RESEARCH PAPER
Determining the hierarchy of risk factors for low-energy fractures in patients of an Osteoporosis Treatment Clinic
1
Central Clinical Hospital, Medical University, Łódź, Poland
2
Faculty of Mathematics and Computer Science, University of Łódź, Poland
3
Department of Internal Medicine, Rehabilitation and Physical Medicine, Medical University, Łódź, Poland
Corresponding author
Dorota Lis-Studniarska
Central Clinical Hospital, Medical University of Łódź, Pomorska 251, 92-213, Łódź, Poland
Central Clinical Hospital, Medical University of Łódź, Pomorska 251, 92-213, Łódź, Poland
KEYWORDS
TOPICS
ABSTRACT
Introduction and objective:
The medical records were examined of 222 patients of the Osteoporosis Treatment Clinic at the Central Clinical Hospital of the Medical University of Łódź, Poland. The influence was analyzed of 27 clinical risk factors on the occurrence of low-energetic fractures in this population. The aim of the research was to find possible dependencies between different risk factors, and the actual fractures that were recorded in the database.
Material and methods:
For each risk factor and for each category (e.g., patients with diabetes and patients without diabetes), the percentage was computed of patients who had incidents osteoporotic fractures, and the percentage of those without fractures. Student’s t-test and Pearson’s chi-squared test were used to find statistically significant risk factors.
Results:
Statistically significant risk factors were found: age, chronic kidney disease, T-scores of the femoral neck and T-score of the lumbar spine, serum phosphate levels, FRAX-BMD, FRAX-BMI, and the type of diet.
Conclusions:
Some observations concerning the influence of individual risk factors on the occurrence of fractures are consistent with those presented in the literature. However, it was also noticed that the patients with hyperthyroidism, rheumatic diseases, diabetes, cancer or gastrointestinal diseases, had a smaller percentage of fractures than the patients who did not have these diseases. This may be explained by the small number of those having these diseases, or by the fact that they had already received appropriate treatment.
The medical records were examined of 222 patients of the Osteoporosis Treatment Clinic at the Central Clinical Hospital of the Medical University of Łódź, Poland. The influence was analyzed of 27 clinical risk factors on the occurrence of low-energetic fractures in this population. The aim of the research was to find possible dependencies between different risk factors, and the actual fractures that were recorded in the database.
Material and methods:
For each risk factor and for each category (e.g., patients with diabetes and patients without diabetes), the percentage was computed of patients who had incidents osteoporotic fractures, and the percentage of those without fractures. Student’s t-test and Pearson’s chi-squared test were used to find statistically significant risk factors.
Results:
Statistically significant risk factors were found: age, chronic kidney disease, T-scores of the femoral neck and T-score of the lumbar spine, serum phosphate levels, FRAX-BMD, FRAX-BMI, and the type of diet.
Conclusions:
Some observations concerning the influence of individual risk factors on the occurrence of fractures are consistent with those presented in the literature. However, it was also noticed that the patients with hyperthyroidism, rheumatic diseases, diabetes, cancer or gastrointestinal diseases, had a smaller percentage of fractures than the patients who did not have these diseases. This may be explained by the small number of those having these diseases, or by the fact that they had already received appropriate treatment.
REFERENCES (47)
1.
Głuszko P, Sewerynek E, Misiorowski W, et al. Guidelines for the diagnosis and management of osteoporosis in Poland. Update 2022. Endokrynol Pol. 2023;74(1):5–15. https://doi.org/10.5603/EP.a20....
2.
NFZ o zdrowiu. 2019. Osteoporoza, Centrala Narodowego Funduszu Zdrowia, Departament Analiz i Strategii, Warszawa. https://ezdrowie.gov.pl/pobier... (access: 2024.01.19).
3.
Leslie WD, Majumdar SR, Lix LM, et al. High fracture probability with FRAX usually indicates densitometric osteoporosis: implications for clinical practice. Osteoporos Int. 2012;23:391–397. https://doi.org/10.1007/s00198....
4.
Gourlay ML, Ritter VS, Fine JP, et al. Comparison of fracture risk assessment tools in older men without prior hip or spine fracture: the MrOS study. Arch Osteoporos. 2017;12:91. https://doi.org/10.1007/s11657....
5.
Miedany IE. FRAX: re-adjust or re-think. Arch Osteoporos. 2020;15:150 https://doi.org/10.1007/s11657....
6.
Cruz AS, Lins HC, Medeiros RVA, et al. Artificial intelligence on the identification of risk groups for osteoporosis, a general review. BioMed Eng OnLine. 2018;17:12. https://doi.org/10.1186/s12938....
7.
Lis-Studniarska D, Lipnicka M, Studniarski M, et al. Applications of artificial intelligence methods for the prediction of osteoporotic fractures. Life. 2023;13:1738. https://doi.org/10.3390/life13....
8.
Rinonapoli G, Ruggiero C, Meccariello L, et al. Osteoporosis in men: a review of underestimated bone condition. Int J Mol Sci. 2021;22(4):2105. https://doi.org/10.3390/ijms22....
9.
Johnston CB, Dagar M. Osteoporosis in older adults. Med Clin N Am. 2020;104:873–884. https://doi.org/10.1016/j.mcna....
11.
Tucker KL. Osteoporosis prevention and nutrition. Curr Osteoporos Rep. 2009;7:111–117 https://doi.org/10.1007/s11914....
12.
Kanis JA, Cooper C, Rizzoli R, et al. European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int. 2019;30:3–44. https://doi.org/10.1007/s00198....
13.
Bagur AC, Mautalen CA. Risk for developing osteoporosis in untreated premature menopause. Calcif Tissue Int. 1992;51(1):4–7. https://doi.org/10.1007/BF0029....
14.
Golds G, Houdek D, Arnason T. Male hypogonadism and osteoporosis: the effects, clinical consequences, and treatment of testosterone deficiency in bone health. Int J Endocrinol. 2017;2017:4602129. https://doi.org/10.1155/2017/4....
15.
Hung CL, Yeh CC, Sung PS, et al. Is partial or total thyroidectomy associated with risk of long-term osteoporosis: a nationwide population-based study. World J Surg. 2018;42:2864–2871. https://doi.org/10.1007/s00268....
16.
Delitala AP, Scuteri A, Doria C. Thyroid hormone diseases and osteoporosis. J Clin Med. 2020;9(4):1034. https://doi.org/10.3390/jcm904....
17.
Lee JH, Suh YS, Koh JH, et al. The risk of osteoporotic fractures according to the FRAX model in Korean patients with rheumatoid arthritis. J Korean Med Sci. 2014;29(8):1082–1089. https://doi.org/10.3346/jkms.2....
18.
Hu Z, Zhang L, Lin Z, et al. Prevalence and risk factors for bone loss in rheumatoid arthritis patients from South China: modeled by three methods. BMC Musculoskelet Disord. 2021;22(1):534. https://doi.org/10.1186/s12891....
19.
Ye C, Leslie WD. Fracture risk and assessment in adults with cancer. Osteoporos Int. 2023;34:449–466. https://doi.org/10.1007/s00198....
20.
Oh HJ, Ryu KH, Park BJ, et al. Osteoporosis and osteoporotic fractures in gastrointestinal disease. J Bone Metab. 2018;25(4):213–217. https://doi.org/10.11005/jbm.2....
21.
Lima GAC, Paranhos-Neto FP, Pereira GRM, et al. Osteoporosis management in patient with renal function impairment. Arq Bras Endocrinol Metab. 2014;58(5):530–539. https://doi.org/10.1590/0004-2....
22.
Trajanoska K, Rivadeneira F. Genomic medicine: lessons learned from monogenic and complex bone disorders. Front Endocrinol. 2020;11:556610. https://doi.org/10.3389/fendo.....
23.
Wnęk D. Dieta w osteoporozie. Medycyna Praktyczna https://www.mp.pl/pacjent/diet... (access: 2023.08.30).
24.
Nawrat-Szołtysik A, Miodońska Z, Zarzeczny R, et al. Osteoporosis in Polish older women: risk factors and osteoporotic fractures: a cross-sectional study. Int J Environ Res Public Health. 2020;17(10):3725. https://doi.org/10.3390/ijerph....
25.
Al-Bashaireh AM, Haddad LG, Weaver M, et al. The effect of tobacco smoking on bone mass: an overview of pathophysiologic mechanisms. J Osteoporos. 2018;2018:1206235. https://doi.org/10.1155/2018/1....
26.
Berman NK, Honig S, Cronstein BN, et al. The effects of caffeine on bone mineral density and fracture risk. Osteoporos Int. 2022;33:235–1241. https://doi.org/10.1007/s00198....
27.
Fasihi L, Tartibian B, Eslami R, et al. Artificial intelligence used to diagnose osteoporosis from risk factors in clinical data and proposing sports protocols. Sci Rep. 2022;12:18330. https://doi.org/10.1038/s41598....
28.
Sözen T, Özişik L, Başaran NÇ. An overview and management of osteoporosis. Eur J Rheumatol. 2017;4(1):46–56. https://doi.org/10.5152/eurjrh....
29.
DiNicolantonio JJ, Mehta V, Zaman SB, et al. Not salt but sugar as aetiological in osteoporosis: a review. Mo Med. 2018;115(3): 247–252.
30.
Ferizi U, Honig S, Chang G. Artificial intelligence, osteoporosis and fragility fractures. Curr Opin Rheumatol. 2019;31(4):368–375. https://doi.org/10.1097/BOR.00....
31.
Grace SJ, Kumar DS, Gautam R, et al. Osteoporosis detection using deep learning. Int J Mod Trends Sci Technol. 2019;05(03):17–20.
32.
Iliou T, Anagnostopoulos C-N, Anastasssopoulos G. Osteoporosis detection using machine learning techniques and feature selection. Int J Artif Intell Tools. 2014;23(5):1450014. https://doi.org/10.1142/S02182....
33.
Moudani W, Shahin A, Chakik F, et al. Intelligent predictive osteoporosis system. Int J Comput Appl. 2011;32(5):28–37. https://doi.org/10.5120/3901-5....
34.
Sathawane KS, Tuteja RR. Data mining in clinical records to foretell the risk of osteoporosis. Int J Res Advent Technol. 2015;3(6):24–31.
35.
Devikanniga D, Raj RJS. Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm. Healthc Technol Lett. 2018;5(2):70–75. https://doi.org/10.1049/htl.20....
36.
Reshmalakshmi C, Sasikumar M. Fuzzy inference system for osteoporosis detection. IEEE 2016 Global Humanitarian Technology Conference; Oct. 13–16; 2016;675–681. https://doi.org/10.1109/GHTC.2....
37.
Shubangi DC, Shilpa K A survey on detection and diagnosis of osteoporosis. Int J Eng Sci Invention. 2017;6(10):30–35.
38.
Ji L, Zhang W, Zhong X, et al. Osteoporosis, fracture and survival: Application of machine learning in breast cancer prediction models. Front Oncol. 2022;12:973307. https://doi.org/10.3389/fonc.2....
39.
Bui, HM, Ha, MH, Pham, HG, et al. Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches. Sci Rep. 2022;12:20160. https://doi.org/10.1038/s41598....
40.
Kong SH, Ahn D, Kim B, et al. A novel fracture prediction model using machine learning in a community-based cohort. JBMR Plus (WOA). 2020;4(3):e10337. https://doi.org/10.1002/jbm4.1....
41.
Suh B, Yu H, Kim H, et al. Interpretable deep-learning approaches for osteoporosis risk screening and individualized feature analysis using large population-based data: model development and performance evaluation. J Med Internet Res. 2023;25:e40179. https://doi.org/10.2196/40179.
43.
Tulchinsky TH. Marc Lalonde, the health field concept and health promotion. Case Studies in Public Health. 2018;523–41. https://doi.org/10.1016/B978-0....
44.
Williams FMK, Cherkas LF, Spector TD, et al. The effect of moderate alcohol consumption on bone mineral density: a study of female twins. Ann Rheum Dis. 2005;64(2):309–310. https://doi.org/10.1136/ard.20....
45.
Godos J, Giampieri F, Chisari E, et al. Alcohol consumption, bone mineral density, and risk of osteoporotic fractures: a dose-response meta-analysis. Int J Environ Res Public Health. 2022;19(3):1515. https://doi.org/10.3390/ijerph....
46.
Zeng X, Su Y, Tan A, et al. The association of coffee consumption with the risk of osteoporosis and fractures: a systematic review and meta-analysis. Osteoporos Int. 2022;33:1871–1893. https://doi.org/10.1007/s00198....
47.
Filip RS, Zagórski J. Osteoporosis risk factors in rural and urban women from the Lublin Region of Poland. Ann Agric Environ Med. 2005;12(1):21–26.
Share
RELATED ARTICLE
| eISSN: | 1898-2263 |
| ISSN: | 1232-1966 |
Improvement of visual identification of the journal - task financed under the agreement No. 618/P-DUN/2019 by the Minister of Science and Higher Education allocated to the activities of disseminating science.
Generation of the DOI (Digital Object Identifier) - task financed under the agreement No. 618/P-DUN/2019 by the Minister of Science and Higher
© 2006-2024 Journal hosting platform by Bentus
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
