---
title: "FRACTURE-ML: Nationwide machine-learning tool for population hip fracture prediction"
id: "plos-medicine-2-a-clinical-decision-support-tool-for-accurate-hip-fracture-prediction-a"
canonical_url: "https://medichelpline.com/clinical-feed/plos-medicine-2-a-clinical-decision-support-tool-for-accurate-hip-fracture-prediction-a"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "PLOS Medicine"
source_url: "https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190"
published_at: "2026-08-27T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# FRACTURE-ML: Nationwide machine-learning tool for population hip fracture prediction
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-medicine-2-a-clinical-decision-support-tool-for-accurate-hip-fracture-prediction-a
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** PLOS Medicine
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190)
- **Published At:** 2026-08-27T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This nationwide Swedish cohort study included all residents aged ≥50 years at randomly assigned baselines between 2011 and 2013 (N = 3,542,647) who had not been prescribed osteoporosis medication in the prior 2 years and followed them through end of 2021. During follow-up, 142,327 individuals sustained a hip fracture. - Investigators constructed an extensive feature set of 139,980 variables derived from diagnoses, medications, procedures, demographics and socioeconomic data across multiple historic windows and levels of detail. - The dataset was split into discovery (25%), development (65%) and holdout (10%) cohorts. Models evaluated included traditional **Cox models**, XGBoost and a neural-network–based survival model **DeepSurv**. - The final clinical decision support tool, **FRACTURE-ML**, used **DeepSurv** and 2,500 predictors and achieved an **AUC** of 0.89 (95% CI 0.88–0.89) at 1 year and 0.88 (95% CI 0.87–0.88) at 2 years. A reduced 35-predictor DeepSurv model had similar performance (AUC ~0.87 at 2 years and 0.85 at 5 years). - Traditional Cox models with 35 and 400 predictors reached AUCs comparable to the machine-learning models. Calibration plots indicated excellent individual-level performance for both DeepSurv and Cox approaches. - Compared with a secondary-prevention Fracture Liaison Service (FLS) screening strategy (recent fracture), which had an AUC of 0.55 (95% CI 0.54–0.55) at 2 years, **FRACTURE-ML** identified nearly seven times more persons at risk for 2-year prediction (sensitivity 0.84 vs 0.12) with a modest decrease in specificity (0.79 vs 0.98). - The authors emphasize potential for population-level, resource-efficient screening without in-person assessment, but note the absence of external validation and implementation studies as a key limitation needing future work. - Data access was restricted by Swedish confidentiality rules; analytic code is publicly available in repositories linked by the authors. Funding sources and competing interests were reported in the manuscript.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosmedicine%2Farticle%3Fid%3D10.1371%2Fjournal.pmed.1005190) * * About * Browse * Publish * [](https://journals.plos.org/plosmedicine/ "PLOS Medicine") * Search [advanced search](https://journals.plos.org/plosmedicine/search) * 0 [Save](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005190#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005190#savedHeader) * 0 [Citation](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005190#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005190#citedHeader) * 151 [View](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005190#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005190#viewedHeader) * 9 [Share](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005190#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005190#discussedHeader) Open Access Peer-reviewed Research Article # A clinical decision support tool for accurate hip fracture prediction: A nationwide cohort study * Kristian F. Axelsson, Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing Affiliations Sahlgrenska Osteoporosis Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Region Västra Götaland, Närhälsan Norrmalm Health Centre, Skövde, Sweden [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-4118-6038 ](https://orcid.org/0000-0002-4118-6038 "ORCID Registry") ⨯ * Henrik Litsne, Roles Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Writing – review & editing Affiliation Sahlgrenska Osteoporosis Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0006-2024-7731 ](https://orcid.org/0009-0006-2024-7731 "ORCID Registry") ⨯ * Konstantinos Konstantinou, Roles Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – review & editing Affiliation APNC Sweden AB, Mölndal, Sweden [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0003-3549-5851 ](https://orcid.org/0000-0003-3549-5851 "ORCID Registry") ⨯ * Hussnain Khalid, Roles Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – review & editing Affiliation APNC Sweden AB, Mölndal, Sweden [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0003-8889-0254 ](https://orcid.org/0009-0003-8889-0254 "ORCID Registry") ⨯ * Aldina Pivodic, Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Writing – review & editing Affiliations APNC Sweden AB, Mölndal, Sweden, Centre for Person-Centered Care (GPCC), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ⨯ * Mattias Lorentzon Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing * E-mail: mattias.lorentzon@medic.gu.se Affiliations Sahlgrenska Osteoporosis Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Department of Medicine, Geriatrics and Emergency Medicine, Sahlgrenska University Hospital, Mölndal, Västra Götaland Region, Sweden [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0003-0749-1431 ](https://orcid.org/0000-0003-0749-1431 "ORCID Registry") ⨯ # A clinical decision support tool for accurate hip fracture prediction: A nationwide cohort study * Kristian F. Axelsson, * Henrik Litsne, * Konstantinos Konstantinou, * Hussnain Khalid, * Aldina Pivodic, * Mattias Lorentzon ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: August 27, 2026 * * [Article](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190) * [Authors](https://journals.plos.org/plosmedicine/article/authors?id=10.1371/journal.pmed.1005190) * [Metrics](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005190) * [Comments](https://journals.plos.org/plosmedicine/article/comments?id=10.1371/journal.pmed.1005190) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pmed.1005190) * [Abstract](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#abstract0) * [Author summary](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#abstract1) * [Introduction](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#sec007) * [Methods](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#sec008) * [Results](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#sec017) * [Discussion](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#sec026) * [Supporting information](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#sec027) * [Acknowledgments](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#ack) * [References](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#references) * [Reader Comments](https://journals.plos.org/plosmedicine/article/comments?id=10.1371/journal.pmed.1005190) * [Figures](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190) ## Abstract ### Background Although hip fractures are commonly associated with functional decline, increased morbidity, and mortality, accurate models for both short- and long-term prediction that do not rely on in-person assessment remain lacking. The aim was to develop and validate a high-performing clinical decision support tool, that can be used for population screening without the need for patient assessment, for predicting hip fracture risk. ### Methods and findings All individuals aged ≥50 years living in Sweden at baseline (randomly set between 2011 and 2013, _N_ = 3,542,647), who had not been prescribed osteoporosis medication within the previous 2 years, were included and followed through the end of 2021. During follow-up, 142,327 individuals sustained a hip fracture. Using a broad unconditional approach, 139,980 variables encompassing diagnoses, medications, procedures, demographics and socioeconomic data with multiple historic windows and level of detail were defined. The dataset was divided into discovery (25%), development (65%) and holdout (10%) cohorts. The risk of hip fracture was evaluated using traditional Cox models, as well as machine learning methods XGBoost and DeepSurv. The developed clinical support tool FRACTURE-ML based on DeepSurv using 2,500 predictors yielded an area under the curve (AUC) of 0.89 (95% CI 0.88, 0.89) at year 1, 0.88 (95% CI 0.87,0.88) at two years, and a reduced model with 35 predictors yielded similar AUCs, 0.87 at 2 years and 0.85 at 5 years. Traditional Cox models with 35 and 400 predictors reached similar AUCs. Both the DeepSurv and the Cox models performed excellently at the individual level based on calibration plot analysis. Screening using the in Sweden advocated fracture liaison services (FLS) secondary prevention approach (recent fracture), resulted in an AUC of 0.55 (95% CI 0.54, 0.55) at two years. For 2-year prediction, FRACTURE-ML, which could be used as a complementary approach for primary prevention, identified nearly seven times more persons at risk (sensitivity 0.84 (95% CI 0.82, 0.85) versus 0.12 (95% CI 0.11, 0.13) than the FLS approach, with limited reduction in specificity (0.79 (95% CI 0.79, 0.79) versus 0.98 (95% CI 0.98, 0.98), respectively). The lack of external validation and implementation studies represents a limitation, as such studies are needed to establish the clinical usefulness of FRACTURE-ML. ### Conclusions FRACTURE-ML was effective in predicting hip fracture and could be used as a resource-efficient solution for population screening to improve the prevention of hip fracture. ## Author summary ### Why was this study done? * Hip fractures are common in older adults and often lead to serious outcomes such as disability, illness, and increased risk of death. * Existing tools to predict fracture risk usually require patient-provided information, making large-scale screening difficult. * There is a need for an accurate and simple way to identify people at high risk without requiring direct patient assessment. ### What did the researchers do and find? * The model identified about seven times more high-risk individuals than current clinical practice, while maintaining relatively high precision. * They developed a prediction model (FRACTURE-ML) using routinely collected health data, which showed high discriminative performance (AUC of up to 0.89) and excellent calibration. * The researchers analyzed data from over 3.5 million individuals aged 50 years and older in Sweden and followed them for up to 10 years. ### What do these findings mean? * The lack of external validation and implementation studies represents a limitation, as such studies are needed to establish the clinical usefulness of FRACTURE-ML. * This approach could help target preventive measures more efficiently and potentially reduce the number of hip fractures. * The findings show that it is possible to predict hip fracture risk at the population level without direct patient interaction. ## Figures ![Fig 3](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005190.g003) ![Fig 4](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005190.g004) ![Fig 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005190.g001) ![Table 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005190.t001) ![Fig 2](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005190.g002) ![Fig 3](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005190.g003) ![Fig 4](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005190.g004) ![Fig 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005190.g001) ![Table 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005190.t001) ![Fig 2](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005190.g002) **Citation:** Axelsson KF, Litsne H, Konstantinou K, Khalid H, Pivodic A, Lorentzon M (2026) A clinical decision support tool for accurate hip fracture prediction: A nationwide cohort study. PLoS Med 23(8): e1005190. https://doi.org/10.1371/journal.pmed.1005190 **Academic Editor:** Jonathan P. Evans, University of Exeter, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND **Received:** December 5, 2025; **Accepted:** July 14, 2026; **Published:** August 27, 2026 **Copyright:** © 2026 Axelsson et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** Data cannot be made publicly available for ethical and legal reasons. Such information is subject to legal restrictions according to national legislation. Specifically, in Sweden confidentiality regarding personal information in studies is regulated in the Public Access to Information and Secrecy Act (SFS 2009:400). The data underlying the results of this study might be made available upon request, after an assessment of confidentiality. There is thus a possibility to apply to get access to certain public documents that an authority holds. In this case, the University of Gothenburg is the specific authority that is responsible for the integrity of the documents with research data. Questions regarding such issues can be directed to the head of the Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. Contact information can be obtained from medicin@gu.se. Code used to generate results in this analysis can be found at and . **Funding:** The study was funded by the Swedish Research Council (Dnr 2023-01976 to ML), the Sahlgrenska University Hospital (ALFGBG-997803/1006873 to KFA; ALFGBG-1006860 to ML), the Gothenburg Society of Medicine (GLS-999015/1022125 to KA), King Gustav V:s and Queen Victoria’s foundation (2023-2024 to ML), the Swedish Society of Medicine (SLS-985867 to KA) and the Skaraborg Research Institute (23-1058 to KA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. **Competing interests:** I have read the journal’s policy and the authors of this manuscript have the following competing interests: ML has received lecture or consulting fees from Astellas, Amgen, UCB Pharma, Medison Pharma, Sandoz, Gedeon Richter, Jansen-Cilag, Medac, Pharmacosmos, Parexel International, and Crinetics, all outside the submitted work. KK, HK and AP received consultancy fees for performing analyses. KFA and HL have no competing interests. **Abbreviations:** AUC, area under the curve; BMD, bone mineral density; FLSs, Fracture Liaison Services; FRACTURE-ML, Fracture Risk Assessment and Classification Using Real-world Evidence and Machine Learning; NAISS, National Academic Infrastructure; NPV, negative predictive values; PPV,, Positive Predictive Value;; ROC, receiver-operating characteristic curves; TRIPOD+AI, Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis using machine learning or Artificial Intelligence; UPPMAX, Uppsala Multidisciplinary Centre for Advanced Computational Science ## Introduction Hip fractures are associated with significant morbidity, disability, mortality and substantial healthcare costs [[1](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref001)–[4](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref004)]. Therefore, it is of utmost importance to identify individuals at high risk of hip fracture in order to initiate effective preventive measures, such as treatment with osteoporosis medications and fall prevention [[5](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref005)–[8](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref008)]. As recently reviewed in JAMA, the US Preventive Services Task Force concluded that screening for osteoporosis to prevent osteoporotic fractures in postmenopausal women has moderate net benefit [[9](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref009)]. Several risk tools to evaluate the risk of hip fracture are available and include FRAX, QFracture and CFracture, but these algorithms are dependent on patient interaction due to their implementation of body mass index (BMI) and lifestyle information on smoking and alcohol in the algorithms [[9](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref009)–[12](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref012)]. A risk model sourced on Danish registry data and developed using traditional statistical methods had high accuracy, but did not consider competing risk of death [[13](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref013),[14](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref014)]. Accurate risk algorithms based on electronic health records alone would allow cost-efficient population screening and an opportunity for prevention without a large impact on the healthcare system. Patients sustaining a first fracture have an increased risk of recurrent fracture, especially during the first 2 years following the index fracture [[15](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref015)–[17](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref017)]. Therefore, structured secondary prevention programs known as Fracture Liaison Services (FLSs) targeting patients with recent fractures have been recommended and launched worldwide [[18](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref018),[19](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref019)], resulting in higher rates of bone mineral density (BMD) testing, treatment initiation, improved medication adherence, and a decrease in the risk of recurrent fracture [[20](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref020),[21](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref021)]. The Swedish National Board of Health and Welfare has identified the establishment of FLSs as a top national priority [[22](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005190#pmed.1005190.ref022)]. Although implementation is strongly advocated in most Western countries, and known to be important for those with osteoporotic fracture, general primary screening for high fracture risk is not. We hypothesized that machine learning algorithms and traditional statistical models applied to national electronic health record data from more than 3.5 million individuals aged 50 years or older could be used to develop and validate _Fracture Risk Assessment and Classification Using Real-world Evidence and Machine Learning_ (FRACTURE-ML), a clinical decision support tool capable of accurately predicting both short- and long-term hip fracture risk. ## Methods ### Study design This nationwide cohort study used multiple national registers in Sweden to develop a clinical decision support tool for hip fracture. A dataset of all Swedish men and women born 1981 or earlier and alive in 2005 was used. All individuals were assigned a random baseline date between 2011 and 2013. Only those aged 50 years or older and alive at baseline were included in the study population. Furthermore, individuals with recent (≤2 years) osteoporosis medica
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