Evidence map›Paper›PMID 39701109›Full record

ArticleJournal of bone metabolism2024

Discriminatory Accuracy of Fracture Risk Assessment Tool in Asian Populations: A Systematic Review and Meta-Analysis.

Dheeraj Jha, Manju Chandran, Namki Hong, Yumie Rhee, Seungjin Baek, Stephen J Ferguson, Benedikt Helgason, Anitha D Praveen

Abstract read
In one paragraph

Article in Journal of bone metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Dheeraj JhaFuture Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.
Manju ChandranOsteoporosis and Bone Metabolism Unit, Department of Endocrinology, Singapore General Hospital, Singapore, Singapore.
Namki HongDepartment of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Yumie RheeDepartment of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Seungjin BaekDepartment of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Stephen J FergusonFuture Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.
Benedikt HelgasonFuture Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.
Anitha D PraveenFuture Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.

Funding

Campus for Research Excellence and Technological EnterpriseETH ZurichKorea Health Industry Development InstituteMinistry of Health and Welfare RS-2023-00265620Ministry of Science and ICTNational Research Foundation of Korea RS-2023-00231864National Research Foundation Singapore
6 · The paper itself

Abstract

backgroundThis review explores the discriminative ability of fracture risk assessment tool (FRAX) in major osteoporotic fracture (MOF) and hip fracture (HF) risk prediction and the densitometric diagnosis of osteoporosis in Asian populations.

methodsWe systematically searched the EMBASE, Cochrane, and PubMed databases from the earliest indexing date to January 2024. Studies were included if FRAX was used to identify future osteoporotic fractures or a densitometric diagnosis of osteoporosis in an Asian population and reported the area under the curve (AUC) values. Meta-analyses were conducted after quality assessment for AUC with 95% confidence intervals across the following categories: standard FRAX without/with bone mineral density (BMD), adjusted FRAX, and BMD alone for fracture prediction, as well as standard FRAX for densitometric diagnosis of osteoporosis.

resultsA total of 42 studies were included. The AUC values for predicting fracture risk using FRAX-MOF with BMD (0.73 [0.70-0.77]) was highest compared to FRAX-MOF without BMD (0.72 [0.66-0.77]), and adjusted FRAX-MOF (0.71 [0.65-0.77]). The AUC values for predicting fracture risk using FRAX-HF with BMD (0.77 [0.71-0.83]) was highest compared to FRAX-HF without BMD (0.72 [0.65-0.80]), and adjusted FRAX-HF (0.75 [0.63-0.86]). The AUC values for BMD alone (0.68 [0.62-0.73]) was lowest for fracture prediction. The AUC values for identifying a densitometric diagnosis of osteoporosis was 0.77 [0.70-0.84] and 0.76 [0.67-0.86] using FRAX-MOF and FRAX-HF, respectively.

conclusionsFRAX with BMD tends to perform more reliably in predicting HF compared to MOF in Asia. However, its accuracy in predicting fracture risk in Asian populations can be improved through region-specific, long-term epidemiological data.

Indexed as

Fractures, boneMeta-analysis as topicOsteoporosisRisk assessmentSystematic reviews as topic

Identifiers

PMID39701109
PMCPMC11658842

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.