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This study investigated whether osteoporosis impacts reoperation or periprosthetic fracture after total ankle arthroplasty. Findings showed no significant difference in reoperation or fracture rates between patients with or without osteoporosis, suggesting osteoporosis may not be a major risk factor for these outcomes.
Background
This study examines the association between osteoporosis and postoperative periprosthetic fracture or reoperation after primary total ankle arthroplasty (TAA) to guide surgical decision-making.
Methods
The United States TriNetX network identified adults undergoing primary TAA. Patients were split per the presence or absence of osteoporosis. The primary outcome was the risk ratio (RR) with 95% confidence intervals (CI) of reoperation within 3 years of primary TAA. Secondary outcome included the RR with 95% CI for postoperative periprosthetic fractures after primary TAA.
Results
There were 270 patients per cohort. There was no statistically significant difference in the likelihood of reoperation in the osteoporosis cohort as compared to the non-osteoporosis cohort through 3 years (5.9% versus 5.6%; p = 0.853). There was also no statistically significant difference in the likelihood of postoperative periprosthetic fractures in the osteoporosis cohort as compared to the non-osteoporosis cohort (6.3% versus 4.1%; p = 0.244).
Conclusion
These findings suggest that osteoporosis may not be a meaningful risk factor for reoperation or postoperative periprosthetic fracture after primary TAA.
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Introduction
Total ankle arthroplasty (TAA) is increasingly favored over arthrodesis for the management of end-stage ankle arthritis due to advancements in implant technology and surgical techniques[1‐5]. Over the last two decades, TAA recipients have experienced greater satisfaction, symptom relief, and improved gait compared to those undergoing ankle arthrodesis[6‐9]. Additionally, recent studies have shown promising early survival data following the emergence of next-generation TAA implants[10‐12]. Despite these advancements within TAA, a subset of patients experience various complications related to the procedure.
The most common intraoperative complication in TAA is a medial malleolar periprosthetic fracture[13], while postoperatively, aseptic loosening and subsidence remain the prevailing causes of implant failure[11, 14‐16]. Thus, ensuring successful outcomes following TAA hinges on optimal patient selection and surgical planning, necessitating a thorough examination of potential risk factors for mechanical failure, such as obesity and diabetes[17‐21]. While osteoporosis is a well-recognized risk factor for increased risk of periprosthetic fractures, aseptic loosening, and revision in patients undergoing total hip or knee arthroplasty[22‐27], we are only aware of one small study that examined the association between bone mineral density (BMD) and outcomes following TAA[28].
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Osteoporosis is a common disorder of low BMD, having a global prevalence of 18% (95% CI 16%, 21%)[29], and affecting 6–11% of adults aged 50 years and older in the United States (US)[30]. Osteoporosis poses a concern for numerous orthopedic reconstructive procedures, as osteoporotic bone may inadequately support implanted materials due to failed bony ingrowth and heightened micromotion, thereby escalating the risk of loosening and subsequent prosthesis failure[31]. Moreover, osteoporotic bones are less resilient to the mechanical stresses encountered during and after surgery, increasing the chances of intraoperative fractures. Given the potential role of bone integrity for success in TAA, it is imperative to investigate how osteoporosis actually impacts surgical outcomes after this procedure. Furthermore, surgeons have noted that low BMD impacts their surgical approach, fixation technique, and choice of implant to mitigate the risk of revision when performing THA, although it is unclear whether this is a routine consideration in TAA[32].
Therefore, this study aims to bridge this gap by examining the association between osteoporosis and likelihood of reoperation and postoperative periprosthetic fracture following TAA. We hypothesize that patients with osteoporosis will be more likely to undergo reoperation as well as have periprosthetic fractures after TAA as measured by risk ratio (RR).
Materials and methods
Study design
This retrospective cohort study adhered to a preregistered protocol (Open Science Framework; https://osf.io/c94np/). The University Hospitals Institutional Review Board (IRB) considers study designs which use de-identified data from TriNetX in accordance with standard operating procedures to meet criteria for Not Human Subjects research, thereby making the present study exempt from IRB review and waiving the need for patient consent. Our data query was performed on May 20, 2024.
Setting and data source
Data for this study was obtained from TriNetX, a US research network based, which includes over 125 million patients across 89 health care organizations. The database may be queried using standardized nomenclature such as International Classification of Disease codes, 10th edition (ICD-10) and Current Procedural Terminology codes (CPT), among others (e.g., RxNorm for medications). TriNetX is compliant with the Health Insurance Portability and Accountability Act (HIPAA), the US federal law which protects the privacy and security of healthcare data. TriNetX is certified to the International Organization for Standardization 27,001:2013 standard and maintains an Information Security Management System to ensure the protection of the healthcare data it has access to and to meet the requirements of the HIPAA Security Rule. Any data displayed on the TriNetX Platform in aggregate form, or any patient-level data provided in a data set generated by the TriNetX Platform, only contains de-identified data as per the de-identification standard defined in Section §164.514(a) of the HIPAA Privacy Rule. The process of de-identifying data is attested to through a formal determination by a qualified expert as defined in Section §164.514(b)(1) of the HIPAA Privacy Rule. The TriNetX network contains data provided by participating healthcare organizations, each of which represents and warrants that it has all necessary rights, consents, approvals, and authority to provide the data to TriNetX under a Business Associate Agreement, so long as their name remains anonymous as a data source and their data are utilized for research purposes. The data shared through the TriNetX Platform are attenuated to ensure that they do not include sufficient information to facilitate the determination of which healthcare organization contributed which specific information about a patient.
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This study also employed the use of TriNetX’s built-in natural language processing function software (Averbis, Freiburg im Breisgau, DE), which has been previously validated against manual chart review[33‐35].
Participant inclusion and exclusion
Patients included adults (≥ 18 years old) who underwent primary TAA the 20 years preceding the data query date. Patients were divided into osteoporosis and non-osteoporosis cohorts based on documented diagnoses within the TriNetX database using ICD-10 codes (Supplementary Tables 1 and 2). The presence of osteoporosis was determined by diagnostic codes explicitly indicating the condition while patients without such codes were categorized in the non-osteoporosis cohort. Osteoporosis was defined as a documented diagnosis using ICD-10 codes (e.g., M81) within the TriNetX database. This diagnosis aligns with the World Health Organization’s criteria for osteoporosis, defined as a T-score of ≤ − 2.5 on dual-energy X-ray absorptiometry (DXA). However, a BMD index, such as T-scores from dual x-ray absorptiometry (DXA), was not directly available in the database and thus was not used to classify patients. Instead, the reliance on ICD-10 codes ensures standardization and consistency in cohort definitions. Furthermore, the database’s integrated natural language processing tool was employed to confirm the presence of these diagnostic codes and mitigate potential errors in identifying osteoporosis-related conditions.
Matching variables
To minimize bias, we used propensity score matching to control for confounding variables present prior to primary TAA and associated with failure or direct complications after primary TAA. Matched variables included demographics (age at index, female/male)[21], nicotine dependence, diabetes mellitus[19, 21], osteochondritis dissecans of the ankle[18], overweight/obesity status[17, 21], inflammatory polyarthropathies and rheumatoid arthritis[21], mental disorders due to known psychological conditions (including dementia), chronic obstructive pulmonary disease[36], alcohol related disorders[36], vascular disease and hyperlipidemia[36, 37], and primary osteoarthritis and/or post traumatic osteoarthritis of the ankle/foot[20]. Furthermore, all patient characteristics listed in Table 1 were ascertained using standardized coding (e.g., ICD-10, CPT) and natural language processing within the TriNetX database. The covariate assessment window included any time preceding the index date in which data were available per patient, ensuring a comprehensive representation of their baseline health status.
Table 1
Baseline characteristics before and after matching. SMD standardized mean difference; variables reported for descriptive purposes that were unmatched (*)
Before matching
After matching
Variable (n (%) or mean (SD))
Osteoporosis
Non-osteoporosis
SMD
Osteoporosis
Non-osteoporosis
SMD
N
279
5251
-
270
270
-
Age
64.9 (11.7)
60.3 (12.4)
0.380
64.8 (11.7)
64.3 (11.0)
0.037
Age [min–max]
25–87
18–86
-
25–87
25–83
-
Black or African American*
10 (4%)
175 (3%)
0.010
10 (4%)
11 (4%)
0.019
White*
238 (87%)
4014 (80%)
0.200
236 (87%)
220 (81%)
0.164
Female
188 (69%)
2223 (44%)
0.514
186 (69%)
188 (70%)
0.016
Male
80 (29%)
2698 (54%)
0.509
79 (29%)
79 (29%)
< 0.001
Primary osteoarthritis ankle and foot
198 (73%)
3069 (61%)
0.247
195 (72%)
201 (74%)
0.050
Inflammatory polyarthropathies
185 (68%)
2286 (45%)
0.462
182 (67%)
187 (69%)
0.040
Hyperlipidemia, unspecified
117 (43%)
1303 (26%)
0.363
114 (42%)
107 (40%)
0.053
Overweight and obesity
84 (31%)
1201 (24%)
0.155
82 (30%)
86 (32%)
0.032
Post-traumatic osteoarthritis, ankle and foot
67 (25%)
1070 (21%)
0.078
67 (25%)
56 (21%)
0.097
Diabetes mellitus
59 (22%)
607 (12%)
0.257
57 (21%)
60 (22%)
0.027
Rheumatoid arthritis
48 (18%)
239 (5%)
0.416
48 (18%)
20 (7%)
0.316
Diseases of arteries, arterioles and capillaries
49 (18%)
403 (8%)
0.299
46 (17%)
39 (14%)
0.071
Nicotine dependence
31 (11%)
467 (9%)
0.068
29 (11%)
25 (9%)
0.049
Chronic obstructive pulmonary disease
26 (10%)
176 (3%)
0.246
24 (9%)
27 (10%)
0.038
Alcohol related disorders
15 (5%)
134 (3%)
0.143
13 (5%)
14 (5%)
0.017
Osteochondritis dissecans of ankle and joints of foot
10 (4%)
60 (1%)
0.161
10 (4%)
10 (4%)
< 0.001
Mental disorders due to known physiological conditions
Our primary composite outcome included several diagnosis codes indicative of reoperation after TAA including TAA revision, removal of TAA implant, and reposition of TAA implant (Supplementary Table 3). Natural language processing was used to enhance ascertainment of reoperation. Our outcome assessment window commenced the day following surgery and was examined through 3 years following primary TAA. Patients were only eligible to be counted for the primary and secondary outcome once, avoiding skewed results due to double counting.
Secondary outcome
Our secondary composite outcome included several diagnosis codes indicative of postoperative periprosthetic fracture after TAA including fractures of the talus, distal end and shaft of the tibia, and shaft of the fibula after TAA (Supplementary Table 4). Natural language processing was used to enhance ascertainment of periprosthetic fracture. Our outcome assessment window commenced the day following surgery and was examined at 3 years after primary TAA.
Power analysis for sample size
A required total sample size of 237 patients per cohort was calculated using the following assumptions for the primary outcome: a 7% risk of reoperation in the non-osteoporosis group[38], a 14% risk of reoperation in the osteoporosis group[39], a two-tailed alpha-error of 0.05, an allocation ratio of one, and a power of 0.80 using GPower (Kiel University, DE) with z-testing function.
Statistical analysis
Initial statistical analysis was completed using the built-in statistical suite within the TriNetX network software. For comparisons of patient baseline demographics, this study used an independent-samples t-test or Pearson chi-square test. We utilized standardized mean difference (SMD) to evaluate meaningful between cohort differences with a threshold of SMD > 0.1. Logistic regression was applied to calculate the propensity scores for patients in each of the two cohorts. This was followed with a propensity score density graph to visually compare the scores before and after propensity matching. We calculated risk ratios (RR) with 95% confidence intervals (CIs) using logistic regression analysis within the TriNetX statistical suite, evaluating significance at p < 0.05. The follow-up period for outcome ascertainment was standardized to 3 years postoperatively (1095 days), consistent with the study’s predefined outcome assessment window. We used R (version 4.2.2, Vienna, AT)[40] to calculate 95% CIs and used the ggplot2 package[41] to plot propensity score density, and plot cumulative incidence with locally weighted scatterplot smoothing.
Results
Patients
The query identified 5530 patients from 53 health care organizations. Prior to propensity matching, the osteoporosis cohort contained 279 patients whereas the cohort without osteoporosis contained 5251 patients. After propensity matching, there were 270 patients in each cohort (Table 1). Prior to matching, cohorts were imbalanced (SMD > 0.1) in terms of age, sex, and multiple comorbidities; however, after matching, both cohorts were similar across all matched variables (SMD < 0.1).
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Descriptive data
The number of data points per patient represents the volume of recorded clinical information available in the TriNetX database, including diagnoses, procedures, medications, and other relevant clinical entries. Both cohorts had sufficient data completeness, with an average of 6253 recorded entries per patient in the osteoporosis cohort and 2154 recorded entries per patient in the non-osteoporosis cohort. Adequate covariate balance was highlighted by a propensity score density plot (Fig. 1). The length of record suggested minimal loss to follow-up, with both cohorts having a median follow-up duration equaling 1095 days (i.e., 3 years), the maximum duration assessed for our outcomes.
Fig. 1
Propensity score plot. The orange bars represent the osteoporosis cohort while the blue bars represent the non-osteoporosis cohort. After matching, the propensity score densities overlap closely, suggesting adequate balance of covariates
There was no statistically significant difference in likelihood of reoperation in the osteoporosis group (5.9%; n = 16 events) as compared to the group without osteoporosis (5.6%; n = 15 events) through 3 years after primary TAA (RR 1.07; 95% CI 0.54, 2.11; p = 0.853; Fig. 2).
Fig. 2
Cumulative incidence plot for reoperation. Incidence curves for osteoporosis (blue) and non-osteoporosis (orange) are shown over the 3-year follow-up period (1095 days). Shaded ribbons indicate 95% confidence intervals
There was also no statistically significant difference in likelihood of postoperative periprosthetic fracture in the osteoporosis group (6.3%; n = 17 events) as compared to the group without osteoporosis (4.1%; n = 11 events) through 3 years after primary TAA (RR 1.55; 95% CI 0.74, 3.24; p = 0.244; Fig. 3).
Fig. 3
Cumulative incidence plot of postoperative periprosthetic fracture. Incidence curves for osteoporosis (blue) and non-osteoporosis (orange) are shown over the 3-year follow-up period (1095 days). Shaded ribbons indicate 95% confidence intervals
Cumulative incidence plots for reoperation showed a similar time-to-event pattern in both cohorts. However, the cumulative incidence plots for postoperative periprosthetic fracture showed a more curvilinear pattern of events in the osteoporosis cohort suggesting events occurred sooner after surgery, compared to a more linear pattern in the non-osteoporosis cohort. Despite this minor difference, the resulting risk ratios suggested a non-meaningful difference for both outcomes.
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Discussion
This propensity-matched cohort study represents the largest study examining the association between reoperation or periprosthetic fracture and osteoporosis after primary TAA. Contrary to our hypothesis, the present study findings suggest that osteoporosis is not a significant risk factor for reoperation or postoperative periprosthetic fracture through 3 years following primary TAA.
In this study, the incidence of reoperation 3 years following primary TAA was 5.9% in the osteoporosis cohort and 5.6% in the non-osteoporosis cohort. When combined, the overall reoperation rate across both cohorts was 5.2%, which aligns with the expected values from the literature on patients over the age of 50[10, 42, 43]. On this topic, Demetracopoulos et al. conducted a comparative study involving 395 TAA recipients sub grouped by age (i.e., < 55, 55–70, > 70 years). After a mean follow-up of 3.5 years, the incidence of revision among those aged 55–70 years was 5.4% and not significantly different compared to the other age groups.[42] Although older individuals in this study may have had a greater prevalence of osteoporosis, the authors did not directly compare bone quality across the three groups, thereby limiting any specific insights into the association between osteoporosis and TAA outcomes[21].
Only one previous small chart review examined the relationship between BMD and TAA outcomes, and found no association between BMD and TAA revision, yet found a positive association between lower BMD and periprosthetic fracture[28]. In this study, including 30 TAA recipients with Housenfield unit (HU) measurements derived from computed tomography of the tibia, Cody et al. found that lower tibial HU values, which correspond with lower BMD, demonstrated a positive significant association with intraoperative and postoperative periprosthetic fracture.[28] There was no association between HU and TAA revision over a median of 2.4 years’ follow-up. However, Cody et al. only controlled for age, sex, and weight, and therefore the study findings are limited by potential unmeasured confounding related to comorbidities associated with complications of TAA, such as diabetes, smoking, and inflammatory arthritis.[18‐21] In addition, it was unclear how many patients in this study had osteoporosis. Differences between the markers used for low BMD in the study by Cody et al. and our present study may also explain the discrepancy compared to the present study regarding the likelihood of periprosthetic fracture. While HU is a continuous measure, a diagnosis of osteoporosis is a binary distinction based on a defined threshold of BMD based on the results of DXA, according to the World Health Organization (T-score of ≤ 2.5). While HU has been used widely for assessing bone quality, DXA remains the gold standard for clinically diagnosing osteoporosis.[44] Accordingly, our population appears more selective, potentially representing osteoporotic patients with a comparatively lower BMD than those with periprosthetic fracture in the Cody et al. study, who had a mean HU value of 204 (SD = 113). As HU values less than 100 are typically considered to represent osteoporosis, while those over 160 are considered normal,[45] the mean HU values among those with fracture in the Cody et al. study fell within a range greater than would be expected for osteoporosis.
Osteoporosis disproportionately affects women, particularly postmenopausal women, due to the rapid decline in estrogen levels, which plays a crucial role in maintaining BMD. In contrast, men experience a more gradual loss of BMD with aging, often driven by declining testosterone levels[46]. Despite the lower prevalence of osteoporosis in men, fractures in this population tend to result in greater morbidity and mortality compared to women[47, 48]. This disparity underscores the need for targeted screening and intervention strategies tailored to each gender. While this study did not stratify outcomes by gender, future research should investigate whether sex differences influence surgical outcomes, such as reoperation or periprosthetic fracture rates, after TAA.
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In general, osteoporosis is highly prevalent in older adults undergoing arthroplasty[49] and contributes to the increasing incidence of periprosthetic fracture and aseptic loosening after primary total knee or hip arthroplasty[22, 24, 50‐52]. In a recent investigation of a national administrative claims database of 418,054 patients by Harris et al., of which 10% had osteoporosis, the 5-year likelihood of all-cause revision after TKA after controlling for age, sex, and the Charlson comorbidity index was slightly higher for patients who had osteoporosis (hazard ratio = 1.1, 95% CI: 1.0, 1.2). Further, in that study, osteoporotic patients had an approximately twofold increased risk of 5-year revision for periprosthetic fracture after TKA after controlling for the aforementioned patient characteristics.[53] While it is unknown why these results for TKA differ than those from TAA in our study, differences may be attributed to implant type or joint mechanics, among other factors. Despite this, preoperative screening with DXA scan remains underutilized and thus the impact on osteoporosis remains underdeveloped[54]. However, there is growing evidence that bisphosphonate treatment pre- and postoperatively may have protective effects, as these medications have been shown to preserve periprosthetic BMD for more than 5 years after THA[55, 56]. Furthermore, in large propensity score analysis study of patients undergoing TKA, bisphosphonate use postoperatively was associated with a 50% reduction in fracture risk[57]. However, it is currently unknown if bisphosphonate or other osteoporosis medication use, such as denosumab, could contribute to a reduction in fracture risk in patients undergoing TAA.
In contrast to the hip and knee arthroplasty literature, our findings suggest that patients with osteoporosis are not at increased risk of requiring revision TAA or experiencing a periprosthetic fracture. The potential impact of osteoporosis on TAA might be counterbalanced by the protective effects of surgical techniques and implant technologies. Accordingly, intraoperative assessment of bone quality remains essential for surgical decision-making during TAA, regardless of the results of this study. Prophylactic fixation of the medial malleolus is advisable in patients with poor bone quality given the risk of medial malleolar stress fracture[58]. Further, increased bony support has been shown to reduce implant and bone micromotion and improve implant stability and decrease mechanical failures. Thus, when securing the implant components in the presence of deficient bone stock and instability around the ankle joint, the adoption of a modular long-stem tibial component and a talar implant reinforced by two talar pegs can enhance stability and reduce the risk of loosening[2]. In addition, patients with osteoporosis may have been more commonly treated with long-stem tibial components relative to patients without osteoporosis. Regardless, while robust tibial fixation may serve as a risk mitigation factor and may be the reason that osteoporosis was not associated with reoperation or periprosthetic fracture in this study, perioperative assessment and medical optimization of low bone density should be further investigated as a possible strategy to improve patient outcomes.
Our study exhibits both strengths and limitations that warrant consideration. Strengths include adherence to a registered protocol, inclusion of a multidisciplinary author team, a relatively large sample size, and robust propensity matching strategy. We were unable to stratify patients according to bone mineral density, or the bone density at the operative site. While these variables may confound the results, DXA scans are not routinely performed prior to TAA, and typically only include measurements of the hips and lumbar spine rather than the ankle. Furthermore, implant type, concomitant procedures, and surgeons’ level of experience may have represented unmeasured confounders. It was not feasible to examine the exact indication for reoperation, thereby limiting our insights into potential reasons for TAA failure. However, we attempted to mitigate this limitation by only selecting codes for TAA revision, implant removal, and implant repositioning, rather than codes directly for irrigation, for our primary outcome of reoperation. Furthermore, we were likewise unable to examine the exact mechanism for postoperative periprosthetic fracture, allowing room for future research on this topic. Another limitation of our study is that we did not examine intraoperative periprosthetic fractures due to methodology limitations, although these fractures may be less common than postoperative periprosthetic fractures[28]. A key limitation of this study is that while our power analysis was designed for the primary outcome (reoperation), the secondary outcome of periprosthetic fracture may have been underpowered. As a result, the lack of statistical significance should be interpreted with caution, and larger studies are needed to better assess this potential association. Finally, as our data derived from US academic medical centers, it remains possible that outcomes are not generalizable to non-academic settings, or countries outside of the US which may have a different prevalence of osteoporosis or different surgical approaches for TAA.
While there was no increased risk of revision TAA or postoperative periprosthetic fracture in patients with osteoporosis in the present study, further research is warranted to corroborate our findings. Studies would ideally have a similar or larger sample size, include more granular measures of bone mineral density such as T-score, and include at least 3 years’ follow-up. In addition, the protective effects of concomitant prophylactic fixation strategies and implant design geared towards osteoporotic bone remain to be studied.
Conclusion
The study found no statistically significant association between osteoporosis and likelihood of reoperation or postoperative periprosthetic fracture following primary TAA performed in academic medical centers, suggesting that osteoporosis is not a risk factor for these complications. While osteoporosis is often considered a complicating factor in orthopedic procedures, comparable reoperation rates between patients with and without osteoporosis underscore the potential efficacy of surgical techniques and implant technologies in TAA, raising hope in the increasing viability of TAA. Despite design-related limitations, this study represents the largest propensity score–matched analysis of patients undergoing TAA, either with or without osteoporosis. To date, this study constitutes the strongest evidence regarding the contribution of osteoporosis to rates of re-operation in TAA, suggesting that osteoporosis does not play a major role in TAA.
Declarations
Conflict of interest
None.
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Grundlagenwissen der Arthroskopie und Gelenkchirurgie erweitert durch Fallbeispiele, Videos und Abbildungen. Zur Fortbildung und Wissenserweiterung, verfasst und geprüft von Expertinnen und Experten der Gesellschaft für Arthroskopie und Gelenkchirurgie (AGA).
Lange Nüchternzeiten vor einer OP können unerwünschte gesundheitliche Konsequenzen haben. Ein multidisziplinäres internationales Team hat deswegen in einem Delphi-Prozess acht Empfehlungen formuliert, die die perioperative Versorgung verbessern sollen.
Es ist jetzt auch schon wieder rund 600 Millionen Jahre her, dass sich mit dem Erscheinen der Zweiseitentiere die Lateralität ausgebildet hat. In chirurgischen Fächern wie der Orthopädie hat das Folgen bis auf den heutigen Tag. Linkshänder wissen, wovon die Rede ist.
Glukokortikoide, die zu Beginn einer krankheitsmodifizierenden Rheumatherapie begleitend eingesetzt werden, lassen sich in den allermeisten Fällen planmäßig ausschleichen und absetzen: Das Ergebnis der ARCTIC-Studie spricht diesbezüglich für die europäische und gegen die US-amerikanische Leitlinie.
Laut einer Querschnittstudie leiden rund 7% der in Deutschland lebenden über 16-Jährigen unter chronischen Schmerzen, die ihren Alltag stark beeinträchtigen. Außer biologischen scheinen auch psychische und soziale Faktoren mit sogenanntem High-Impact Chronic Pain assoziiert zu sein.
