RESUMEN
OBJECTIVE: To evaluate the predictive capacity of different frailty scales, as well as the strength of the handgrip, and to determine their relationship with clinical favourable outcomes. PATIENTS AND METHOD: Prospective study of patients admitted to the Geriatric Functional Recovery Unit (GFRU) of the Hospital Central Cruz Roja. The «FRAIL¼ scale, «Clinical Frailty Scale¼ (CFS) and «Fragil-VIG¼ index, and handgrip strength by hydraulic dynamometer were completed on admission. A functional gain was assumed as 20 or more points in the Barthel Index and return to home, as good outcomes at discharge. The discriminative capacity of favourable outcomes for each frailty scale and handgrip strength was analysed by means of ROC curves, calculating the C statistic (area under the curve = AUC). RESULTS: The analysis included 74 patients (median age 82 years; 48.5% women), admitted for stroke recovery (65%), orthopaedic pathology (16%), and other causes (19%). The prevalence of frailty varied between 31% (FRAIL scale), 40% (CFS), and 57.5% («Fragil-VIG¼). Median handgrip strength was 15 Kg in males (interquartile range 11-21), and 9 Kg in females (interquartile range 7-12). At discharge, 51.5% of patients had a functional gain of 20 or more points in Barthel index, and 63% returned to their previous home. The discriminating ability to achieve acceptable functional gain at discharge was good for CFS (AUC = 0.72; 95% CI; 0.60-0.84) and «Fragil-VIG¼ (AUC = 0.72; 95% CI;0.58-0.82), and handgrip strength was the only tool related to return home (AUC = 0.68; 95% CI;0.56-0.81). CONCLUSION: To evaluate frailty on admission to a GFRU contributes to predicting favourable clinical outcomes, but the discriminating capacity of each scale is variable.
Asunto(s)
Fragilidad , Fuerza de la Mano , Hospitalización , Anciano , Anciano de 80 o más Años , Femenino , Anciano Frágil , Fragilidad/diagnóstico , Evaluación Geriátrica , Humanos , Masculino , Alta del Paciente , Estudios ProspectivosRESUMEN
The capture, processing and distribution of visual information is one of the major challenges for the paradigm of the Internet of Things. Privacy emerges as a fundamental barrier to overcome. The idea of networked image sensors pervasively collecting data generates social rejection in the face of sensitive information being tampered by hackers or misused by legitimate users. Power consumption also constitutes a crucial aspect. Images contain a massive amount of data to be processed under strict timing requirements, demanding high-performance vision systems. In this paper, we describe a hardware-based strategy to concurrently address these two key issues. By conveying processing capabilities to the focal plane in addition to sensing, we can implement privacy protection measures just at the point where sensitive data are generated. Furthermore, such measures can be tailored for efficiently reducing the computational load of subsequent processing stages. As a proof of concept, a full-custom QVGA vision sensor chip is presented. It incorporates a mixed-signal focal-plane sensing-processing array providing programmable pixelation of multiple image regions in parallel. In addition to this functionality, the sensor exploits reconfigurability to implement other processing primitives, namely block-wise dynamic range adaptation, integral image computation and multi-resolution filtering. The proposed circuitry is also suitable to build a granular space, becoming the raw material for subsequent feature extraction and recognition of categorized objects.