The high transmissibility of SARS-CoV-2 before and shortly after the onset of symptoms suggests that only diagnosing and isolating symptomatic patients may not be sufficient to interrupt the spread of infection; therefore, public health measures such as personal distancing are also necessary. Additionally, it will be important to detect the newly infected individuals who remain asymptomatic, which may account for 50% or more of the cases. Molecular techniques are the “gold standard” for the diagnosis of SARS-CoV-2 infection. However, the massive use of these techniques has generated some problems. On the one hand, the scarcity of resources (analyzers, fungibles and reagents), and on the other the delay in the notification of results. These two facts translate into a lag in the application of isolation measures among cases and contacts, which favors the spread of the infection. Antigen detection tests are also direct diagnostic methods, with the advantage of obtaining the result in a few minutes and at the very “pointof-care”. Furthermore, the simplicity and low cost of these tests allow them to be repeated on successive days in certain clinical settings. The sensitivity of antigen tests is generally lower than that of nucleic acid tests, although their specificity is comparable. Antigenic tests have been shown to be more valid in the days around the onset of symptoms, when the viral load in the nasopharynx is higher. Having a rapid and real-time viral detection assay such as the antigen test has been shown to be more useful to control the spread of the infection than more sensitive tests, but with greater cost and response time, such as in case of molecular tests. The main health institutions such as the WHO, the CDC and the Ministry of Health of the Government of Spain propose the use of antigenic tests in a wide variety of strategies to respond to the pandemic. This document aims to support physicians involved in the care of patients with suspected SC2 infection, in the context of a growing incidence in Spain since September 2020, which already represents the second pandemic wave of COVID-19.
The aim of this study was to develop a predictive model of gait recovery after hip fracture. Data was obtained from a sample of 25,607 patients included in the Spanish National Hip Fracture Registry from 2017 to 2019. The primary outcome was recovery of the baseline level of ambulatory capacity. A logistic regression model was developed using 40% of the sample and the model was validated in the remaining 60% of the sample. The predictors introduced in the model were: age, prefracture gait independence, cognitive impairment, anesthetic risk, fracture type, operative delay, early postoperative mobilization, weight bearing, presence of pressure ulcers and destination at discharge. Five groups of patients or clusters were identified by their predicted probability of recovery, including the most common features of each. A probability threshold of 0.706 in the training set led to an accuracy of the model of 0.64 in the validation set. We present an acceptably accurate predictive model of gait recovery after hip fracture based on the patients’ individual characteristics. This model could aid clinicians to better target programs and interventions in this population.
Older adults living in nursing homes are the most vulnerable group of the COVID-19 pandemic. There are many difficulties in isolating residents and limiting the spread in this setting. We have developed a simple algorithm with a traffic light shape for resident classification and sectorization within nursing homes, based on basic diagnostic tests, surveillance of symptoms onset and close contact monitoring. We have implemented the algorithm in several centers with good data on adherence. Suggestions for implementation and evaluation are discussed.
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