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© 2020 Sociedade Brasileira de Geriatria e Gerontologia This is an open access article distributed under the terms of the Creative Commons license. Mendonça SS, Marques APO, Nunes MGS, D’Angelo ER, Leal MCC Geriatr Gerontol Aging. 2020;14(1):52-60 53 INTRODUCTION With the ageing of the population and increasing global life expectancy, the segment of the oldest old, i.e., individuals aged 80 and over, has become the fastest growing age subgroup in the world. This fact, however, brings concern with the increased prevalence of frailty and functional dependence, in addition to the increase in health care costs.1 The term functional capacity refers to the maintenance of the physical and mental abilities necessary for an independent and autonomous life, and is directly related to the ability of performing activities of daily living (ADLs), fundamental for the assurance of self-preservation and survival of the individual. The reduction of functional capacity predisposes the older population to dependence, falls, and institutionalization, representing a factor for hospitalization and death of this population.2 The evaluation of functional capacity emerges as a paradigm in the health of the aged person due to the viability of keeping these individuals active, independent and autonomous for a longer time and actively participating in society, even in the presence of diseases.3 Recife is the sixth capital city with the highest proportion of older people in the country, 4 prevailing those who depend on the assistance provided by the Brazilian Unified Health System (SUS). 5 Given this reality, this research aimed to evaluate the functional capacity of the oldest old assisted by the Family Health Strategy of Recife, an age segment still under investigation, and its association with socio-demographic, economic, and clinical variables, based on the decision tree model, a statistical model based on successive χ2 association tests. Based on a list of possible predictors, this model traces a path from the most influential to the least influential association, reducing a complex decision to a finite number of simple elements in relation to the dependent variable, justifying the choice of the method. METHODS This was a quantitative cross-sectional study with non-institutionalized oldest old adults of both sexes, assisted by Family Health teams from the micro-region 4.2 of the city of Recife. The oldest old population was identified by surveying all medical records of patients within this age group in the health units of the territory, corresponding to 485 individuals aged 80 years and above. The present investigation represents an excerpt of the study ‘Successful aging in the oldest old and associated factors’ (Envelhecimento bem-sucedido em idosos longevos e fatores associados), approved by the Research Ethics Committee of Universidade Federal de Pernambuco (under approval number CAEE 34900514.0.0000.5208), whose sample was calculated considering the population estimate of older adults living in the 4.2 micro-region of Recife. The dependent variable was equal to 31.3196, margin of error of 3.7644 and 95% confidence level. Thus, 172 individuals were selected by probabilistic sample, and those with cognitive impairment that prevented them from responding to the instruments were excluded. The participants themselves had to answer questions related to self-perception of health, social support network, and depressive R E S U M O INTRODUÇÃO: Idosos com 80 anos ou mais, ou longevos, são o subgrupo etário que mais cresce no mundo. Nesse segmento, a incapacidade funcional (IF) é mais prevalente que em outras faixas etárias. OBJETIVO: Caracterizar a capacidade funcional e analisar potenciais associações em idosos longevos de uma capital brasileira, com base em um modelo de decisão. MÉTODO: Estudo transversal com 100 idosos longevos, não institucionalizados, assistidos pela Estratégia Saúde da Família do Recife (PE), selecionados por amostra probabilística. Foram avaliados dados sociodemográficos, econômicos e clínicos coletados por meio de entrevistas domiciliares, aferição de medidas antropométricas e buscas nos prontuários. Para análise bivariada, foi empregado o teste χ2 de Pearson, considerando o nível de significância p < 0,05, e para a multivariada foi construída uma árvore de decisão com base em um algoritmo Exhaustive CHAID. RESULTADOS: A prevalência de IF na amostra correspondeu a 67,0%. Na análise bivariada, contribuíram para esse desfecho: renda (p = 0,032), situação previdenciária (p < 0,01), situação nutricional (p = 0,010), doenças neurológicas (p < 0,01), neoplasias (p < 0,01), saúde autopercebida (p = 0,025) e rede de apoio social (p = 0,032), permanecendo na análise multivariada: renda (p = 0,003), rede de apoio social (p = 0,032) e situação nutricional (p = 0,040). A árvore de decisão possibilitou a identificação das variáveis mais associadas ao desfecho, sendo capaz de prever adequadamente a dependência moderada, com assertividade de 72,1%. CONCLUSÃO: O modelo de decisão mostrou-se uma ferramenta oportuna na dedução dos determinantes mais relevantes da IF. Seu uso potencialmente contribui para ampliar a precisão diagnóstica e identificação de populações de risco. PALAVRAS-CHAVE: idoso de 80 anos ou mais; atividades cotidianas; avaliação geriátrica; atenção primária à saúde; árvores de decisões. Functional capacity and decision model Geriatr Gerontol Aging. 2020;14(1):52-60 54 symptoms, quantitative that corresponding to the 72 participants. At the end, a total of 100 older people composed the sample. The subjects were only evaluated after they were aware of the objectives of the study and registered their consent, by signature or fingerprint, in the Informed Consent Form. After this stage, home interviews were conducted between April and August 2015, using a semi-structured questionnaire to record the information, organized into thematic blocks: sociodemographic, economic, and clinical data. Functional capacity was assessed using the Barthel Index6 and the cutoff points adopted in this study were those proposed by Azeredo and Matos.7 For analysis purposes, the functionality variable was previously aggregated into 3 categories: severe dependency (0 to 60 points), moderate dependence (61 to 99 points), and independence (100 points). The cognitive capacity was assessed using the Mini Mental State Examination (MMSE), based on the standard deviation below the education means for the MMSE, according to the schooling cutoff points proposed by Bertolucci et al.8 To identify the support from family and friends received and perceived by this population, the Lubben Social Networking Brief Scale9 was used, which evaluates the social support network using 2 domains: family and friends, in which the score ≥ 12 points represents a good social support network, while values below 12 characterize an insufficient support network. In screening for depressive symptoms, the chosen resource corresponded to the 15-item Geriatric Depression Scale (Geriatric Depressive Symptoms — GDS-15),10 whose scores below 5 represents absence of depression, between 5 and 10, minor depression, and above 10, major depression. As for self-perceived health, this was categorized as positive (excellent, very good and good) and negative (regular and bad). The presence of nutritional risk was determined according to the Mini Nutritional Assessment. 11 To calculate body mass index (weight/height2), weight was obtained using a portable G-Teck® digital scale Glass 8 model, with a maximum capacity of 150 kg and a graduation of 100g. Height was estimated using the measurement of knee height with the aid of a Carci® child stadiometer 1.000 mm long, using the equations proposed by Chumlea et al.12 The SPSS software version 19.0 was used for data tabulation and analysis. The association of independent variables and functional capacity was evaluated by means of bivariate analysis using Pearson’s χ2 test, with significance established at 5%. In the multivariate analysis, a decision tree was generated using the CHAID exhaustive algorithm. According to Rokach and Maimon, 13 decision trees are hierarchical structures that, using a database, repeatedly divide the base into subsets, providing each time a partial result that will feed the next step of the process, making the tree less complex and more understandable. The algorithm does not accept continuous variables as predictors and therefore all those used in this study were categorized. At the top of the structure lies the root node, with the internal nodes below. These represent decision nodes and each contains a test on an independent variable whose outcomes form the branches of the tree. The nodes at the end of the tree represent predictive values for the dependent variable or probability distributions of those values.13 RESULTS The sample of the present study corresponded to 100 ageing adults, with a mean age of 85.5 years, ranging from 80 to 100 years (± 4.39), of which 77 (77.0%) were female. The most prevalent sociodemographic variables were brown race/color (42.0%), absence of a spouse or partner (78.0%), 1 to 4 years of schooling (46.0%), and individual monthly income from 1 to 2 minimum wages (76.0%), from retirement pays in 73.0% of the cases (Table 1). As for clinical characteristics, 28.0% of the sample presented some degree of cognitive impairment and 44.0% of the oldest old were at risk of malnutrition. In relation to the diagnosis of chronic conditions registered in the medical records, hypertension was the most prevalent (77.0%), followed by depressive symptoms (36.0%) (Table 2). Diabetes was observed in approximately 34% (n = 34) of participants, 62% (n = 62) of respondents classified their health negatively, and 63% (n = 63) mentioned having an insufficient social support network (Table 2). Regarding functional capacity, based on the Barthel Index, 7.0% of the older adults were identified as severely dependen

FUNCTIONAL CAPACITY IN THE OLDEST OLD: CROSS-SECTIONAL ANALYSIS BASED ON A DECISION MODEL