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

A. de Sire, N. Marotta, E. Prestifilippo, D. Calafiore, L. Lippi, C. Sconza, L. Muraca, M. Invernizzi, K. Mezian, A. Ammendolia

. 2024 ; 64 (3) : 293-300. [pub] 20231221

Jazyk angličtina Země Itálie

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc24006941

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 relationship with the FRAX Index. This machine learning approach allowed to create a visual decision boundaries plot, providing a dynamic representation 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. Concurrently, 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.

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$a de Sire, Alessandro $u Department of Medical and Surgical Sciences, The Magna Græcia University of Catanzaro, Catanzaro, Italy - alessandro.desire@unicz.it $u MusculoSkeletalHealth@UMG, The Magna Græcia University of Catanzaro, Catanzaro, Italy - alessandro.desire@unicz.it $u Department of Rehabilitation and Sports Medicine, Second Faculty of Medicine, University Hospital Motol, Charles University, Prague, Czech Republic - alessandro.desire@unicz.it
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$a 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 relationship with the FRAX Index. This machine learning approach allowed to create a visual decision boundaries plot, providing a dynamic representation 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. Concurrently, 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.
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$a Lippi, Lorenzo $u Department of Health Sciences, University of Eastern Piedmont, Novara, Italy $u Unit of Translational Medicine, Department of Integrated Activities Research and Innovation, Azienda Ospedaliera Nazionale SS. Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
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