Smart cities face the challenge of combining sustainable national welfare with high living standards. In the last decades, life expectancy increased globally, leading to various age-related issues in almost all developed countries. Frailty affects elderly who are experiencing daily life limitations due to cognitive and functional impairments and represents a remarkable burden for national health systems. In this paper, we proposed two different predictive models for frailty by exploiting 12 socioclinical databases. Emergency hospitalization or all-cause mortality within a year were used as surrogates of frailty. The first model was able to assign a frailty risk score to each subject older than 65 years old, identifying five different classes for tailor made interventions. The second prediction model assigned a worsening risk score to each subject in the first nonfrail class, namely the probability to move in a higher frailty class within the year. We conducted a retrospective cohort study based on the whole elderly population of the Municipality of Bologna, Italy. We created a baseline cohort of 95 368 subjects for the frailty risk model and a baseline cohort of 58 789 subjects for the worsening risk model, respectively. To evaluate the predictive ability of our models through calibration and discrimination estimates, we used, respectively, a six-year and a four-year observation period. Good discriminatory power and calibration were obtained, demonstrating a good predictive ability of the models.

Predicting Frailty Condition in Elderly Using Multidimensional Socioclinical Databases / Bertini, Flavio; Bergami, Giacomo; Montesi, Danilo; Veronese, Giacomo; Marchesini, Giulio; Pandolfi, Paolo. - In: PROCEEDINGS OF THE IEEE. - ISSN 0018-9219. - 106:(2018), pp. 1-15. [10.1109/JPROC.2018.2791463]

Predicting Frailty Condition in Elderly Using Multidimensional Socioclinical Databases

Bertini, Flavio;
2018-01-01

Abstract

Smart cities face the challenge of combining sustainable national welfare with high living standards. In the last decades, life expectancy increased globally, leading to various age-related issues in almost all developed countries. Frailty affects elderly who are experiencing daily life limitations due to cognitive and functional impairments and represents a remarkable burden for national health systems. In this paper, we proposed two different predictive models for frailty by exploiting 12 socioclinical databases. Emergency hospitalization or all-cause mortality within a year were used as surrogates of frailty. The first model was able to assign a frailty risk score to each subject older than 65 years old, identifying five different classes for tailor made interventions. The second prediction model assigned a worsening risk score to each subject in the first nonfrail class, namely the probability to move in a higher frailty class within the year. We conducted a retrospective cohort study based on the whole elderly population of the Municipality of Bologna, Italy. We created a baseline cohort of 95 368 subjects for the frailty risk model and a baseline cohort of 58 789 subjects for the worsening risk model, respectively. To evaluate the predictive ability of our models through calibration and discrimination estimates, we used, respectively, a six-year and a four-year observation period. Good discriminatory power and calibration were obtained, demonstrating a good predictive ability of the models.
2018
Predicting Frailty Condition in Elderly Using Multidimensional Socioclinical Databases / Bertini, Flavio; Bergami, Giacomo; Montesi, Danilo; Veronese, Giacomo; Marchesini, Giulio; Pandolfi, Paolo. - In: PROCEEDINGS OF THE IEEE. - ISSN 0018-9219. - 106:(2018), pp. 1-15. [10.1109/JPROC.2018.2791463]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2901216
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