Muscle strength, not muscle mass, determines the health-related quality of life in Indonesian women with systemic lupus erythematosus.

OBJECTIVE: No study evaluated the impact of low muscle strength and mass on the Sarcopenia-related Quality of Life (SarQoL) in women with SLE. METHODS: This cross-sectional study recruited 145 women with SLE consecutively; muscle strength was measured with a calibrated Jamar handheld dynamometer, muscle mass was measured with appendicular muscle mass index (Tanita MC-780 MAP body impedance analyser) and health-related quality of life with SarQoL Questionnaire. The cut-off points for low muscle strength, low muscle mass and sarcopenia were derived from the Asian Working Group on Sarcopenia 2019. Statistical analysis was conducted with a t-test for mean difference, and logistic regression was used to evaluate for low muscle strength contributing factors. RESULTS: There was a significant difference in the mean total score of SarQoL in individuals with normal compared with low muscle strength (74.36 vs 64.85; mean difference 9.50; 95% CI 2.10 to 5.33; p<0.001). On the other hand, there was no difference in individuals with normal compared with low muscle mass (71.07 vs 70.79; mean difference 0.28; -5.18 to 5.74; p=0.91). After minimally adjusted with age, we found moderate-severe joint pain (B -9.280; p<0.001) and low muscle strength (B -6.979; p=0.001) to be independently associated with low mean SarQoL total score. CONCLUSION: There was a lower total SarQoL score in individuals with low muscle strength but not with low muscle mass.

Frailty state among Indonesian elderly: prevalence, associated factors, and frailty state transition.

BACKGROUND: Information about frailty status and its transition is important to inform clinical decisions. Predicting frailty transition is beneficial for its prevention. While Indonesia is the 4th largest geriatric population in Asia, data about frailty transition is limited. This study aimed to obtain data on prevalence of frailty, its risk factors, frailty state transition and its prognostic factors, as well as to develop prognostic score for frailty state transition. METHODS: Multicenter study on subjects aged ≥60 years old was done to obtain the prevalence of frailty status and to identify risk factors of frailty. Prospective cohort over 12 months was done to obtain data on frailty state transition. Multiple logistic regression analysis was performed to identify its prognostic factors from several clinical data, which then were utilized to develop prognostic score for frailty state worsening. RESULTS: Cross-sectional data from 448 subjects showed that 25.2% of the subjects were frail based on Frailty index-40 items. Risk factors of frailty were age (OR 2.72; 95% CI 1.58-4.76), functional status (OR 2.89; 95% CI 1.79-4.67), and nutritional status (OR 3.75; 95% CI 2.29-6.13). Data from the 162 subjects who completed the cohort showed 27.2% of the cohort had frailty state worsening. Prognostic factors for frailty state worsening were being 70 years or older (OR 3.9; 95% CI 1.2-12.3, p < 0.05), negative QoL, i.e., fair and poor QoL (OR 2.5; 95% CI 1.1-5.9, p < 0.05), and slow gait speed (OR 2.8; 95% CI 1.3-6.4, p < 0.05). The internal validation of the prognostic score consisted of those three variables showed good performance. CONCLUSION: The prevalence of frailty in this study among Indonesian elderly in outpatient setting was 25.2%. The risk factors of frailty were age, functional status and nutritional status. The prognostic factors for frailty state worsening were being 70 years old or older, negative QoL (fair or poor quality of life), and slow gait speed. A prognostic score to predict frailty state worsening in 12 months had been developed.

Diagnostic Test of a Scoring System for Frailty Syndrome in the Elderly According to Cardiovascular Health Study, Study of Osteoporotic Fracture and Comprehensive Geriatric Assessment Based Frailty Index Compared with Frailty Index 40 Items.

AIM: to get a recommendation on the best frailty syndrome diagnostic tools, that will be able to be practiced on a daily setting in Indonesia. METHODS: this is a cross-sectional study with diagnostic test approach, conducted to patients in the Geriatric Outpatient Clinic of Cipto Mangunkusumo National Referral Hospital on May-June 2013. Each subject underwent a frailty evaluation using CHS, SOF, FI-CGA and FI-40 scoring systems. Then, we calculate the sensitivity, specificity, PPV, NPV, LR+ and LR- of each scoring system compared to FI-40. RESULTS: the proportion of frail, pre-frail and fit according to FI-40 are 25.3%, 71% and 3.7% respectively. In terms of differentiation frail to non-frail, CHS had 41.2% sensitivity, 95% specificity, PPV 73.7%, NPV 82.7%, LR+ 8.41 and LR- 0.62. SOF scoring system had 17.6% sensitivity, 99.5% specificity, PPV 92.3%, NPV 78.1%, LR+ 35.2 and LR- 0.83. Furthermore FI-CGA had 8.8% sensitivity, 100% specificity, PPV 100%, NPV 76.4%, LR+ and LR- 0.91. CONCLUSION: There is no better scoring system that could be implemented to screen for frailty syndrome other than FI-40 items. However, other scoring systems could be used as a good diagnostic tool for the syndrome.