PDF(Pubmed)
Abstract：
UNASSIGNED: Long COVID, marked by persistent, recurring, or new symptoms post-COVID-19 infection, impacts children\'s well-being yet lacks a unified clinical definition. This study evaluates the performance of an empirically derived Long COVID case identification algorithm, or computable phenotype, with manual chart review in a pediatric sample. This approach aims to facilitate large-scale research efforts to understand this condition better.
UNASSIGNED: The algorithm, composed of diagnostic codes empirically associated with Long COVID, was applied to a cohort of pediatric patients with SARS-CoV-2 infection in the RECOVER PCORnet EHR database. The algorithm classified 31,781 patients with conclusive, probable, or possible Long COVID and 307,686 patients without evidence of Long COVID. A chart review was performed on a subset of patients (n=651) to determine the overlap between the two methods. Instances of discordance were reviewed to understand the reasons for differences.
UNASSIGNED: The sample comprised 651 pediatric patients (339 females, M age = 10.10 years) across 16 hospital systems. Results showed moderate overlap between phenotype and chart review Long COVID identification (accuracy = 0.62, PPV = 0.49, NPV = 0.75); however, there were also numerous cases of disagreement. No notable differences were found when the analyses were stratified by age at infection or era of infection. Further examination of the discordant cases revealed that the most common cause of disagreement was the clinician reviewers\' tendency to attribute Long COVID-like symptoms to prior medical conditions. The performance of the phenotype improved when prior medical conditions were considered (accuracy = 0.71, PPV = 0.65, NPV = 0.74).
UNASSIGNED: Although there was moderate overlap between the two methods, the discrepancies between the two sources are likely attributed to the lack of consensus on a Long COVID clinical definition. It is essential to consider the strengths and limitations of each method when developing Long COVID classification algorithms.
UNASSIGNED: The algorithm, composed of diagnostic codes empirically associated with Long COVID, was applied to a cohort of pediatric patients with SARS-CoV-2 infection in the RECOVER PCORnet EHR database. The algorithm classified 31,781 patients with conclusive, probable, or possible Long COVID and 307,686 patients without evidence of Long COVID. A chart review was performed on a subset of patients (n=651) to determine the overlap between the two methods. Instances of discordance were reviewed to understand the reasons for differences.
UNASSIGNED: The sample comprised 651 pediatric patients (339 females, M age = 10.10 years) across 16 hospital systems. Results showed moderate overlap between phenotype and chart review Long COVID identification (accuracy = 0.62, PPV = 0.49, NPV = 0.75); however, there were also numerous cases of disagreement. No notable differences were found when the analyses were stratified by age at infection or era of infection. Further examination of the discordant cases revealed that the most common cause of disagreement was the clinician reviewers\' tendency to attribute Long COVID-like symptoms to prior medical conditions. The performance of the phenotype improved when prior medical conditions were considered (accuracy = 0.71, PPV = 0.65, NPV = 0.74).
UNASSIGNED: Although there was moderate overlap between the two methods, the discrepancies between the two sources are likely attributed to the lack of consensus on a Long COVID clinical definition. It is essential to consider the strengths and limitations of each method when developing Long COVID classification algorithms.
摘要：
■长型COVID，以持久性为标志，经常性的,或COVID-19感染后出现新症状，影响儿童的福祉，但缺乏统一的临床定义。这项研究评估了经验推导的LongCOVID病例识别算法的性能，或可计算的表型，在儿科样本中进行手动图表审查。这种方法旨在促进大规模研究工作，以更好地了解这种情况。
■算法，由经验上与长COVID相关的诊断代码组成，在RECOVERPCORnetEHR数据库中应用于一组SARS-CoV-2感染的儿科患者。该算法对31,781名患者进行了分类，可能,或可能长COVID和307,686例没有长COVID证据的患者。对患者的子集(n=651)进行图表审查以确定两种方法之间的重叠。审查了不一致的情况，以了解差异的原因。
■样本包括651名儿科患者（339名女性，法师=10.10年）在16个医院系统中。结果显示，表型和图表审查长COVID鉴定之间存在中度重叠(准确度=0.62，PPV=0.49，NPV=0.75)；然而，也有许多分歧的案例。当分析按感染时的年龄或感染时代分层时，没有发现显着差异。对不和谐病例的进一步检查显示，引起分歧的最常见原因是临床医生审核员倾向于将长期COVID样症状归因于先前的医疗状况。当考虑先前的医疗状况时,表型的性能改善(准确度=0.71,PPV=0.65,NPV=0.74)。
■尽管两种方法之间存在适度的重叠，两种来源之间的差异可能是由于对COVID的长期临床定义缺乏共识。在开发LongCOVID分类算法时，必须考虑每种方法的优势和局限性。
■算法，由经验上与长COVID相关的诊断代码组成，在RECOVERPCORnetEHR数据库中应用于一组SARS-CoV-2感染的儿科患者。该算法对31,781名患者进行了分类，可能,或可能长COVID和307,686例没有长COVID证据的患者。对患者的子集(n=651)进行图表审查以确定两种方法之间的重叠。审查了不一致的情况，以了解差异的原因。
■样本包括651名儿科患者（339名女性，法师=10.10年）在16个医院系统中。结果显示，表型和图表审查长COVID鉴定之间存在中度重叠(准确度=0.62，PPV=0.49，NPV=0.75)；然而，也有许多分歧的案例。当分析按感染时的年龄或感染时代分层时，没有发现显着差异。对不和谐病例的进一步检查显示，引起分歧的最常见原因是临床医生审核员倾向于将长期COVID样症状归因于先前的医疗状况。当考虑先前的医疗状况时,表型的性能改善(准确度=0.71,PPV=0.65,NPV=0.74)。
■尽管两种方法之间存在适度的重叠，两种来源之间的差异可能是由于对COVID的长期临床定义缺乏共识。在开发LongCOVID分类算法时，必须考虑每种方法的优势和局限性。
