{"title":"使用CHARMS进行早产预测模型的系统综述。","authors":"Jeung-Im Kim, Joo Yun Lee","doi":"10.1177/10998004211025641","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study sought to evaluate prediction models for preterm birth (PTB) and to explore predictors frequently used in PTB prediction models.</p><p><strong>Methods: </strong>A systematic review was conducted. We selected studies according to the PRISMA, classified studies according to TRIPOD, appraised studies according to the PROBAST, and extracted and synthesized the data narratively according to the CHARMS. We classified the predictors in the models into socio-economic factors with demographic, psychosocial, biomedical, and health behavioral factors.</p><p><strong>Results: </strong>Twenty-one studies with 27 prediction models were selected for the analysis. Only 16 models (59.3%) defined PTB outcomes as 37 weeks or less, and seven models (25.9%) defined PTB as 32 weeks or less. The PTB rates varied according to whether high-risk pregnant women were included and according to the outcome definition used. The most frequently included predictors were age (among demographic factors), height, weight, body mass index, and chronic disease (among biomedical factors), and smoking (among behavioral factors).</p><p><strong>Conclusion: </strong>When using the PTB prediction model, one must pay attention to the outcome definition and inclusion criteria to select a model that fits the case. Many studies use the sub-categories of PTB; however, some of these sub-categories are not correctly indicated, and they can be misunderstood as PTB (≤ 37 weeks). To develop further PTB prediction models, it is necessary to set the target population and identify the outcomes to predict.</p>","PeriodicalId":8997,"journal":{"name":"Biological research for nursing","volume":"23 4","pages":"708-722"},"PeriodicalIF":1.9000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/10998004211025641","citationCount":"6","resultStr":"{\"title\":\"Systematic Review of Prediction Models for Preterm Birth Using CHARMS.\",\"authors\":\"Jeung-Im Kim, Joo Yun Lee\",\"doi\":\"10.1177/10998004211025641\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This study sought to evaluate prediction models for preterm birth (PTB) and to explore predictors frequently used in PTB prediction models.</p><p><strong>Methods: </strong>A systematic review was conducted. We selected studies according to the PRISMA, classified studies according to TRIPOD, appraised studies according to the PROBAST, and extracted and synthesized the data narratively according to the CHARMS. We classified the predictors in the models into socio-economic factors with demographic, psychosocial, biomedical, and health behavioral factors.</p><p><strong>Results: </strong>Twenty-one studies with 27 prediction models were selected for the analysis. Only 16 models (59.3%) defined PTB outcomes as 37 weeks or less, and seven models (25.9%) defined PTB as 32 weeks or less. The PTB rates varied according to whether high-risk pregnant women were included and according to the outcome definition used. The most frequently included predictors were age (among demographic factors), height, weight, body mass index, and chronic disease (among biomedical factors), and smoking (among behavioral factors).</p><p><strong>Conclusion: </strong>When using the PTB prediction model, one must pay attention to the outcome definition and inclusion criteria to select a model that fits the case. Many studies use the sub-categories of PTB; however, some of these sub-categories are not correctly indicated, and they can be misunderstood as PTB (≤ 37 weeks). To develop further PTB prediction models, it is necessary to set the target population and identify the outcomes to predict.</p>\",\"PeriodicalId\":8997,\"journal\":{\"name\":\"Biological research for nursing\",\"volume\":\"23 4\",\"pages\":\"708-722\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2021-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1177/10998004211025641\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biological research for nursing\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/10998004211025641\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/6/23 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biological research for nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/10998004211025641","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/6/23 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"NURSING","Score":null,"Total":0}
Systematic Review of Prediction Models for Preterm Birth Using CHARMS.
Objective: This study sought to evaluate prediction models for preterm birth (PTB) and to explore predictors frequently used in PTB prediction models.
Methods: A systematic review was conducted. We selected studies according to the PRISMA, classified studies according to TRIPOD, appraised studies according to the PROBAST, and extracted and synthesized the data narratively according to the CHARMS. We classified the predictors in the models into socio-economic factors with demographic, psychosocial, biomedical, and health behavioral factors.
Results: Twenty-one studies with 27 prediction models were selected for the analysis. Only 16 models (59.3%) defined PTB outcomes as 37 weeks or less, and seven models (25.9%) defined PTB as 32 weeks or less. The PTB rates varied according to whether high-risk pregnant women were included and according to the outcome definition used. The most frequently included predictors were age (among demographic factors), height, weight, body mass index, and chronic disease (among biomedical factors), and smoking (among behavioral factors).
Conclusion: When using the PTB prediction model, one must pay attention to the outcome definition and inclusion criteria to select a model that fits the case. Many studies use the sub-categories of PTB; however, some of these sub-categories are not correctly indicated, and they can be misunderstood as PTB (≤ 37 weeks). To develop further PTB prediction models, it is necessary to set the target population and identify the outcomes to predict.
期刊介绍:
Biological Research For Nursing (BRN) is a peer-reviewed quarterly journal that helps nurse researchers, educators, and practitioners integrate information from many basic disciplines; biology, physiology, chemistry, health policy, business, engineering, education, communication and the social sciences into nursing research, theory and clinical practice. This journal is a member of the Committee on Publication Ethics (COPE)