Jun Zhou, Mengxiao Xie, Ning Dong, Mingjun Xie, Jingping Liu, Min Wang, Yaman Wang, Hua-Guo Xu
{"title":"预测成人嗜血细胞淋巴组织细胞增多症 30 天死亡率的实验室数据机器学习。","authors":"Jun Zhou, Mengxiao Xie, Ning Dong, Mingjun Xie, Jingping Liu, Min Wang, Yaman Wang, Hua-Guo Xu","doi":"10.1007/s10875-024-01806-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Hemophagocytic Lymphohistiocytosis (HLH) carries a high mortality rate. Current existing risk-evaluation methodologies fall short and improved predictive methods are needed. This study aimed to forecast 30-day mortality in adult HLH patients using 11 distinct machine learning (ML) algorithms.</p><p><strong>Methods: </strong>A retrospective analysis on 431 adult HLH patients from January 2015 to September 2021 was conducted. Feature selection was executed using the least absolute shrinkage and selection operator. We employed 11 ML algorithms to create prediction models. The area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve and decision curve analysis were used to evaluate these models. We assessed feature importance using the SHapley Additive exPlanation (SHAP) approach.</p><p><strong>Results: </strong>Seven independent predictors emerged as the most valuable features. An AUC between 0.65 and 1.00 was noted among the eleven ML algorithms. The gradient boosting decision tree (GBDT) algorithms demonstrated the most optimal performance (1.00 in the training cohort and 0.80 in the validation cohort). By employing the SHAP method, we identified the variables that contributed to the model and their correlation with 30-day mortality. The AUC of the GBDT algorithms was the highest when using the top 4 (ferritin, UREA, age and thrombin time (TT)) features, reaching 0.99 in the training cohort and 0.83 in the validation cohort. Additionally, we developed a web-based calculator to estimate the risk of 30-day mortality.</p><p><strong>Conclusions: </strong>With GBDT algorithms applied to laboratory data, accurate prediction of 30-day mortality is achievable. Integrating these algorithms into clinical practice could potentially improve 30-day outcomes.</p>","PeriodicalId":15531,"journal":{"name":"Journal of Clinical Immunology","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning of Laboratory Data in Predicting 30-Day Mortality for Adult Hemophagocytic Lymphohistiocytosis.\",\"authors\":\"Jun Zhou, Mengxiao Xie, Ning Dong, Mingjun Xie, Jingping Liu, Min Wang, Yaman Wang, Hua-Guo Xu\",\"doi\":\"10.1007/s10875-024-01806-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Hemophagocytic Lymphohistiocytosis (HLH) carries a high mortality rate. Current existing risk-evaluation methodologies fall short and improved predictive methods are needed. This study aimed to forecast 30-day mortality in adult HLH patients using 11 distinct machine learning (ML) algorithms.</p><p><strong>Methods: </strong>A retrospective analysis on 431 adult HLH patients from January 2015 to September 2021 was conducted. Feature selection was executed using the least absolute shrinkage and selection operator. We employed 11 ML algorithms to create prediction models. The area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve and decision curve analysis were used to evaluate these models. We assessed feature importance using the SHapley Additive exPlanation (SHAP) approach.</p><p><strong>Results: </strong>Seven independent predictors emerged as the most valuable features. An AUC between 0.65 and 1.00 was noted among the eleven ML algorithms. The gradient boosting decision tree (GBDT) algorithms demonstrated the most optimal performance (1.00 in the training cohort and 0.80 in the validation cohort). By employing the SHAP method, we identified the variables that contributed to the model and their correlation with 30-day mortality. The AUC of the GBDT algorithms was the highest when using the top 4 (ferritin, UREA, age and thrombin time (TT)) features, reaching 0.99 in the training cohort and 0.83 in the validation cohort. Additionally, we developed a web-based calculator to estimate the risk of 30-day mortality.</p><p><strong>Conclusions: </strong>With GBDT algorithms applied to laboratory data, accurate prediction of 30-day mortality is achievable. Integrating these algorithms into clinical practice could potentially improve 30-day outcomes.</p>\",\"PeriodicalId\":15531,\"journal\":{\"name\":\"Journal of Clinical Immunology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Immunology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10875-024-01806-6\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Immunology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10875-024-01806-6","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
Machine Learning of Laboratory Data in Predicting 30-Day Mortality for Adult Hemophagocytic Lymphohistiocytosis.
Background: Hemophagocytic Lymphohistiocytosis (HLH) carries a high mortality rate. Current existing risk-evaluation methodologies fall short and improved predictive methods are needed. This study aimed to forecast 30-day mortality in adult HLH patients using 11 distinct machine learning (ML) algorithms.
Methods: A retrospective analysis on 431 adult HLH patients from January 2015 to September 2021 was conducted. Feature selection was executed using the least absolute shrinkage and selection operator. We employed 11 ML algorithms to create prediction models. The area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve and decision curve analysis were used to evaluate these models. We assessed feature importance using the SHapley Additive exPlanation (SHAP) approach.
Results: Seven independent predictors emerged as the most valuable features. An AUC between 0.65 and 1.00 was noted among the eleven ML algorithms. The gradient boosting decision tree (GBDT) algorithms demonstrated the most optimal performance (1.00 in the training cohort and 0.80 in the validation cohort). By employing the SHAP method, we identified the variables that contributed to the model and their correlation with 30-day mortality. The AUC of the GBDT algorithms was the highest when using the top 4 (ferritin, UREA, age and thrombin time (TT)) features, reaching 0.99 in the training cohort and 0.83 in the validation cohort. Additionally, we developed a web-based calculator to estimate the risk of 30-day mortality.
Conclusions: With GBDT algorithms applied to laboratory data, accurate prediction of 30-day mortality is achievable. Integrating these algorithms into clinical practice could potentially improve 30-day outcomes.
期刊介绍:
The Journal of Clinical Immunology publishes impactful papers in the realm of human immunology, delving into the diagnosis, pathogenesis, prognosis, or treatment of human diseases. The journal places particular emphasis on primary immunodeficiencies and related diseases, encompassing inborn errors of immunity in a broad sense, their underlying genotypes, and diverse phenotypes. These phenotypes include infection, malignancy, allergy, auto-inflammation, and autoimmunity. We welcome a broad spectrum of studies in this domain, spanning genetic discovery, clinical description, immunologic assessment, diagnostic approaches, prognosis evaluation, and treatment interventions. Case reports are considered if they are genuinely original and accompanied by a concise review of the relevant medical literature, illustrating how the novel case study advances the field. The instructions to authors provide detailed guidance on the four categories of papers accepted by the journal.