INTRODUCTION:
Since the SARS-CoV-2 pandemic began in late 2019, over 13 million children in the United States have been infected with the virus [1]. Although many of these acute infections have not resulted in severe morbidity or mortality, a subset of children and adolescents have experienced recurrent or persistent symptoms beyond the typical recovery period [2]. The constellation of findings that occur postinfection is known as postacute sequelae of SARS-CoV-2 (PASC), or colloquially as “long-Covid.” The U.S. Centers for Disease Control and Prevention (CDC) defines PASC as a wide range of health problems that linger for more than 4 weeks following an acute COVID-19 infection [3]. Although this is an area of active research, relatively little is currently known about its clinical epidemiology in the pediatric population.
Considering the large number of children who have been affected by COVID-19, it is critical that we monitor the rates, trends, and outcomes of PASC in this population. An important first step toward these efforts is the development of a tool that can quickly and easily identify cases in large clinical populations. With the widespread adoption of electronic health records (EHR), it is now possible to develop computable phenotypes using data that are collected for clinical care, which can be used for population-level analysis to inform the public health response [4, 5]. In this report, we describe a novel phenotyping algorithm to define the burden, clinical spectrum, and outcomes of pediatric PASC using real-world data.
Source: Tomini A Fashina, Christine M Miller, Elijah Paintsil, Linda M Niccolai, Cynthia Brandt, Carlos R Oliveira, Computable Clinical Phenotyping of Postacute Sequelae of COVID-19 in Pediatrics Using Real-World Data, Journal of the Pediatric Infectious Diseases Society, 2022;, piac132, https://doi.org/10.1093/jpids/piac132 https://academic.oup.com/jpids/advance-article/doi/10.1093/jpids/piac132/6957369 (Full text)