Development of a Prediction Model for the Risk of Hospitalized Infection in Patients With Chronic Inflammatory Arthritis Treated With Biological Drugs
Conditions: Rheumatoid Arthritis, Spondyloarthritis, Psoriatic Arthritis
Sponsor: Simon Krabbe

Predicting Hospitalized Infection in Patients With Chronic Inflammatory Arthritis Treated With Biological Drugs, April 5, 2018 
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PROTOCOL 
 
Official Title 
Development of a Prediction Model for the Risk of Hospitalized Infection in Patients With Chronic Inflammatory 
Arthritis Treated With Biological Drugs 
Brief Title 
Predicting Hospitalized Infection in Patients With Chronic Inflammatory Arthritis Treated With Biological Drugs 
Authors 
Simon Krabbe1,2, Kathrine Grøn1,2, Bente Glintborg1, Mette Nørgaard3, Frank Mehnert3, Dorte Ejg Jarbøl4, Nina Friis-
Møller5, Mikkel Østergaard1,2, Merete Lund Hetland1,2 
Affiliations 
1 Copenhagen Center for Arthritis Research, Center for Rheumatology and Spine Diseases, Rigshospitalet 
2 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen 
3 Department of Clinical Epidemiology, Aarhus University 
4 Department of Public Health, University of Southern Denmark 
5 Department of Infectious Diseases, Zealand University Hospital 
Study Registration 
The study will be registered on clinicaltrials.gov. 
Conflicts of Interests 
B.G. reported receiving funding from AbbVie and Biogen. All other authors report no relevant conflicts of interest with 
regards to this study. 
Ethics Committee 
This study does not require approval by Ethics Committee. 
Data Approval 
Approval to use data has been obtained from The Capital Region of Denmark (RH-2015-209). 
Timeline 
The merged dataset is expected to be ready for analysis in June 2018. The manuscript is expected to be submitted for 
publication in a peer-reviewed journal in December 2018. 
Contributors 
The protocol was drafted by S.K. and all authors critically revised it. All statistical analyses will be performed by S.K. The 
project was conceived by S.K., D.E.J., N.F.M., B.G. and M.L.H. 
Acknowledgement 
We thank Niels Steen Krogh, ZiteLab, for extracting the necessary data from DANBIO and all the Danish departments of 
rheumatology that report to DANBIO. 
 
 
 
 

Predicting Hospitalized Infection in Patients With Chronic Inflammatory Arthritis Treated With Biological Drugs, April 5, 2018 
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Brief Summary 
Background 
The risk for hospitalized infection (i.e. infection leading to hospitalization) in patients with inflammatory arthritis 
(rheumatoid arthritis (RA), psoriatic arthritis (PsA) or axial spondyloarthritis (axSpA) treated with biological drugs is 
known to be increased compared to the background population. In daily clinical practice, there is a need for a simple 
way to assess the absolute risk for hospitalized infection in individual patients based on easily available information 
such as age, diagnosis, functional status, comorbidities and medication. This risk estimate will be useful in clinical 
decision making e.g. when advising patients on whether or not to initiate biologic therapy or when advising patients on 
influenza or pneumococcal vaccination. 
 
Objectives 
The objectives are 1) to assess the risk for hospitalized infection (infection leading to hospitalization) in patients with 
inflammatory arthritis during 12 months of follow-up after initiating treatment with their first biological drug (bDMARD) 
with the risk in the general population, and 2) to develop a simple, clinically useful algorithm that allows prediction of 
the risk of hospitalized infection in individual patients. 
 
Methods 
Observational cohort study based on existing data in: The Danish Rheumatology Register (DANBIO), The Danish 
National Patient Register, The Danish National Prescription Register and The Danish Register of Causes of Death. All 
patients registered in DANBIO with RA, PsA or axSpA who initiated treatment with their first biological drug between 
January 1, 2006 and December 31, 2016 will be identified. Baseline predictors and outcomes (hospitalized infection or 
death) during 12 months of follow-up are obtained. Logistic regression analysis and 10-fold cross-validation will be used 
to develop and internally validate the prediction model. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Introduction 
The risk of hospitalized infection in patients with inflammatory arthritis, here defined as rheumatoid arthritis (RA), 
psoriatic arthritis (PsA) or axial spondyloarthritis (axSpA), may be increased due to the immunosuppressive treatments. 
The seriousness of hospitalized infections is reflected by increased mortality. RA patients have an increased mortality 
compared to the general population, with more deaths caused by infections1,2. Similar results have been found in 
axSpA3,4, whereas a single study in PsA found an infection-related mortality at the level of the general population5. 
In most studies, severe infection has been defined as hospitalized infection (i.e. infection leading to 
hospitalization). In patients with unselected inflammatory polyarthritis6 and in patients with RA7,8 a doubled risk of 
hospitalized infection was found compared to that of the general population. Risk of hospitalized infections varies 
across studies and across indications. For RA, the risk of severe infection according to the British Society for 
Rheumatology Biologics Register9, the Swedish ARTIS Register10, the German RABBIT Register11 and the Canadian 
Ontario Health Insurance Plan12 have all been in the range of 2-5 events per 100 patient years. Patients with axSpA had 
1-2 severe infections per 100 patient years13; these patients were younger than in the RA studies. In one study of 
patients with PsA, the risk of opportunistic infections was higher than in the general population14, but we are not aware 
of population-based observational studies of the overall risk of hospitalized infections in patients with PsA. 
A model for the individual RA patient’s risk of hospitalized infection based on age, functional status, 
selected comorbidities, glucocorticoid dose, and treatment has been developed based on data from the Germany 
RABBIT register11. This model has later been validated in the same registry during a later time period15. In routine care, 
such a prediction model would potentially be useful when advising the patient on vaccination. It has been suggested to 
vaccinate all patients with chronic inflammatory arthritis against influenza and pneumococcal disease16, but in routine 
care vaccination uptake is low and it is costly to vaccinate all patients. The benefit for each patient depends on the 
absolute risk that the patient, if not vaccinated, would get the infectious disease. Thus, a more targeted approach of 
vaccinating only patients of moderate to high risk may be more cost-effective. This may also be assist in the decision 
making of whether to start a biological disease-modifying anti-rheumatic drug (bDMARD) in patients who are at high 
risk for hospitalized infection. 
The RABBIT risk score has some potential drawbacks. Diabetes and cardiovascular diseases are common 
comorbidities that increase the risk of hospitalized infections in patients with chronic inflammatory arthrits17,18, and 
smoking is also a predictor for hospitalized infections6,9, but these variables were not included in the development of 
the RABBIT risk score. Also, when the age-related risk of hospitalized infections is modelled as a dichotomous variable 
using 60 years as cut-off, much predictive information based on different ages within the two broad age groups is likely 
to be lost. 
The objectives are 1) to compare the risk for hospitalized infection in patients with inflammatory 
arthritis during 12 months of follow-up after initiating treatment with their first bDMARD with the risk in the general 
population, 2) to investigate if infection risk varies across diagnoses (RA/PsA/axSpA), and 3) to develop a simple, 
clinically useful algorithm that allows prediction of the risk of hospitalized infection in individual patients. 
 
 
 

Predicting Hospitalized Infection in Patients With Chronic Inflammatory Arthritis Treated With Biological Drugs, April 5, 2018 
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Methods 
Study design 
This is an observational registry-based study using data from the following Danish nationwide registries: The Danish 
Rheumatology Register (DANBIO)19 (clinical data for patients with inflammatory rheumatic diseases), The Danish 
National Patient Register20 (International Classification of Diseases ICD-10 codes for all contacts to hospitals), The 
Danish National Prescription Registry21, and The Danish Register of Causes of Death22.  
 
Study population 
We will identify three cohorts of patients in DANBIO with RA, SpA and PsA, respectively, who started treatment with 
the first bDMARD between January 1, 2006 and December 31, 2016 in Denmark. Patients will be identified based on the 
diagnosis recorded in DANBIO, which is known to be of high validity23. Index date is the date of start of first bDMARD. 
Inclusion criteria: 
- 
Patients with RA: Registered in DANBIO with a diagnosis of M05.9, M06.0 or M06.9. 
- 
Patients with SpA: Registered in DANBIO with a diagnosis of M45.9, M46.1, M46.8+M02.9, M46.8+M07.4, 
M46.8+M07.5 or M46.9. 
- 
Patients with PsA: Registered in DANBIO with a diagnosis of M07.3 or M46.8+M07.2. 
- 
First bDMARD treatment course. 
- 
Start of treatment with first bDMARD in the period January 1, 2006 to December 31, 2016. 
- 
Age at start of treatment with first bDMARD ≥ 18 years. 
Exclusion criteria: 
- 
Not followed in DANBIO since start of first bDMARD. 
 
Control group 
For each cohort, a set of matched controls from the general population will be obtained, so that outcomes in each 
diagnosis group can be compared with its own matched controls. Therefore, 3 groups of matched controls are 
constructed. Ten controls from the general population will be drawn for each patient matched by age, sex and postal 
code (replacement is allowed). Index date equal to date of start of first bDMARD. At the index date, controls must not 
have or have had one the diagnoses of RA, SpA or PsA listed above. Baseline variables (predictors) and outcomes (for 
definitions and details, see below) will be collected in the same time periods for each individual patient and his/her 10 
matched controls. 
 
Exposure and follow-up time 
For patients, the index date is the start date of the first bDMARD: abatacept, adalimumab, anakinra, certolizumab, 
etanercept, golimumab, infliximab, rituximab, secukinumab tocilizumab or ustekinumab. End of follow-up is defined as 
12 months after the index date for each patient, regardless of whether the bDMARD is discontinued during this period 
or not. This reflects the clinical situation: When the decision to start treatment with bDMARD is made, it is not known if 
the patient will continue or discontinue the bDMARD. 
 

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Baseline variables (predictors) 
Baseline variables are collected from three registries: The Danish National Patient Register, The Danish National 
Prescription Registry, and DANBIO. The baseline variables include all variables in the RABBIT risk score15, as well as 
other important predictors previously identified in other studies as predictors of severe infections17,18. Included 
variables should be readily available for a prediction algorithm applied in routine care. A few remarks on selected 
variables: 
 
- 
Index of disease activity in patients with RA: Disease Activity Score 28 Joint Count (DAS28) 
- 
Index of disease activity in patients with PsA: Disease Activity Index for Psoriatic Arthritis 28 Joint Count 
(DAPSA28, a modified version of DAPSA based on 28 joints instead of 66/68 joints) 
- 
Indices of disease activity in patients with axSpA: Ankylosing Spondylitis Disease Activity Score (ASDAS) and 
Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) 
- 
Diabetes yes/no: Subjects are defined as having diabetes if fulfilling the codes (Table 1) in the National Patient 
Register and/or having ≥2 separate purchases of drugs used in diabetes from baseline and 5 years back 
according to the Danish National Prescription Registry21 (Table 2). This follows the algorithm in the Danish 
National Diabetes Register24, except that patients with only multiple blood sugar measurements or a referral 
to podiatric care will not be included. 
- 
Lung disease yes/no: Subjects are defined as having asthma, chronic obstructive pulmonary disease, interstitial 
pulmonary disease or bronchiectasis if fulfilling the codes in The Danish National Patient Register (Table 1) or 
having ≥2 separate purchases of drugs for obstructive airway diseases from baseline and 5 years back 
according to The Danish National Prescription Register (Table 2). This is in line with the approach used in the 
Danish National Database for Asthma25, except that we allow age >44 years and do not discriminate between 
asthma and COPD, as this was not deemed possible in this context. Therefore all diagnoses of obstructive 
airway diseases in The Danish National Patient Registry, as well as interstitial lung disease and bronchiectasis, 
will be included. 
- 
Systemic or intraarticular glucocorticoids yes/no: Data on glucocorticoids will be merged from The Danish 
National Prescription Register (Table 2) and DANBIO (Table 3) into a single variable that indicates whether the 
patient has received more than a trivial amount of glucocorticoids, i.e. one of the following: A) injection at ≥2 
separate dates from baseline and 12 months back and >2 mL in total, B) use of oral glucocorticoids registered 
in DANBIO from baseline and 12 months back, or C) ≥2 purchases of prednisolone for systemic use from 
baseline and 12 months back. 
 
Further details of all baseline variables for patients and matched controls are shown in Tables 1-3.  
 
 
 
 
 
 

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Table 1. Baseline variables identified in The Danish National Patient Register (patients and matched controls) 
 
Variable 
Description 
Comment 
Values 
Malignancy, within 10 
years 
C00-C43, C45-C96 
Time: from baseline and 10 
years back 
yes/no 
Malignancy, ever 
C00-C43, C45-C96 
Time: At any time before 
baseline 
yes/no 
Hospitalized infection 
within 5 years 
A00-B99, D73.3, E06.0, E32.1, G00-G02, G04.2, G05-
G07, H00.0, H44.0, H60.0-H60.3, H66-H67, H70, I30.1, 
I40.0, J00-J22, J32, J34.0, J36, J38.3, J39.0-J39.1, J44.0, 
J85, J86, K04.4, K04.6, K04.7, K10.2,  K11.3, K12.2, 
K14.0, K57.0, K57.2, K57.4, K57.8, K61, K63.0, K65.0, 
K65.1, K65.2, K65.9, L00-L08, L30.3, M00-M01, M46.2-
M46.5, M60.0, M65.0, M71.0, M71.1, M72.6, M86, 
N10, N11, N12, N13.6, N15.1, N15.9, N30.0, N30.8, 
N34.0, N39.0, N41.2, N43.1, N45.2, N45.3, N45.4, 
N48.2, N61, N70, N73, N75.1, O23, O26.4, O41.1, 
O75.3, O85, O86, O88.3, O91, O98 
Time: from baseline and 5 
years back 
yes/no 
Knee or hip prosthesis 
within 5 years 
NGB, NFB 
Time: from baseline and 5 
years back 
yes/no 
Lung disease within 5 
years 
J41-J45, J47, J84.1, J84.9 
Time: from baseline and 5 
years back 
yes/no 
Diabetes within 5 
years 
E10-E14, O24 
Time: from baseline and 5 
years back 
yes/no 
Myocardial infarction 
within 5 years 
I20.0, I21, I22 
Time: from baseline and 5 
years back 
yes/no 
Chronic kidney disease 
within 5 years 
I12, I13, N00-N05, N07, N11, N18-N19, Q61 
Time: from baseline and 5 
years back 
yes/no 
Inflammatory bowel 
disease within 5 years 
K50, K51 
Time: from baseline and 5 
years back 
yes/no 
International Classification of Diseases ICD-10 codes for all diseases listed. For surgical procedures (knee or hip prosthesis), the Nordic 
Medico-Statistical Committee (NOMESCO) Classification of Surgical Procedures is used. 
 
 
 
Table 2. Baseline variables identified in The Danish National Prescription Register (patients and matched controls) 
 
Variable 
Description 
Comment 
Value 
Drugs used in diabetes 
ATC: A10 
Time: ≥2 separate purchases 
from baseline and 5 years 
back 
yes/no 
Drugs for obstructive 
airway diseases 
ATC: R03 
Time: ≥2 separate purchases 
from baseline and 5 years 
back 
yes/no 
Corticosteroids for 
systemic use at 
baseline 
ATC: H02 
Time: ≥2 separate purchases 
from baseline and 12 months 
back 
yes/no 
 
 
 
Table 3. Baseline variables from DANBIO (only patients) 
 
Variable 
Description 
Comment 
Value 
Social security number 
 
 
 
Gender 
 
 
male/female 
Specific diagnosis 
code 
ICD-10 Code in DANBIO 
 
 
Date of start of 
symptoms 
 
 
Date (MM/YYYY) 
Date of diagnosis 
 
 
Date (MM/YYYY) 
bDMARD – drug name 
Orencia/Abatacept, Humira/Adalimumab, 
Kineret/Anakinra, Cimzia/Certolizumab pegol, 
Enbrel/Etanercept, Benepali/Etanercept, 
Simponi/Golimumab, Remicade/Infliximab, 
Inflectra/Infliximab, Remsima/Infliximab, 
First biological drug 
Character 
(”abatacept”, 
”adalimumab”, 
”anakinra”, 
”certolizumab”, 

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MabThera/Rituximab, Cosentyx/Secukinumab, 
RoActemra/Tocilizumab, Stelara/Ustekinumab 
”etanercept”, 
”golimumab”, 
”infliximab”, 
”rituximab”, 
”secukinumab”, 
”tocilizumab”, 
”ustekinumab”) 
bDMARD – start date 
 
First biological drug 
Date 
(DD/MM/YYYY) 
csDMARD – drug 
name 
Methotrexate, sulphasalazine, hydroxychloroquine, 
leflunomide 
 
Only  if csDMARD was given 
at baseline 
Character 
(“methotrexate”, 
“sulphasalazine”, 
“hydroxychloroqu
ine”, 
“leflunomide”) 
csDMARD – start date 
 
Only if csDMARD was given 
at baseline 
Date 
(DD/MM/YYYY) 
Oral glucocorticoids 
 
Time: from baseline and 12 
months back 
yes/no 
Glucocorticoid 
injections 
(intramuscular or 
intraarticular) 
 
Time: injection at ≥2 
separate dates from baseline 
and 12 months back and >2 
mL in total 
yes/no 
Health Assessment 
Questionnaire (HAQ) 
 
At baseline (as close to 
baseline as possible, up to 12 
months before is allowed) 
Numeric (range 0-
3) 
C-reactive protein 
 
Time: from baseline and 90 
days back or 6 days after 
 
Disease activity 
Separate columns for DAS28, DAPSA28, ASDAS, BASDAI 
Time: from baseline and 90 
days back or 6 days after 
Numeric 
Smoking status 
 
At baseline (first, this should 
be searched back in time 
from baseline and data 
closest to baseline should be 
used; if no data are found 
back in time, data on 
smoking status will be 
searched from baseline and 
onwards) 
Character 
(“current”, 
“occasionally”, 
“previous”, 
“never”) 
Number of csDMARD 
treatment failures 
Number of previously received DMARDs that the 
patient no longer receives at baseline (stop date at 
baseline date or before). If a patient switches from oral 
to subcutaneous methotrexate it is not counted as a 
treatment failure. If a patient stops a drug and starts it 
again within ≤3 months, the first stop is not counted as 
a treatment failure; however, if the patient received 
another csDMARD in the intervening period, the first 
treatment period will count as a failure. The patient 
can only fail each drug once. 
Before baseline 
Integer 
 
Outcomes 
Hospitalized infections are defined as hospitalization caused by infection according to The Danish National Patient 
Register (Table 4). Overall mortality and cause-specific mortality are identified in The Danish Register of Causes of 
Death (Table 5). The risk of hospitalized infection, overall mortality and mortality caused by infection will be calculated 
for the 12 months of follow-up after start of treatment with the first biological DMARD. Each patient will be counted as 
either having an event (hospitalized infection and/or death from any cause) or not during the 12 month of follow-up. 
The number of deaths is expected to be very low. 
 
 
 

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Table 4. Outcome variables identified in National Patient Register (patients and matched controls) 
 
Variable 
Description 
Comment 
Values 
Hospitalized infections 
during 12 months of 
follow-up 
A00-B99, D73.3, E06.0, E32.1, G00-G02, G04.2, G05-
G07, H00.0, H44.0, H60.0-H60.3, H66-H67, H70, I30.1, 
I40.0, J00-J22, J32, J34.0, J36, J38.3, J39.0-J39.1, J44.0, 
J85, J86, K04.4, K04.6, K04.7, K10.2,  K11.3, K12.2, 
K14.0, K57.0, K57.2, K57.4, K57.8, K61, K63.0, K65.0, 
K65.1, K65.2, K65.9, L00-L08, L30.3, M00-M01, M46.2-
M46.5, M60.0, M65.0, M71.0, M71.1, M72.6, M86, 
N10, N11, N12, N13.6, N15.1, N15.9, N30.0, N30.8, 
N34.0, N39.0, N41.2, N43.1, N45.2, N45.3, N45.4, 
N48.2, N61, N70, N73, N75.1, O23, O26.4, O41.1, 
O75.3, O85, O86, O88.3, O91, O98 
Visits to emergency room 
(Danish: “skadestue”) are 
not included. 
 
If a patient is discharged 
from hospital and is at home 
one night, it counts as one 
hospitalization. If the patient 
is hospitalized the day after, 
it counts as a new 
hospitalization. 
 
Patients that are seen as 
outpatients (Danish: 
“ambulante kontakter”) are 
not included. 
All primary and 
contributory 
diagnoses (ICD-10 
codes), admission 
and discharge 
dates for all 
hospitalizations 
with infection as 
primary diagnosis 
during 12 months 
of follow-up. If 
the primary 
diagnosis is RA, 
SpA or PsA, then 
infection should 
also be searched 
for in the 
secondary 
diagnoses. 
 
 
 
Table 5. Outcome variables identified in The Danish Register of Causes of Death (patients and matched controls) 
 
Variable 
Description 
Comment 
Values 
Cause of death 
Information on all deaths 
 
As appropriate 
Mortality is ascribed to infection if any of the diagnoses in Table 4 are entered on the death certificate in any data field (direct cause, 
underlying cause, or contributing cause). 
 
 
Statistical analysis 
The expected number of patients to be included is 7,500. The expected number of patients with at least one 
hospitalized infection during the 12 months of follow-up is 300, based on incidence rates previously published. 
The number of participants with hospitalized infection or death during the 12 months of follow-up will 
be compiled for each diagnosis group (RA, SpA, PsA) stratified by age. Age will be categorized as <40 years, 40-49 years, 
50-59 years, 60-69 years, ≥70 years (this may need to be adapted based on the observed number of events). 
The crude risk of hospitalized infections will be calculated in the 6 groups (RA-bDMARD, SpA-bDMARD, 
PsA-bDMARD and the 3 groups of matched controls) stratified by age as the 12-month risk of any hospitalized infection 
with 95% confidence intervals. The risk in each patient group, stratified by age, will be compared to the risk for its 
matched controls as risk difference (absolute risk) and risk ratio (relative risk). 
Multivariable logistic regression will be performed to develop the prediction model. Missing values of 
predictor variables will be imputed after an analysis of possible patterns of missing data. Unless otherwise indicated, 
multiple imputation by chained equations (MICE) will be performed under the assumption that data are missing at 
random. Baseline age and comorbidities (yes/no, as described in methods section above: malignancy, previous 
hospitalized infection, knee or hip prosthesis, lung disease, diabetes, myocardial infarction, chronic kidney disease and 
inflammatory bowel disease), will be included in the model. We aim to develop a simple prediction algorithm for the 
risk of hospitalized infection based on the results from the stratified analysis and the multivariable regression analysis. 

Predicting Hospitalized Infection in Patients With Chronic Inflammatory Arthritis Treated With Biological Drugs, April 5, 2018 
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The model form will be built gradually and improved based on the data. The performance will be evaluated by the 
overall explained variation, ROC curve and calibration plot, and it will be internally validated using 10-fold cross-
validation. The regression formula of the final model and, possibly, a simple presentation in the form of a score chart or 
a table with predictions will be provided. 
If the number of hospitalized infections is sufficiently large, an analysis of different sites of infection 
may be performed (e.g. pneumonia/respiratory tract infections, skin/subcutaneous tissue/musculoskeletal system 
infection, genitourinary tract infections, infections of the gastrointestinal tract, sepsis, and other infections). 
Complete case analysis (patients with no missing data that have to be imputed) will be performed as 
sensitivity analysis. It is possible that the case mix of patients treated with bDMARD may change over time, and a 
sensitivity analysis of the effect of calendar years will be performed. 
It is possible that separate prediction models are needed for each of the 3 disease groups. However, if 
no major differences exist between the infection risks in the 3 disease groups across age, comorbidities and 
glucocorticoid use, a single model will be chosen. 
 
Data flow 
This figure shows the data flow to create the final merged dataset. 
 
 
 
 
 
 
 
 
 
 

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Results 
During data analysis, Tables 6-11 will be produced. 
 
 
 
Table 6. Baseline characteristics (separate tables for RA patients, SpA patients and PsA patients) 
 
 
 
Rheumatoid arthritis 
General population 
 
 
Available 
data 
 
Available 
data 
 
Number of patients/controls 
Number 
 
 
 
 
Age, years 
Median, IQR 
 
 
 
 
Male/female 
%/% 
 
 
 
 
Duration of symptoms, years 
Median, IQR 
 
 
NA 
NA 
Disease duration (after diagnosis), years 
Median, IQR 
 
 
NA 
NA 
Methotrexate 
% 
 
 
NA 
NA 
Other csDMARDs (sulfasalazine, hydroxychloroquine, 
leflunomide) 
% 
 
 
NA 
NA 
Oral glucocorticoids for systemic use at baseline 
% / Median, IQR 
 
 
 
 
Injections of glucocorticoids for systemic use at baseline 
% / Median, IQR 
 
 
NA 
NA 
TNF inhibitor/other bDMARD 
%/% 
 
 
NA 
NA 
Number of previous DMARD treatment failures 
% 
 
 
NA 
NA 
Smoking status: Current/Occasionally/Previous/Never 
%/%/%/% 
 
 
NA 
NA 
Health Assessment Questionnaire (HAQ) 
Median, IQR 
 
 
NA 
NA 
Malignancy, within 10 years 
% 
 
 
 
 
Malignancy, ever 
% 
 
 
 
 
Hospitalized infection, within 5 years 
% 
 
 
 
 
Knee or hip prosthesis, within 5 years 
% 
 
 
 
 
Lung disease, within 5 years 
% 
 
 
 
 
Diabetes, within 5 years 
% 
 
 
 
 
Myocardial infarction, within 5 years 
% 
 
 
 
 
Chronic kidney disease, within 5 years 
% 
 
 
 
 
Inflammatory bowel disease, within 5 years 
% 
 
 
 
 
 
 
 
Table 7. Risk of hospitalized infection by disease 
 
 
RA 
SpA 
PsA 
Matched 
controls 
for 
RA patients 
Matched 
controls 
for 
SpA patients 
Matched 
controls 
for 
PsA patients 
No. of patients/controls 
 
 
 
 
 
 
No. of patients/controls with at 
least one hospitalized infection, 
n 
 
 
 
 
 
 
Risk of hospitalized infection 
(95% CI) 
 
 
 
 
 
 
Risk difference1 
 
 
 
NA 
NA 
NA 
Risk ratio1 
 
 
 
NA 
NA 
NA 
1Compared to the general population, matched for age, sex and postal code. 
 
 
 
Table 8. Risk of hospitalized infection by age (separate tables for RA patients, SpA patients and PsA patients) 
 
 
<40 years 
40-50 years 
50-60 years 
60-70 years 
>70 years 
No. of patients 
 
 
 
 
 
No. of patients with at least 
one hospitalized infection, n 
 
 
 
 
 
Risk of hospitalized 
infection (95% CI) 
 
 
 
 
 
Risk difference1 
 
 
 
 
 
Risk ratio1 
 
 
 
 
 
1Compared to the general population, matched for age, sex and postal code. 

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Table 9. Risk of hospitalized infection by comorbidities (separate tables for RA patients, SpA patients and PsA patients) 
 
 
No comorbidities 
1 comorbidity 
2 or more comorbidities 
No. of patients 
 
 
 
No. 
of 
patients 
with 
at 
least 
one 
hospitalized infection, n 
 
 
 
Risk of hospitalized infection (95% CI) 
 
 
 
Risk difference1 
 
 
 
Risk ratio1 
 
 
 
Comorbidities are: Malignancy, previous hospitalized infection, knee or hip prosthesis, lung disease, diabetes, myocardial infarction, chronic 
kidney disease and inflammatory bowel disease. 1Compared to the general population, matched for age, sex and postal code. 
 
 
 
Table 10. Risk of hospitalized infection by glucocorticoid use (separate tables for RA patients, SpA patients and PsA patients) 
 
 
No glucocorticoids 
Glucocorticoids* 
No. of patients 
 
 
No. 
of 
patients 
with 
at 
least 
one 
hospitalized infection, n 
 
 
Risk of hospitalized infection (95% CI) 
 
 
Risk difference1 
 
 
Risk ratio1 
 
 
* One of the following: A) injection at ≥2 separate dates from baseline and 12 months back and >2 mL in total, B) use of oral glucocorticoids 
registered in DANBIO from baseline and 12 months back, C) ≥2 purchases of corticosteroids for systemic use from baseline and 12 months back. 
1Compared to the general population, matched for age, sex and postal code. 
 
 
 
Table 11. All-cause mortality and cause-specific mortality due to infection 
 
 
RA 
SpA 
PsA 
No. of patients 
 
 
 
No. of all deaths, n 
 
 
 
Mortality rate (all-cause) 
 
 
 
Standardized mortality rate (all-cause) 
 
 
 
No. of deaths due to infection, n 
 
 
 
Mortality rate (infection) 
 
 
 
Standardized mortality rate (infection) 
 
 
 
 
 
 
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