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Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. ROLE OF THE STUDY SPONSOR
  8. REFERENCES

Objective

Infection risk is increased in patients with rheumatoid arthritis (RA), and accurate assessment of the risk of infection could inform clinical decision-making. This study was undertaken to develop and validate a score to predict the 1-year risk of serious infection in patients with RA.

Methods

We studied a population-based cohort of Olmsted County, Minnesota residents with incident RA ascertained in 1955–1994 whose members were followed up longitudinally, via complete medical records, until January 2000. The validation cohort included residents with incident RA ascertained in 1995–2007. The outcome measure included all serious infections (requiring hospitalization or intravenous antibiotics). Potential predictors were examined using multivariable Cox models. The risk score was estimated directly from the multivariable model, and performance was assessed in the validation cohort using Harrell's C statistic.

Results

Among the 584 RA patients in the original cohort (72% female; mean age 57.5 years), who were followed up for a median of 9.9 years, 252 had ≥1 serious infection (646 total infections). Components of the risk score included age, previous serious infection, corticosteroid use, elevated erythrocyte sedimentation rate, extraarticular manifestations of RA, and comorbidities (coronary heart disease, heart failure, peripheral vascular disease, chronic lung disease, diabetes mellitus, alcoholism). Validation analysis revealed good discrimination (C statistic 0.80).

Conclusion

RA disease characteristics and comorbidities can be used to accurately assess the risk of serious infection in patients with RA. Knowledge of risk of serious infection in RA patients can influence clinical decision making and inform strategies to reduce and prevent the occurrence of these infections.

Patients with rheumatoid arthritis (RA) have increased age-adjusted all-cause mortality (1). In addition, a high frequency of opportunistic and common infections complicates RA and partially contributes to this increased mortality (2–7). Potential predictors of infection in RA, including comorbidities, RA disease characteristics, and medications, have been examined in previous studies (8). However, other potential risk factors for infection, such as lymphopenia and neutropenia, which occur commonly over the long disease course, have not been evaluated in RA. The risk of infection is increased by a constellation of comorbidities and is further influenced by RA disease characteristics and treatment (9–11). A risk score that accurately predicts the risk of infection among patients with RA would be useful. The purpose of this study was to develop and validate a score to predict the 1-year risk of serious infection among patients with RA.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. ROLE OF THE STUDY SPONSOR
  8. REFERENCES

Study population.

The original cohort consisted of a previously assembled inception cohort of Rochester, Minnesota residents ≥18 years of age who first fulfilled the 1987 American College of Rheumatology (ACR) criteria for RA (12) (applied retrospectively, as appropriate) between January 1, 1955 and December 31, 1994 and were followed up until January 1, 2000 (13). This cohort was assembled utilizing the resources of the Rochester Epidemiology Project, which is a medical records linkage system that allows access to the complete inpatient and outpatient medical records of study subjects from all health care providers in Olmsted County, Minnesota, including the Mayo Clinic, Olmsted Medical Center, their affiliated hospitals, and others (14, 15). For the purposes of developing an infection risk score, patients were censored at a diagnosis of cancer and patients with cancer prior to RA incidence were excluded, leaving a total of 584 RA patients in the study group. Due to the very small number and short followup (through January 1, 2000), the few patients who received biologic agents were also censored for this analysis at the time of initiation of biologic treatment.

A second cohort was used to validate the risk score. The validation cohort consisted of 464 Olmsted County residents who fulfilled the ACR criteria for RA between January 1, 1995 and December 31, 2007 and were followed up through December 31, 2008 or until the time of migration from Olmsted County or death, if prior to December 31, 2008. Patients in this cohort were also censored at a diagnosis of cancer, and after exclusion of patients with cancer prior to RA incidence, the validation cohort comprised a total of 410 RA patients. These patients were not censored at the initiation of biologic treatment.

Data collection.

In the original cohort, the complete medical records of all those who had RA were previously reviewed by trained nurse abstractors (2, 8). In the validation cohort, data collection was performed by the same abstractors using the same definitions of variables as were used with the original cohort. The data were collected from complete (inpatient and outpatient) medical records. Data collected included demographic characteristics, comorbidities, disease-related variables, and treatment (use of corticosteroids and disease-modifying antirheumatic drugs [DMARDs] as listed in Table 1, and daily dosage of corticosteroids). In the original cohort, laboratory data collection included all measured erythrocyte sedimentation rate (ESR) values, the first occurrence of leukopenia (defined as a white blood cell count of <4,000/ml on 2 or more occasions), and the first occurrence of lymphopenia (defined as a lymphocyte count of <1,500/ml on 2 or more occasions). In the validation cohort, laboratory data collection included all measured values of ESR, C-reactive protein (CRP), and leukocyte, lymphocyte, and neutrophil counts; data on use of biologic agents were also collected. Leukopenia was defined as a white blood cell count of <3.5 × 109/liter, lymphopenia as a lymphocyte count of <0.9 × 109/liter, and neutropenia as a neutrophil count of <1.7 × 109/liter. All blood cell counts were performed in the course of clinical care, predominantly to monitor medication effects, disease activity, and other conditions (e.g., anemia). Also, in the validation cohort, abnormal values on more than 1 occasion were not required, and leukopenia, lymphopenia, and neutropenia were defined based on the most recent test values at each time point throughout followup.

Table 1. Potential predictors considered in development of the risk score for serious infection
Stage of development processPotential predictors considered
Predictors included in model of serious infection risk proposed by Doran et al (8)Age, sex, alcoholism, history of leukopenia, dementia/Alzheimer's disease, diabetes mellitus, chronic lung disease, extraarticular manifestations of rheumatoid arthritis (including amyloidosis, Felty's syndrome, rheumatoid vasculitis, and rheumatoid lung disease), rheumatoid factor, functional capacity, corticosteroid use, interaction between age and diabetes mellitus
Additional predictors included in Doran et al's model of objectively confirmed infectionsErythrocyte sedimentation rate, rheumatoid nodules
Other predictors considered by Doran et alSmoking status, body mass index, disease-modifying antirheumatic drug treatment (methotrexate, azathioprine, hydroxychloroquine, sulfasalazine, intramuscular gold, oral gold, D-penicillamine, leflunomide, and cyclophosphamide)
Additional predictors considered in the original cohortCoronary heart disease, heart failure, peripheral vascular disease, presence of large joint swelling, erosions/destructive changes seen on radiography, joint surgeries, rheumatoid arthritis disease duration, calendar year, and previous serious infections
Additional predictors considered in the validation cohortLeukocytes, lymphocytes, neutrophils, C-reactive protein level, biologic treatment

Comorbidities such as chronic lung disease and alcoholism based on physician diagnosis were included in the analysis. Coronary heart disease was defined as myocardial infarction or revascularization procedures (i.e., bypass grafting, percutaneous coronary intervention). Myocardial infarctions were classified as definite or probable, based on the presence of cardiac pain, biomarker values, and the Minnesota electrocardiogram coding system (16). Heart failure was defined according to the Framingham criteria (17). Diabetes mellitus was defined as at least 2 measurements of fasting plasma glucose of ≥126 mg/dl or a 2-hour plasma glucose value of ≥200 mg/dl, or a clearly documented history of diabetes mellitus or treatment with hypoglycemic agents (18). Extraarticular manifestations of RA, i.e., amyloidosis, Felty's syndrome, rheumatoid vasculitis, and rheumatoid lung disease, were assessed.

Data were collected on all documented episodes of infection occurring after the RA incidence date as described previously (2). Minor upper respiratory tract infections and uncomplicated urinary tract infections were not included. For each episode of infection, information was collected regarding accompanying fever, leukocytosis, and findings of relevant investigations, including microbiologic culture and radiologic findings. Also recorded was whether the infection required treatment with intravenous antibiotics or hospitalization, and length of hospital stay. Serious infections were defined as infections requiring hospitalization and/or intravenous antibiotic treatment. Objectively confirmed infections were defined as infections with positive results of microbiologic culture and/or radiologic imaging.

Statistical analysis.

Descriptive statistics (means, proportions, etc.) were used to summarize the data. Characteristics that developed during followup were summarized as “ever” for descriptive purposes only. The outcome of interest was serious infection. Objectively confirmed infections were also examined, but these results were not reported because most serious infections were also objectively confirmed, and the risk models for these two outcomes were not meaningfully different.

Since our goal was to predict the risk of infection in the next year among patients with RA at any point in the disease course, each patient's followup time was divided into 1-year intervals. Covariate values were assessed at the beginning of each yearly interval. The outcome measure was occurrence of at least 1 serious infection in that year. Subsequent infections in the same year were not included. Andersen-Gill models, which are a variation of Cox models allowing inclusion of multiple events in the same patient (19), were used. The Andersen-Gill model is a marginal model, in which it is assumed that the overall effects for each covariate, rather than the subject-specific effects, are of interest, and then a robust variance corrects the variability to account for possible correlations within patients. It is assumed in this model that events occurring in the same patient are independent, meaning the baseline risk of experiencing a subsequent event does not change when a patient has an event. In the case of infections, having an infection may increase susceptibility to subsequent infections. To address this, we included covariates for previous infections in the past 1–3 years to allow for this increased risk, and we excluded multiple infections occurring in the same year to minimize this issue. In addition, model assumptions, such as the assumption of proportional hazards (20), were assessed, and no violations were found.

Model building began with the multivariable model for serious infections that Doran et al previously defined using this cohort (8) (Table 1). Since the outcomes objectively confirmed and serious infection were quite similar, we considered the predictors that had been previously included in either model, but other variables previously assessed during Doran's modeling process were presumed to be insignificant and were not reconsidered, to avoid introducing bias.

Each of the additional potential predictors was assessed individually to determine whether it would add significantly (P < 0.05) to the previously established multivariable model (Table 1). In addition, the daily dosage of corticosteroids was assessed. Age, RA duration, and ESR were also modeled, using smoothing splines to allow nonlinear effects, and these analyses were used to inform the choice of cut points for age and ESR. Two-way interactions between predictors in the model were also evaluated. In particular, interactions with previous infections were assessed to determine whether separate models were warranted to predict the risk of infection among patients with and those without previous infections, but no significant interactions were found. The general formula for obtaining a risk estimate from a Cox model is

  • equation image

where S0(t) is the baseline infection-free rate at followup time t (here, t = 1 year), βi is the estimated regression coefficient, Xi is the value of the ith risk factor, and p is the number of risk factors.

Validation analysis was performed by assessing the performance of the risk score in the validation cohort. Discrimination, which is the ability to accurately rank risk levels to distinguish low risk from high risk, was assessed using concordance statistics (21). For a binary outcome, the C statistic is analogous to the area under the receiver operating characteristic curve. The C statistic ranges from 0.5 to 1, with 0.5 indicating that the risk score is uninformative. This method has been extended by Harrell (22) for use in Cox models. Calibration is the ability to accurately predict the absolute risk level. To assess calibration, the cohort was divided into deciles and the risk score estimates were plotted beside the Kaplan-Meier estimates to allow visual assessment of agreement. In addition, standardized incidence ratios (SIRs), which are the ratio of observed to predicted events, were used to assess overall calibration. Ninety-five percent confidence intervals (95% CIs) for SIRs were computed under the assumption that the observed number of events follows a Poisson distribution.

Recalibration is often necessary when a risk score is translated from one population to another. Our risk score was developed in one time period and validated in another time period. Since the underlying risks of first and subsequent serious infection had changed over time, the predicted risks systematically exceeded the observed risks. The risk score was recalibrated to the new cohort by modifying the baseline infection-free rate—i.e., S0(t) in the risk score equation above —and the coefficients for previous infections. Coefficients of all other factors in the risk model were held fixed when estimating the recalibrated coefficients.

Following recalibration, a few potential predictors for which information was not available in the original cohort were assessed in the validation cohort (Table 1). For each yearly interval, the most recently measured value was used, and each potential predictor was added to the previously developed risk score model individually to assess its potential significance. Nonlinear effects of the continuous variables were also examined using smoothing splines.

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. ROLE OF THE STUDY SPONSOR
  8. REFERENCES

Of the 584 RA patients in the original cohort (patients diagnosed in 1955–1994), the mean age was 57.5 years and the majority of patients (72%) were female (Table 2). The median length of followup was 9.9 years (7,096 total person-years), during which 252 of the patients had ≥1 serious infection (total of 646 infections, 491 of which were used in the models, which included only 1 infection per patient per year). In the validation cohort (patients diagnosed in 1995–2007), the mean age was 54.9 years and 69% were female. The median length of followup was 5.2 years (2,292 total person-years), during which 55 patients had ≥1 serious infection (total of 166 total infections; 103 were the first infection in a given year). Characteristics of both cohorts are shown in Table 2. Due to the difference in length of followup between the cohorts, formal comparisons of characteristics were not performed.

Table 2. Characteristics of the 994 patients with incident RA diagnosed between 1955 and 2007*
CharacteristicOriginal cohort, 1955–1994 (n = 584)Validation cohort, 1995–2007 (n = 410)
  • *

    Except where indicated otherwise, values are the number (%). BMI = body mass index; ESR = erythrocyte sedimentation rate; CRP = C-reactive protein; NA = not available or not applicable; RF = rheumatoid factor; DMARDs = disease-modifying antirheumatic drugs.

  • Data available in 366 (89%) of patients in the validation cohort.

  • At the time of rheumatoid arthritis (RA) incidence or during followup.

  • §

    Data not available for all patients.

  • Amyloidosis, Felty's syndrome, rheumatoid vasculitis, and rheumatoid lung disease.

At RA incidence  
 Age, mean ± SD years57.5 ± 15.154.9 ± 15.5
 Female sex422 (72)284 (69)
 BMI, mean ± SD kg/m225.6 ± 4.928.6 ± 6.4
 ESR, mean ± SD mm/hour34.4 ± 25.923.1 ± 19.1
 CRP, mean ± SD mg/literNA27.4 ± 57.9
Ever  
 RF positivity331 (65)§267 (65)
 Extraarticular manifestations of RA50 (9)33 (8)
 Chronic lung disease149 (26)118 (29)
 Leukopenia87 (15)40 (10)
 Lymphopenia303 (52)136 (33)
 NeutropeniaNA35 (9)
 Diabetes mellitus60 (10)63 (15)
 Alcoholism40 (7)37 (9)
 Heart failure160 (27)10 (2)
 Peripheral vascular disease91 (16)36 (9)
 Coronary heart disease57 (10)25 (6)
Ever use of medications  
 Methotrexate122 (21)263 (64)
 Hydroxychloroquine214 (37)259 (63)
 Other DMARDs211 (36)92 (22)
 Corticosteroids250 (43)323 (79)
 Biologic agentsNA83 (20)
Length of followup, mean ± SD years14.2 ± 9.55.6 ± 9.5

The overall rate of serious infection in the validation cohort (7.2 per 100 person-years) was significantly lower than in the original cohort (9.1 per 100 person-years) (rate ratio 0.80 [95% CI 0.69–0.93], P < 0.001), reflecting the general decline in hospitalization rates that occurred over this extensive time period. Similarly, the rate of first infection was lower in the validation cohort (2.6 per 100 person-years) than in the original cohort (3.9 per 100 person-years) (P < 0.001). However, among those who experienced a first infection, the rate of second infection was higher in the validation cohort (36.5 per 100 person-years) than in the original cohort (13.6 per 100 person-years) (P < 0.001).

A multivariable model for serious infections was developed using the original cohort as described above. This model, which defines our risk score for serious infection, is shown in Table 3. Increasing age was associated with high infection risk, with the greatest risk identified in those age ≥80 years (hazard ratio [HR] 2.36 [95% CI 1.71–3.24] compared to patients age <60 years). Another factor found to be a strong predictor of infection risk was having had a previous serious infection in the past year, with an HR of 3.48 (95% CI 2.67–4.54). Elevated ESR was also associated with infection risk, with the greatest risk identified in those with an ESR of >50 mm/hour (HR 1.84 [95% CI 1.45–2.34]). Corticosteroid use was dose-dependently associated with higher risk of serious infection. For corticosteroid use in dosages of up to 10 mg daily prednisone equivalent, the HR was 1.74 (95% CI 1.35–2.24) compared to patients not taking corticosteroids. With daily dosages of >10 mg, the HR compared to those not taking corticosteroids increased to 3.60 (95% CI 1.90–6.82).

Table 3. Multivariable models, developed in the original cohort and assessed in the validation cohort, for 1-year risk of serious infection in patients with RA*
Predictor, levelModel developed in original cohort, HR (95% CI)Model assessed in validation cohort, HR (95% CI)
  • *

    RA = rheumatoid arthritis; HR = hazard ratio; 95% CI = 95% confidence interval; ESR = erythrocyte sedimentation rate.

  • Amyloidosis, Felty's syndrome, rheumatoid vasculitis, and rheumatoid lung disease.

  • Diabetes mellitus, chronic lung disease, alcoholism, coronary heart disease, heart failure, and peripheral vascular disease.

Age  
 <60 yearsReferentReferent
 60 to <80 years1.50 (1.15–1.95)1.53 (0.98–2.40)
 ≥80 years2.36 (1.71–3.24)2.18 (1.21–3.91)
Previous serious infection  
 Never or >3 years agoReferentReferent
 In the past year3.48 (2.67–4.54)10.13 (6.00–17.12)
 In the past 2–3 years1.95 (1.45–2.63)5.59 (2.69–11.64)
Extraarticular manifestation of RA1.86 (1.33–2.60)1.59 (0.88–2.87)
ESR  
 <30 mm/hourReferentReferent
 30 to ≤50 mm/hour1.20 (0.93–1.55)1.50 (0.98–2.32)
 >50 mm/hour1.84 (1.45–2.34)1.05 (0.54–2.03)
Corticosteroid dosage  
 NoneReferentReferent
 ≤10 mg/day1.74 (1.35–2.24)2.14 (1.42–3.23)
 >10 mg/day3.60 (1.90–6.82)3.97 (1.79–8.82)
Comorbidities  
 NoneReferentReferent
 11.96 (1.55–2.49)1.25 (0.77–2.05)
 >12.79 (2.17–3.58)2.67 (1.45–4.90)

Several comorbidities were associated with risk of serious infection, including chronic lung disease (HR 1.56 [95% CI 1.24–1.95], adjusted for all other predictors in the risk score), diabetes mellitus (HR 1.35 [95% CI 1.01–1.81]), alcoholism (HR 1.50 [95% CI 1.05–2.16]), coronary heart disease (HR 1.47 [95% CI 1.08–2.01]), heart failure (HR 1.70 [95% CI 1.34–2.16]), and peripheral vascular disease (HR 1.50 [95% CI 1.13–2.10]). To simplify the model, comorbidities were combined and modeled as no comorbidity, 1 comorbidity, or >1 comorbidity; the C statistic did not change with this simplification. The presence of 1 comorbidity was associated with increased risk of infection (HR 1.96; 95% CI 1.55, 2.49), with further risk when there was >1 comorbidity (HR 2.79 [95% CI 2.17–3.58]).

When the risk score developed using the original cohort was tested in the validation cohort, it showed excellent discrimination (C statistic 0.80 [95% CI 0.74–0.85]). Figure 1 shows that the observed risk increased as the predicted risk increased. However, the calibration of the risk score was poor in that the risk score predicted a higher risk of infection than was observed, due to the lower event rate in the validation cohort (103 observed serious infections versus 148.0 predicted infections; SIR 0.70 [95% CI 0.57–0.84]).

thumbnail image

Figure 1. Comparison of predicted and observed 1-year risk of serious infection in the validation rheumatoid arthritis cohort according to decile of predicted risk obtained from our risk score for serious infections developed in the original cohort. The observed risk data were obtained using Kaplan-Meier methods. Predicted risk was identical for patients in deciles 1–3; therefore, the bars shown for decile 3 include all 3 of these deciles.

Download figure to PowerPoint

The calibration was explored further by refitting the model from the original cohort in the validation cohort (Table 3). In most cases, the coefficients in the model fit in the validation cohort were close to those in the original cohort (i.e., were within the 95% CI in the original model). However, some of the coefficients were not statistically significant in the validation cohort (e.g., age between 60 and 80 years, extraarticular manifestations of RA, ESR between 30 and 50 mm/hour, and presence of 1 comorbidity), likely due to the smaller sample size or shorter followup of this cohort, which limited statistical power somewhat. Striking differences in coefficients for previous infection were noted, due to the increased risk of subsequent infection in the validation cohort. Recalibration, which involved replacing the 1-year baseline infection-free rate from the original cohort, i.e., S0(1) = 0.980, with that from the validation cohort, i.e., S0(1) = 0.989, and modifying the coefficients for previous infections, improved the calibration of the risk score (SIR 0.96 [95% CI 0.78–1.16]) and had little impact on the discrimination (C statistic 0.81 [95% CI 0.75–0.86]) (Figure 2).

thumbnail image

Figure 2. Comparison of predicted and observed 1-year risk of serious infection in the validation rheumatoid arthritis cohort according to decile of predicted risk obtained from our recalibrated score for serious infections. The observed risk data were obtained using Kaplan-Meier methods. Predicted risk was identical for patients in deciles 1–3; therefore, the bars shown for decile 3 include all 3 of these deciles.

Download figure to PowerPoint

Data on several additional risk factors were available in the validation cohort (i.e., leukopenia, lymphopenia, neutropenia, CRP level, and use of biologic agents). Each of these potential predictors was assessed by adding it to the recalibrated risk score model. Infection risks were increased by 20–80% in the presence of leukopenia (HR 1.23 [95% CI 0.34–4.47]), lymphopenia (HR 1.45 [95% CI 0.86–2.43]), and neutropenia (HR 1.76 [95% CI 0.42–7.37]). However, these increases did not reach statistical significance, likely due to the low prevalence of the abnormalities, as well as the inclusion of previous infections in the risk score models. Analyses of grades of leukopenia, lymphopenia, and neutropenia, as well as of nonlinear effects, did not reveal improved associations with risk of serious infection. Abnormal CRP levels were not significantly associated with risk of serious infection (HR 1.04 [95% CI 0.64–1.67]). Similar results were seen for continuous (linear and nonlinear) and log-transformed CRP values.

In the validation cohort, 20% of patients had been treated with biologic agents. The majority (95%) of biologic agents used were tumor necrosis factor antagonists. Biologic treatment was not associated with risk of serious infections (HR 1.27 [95% CI 0.76–2.12] compared to those who had not received biologic treatment).

Table 4 lists the coefficients of the risk score, which include the recalibrated coefficients for previous infections and the coefficients from the model developed in the original cohort for all other risk factors. Since all items in the score are either present or absent, the coefficients are simply added together to obtain the score in an individual patient. To convert the score into a risk percentage, the cutoffs shown in the Table 4 footnote can be used. For example, a 70-year-old woman with RA who has an ESR of 35 mm/hour, has diabetes, takes prednisone at 15 mg/day, and has had no previous serious infection has a risk score of 2.54, which corresponds to a 10–15% risk of developing an infection in the next year. If her prednisone dose was reduced to 5 mg/day, her risk score would be 1.81, corresponding to a 5–10% risk of serious infection within the next year.

Table 4. Risk assessment for 1-year risk of serious infection in patients with rheumatoid arthritis*
Predictor, levelCoefficient
  • *

    One-year risk of serious infection (%) = [1– 0.989 (exp[A])] × 100%, where A = 0.404 (if age 60 to <80 years) + 0.857 (if age ≥80 years) + 2.138 (if serious infection in the past year) + 1.670 (if serious infection in the past 2–3 years) + 0.620 (if extraarticular RA) + 0.180 (if ESR 30 to ≤50 mm/hour) + 0.611 (if ESR >50 mm/hour) + 0.553 (if corticosteroids ≤10 mg/day) + 1.281 (if corticosteroids >10 mg/day) + 0.675 (if 1 comorbidity) + 1.024 (if >1 comorbidity). To use the risk score, add the coefficients for the relevant items, then refer to the following cutoffs: score of 1.53 = 5% risk; score of 2.25 = 10% risk; score of 2.69 = 15% risk; score of 3.00 = 20% risk.

Age 
 <60 years0
 60 to <80 years0.404
 ≥80 years0.857
Previous serious infection 
 Never or >3 years ago0
 In the past year2.138
 In the past 2–3 years1.670
Extraarticular manifestation of RA0.620
ESR 
 <30 mm/hour0
 30 to ≤50 mm/hour0.180
 >50 mm/hour0.611
Corticosteroid dosage 
 None0
 ≤10 mg/day0.553
 >10 mg/day1.281
Comorbidities 
 None0
 10.675
 >11.024

DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. ROLE OF THE STUDY SPONSOR
  8. REFERENCES

In this study we developed a risk score for serious infection in patients with RA, which encompasses age, comorbidities, measures of RA disease severity (e.g., extraarticular RA) and disease activity (e.g., increased ESR), corticosteroid treatment, and previous infection. We expanded upon knowledge of risk factors for infection that have been determined previously in this RA cohort (8) and demonstrated that cardiovascular comorbidities also increased the risk of infection. We validated the risk score using patients who developed RA more recently (since 1995), and demonstrated that the risk score had good discrimination in the validation cohort.

Although it is recognized that RA is associated with increased risk of infection, there are few risk scores to help clinicians assess this risk. Franklin et al created a score using a population of patients with new-onset polyarthritis (23); however, this strategy may not accurately predict risk in patients with disease of longer duration. Additionally, the risk score was based on a multivariable model with a limited set of predictors (i.e., history of smoking, presence of rheumatoid factor (RF), and use of corticosteroids), which was less comprehensive than ours.

In addition to the patient and disease characteristics included in our risk score, we examined the effect of medications used in the treatment of RA. In our analysis, only corticosteroid use was associated with increased infection risk, as has been reported by our group and others (3, 8, 24, 25).

There is conflicting information regarding the increased risk of infection with traditional DMARD therapy. In the current study, traditional DMARDs were assessed and were not found to be significantly associated with infection risk. In our original cohort, use of methotrexate was not associated with increased risk, consistent with the results of other studies (8, 26). In contrast, Smitten et al found a reduced risk of infection (25), and other investigators have found an increased risk of infection (24, 27), associated with methotrexate use. In our original cohort, cytotoxic medications (e.g., cyclophosphamide, azathioprine) were assessed and appeared to confer increased risk of infection, but these associations did not reach statistical significance due to low prevalence of use of these medications (8).

We found no evidence of increased risk of serious infection associated with biologic therapy, which is consistent with the findings of some authors (11). Still other authors have noted an increased risk of serious infection in patients receiving biologic agents, however (9, 10, 28, 29). The recent assessment of infection risk performed using the German biologics register RABBIT (German acronym for Rheumatoid Arthritis—Observation of Biologic Therapy) (30) helped to make sense of these conflicting reports. An increased risk of infection during the first year of treatment with biologic agents was demonstrated, with a subsequent decline in infection risk due to improvement in disease activity, reduction in concomitant corticosteroid use, and discontinuation of biologic treatment among patients at high risk for infection. The low prevalence of biologic treatment in our cohort did not allow us to perform detailed examination of patient selection and time-varying effects of this treatment; however, findings regarding the impact of other risk factors were similar between the present study and the study using the RABBIT (30).

Another objective of the present investigation was to identify predictors of infection that were not considered in the earlier study by Doran et al (8). Lymphopenia occurred in ∼80% of patients in our original cohort during the followup period (mean 14 years) (31), and may have been a useful indicator of increased infection risk. However, lymphopenia may also be associated with previous infection or corticosteroid use. While lymphopenia was modestly related to risk of serious infection after adjustment for the other predictors in our risk score, the association did not reach statistical significance. Our finding of a relatively weaker association between lymphopenia and infection risk is consistent with a prior analysis of morbidity and mortality in a small cohort of 53 patients with RA who were treated with the lymphocytotoxic monoclonal antibody alemtuzumab, resulting in prolonged therapy-induced lymphopenia (32, 33). Twelve-year outcome data from that cohort did not reveal increased infection-related mortality, or excess mortality when compared to matched controls with long-term followup. Possible explanations for this lack of association include immunosufficiency at other sites (e.g., lymph nodes) or preserved immunologic memory.

Although we did not have data on disease activity scores (e.g., 28-joint Disease Activity Score [DAS28]) (34), we were able to assess measures reflecting RA severity (e.g., RF positivity, rheumatoid nodules, extraarticular manifestations of RA, and poor functional status) and markers of inflammation (e.g., ESR and CRP) as predictors of infection. Additionally, we were able to demonstrate that cardiovascular comorbidities, which have also been linked to measures of disease severity (35), are associated with increased risk of infection. Other investigators have also reported increased infection risk with cardiovascular comorbidities and with increased disease activity based on the Clinical Disease Activity Index (36) and DAS28, in RA patients from the Consortium of Rheumatology Researchers of North America registry (24).

Application of our risk score in the clinic may alert physicians to an increased risk of infection, triggering heightened vigilance with followup, education regarding preventive measures for infection, and possible modification of treatment decisions. In fact, quantifying the risk of infection could help with decision-making regarding antimicrobial prophylaxis. There are currently immunization guidelines, but no guidelines for identification of RA patients in whom prophylaxis might be useful. Further research is needed to determine the level of infection risk at which patients receive the most benefit from prophylaxis, and how formal infection risk assessment might influence the choice of DMARD, including biologic therapy, in RA.

A limitation in the original cohort was that the patients studied did not reflect current treatment patterns for RA, since followup extended only through the year 2000. The validation cohort included patients with more recently diagnosed RA, but confounding by indication in this observational study may still affect our ability to draw conclusions about the effects of medication on infection risk. Another limitation is that potential risk factors with low prevalence (<5%) would have less likelihood of inclusion in the risk score due to limited statistical power for identifying an association with infection. The duration of followup was shorter in our validation cohort than in the original cohort. While we did not find disease duration to be associated with risk of serious infection in the original cohort, the shorter disease duration in the validation cohort would allow less time for development of comorbidities, and a lower prevalence of comorbidities could affect our ability to estimate their effects in the validation.

A further potential limitation is the retrospective nature of the study. Our definition of serious infection required hospitalization or intravenous antibiotics, and it is unlikely that we failed to capture any of these infections due to the comprehensive nature of the Rochester Epidemiology Project resources. In contrast, some of the risk factors, such as lymphopenia, may have been missed, as it is possible that some may not have come to medical attention. We believe, however, that the majority of clinically relevant risk factors would have been assessed clinically and would therefore be captured in our extensive medical records review. Finally, the majority of the Olmsted County population is white, which may limit the generalizability of the study results.

In conclusion, our risk score for serious infection has potential clinical utility for predicting the occurrence of infection within the next year in patients with RA. Validation of this risk score in patients with RA diagnosed since 1995 has enhanced understanding of the infection risk in these patients, and demonstrated excellent performance of the risk score. Assessment of the risk of serious infection in patients with RA can influence clinical decision-making and inform strategies to reduce and prevent the occurrence of these infections.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. ROLE OF THE STUDY SPONSOR
  8. REFERENCES

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Ms Crowson had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Crowson, Matteson.

Acquisition of data. Crowson, Hoganson, Matteson.

Analysis and interpretation of data. Crowson, Hoganson, Fitz-Gibbon, Matteson.

ROLE OF THE STUDY SPONSOR

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. ROLE OF THE STUDY SPONSOR
  8. REFERENCES

Genentech approved the study design and reviewed and approved the manuscript prior to submission. The authors independently designed the study, collected the data, interpreted the results, wrote the manuscript, and had the final decision to submit the manuscript for publication. Publication of this article was not contingent upon approval by Genentech.

REFERENCES

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. ROLE OF THE STUDY SPONSOR
  8. REFERENCES
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