Morbidity and Mortality After Second Hip Fracture With and Without Nursing Care Program

Solou, Konstantina; Tyllianakis, Minos; Kouzelis, Antonis; Lakoumentas, John; Panagopoulos, Andreas

doi:10.7759/cureus.23373

Cited by 5 publications

(5 citation statements)

References 28 publications

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“…Omsland et al reported that 11% of males and 15% of females will experience a contralateral hip fracture within 10 years of a first hip fracture 1 . The incidence of the contralateral hip fracture is estimated to be between 6.8% and 16% in elderly patients after a first hip fracture 2 , 3 , with mortality rates of 15.9% within one year of an initial hip fracture and 37.3% within one year of a contralateral hip fracture 4 , 5 . Elderly patients with a second hip fracture frequently show reduced mobility, less social independence, and higher rates of mortality, as well as higher rates of dementia 6 .…”

Section: Introductionmentioning

confidence: 99%

Analysis of the clinical characteristics and risk factors associated with contralateral hip fracture after initial hip fracture in elderly patients: a retrospective cohort study

Zhao,

Tian,

Sha

et al. 2024

Sci Rep

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Fractures of the contralateral hip may easily occur in elderly patients after an initial hip fracture. The aim of this study was to investigate the clinical characteristics and major predisposing risk factors of contralateral hip fracture after initial hip fracture in the elderly, to provide a clinical basis for preventing contralateral hip fracture. The data of 1586 patients who had sustained first or second hip fractures and had been surgically treated in our department were retrospectively analyzed. Potential predictive factors for contralateral hip fracture and descriptive statistics associated with surgery (such as blood loss, operation time, and length of hospital stay) were recorded. Of these patients, 133 (8.4%) suffered contralateral hip fracture. The rates of contralateral fracture after femoral neck and intertrochanteric fracture were 5.4% and 10.7% respectively (P < 0.01). Fifty-four cases of contralateral hip fracture occurred within one year, an incidence of 40.6%, while 95 cases (71.4%) and 105 cases (78.9%) occurred within two and three years, respectively, with a interval duration of 21.6 months. The risk factors for contralateral hip fracture were found to be age, type of first fracture, bone mineral density, the Singh index, and concomitant internal medical diseases, which were found to be significantly associated with an increased risk of contralateral hip fracture in multivariate logistic regression analysis (P < 0.05). In conclusion, the presence of concomitant internal diseases, type of first fracture, bone mineral density, the Singh index, and age were found to be significant predictors of the risk of contralateral hip fracture in elderly patients after a first hip fracture.

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Section: Introductionmentioning

confidence: 99%

Analysis of the clinical characteristics and risk factors associated with contralateral hip fracture after initial hip fracture in elderly patients: a retrospective cohort study

Zhao,

Tian,

Sha

et al. 2024

Sci Rep

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“…12 The mortality within 42 months after a second hip fracture was 57.2%. 13 Because the mortality rate of patients with hip fractures is high, it is important to predict or diagnose OP early to prevent hip fractures from occurring. BMD is measured using dual energy X-ray absorptiometry (DXA) scans of the spine and hip to diagnose OP.…”

Section: Introductionmentioning

confidence: 99%

A Multi-layer Perceptron Neural Network for Predicting the Osteoporosis in Women Using Physical Activity Factors

Kim

2023

Preprint

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Background: Osteoporosis (OP) is a bone disease caused by a decrease in bone mineral density (BMD). OP is common in women because BMD gradually decreases after age 35. OP due to decreased BMD is highly likely to cause fatal traumatic injuries such as hip fracture. Purpose: To develop and evaluate a multi-layer perceptron neural network model that predicts OP using physical characteristics and activity factors of adult women over the age of 35 whose BMD begins to decline. Method: Data from KNHANES were used to develop a multi-layer perceptron model for predicting OP. Data preprocessing included variable selection and sample balancing, and LASSO was used for feature selection. The model used 5 hidden layers, dropout and batch normalization and was evaluated using evaluation scores such as accuracy and recall score calculated from a confusion matrix. Result: Models were trained and evaluated to predict OP using selected features including age, quality of life index, weight, grip strength and average working hours per week. The model achieved 76.8% accuracy, 74.5% precision, 80.5% recall, 77.4% F1 score, and 74.8% ROC AUC. Conclusion: A multi-layer perceptron neural network for predicting OP diagnosis using physical characteristics and activity factors in women aged 35 years or older showed relatively good performance. Since the selected variables can be easily measured through surveys, assessment tool, and digital hand dynamometer, this model will be useful for screening elderly women with OP or not in areas with poor medical facilities or difficult access.

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Section: Introductionmentioning

confidence: 99%

A Multi-Layer Perceptron Neural Network for Predicting the Diagnosis of Osteoporosis in Women Using Physical Activity Factors

Gwak,

Kim,

Hwang

et al. 2023

J Musculoskelet Sci Technol

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Morbidity and Mortality After Second Hip Fracture With and Without Nursing Care Program

Cited by 5 publications

References 28 publications

Analysis of the clinical characteristics and risk factors associated with contralateral hip fracture after initial hip fracture in elderly patients: a retrospective cohort study

Analysis of the clinical characteristics and risk factors associated with contralateral hip fracture after initial hip fracture in elderly patients: a retrospective cohort study

A Multi-layer Perceptron Neural Network for Predicting the Osteoporosis in Women Using Physical Activity Factors

A Multi-Layer Perceptron Neural Network for Predicting the Diagnosis of Osteoporosis in Women Using Physical Activity Factors

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