, MS characteristics adjusted) n=47051COVID-19 Hospitalization (Fully adjusted) n=47051
      HR95% CIp-valueHR95% CIp-valueHR95% CIp-value
      Age 18-49Ref
      Age 50-591.771.122.800.0141.671.052.650.0321.240.782.000.366
      Age 60-692.861.864.39<0.0012.541.633.98<0.0011.540.962.460.074
      Age 70-794.522.87.31<0.0013.442.075.71<0.0011.520.872.660.137
      Age 80+11.206.1820.40<0.0017.474.0013.94<0.0012.671.335.330.006
      Male1.821.342.46<0.0011.721.272.330.0011.761.292.39<0.001
      Asian/Caucasian/Other/UnknownRef
      African American3.682.665.11<0.0012.922.094.06<0.0012.351.683.29<0.001
      Current smoker0.920.581.470.7350.860.541.370.5210.760.471.230.260
      Years since MS Diagnosis, mean (sd)0.960.921.010.0940.960.921.010.0970.970.921.020.211
      Fully ambulatoryRef
      Walks with assistance2.501.693.70<0.0012.051.383.04<0.0011.841.232.750.003
      Non ambulatory6.464.459.39<0.0014.873.317.17<0.0013.642.455.42<0.001
      No RelapseRef
      Moderate Relapse1.621.052.520.0301.540.992.390.0551.260.801.960.316
      Severe Relapse5.243.547.74<0.0012.921.954.37<0.0012.061.363.120.001
      Arrhythmia2.091.443.03<0.001----0.790.531.190.255
      Asthma1.921.193.090.007----0.970.591.590.895
      Chronic Heart Failure4.432.936.7<0.001----1.581.002.490.050
      Chronic Respiratory Diseases3.062.104.46<0.001----1.611.072.420.021
      Dementia5.763.898.53<0.001----3.062.054.58<0.001
      Diabetes with complications3.652.465.4<0.001----1.520.992.330.055
      Hypertension3.232.334.48<0.001----1.791.262.560.001
      Kidney Disease2.361.563.55<0.001----0.800.521.240.318
      Malignancy (solid tumour)1.190.702.030.531----1.120.661.920.674
      Obesity2.621.913.6<0.001----1.461.032.060.033
      Other cardiovascular disease3.582.624.91<0.001----1.651.152.370.006
      Stroke/Cerebrovascular Disease2.221.423.48<0.001----0.900.571.430.660
      Figure 2
      Figure 2Finding of LASSO regression analysis. The green-dotted line represents a 90% probability of inclusion
      In subgroup analyses (n=18,039), patients with PPMS had a higher risk of COVID-19 hospitalization (n=72) in a Cox regression model adjusted for age and gender (Table 3). In multivariate models the risk was attenuated and non-significant when adjusting for MS characteristics, and comorbidities. As ambulatory status and MS subtype are highly correlated (χ2-test, p<0.001), ambulatory status partially explains the variance in hospitalization for COVID-19 arising from MS subtype.
      Table 3Subgroup analyses amongst patients with MS Subtype information available
      COVID-19 Hospitalization (Age, gender adjusted)

      n=18039
      COVID-19 Hospitalization (Age, gender, MS characteristics adjusted)

      n=18039
      COVID-19 Hospitalization (Fully adjusted)

      n=18039
      HR95% CIp-valueHR95% CIp-valueHR95% CIp-value
      RRMSRef
      PPMS + SPMS2.051.223.450.0071.280.742.210.3711.320.762.290.323
      MS=multiple sclerosis, PPMS=primary-progressive MS, RRMS=relapsing-remitting MS, SPMS=secondary progressive

      3.5 Channeling bias assessment

      There were 5,169 pwMS using OCR and 3,351 using DMF. The identified COVID-19 hospitalization risk factors including ambulatory status and MS subtypes were highly imbalanced between the groups, including a larger proportion of non-ambulatory and PPMS patients in the OCR group, potentially arising from key differences in the labelled indications for these therapies (Table 4). Furthermore, age, gender and relapse history also displayed some imbalance. The identified risk factors were included in the PS model and sufficient balance was achieved after IPTW-ATT weighting, which resulted in SMDs below 0.1. However IPTW-ATT weighting did not include MS subtype due to the extent of missing data, and remained imbalanced with a significantly larger proportion of PPMS patients amongst the OCR group (Table 4).
      Table 4Risk factors for COVID-19 hospitalization at baseline for dimethyl fumarate and ocrelizumab users before and after propensity score weighting
      Prior to IPTW-ATTAfter IPTW-ATT
      DMF N=3351OCR N=5169SMDDMF N=5157.1OCR N=5169.0SMD
      Age 18-491592 (47.5)2811 (54.4)0.1512829.9 (54.9)2811.0 (54.4)0.034
      Age 50-591066 (31.8)1386 (26.8)1362.4 (26.4)1386.0 (26.8)
      Age 60-69603 (18.0)803 (15.5)816.1 (15.8)803.0 (15.5)
      Age 70-7984 (2.5)159 (3.1)135.0 (2.6)159.0 (3.1)
      Age 80+<11<1113.7 (0.3)<11
      Male880 (26.3)1591 (30.8)0.1001586.7 (30.8)1591.0 (30.8)<0.001
      African American508 (15.2)610 (11.8)0.099619.4 (12.0)610.0 (11.8)0.050
      Caucasian2555 (76.2)4117 (79.6)4023.6 (78.0)4117.0 (79.6)
      Other/Unknown288 (8.6)442 (8.6)514.1 (10.0)442.0 (8.6)
      PPMS111 (3.3)613 (11.9)0.339209.1 (4.1)613.0 (11.9)0.299
      RRMS1416 (42.3)2105 (40.7)2203.3 (42.7)2105.0 (40.7)
      SPMS25 (0.7)70 (1.4)46.8 (0.9)70.0 (1.4)
      Unknown MS Subtype1799 (53.7)2381 (46.1)2697.9 (52.3)2381.0 (46.1)
      Fully ambulatory2425 (72.4)3043 (58.9)0.2983045.6 (59.1)3043.0 (58.9)0.012
      Walks with assistance726 (21.7)1540 (29.8)1546.3 (30.0)1540.0 (29.8)
      Non ambulatory200 (6.0)586 (11.3)565.2 (11.0)586.0 (11.3)
      No Relapse2830 (84.5)4145 (80.2)0.1134123.3 (80.0)4145.0 (80.2)0.020
      Moderate Relapse396 (11.8)791 (15.3)779.9 (15.1)791.0 (15.3)
      Severe Relapse125 (3.7)233 (4.5)253.9 (4.9)233.0 (4.5)
      Chronic Heart Failure41 (1.2)41 (0.8)0.04339.5 (0.8)41.0 (0.8)0.003
      Chronic Respiratory Diseases121 (3.6)134 (2.6)0.059140.9 (2.7)134.0 (2.6)0.009
      Dementia44 (1.3)75 (1.5)0.01278.4 (1.5)75.0 (1.5)0.006
      Diabetes with complications85 (2.5)122 (2.4)0.011117.0 (2.3)122.0 (2.4)0.006
      Hypertension699 (20.9)1003 (19.4)0.036999.5 (19.4)1003.0 (19.4)0.001
      Obesity483 (14.4)692 (13.4)0.030713.6 (13.8)692.0 (13.4)0.013
      Other cardiovascular disease305 (9.1)441 (8.5)0.020440.7 (8.5)441.0 (8.5)0.001
      IPTW=Inverse probability of treatment weighting, ATT=Average treatment effect in the treated, DMF=dimethyl fumarate, OCR=ocrelizumab, MS=multiple sclerosis, PPMS=primary-progressive MS, RRMS=relapsing-remitting MS, SPMS=secondary progressive MS. All variables listed were included in the propensity score model except for MS Subtype due to high missingness. Note subgroups with 10 or less people are represented as <11.

      3.6 Comparison of OCR and DMF

      In unadjusted, adjusted, PS weighted, and doubly robust models, compared to DMF, no significantly higher risk of COVID-19 hospitalization was observed with OCR use (Figure 3A). In secondary outcome analyses, similarly no significantly higher risk of COVID-19 was associated with OCR (Figure 3B). For both analyses, adjustment for the identified confounders led to a decrease in the point estimate towards the null.
      Figure 3
      Figure 3A: Comparison of risk of hospitalization for COVID-19 between patients treated with OCR and DMF. B: Comparison of risk of COVID-19 between patients treated with OCR and DMF
      Note: the unadjusted and adjusted models include 5169 OCR and 3351 DMF patients, and the unadjusted and adjusted propensity score weighted models include 5169.0 OCR and 5157.1 DMF patients.

      4. Discussion

      In this retrospective cohort of 47,051 pwMS active in a large US EHR and claims database during the COVID-19 pandemic, 799 COVID-19 cases and 182 COVID-19 hospitalizations were identified. Comparing the COVID-19 cohorts to the overall pwMS cohort highlights the differential sampling bias for certain risk factors. Multivariate cox proportional hazard modelling identified age, gender, African-American race, ambulatory status, recent relapses, dementia, cardiovascular disease, chronic heart failure, chronic respiratory diseases, diabetes with complications, hypertension, and obesity as risk factors for COVID-19 hospitalization. Further analyses highlight the importance of considering each comorbidity separately for its impact on hospitalization for COVID-19 (Appendix D). We identified a clear potential for channeling bias, with significant differences in risk factors for COVID-19 hospitalization, between OCR and DMF use. These factors are also associated with COVID-19 hospitalization and potentially confound the association between particular DMT exposure and risk of COVID-19 hospitalization. No significant increased risk of COVID-19 hospitalization was associated with OCR vs. DMF in the unadjusted comparison, which was confirmed in adjusted analyses with attenuation of the HR towards the null.
      Previous studies reported COVID-19 hospitalization rates amongst pwMS COVID-19 cohorts ranging from 11.4%–23.2% of COVID-19 cases (
      • Langer-Gould A
      • Smith JB
      • Li BH
      • et al.
      Multiple sclerosis, rituximab, and COVID-19.
      ,
      • Salter A
      • Fox RJ
      • Newsome SD
      • et al.
      Outcomes and risk factors associated with SARS-CoV-2 infection in a North American registry of patients with multiple sclerosis.
      ,
      • Pérez CA
      • Zhang GQ
      • Li X
      • et al.
      COVID-19 severity and outcome in multiple sclerosis: results of a national, registry-based, matched cohort study.
      ,
      • Reder AT
      • Centonze D
      • Naylor ML
      • et al.
      COVID-19 in patients with multiple sclerosis: associations with disease-modifying therapies.
      ,
      • Sormani MP
      • De Rossi N
      • Schiavetti I
      • et al.
      Disease-modifying therapies and coronavirus disease 2019 severity in multiple sclerosis.
      A,
      • Sormani MP
      • De Rossi N
      • Schiavetti I
      • et al.
      Disease-modifying therapies and coronavirus disease 2019 severity in multiple sclerosis.
      B, Simpson-Yap et al 2022,
      • Spelman T
      • Forsberg L
      • McKay K
      • et al.
      Increased rate of hospitalization for COVID-19 among rituximab-treated multiple sclerosis patients: a study of the Swedish multiple sclerosis registry.
      ,
      • Louapre C
      • Collongues N
      • Stankoff B
      • et al.
      Clinical characteristics and outcomes in patients with coronavirus disease 2019 and multiple sclerosis.
      ), similar to the hospitalization rate of 22.8% observed in our study. The variation observed in hospitalization rates may be partly attributed to geographic differences in standard clinical practice, and also temporal effects of capacity constraints during the pandemic. In our study, the rate of COVID-19 hospitalization amongst all pwMS appears to be <0.5% during the first 7-15 months of the pandemic, slightly higher than the rate of 0.2% and 0.01% reported in
      • Reder AT
      • Centonze D
      • Naylor ML
      • et al.
      COVID-19 in patients with multiple sclerosis: associations with disease-modifying therapies.
      and Smith et al. 2022, respectively. However, these studies were restricted to pwMS treated with DMTs, and the latter study was performed over a longer follow-up time during the pandemic.
      We identified individual comorbidities prognostic of COVID-19 hospitalization relevant for a population of pwMS similar to those found in the general population (
      • Williamson E.J.
      • Walker A.J.
      • Bhaskaran K.
      • et al.
      Factors associated with COVID-19-related death using OpenSAFELY.
      ,
      • Docherty AB
      • Harrison EM
      • Green CA
      • et al.
      Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study.
      ,
      • Petrilli CM
      • Jones SA
      • Yang J
      • et al.
      Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study.
      ,
      • Cummings MJ
      • Baldwin MR
      • Abrams D
      • et al.
      Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study.
      ). We also confirmed findings of prior MS studies that ambulatory status and relapse history are important prognostic factors for COVID-19 hospitalization for pwMS (
      • Louapre C
      • Collongues N
      • Stankoff B
      • et al.
      Clinical characteristics and outcomes in patients with coronavirus disease 2019 and multiple sclerosis.
      ,
      • Sormani MP
      • De Rossi N
      • Schiavetti I
      • et al.
      Disease-modifying therapies and coronavirus disease 2019 severity in multiple sclerosis.
      A,
      • Salter A
      • Fox RJ
      • Newsome SD
      • et al.
      Outcomes and risk factors associated with SARS-CoV-2 infection in a North American registry of patients with multiple sclerosis.
      , Parotta et al. 2020,
      • Chaudhry F
      • Bulka H
      • Rathnam AS
      • et al.
      COVID-19 in multiple sclerosis patients and risk factors for severe infection.
      ), as are age, gender and race (Smith et al. 2022,
      • Salter A
      • Fox RJ
      • Newsome SD
      • et al.
      Outcomes and risk factors associated with SARS-CoV-2 infection in a North American registry of patients with multiple sclerosis.
      ). In subgroup analyses we also identified PPMS to have a higher risk of hospitalization for COVID-19 which was attenuated when adjusting for ambulatory status in particular.
      We evaluated the propensity for these prognostic factors for COVID-19 hospitalization to differ between OCR and DMF use, and identified significant differences related to MS disease characteristics, potentially explained by their different licensed indications for MS. To address the potential confounding we applied both direct adjustment and PS weighting techniques (Petri et al. 1991,
      • Lobo FS
      • Wagner S
      • Gross CR
      • et al.
      Addressing the issue of channeling bias in observational studies with propensity scores analysis.
      ). We had sufficient power to detect a relative hazard risk increase of 180% in COVID-19 hospitalizations amongst OCR treated pwMS. Across all analyses we did not identify a statistically significant increased risk of COVID-19 hospitalization associated with OCR use. The null finding in our unadjusted analysis provides support for potential distortion of HRs in prior studies due to sampling bias, while the influence of confounders was observed in adjusted analyses with an attenuation of the HR towards the null.
      Findings from prior EHR studies may be limited due to sampling bias conditioned on COVID-19 potentially causing collider bias and distorted risk ratios (Griffiths et al 2021); and residual confounding due to inadequate control for key prognostic factors (Smith et al. 2022, Longinetti et al. 2022). Further limitations of prior studies including the selection of multiple comparator DMTs have recently been highlighted (Smith et al. 2022). The current study is thus strengthened by the head-to-head analysis of two therapies (
      • Hernán MA
      • Robins JM
      Causal Inference: What If.
      ).
      A strength of this study is the large sample size of pwMS in comparison to prior studies. To identify an MS population we rely on recorded encounters for MS within EHR and claims data. Although these algorithms have demonstrated good validity (
      • Culpepper WJ
      • Marrie RA
      • Langer-Gould A
      • et al.
      Validation of an algorithm for identifying MS cases in administrative health claims datasets.
      ), they may exclude certain pwMS who do not require DMTs or regularly attend healthcare settings. Another major challenge is identifying persons at-risk in EHRs and may potentially underestimate the population considered at-risk for COVID-19 (Casey et al 2016). Patients without an interaction with the health system during follow-up for diagnosis or testing for COVID-19 will not be captured, such as asymptomatic patients, patients with mild COVID-19 symptoms or patients who received diagnoses in alternate settings, which is further complicated by temporal trends of laboratory testing capacity. COVID-19 hospitalization is a more valid and reliable outcome to evaluate in EHR and claims data due to better objectivity of disease severity and higher sensitivity to capture such outcomes. Furthermore, COVID-19 related mortality was not evaluated in our study due to missing information on cause and date of death, limiting the reliability of this endpoint. It must also be noted that the period studied corresponds to the early era of the pandemic. This has resulted in limited clinical insights on research questions relevant to latter stages of the pandemic such as further understanding COVID-19 severity in pwMS associated with DMTs or vaccine effectiveness. As our study objectives were concerned with methodological challenges when comparing COVID-19 outcomes amongst two DMT groups, we did not evaluate objectives concerning risk factors within a single DMT group e.g. duration of use. Finally, there was an absence of information on key risk factors (EDSS and MS subtypes) for a large proportion of pwMS. Imputation methods were not considered due to the limited understanding of the missing data mechanism (missing completely or partially at random). However, we derived ambulatory status as a proxy indicator for EDSS, which demonstrated moderate concordance with EDSS, and we performed subgroup analyses amongst those with MS subtype information.

      5. Conclusions

      We highlight the need for careful consideration of cohort sampling and confounding control within EHR-based studies to ensure valid conclusions. Our findings highlight prognostic risk factors in pwMS for COVID-19 hospitalization, which includes age, gender, individual comorbidities, MS disease characteristics such as disability status and recent relapse history. We also identified a clear potential for channeling bias, with significant differences in risk factors for COVID-19 hospitalization, in particular MS disease characteristics, between OCR and DMF use. Finally the methodological challenges highlighted here are not solely applicable to EHR studies, but should be considered in all COVID-19 research amongst pwMS using other real-world data sources.

      Author Contributions

      Concept and design: All authors
      Acquisition of data: Dillon, Siadimas, Fajardo, Roumpanis, Muros-Le Rouzic
      Analysis and interpretation of data: Dillon, Siadimas, Fajardo, Roumpanis, Muros-Le Rouzic
      Drafting of manuscript: Dillon, Muros-Le Rouzic, Fitovski, Jessop, Whitley
      Critical revision of paper for important intellectual content: All authors
      Obtaining funding:
      Administrative, technical, or logistic support:
      Supervision: Muros-Le Rouzic

      Funding/Support

      This study was sponsored by F. Hoffmann-La Roche Ltd, Basel, Switzerland.

      Role of the Funder/Sponsor

      F. Hoffmann-La Roche Ltd, Basel, Switzerland was involved in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

      Data Availability

      For up-to-date details on Roche's Global Policy on the Sharing of Clinical Information and how to request access to related clinical study documents, see here: https://go.roche.com/data_sharing

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      Conflict of Interest Disclosures

      P Dillon was an employee of F. Hoffmann-La Roche Ltd during completion of the work related to this manuscript.A Siadimas is an employee of F. Hoffmann-La Roche Ltd
      O Fajardo is an employee of F. Hoffmann-La Roche Ltd
      S Roumpanis is an employee of F. Hoffmann-La Roche Ltd
      K Fitovksi is an employee of F. Hoffmann-La Roche Ltd
      N Jessop is an employee of F. Hoffmann-La Roche Ltd
      L Whitley is a Senior Partner at TranScrip Partners LLP and a consultant to F. Hoffmann-La Roche Ltd
      E Muros-Le Rouzic is an employee of F. Hoffmann-La Roche Ltd

      Appendix A

      Positive or clinically confirmed COVID-19 diagnosis will be determined by the presence of ICD-10 diagnostic code for COVID-19 (‘U07.1’ or ‘U07.2), or a positive diagnostic laboratory test, or an ICD-10 diagnostic code for coronaviruses (‘B97.29’). One of the following three criteria must be met to classify a positive or clinically confirmed COVID-19 diagnosis:
      • 1
        Patients with a ‘U07.1’ or ‘U07.2’ ICD-10 code. These are emergency use ICD codes for COVID-19 disease. An emergency ICD-10 code of ‘U07.1 COVID-19, virus identified’ is assigned to a disease diagnosis of COVID-19 confirmed by laboratory testing. An emergency ICD-10 code of ‘U07.2 COVID-19, virus not identified’ is assigned to suspected or probable cases with known contact or exposures to COVID-19, symptoms of COVID-19 requiring hospitalization, or clinical diagnosis of COVID-19 in absence of documented test. These codes became effective as of April 1st 2020 (WHO 2020).
      • 2
        Patients with a positive diagnostic lab for COVID-19. Diagnostic tests are defined as having the following combination of words in their descriptions: ‘rna’, ‘pcr’, ‘np’, ‘op’, or ‘naat’ plus ‘sars coronavirus 2’, ‘covid 19’, ‘coronavirus 2’, ‘2019 novel coronavirus’, ‘2019 coronavirus’, ‘sars-cov-2’ or ‘sars coronavirus’. In addition, 'rapid covid-19’ tests will be included. (Note abbreviations: rna= ribonucleic acid, pcr=polymerase chain reaction, np=nasopharyngeal, op=oropharyngeal, naat= nucleic acid amplification test).
      • 3
        Patients with a B97.29 diagnosis code, “other coronavirus as the cause of diseases classified elsewhere”, (with the exception of patients who: received a negative diagnostic lab test within a 14-day window of the B97.29 diagnosis code, and did not have a positive lab test). The 14-day window represents 7-days before and 7-days after. This code was the recommended code during February 20th to March 31st 2020, in combination with symptoms codes (pneumonia, acute bronchitis, lower respiratory infection and acute respiratory distress syndrome) for confirmed COVID-19 cases. The code was not recommended for use with suspected, possible, or probable COVID-19 cases (CDC 2020).

      Appendix B

      Table B1.
      Table B1Comorbidities identified in general population studies to be associated with poorer COVID-19 outcomes (Column B, C), studies of COVID-19 outcomes in MS populations and comorbidities considered (Column D)
      A. ComorbidityB. Original articlesC. Meta- AnalysisD. MS StudiesE. ICD10-CM codesF. ICD9-CM codesG. ICD-10-CM Code DescriptorsH. ICD-9-CM Descriptors
      Chronic respiratory diseases (excluding asthma)1, 2, 46, 8, 910, 14, 16J40, J41, J42, J43, J44, J47, J60, J61, J62, J63, J64, J65, J66, J98.2, J98.3,490, 491, 492, 494, 496, 500, 501, 502, 503, 504, 505, 518.1, 518.2Bronchitis, not specified as acute or chronic, Simple and mucopurulent chronic bronchitis, Unspecified chronic bronchitis, Emphysema, Other chronic obstructive pulmonary disease, Bronchiectasis, Coalworker's pneumoconiosis, Pneumoconiosis due to asbestos and other mineral fibers, Pneumoconiosis due to dust containing silica, Pneumoconiosis due to other inorganic dusts, Unspecified pneumoconiosis, Pneumoconiosis associated with tuberculosis, Airway disease due to specific organic dust, Interstitial emphysema, Compensatory emphysema,Bronchitis, not specified as acute or chronic, Chronic bronchitis, Emphysema, Bronchiectasis, Chronic airway obstruction, not elsewhere classified, Coal workers' pneumoconiosis, Asbestosis, Pneumoconiosis due to other silica or silicates, Pneumoconiosis due to other inorganic dust, Pneumonopathy due to inhalation of other dust, Pneumoconiosis unspecified, Compensatory emphysema, Interstitial emphysema,
      Asthma1810, 14, 16J45493Asthma, Status asthmaticusAsthma
      Hypertension1, 3, 56, 7, 8, 910, 13, 14, 16I10, I11, I12, I13, I15, I16,401, 402, 403, 404, 405Essential hypertension, Hypertensive disease, Hypertensive chronic kidney disease, Hypertensive heart and chronic kidney disease, Secondary Hypertension, Hypertensive crisisEssential hypertension, Hypertensive heart disease, Hypertensive chronic kidney disease, Hypertensive heart and chronic kidney disease, Secondary hypertension
      Chronic Heart Failure1, 2, 3, 46, 7, 810I11.0, I13.0, I13.2, I50402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428Hypertensive heart disease with heart failure, Hypertensive heart and chronic kidney disease with heart failure and stage 1 through stage 4 chronic kidney disease, or unspecified chronic kidney disease, Hypertensive heart and chronic kidney disease with heart failure and with stage 5 chronic kidney disease, or end stage renal disease, Heart failureMalignant hypertensive heart disease with heart failure, Heart failure, Benign hypertensive heart disease with heart failure, Unspecified hypertensive heart disease with heart failure, Hypertensive heart and chronic kidney disease malignant with heart failure and with chronic kidney disease stage I through stage IV or unspecified, Hypertensive heart and chronic kidney disease malignant with heart failure and with chronic kidney disease stage V or end stage renal disease, Hypertensive heart and chronic kidney disease benign with heart failure and with chronic kidney disease stage I through stage IV or unspecified, Hypertensive heart and chronic kidney disease benign with heart failure and chronic kidney disease stage V or end stage renal disease, Hypertensive heart and chronic kidney disease unspecified with heart failure and with chronic kidney disease stage I through stage IV or unspecified, Hypertensive heart and chronic kidney disease unspecified with heart failure and chronic kidney disease stage V or end stage renal disease, Heart failure
      Arrhythmia10I44, I45.6, I45.9, I47, I48, I49, R00.0, R00.1, R00.8, Z95.0426, 427.0, 427.1, 427.2, 427.3, 427.4, 427.6, 427.8, 427.9, 785.0, V45.0, V53.3Atrioventricular and left bundle-branch block, Paroxysmal tachycardia, Atrial fibrillation and flutter, Other cardiac arrhythmias, Tachycardia, unspecified, Bradycardia, unspecified, Presence of cardiac pacemakerConduction disorders, Paroxysmal supraventricular tachycardia, Paroxysmal ventricular tachycardia, Paroxysmal tachycardia unspecified, Atrial fibrillation and flutter, Ventricular fibrillation and flutter, Premature beats, Other specified cardiac dysrhythmias, Cardiac dysrhythmia unspecified, Tachycardia, unspecified, Cardiac pacemaker in situ, Fitting and adjustment of cardiac pacemaker
      Other Cardiovascular Disease1, 2, 3, 46, 7, 8, 910, 13, 16I05, I06, I07, I08, I09, I20, I21, I22, I23, I24, I25, I30, I31, I32, I33, I34, I35, I36, I37, I38, I39, I40, I41, I42, I43, I45, I46, I51, I52, I70, I71, I72, I73, I74, I75, I76, I77, I78, I79, I80, I81, I82, I95, I99394, 395, 396, 397, 398, 410, 411, 412, 413, 414, 420, 421, 422, 423, 424, 425, 427.5, 429, 440, 441, 442, 443, 444, 445, 447, 448, 449, 451, 452, 453, 458, 459Rheumatic mitral valve diseases, Rheumatic aortic valve diseases, Rheumatic tricuspid valve diseases, Multiple valve diseases, Other rheumatic heart diseases, Angina pectoris, Acute myocardial infarction, Subsequent ST elevation (STEMI) and non-ST elevation (NSTEMI) myocardial infarction, Certain current complications following ST elevation (STEMI) and non-ST elevation (NSTEMI) myocardial infarction (within the 28 day period), Other acute ischemic heart diseases, Chronic ischemic heart disease, Acute pericarditis, Other diseases of pericardium, Pericarditis in diseases classified elsewhere, Acute and subacute endocarditis, Nonrheumatic mitral valve disorders, Nonrheumatic aortic valve disorders, Nonrheumatic tricuspid valve disorders, Nonrheumatic pulmonary valve disorders, Endocarditis valve unspecified, Endocarditis and heart valve disorders in diseases classified elsewhere, Acute myocarditis, Myocarditis in diseases classified elsewhere, Cardiomyopathy, Cardiomyopathy in diseases classified elsewhere, Other conduction disorders, Cardiac arrest, Complications and ill-defined descriptions of heart disease, Other heart disorders in diseases classified elsewhere, Atherosclerosis, Aortic aneurysm and dissection, Other aneurysm, Other peripheral vascular diseases, Arterial embolism and thrombosis, Atheroembolism, Septic arterial embolism, Other disorders of arteries and arterioles, Diseases of capillaries, Disorders of arteries arterioles and capillaries in diseases classified elsewhere, Phlebitis and thrombophlebitis, Portal vein thrombosis, Other venous embolism and thrombosis, Hypotension, Other and unspecified disorders of circulatory systemDiseases of mitral valve, Diseases of aortic valve, Diseases of mitral and aortic valves, Diseases of other endocardial structures, Other rheumatic heart disease, Acute myocardial infarction, Other acute and subacute forms of ischemic heart disease, Old myocardial infarction, Angina pectoris, Other forms of chronic ischemic heart disease, Acute pericarditis, Acute and subacute endocarditis, Acute myocarditis Other diseases of pericardium, Other diseases of endocardium, Cardiomyopathy, Cardiac arrest, Ill-defined descriptions and complications of heart disease, Atherosclerosis, Aortic aneurysm and dissection, Other aneurysm, Other peripheral vascular disease, Arterial embolism and thrombosis, Atheroembolism, Other disorders of arteries and arterioles, Disease of capillaries, Septic arterial embolism, Phlebitis and thrombophlebitis, Portal vein thrombosis, Other venous embolism and thrombosis, Hypotension, Other disorders of circulatory system
      Diabetes without chronic complications1, 36, 7, 8, 910, 13, 14, 16E10.1, E10.6, E10.8, E10.9, E11.0, E11.1, E11.6, E11.8, E11.9, E13.0, E13.1, E13.6, E13.8, E13.9250.0, 250.1, 250.2, 250.3, 250.8, 250.9Type 1 diabetes mellitus with ketoacidosis, Type 1 diabetes mellitus with other specified complications, Type 1 diabetes mellitus with unspecified complications, Type 1 diabetes mellitus without complications, Type 2 diabetes mellitus with hyperosmolarity, Type 2 diabetes mellitus with ketoacidosis, Type 2 diabetes mellitus with other specified complications, Type 2 diabetes mellitus with unspecified complications, Type 2 diabetes mellitus without complications, Other specified diabetes mellitus with hyperosmolarity, Other specified diabetes mellitus with ketoacidosis, Other specified diabetes mellitus with other specified complications, Other specified diabetes mellitus with unspecified complications, Other specified diabetes mellitus without complicationsDiabetes mellitus without mention of complication, Diabetes with ketoacidosis, Diabetes mellitus with hyperosmolarity, Diabetes with other coma, Diabetes with other specified manifestations, Diabetes with unspecified complication
      Diabetes with chronic complications (neurological, circulatory, ophthalmic, renal)1E10.2, E10.3, E10.4, E10.5, E11.2, E11.3, E11.4, E11.5, E13.2, E13.3, E13.4, E13.5250.4, 250.5, 250.6, 250.7Type 1 diabetes mellitus with kidney complications, Type 1 diabetes mellitus with ophthalmic complications, Type 1 diabetes mellitus with neurological complications, Type 1 diabetes mellitus with circulatory complications, Type 2 diabetes mellitus with kidney complications, Type 2 diabetes mellitus with ophthalmic complications, Type 2 diabetes mellitus with neurological complications, Type 2 diabetes mellitus with circulatory complications, Other specified diabetes mellitus with kidney complications, Other specified diabetes mellitus with ophthalmic complications, Other specified diabetes mellitus with neurological complicationsDiabetes with renal manifestations, Diabetes with ophthalmic manifestations, Diabetes with neurological manifestations, Diabetes with peripheral circulatory disorders
      Malignancy (non-hematologic)1, 2, 36, 7, 8, 916C00-C14, C15-26, C30-C39, C40-C41, C43-C44, C45-C49, C50, C51-C58C60-C63, C64-C68, C69-C72, C73-C75, C76-C80140-149, 150-159, 160-165, 170-176, 179-189, 190-199Malignant neoplasms of lip, oral cavity and pharynx, Malignant neoplasms of digestive organs, Malignant neoplasms of respiratory and intrathoracic organs, Malignant neoplasms of bone and articular cartilage, Melanoma and other malignant neoplasms of skin, Malignant neoplasms of mesothelial and soft tissue, Malignant neoplasms of breast, Malignant neoplasms of female genital organs, Malignant neoplasms of male genital organs, Malignant neoplasms of urinary tract, Malignant neoplasms of eye, brain and other parts of central nervous system, Malignant neoplasms of thyroid and other endocrine glands, Malignant neoplasms of ill-defined other secondary and unspecified sitesMalignant Neoplasm Of Lip, Oral Cavity, And Pharynx, Malignant Neoplasm Of Digestive Organs And Peritoneum, Malignant Neoplasm Of Respiratory And Intrathoracic Organs, Malignant Neoplasm Of Bone, Connective Tissue, Skin, And Breast, Malignant Neoplasm Of Genitourinary Organs, Malignant Neoplasm Of Other And Unspecified Sites
      Haematological malignancy1, 2, 36, 7, 8, 916C81-C96200-209Malignant neoplasms of lymphoid, hematopoietic and related tissueMalignant Neoplasm Of Lymphatic And Hematopoietic Tissue
      Stroke/Cerebrovascular disease16, 8, 916I60, I61, I62, I63, I65, I66, I67, I68, I69, G45, G46,430, 431, 432, 433, 434, 435, 436, 437, 438Nontraumatic subarachnoid hemorrhage, Nontraumatic intracerebral hemorrhage, Other and unspecified nontraumatic intracranial hemorrhage, Cerebral infarction, Occlusion and stenosis of precerebral arteries, not resulting in cerebral infarction, Occlusion and stenosis of cerebral arteries, not resulting in cerebral infarction, Other cerebrovascular diseases, Cerebrovascular disorders in diseases classified elsewhere, Sequelae of cerebrovascular disease, Transient cerebral ischemic attacks and related syndromes,Vascular syndromes of brain in cerebrovascular diseasesSubarachnoid hemorrhage, Intracerebral hemorrhage, Other and unspecified intracranial hemorrhage, Occlusion and stenosis of precerebral arteries, Occlusion of cerebral arteries, Transient cerebral ischemia, Acute, but ill-defined, cerebrovascular disease, Other and ill-defined cerebrovascular disease, Late effects of cerebrovascular disease
      Liver disease1, 26, 816B15, B16, B17, B18, B19, K70, K71, K72, K73, K74, K75, K76, K77, I85, I86.4, Z94.4, R18070, 570, 571, 572, 573, 456.0, 456.1, 456.2, 456.8, V42.7, 789.5Acute hepatitis A, Acute hepatitis B, Other acute viral hepatitis, Chronic viral hepatitis, Unspecified viral hepatitis, Alcoholic liver disease, Toxic liver disease, Hepatic failure, not elsewhere classified, Chronic hepatitis, not elsewhere classified, Fibrosis and cirrhosis of liver, Other inflammatory liver diseases, Other diseases of liver, Liver disorders in diseases classified elsewhere, Esophageal varices, Gastric varices, Liver transplant status, AscitesViral hepatitis, Acute and subacute necrosis of liver, Chronic liver disease and cirrhosis, Liver abscess and sequelae of chronic liver disease, Other disorders of liver, Esophageal varices with bleeding, Esophageal varices without mention of bleeding, Esophageal varices in diseases classified elsewhere, Varices of other sites, Liver replaced by transplant, Ascites
      Dementia1, 29F01, F02, F03, F10.27, F10.97, G30, G31290, 291.1, 291.2, 294.1, 294.2, 331.0, 331.1, 331.2, 331.7, 331.9Vascular dementia, Dementia in other diseases classified elsewhere, Unspecified dementia, Alcohol dependence with alcohol-induced persisting dementia, Alcohol use, unspecified with alcohol-induced persisting dementia, Alzheimer's disease, Other degenerative diseases of nervous system, not elsewhere classifiedDementias, Alcohol-induced persisting amnestic disorder, Alcohol-induced persisting dementia Dementia in conditions classified elsewhere, Dementia, unspecified, Alzheimer's disease, Frontotemporal dementia, Senile degeneration of brain, Cerebral degeneration in diseases classified elsewhere, Cerebral degeneration, unspecified
      Other neurological conditions which may affect respiratory system (i.e. motor neurone disease, myasthenia gravis, Parkinson's disease, cerebral palsy, progressive cerebellar disease)1, 2G11, G12.2, G20, G21, G70.0, G80332, 334, 335.2, 343, 358.0Hereditary ataxia, Motor neuron disease, Parkinson's disease, Secondary parkinsonism, Myasthenia gravis, Cerebral palsyParkinson's disease, Spinocerebellar disease, Motor neuron disease, Infantile cerebral palsy, Myasthenia gravis
      Chronic kidney disease or renal impairment/failure1, 2, 3, 56, 7, 8, 9I12, I13, N17, N18, N19, Z49, Z94.0, Z99.2, K76.7, R94.4403, 404, 584, 585, 586, V42.0, V45.11, V56, 572.4, 794.4Hypertensive chronic kidney disease, Hypertensive heart and chronic kidney disease, Acute kidney failure, Chronic kidney disease (CKD), Unspecified kidney failure, Encounter for care involving renal dialysis, Kidney transplant status, Dependence on renal dialysis, Hepatorenal syndrome, Abnormal results of kidney function studiesHypertensive chronic kidney disease, Hypertensive heart and chronic kidney disease, Acute kidney failure, Chronic kidney disease (CKD), Renal failure, unspecified, Kidney replaced by transplant, Renal dialysis status, Encounter for dialysis and dialysis catheter care, Hepatorenal syndrome, Nonspecific abnormal results of function study of kidney
      Organ transplant1Z94, T86V42, 996.8Transplanted organ and tissue status, Complications of transplanted organs and tissueOrgan or tissue replaced by transplant, Complications of transplanted organ
      Spleen disease1D73, Q89.0289.4, 289.5 759.0Diseases of spleen, Congenital absence and malformations of spleenHypersplenism, Other diseases of spleen, Anomalies of spleen
      Rheumatoid Arthritis1M05, M06714.0, 714.1, 714.2Rheumatoid arthritis with rheumatoid factor, Other rheumatoid arthritis,Rheumatoid arthritis, Felty's syndrome, Other rheumatoid arthritis with visceral or systemic involvement
      Systemic lupus erythematosus1M32, L93, H01.12710.0, 695.4, 373.34Systemic lupus erythematosus (SLE), Lupus erythematosus, Discoid lupus erythematosus of eyelidSystemic lupus erythematosus, Lupus erythematosus ,Discoid lupus erythematosus of eyelid
      Psoriasis1L40696.0, 696.1, 696.2, 696.8PsoriasisPsoriatic arthropathy, Other psoriasis, Parapsoriasis, Other psoriasis and similar disorders
      Other immunological conditions (includes Rheumatoid arthritis, SLE, Psoriasis)1(See above)(See above)(See above)(See above)
      Obesity2, 3, 5910, 13, 16E66.0, E66.1, E66.2, E66.8, E66.9, Z68.3, Z68.4278.00, 278.01, 278.03, V85.3, V85.4Obesity due to excess calories, Drug-induced obesity, Morbid (severe) obesity with alveolar hypoventilation, Other obesity, Obesity, unspecified, Body mass index [BMI] 30-39, adult, Body mass index [BMI] 40 or greater, adultObesity, unspecified, Morbid obesity, Obesity hypoventilation syndrome, Body Mass Index between 30-39, adult, Body Mass Index 40 and over, adult
      HIV5B20, Z21, B97.35042, V08, 079.53Human immunodeficiency virus [HIV] disease, Asymptomatic human immunodeficiency virus [HIV] infection status, Human immunodeficiency virus, type 2 [HIV 2] as the cause of diseases classified elsewhereHuman immunodeficiency virus [HIV] disease, Asymptomatic human immunodeficiency virus [HIV] infection status, Human immunodeficiency virus, type 2 [HIV-2]
      One or more comorbidities11, 12, 15N/A
      1. Williamson, E.J., Walker, A.J., Bhaskaran, K. et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature 584, 430–436 (2020).
      2. Docherty AB, Harrison EM, Green CA, et al. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ. 2020 May 22;369:m1985.
      3. Petrilli CM, Jones SA, Yang J, et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ. 2020 May 22;369:m1966.
      4. Cummings MJ, Baldwin MR, Abrams D, et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study. Lancet. 2020 Jun 6;395(10239):1763-1770
      5. Argenziano MG, Bruce SL, Slater CL, et al. Characterization and clinical course of 1000 patients with coronavirus disease 2019 in New York: retrospective case series. BMJ. 2020 May 29;369:m1996.
      6. Figliozzi S, Masci PG, Ahmadi N, et al. Predictors of adverse prognosis in COVID-19: A systematic review and meta-analysis. Eur J Clin Invest. 2020 Oct;50(10):e13362.
      7. Ssentongo P, Ssentongo AE, Heilbrunn ES, Ba DM, Chinchilli VM. Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: A systematic review and meta-analysis. PLoS One. 2020 Aug 26;15(8):e0238215.
      8. Khan MMA, Khan MN, Mustagir MG, Rana J, Islam MS, Kabir MI. Effects of underlying morbidities on the occurrence of deaths in COVID-19 patients: A systematic review and meta-analysis. J Glob Health. 2020 Dec;10(2):020503.
      9. Izcovich A, Ragusa MA, Tortosa F, et al. Prognostic factors for severity and mortality in patients infected with COVID-19: A systematic review. PLoS One. 2020 Nov 17;15(11):e0241955.
      10. Louapre C, Collongues N, Stankoff B, et al. Clinical characteristics and outcomes in patients with coronavirus disease 2019 and multiple sclerosis. JAMA Neurol. 2020;77:1079-1088.
      11. Sormani MP, De Rossi N, Schiavetti I, et al. Disease-modifying therapies and coronavirus disease 2019 severity in multiple sclerosis. Ann Neurol. 2021;89:780-789.
      12. Loonstra FC, Hoitsma E, van Kempen ZL, Killestein J, Mostert JP. COVID-19 in multiple sclerosis: The Dutch experience. Mult Scler. 2020 Sep;26(10):1256-1260.
      13. Parrotta E, Kister I, Charvet L, et al. COVID-19 outcomes in MS: Observational study of early experience from NYU Multiple Sclerosis Comprehensive Care Center. Neurol Neuroimmunol Neuroinflamm. 2020 Jul 9;7(5):e835.
      14. Chaudhry F, Bulka H, Rathnam AS, et al. COVID-19 in multiple sclerosis patients and risk factors for severe infection. J Neurol Sci. 2020 Nov 15;418:117147.
      15. Zabalza A, Cárdenas-Robledo S, Tagliani P, et al. COVID-19 in multiple sclerosis patients: susceptibility, severity risk factors and serological response. Eur J Neurol. 2020 Dec 19.
      16. Salter A, Fox RJ, Newsome SD, et al. Outcomes and risk factors associated with SARS-CoV-2 infection in a North American registry of patients with multiple sclerosis. JAMA Neurol. 2021;78:699-708.

      Appendix C

      Table C1.
      Table C1Estimation of the MDRR for proposed statistical analyses
      TargetComparatorOutcomeTarget YearsComparator YearsTarget EventsComparator EventsMDRR
      OcrelizumabDimethyl FumarateCOVID-19 Hospitalization2,9861,93422<112.80
      OcrelizumabDimethyl FumarateCOVID-19 Diagnosis3060197381431.67
      MDRR=minimum detectable relative risk

      Appendix D

      Model performance statistics indicate that the subsequent addition of MS characteristics and individual comorbidities to an age and gender model resulted in increases in the concordance index for predicting COVID-19 hospitalization, indicating that these variables add additional prognostic information on probability of the COVID-19 hospitalization occurring. Furthermore, replacing the individual comorbidities with a simple summary measure of comorbidity (i.e. ≥1 Comorbidity etc.) led to a reduction in the concordance index, indicating that individual comorbidities provide additional prognostic information on the risk of these outcomes above a simple summary measure (Table D.1).
      Table D1Model performance statistics for each statistical model
      ModelCOVID-19 Hospitalization
      ConcordanceSE
      Age, gender0.6720.022
      Age, gender, demographics, MS Characteristics0.7990.019
      Fully adjusted (Individual Comorbidities)0.8280.019
      Fully adjusted (≥1 Comorbidity)0.8080.019
      Fully adjusted (≥2 Comorbidity)0.8150.019
      Fully adjusted (≥3 Comorbidity)0.8130.019
      SE=Standard Error

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