Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients: the Dutch Hip Fracture Audit algorithms in 74,396 cases
DOI:
https://doi.org/10.2340/17453674.2025.44248Keywords:
Hip, Hip Fracture, Machine Learning, Older Adults, Prediction, Shared Decision-MakingAbstract
Background and purpose: Treatment-related shared decision-making (SDM) in older adults with hip fractures is complex due to the need to balance patient-specific factors such as life goals, frailty, and surgical risks. It includes considerations such as prognosis and decisions concerning whether to operate or not on frail, life-limited patients. We aimed to develop machine learning (ML)-driven prediction models for short- and long-term mortality in a large cohort of patients with hip fractures.
Methods: In this national registry-based retrospective cohort study, patients aged ≥ 70 years registered in the nationwide Dutch Hip Fracture Audit from 2018–2023 were included. Predictive variables were selected based on the literature and/or clinical relevance. 6 ML algorithms, including logistic regression, were trained with internal cross-validation and evaluated on discrimination (c-statistic), sensitivity, specificity, calibration, and interpretability.
Results: 74,396 patients (median age 84, IQR 78–89; 68% female) were analyzed. Most patients lived at home (69%) and high malnutrition risk was seen in 10%. 18% had dementia. Mortality rates were 9.1% (30-day), 15% (90-day), and 26% (1-year). Logistic regression performed comparably to other algorithms, but was chosen as the preferred algorithm due to its superior interpretability (c-statistic: 30-day 0.82, 90-day 0.81, 1-year 0.80).
Conclusion: We developed and validated ML algorithms, including logistic regression, for mortality prediction in older hip fracture patients with adequate performance. This information may inform SDM.
Downloads
References
van Bremen H-E, Seppala L J, Hegeman J H, van der Velde N, Willems H C. Characteristics and outcomes of nonoperatively managed patients with hip fracture using the Dutch Hip Fracture Audit. J Orthop Trauma 2024; 38: 265-72. doi: 10.1097/BOT.0000000000002778. DOI: https://doi.org/10.1097/BOT.0000000000002778
Peeters C M M, Visser E, Van de Ree C L P, Gosens T, Den Oudsten B L, De Vries J. Quality of life after hip fracture in the elderly: s systematic literature review. Injury 2016; 47: 1369-82. doi: 10.1016/j.injury.2016.04.018. DOI: https://doi.org/10.1016/j.injury.2016.04.018
Haleem S, Choudri M J, Kainth G S, Parker M J. Mortality following hip fracture: trends and geographical variations over the last SIXTY years. Injury 2023; 54: 620-9. doi: 10.1016/j.injury.2022.12.008. DOI: https://doi.org/10.1016/j.injury.2022.12.008
Loggers S A I, Willems H C, Van Balen R, Gosens T, Polinder S, Ponsen K J, et al. Evaluation of quality of life after nonoperative or operative management of proximal femoral fractures in frail institutionalized patients: the FRAIL-HIP study. JAMA Surg 2022; 157: 424-34. doi: 10.1001/jamasurg.2022.0089. DOI: https://doi.org/10.1001/jamasurg.2022.0089
Loggers S A I, Geraerds A J L M, Joosse P, Willems H C, Gosens T, Van Balen R, et al. Nonoperative versus operative management of frail institutionalized older patients with a proximal femoral fracture: a cost-utility analysis alongside a multicenter prospective cohort study. Osteoporos Int 2023; 34: 515-25. doi: 10.1007/s00198-023-06673-2. DOI: https://doi.org/10.1007/s00198-022-06638-x
Laane D, Kroes T, van den Berg A, de Jongh M, The R, Van der Velde D, et al. Patient and proxy perspectives in decision-making for geriatric hip fracture management in the Netherlands: a qualitative study. BMJ Open 2024; 14. doi: 10.1136/bmjopen-2023-082093. DOI: https://doi.org/10.1136/bmjopen-2023-082093
van de Ree C L P, De Jongh M A C, Peeters C M M, de Munter L, Roukema J A, Gosens T. Hip fractures in elderly people: surgery or no surgery? A systematic review and meta-analysis. Geriatr Orthop Surg Rehabil 2017; 8: 173-80. doi: 10.1177/2151458517713821. DOI: https://doi.org/10.1177/2151458517713821
Dijkstra H, Oosterhoff J H F, van de kuit A, IJpma F F A, Schwab J H, et al. Development of machine-learning algorithms for 90-day and one-year mortality prediction in the elderly with femoral neck fractures based on the HEALTH and FAITH trials. Bone Jt Open 2023; 4: 168-81. doi: 10.1302/2633-1462.43.BJO-2022-0162.R1. DOI: https://doi.org/10.1302/2633-1462.43.BJO-2022-0162.R1
Würdemann F S, van Zwet E W, Krijnen P, Hegeman J H, Schipper I B, Calf A H, et al. Is hospital volume related to quality of hip fracture care? Analysis of 43,538 patients and 68 hospitals from the Dutch Hip Fracture Audit. Eur J Trauma Emerg Surg 2023; 49: 1525-34. doi: 10.1007/s00068-022-02205-5. DOI: https://doi.org/10.1007/s00068-022-02205-5
Würdemann F S, Krijnen P, van Zwet E W, Arends A J, Heetveld M J, Trappenburg M C, et al. Trends in data quality and quality indicators 5 years after implementation of the Dutch Hip Fracture Audit. Eur J Trauma Emerg Surg 2022; 48: 4783-96. doi: 10.1007/s00068-022-02012-y. DOI: https://doi.org/10.1007/s00068-022-02012-y
van der Werf L R, Voeten S C, van Loe C M M, Karthaus E G, Wouters M W J M, Prins H A. Data verification of nationwide clinical quality registries. BJS Open 2019; 3: 857-64. doi: 10.1007/s00068-022-02012-y. DOI: https://doi.org/10.1002/bjs5.50209
Bui M, Nijmeijer W S, Hegeman J H, Witteveen A, Groothuis-Oudshoorn C G M. Systematic review and meta-analysis of preoperative predictors for early mortality following hip fracture surgery. Osteoporos Int 2024; 35: 561-74. doi: 10.1007/s00198-023-06942-0. DOI: https://doi.org/10.1007/s00198-023-06942-0
Nijmeijer W S, Voorthuis B J, Groothuis-Oudshoorn C G M, Würdemann F S, van der Velde D, Vollenbroek-Hutten M M R, et al. . The prediction of early mortality following hip fracture surgery in patients aged 90 years and older: the Almelo Hip Fracture Score 90 (AHFS90). Osteoporos Int 2023; 34: 867-77. doi: 10.1007/s00198-023-06696-9. DOI: https://doi.org/10.1007/s00198-023-06696-9
Liu F, Liu C, Tang X, Gong D, Zhu J, Zhang X. Predictive value of machine learning models in postoperative mortality of older adults patients with hip fracture: a systematic review and meta-analysis. Arch Gerontol Geriatr 2023; 115: 105120. doi: 10.1016/j.archger.2023.105120. DOI: https://doi.org/10.1016/j.archger.2023.105120
Oosterhoff J H F, Dijkstra H, Karhade A V, Poolman R W, Schipper I B, Nelissen R G H H, et al. . Clockwise torque results in higher reoperation rates in left-sided femur fractures. Injury 2023; 54: 110757. doi: 10.1016/j.injury.2023.04.044. DOI: https://doi.org/10.1016/j.injury.2023.04.044
Wong A M, Xu B Y, Low L L, Allen J C, Low S G. Impact of malnutrition in surgically repaired hip fracture patients admitted for rehabilitation in a community hospital: a cohort prospective study. Clin Nutr ESPEN 2021; 44: 188-93. doi: 10.1016/j.clnesp.2021.06.024. DOI: https://doi.org/10.1016/j.clnesp.2021.06.024
Reijneveld S A, Spijker J, Dijkshoorn H. Katz’ ADL index assessed functional performance of Turkish, Moroccan, and Dutch elderly. J Clin Epidemiol 2007; 60: 382-8. doi: 10.1016/j.jclinepi.2006.02.022. DOI: https://doi.org/10.1016/j.jclinepi.2006.02.022
Kruizenga H M, Seidell J C, de Vet H C W, Wierdsma N J, van Bokhorst-de van der Schueren M A E. Development and validation of a hospital screening tool for malnutrition: the short nutritional assessment questionnaire (SNAQ©). Clin Nutr 2005; 24: 75-82. doi: 10.1016/j.clnu.2004.07.015. DOI: https://doi.org/10.1016/j.clnu.2004.07.015
Stratton R J, King C L, Stroud M A, Jackson A A, Elia M. ‘Malnutrition Universal Screening Tool’ predicts mortality and length of hospital stay in acutely ill elderly. Br J Nutr 2006; 95: 325-30. doi: 10.1079/bjn20051622. DOI: https://doi.org/10.1079/BJN20051622
Voeten S C, Nijmeijer W S, Vermeer M, Schipper I B, Hegeman J H. Validation of the Fracture Mobility Score against the Parker Mobility Score in hip fracture patients. Injury 2020; 51: 395-9. doi: 10.1016/j.injury.2019.10.035. DOI: https://doi.org/10.1016/j.injury.2019.10.035
Steyerberg E W, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J 2014; 35: 1925-31. doi: 10.1093/eurheartj/ehu207. DOI: https://doi.org/10.1093/eurheartj/ehu207
Van Calster B, Vickers A J. Calibration of risk prediction models: impact on decision-analytic performance. Med Decis Mak 2015; 35: 162-9. doi: 10.1177/0272989X14547233. DOI: https://doi.org/10.1177/0272989X14547233
Dijkstra H, van de Kuit A, de Groot T, Canta O, Groot O Q, Oosterhoff J H F, et al. Systematic review of machine-learning models in orthopaedic trauma. Bone Jt Open 2024; 5: 9-19. doi: 10.1302/2633-1462.51.BJO-2023-0095.R1. DOI: https://doi.org/10.1302/2633-1462.51.BJO-2023-0095.R1
World Health Organization. WHO data policy 2025. Available from: https://www.who.int/about/policies/publishing/data-policy. Accessed Mar 14, 2021.
Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res 2016; 18: 1-10. doi: 10.2196/jmir.5870. DOI: https://doi.org/10.2196/jmir.5870
Collins G S, Moons K G M, Dhiman P, Riley R D, Beam A L, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024; 385: e078378. doi: 10.1136/bmj.q902. DOI: https://doi.org/10.1136/bmj.q902
Moons K G M, Damen J A A, Kaul T, Hooft L, Andaur Navarro C, Dhiman P, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ 2025; 388: e082505. doi: 10.1136/bmj-2024-082505. DOI: https://doi.org/10.1136/bmj-2024-082505
Wesdorp M A, Moerman S, Vochteloo A J H, Mathijssen N M C. External validation of the Almelo Hip Fracture Score, a prediction model for early mortality following hip fracture surgery. Eur J Trauma Emerg Surg 2022; 48: 1871-7. doi: 10.1007/s00068-021-01619-x. DOI: https://doi.org/10.1007/s00068-021-01619-x
Salari N, Darvishi N, Bartina Y, Larti M, Kiaei A, Hemmati M, et al. Global prevalence of osteoporosis among the world older adults: a comprehensive systematic review and meta-analysis. J Orthop Surg Res 2021; 16: 1-13. doi: 10.1186/s13018-021-02821-8. DOI: https://doi.org/10.1186/s13018-021-02821-8
Lems W F, Geusens P. Osteoporose en fractuurpreventie. Leerboek Reumatologie en Klinische Immunologie 2013: 215-26. DOI: https://doi.org/10.1007/978-90-313-9894-2_20
Additional Files
Published
How to Cite
License
Copyright (c) 2025 Hidde Dijkstra, Cathleen S Parsons, Hanne-Eva VAN Bremen, Hanna C Willems, Anne A H De Hond, Barbara C van Munster, Job N Doornberg, Jacobien H F Oosterhoff
This work is licensed under a Creative Commons Attribution 4.0 International License.
