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Open Access

Peer-reviewed

Research Article

Clinical Value of Prognostic Instruments to Identify Patients with an Increased Risk for Osteoporotic Fractures: Systematic Review

  • Johann Steurer ,

    Johann.steurer@usz.ch

    Affiliation Horten Center for Patient-Oriented Research and Knowledge Transfer, University of Zurich, Zurich, Switzerland

    ⨯
  • Cyrill Haller,

    Affiliation Horten Center for Patient-Oriented Research and Knowledge Transfer, University of Zurich, Zurich, Switzerland

    ⨯
  • HansJörg Häuselmann,

    Affiliation Center for Rheumatology and Bone Disease Clinic, Zurich, Switzerland

    ⨯
  • Florian Brunner,

    Affiliations Horten Center for Patient-Oriented Research and Knowledge Transfer, University of Zurich, Zurich, Switzerland, Department of Physical Medicine and Rheumatology, Balgrist University Hospital, Zurich, Switzerland

    ⨯
  • Lucas M. Bachmann

    Affiliation Horten Center for Patient-Oriented Research and Knowledge Transfer, University of Zurich, Zurich, Switzerland

    ⨯

Correction

22 Aug 2011: Steurer J, Haller C, Häuselmann H, Brunner F, Bachmann LM (2011) Correction: Clinical Value of Prognostic Instruments to Identify Patients with an Increased Risk for Osteoporotic Fractures: Systematic Review. PLOS ONE 6(8): 10.1371/annotation/7596ad0d-c602-4e5f-a7ae-caf7d5802720. https://doi.org/10.1371/annotation/7596ad0d-c602-4e5f-a7ae-caf7d5802720 View correction

Abstract

Background

With the broad availability of effective medications, identifying individuals bearing a higher risk for osteoporotic fractures has become an issue of major concern in modern medicine. In recent years various prognostic instruments have become available showing conflicting results regarding estimated risks for individual patients.

Objective

To provide an overview of current evidence and of opportunities for further research.

Methodology/Principal Findings

Systematic Review: We identified studies describing the development of instruments and all subsequent validations in electronic databases and reference lists of included studies. We screened for inclusion, read full papers and extracted data on salient clinical features, performance characteristics and quality in duplicate. Searches retrieved 5,275 records of which full texts of 167 papers were obtained after screening titles and abstract. We included 35 studies enrolling a total of 609,969 patients (median 2546) reporting on 31 derivations and 12 validations after assessing full texts. Median follow-up time was 4.1 years (IQR 3 to 7.7). Only four studies validated an instrument that was developed by another group. None of the existing instruments was validated more than once. The five most frequent included variables in the final model were age, body mass index, bone mass index, past history of falls, and maternal history of fractures. The methodological quality of the studies was moderate.

Conclusion

There is a plethora of evidence available studying the association of risk profiles and the development of osteoporotic fractures. The small number of out-of-sample validations, the large variety of study characteristics, outcomes and follow-up periods impedes from deriving robust summaries and from conclusions regarding the clinical performance of many tools. First and foremost, future activity in this field should aim at reaching a consensus among clinical experts in respect to the existing instruments. Then we call for careful validations and expedient adaptations for local circumstances of the most promising candidates.

Citation: Steurer J, Haller C, Häuselmann H, Brunner F, Bachmann LM (2011) Clinical Value of Prognostic Instruments to Identify Patients with an Increased Risk for Osteoporotic Fractures: Systematic Review. PLoS ONE 6(5): e19994. https://doi.org/10.1371/journal.pone.0019994

Editor: Ulrich Thiem, Marienhospital Herne - University of Bochum, Germany

Received: January 18, 2011; Accepted: April 8, 2011; Published: May 18, 2011

Copyright: © 2011 Steurer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was sponsored by an unrestricted grant of the Swiss Association against Osteoporosis. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Osteoporotic fractures are a major cause of morbidity in the elderly and a considerable burden to health care services. Fractures necessitate hospitalizations in many cases and result in loss of function and autonomy of elderly people. Derived from present data it has been forecasted that 20% of 50-year old men and half of 50-year-old women will suffer from at least one clinical fracture during their remaining lifetime [1]. In Switzerland, the direct and indirect costs spent in caring for one patient with hip fracture is about 70.000 CHF (about 70.000 US $).

Preventive measures to reduce fractures therefore have high priority. Various lifestyle modifications and drugs have shown to reduce the decline in bone mineral density and the risk of osteoporotic fractures. An efficient strategy would be to identify persons with a moderate or high risk of fractures who might benefit from preventive or therapeutic measures. Measurement of bone mineral density alone may represent an inaccurate predictor of osteoporotic fractures and most fractures occur in women with normal bone mineral density [2]. A risk prediction rule to estimate the individual future fracture risk accurately would be helpful. In the last twenty years different risk prediction rules have been developed, but none of them, like other prediction rules, has found broad acceptance in medical practice so far.

The purpose of this review was to provide an overview of current evidence and of opportunities for further research. Moreover we explored whether setting-up a study developing a new prognostic instrument for the Swiss population is justified in light of the existing literature.

Methods

Literature search

In a first phase we searched for derivation studies in the following databases; Ovid MEDLINE (Ovid version, from inception to May 2009), and EMBASE and Scopus (from inception to May 2009). We used the following search terms: osteoporosis, bone loss, fractures, risk, model, logistic regression, decision, and validation. The detailed search strategy in Ovid Medline is available on Table S5. The search was conducted without restrictions to language or year of publication. We also hand-searched the bibliography of all studies ordered in full text.

In a second phase, after identifying the original derivation studies of prediction rules, we used these references to search in “ISI Web of Knowledge” (http://apps.isiknowledge.com) for corresponding validation studies. This database provides detailed information on how often and by whom a published paper has been cited (last access date on May 25, 2009). We assumed that all validations of an existing prediction rule would cite the derivation study.

Selection criteria

We included studies describing the development or validation of prognostic instruments to identify individuals with an increased risk for osteoporotic fractures. The minimum requirement was that the study reported the final model, i.e. the selected indicators along with the strength of association with one of the following outcomes: hip/femur fracture, vertebral fracture or any/non-vertebral fracture. Moreover, a study had to provide a description about recruitment of participants and methods for variable selection.

Study selection

Derivation studies.

Two reviewers independently screened the titles and the abstracts of all retrieved references to identify derivation studies of prognostic instruments. Full text versions were ordered for all publications classified by any one of the reviewers as potentially relevant.

Validation studies.

Two reviewers independently screened the titles and abstracts of all references to identify validation studies of one of the derivation studies. Full text versions were ordered for all potentially relevant publications.

Data extraction

We developed article review forms that were pilot tested and revised before use. The two reviewers independently assessed each selected study by recording details about study sites, inclusion and exclusion criteria, number of eligible patients and number of included patients. In addition, the reviewers recorded the definitions for various risk factors and outcomes, kind of data collection (prospective or retrospective) and the statistical method to derive the prediction rule. We contacted one author of for further information about missing data but did not get any response [3].

The two reviewers determined the agreement about data extraction and final inclusion by consensus. If there was a disagreement, a third author made the conclusive decision.

Results

In duplicate we screened 5275 records and ordered full texts of 167 papers. In total we included 35 studies reporting on 31 derivations [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34] and 12 validations [8], [11], [13], [15], [23], [26], [32], [33], [35], [36], [37], [38] of instruments published between 1990 and 2009. In total 609,969 patients (median 2546, interquartile range: 1435 to 7654, range: 672 to 197,848) have been enrolled in the 35 studies. The study selection process is summarized in Figure 1. Median follow-up time was 4.1 years (interquartile range 3 to 7.7; range 1 to 15). In five studies duration of follow up was not reported [6], [13], [29], [30], [33]. Only four studies [35], [36], [37], [38] validated an instrument that was developed by another group. None of the existing instruments was validated more than once. We found substantial variation in respect to variable selection, definition of risk indicators, outcome assessment and forms of the final model. For details please refer to Table S1.

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Figure 1. Flow of included studies.

https://doi.org/10.1371/journal.pone.0019994.g001

Assessment of methodological quality

In 29 studies data were collected prospectively [4], [5], [7], [8], [9], [11], [12], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [34], [35], [36], [37], six studies were based on registry data [6], [10], [13], [32], [33], [38]. Measurement of the outcome, vertebral fractures, was executed differently; in some studies the rate of vertebral fractures has been determined on clinical symptoms only [6], [8], [15], [16], [18], [23], [25], [31], [32], [33], [36], [37], [38], in others by radiography [4], [34], [35]. In six studies a random sample of participants were included [4], [5], [11], [14], [26], [35], in 3 participants were included in a consecutive manner [12], [18], [32] and in 17 studies an arbitrary sample has been evaluated [7], [8], [9], [15], [16], [19], [20], [21], [22], [23], [24], [25], [27], [28], [31], [34], [38] and in 9 no information about inclusion has been reported [6], [10], [13], [15], [17], [29], [30], [33], [37]. Variables were defined clearly and clinically sensible in 34 studies [4], [5], [6], [7], [8], [9], [10], [11], [12], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38]. In general the quality of the studies was moderate. The details are shown in Table S2.

Assessed Outcomes

Hip fracture only was the assessed outcome in 12 studies [7], [8], [11], [16], [19], [21], [22], [26], [30], [34], [35], [36], non-vertebral fractures only in 3 studies [12], [24], [27] and all fractures in 19 studies [4], [5], [6], [9], [10], [14], [18], [20], [23], [28], [29], [31], [33], [37], [38]. The details are given in Table S3.

Statistical methods applied in derivation studies

Logistic regression analysis was the most commonly used statistical model (n = 14) to derive the instrument [4], [5], [6], [7], [8], [10], [17], [19], [23], [25], [27], [28], [30], [31] and 12 studies used survival models (proportional hazard regression) [11], [12], [15], [18], [20], [21], [22], [24], [26], [32], [33], [34]. A Classification and Regression Tree (CART) algorithm was used in one study [9] and Poisson regression in another study [16].

Variables in the final model

The five most frequent included variables in the final model were age, body mass index, bone mass index, past history of falls, and maternal history of fractures. Details on all included variables are given in Table S4.

Discussion

Main findings

Out of 35 studies enrolling more than 600,000 patients only four studies performed out of population validations of existing instruments. In many instances details of reporting of model development, form of the final model, descriptions of calibration, and discrimination and adjustments were unavailable. The five most frequent included variables in the final model were age, body mass index, bone mass index, past history of falls, and maternal history of fractures.

Strength and Limitations

To our knowledge this is the first comprehensive assessment of prognostic instruments to identify patients with an increased risk for osteoporotic fractures. The strength of this study includes the application of robust systematic review methodology. We believe that our methods to search, identify and select relevant papers were sound. We made strenuous efforts to minimize the risk of selection bias. Relevant reports were searched systematically and without language restriction. The minimum requirement to be included in this review was that the study reported the final model, i.e. the selected indicators along with the strength of association with hip/femur fracture, vertebral fracture or any/non-vertebral fracture. The fact that we were unable performing a formal meta-analysis but were limited to systematic description alone is another limitation of this study. Study characteristics, outcomes and follow-up periods ranged substantially and impeded us thus from providing a statistically robust and clinically meaningful summary.

It may be argued that we did not include an eminent study, describing the development of the FRAX algorithm. This exclusion resulted after careful after a lengthy discourse among the authors. We finally refrained from including the FRAX algorithm in the study, because it failed to provide the minimum of information. We think that reports on prognostic models should always allow the reader to see the final form of the model and should allow re-calculating estimated risks for an individual patient. The provision of the model in a web interface is laudable but no substitute for complete reporting.

Implications for Research

Two issues emerge from this review. The time has come for clinical experts to reach consensus about the validity of the existing evidence. We propose Delphi-type consensus studies that allow securing experts views on the correct type and form of input into these models. Once this has been assessed the available instruments should be checked against this consensus. If no instrument fulfilling the expert consensus exists, the international societies and other relevant healthcare bodies should set the research agenda for a well designed, carefully executed and validly analysed primary study. Researches should execute the studies carefully and provide an optimal reporting. Second, the models should be adapted to local circumstances, e.g. by careful calibration. Finally, impact studies should assess the transfer value of these instruments in practice.

Conclusion

There is a plethora of evidence available studying the association of risk profiles and the development of osteoporotic fractures. The small number of out-of-sample validations, the large variety of study characteristics, outcomes and follow-up periods impedes from deriving robust summaries and from conclusions regarding the clinical performance of many tools. First and foremost, future activity in this field should aim at reaching a consensus among clinical experts in respect to the existing instruments. Then we call for careful validations and expedient adaptations for local circumstances of the most promising candidates.

Supporting Information

Table S1.

Descriptives of included Studies.

https://doi.org/10.1371/journal.pone.0019994.s001

(DOC)

Table S2.

(a) Methodological features of derivation studies. (b) Methodological features of validation studies.

https://doi.org/10.1371/journal.pone.0019994.s002

(DOC)

Table S3.

Outcomes.

https://doi.org/10.1371/journal.pone.0019994.s003

(DOC)

Table S4.

Variables included in the final model.

https://doi.org/10.1371/journal.pone.0019994.s004

(DOC)

Table S5.

Search history.

https://doi.org/10.1371/journal.pone.0019994.s005

(DOC)

Author Contributions

Conceived and designed the experiments: JS HH LMB. Performed the experiments: JS CH HH LMB. Analyzed the data: JS CH HH FB LMB. Contributed reagents/materials/analysis tools: JS CH HH LMB. Wrote the paper: JS CH HH FB LMB.

References

  1. 1. Lippuner K, Johansson H, Kanis JA, Rizzoli R (2009) Remaining lifetime and absolute 10-year probabilities of osteoporotic fracture in Swiss men and women. Osteoporos Int 20: 1131–1140.
  2. 2. Wainwright SA, Marshall LM, Ensrud KE, Cauley JA, Black DM, et al. (2005) Hip fracture in women without osteoporosis. J Clin Endocrinol Metab 90: 2787–2793.
  3. 3. Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E (2008) FRAX and the assessment of fracture probability in men and women from the UK. Osteoporosis International 19: 385–397.
  4. 4. Albrand G, Munoz F, Sornay RE, DuBoeuf F, Delmas PD (2003) Independent predictors of all osteoporosis-related fractures in healthy postmenopausal women: The OFELY Study. Bone 32: 78–85.
  5. 5. Barrett CE, Siris ES, Wehren LE, Miller PD, Abbott TA, et al. (2005) Osteoporosis and fracture risk in women of different ethnic groups. Journal of Bone and Mineral Research 20: 185–194.
  6. 6. Bensen R, Adachi JD, Papaioannou A, Ioannidis G, Olszynski WP, et al. (2005) Evaluation of easily measured risk factors in the prediction of osteoporotic fractures. BMC Musculoskeletal Disorders 6: 47.
  7. 7. Black DM, Steinbuch M, Palermo L, Dargent-Molina P, Lindsay R, et al. (2001) An assessment tool for predicting fracture risk in postmenopausal women. Osteoporosis International 12: 519–528.
  8. 8. Burger H, De LCEDH, Weel AEAM, Hofman A, Pols HAP (1999) Added value of bone mineral density in hip fracture risk scores. Bone 25: 369–374.
  9. 9. Chen YT, Miller PD, Barrett-Connor E, Weiss TW, Sajjan SG, et al. (2007) An approach for identifying postmenopausal women age 50–64 years at increased short-term risk for osteoporotic fracture. Osteoporosis International 18: 1287–1296.
  10. 10. Colon-Emeric CS, Pieper CF, Artz MB (2002) Can historical and functional risk factors be used to predict fractures in community-dwelling older adults? development and validation of a clinical tool. Osteoporosis International 13: 955–961.
  11. 11. Dargent-Molina P, Douchin MN, Cormier C, Meunier PJ, Breart G, et al. (2002) Use of clinical risk factors in elderly women with low bone mineral density to identify women at higher risk of hip fracture: The EPIDOS prospective study. Osteoporosis International 13: 593–599.
  12. 12. Diez-Perez A, Gonzalez-Macias J, Marin F, Abizanda M, Alvarez R, et al. (2007) Prediction of absolute risk of non-spinal fractures using clinical risk factors and heel quantitative ultrasound. Osteoporosis International 18: 629–639.
  13. 13. Ettinger B, Hillier TA, Pressman A, Che M, Hanley DA (2005) Simple computer model for calculating and reporting 5-year osteoporotic fracture risk in postmenopausal women. Journal of Women's Health 14: 159–171.
  14. 14. Girman CJ, Chandler JM, Zimmerman SI, Martin AR, Hawkes W, et al. (2002) Prediction of fracture in nursing home residents.[see comment]. Journal of the American Geriatrics Society 50: 1341–1347.
  15. 15. Guessous I, Cornuz J, Ruffieux C, Burckhardt P, Krieg M-A (2008) Osteoporotic fracture risk in elderly women: estimation with quantitative heel US and clinical risk factors. Radiology 248: 179–184.
  16. 16. Hans D, Durosier C, Kanis JA, Johansson H, Schott-Pethelaz A-M, et al. (2008) Assessment of the 10-year probability of osteoporotic hip fracture combining clinical risk factors and heel bone ultrasound: the EPISEM prospective cohort of 12,958 elderly women. Journal of Bone & Mineral Research 23: 1045–1051.
  17. 17. Krege JH, Siminoski K, Adachi JD, Misurski DA, Chen P (2006) A simple method for determining the probability a new vertebral fracture is present in postmenopausal women with osteoporosis. Osteoporosis International 17: 379–386.
  18. 18. Kung AWC, Lee K-K, Ho AY, Tang G, Luk KD (2007) Ten-year risk of osteoporotic fractures in postmenopausal Chinese women according to clinical risk factors and BMD T-scores: a prospective study. Journal of Bone & Mineral Research 22: 1080–1087.
  19. 19. McGrother CW, Donaldson MMK, Clayton D, Abrams KR, Clarke M (2002) Evaluation of a hip fracture risk score for assessing elderly women: the Melton Osteoporotic Fracture (MOF) study. Osteoporosis International 13: 89–96.
  20. 20. Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV (2008) Development of prognostic nomograms for individualizing 5-year and 10- year fracture risks. Osteoporosis International 19: 1431–1444.
  21. 21. Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV (2007) Development of a nomogram for individualizing hip fracture risk in men and women. Osteoporosis International 18: 1109–1117.
  22. 22. Nguyen ND, Pongchaiyakul C, Center JR, Eisman JA, Nguyen TV (2005) Identification of high-risk individuals for hip fracture: a 14-year prospective study. Journal of Bone & Mineral Research 20: 1921–1928.
  23. 23. Nguyen TV, Center JR, Pocock NA, Eisman JA (2004) Limited utility of clinical indices for the prediction of symptomatic fracture risk in postmenopausal women. Osteoporosis International 15: 49–55.
  24. 24. Nguyen TV, Center JR, Sambrook PN, Eisman JA (2001) Risk factors for proximal humerus, forearm, and wrist fractures in elderly men and women: the Dubbo Osteoporosis Epidemiology Study. American Journal of Epidemiology 153: 587–595.
  25. 25. Porthouse J, Birks YF, Torgerson DJ, Cockayne S, Puffer S, et al. (2004) Risk factors for fracture in a UK population: a prospective cohort study. Qjm 97: 569–574.
  26. 26. Robbins J, Aragaki AK, Kooperberg C, Watts N, Wactawski-Wende J, et al. (2007) Factors associated with 5-year risk of hip fracture in postmenopausal women. JAMA 298: 2389–2398.
  27. 27. Roux C, Briot K, Horlait S, Varbanov A, Watts NB, et al. (2007) Assessment of non-vertebral fracture risk in postmenopausal women. Annals of the Rheumatic Diseases 66: 931–935.
  28. 28. Torgerson DJ, Campbell MK, Thomas RE, Reid DM (1996) Prediction of perimenopausal fractures by bone mineral density and other risk factors. Journal of Bone & Mineral Research 11: 293–297.
  29. 29. Turner LW, Fu Q, Taylor JE, Wang MQ (1998) Osteoporotic fracture among older U.S. women: risk factors quantified. Journal of Aging & Health 10: 372–391.
  30. 30. Turner LW, Wang MQ, Fu Q (1998) Risk factors for hip fracture among southern older women. Southern Medical Journal 91: 533–540.
  31. 31. van Hemert AM, Vandenbroucke JP, Birkenhager JC, Valkenburg HA (1990) Prediction of osteoporotic fractures in the general population by a fracture risk score. A 9-year follow-up among middle-aged women. American Journal of Epidemiology 132: 123–135.
  32. 32. Hippisley-Cox J, Coupland C (2009) Predicting risk of osteoporotic fracture in men and women in England and Wales: prospective derivation and validation of QFractureScores. BMJ 339: b4229.
  33. 33. van Staa TP, Geusens P, Kanis JA, Leufkens HG, Gehlbach S, et al. (2006) A simple clinical score for estimating the long-term risk of fracture in post-menopausal women. QJM 99: 673–682.
  34. 34. Cummings SR, Nevitt MC, Browner WS, Stone K, Fox KM, et al. (1995) Risk factors for hip fracture in white women. Study of Osteoporotic Fractures Research Group. N Engl J Med 332: 767–773.
  35. 35. Ahmed LA, Schirmer H, Fonnebo V, Joakimsen RM, Berntsen GK (2006) Validation of the Cummings' risk score; how well does it identify women with high risk of hip fracture: the Tromso Study. European Journal of Epidemiology 21: 815–822.
  36. 36. Elffors L, Gullberg B, Allander E, Johnell O, Kanis JA (1993) Methodology of MEDOS multicentre study of hip fracture incidence: validity and relevance considerations. Bone 14: Suppl 1S45–49.
  37. 37. Fujiwara S, Nakamura T, Orimo H, Hosoi T, Gorai I, et al. (2008) Development and application of a Japanese model of the WHO fracture risk assessment tool (FRAX). Osteoporosis International 19: 429–435.
  38. 38. Leslie WD, Tsang JF, Lix LM (2008) Validation of ten-year fracture risk prediction: A clinical cohort study from the Manitoba Bone Density Program. Bone 43: 667–671.