The results of investigation of sample of 107 patients 60 years and older during one month are described. This sample was collected in geriatric outpatient departments. The prevalence of geriatric syndromes was assessed. The main geriatric syndromes revealed were high risk of falls, risk of malnutrition, depression and cognitive disorders. 67% of patients were frail. All geriatric syndromes caused high level of dependency. The geriatrician made recommendations for correction of main problems. All recommendations need to use multidisciplinary approach.

Introduction. Grip strength is a reflection not only of the strength of the hands, but also the strength of the muscles of the whole body, the functional capabilities of the body and an important diagnostic marker of the overall health of a person. The aim of this work was to compare the measurements obtained with the DK-50 and JAMAR® Plus digital handheld dynamometers.Methods. A convenience sample was used of 94 health participants, men and women, aged from 15 to 65 years old. Grip strength of a dominant hand was conducted using a carpal mechanical dynamometer DK-50 (Nizhni Tagil, Russian Federation) and JAMAR® Plus digital handheld dynamometer. The simple Pearson correlation test, linear regression method and the procedure of Bland and Altman were used to estimate difference between an average value of results of measurements of grip strength (AGS) and maximum measurement of grip strength (MGS) of the dominant hand of two dynamometers.Results. The grip strength using JAMAR® Plus dynamometer was higher than with the DK-50 dynamometer by 5.6 ± 4.2 kg for the average grip strength (AGS) and by 6.7 ± 4.3 kg for the maximum grip strength (MGS). The formulas for transferring the data of the car dynamometry of the DK-50 dynamometer to the values obtained from the JAMAR® Plus dynamometer are calculated: AGS JAMAR® Plus == 1,7874 + 1,1208 × AGS DK-50 and MGS JAMAR® Plus = 1.7667 + 1, 1275 × MGS DK-50.Conclusion. For avoiding errors in the interpretation of the results from different studies, it is necessary to take into account which type of dynamometer was used. The resulting formulas (AGS JAMAR® Plus == 1,7874 + 1,1208 × AGS DK50 and MGS JAMAR® Plus = 1,7667 + 1,1275 × MGS DK50) can be used to correct the data of the dynamometer DK-50 for value of JAMAR® Plus dynamometer and to compare the results of Russian studies with data from foreign studies organized using JAMAR® Plus dynamometers. (For citation: Turusheva AV, Frolova EV, Degryse J-M. Comparison of measurement results are obtained with dynamometers DK-50 and JAMAR® Plus. Russian Family Doctor. 2018;22(1):12-17. doi 10.17816/RFD2018112-17).

Atrial fibrillation (AF) is the most common rhythm disorder. The consequences of undiagnosed AF are an increased risk of developing of heart failure and thromboembolic complications. The article is devoted to study of the possibility of using the medical device MyDiagnostick 1001R® in clinical practice for patients with the risk of AF development for early stage diagnosis. Methods. A group of subjects included 30 patients who did not have a history of AF, but who had the risk factors for its development — arterial hypertension, diabetes, obesity, ischemic heart disease (IHD). The average age of participants was 65.9 ± 12.1 years. A questionnaire containing 16 questions was developed to identify the risk factors of AF development. Results. From 30 participants 80% had arterial hypertension, 43.3% diabetes, 3.3% were obese, 50% suffered from IHD, 16.6% had stroke in anamnesis and 23.3% were smokers. All patients had a combination of risk factors. As a result, with the help of MyDiagnostick 1001R® 26.7% (8 patients) of the group was discovered to have AF episodes. For all participants recommendations for further examination and treatment were given. Conclusions. MyDiagnostick 1001R® is a simple device that does not require additional equipment, and allows to identify previously undiagnosed AF within 24 hours for subsequent diagnosis. It allows us to recommend this device for screening of patients with AF risk factors.

Objectives. This paper sought to provide normative values for grip strength among older adults 65+ across different age groups in northwest Russia. Methods. A population-based prospective cohort study of 611 community-dwelling individuals 65+. Grip strength was measured using the standard protocol applied in the Groningen Elderly Tests. The cut-off thresholds for grip strength were defined separately for men and women of different ages using a weighted polynomial regression. Results and conclusions. 1. This study presents age- and sex-specific reference values for grip strength in the 65+ Russian population derived from a prospective cohort study. 2. The grip strength values obtained in the current study were lower than those reported in other population-based studies in Europe and USA.

INTRODUCTION: Frailty prevalence differs across different population depending on the models used to assess, age, economic situation, social status, and the proportion of men and women in the study. The diagnostic value of different models of frailty varies from population to population. OBJECTIVES: To assess the prevalence of frailty using 4 different diagnostic models and their sensitivity for identifying persons with autonomy decline. MATERIAL AND METHODS: A random sample of 611 people aged 65 and over. Models used: the Age is not a blocking factor model, the SOF Frailty Index, the Groningen Frailty Indicator, L. Fried model. Covariates: nutritional status, anemia, functional status, depression, dementia, chronic diseases, grip strength, physical function. RESULTS: The prevalence of the Frailty Phenotype ranged from 16.6 to 20.4% and the Frailty Index was 32.6%. Frailty, regardless of the used models was associated with an increase in the prevalence of the geriatric syndromes: urinary incontinence, hearing and vision loss, physical decline, malnutrition and the risk of malnutrition, low cognitive functions and autonomy decline (p 0.05). The negative predictive value (NPV) of the Age is not a blocking factor model, the SOF Frailty Index, the Groningen Frailty Indicator for identifying individuals with autonomy decline was 8690%. CONCLUSION: The prevalence of frailty depended on the operational definition and varied from 16.6 to 32.6%. The Age is not a blocking factor model, the SOF Frailty Index, the Groningen Frailty Indicator, L. Fried model can be used as screening tools to identify older patient with autonomy decline. Regardless of the model used, frailty is closely associated with an increase in the prevalence of major geriatric syndromes.