Evidence map›Paper›PMID 27399597›Full record

ArticlePloS one2016

Development of a Korean Fracture Risk Score (KFRS) for Predicting Osteoporotic Fracture Risk: Analysis of Data from the Korean National Health Insurance Service.

Ha Young Kim, Eun Jin Jang, ByeongJu Park, Tae-Young Kim, Soon-Ae Shin, Yong-Chan Ha, Sunmee Jang

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
3.0field-weighted citation impact, top 10% of its field
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

22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 33 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Prevalence of osteoporosis and incidence of related fractures in developed economies in the Asia Pacific region: a systematic review.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2023
    Pooled it
  4. Article
  5. Article
  6. Development and validation of common data model-based fracture prediction model using machine learning algorithm.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2023
    Article
  7. Article
  8. Review
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  11. Long-Term Treatment of Postmenopausal Osteoporosis.Endocrinology and metabolism (Seoul, Korea) · 2021
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Associated Factors for Osteoporosis and Fracture in Chinese Elderly.Medical science monitor : international medical journal of experimental and clinical research · 2019
    Article
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  18. Observational
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  20. 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

7 authors at 7 institutions in 1 country.

Ha Young KimDepartment of Internal Medicine, Wonkwang University Sanbon Hospital, Gunpo, Republic of Korea.ORCID http://orcid.org/0000-0002-0651-2213
Eun Jin JangDepartment of information Statistics, Andong National University, Andong, South Korea.
ByeongJu ParkDepartment of Statistics, Kyungpook National University, Daegu, South Korea.
Tae-Young KimDepartment of Orthopaedic Surgery, School of Medicine, Hallym University, Anyang, Korea.
Soon-Ae ShinBig Data Steering Department, National Health Insurance Service, Seoul, Korea.
Yong-Chan HaDepartment of Orthopaedic Surgery, Chung-Ang University College of Medicine, Seoul, South Korea.
Sunmee JangCollege of Pharmacy, Gachon University, Incheon, Korea.
Chung-Ang University · KRGachon University · KRGyeongguk National University · KRHallym University · KRKyungpook National University · KRNational Health Insurance Service · KRWonkwang University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAsian-specific prediction models for estimating individual risk of osteoporotic fractures are rare. We developed a Korean fracture risk prediction model using clinical risk factors and assessed validity of the final model.

methodsA total of 718,306 Korean men and women aged 50-90 years were followed for 7 years in a national system-based cohort study. In total, 50% of the subjects were assigned randomly to the development dataset and 50% were assigned to the validation dataset. Clinical risk factors for osteoporotic fracture were assessed at the biennial health check. Data on osteoporotic fractures during the follow-up period were identified by ICD-10 codes and the nationwide database of the National Health Insurance Service (NHIS).

resultsDuring the follow-up period, 19,840 osteoporotic fractures were reported (4,889 in men and 14,951 in women) in the development dataset. The assessment tool called the Korean Fracture Risk Score (KFRS) is comprised of a set of nine variables, including age, body mass index, recent fragility fracture, current smoking, high alcohol intake, lack of regular exercise, recent use of oral glucocorticoid, rheumatoid arthritis, and other causes of secondary osteoporosis. The KFRS predicted osteoporotic fractures over the 7 years. This score was validated using an independent dataset. A close relationship with overall fracture rate was observed when we compared the mean predicted scores after applying the KFRS with the observed risks after 7 years within each 10th of predicted risk.

conclusionWe developed a Korean specific prediction model for osteoporotic fractures. The KFRS was able to predict risk of fracture in the primary population without bone mineral density testing and is therefore suitable for use in both clinical setting and self-assessment. The website is available at http://www.nhis.or.kr.

Indexed as

Insurance, HealthAgedAged, 80 and overCohort StudiesDatabases, FactualFemaleHumansIncidenceMaleMiddle AgedModels, StatisticalOsteoporotic FracturesRepublic of KoreaRisk AssessmentRisk Factors

Identifiers

PMID27399597
PMCPMC4939957
OpenAlexW2396728882

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.