Influence of cognitive impairment on fall risk among elderly nursing home residents

Seijo-Martínez, Manuel; Cancela, José M.; Varela, Silvia; Suárez, Helena Vila

doi:10.1017/s1041610216001113

Cited by 25 publications

(17 citation statements)

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“…Only RF reported that the total number of residents was an important factor in falls, consistent with Thomas et al [42], but not with Van Doorn et al [7]. The RF and SVM radical reported that the proportion of residents with cognitive dysfunction related to falls, consistent with preceding research [6][7][8], but inconsistent with Datta et al [34].…”

Section: Discussionsupporting

confidence: 57%

“…The proportion of residents prescribed psychiatric medications and urinary incontinence were top factors in the logistic regression, RF, SVM polynomial, SVM radial, and SVM sigmoid. Psychiatric medication was a very strong predictor of falls, consistent with previous research [6,7,9,10,[31][32][33], but inconsistent with a couple of studies [34,35]. The proportion of residents with urinary incontinence was an important factor in falls, supported in previous research [9][10][11][12], but not supported in other studies [7,32].…”

Section: Discussionsupporting

confidence: 46%

“…These stakeholders develop targeted fall-prevention management and assess residents based on the factors associated with a fall [5]. Prior studies identified a number of risk factors for falls, including age, sex, visual deficits, psychotropic medications, cognitive dysfunction [6][7][8], range of motion, urinary incontinence [9-12], hours per residents day (HPRD) of registered…”

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confidence: 99%

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Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Lee

Ahn

Shin

et al. 2020

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Background: To compare machine learning (ML) methods beyond traditional regression analysis using a real dataset on nurse staffing and falls in nursing homes. Methods:We used a total of 6 ML algorithms to compare the superiority of ML over traditional regression methods. We applied three representative ML algorithms-random forest (RF), logistics regression, support vector machine (SVM): linear, polynomial, radial, and sigmoid-to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models.Results: RF was the most accurate model (0.883), followed by the logistic regression model (0.867).Using RF procedures, we identified a total of 6 variables as predictors of falls including hours per resident day of administrative staff, proportion of nursing home residents with psychiatric medications, aggressive behaviors and cognitive dysfunction, urinary incontinence, current number of residents in each nursing home, and the maximum capacity of each nursing home. Conclusions:The appropriate choice of prediction model is quite challenging for nursing researchers.For effective fall management, researchers should consider organizational characteristics as well as personal factors. The examination of related factors on falls is quite meaningful in that preventing falls contributes to improving the quality of life and care of residents and decreases health care costs. BackgroundThe most frequently reported adverse event among nursing home (NH) residents, falls are associated with increased morbidity and mortality, and with reduced functioning [1-3]. The U.S. fall prevalence is 1.7 falls per resident yearly in NHs [1]; about 50% of NH residents in developed countries fall eachyear [4]. Accurate prediction of factors associated with a fall in NHs is important because nurses, health care professionals, researchers in practice, administrative staff, researchers, and politicians address fall issues. These stakeholders develop targeted fall-prevention management and assess residents based on the factors associated with a fall [5]. Prior studies identified a number of risk factors for falls, including age, sex, visual deficits, psychotropic medications, cognitive dysfunction [6][7][8], range of motion, urinary incontinence [9-12], hours per residents day (HPRD) of registered

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Section: Discussionsupporting

confidence: 57%

Section: Discussionsupporting

confidence: 46%

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confidence: 99%

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Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Lee

Ahn

Shin

et al. 2020

Preprint

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“…En nuestro estudio, el deterioro cognitivo se asoció al riesgo de caídas según AF, pues encontramos que los adultos mayores con deterioro cognitivo tienen mayor probabilidad de riesgo de caídas, con una razón de prevalencias que triplica las probabilidades de tener un AF alterado, en un modelo ajustado por educación, número de comorbilidades, polifarmacia y edad. Este hallazgo está en línea con otros estudios como el de Seijo-Martínez et al, [23] y Gómez et al, [24] donde el estado cognitivo de los adultos mayores tuvo una asociación estadísticamente significativa con el riesgo de caídas, demostrando que a mayor severidad de deterioro cognitivo en el adulto mayor se incrementa la probabilidad del riesgo de caídas; de manera similar, Thanthrige et al, [25] y Azevedo et al, [26] evidenciaron también, la relación existente entre el desempeño cognitivo y el riesgo de caídas.…”

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“…Análisis bivariado de prueba de alcance funcional y covariables del estudio (n=1786). De forma similar, también se encontró asociación entre el sexo femenino y a la polifarmacia como factores de riesgo para caídas, acorde a lo hallado por estudios previos [23,27] . Adicionalmente, el estudio de Azevedo et al, [26] evidenció que los adultos mayores del sexo femenino tenían mayor riesgo de presentar caídas y que este riesgo se incrementaba ante la presencia de comorbilidades.…”

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Relación entre el riesgo de caídas y deterioro cognitivo en adultos mayores atendidos en el Centro Médico Naval “Cirujano Mayor Santiago Távara” 2010-2015*

2019

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Objetivo: Determinar la relación entre el riesgo de caídas y deterioro cognitivo en adultos mayores del Centro Médico Naval “Cirujano Mayor Santiago Távara” 2010-2015. Materiales y métodos: Fue un estudio observacional, analítico, retrospectivo, que consistió en el análisis secundario de una base de datos. Se evaluó el riesgo de caídas mediante los resultados de la prueba de alcance funcional (AF), mientras que para determinar el deterioro cognitivo se utilizó el cuestionario de Pfeiffer. Adicionalmente, se estudiaron factores socio-demográficos como la edad, sexo, nivel educativo; y clínicos como la presencia de comorbilidades, polifarmacia o antecedentes de caídas. Resultados: La muestra estuvo conformada por 1786 adultos mayores. El 45,1% tenían entre 71 y 80 años, de sexo masculino (58,9%), con nivel educativo técnico/ superior (70,9%), tenían 2 o más comorbilidades (48,9%). Se encontró asociación estadística (p<0,05) entre AF alterado y la edad, el nivel educativo, la presencia de comorbilidades, y el deterioro cognitivo. Al realizar el modelo multivariado, se encontró que el deterioro cognitivo se asoció al riesgo de caídas con un PR ajustado de 3,05 (IC 95%: 2,45-3,79). Conclusión: Se encontró que el deterioro cognitivo estuvo asociado a un riesgo tres veces mayor de caídas en adultos mayores del Centro Médico Naval “Cirujano Mayor Santiago Távara” durante los años 2010-2015.

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Organizational factors of fall injuries among residents within German nursing homes: secondary analyses of cross-sectional data

et al. 2019

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Influence of cognitive impairment on fall risk among elderly nursing home residents

Abstract: This study provides evidence indicating that fall risk factors do not hold a direct correlation with the level of cognitive impairment among elderly nursing home care residents.

Cited by 25 publications

References 44 publications

Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Relación entre el riesgo de caídas y deterioro cognitivo en adultos mayores atendidos en el Centro Médico Naval “Cirujano Mayor Santiago Távara” 2010-2015*

Organizational factors of fall injuries among residents within German nursing homes: secondary analyses of cross-sectional data

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