Construction and validation of a risk prediction model for water-loss dehydration in elderly hip fracture patients based on machine learning_West China Medical Journal

Authors：

BI Zhaodong ¹ , REN Jiachen ^2,3 , ZHANG Rongli ² , NIU Na ² , SONG Meiyi ² , LIU Mengyuan ^2,3 , LI Xiuting ^2,4 ,  ZHANG Liwei ²

1. Department of Rehabilitation Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P. R. China;
2. Trauma and Emergency Center, the Third Hospital of Hebei Medical University, Shijiazhuang, Hebei 050051, P. R. China;
3. School of Nursing, Hebei Medical University, Shijiazhuang, Hebei 050000, P. R. China;
4. Department of Nursing, the Third Hospital of Hebei Medical University, Shijiazhuang, Hebei 050051, P. R. China;

Corresponding?author：

ZHANG Liwei, Email: 973150956@qq.com

Keywords：

Machine learning; hip fracture; water-loss dehydration; prediction model

DOI：

10.7507/1002-0179.202606087

Video：

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Objective To investigate the prevalence of water-loss dehydration on admission among older patients with hip fractures and to develop a machine learning-based risk prediction model. Methods A retrospective cohort study was conducted in older patients with hip fractures admitted to the Third Hospital of Hebei Medical University between January and December 2024. Ten machine learning algorithms were used to develop prediction models. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, specificity, and F1 score. Shapley additive explanations (SHAP) were applied to interpret the optimal model. Results A total of 529 patients were included, of whom 165 patients (31.2%) had water-loss dehydration. The random forest model showed the best overall performance, with ROC-AUC values of 0.862 [95% confidence interval (CI) (0.825, 0.899)] in the training set and 0.831 [95%CI (0.762, 0.899)] in the internal validation set. In the internal validation set, the accuracy, sensitivity, specificity, and F1 score were 0.744, 0.800, 0.718, and 0.661, respectively. SHAP analysis showed that time from injury to admission, history of diabetes, and history of previous surgery made the greatest contributions to model predictions. Conclusions Water-loss dehydration is common on admission among older patients with hip fractures. The random forest model demonstrated good performance in internal validation and may support early risk stratification for dehydration in this population. Further external validation is warranted.

Citation： BI Zhaodong, REN Jiachen, ZHANG Rongli, NIU Na, SONG Meiyi, LIU Mengyuan, LI Xiuting, ZHANG Liwei. Construction and validation of a risk prediction model for water-loss dehydration in elderly hip fracture patients based on machine learning. West China Medical Journal, 2026, 41(9): 1447-1452. doi: 10.7507/1002-0179.202606087 Copy

1.	Gong XF, Li XP, Zhang LX, et al. Current status and distribution of hip fractures among older adults in China[J]. Osteoporos Int, 2021, 32(9): 1785-1793.
2.	Goh EL, Khatri A, Costa AB, et al. Prevalence of complications in older adults after hip fracture surgery: a systematic review and meta-analysis[J]. Bone Joint J, 2025, 107-B(2): 139-148.
3.	Downey C, Kelly M, Quinlan JF. Changing trends in the mortality rate at 1-year post hip fracture: a systematic review[J]. World J Orthop, 2019, 10(3): 166-175.
4.	Dyer SM, Crotty M, Fairhall N, et al. A critical review of the long-term disability outcomes following hip fracture[J]. BMC Geriatr, 2016, 16(1): 158.
5.	Chan HYL, Cheng A, Cheung SSS, et al. Association between dehydration on admission and postoperative complications in older persons undergoing orthopaedic surgery[J]. J Clin Nurs, 2018, 27(19/20): 3679-3686.
6.	Ekman L, Johnson P, Hahn RG. Signs of dehydration after hip fracture surgery: an observational descriptive study[J]. Medicina (Kaunas), 2020, 56(7): 361.
7.	Zanetti M, De Colle P, Omiciuolo C, et al. Postoperative dehydration is associated with frailty and decreased survival in older patients with hip fracture[J]. Nutrients, 2022, 14(4): 820.
8.	中華醫學會老年醫學分會, 中國醫師協會老年醫學科醫師分會. 中國老年人臨床水化實踐指南(2024 版)[J]. 中華老年醫學雜志, 2024, 43(8): 954-970.
9.	Volkert D, Beck AM, Cederholm T, et al. ESPEN practical guideline: clinical nutrition and hydration in geriatrics[J]. Clin Nutr, 2022, 41(4): 958-989.
10.	Botigué T, Miranda J, Escobar-Bravo M, et al. Analysis of dehydration in older people in a nursing home in Spain: prevalence and associated factors[J]. Nutr Hosp, 2021, 38(2): 252-259.
11.	Hooper L, Abdelhamid A, Ali A, et al. Diagnostic accuracy of calculated serum osmolarity to predict dehydration in older people: adding value to pathology laboratory reports[J]. BMJ Open, 2015, 5(10): e008846.
12.	郭大鑫, 黃曉麗. 老年人失水性脫水的診斷與治療[J]. 中華老年醫學雜志, 2023, 42(5): 598-602.
13.	中華醫學會. 臨床診療指南: 骨科分冊[M]. 北京: 人民衛生出版社, 2009.
14.	Lundberg SM, Lee SI. A unified approach to interpreting model predictions[M]//Guyon I, von Luxburg U, Bengio S, et al. Advances in neural information processing systems 30. Red Hook (NY): Curran Associates, Inc., 2017: 4765-4774.
15.	Dhar M, Mittal K, Pathania M, et al. Dehydration in elderly: revisiting the assessment and management strategies[J]. J Assoc Physicians India, 2023, 71(12): 47-50.
16.	Mohan V, Kalra S, Zargar AH, et al. Diabetes mellitus and fluid imbalance: the need for adequate hydration[J]. J Assoc Physicians India, 2024, 72(6S): 16-24.
17.	Hooper L, Bunn DK, Downing A, et al. Which frail older people are dehydrated? The UK DRIE study[J]. J Gerontol A Biol Sci Med Sci, 2016, 71(10): 1341-1347.
18.	Kinoshita A, Yonemitsu T, Miyai N, et al. Factors associated with hypertonic dehydration in older Japanese outpatients[J]. J Gen Fam Med, 2025, 26(3): 205-212.
19.	Coppes T, Hazen ACM, Zwart DLM, et al. Characteristics and preventability of medication-related admissions for acute kidney injury and dehydration in elderly patients[J]. Eur J Clin Pharmacol, 2024, 80(9): 1355-1362.
20.	Puga AM, Lopez-Oliva S, Trives C, et al. Effects of drugs and excipients on hydration status[J]. Nutrients, 2019, 11(3): 669.
21.	Luo X, He S, Li Z, et al. Quantification and influencing factors of perioperative hidden blood loss during intramedullary fixation for intertrochanteric fractures in the elderly[J]. Arch Orthop Trauma Surg, 2020, 140(10): 1339-1348.
22.	鄭曉玲, 陳希偉, 陳小岑, 等. 老年腫瘤患者入院時低攝入性脫水的評估及原因分析[J]. 中華護理雜志, 2023, 58(16): 1964-1969.
23.	Rowat A, Graham C, Dennis M. Dehydration in hospital-admitted stroke patients: detection, frequency, and association[J]. Stroke, 2012, 43(3): 857-859.

1. Gong XF, Li XP, Zhang LX, et al. Current status and distribution of hip fractures among older adults in China[J]. Osteoporos Int, 2021, 32(9): 1785-1793.
2. Goh EL, Khatri A, Costa AB, et al. Prevalence of complications in older adults after hip fracture surgery: a systematic review and meta-analysis[J]. Bone Joint J, 2025, 107-B(2): 139-148.
3. Downey C, Kelly M, Quinlan JF. Changing trends in the mortality rate at 1-year post hip fracture: a systematic review[J]. World J Orthop, 2019, 10(3): 166-175.
4. Dyer SM, Crotty M, Fairhall N, et al. A critical review of the long-term disability outcomes following hip fracture[J]. BMC Geriatr, 2016, 16(1): 158.
5. Chan HYL, Cheng A, Cheung SSS, et al. Association between dehydration on admission and postoperative complications in older persons undergoing orthopaedic surgery[J]. J Clin Nurs, 2018, 27(19/20): 3679-3686.
6. Ekman L, Johnson P, Hahn RG. Signs of dehydration after hip fracture surgery: an observational descriptive study[J]. Medicina (Kaunas), 2020, 56(7): 361.
7. Zanetti M, De Colle P, Omiciuolo C, et al. Postoperative dehydration is associated with frailty and decreased survival in older patients with hip fracture[J]. Nutrients, 2022, 14(4): 820.
8. 中華醫學會老年醫學分會, 中國醫師協會老年醫學科醫師分會. 中國老年人臨床水化實踐指南(2024 版)[J]. 中華老年醫學雜志, 2024, 43(8): 954-970.
9. Volkert D, Beck AM, Cederholm T, et al. ESPEN practical guideline: clinical nutrition and hydration in geriatrics[J]. Clin Nutr, 2022, 41(4): 958-989.
10. Botigué T, Miranda J, Escobar-Bravo M, et al. Analysis of dehydration in older people in a nursing home in Spain: prevalence and associated factors[J]. Nutr Hosp, 2021, 38(2): 252-259.
11. Hooper L, Abdelhamid A, Ali A, et al. Diagnostic accuracy of calculated serum osmolarity to predict dehydration in older people: adding value to pathology laboratory reports[J]. BMJ Open, 2015, 5(10): e008846.
12. 郭大鑫, 黃曉麗. 老年人失水性脫水的診斷與治療[J]. 中華老年醫學雜志, 2023, 42(5): 598-602.
13. 中華醫學會. 臨床診療指南: 骨科分冊[M]. 北京: 人民衛生出版社, 2009.
14. Lundberg SM, Lee SI. A unified approach to interpreting model predictions[M]//Guyon I, von Luxburg U, Bengio S, et al. Advances in neural information processing systems 30. Red Hook (NY): Curran Associates, Inc., 2017: 4765-4774.
15. Dhar M, Mittal K, Pathania M, et al. Dehydration in elderly: revisiting the assessment and management strategies[J]. J Assoc Physicians India, 2023, 71(12): 47-50.
16. Mohan V, Kalra S, Zargar AH, et al. Diabetes mellitus and fluid imbalance: the need for adequate hydration[J]. J Assoc Physicians India, 2024, 72(6S): 16-24.
17. Hooper L, Bunn DK, Downing A, et al. Which frail older people are dehydrated? The UK DRIE study[J]. J Gerontol A Biol Sci Med Sci, 2016, 71(10): 1341-1347.
18. Kinoshita A, Yonemitsu T, Miyai N, et al. Factors associated with hypertonic dehydration in older Japanese outpatients[J]. J Gen Fam Med, 2025, 26(3): 205-212.
19. Coppes T, Hazen ACM, Zwart DLM, et al. Characteristics and preventability of medication-related admissions for acute kidney injury and dehydration in elderly patients[J]. Eur J Clin Pharmacol, 2024, 80(9): 1355-1362.
20. Puga AM, Lopez-Oliva S, Trives C, et al. Effects of drugs and excipients on hydration status[J]. Nutrients, 2019, 11(3): 669.
21. Luo X, He S, Li Z, et al. Quantification and influencing factors of perioperative hidden blood loss during intramedullary fixation for intertrochanteric fractures in the elderly[J]. Arch Orthop Trauma Surg, 2020, 140(10): 1339-1348.
22. 鄭曉玲, 陳希偉, 陳小岑, 等. 老年腫瘤患者入院時低攝入性脫水的評估及原因分析[J]. 中華護理雜志, 2023, 58(16): 1964-1969.
23. Rowat A, Graham C, Dennis M. Dehydration in hospital-admitted stroke patients: detection, frequency, and association[J]. Stroke, 2012, 43(3): 857-859.

West China Medical Journal

Construction and validation of a risk prediction model for water-loss dehydration in elderly hip fracture patients based on machine learning

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