Background: Due to differences in the definition of frailty, many different screening instruments have been developed. However, the predictive validity of these instruments among community-dwelling older people remains uncertain. Objective: To investigate whether combined (i.e. sequential or parallel) use of available frailty instruments improves the predictive power of dependency in (instrumental) activities of daily living ((I)ADL), mortality and hospitalization. Design, setting and participants: A prospective cohort study with two-year followup was conducted among pre-frail and frail community-dwelling older people in the Netherlands. Measurements: Four combinations of two highly specific frailty instruments (Frailty Phenotype, Frailty Index) and two highly sensitive instruments (Tilburg Frailty Indicator, Groningen Frailty Indicator) were investigated. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for all single instruments as well as for the four combinations, sequential and parallel. Results: 2,420 individuals participated (mean age 76.3 ± 6.6 years, 60.5% female) in our study. Sequential use increased the levels of specificity, as expected, whereas the PPV hardly increased. Parallel use increased the levels of sensitivity, although the NPV hardly increased. Conclusions: Applying two frailty instruments sequential or parallel might not be a solution for achieving better predictions of frailty in community-dwelling older people. Our results show that the combination of different screening instruments does not improve predictive validity. However, as this is one of the first studies to investigate the combined use of screening instruments, we recommend further exploration of other combinations of instruments among other study populations.
The aim of this study was to assess the predictive ability of the frailty phenotype (FP), Groningen Frailty Indicator (GFI), Tilburg Frailty Indicator (TFI) and frailty index (FI) for the outcomes mortality, hospitalization and increase in dependency in (instrumental) activities of daily living ((I)ADL) among older persons. This prospective cohort study with 2-year follow-up included 2420 Dutch community-dwelling older people (65+, mean age 76.3±6.6 years, 39.5% male) who were pre-frail or frail according to the FP. Mortality data were obtained from Statistics Netherlands. All other data were self-reported. Area under the receiver operating characteristic curves (AUC) was calculated for each frailty instrument and outcome measure. The prevalence of frailty, sensitivity and specifcity were calculated using cutoff values proposed by the developers and cutoff values one above and one below the proposed ones (0.05 for FI). All frailty instruments poorly predicted mortality, hospitalization and (I)ADL dependency (AUCs between 0.62–0.65, 0.59–0.63 and 0.60–0.64, respectively). Prevalence estimates of frailty in this population varied between 22.2% (FP) and 64.8% (TFI). The FP and FI showed higher levels of specifcity, whereas sensitivity was higher for the GFI and TFI. Using a different cutoff point considerably changed the prevalence, sensitivity and specifcity. In conclusion, the predictive ability of the FP, GFI, TFI and FI was poor for all outcomes in a population of pre-frail and frail community-dwelling older people. The FP and the FI showed higher values of specifcity, whereas sensitivity was higher for the GFI and TFI.
Background: Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging. Results: We developed the MultipleXLab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the MultipleXLab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants. Conclusion: MultipleXLab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.
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