| Autor: |
Ming Zhou, Gongzi Zhang, Na Wang, Tianshu Zhao, Yangxiaoxue Liu, Yuhan Geng, Jiali Zhang, Ning Wang, Nan Peng, Liping Huang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
BMC Geriatrics, Vol 24, Iss 1, Pp 1-9 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2318 |
| DOI: |
10.1186/s12877-024-05064-4 |
| Popis: |
Abstract Background Early detection of patients at risk of falling is crucial. This study was designed to develop and internally validate a novel risk score to classify patients at risk of falls. Methods A total of 334 older people from a fall clinic in a medical center were selected. Least absolute shrinkage and selection operator (LASSO) regression was used to minimize the potential concatenation of variables measured from the same patient and the overfitting of variables. A logistic regression model for 1-year fall prediction was developed for the entire dataset using newly identified relevant variables. Model performance was evaluated using the bootstrap method, which included measures of overall predictive performance, discrimination, and calibration. To streamline the assessment process, a scoring system for predicting 1-year fall risk was created. Results We developed a new model for predicting 1-year falls, which included the FRQ-Q1, FRQ-Q3, and single-leg standing time (left foot). After internal validation, the model showed good discrimination (C statistic, 0.803 [95% CI 0.749–0.857]) and overall accuracy (Brier score, 0.146). Compared to another model that used the total FRQ score instead, the new model showed better continuous net reclassification improvement (NRI) [0.468 (0.314–0.622), P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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