{"title":"SARS冠状病毒感染急性后后遗症的分类系统","authors":"Leonard A. Jason, Mohammed F. Islam","doi":"10.47316/cajmhe.2022.3.1.04","DOIUrl":null,"url":null,"abstract":"This study aimed to contribute to the development of a research case definition for post-acute sequelae of SARS CoV-2 infection (PASC) using a PASC data set and experiences from case definitions developed for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Our database included patients with PASC who provided self-report symptomology during the onset of infection and the time of survey completion (post-infection). We found that we could distinguish between those with mild, moderate, and severe PASC. Regarding the proportion meeting an ME/CFS case definition, we found 0% in the mildly impaired group, 30.6% to 62.6% in the moderately impaired group, and 74.3% to 89.0% in the severely impaired group. Based on these preliminary data, we propose a 5-part classification system for PASC. Axis 1 involves the variant of the COVID infection and the type of documentation of the infection. Axis 2 involves the time elapsed since infection. Axis 3 involves the type of medical collateral damage to different organs. Axis 4 involves functional impairment classified into three categories: mild, moderate, or severe. Finally, Axis 5 is the identified symptoms. Finally, if the patient has been sick for 6 or more months, it is important to determine whether the person has met the ME/CFS criteria. This proposed 5-part classification system for PASC might bring considerable clarity to diagnosing PASC. ","PeriodicalId":388483,"journal":{"name":"Central Asian Journal of Medical Hypotheses and Ethics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A CLASSIFICATION SYSTEM FOR POST-ACUTE SEQUELAE OF SARS CoV-2 INFECTION\",\"authors\":\"Leonard A. Jason, Mohammed F. Islam\",\"doi\":\"10.47316/cajmhe.2022.3.1.04\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aimed to contribute to the development of a research case definition for post-acute sequelae of SARS CoV-2 infection (PASC) using a PASC data set and experiences from case definitions developed for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Our database included patients with PASC who provided self-report symptomology during the onset of infection and the time of survey completion (post-infection). We found that we could distinguish between those with mild, moderate, and severe PASC. Regarding the proportion meeting an ME/CFS case definition, we found 0% in the mildly impaired group, 30.6% to 62.6% in the moderately impaired group, and 74.3% to 89.0% in the severely impaired group. Based on these preliminary data, we propose a 5-part classification system for PASC. Axis 1 involves the variant of the COVID infection and the type of documentation of the infection. Axis 2 involves the time elapsed since infection. Axis 3 involves the type of medical collateral damage to different organs. Axis 4 involves functional impairment classified into three categories: mild, moderate, or severe. Finally, Axis 5 is the identified symptoms. Finally, if the patient has been sick for 6 or more months, it is important to determine whether the person has met the ME/CFS criteria. This proposed 5-part classification system for PASC might bring considerable clarity to diagnosing PASC. \",\"PeriodicalId\":388483,\"journal\":{\"name\":\"Central Asian Journal of Medical Hypotheses and Ethics\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Central Asian Journal of Medical Hypotheses and Ethics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47316/cajmhe.2022.3.1.04\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Central Asian Journal of Medical Hypotheses and Ethics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47316/cajmhe.2022.3.1.04","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A CLASSIFICATION SYSTEM FOR POST-ACUTE SEQUELAE OF SARS CoV-2 INFECTION
This study aimed to contribute to the development of a research case definition for post-acute sequelae of SARS CoV-2 infection (PASC) using a PASC data set and experiences from case definitions developed for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Our database included patients with PASC who provided self-report symptomology during the onset of infection and the time of survey completion (post-infection). We found that we could distinguish between those with mild, moderate, and severe PASC. Regarding the proportion meeting an ME/CFS case definition, we found 0% in the mildly impaired group, 30.6% to 62.6% in the moderately impaired group, and 74.3% to 89.0% in the severely impaired group. Based on these preliminary data, we propose a 5-part classification system for PASC. Axis 1 involves the variant of the COVID infection and the type of documentation of the infection. Axis 2 involves the time elapsed since infection. Axis 3 involves the type of medical collateral damage to different organs. Axis 4 involves functional impairment classified into three categories: mild, moderate, or severe. Finally, Axis 5 is the identified symptoms. Finally, if the patient has been sick for 6 or more months, it is important to determine whether the person has met the ME/CFS criteria. This proposed 5-part classification system for PASC might bring considerable clarity to diagnosing PASC.