Influence of hand grip strength test and short physical performance battery on FRAX in post-menopausal women: a machine learning cross-sectional study

被引:3
|
作者
de Sire, Alessandro [1 ,2 ,3 ,13 ]
Marotta, Nicola [2 ,4 ]
Prestifilippo, Emanuele [1 ]
Calafiore, Dario [5 ]
Lippi, Lorenzo [6 ,7 ]
Sconza, Cristiano [8 ,9 ]
Muraca, Lucia [10 ,11 ]
Invernizzi, Marco [6 ,7 ]
Mezian, Kamal [12 ]
Ammendolia, Antonio [1 ,2 ]
机构
[1] Magna Graecia Univ Catanzaro, Dept Med & Surg Sci, Catanzaro, Italy
[2] Magna Graecia Univ Catanzaro, MusculoSkeletalhlth UMG, Catanzaro, Italy
[3] Charles Univ Prague, Univ Hosp Motol, Fac Med 2, Dept Rehabil & Sports Med, Prague, Czech Republic
[4] Charles Univ Prague, Motol Univ Hosp, Dept Rehabil & Sports Med, Fac Med 2, Prague, Czech Republic
[5] ASST Carlo Poma, Dept Neurosci, Unit Phys Med & Rehabil, Mantua, Italy
[6] Univ Piemonte Orientale, Dept Hlth Sci, Novara, Italy
[7] Azienda Ospedaliera Nazl SS Antonio & Biagio & Ces, Dept Integrated Act Res & Innovat, Unit Translat Med, Alessandria, Italy
[8] Humanitas Univ, Dept Biomed Sci, Pieve Emanuele, Milan, Italy
[9] IRCCS Human Clin, Rozzano, Milan, Italy
[10] ASP 203, Dept Gen Med, Catanzaro, Italy
[11] Magna Graecia Univ Catanzaro, FAS UMG Res Ctr, Catanzaro, Italy
[12] Charles Univ Prague, Gen Univ Hosp Prague, Fac Med 1, Dept Rehabil Med, Prague, Czech Republic
[13] Magna Graecia Univ Catanzaro, Dept Med & Surg Sci, MusculoSkeletalHlthUMG, Via Campanella 115, I-88100 Catanzaro, Italy
来源
JOURNAL OF SPORTS MEDICINE AND PHYSICAL FITNESS | 2024年 / 64卷 / 03期
关键词
Hand strength; Physical functional performance; Osteoporosis; Sarcopenia; Machine learning; FRACTURE RISK-ASSESSMENT; SARCOPENIA; OSTEOPOROSIS; RATIONALE;
D O I
10.23736/S0022-4707.23.15417-X
中图分类号
G8 [体育];
学科分类号
04 ; 0403 ;
摘要
BACKGROUND: Impaired physical performance and muscle strength are recognized risk factors for fragility fractures, frequently associated with osteoporosis and sarcopenia. However, the integration of muscle strength and physical performance in the comprehensive assessment of fracture risk is still debated. Therefore, this cross-sectional study aimed to assess the potential role of hand grip strength (HGS) and short physical performance battery (SPPB) for predicting fragility fractures and their correlation with Fracture Risk Assessment Tool (FRAX) with a machine learning approach.METHODS: In this cross-sectional study, a group of postmenopausal women underwent assessment of their strength, with the outcome measured using the HSG, their physical performance evaluated using the SPPB, and the predictive algorithm for fragility fractures known as FRAX. The statistical analysis included correlation analysis using Pearson's r and a decision tree model to compare different variables and their relation-ship with the FRAX Index. This machine learning approach allowed to create a visual decision boundaries plot, providing a dynamic representa-tion of variables interactions in predicting fracture risk.RESULTS: Thirty-four patients (mean age 63.8 +/- 10.7 years) were included. Both HGS and SPPB negatively correlate with FRAX major (r=-0.381, P=0.034; and r=-0.407, P=0.023 respectively), whereas only SPPB significantly correlated with an inverse proportionality to FRAX hip (r=-0.492, P=0.001). According to a machine learning approach, FRAX major >20 and/or hip >3 might be reported for an SPPB<6. Concur-rently, HGS<17.5 kg correlated with FRAX major >20 and/or hip >3.CONCLUSIONS: In light of the major findings, this cross-sectional study using a machine learning model related SPPB and HGS to FRAX. Therefore, a precise assessment including muscle strength and physical performance might be considered in the multidisciplinary assessment of fracture risk in post-menopausal women.
引用
收藏
页码:293 / 300
页数:8
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