{"title":"基于 LASSO-Cox 回归的坏死性小肠结肠炎不良后果风险模型的构建与评估。","authors":"HaiJin Zhang, RongWei Yang, Yuan Yao","doi":"10.3389/fped.2024.1366913","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to develop a nomogram to predict adverse outcomes in neonates with necrotizing enterocolitis (NEC).</p><p><strong>Methods: </strong>In this retrospective study on neonates with NEC, data on perinatal characteristics, clinical features, laboratory findings, and x-ray examinations were collected for the included patients. A risk model and its nomogram were developed using the least absolute shrinkage and selection operator (LASSO) Cox regression analyses.</p><p><strong>Results: </strong>A total of 182 cases of NEC were included and divided into a training set (148 cases) and a temporal validation set (34 cases). Eight features, including weight [<i>p</i> = 0.471, HR = 0.99 (95% CI: 0.98-1.00)], history of congenital heart disease [<i>p</i> < 0.001, HR = 3.13 (95% CI:1.75-5.61)], blood transfusion before onset [<i>p</i> = 0.757, HR = 0.85 (95%CI:0.29-2.45)], antibiotic exposure before onset [<i>p</i> = 0.003, HR = 5.52 (95% CI:1.81-16.83)], C-reactive protein (CRP) at onset [<i>p</i> = 0.757, HR = 1.01 (95%CI:1.00-1.02)], plasma sodium at onset [<i>p</i> < 0.001, HR = 4.73 (95%CI:2.61-8.59)], dynamic abdominal x-ray score change [<i>p</i> = 0.001, HR = 4.90 (95%CI:2.69-8.93)], and antibiotic treatment regimen [<i>p</i> = 0.250, HR = 1.83 (0.65-5.15)], were ultimately selected for model building. The C-index for the predictive model was 0.850 (95% CI: 0.804-0.897) for the training set and 0.7880.788 (95% CI: 0.656-0.921) for the validation set. The area under the ROC curve (AUC) at 8-, 10-, and 12-days were 0.889 (95% CI: 0.822-0.956), 0.891 (95% CI: 0.829-0.953), and 0.893 (95% CI:0.832-0.954) in the training group, and 0.812 (95% CI: 0.633-0.991), 0.846 (95% CI: 0.695-0.998), and 0.798 (95%CI: 0.623-0.973) in the validation group, respectively. Calibration curves showed good concordance between the predicted and observed outcomes, and DCA demonstrated adequate clinical benefit.</p><p><strong>Conclusions: </strong>The LASSO-Cox model effectively identifies NEC neonates at high risk of adverse outcomes across all time points. Notably, at earlier time points (such as the 8-day mark), the model also demonstrates strong predictive performance, facilitating the early prediction of adverse outcomes in infants with NEC. This early prediction can contribute to timely clinical decision-making and ultimately improve patient prognosis.</p>","PeriodicalId":12637,"journal":{"name":"Frontiers in Pediatrics","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491366/pdf/","citationCount":"0","resultStr":"{\"title\":\"Construction and evaluation of a risk model for adverse outcomes of necrotizing enterocolitis based on LASSO-Cox regression.\",\"authors\":\"HaiJin Zhang, RongWei Yang, Yuan Yao\",\"doi\":\"10.3389/fped.2024.1366913\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This study aimed to develop a nomogram to predict adverse outcomes in neonates with necrotizing enterocolitis (NEC).</p><p><strong>Methods: </strong>In this retrospective study on neonates with NEC, data on perinatal characteristics, clinical features, laboratory findings, and x-ray examinations were collected for the included patients. A risk model and its nomogram were developed using the least absolute shrinkage and selection operator (LASSO) Cox regression analyses.</p><p><strong>Results: </strong>A total of 182 cases of NEC were included and divided into a training set (148 cases) and a temporal validation set (34 cases). Eight features, including weight [<i>p</i> = 0.471, HR = 0.99 (95% CI: 0.98-1.00)], history of congenital heart disease [<i>p</i> < 0.001, HR = 3.13 (95% CI:1.75-5.61)], blood transfusion before onset [<i>p</i> = 0.757, HR = 0.85 (95%CI:0.29-2.45)], antibiotic exposure before onset [<i>p</i> = 0.003, HR = 5.52 (95% CI:1.81-16.83)], C-reactive protein (CRP) at onset [<i>p</i> = 0.757, HR = 1.01 (95%CI:1.00-1.02)], plasma sodium at onset [<i>p</i> < 0.001, HR = 4.73 (95%CI:2.61-8.59)], dynamic abdominal x-ray score change [<i>p</i> = 0.001, HR = 4.90 (95%CI:2.69-8.93)], and antibiotic treatment regimen [<i>p</i> = 0.250, HR = 1.83 (0.65-5.15)], were ultimately selected for model building. The C-index for the predictive model was 0.850 (95% CI: 0.804-0.897) for the training set and 0.7880.788 (95% CI: 0.656-0.921) for the validation set. The area under the ROC curve (AUC) at 8-, 10-, and 12-days were 0.889 (95% CI: 0.822-0.956), 0.891 (95% CI: 0.829-0.953), and 0.893 (95% CI:0.832-0.954) in the training group, and 0.812 (95% CI: 0.633-0.991), 0.846 (95% CI: 0.695-0.998), and 0.798 (95%CI: 0.623-0.973) in the validation group, respectively. Calibration curves showed good concordance between the predicted and observed outcomes, and DCA demonstrated adequate clinical benefit.</p><p><strong>Conclusions: </strong>The LASSO-Cox model effectively identifies NEC neonates at high risk of adverse outcomes across all time points. Notably, at earlier time points (such as the 8-day mark), the model also demonstrates strong predictive performance, facilitating the early prediction of adverse outcomes in infants with NEC. This early prediction can contribute to timely clinical decision-making and ultimately improve patient prognosis.</p>\",\"PeriodicalId\":12637,\"journal\":{\"name\":\"Frontiers in Pediatrics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491366/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Pediatrics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fped.2024.1366913\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"PEDIATRICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Pediatrics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fped.2024.1366913","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"PEDIATRICS","Score":null,"Total":0}
Construction and evaluation of a risk model for adverse outcomes of necrotizing enterocolitis based on LASSO-Cox regression.
Objective: This study aimed to develop a nomogram to predict adverse outcomes in neonates with necrotizing enterocolitis (NEC).
Methods: In this retrospective study on neonates with NEC, data on perinatal characteristics, clinical features, laboratory findings, and x-ray examinations were collected for the included patients. A risk model and its nomogram were developed using the least absolute shrinkage and selection operator (LASSO) Cox regression analyses.
Results: A total of 182 cases of NEC were included and divided into a training set (148 cases) and a temporal validation set (34 cases). Eight features, including weight [p = 0.471, HR = 0.99 (95% CI: 0.98-1.00)], history of congenital heart disease [p < 0.001, HR = 3.13 (95% CI:1.75-5.61)], blood transfusion before onset [p = 0.757, HR = 0.85 (95%CI:0.29-2.45)], antibiotic exposure before onset [p = 0.003, HR = 5.52 (95% CI:1.81-16.83)], C-reactive protein (CRP) at onset [p = 0.757, HR = 1.01 (95%CI:1.00-1.02)], plasma sodium at onset [p < 0.001, HR = 4.73 (95%CI:2.61-8.59)], dynamic abdominal x-ray score change [p = 0.001, HR = 4.90 (95%CI:2.69-8.93)], and antibiotic treatment regimen [p = 0.250, HR = 1.83 (0.65-5.15)], were ultimately selected for model building. The C-index for the predictive model was 0.850 (95% CI: 0.804-0.897) for the training set and 0.7880.788 (95% CI: 0.656-0.921) for the validation set. The area under the ROC curve (AUC) at 8-, 10-, and 12-days were 0.889 (95% CI: 0.822-0.956), 0.891 (95% CI: 0.829-0.953), and 0.893 (95% CI:0.832-0.954) in the training group, and 0.812 (95% CI: 0.633-0.991), 0.846 (95% CI: 0.695-0.998), and 0.798 (95%CI: 0.623-0.973) in the validation group, respectively. Calibration curves showed good concordance between the predicted and observed outcomes, and DCA demonstrated adequate clinical benefit.
Conclusions: The LASSO-Cox model effectively identifies NEC neonates at high risk of adverse outcomes across all time points. Notably, at earlier time points (such as the 8-day mark), the model also demonstrates strong predictive performance, facilitating the early prediction of adverse outcomes in infants with NEC. This early prediction can contribute to timely clinical decision-making and ultimately improve patient prognosis.
期刊介绍：
Frontiers in Pediatrics (Impact Factor 2.33) publishes rigorously peer-reviewed research broadly across the field, from basic to clinical research that meets ongoing challenges in pediatric patient care and child health. Field Chief Editors Arjan Te Pas at Leiden University and Michael L. Moritz at the Children''s Hospital of Pittsburgh are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
Frontiers in Pediatrics also features Research Topics, Frontiers special theme-focused issues managed by Guest Associate Editors, addressing important areas in pediatrics. In this fashion, Frontiers serves as an outlet to publish the broadest aspects of pediatrics in both basic and clinical research, including high-quality reviews, case reports, editorials and commentaries related to all aspects of pediatrics.
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