Yuwen Liu, Yuhan Wu, Tao Zhang, Jie Chen, Wei Hu, Guixin Sun, Pengfei Zheng
{"title":"用于骨关节感染儿童血液感染早期检测的机器学习算法。","authors":"Yuwen Liu, Yuhan Wu, Tao Zhang, Jie Chen, Wei Hu, Guixin Sun, Pengfei Zheng","doi":"10.3389/fped.2024.1398713","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Bloodstream infection (BSI) poses a significant life-threatening risk in pediatric patients with osteoarticular infections. Timely identification of BSI is crucial for effective management and improved patient outcomes. This study aimed to develop a machine learning (ML) model for the early identification of BSI in children with osteoarticular infections.</p><p><strong>Materials and methods: </strong>A retrospective analysis was conducted on pediatric patients diagnosed with osteoarticular infections admitted to three hospitals in China between January 2012 and January 2023. All patients underwent blood and puncture fluid bacterial cultures. Sixteen early available variables were selected, and eight different ML algorithms were applied to construct the model by training on these data. The accuracy and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of these models. The Shapley Additive Explanation (SHAP) values were utilized to explain the predictive value of each variable on the output of the model.</p><p><strong>Results: </strong>The study comprised 181 patients in the BSI group and 420 in the non-BSI group. Random Forest exhibited the best performance, with an AUC of 0.947 ± 0.016. The model demonstrated an accuracy of 0.895 ± 0.023, a sensitivity of 0.847 ± 0.071, a specificity of 0.917 ± 0.007, a precision of 0.813 ± 0.023, and an F1 score of 0.828 ± 0.040. The four most significant variables in both the feature importance matrix plot of the Random Forest model and the SHAP summary plot were procalcitonin (PCT), neutrophil count (N), leukocyte count (WBC), and fever days.</p><p><strong>Conclusions: </strong>The Random Forest model proved to be effective in early and timely identification of BSI in children with osteoarticular infections. Its application could aid in clinical decision-making and potentially mitigate the risk associated with delayed or inaccurate blood culture results.</p>","PeriodicalId":12637,"journal":{"name":"Frontiers in Pediatrics","volume":"12 ","pages":"1398713"},"PeriodicalIF":2.1000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668579/pdf/","citationCount":"0","resultStr":"{\"title\":\"Machine learning algorithms for the early detection of bloodstream infection in children with osteoarticular infections.\",\"authors\":\"Yuwen Liu, Yuhan Wu, Tao Zhang, Jie Chen, Wei Hu, Guixin Sun, Pengfei Zheng\",\"doi\":\"10.3389/fped.2024.1398713\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Bloodstream infection (BSI) poses a significant life-threatening risk in pediatric patients with osteoarticular infections. Timely identification of BSI is crucial for effective management and improved patient outcomes. This study aimed to develop a machine learning (ML) model for the early identification of BSI in children with osteoarticular infections.</p><p><strong>Materials and methods: </strong>A retrospective analysis was conducted on pediatric patients diagnosed with osteoarticular infections admitted to three hospitals in China between January 2012 and January 2023. All patients underwent blood and puncture fluid bacterial cultures. Sixteen early available variables were selected, and eight different ML algorithms were applied to construct the model by training on these data. The accuracy and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of these models. The Shapley Additive Explanation (SHAP) values were utilized to explain the predictive value of each variable on the output of the model.</p><p><strong>Results: </strong>The study comprised 181 patients in the BSI group and 420 in the non-BSI group. Random Forest exhibited the best performance, with an AUC of 0.947 ± 0.016. The model demonstrated an accuracy of 0.895 ± 0.023, a sensitivity of 0.847 ± 0.071, a specificity of 0.917 ± 0.007, a precision of 0.813 ± 0.023, and an F1 score of 0.828 ± 0.040. The four most significant variables in both the feature importance matrix plot of the Random Forest model and the SHAP summary plot were procalcitonin (PCT), neutrophil count (N), leukocyte count (WBC), and fever days.</p><p><strong>Conclusions: </strong>The Random Forest model proved to be effective in early and timely identification of BSI in children with osteoarticular infections. 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Machine learning algorithms for the early detection of bloodstream infection in children with osteoarticular infections.
Background: Bloodstream infection (BSI) poses a significant life-threatening risk in pediatric patients with osteoarticular infections. Timely identification of BSI is crucial for effective management and improved patient outcomes. This study aimed to develop a machine learning (ML) model for the early identification of BSI in children with osteoarticular infections.
Materials and methods: A retrospective analysis was conducted on pediatric patients diagnosed with osteoarticular infections admitted to three hospitals in China between January 2012 and January 2023. All patients underwent blood and puncture fluid bacterial cultures. Sixteen early available variables were selected, and eight different ML algorithms were applied to construct the model by training on these data. The accuracy and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of these models. The Shapley Additive Explanation (SHAP) values were utilized to explain the predictive value of each variable on the output of the model.
Results: The study comprised 181 patients in the BSI group and 420 in the non-BSI group. Random Forest exhibited the best performance, with an AUC of 0.947 ± 0.016. The model demonstrated an accuracy of 0.895 ± 0.023, a sensitivity of 0.847 ± 0.071, a specificity of 0.917 ± 0.007, a precision of 0.813 ± 0.023, and an F1 score of 0.828 ± 0.040. The four most significant variables in both the feature importance matrix plot of the Random Forest model and the SHAP summary plot were procalcitonin (PCT), neutrophil count (N), leukocyte count (WBC), and fever days.
Conclusions: The Random Forest model proved to be effective in early and timely identification of BSI in children with osteoarticular infections. Its application could aid in clinical decision-making and potentially mitigate the risk associated with delayed or inaccurate blood culture results.
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.