Use of a baseline risk score to identify the risk of serious infectious events in patients with rheumatoid arthritis during certolizumab pegol treatment

Anti-tumor necrosis factor (anti-TNF) agents are often the first class of biologic drugs prescribed to patients with rheumatoid arthritis (RA), providing an effective therapeutic option that improves clinical, radiographic, and functional outcomes [1]. Owing to their immunomodulatory action, anti-TNFs have been linked to an increased risk of serious infectious events (SIEs), although the strength of this association remains a topic of debate [2–5]. In light of these concerns, biologic registries have been established in several countries to examine the long-term safety of anti-TNFs and to investigate how different patient characteristics affect the risk of serious infection [6–9].

In addition, older age and specific comorbidities that are relatively prevalent in the RA population, such as diabetes mellitus or chronic obstructive pulmonary disease (COPD), have been associated with an increased risk of SIEs during anti-TNF therapy [9–14]. Concomitant treatment with systemic glucocorticoids has also been linked to an increased susceptibility to infection [6, 8, 12, 15]. In clinical practice, physicians have to balance the benefits of the different available treatments against their overall risk, as well as the risks associated with the characteristics of each patient. This often involves the complex task of extrapolating data from study populations to the individual patient level. Infection risk scores, which summarize the relative contributions of various patient characteristics in a single composite measure, may help clinicians to anticipate the potential risk of SIEs in individual patients and make better-informed treatment decisions when balancing the expected therapeutic risks and benefits for an individual patient [11, 16, 17].

Certolizumab pegol (CZP) is a PEGylated, Fc-free anti-TNF approved for the treatment of adult patients with moderate to severe active RA [18–21]. Using baseline patient characteristics, we developed and tested an age-adjusted comorbidity index (AACI) to predict the risk of SIEs in patients with RA at the start of CZP treatment. In addition, we investigated if the predicted SIE risk was associated with the likelihood of clinical response at 1 year of CZP treatment.

Patients with RA are reported to have an increased susceptibility to infection compared with patients without RA [28]. Although the strength of association between anti-TNFs and the risk of infection remains a topic of debate [2–5], the evidence published so far underscores the need for clinicians to carefully balance the clinical benefits and potential harms of initiating anti-TNF therapy in individual patients.

Using data pooled from the pivotal RAPID1 and RAPID2 RCTs and OLEs [20–23], we derived and tested a new AACI to predict the risk of SIEs during CZP treatment, based on the baseline characteristics of anti-TNF naive patients with moderate to severe active RA and inadequate response to prior disease-modifying antirheumatic drugs. AACI ≥ 2 and systemic glucocorticoid use were identified as the main baseline risk factors for the occurrence of SIEs. With up to 2 years of CZP exposure, predicted SIE rates matched the observed rates, suggesting that the model was well-calibrated for the outcome. Discrimination between different risk groups in the model was fair (RCT CZP) to excellent (All CZP).

As expected, the IRs of SIEs in groups defined as being at high risk were higher than reported previously in a pooled safety analysis of patients with RA in CZP clinical trials (IR 3.7/100 PY [3.3–4.1] [25]). Consistent with previous CZP safety data [25] and registry data [10, 13], the highest incidence of SIEs was seen during the first 6 months of anti-TNF treatment and decreased thereafter.

In All CZP, the rates of LDA and remission observed at 1 year were similar across all six risk groups, suggesting that the baseline risk of SIEs was not associated with the likelihood of response to CZP treatment. Indeed, poor prognostic factors for RA progression, such as longer disease duration, greater disability, and erosive disease [29, 30], were not associated with SIE risk when tested in the Cox model.

In line with previous studies [9–14], older age (≥70 years), diabetes mellitus, and COPD/asthma were strongly associated with SIE risk in this RA population, making an important contribution to patients’ AACI scores. Consistent with our results, older age and comorbidities also featured in other SIE risk scores in RA, developed using data from biologics registries and administrative databases [11, 16, 17]. In addition, we found that hyperlipidemia, osteoporosis, and depression were associated with an increased risk of SIEs in the RAPID1/RAPID2 patient population. Depression and the use of certain antidepressant medications have previously been associated with an increased risk of some infections [31]. However, it is uncertain whether some of these associations might have a true biologic basis, be driven by the effects of medications used to treat the condition, be a proxy for a shared risk factor (e.g., frailty), or reflect other confounding factors.

The increased risk of infection associated with systemic glucocorticoids is widely recognized [6, 8, 12, 15]. Our data revealed a dose-independent association between baseline systemic glucocorticoid use and SIEs in the RCT CZP group, where doses were required to remain stable throughout the RCTs [20, 21]. By contrast, systemic glucocorticoid doses were allowed to change during the OLEs, which may have minimized our ability to detect a stronger association with SIE risk in the All CZP group. Furthermore, we cannot discount the possibility that doses taken over the previous 2–3 years may have had a cumulative impact on the risk of SIEs [32]. Despite such limitations, our results suggest potential benefits of avoiding concomitant systemic glucocorticoid use in CZP-treated patients with RA as early as possible, as recommended by the 2015 American College of Rheumatology RA guidelines [33].

High disease activity has been associated with an increased risk of infection [34, 35]. However, in our model, baseline disease activity showed only a modest association with the risk of SIEs. One likely explanation is the fact that all patients enrolled in the RAPID1/RAPID2 RCTs had moderate to high disease activity at baseline. The relatively narrow range of baseline DAS28(CRP) scores in this RA population may have concealed a stronger association with SIE risk.

Several studies have reported that the relative risk of infection decreases over time in patients with RA treated with anti-TNF [13, 25, 36–38]. This has been attributed not only to the depletion of susceptible patients from the study cohorts but also to an improvement in the clinical status of patients, either as a direct result of reduced disease activity or owing to a decline in the use of concomitant glucocorticoids [8]. The model that we developed tested only the effect of baseline patient characteristics. Although AACI, which is a static patient characteristic, was the strongest predictor of SIEs, clinical parameters such as disease activity and physical function (HAQ-DI) may have changed considerably over time as a result of CZP treatment, resulting in weaker prediction of SIE rates for CZP exposures > 2 years.

A limitation of the method used to derive the AACI is the fact that the HRs for each age category and individual comorbidity were averaged across the RCT CZP and All CZP groups in order to assign the respective weighting. Our intention with the AACI was to provide an indication of SIE risk not only in the first year of treatment but also over a longer period of time. Had we used just the RCT period to derive the weightings for each age category and comorbidity, we would have potentially overestimated the risk of SIEs during the OLE. For this reason, the averaging of HRs seemed to be a reasonable approach, albeit at the expense of some predictive accuracy.

Prior epidemiologic work has suggested that although some risk estimation models can be generalized, model performance is optimized when population-specific weights can be derived [11]. As such, the weighting for each risk factor in one population may not be generalizable to a different RA population. For instance, the RABBIT Risk Score performed well in the All CZP group, but SIE rates were underestimated at the lower end of the risk spectrum. One possible explanation is the fact that the RABBIT Risk Score may be better calibrated for real-world patient populations with a higher propensity for SIEs [17]. For example, chronic renal disease and chronic lung disease, which contribute to the RABBIT Risk Score, were less prevalent or absent in our study population. Furthermore, our assumption that there were zero patients with a history of prior SIEs may also have contributed to the underestimation of RABBIT Risk Scores.

At initiation of anti-TNF therapy, prescribers need to consider the infection risk in each individual patient, which must take into account the patient’s age, comorbidities, concomitant medications, and clinical history. However, most data currently available consist of mean infection rates observed in relatively large, heterogeneous populations. Although group-level infection rates are informative, they are not always relevant at the individual patient level in clinical practice. This is why stratifying patients according to comorbidity burden and other infection risk factors can help clinicians to make more accurate risk-benefit assessments regarding anti-TNF therapy and ultimately make better-informed treatment decisions at the individual-patient level.

However, in clinical practice, prescribers tend to channel higher-risk patients away from anti-TNF therapy, owing to the commonly held view that older patients or those with more severe comorbidities are less likely to benefit clinically from anti-TNF treatment [11, 39, 40]. However, there is published evidence suggesting that responsiveness to treatment and the incremental infection risk associated with anti-TNFs is similar for higher- and lower-risk groups [41, 42]. Consistent with this, our data showed that the rates of LDA and remission at 1 year of CZP treatment were similar across all AACI risk groups. Although the limited placebo exposure did not allow us to quantify the incremental risk associated with CZP, the results of our study are reassuring for patients with higher AACI scores at baseline and may encourage clinicians to avoid concomitant glucocorticoids to reduce the SIE risk. Further research is needed to examine how the disease-modifying effects of CZP treatment, as well as changes in glucocorticoid use, influence the risk of SIEs in a time-dependent manner.

In this population of patients with moderate to severe active RA and no prior anti-TNF use, the rate of SIEs during CZP treatment was predicted with considerable accuracy using a combination of AACI score and baseline systemic glucocorticoid use, with good discrimination between high- and low-risk patient groups. This SIE risk score may provide a valuable tool for clinicians when considering the risk of infection in individual patients with RA. One of the greatest strengths of our analysis was the use of a large, very comprehensive clinical trial database to derive the AACI. However, we should note that the AACI model has not been validated in a different population. It would be valuable to assess how the AACI performs in other RA trials and in real-world settings.