German DVO risk score identified more patients requiring treatment compared to FRAX

in Endocrine Connections
Authors:
Anna Frank University Children’s Hospital Oldenburg, Oldenburg, Germany

Search for other papers by Anna Frank in
Current site
Google Scholar
PubMed
Close
,
Judith Charlotte Witzel Department of Trauma, Orthopedics and Reconstructive Surgery, University Medical Center Göttingen, Göttingen, Germany

Search for other papers by Judith Charlotte Witzel in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0009-0007-9852-2905
,
Christina Heppner MVZ endokrinologikum Göttingen, Göttingen, Germany

Search for other papers by Christina Heppner in
Current site
Google Scholar
PubMed
Close
,
Annette Lamersdorf MVZ endokrinologikum Göttingen, Göttingen, Germany

Search for other papers by Annette Lamersdorf in
Current site
Google Scholar
PubMed
Close
,
Andreas Leha Institute for Medical Statistics, University Medical Center Göttingen, Göttingen, Germany

Search for other papers by Andreas Leha in
Current site
Google Scholar
PubMed
Close
, and
Heide Siggelkow Department of Trauma, Orthopedics and Reconstructive Surgery, University Medical Center Göttingen, Göttingen, Germany
MVZ endokrinologikum Göttingen, Göttingen, Germany

Search for other papers by Heide Siggelkow in
Current site
Google Scholar
PubMed
Close

Correspondence should be addressed to J C Witzel: judith.witzel1@agaplesion.de

(A Frank and J C Witzel contributed equally to this work)

Open access

Sign up for journal news

Purpose

Fracture risk determination is essential when recommending treatment in osteoporosis management. We calculated the risk probabilities of major osteoporotic and hip fractures using the DVO score established in German-speaking countries and the FRAX tool.

Methods

We retrospectively analysed data from 555 female patients (mean age 64.2 ± 10.3 years) evaluated for osteoporosis. As suggested by DVO guidelines before 2023, we set the therapy threshold of >30% for vertebral and hip fractures. Major osteoporotic fracture (MOF) and hip fracture risk (HF) were calculated based on corresponding FRAX scores. We applied the internationally most common therapy threshold of ≥20% for MOF and ≥3% for HF and determined the ‘DVO-equivalent risk levels’ for FRAX-based assessment.

Results

The DVO score identified 52.8% of women as having a 10-year risk of hip and vertebral fractures >30%. FRAX score for HF ≥ 3% without bone mineral density (BMD) identified the highest number of patients (56%). The proportion of female patients identified for treatment only by DVO score (14.6%) were more likely to present spinal fractures (38.3 vs 18.6%), whereas the 10.6% of patients only identified by FRAX including BMD presented more peripheral fractures (40.7 vs 29.6%). The thresholds for this ‘DVO-equivalent risk level’ for ‘FRAX with BMD’ would be ≥10% for MOF and ≥2.6% for HF.

Conclusion

Given the differences in the DVO and FRAX scores, we highly recommend considering both scores when assessing individual women for treatment.

Abstract

Purpose

Fracture risk determination is essential when recommending treatment in osteoporosis management. We calculated the risk probabilities of major osteoporotic and hip fractures using the DVO score established in German-speaking countries and the FRAX tool.

Methods

We retrospectively analysed data from 555 female patients (mean age 64.2 ± 10.3 years) evaluated for osteoporosis. As suggested by DVO guidelines before 2023, we set the therapy threshold of >30% for vertebral and hip fractures. Major osteoporotic fracture (MOF) and hip fracture risk (HF) were calculated based on corresponding FRAX scores. We applied the internationally most common therapy threshold of ≥20% for MOF and ≥3% for HF and determined the ‘DVO-equivalent risk levels’ for FRAX-based assessment.

Results

The DVO score identified 52.8% of women as having a 10-year risk of hip and vertebral fractures >30%. FRAX score for HF ≥ 3% without bone mineral density (BMD) identified the highest number of patients (56%). The proportion of female patients identified for treatment only by DVO score (14.6%) were more likely to present spinal fractures (38.3 vs 18.6%), whereas the 10.6% of patients only identified by FRAX including BMD presented more peripheral fractures (40.7 vs 29.6%). The thresholds for this ‘DVO-equivalent risk level’ for ‘FRAX with BMD’ would be ≥10% for MOF and ≥2.6% for HF.

Conclusion

Given the differences in the DVO and FRAX scores, we highly recommend considering both scores when assessing individual women for treatment.

Introduction

Worldwide, 6% of men and 21% of women between 50 and 84 years of age suffer from osteoporosis. Along with cardiovascular disease, stroke and cancer, osteoporosis is one of the most common health problems (1, 2). The disease is characterised by a pathological increase in fracture risk resulting from a reduction in bone mineral density (BMD) and bone quality.

The determination of individual fracture risks is becoming more relevant to the assessment and timely treatment of osteoporosis. Lately, the availability of anabolic therapeutic agents has increased the necessity to improve the prediction of fragility fractures and thus target therapeutic interventions in postmenopausal women more effectively (3). Risk factors other than BMD influence the fracture risk. Different tools have thus been developed to assess osteoporosis-associated fracture risk. (4, 5). The FRAX score is one such tool that was established in 2008 and updated several times to adapt to new developments (6). The FRAX score calculates an individual 10-year fracture risk by considering defined clinical risk factors and optionally the BMD at the femoral neck. The probability calculated is a country-specific risk of suffering a major osteoporotic fracture (MOF), including spinal, hip, shoulder and wrist fracture, or a hip fracture (HF), using an algorithm. There are no official therapy thresholds above which scores patients are recommended treatment. However, the internationally most commonly implemented therapy thresholds for FRAX are ≥20% for MOF and ≥3% for HF (7, 8, 9). Nowadays, age-dependent therapy thresholds are much more the norm, but were not available at the time of our data collection (10, 11, 12).

The score commonly used in German-speaking countries is based on the German guidelines for diagnostics and treatment of osteoporosis developed by the Dachverband Osteologie (German Confederation of Osteologists, DVO). The DVO score is a step-by-step approach (Table 1) that classifies patients in groups of 10-year fracture-risk incidences of suffering a hip or clinical vertebral fracture. Based on forty risk factors, patients are assessed whether they need further diagnostics. Starting from a 10-year fracture incidence >20%, further diagnostics including BMD measurement via dual-energy absorptiometry (DXA) are advised. Patients are stratified into groups with either a risk between 20 and 30% or >30%, depending on the combination of risk factors and BMD. Specific treatment for osteoporosis is recommended for patients with a 10-year fracture risk >30% (13, 14, 15). The first edition of these guidelines was published in 2003 and was revised in 2006, 2009, 2014 and 2017, in accordance with the relevant literature. In 2023, a new DVO score was published, representing a major change in risk assessment by calculating the 3-year fracture risk for 10-year fracture-risk probabilities of suffering a hip or clinical vertebral fracture and adapting different risk categories (16, 17).

Table 1

Comparison of DVO and FRAX score.

DVO score FRAX score
Many (version 2017 = 40) risk factors listed in the DVO guidelines 12 risk factors, DXA value optional
Different fracture types Prior fractures
DXA at lumbar spine OR total femur OR femoral neck DXA at femoral neck only, optional
Step-by-step evaluation of the 10-year fracture probability One step to the 10-year fracture incidence
Consistent therapy threshold >30% No therapy threshold set by tool
Dependent variable: 10-year fracture incidence of hip and vertebral fracture Dependent variable: 10-year fracture probability of major osteoporotic fracture or hip fracture
Published 10-year fracture risk calculation method 10-year fracture risk calculation algorithm
Additional risk factors with direct therapy recommendation

Aims

In respect of developing the new DVO score in 2023, it was discussed whether German-speaking countries would also apply the FRAX score in the clinical care of patients with osteoporosis. We were therefore interested in whether the FRAX score would have characterised women treated according to the DVO score before 2023. In this retrospective study, we compared risk scores and treatment recommendations of female patients in Germany based on DVO score, with the FRAX score effective at the time of clinical presentation. We adapted therapy thresholds frequently employed internationally (≥20% for MOF and ≥3% for HF) (15) for FRAX to our study group and contrasted them with therapy recommendations based on the DVO score. Owing to the fact that our patients were treated according to DVO score during the subsequent years, it is not possible to conclude which score is more accurate regarding fracture risk prediction.

Material and methods

Patients

We recorded data between July 2007 and June 2014 in the specialised MVZ Endokrinologikum Centre in Göttingen. The centre specialises in the diagnosis and treatment of patients with osteoporosis. Patients were mainly referred from other clinics or practices for evaluation of secondary osteoporosis, fracture risk and treatment consideration. Patients presented a high risk of suffering osteoporosis resulting from risk factors or fragility fractures.

In total, 710 female patients were initially assessed, with 555 (age 40–91 years) meeting the study criteria (documented risk factors for DVO score and FRAX score, DXA measurement at least one side of the femoral neck). Five hundred of these 555 patients underwent DXA measurement of the lumbar spine. A physician documented and validated all the data.

All patients provided informed consent to use of their individual data in an anonymised form for research purposes. The local ethics review board in Göttingen approved this approach with their decision dated February 18, 2007 (Ref. 18-2-07).

Data

Available data on general patient information (age, height and loss of height and body mass), medical history (medication, illnesses, lifestyle factors and risk factors for osteoporosis), family and fracture history (previously experienced, pathological and/or traumatic and fracture side) and laboratory parameters and DXA measurements were collected from the patients’ electronic medical records (MEDISTAR) retrospectively.

BMD and T-score at the femoral neck, the total femur and the lumbar spine (L1-L4) were measured by DXA. The majority (57.4%) of the measurements were performed at MVZ Endokrinologikum Göttingen using a GE Lunar Prodigy densitometer (GE Healthcare GmbH, Germany), the other 42.6% were performed in other radiological centres. We used the minimum T-score at the femoral neck to calculate the respective FRAX score.

Analysis

We utilised the web-based data interface of the country-specific version of the FRAX risk calculator for Germany, which is available on the University of Sheffield website (https://www.sheffield.ac.uk/FRAX/tool.aspx?lang=de). The required data include age, body mass, previous fracture, parental history of HF, current smoking, long-term use of oral glucocorticoids, rheumatoid arthritis, daily alcohol consumption, other causes of secondary osteoporosis and optionally the minimum T-score of the left and right femoral neck.

We implemented therapy thresholds for FRAX scores of ≥20% for a MOF and ≥3% for a HF (15).

Subsequently, we compared the calculated FRAX scores with the recommendations based on the DVO guidelines. Using a multistep approach, the DVO guidelines estimate 10-year fracture probabilities based on various risk factors and their different impact on fracture risk. Based on the DVO guidelines in effect at the time of the initial presentation, we used the valid DVO score with all risk factors (40 risk factors in the 2017 version) to determine the 10-year fracture risk. Patients with a 10-year fracture risk >20% are recommended to undergo DXA measurement. The therapy-indication table provided by the DVO guidelines, based on gender, age, a number of BMD-independent risk factors and DXA T-score, is used to determine the 10-year fracture risk. The DVO-score uses the minimum T-score at all sites including femoral neck, total femur and lumbar spine. A 10-year fracture probability >30% leads to the recommendation of specific anti-osteoporotic treatment.

Statistical analysis was performed with the software packages IBM SPSS Statistics Version 25 and Microsoft Excel 365 using descriptive statistics and Wilcoxon signed-ranked and McNemar tests. We used the McNemar test to analyse statistically the agreement of the respective therapy recommendations based on DVO and FRAX scores. The significance level was set at P < 0.05. Furthermore, we used a confusion matrix to analyse and visualise the sensitivities and specificities of the DVO and FRAX scores based on therapy recommendations and the prevalence of spinal fractures. The significance level was set at P < 0.05. Owing to the fact that women were treated according to their fracture risk, we cannot evaluate the true incidence of new fractures. Therefore, we cannot conclude which score predicts the actual fracture incidence more accurately from our data.

Results

In total, 555 female patients with a complete set of data were analysed. Table 2 summarises the baseline characteristics of these patients. The two scores approach risk factors differently. Smoking, glucocorticoid intake, rheumatoid arthritis and alcohol consumption are treated as causes of secondary osteoporosis in DVO, whereas these risk factors have to be entered separately into the FRAX interface. Viewing the secondary osteoporosis causes according to FRAX, we see that the onset of menopause before the age of 45 was the most common (95.23%). The most common secondary cause in DVO was vitamin-D deficiency in 28 of 116 patients (24.13%).

Table 2

Baseline characteristics of patients.

Baseline characteristics of patients n MVZ Endokrinologikum Göttingen
Age in years ±SD 555 64.21 (±10.3)
BMI in kg/m2 ±SD 555 24.94 (±4.66)
Prior fracture 292 52.61%
Parental hip fractures 77 13.87%
Current smoking 91 16.40%
Oral glucocorticoids (current intake >5 mg) 93 16.76%
Rheumatoid arthritis 39 7.03%
Alcohol three or more units a day 48 8.65%
Femoral neck T-score 555 −1.86 (±0.98)
Secondary osteoporosis (as described in FRAX) 126 22.70%
Secondary osteoporosis (as described in DVO) 116 20.90%

The average T-score at the femoral neck was −1.86 ± 0.98, and thus higher than the average T-score at the lumbar spine of −2.21 ± 1.23. The lowest T-score (−2.51 ± 0.99) of all measurement sites (femoral neck, total femur and lumbar spine) was even lower than at the lumbar spine. When only patients with any prior fracture were analysed, the minimum T-score at the femoral neck was significantly lower (P < 0.01, Wilcoxon test) than in those without any fracture (femoral T-score −1.99 ± 0.93 versus −1.72 ± 1.01).

Figure 1 illustrates fracture risk probabilities (vertical axis) according to the DVO score in our study population. The DVO score identified 52.8% (293 out of 555) as having a 10-year fracture risk of suffering a hip or vertebral fracture >30%. We identified a secondary cause of osteoporosis in 22.9% (67 out of 293) of these patients (12.1% of the total 555 patients).

Figure 1
Figure 1

10-year fracture probabilities (vertical axis) for hip or vertebral fractures according to the DVO score, split into primary (black) and secondary (grey) causes of osteoporosis.

Citation: Endocrine Connections 14, 5; 10.1530/EC-25-0048

The mean 10-year fracture probabilities according to FRAX were significantly higher when including BMD, both for MOF (P < 0.001) and HF (P < 0.001). We additionally calculated the mean individual FRAX 10-year fracture probability within the subgroups of <20%, 20–30%, and >30% fracture risk according to the DVO score. The mean FRAX score fracture probabilities with and without BMD proved to be lower than those determined for the DVO scores in all groups. Hence, the FRAX score identified fewer patients at risk. The mean FRAX score fracture probabilities for patients with a DVO score >30% without BMD proved to be 18.2 ± 11.4 for FRAX MOF and 8.9 ± 9.5 for FRAX HF. Including BMD, they were 17.4 ± 10.8 for FRAX MOF and 8.1 ± 9.1 for FRAX HF.

We calculated FRAX score thresholds that would identify the same percentage of patients (52.5%) as the DVO score. We found the adapted therapy thresholds for MOF to be markedly lower (without BMD 11.0%, with BMD 10.0%) in our female study population than internationally employed thresholds of ≥20%. Looking at the internationally common therapy threshold of ≥3% for HF, our adapted therapy threshold without BMD was higher (3.4%), whereas the value with BMD (2.6%) was below the common internationally implemented threshold.

We applied the therapy threshold of ≥3% for HF and ≥20 for MOF to compare the therapy indications for both the DVO and FRAX scores (Fig. 2). The most patients were identified by the FRAX score as requiring treatment adopting a threshold of 3% for HF without BMD followed by the DVO score, with the small difference being statistically insignificant (P = 0.705). The fewest patients were recommended treatment by the FRAX score based on a therapy threshold of≥ 20% for MOF with or without BMD. All FRAX scores, except FRAX for HF without BMD, showed a significant (P < 0.05) difference regarding their therapy recommendations in respect to the DVO score.

Figure 2
Figure 2

Therapy recommendations ‘yes’ (black) and ‘no’ (grey) based on DVO guidelines, FRAX score (grouped by therapy thresholds for MOF and HF). The figure displays a difference in the total number of therapy recommendations based on the score.

Citation: Endocrine Connections 14, 5; 10.1530/EC-25-0048

Aiming to determine the overlap of patients identified by each score, the greatest percentage concordance of 39.3% for women with therapy recommended was found between FRAX HF ≥ 3% without BMD and DVO. However, both scores identified different individual women in need of therapy. The FRAX score classified 16.8% of patients as in need of therapy who were not identified by DVO score. In comparison, 13.5% were identified by DVO but not by FRAX. Furthermore, FRAX HF ≥ 3% with BMD identified 72.4% of the patients recommended treatment according to the DVO score. The FRAX score for MOF ≥20% with BMD identified the fewest individual patients also identified by DVO score as requiring treatment.

Table 3 depicts specific differences for patients identified as in need of therapy by the DVO score and the FRAX score HF with BMD. These scores reveal the greatest overlap (38.2% of the total population) of patient-specific therapy decisions. We compared patient-specific parameters of females identified by both DVO and FRAX HF with BMD with those of each score to describe the difference in the risk profile of each score.

Table 3

Comparison of risk factors in patient groups identified as in need of therapy by DVO and FRAX HF with BMD, only by DVO score and only by FRAX HF with BMD.

Risk factors of patients DVO and FRAX HF with BMD overlap (n = 212) DVO only (n = 81) FRAX HF with BMD only (n = 59)
Prior fracture 69.8% 49.4% 62.7%
• Spinal fracture 42.0% 38.3% 18.6%
• Peripheral fractures at the age of 50 years 49.1% 29.6% 40.7%
Parent hip fractures 18.4% 13.6% 8.5%
Current smoking 18.9% 13.6% 22.0%
Oral glucocorticoids (current intake >5 mg) 23.1% 11.1% 16.9%
Rheumatoid arthritis 11.3% 2.5% 5.1%
Alcohol three units or more 9.9% 8.6% 10.2%
Secondary osteoporosis 20.8% 28.4% 15.3%
Femoral neck T-score −2.5 ± 0.7 −1.4 ± 0.7 −2.3 ± 0.7
Total femur T-score −2.4 ± 0.8 −1.5 ± 0.9 −2.0 ± 0.9
Lumbar spine T-score −2.7 ± 1.1 −2.4 ± 1.4 −2.1 ± 1.2

We conclude that certain risk factors have differing influence on the calculation of the DVO and FRAX scores, resulting in different treatment recommendations to patients. The number of prior spinal fractures was more than twice as high in the females identified only by the DVO score (38.3 vs 18.6%). Furthermore, those patients only identified by DVO score were revealed to have a higher percentage of parental HFs in their history (13.6 vs 8.5%). Patients only identified by the FRAX score were also revealed as having a higher prevalence of peripheral fractures (40.7 vs 29.6%).

Looking at risk factors other than fracture history, those patients only identified by FRAX HF with BMD were found to have a higher percentage prevalence of the risk factor ‘current smoking’ (22 vs 16.6%). In addition, the risk factor ‘rheumatoid arthritis’ was 5.1 vs 2.5% in DVO score. Nevertheless, patients identified by the DVO score presented secondary osteoporosis more often (28.4 vs 15.3%).

We used a confusion matrix to analyse the respective sensitivities and specificities of the two scores in regard of the predictive value of prevalent spinal fractures. In comparison, the DVO score proved to have a greater and significant sensitivity (0.8, P < 0.001), precision (0.41, P = 0.002), negative predictive value (0.86, P < 0.001) and accuracy (0.63, P < 0.001) than the FRAX score for HF with BMD (sensitivity (0.67, P < 0.001), precision (0.37, P < 0.001), negative predictive value (0.82, P < 0.001) and accuracy (0.60, P < 0.001)). Specificity was nearly identical between FRAX (0.58, P = 0.002) and DVO score 0.57 (P = 0.004).

Discussion

This single-centre retrospective study compared the respective fracture risks calculated by the DVO and FRAX scores with and without BMD measurements in 555 female patients evaluated for osteoporosis in a specialised endocrinological centre between 2007 and 2014. The study population was not a representative sample of the female population, presenting a higher risk of suffering from osteoporosis compared to the general population. The FRAX and DVO scores applied for these women differ in the number of risk factors assessed and their algorithms of 10-year fracture-risk calculation. The FRAX score implements an algorithm to calculate 10-year fracture probabilities of a MOF or HF. The score may be used without any DXA measurements, or with BMD or T-score values (which are based on a female reference population that is applied to both men and women alike). In contrast, the DVO score used during the investigated period presents a step-by-step, replicable 10-year fracture assessment. The risk of suffering an osteoporotic hip or vertebral fracture is calculated based on the T-score (using a female reference population). The DVO guideline recommends specific anti-osteoporotic therapy at a fixed threshold of a 10-year fracture risk >30%. On the other hand, the FRAX score does not supply any official therapy threshold. A systematic review of numerous countries revealed a therapy threshold for MOF is commonly ≥20% and ≥3% for HF (15, 18).

There are hardly any data published with the German DVO score in comparison with the FRAX score (19, 20). To better analyse the similarities and differences of the scores, we compared both scores with respect to their indication for anti-osteoporotic treatment. As already stated above, we were not able to determine which score predicted the 10-year fracture risk more accurately with this approach.

In accordance with the version of the DVO guidelines applied, patients were recommended further treatment when the estimated 10-year fracture risk is ≥30%. Comparing the 10-year fracture risks that FRAX calculated to those determined by the DVO guidelines, only the FRAX score for HF without BMD was similar. The FRAX score for MOF (with and without BMD) indicated a lower risk than the DVO score. We assume the following reasons for this observation: the DVO score and the FRAX score do not estimate the same fracture outcomes. The DVO score estimates fracture risks based on prevalent and clinical hip and vertebral fractures, whereas FRAX only estimates the risk dependent on clinical fractures including those of the spine, shoulder and forearm in the MOF category. Furthermore, our study population represented females suffering from severe forms of osteoporosis, with a large prevalence (52.6%) of prior fractures including those of the spine.

Interestingly, below the age of 65 years, the fracture risk calculated by FRAX was higher when including BMD compared to FRAX without BMD (data not shown). This relationship reversed for women above the age of 65 years, with higher percentages for FRAX without BMD. This may reflect the changing influence of BMD in FRAX score calculation, possibly caused by the increasing importance of the different risk factors during ageing. There was no further increase in FRAX scores after the age of 80 years, perhaps owing to the inclusion of mortality risk in the FRAX score calculation.

Taking the internationally common therapy thresholds of 10-year fracture risk ≥20% for MOF and ≥3% for HF into account, therapy recommendations did not differ significantly with BMD scores. However, the individual patients identified were not the same and there were patients only identified either by FRAX or by DVO as requiring treatment.

If exactly the 52.8% of patients identified by the DVO score as in need of therapy were to be treated according to the FRAX scores, the therapy thresholds would need to be ≥2.6% with BMD and ≥3.4% without BMD for HF. The adapted therapy thresholds nearly match or even exceed the internationally recommended therapy threshold of ≥3% for HF. The adapted therapy thresholds for the 52.8% of patients identified by the DVO score as requiring therapy strongly differ (≥10% without BMD; ≥11% with BMD) from the internationally recommended therapy threshold of ≥20% for FRAX MOF. This implies that female patients in Germany are treated at an earlier time point and at lower fracture risk compared to other European countries implementing FRAX. We concluded that the FRAX score appears to discriminate better for HF than for MOF. This observation is in accordance with the findings of other studies (20, 21, 22).

There might be several reasons for the difference between the FRAX and DVO scores and the differing therapy recommendations for the patients in our study population. One explanation might be that the DVO implements the lowest T-score of all measurement sites, and specifically including the spine. DXA measurement of the total femur or femoral neck has the best predictive value for HFs, whereas the measurement of the lumbar spine has a better predictive value for vertebral fractures (23). There is no evidence of any improvement in stratification of fracture risk by using the lowest T-score of several measurement sites including the spine versus taking T-score of the hip alone (24, 25). An epidemiological study from Canada even revealed an overestimation of the fracture risk by taking the lumbar spine instead of femoral neck as a BMD parameter for FRAX. One might discuss whether the DVO score overestimates 10-year fracture risks by using the lowest T-score of all measurement sites.

To analyse the scores further, we contrasted the patient-specific sets of risk factors of patients identified by each score. Spinal fractures were identified as being twice as common in women identified only by the DVO score as requiring therapy. The possible assumption is that the DVO score is more sensitive when detecting vertebral fractures in female patients by considering the lowest T-scores. Women only identified by the DVO score as in need of treatment were positive for more secondary osteoporosis risk factors. In contrast, those only identified by the FRAX HF score as in need of treatment had more peripheral fractures at the age of 50 were more likely to be smokers, took more glucocorticoids and the T-score at the femoral neck was nearly one point lower compared to those only identified by the DVO score (−2.3 versus −1.4, respectively). We applied a confusion matrix to analyse the respective sensitivities and specificities of each score in terms of the predictive value of recording prevalent spinal fractures. Whereas the DVO score was more sensitive (0.8 versus 0.67) for patients with prevalent spinal fractures, both scores demonstrated similar specificities (0.57 versus 0.58).

In summary, the DVO score and FRAX score do not identify the same individual female patients at risk of fracture in our study population. In this retrospective study of females presenting a higher risk of suffering from osteoporosis compared to the general population, only the FRAX score for HF without BMD (internationally common therapy threshold ≥3%) identified a similar percentage of patients compared to the DVO score. The patients in our study who were identified by the DVO score as requiring treatment corresponded to a FRAX with BMD hip-fracture risk threshold of ≥2.6%. However, using the FRAX score for MOF with and without BMD for our study population, the results demonstrate that the German therapy threshold corresponds to ≥11% (MOF with BMD) or ≥10% (MOF without BMD). Hence, therapy was clearly recommended at a lower risk compared to the internationally common therapy threshold of ≥20%.

Our study results have a number of practical implications for the treatment of patients with osteoporosis in German-speaking countries. The DVO score was better at identifying particularly women with a high-risk profile of vertebral fractures. Given the fact that the German version of the FRAX score is used in some areas of Austria and Switzerland, our data increase the awareness of differences in treatment decision. Our results also had major impact on the development of the new DVO guidelines. Initially, there was discussion on using the German version of the FRAX score in Germany also; however, our comparison of these two scores in men (20) and women supported the revision of the DVO score instead. More generally, our data may also contribute to the discussion on country-specific solutions to identify high-risk patients with osteoporosis as early as possible (26). Furthermore, the German government and institutions responsible for treatment decisions and reimbursement policies accept the DVO score in the version valid at any specific time.

There are a number of limitations to our study. The study group does not reflect epidemiological data, but women evaluated for osteoporosis in a specialised osteoporosis centre. In addition, there are no valid data on fracture incidence in these women, because the patients included in this study were treated based on their individual risk after being evaluated. Therefore, we cannot conclude which score predicts the actual fracture incidence more accurately.

Since the analysis of the women in our study, FRAX and the DVO score have been continuously undergoing revision. It will be interesting to compare these tools in future trials, especially with respect to the identification of high-risk patients. The ultimate priority of any of these risk scores should be to identify the high-risk patients as early as possible to prevent fractures.

In conclusion, the comparison of the international FRAX score and the German DVO score valid before 2023 in a preselected female study population with a higher risk of suffering from osteoporosis compared to the general population revealed clear differences. The DVO score proved to be better at identifying particularly women with a high-risk profile of vertebral fractures. Given the goal is to identify women at high risk of fracture as early as possible to prevent fractures, it would be highly advisable to use the score most appropriate in the respective country.

Declaration of interest

Anna Frank, Judith Charlotte Witzel, Christina Heppner, Annette Lamersdorf and Andreas Leha declare that they have no conflict of interest. Heide Siggelkow is a member of the committee of the German guidelines for diagnostics and treatment of osteoporosis developed by the Dachverband Osteologie (German Confederation of Osteologists, DVO).

Funding

We acknowledge support of open access publication charges by Göttingen University.

Acknowledgements

We thank A Entwistle for his assistance with the English version of the manuscript.

References

  • 1

    Shariati-Sarabi Z , Rezaie HE , Milani N , et al. Evaluation of bone mineral density in perimenopausal period. Arch Bone Jt Surg 2018 6 5762.

  • 2

    Hernlund E , Svedbom A , Ivergård M , et al. Osteoporosis in the European Union: medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA). Arch Osteoporos 2013 8 136. (https://doi.org/10.1007/s11657-013-0136-1)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Kanis JA , Harvey NC , McCloskey E , et al. Algorithm for the management of patients at low, high and very high risk of osteoporotic fractures. Osteoporos Int 2020 31 112. (https://doi.org/10.1007/s00198-019-05176-3)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    Professor Kanis JA , Melton LJ 3rd , Christiansen C , et al. The diagnosis of osteoporosis. J Bone Miner Res 1994 9 11371141. (https://doi.org/10.1002/jbmr.5650090802)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Cruz AS , Lins HC , Medeiros RVA , et al. Artificial intelligence on the identification of risk groups for osteoporosis, a general review. Biomed Eng Online 2018 17 12. (https://doi.org/10.1186/s12938-018-0436-1)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    McCloskey EV , Harvey NC , Johansson H , et al. Fracture risk assessment by the FRAX model. Climacteric 2022 25 2228. (https://doi.org/10.1080/13697137.2021.1945027)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    Kanis JA , Johnell O , Oden A , et al. Ten-year risk of osteoporotic fracture and the effect of risk factors on screening strategies. Bone 2002 30 251258. (https://doi.org/10.1016/s8756-3282(01)00653-6)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Kanis JA , Oden A , Johnell O , et al. The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int 2007 18 10331046. (https://doi.org/10.1007/s00198-007-0343-y)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Kanis JA , McCloskey EV , Johansson H , et al. Development and use of FRAX® in osteoporosis. Osteoporos Int 2010 21 407413. (https://doi.org/10.1007/s00198-010-1253-y)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Grigorie D , Sucaliuc A , Johansson H , et al. FRAX-based intervention and assessment thresholds for osteoporosis in Romania. Arch Osteoporos 2013 8 164. (https://doi.org/10.1007/s11657-013-0164-x)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Khashayar P , Keshtkar A , Ostovar A , et al. FRAX-based intervention and assessment thresholds for osteoporosis in Iran. Osteoporos Int 2019 30 22252230. (https://doi.org/10.1007/s00198-019-05078-4)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Johansson H , Azizieh F , Al Ali N , et al. FRAX- vs T-score-based intervention thresholds for osteoporosis. Osteoporos Int 2017 28 30993105. (https://doi.org/10.1007/s00198-017-4160-7)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Faßbender W , Scheidt-Nave C & Pfeilschifter J . Die neuen Leitlinien zur Osteoporose. Dtsch Med Wochenschr 2003 128 16151617. (https://doi.org/10.1055/s-2003-40930)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Neuerburg C , Schmidmaier R , Schilling S , et al. Identification, diagnostics and guideline conform therapy of osteoporosis (DVO) in trauma patients: a treatment algorithm. Unfallchirurg 2015 118 913924. (https://doi.org/10.1007/s00113-015-0071-2)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15

    Kanis JA , Harvey NC , Cooper C , et al. A systematic review of intervention thresholds based on FRAX: a report prepared for the National Osteoporosis Guideline Group and the International Osteoporosis Foundation. Arch Osteoporos 2016 11 25. (https://doi.org/10.1007/s11657-016-0278-z)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16

    Siggelkow H & Thomasius F . Osteoporosis – implications of the new guidelines in practice. Dtsch Med Wochenschr 2024 149 684689. (https://doi.org/10.1055/a-2127-2927)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17

    Ralf S , Hadji P , Kern P , et al. Recommendations for the pharmacological treatment of osteoporosis – update 2023 of the German osteoporosis guideline. Osteologie 2023 32 115122. (https://doi.org/10.1055/a-2034-6086)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18

    Kanis JA , Compston J , Cooper C , et al. SIGN guidelines for Scotland: BMD versus FRAX versus QFracture. Calcif Tissue Int 2016 98 417425. (https://doi.org/10.1007/s00223-015-0092-4)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19

    Hadji P , Klein S , Gothe H , et al. The epidemiology of osteoporosis--Bone Evaluation Study (BEST): an analysis of routine health insurance data. Dtsch Arztebl Int 2013 110 5257. (https://doi.org/10.3238/arztebl.2013.0052)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20

    Witzel JC , Giessel A , Heppner C , et al. Discrepancies between osteoporotic fracture evaluations in men based on German (DVO) osteoporosis guidelines or the FRAX score. Exp Clin Endocrinol Diabetes 2023 131 114122. (https://doi.org/10.1055/a-1977-4413)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Bolland MJ , Siu AT , Mason BH , et al. Evaluation of the FRAX and Garvan fracture risk calculators in older women. J Bone Miner Res 2011 26 420427. (https://doi.org/10.1002/jbmr.215)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22

    Sandhu SK , Nguyen ND , Center JR , et al. Prognosis of fracture: evaluation of predictive accuracy of the FRAX algorithm and Garvan nomogram. Osteoporos Int 2010 21 863871. (https://doi.org/10.1007/s00198-009-1026-7)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    El Maghraoui A & Roux C . DXA scanning in clinical practice. QJM 2008 101 605617. (https://doi.org/10.1093/qjmed/hcn022)

  • 24

    Kanis JA , Johnell O , Oden A , et al. The use of multiple sites for the diagnosis of osteoporosis. Osteoporos Int 2006 17 527534. (https://doi.org/10.1007/s00198-005-0014-9)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    Leslie WD , Lix LM , Tsang JF , et al. Single-site vs multisite bone density measurement for fracture prediction. Arch Intern Med 2007 167 16411647. (https://doi.org/10.1001/archinte.167.15.1641)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    Cortet B , Guañabens N , Brandi ML , et al. Similarities and differences between European guidelines for the management of postmenopausal osteoporosis. Arch Osteoporos 2024 19 84. (https://doi.org/10.1007/s11657-024-01441-z)

    • PubMed
    • Search Google Scholar
    • Export Citation

 

  • Collapse
  • Expand
  • Figure 1

    10-year fracture probabilities (vertical axis) for hip or vertebral fractures according to the DVO score, split into primary (black) and secondary (grey) causes of osteoporosis.

  • Figure 2

    Therapy recommendations ‘yes’ (black) and ‘no’ (grey) based on DVO guidelines, FRAX score (grouped by therapy thresholds for MOF and HF). The figure displays a difference in the total number of therapy recommendations based on the score.

  • 1

    Shariati-Sarabi Z , Rezaie HE , Milani N , et al. Evaluation of bone mineral density in perimenopausal period. Arch Bone Jt Surg 2018 6 5762.

  • 2

    Hernlund E , Svedbom A , Ivergård M , et al. Osteoporosis in the European Union: medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA). Arch Osteoporos 2013 8 136. (https://doi.org/10.1007/s11657-013-0136-1)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Kanis JA , Harvey NC , McCloskey E , et al. Algorithm for the management of patients at low, high and very high risk of osteoporotic fractures. Osteoporos Int 2020 31 112. (https://doi.org/10.1007/s00198-019-05176-3)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    Professor Kanis JA , Melton LJ 3rd , Christiansen C , et al. The diagnosis of osteoporosis. J Bone Miner Res 1994 9 11371141. (https://doi.org/10.1002/jbmr.5650090802)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Cruz AS , Lins HC , Medeiros RVA , et al. Artificial intelligence on the identification of risk groups for osteoporosis, a general review. Biomed Eng Online 2018 17 12. (https://doi.org/10.1186/s12938-018-0436-1)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    McCloskey EV , Harvey NC , Johansson H , et al. Fracture risk assessment by the FRAX model. Climacteric 2022 25 2228. (https://doi.org/10.1080/13697137.2021.1945027)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    Kanis JA , Johnell O , Oden A , et al. Ten-year risk of osteoporotic fracture and the effect of risk factors on screening strategies. Bone 2002 30 251258. (https://doi.org/10.1016/s8756-3282(01)00653-6)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Kanis JA , Oden A , Johnell O , et al. The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int 2007 18 10331046. (https://doi.org/10.1007/s00198-007-0343-y)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Kanis JA , McCloskey EV , Johansson H , et al. Development and use of FRAX® in osteoporosis. Osteoporos Int 2010 21 407413. (https://doi.org/10.1007/s00198-010-1253-y)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Grigorie D , Sucaliuc A , Johansson H , et al. FRAX-based intervention and assessment thresholds for osteoporosis in Romania. Arch Osteoporos 2013 8 164. (https://doi.org/10.1007/s11657-013-0164-x)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Khashayar P , Keshtkar A , Ostovar A , et al. FRAX-based intervention and assessment thresholds for osteoporosis in Iran. Osteoporos Int 2019 30 22252230. (https://doi.org/10.1007/s00198-019-05078-4)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Johansson H , Azizieh F , Al Ali N , et al. FRAX- vs T-score-based intervention thresholds for osteoporosis. Osteoporos Int 2017 28 30993105. (https://doi.org/10.1007/s00198-017-4160-7)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Faßbender W , Scheidt-Nave C & Pfeilschifter J . Die neuen Leitlinien zur Osteoporose. Dtsch Med Wochenschr 2003 128 16151617. (https://doi.org/10.1055/s-2003-40930)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Neuerburg C , Schmidmaier R , Schilling S , et al. Identification, diagnostics and guideline conform therapy of osteoporosis (DVO) in trauma patients: a treatment algorithm. Unfallchirurg 2015 118 913924. (https://doi.org/10.1007/s00113-015-0071-2)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15

    Kanis JA , Harvey NC , Cooper C , et al. A systematic review of intervention thresholds based on FRAX: a report prepared for the National Osteoporosis Guideline Group and the International Osteoporosis Foundation. Arch Osteoporos 2016 11 25. (https://doi.org/10.1007/s11657-016-0278-z)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16

    Siggelkow H & Thomasius F . Osteoporosis – implications of the new guidelines in practice. Dtsch Med Wochenschr 2024 149 684689. (https://doi.org/10.1055/a-2127-2927)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17

    Ralf S , Hadji P , Kern P , et al. Recommendations for the pharmacological treatment of osteoporosis – update 2023 of the German osteoporosis guideline. Osteologie 2023 32 115122. (https://doi.org/10.1055/a-2034-6086)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18

    Kanis JA , Compston J , Cooper C , et al. SIGN guidelines for Scotland: BMD versus FRAX versus QFracture. Calcif Tissue Int 2016 98 417425. (https://doi.org/10.1007/s00223-015-0092-4)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19

    Hadji P , Klein S , Gothe H , et al. The epidemiology of osteoporosis--Bone Evaluation Study (BEST): an analysis of routine health insurance data. Dtsch Arztebl Int 2013 110 5257. (https://doi.org/10.3238/arztebl.2013.0052)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20

    Witzel JC , Giessel A , Heppner C , et al. Discrepancies between osteoporotic fracture evaluations in men based on German (DVO) osteoporosis guidelines or the FRAX score. Exp Clin Endocrinol Diabetes 2023 131 114122. (https://doi.org/10.1055/a-1977-4413)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Bolland MJ , Siu AT , Mason BH , et al. Evaluation of the FRAX and Garvan fracture risk calculators in older women. J Bone Miner Res 2011 26 420427. (https://doi.org/10.1002/jbmr.215)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22

    Sandhu SK , Nguyen ND , Center JR , et al. Prognosis of fracture: evaluation of predictive accuracy of the FRAX algorithm and Garvan nomogram. Osteoporos Int 2010 21 863871. (https://doi.org/10.1007/s00198-009-1026-7)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    El Maghraoui A & Roux C . DXA scanning in clinical practice. QJM 2008 101 605617. (https://doi.org/10.1093/qjmed/hcn022)

  • 24

    Kanis JA , Johnell O , Oden A , et al. The use of multiple sites for the diagnosis of osteoporosis. Osteoporos Int 2006 17 527534. (https://doi.org/10.1007/s00198-005-0014-9)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    Leslie WD , Lix LM , Tsang JF , et al. Single-site vs multisite bone density measurement for fracture prediction. Arch Intern Med 2007 167 16411647. (https://doi.org/10.1001/archinte.167.15.1641)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    Cortet B , Guañabens N , Brandi ML , et al. Similarities and differences between European guidelines for the management of postmenopausal osteoporosis. Arch Osteoporos 2024 19 84. (https://doi.org/10.1007/s11657-024-01441-z)

    • PubMed
    • Search Google Scholar
    • Export Citation