Physical frailty prediction model for the oldest old
ABSTRACT Objective: to present a physical frailty prediction model for oldest old users of primary health care, according to clinical variables. Method: cross-sectional study with proportional stratified sample of 243 oldest old subjects. Data were collected through a structured clinical questionnaire, handgrip strength test, walking speed, weight loss, fatigue/exhaustion, and physical activity level. For the analysis of the data, univariate and multivariate analysis by logistic regression were used (p<0.05), which resulted in prediction models. The odds ratios (95% Confidence Interval) of the models were calculated. Each model was evaluated by deviance analysis, likelihood ratios, specificity and sensitivity, considering the most adequate. All ethical and legal precepts were followed. Results: the prediction model elected was composed of metabolic diseases, dyslipidemias and hospitalization in the last 12 months. Conclusion: clinical variables interfere in the development of the physical frailty syndrome in oldest old users of basic health unit. The choice of a physical frailty regression model is the first step in the elaboration of clinical methods to evaluate the oldest old in primary care.
| Main Authors: | , , , , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Escola de Enfermagem de Ribeirão Preto / Universidade de São Paulo
2018
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| Online Access: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-11692018000100346 |
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| Summary: | ABSTRACT Objective: to present a physical frailty prediction model for oldest old users of primary health care, according to clinical variables. Method: cross-sectional study with proportional stratified sample of 243 oldest old subjects. Data were collected through a structured clinical questionnaire, handgrip strength test, walking speed, weight loss, fatigue/exhaustion, and physical activity level. For the analysis of the data, univariate and multivariate analysis by logistic regression were used (p<0.05), which resulted in prediction models. The odds ratios (95% Confidence Interval) of the models were calculated. Each model was evaluated by deviance analysis, likelihood ratios, specificity and sensitivity, considering the most adequate. All ethical and legal precepts were followed. Results: the prediction model elected was composed of metabolic diseases, dyslipidemias and hospitalization in the last 12 months. Conclusion: clinical variables interfere in the development of the physical frailty syndrome in oldest old users of basic health unit. The choice of a physical frailty regression model is the first step in the elaboration of clinical methods to evaluate the oldest old in primary care. |
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