Clustering of Mental and Physical Comorbidity and the Risk of Frailty in Patients Aged 60 Years or More in Primary Care

Abstract

BackgroundThis study aimed to identify the clustering of comorbidities, cognitive, and mental factors associated with increased risk of pre-frailty and frailty in patients ≥60 years in a primary healthcare setting in eastern Croatia.Material/MethodsThere were 159 patients included in the cluster analysis who were ≥60 years and who underwent four-month follow-up. The first cluster contained 50 patients, the second cluster contained 74 patients, and the third cluster contained 35 patients. Clinical parameters were identified from electronic health records and patient questionnaires. Laboratory tests, anthropometric measurements, the number of chronic diseases, the number of prescribed medications were recorded. Frailty was determined using the five criteria of Fried’s phenotype model. Levels of anxiety and depression were recorded using the Geriatric Anxiety Scale (GAS) and the Geriatric Depression Scale (GDS), and the Mini-Mental State Examination (MMSE) score assessed cognitive impairment. Logistic regression models were used to identify predictors of frailty and pre-frailty.ResultsThree overlapping clusters of phenotypes predicted frailty, and included obesity (n=50), multimorbidity with mental impairment (n=74), and decline in renal function with cognitive impairment (n=35). The predictors of outcome included increasing age, number of chronic diseases, inflammation, anemia, anxiety, and cognitive impairment, and reduced muscle mass.ConclusionsIn patients ≥60 years in a primary healthcare setting, multimorbidity predictors of pre-frailty and frailty included a decline in cognitive function and renal function.

Cluster Analysis of the Associations among Physical Frailty, Cognitive Impairment and Mental Disorders

“…Moreover, to our knowledge, this study is the first attempt to identify patterns of physical and cognitive performances among older individuals. In our previous study, we used the k-means clustering technique, which compares distances between numerical variables, to determine the relationship of factors associated with comorbidities and the cognitive and mental functioning of older primary care patients with pre-frail and frail status [ 42 ]. A recent large-scale study used a fuzzy c-means algorithm to identify multimorbidity patterns in older individuals, and to analyse their sociodemographic, lifestyle, clinical, and functional characteristics [ 10 ].…”

Assessing timewise changes over 15 months in life-space mobility among community-dwelling elderly persons

“…However, the timewise changes in life-space mobility of elderly adults who live alone were better than those who live with others. To date, living alone has been noted as a risk factor for social frailty [15,16,18]; however, it appears that those who live with others have another risk. One of the reasons for the diminished life-space mobility of elderly persons who live with others could be that they rely on others in daily life activities, such as for shopping, so their life-space mobility decreases.…”

“…Over recent decades, frailty has received increasing worldwide attention from researchers. Recent research has assessed the relationship between frailty and risk factors for falls [13], hospital use and mortality [14], sarcopenia and osteoporosis [15], dental care [16], oral environment [17], and mental and physical comorbidity [18]. Although there is no consensus for definition of frailty, the two most common measurements of frailty are Fried's Frailty Phenotype [19] and the Frailty Index (FI) [20].…”

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

“…Our research group used non-hierarchical (k-means) and hierarchical (LCA) cluster analyses in the pilot exploratory study, aimed at getting some insights into the mechanisms that might have stayed behind the clustering of physical frailty and cognitive impairment–the two major aging entropy states [ 93 , 94 ]. We had firstly targeted these functional disorders, and then provided descriptions of the identified clusters by assessing differences in diagnoses of chronic diseases and other clinical and socio-demographic variables, by means of phenotyping the heterogeneous patients at risk for these outcomes.…”

“…Like the idea of this study, our research group assumes that, when studying multimorbidity problems, the multi-modal data that describe patients with many aspects should create the input of classification or prediction models. That measures of health status functional decline, rather than disease labels, should be used as the outcome measures [ 93 , 94 ]. In line with the paradigm of complex thinking, the authors in this study used a combination of well-proven methods, prioritizing the problem-solving task over the assessment of new techniques.…”