Rationale: Diagnosis of sarcopenia in older adults is essential for early treatment in clinical practice. Bio-electrical impedance analysis (BIA) may be a valid means to assess appendicular lean mass (ALM) in older adults, but limited evidence is available. Therefore, we aim to evaluate the validity of BIA to assess ALM in older adults.Methods: In 215 community dwelling older adults (age ≥ 55 years), ALM was measured by BIA (Tanita MC-780; 8-points) and compared with dual-energy X-ray absorptiometry (DXA, Hologic Discovery A) as reference. Validity for assessing absolute values of ALM was evaluated by: 1) bias (mean difference), 2) percentage of accurate predictions (within 5% of DXA values), 3) individual error (root mean squared error (RMSE), mean absolute deviation), 4) limits of agreement (Bland-Altman analysis). For diagnosis of low ALM, the lowest quintile of ALM by DXA was used (below 21.4 kg for males and 15.4 for females). Sensitivity and specificity of detecting low ALM by BIA were assessed.Results: Mean age of the subjects was 71.9 ± 6.4, with a BMI of 25.8 ± 4.2 kg/m2, and 70% were females. BIA slightly underestimated ALM compared to DXA with a mean bias of -0.6 ± 0.2 kg. The percentage accurate predictions was 54% with RMSE 1.6 kg and limits of agreements −3.0 to +1.8 kg. Sensitivity was 79%, indicating that 79% of subjects with low ALM according to DXA also had low ALM with the BIA. Specificity was 90%, indicating that 90% of subjects with ‘no low’ ALM according to DXA also had ‘no low’ ALM with the BIA.Conclusions: This comparison showed a poor validity of BIA to assess absolute values of ALM, but a reasonable sensitivity and specificity to diagnose a low level of ALM in community-dwelling older adults in clinical practice.Disclosure of interest: None declared.
Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
