{"title":"中国武汉社区隔离的3145名非严重sars - cov -2感染者病情恶化的早期预测指标:机器学习算法和竞争风险生存分析的结合","authors":"Kaiyuan Min, Zhenshun Cheng, Jiangfeng Liu, Yanhong Fang, Weichen Wang, Yehong Yang, Pascal Geldsetzer, Till Bärnighausen, Juntao Yang, Depei Liu, Simiao Chen, Chen Wang","doi":"10.1111/jebm.12529","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>To determine which early-stage variables best predicted the deterioration of coronavirus disease 2019 (COVID-19) among community-isolated people infected with severe acute respiratory syndrome coronavirus 2 and to test the performance of prediction using only inexpensive-to-measure variables.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Medical records of 3145 people isolated in two Fangcang shelter hospitals (large-scale community isolation centers) from February to March 2020 were accessed. Two complementary methods—machine learning algorithms and competing risk survival analyses—were used to test potential predictors, including age, gender, severity upon admission, symptoms (general symptoms, respiratory symptoms, and gastrointestinal symptoms), computed tomography (CT) signs, and comorbid chronic diseases. All variables were measured upon (or shortly after) admission. The outcome was deterioration versus recovery of COVID-19.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>More than a quarter of the 3145 people did not present any symptoms, while one-third ended isolation due to deterioration. Machine learning models identified moderate severity upon admission, old age, and CT ground-glass opacity as the most important predictors of deterioration. Removing CT signs did not degrade the performance of models. Competing risk models identified age ≥ 35 years, male gender, moderate severity upon admission, cough, expectoration, CT patchy opacity, CT consolidation, comorbid diabetes, and comorbid cardiovascular or cerebrovascular diseases as significant predictors of deterioration, while a stuffy or runny nose as a predictor of recovery.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Early-stage prediction of COVID-19 deterioration can be made with inexpensive-to-measure variables, such as demographic characteristics, severity upon admission, observable symptoms, and self-reported comorbid diseases, among asymptomatic people and mildly to moderately symptomatic patients.</p>\n </section>\n </div>","PeriodicalId":16090,"journal":{"name":"Journal of Evidence‐Based Medicine","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jebm.12529","citationCount":"1","resultStr":"{\"title\":\"Early-stage predictors of deterioration among 3145 nonsevere SARS-CoV-2-infected people community-isolated in Wuhan, China: A combination of machine learning algorithms and competing risk survival analyses\",\"authors\":\"Kaiyuan Min, Zhenshun Cheng, Jiangfeng Liu, Yanhong Fang, Weichen Wang, Yehong Yang, Pascal Geldsetzer, Till Bärnighausen, Juntao Yang, Depei Liu, Simiao Chen, Chen Wang\",\"doi\":\"10.1111/jebm.12529\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>To determine which early-stage variables best predicted the deterioration of coronavirus disease 2019 (COVID-19) among community-isolated people infected with severe acute respiratory syndrome coronavirus 2 and to test the performance of prediction using only inexpensive-to-measure variables.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Medical records of 3145 people isolated in two Fangcang shelter hospitals (large-scale community isolation centers) from February to March 2020 were accessed. Two complementary methods—machine learning algorithms and competing risk survival analyses—were used to test potential predictors, including age, gender, severity upon admission, symptoms (general symptoms, respiratory symptoms, and gastrointestinal symptoms), computed tomography (CT) signs, and comorbid chronic diseases. All variables were measured upon (or shortly after) admission. The outcome was deterioration versus recovery of COVID-19.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>More than a quarter of the 3145 people did not present any symptoms, while one-third ended isolation due to deterioration. Machine learning models identified moderate severity upon admission, old age, and CT ground-glass opacity as the most important predictors of deterioration. Removing CT signs did not degrade the performance of models. Competing risk models identified age ≥ 35 years, male gender, moderate severity upon admission, cough, expectoration, CT patchy opacity, CT consolidation, comorbid diabetes, and comorbid cardiovascular or cerebrovascular diseases as significant predictors of deterioration, while a stuffy or runny nose as a predictor of recovery.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Early-stage prediction of COVID-19 deterioration can be made with inexpensive-to-measure variables, such as demographic characteristics, severity upon admission, observable symptoms, and self-reported comorbid diseases, among asymptomatic people and mildly to moderately symptomatic patients.</p>\\n </section>\\n </div>\",\"PeriodicalId\":16090,\"journal\":{\"name\":\"Journal of Evidence‐Based Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jebm.12529\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Evidence‐Based Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jebm.12529\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Evidence‐Based Medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jebm.12529","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Early-stage predictors of deterioration among 3145 nonsevere SARS-CoV-2-infected people community-isolated in Wuhan, China: A combination of machine learning algorithms and competing risk survival analyses
Objective
To determine which early-stage variables best predicted the deterioration of coronavirus disease 2019 (COVID-19) among community-isolated people infected with severe acute respiratory syndrome coronavirus 2 and to test the performance of prediction using only inexpensive-to-measure variables.
Methods
Medical records of 3145 people isolated in two Fangcang shelter hospitals (large-scale community isolation centers) from February to March 2020 were accessed. Two complementary methods—machine learning algorithms and competing risk survival analyses—were used to test potential predictors, including age, gender, severity upon admission, symptoms (general symptoms, respiratory symptoms, and gastrointestinal symptoms), computed tomography (CT) signs, and comorbid chronic diseases. All variables were measured upon (or shortly after) admission. The outcome was deterioration versus recovery of COVID-19.
Results
More than a quarter of the 3145 people did not present any symptoms, while one-third ended isolation due to deterioration. Machine learning models identified moderate severity upon admission, old age, and CT ground-glass opacity as the most important predictors of deterioration. Removing CT signs did not degrade the performance of models. Competing risk models identified age ≥ 35 years, male gender, moderate severity upon admission, cough, expectoration, CT patchy opacity, CT consolidation, comorbid diabetes, and comorbid cardiovascular or cerebrovascular diseases as significant predictors of deterioration, while a stuffy or runny nose as a predictor of recovery.
Conclusions
Early-stage prediction of COVID-19 deterioration can be made with inexpensive-to-measure variables, such as demographic characteristics, severity upon admission, observable symptoms, and self-reported comorbid diseases, among asymptomatic people and mildly to moderately symptomatic patients.
期刊介绍:
The Journal of Evidence-Based Medicine (EMB) is an esteemed international healthcare and medical decision-making journal, dedicated to publishing groundbreaking research outcomes in evidence-based decision-making, research, practice, and education. Serving as the official English-language journal of the Cochrane China Centre and West China Hospital of Sichuan University, we eagerly welcome editorials, commentaries, and systematic reviews encompassing various topics such as clinical trials, policy, drug and patient safety, education, and knowledge translation.