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FALL RISK PREDICTION MODEL FOR PATIENTS IN HOSPITALS: A MULTI-CENTER STUDY IN HO CHI MINH CITY
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Abstract
A multicenter study was conducted to collect real-world data and identify factors related to falls, thereby developing a fall risk prediction model specifically for Vietnamese patients. Methods: A case-control study was performed at nine public hospitals in Ho Chi Minh City, utilizing logistic regression analysis to identify associated factors and build a multivariable model. The model was internally validated using the Bootstrap method with 100 iterations. Results: Univariate regression analysis identified 18 statistically significant risk factors (p < 0.05) related to falls. Based on these factors, a predictive model was developed. Internal validation with Bootstrap demonstrated high accuracy and minimal deviation from actual results, confirming the model's reliability and strong validity. Conclusion: The study identified potential fall risk factors and developed a robust prediction model tailored for Vietnamese patients, contributing to improving fall risk assessment tools.
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Keywords
falls, inpatients, case-control study
References
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