Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments
<b>Background:</b> With 11 million sepsis-related deaths worldwide, the development of tools for early prediction of sepsis onset in hospitalized patients is a global health priority. We developed a machine learning algorithm, capable of detecting the early onset of sepsis in all hospita...
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MDPI AG
2025-01-01
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| author | Pierre-Elliott Thiboud Quentin François Cécile Faure Gilles Chaufferin Barthélémy Arribe Nicolas Ettahar |
| author_facet | Pierre-Elliott Thiboud Quentin François Cécile Faure Gilles Chaufferin Barthélémy Arribe Nicolas Ettahar |
| author_sort | Pierre-Elliott Thiboud |
| collection | DOAJ |
| description | <b>Background:</b> With 11 million sepsis-related deaths worldwide, the development of tools for early prediction of sepsis onset in hospitalized patients is a global health priority. We developed a machine learning algorithm, capable of detecting the early onset of sepsis in all hospital departments. <b>Methods:</b> Predictors of sepsis from 45,127 patients from all departments of Valenciennes Hospital (France) were retrospectively collected for training. The binary classifier SEPSI Score for sepsis prediction was constructed using a gradient boosted trees approach, and assessed on the study dataset of 5270 patient stays, including 121 sepsis cases (2.3%). Finally, the performance of the model and its ability to detect early sepsis onset were evaluated and compared with existing sepsis scoring systems. <b>Results:</b> The mean positive predictive value of the SEPSI Score was 0.610 compared to 0.174 for the SOFA (Sepsis-related Organ Failure Assessment) score. The mean area under the precision–recall curve was 0.738 for SEPSI Score versus 0.174 for the most efficient score (SOFA). High sensitivity (0.845) and specificity (0.987) were also reported for SEPSI Score. The model was more accurate than all tested scores, up to 3 h before sepsis onset. Half of sepsis cases were detected by the model at least 48 h before their medically confirmed onset. <b>Conclusions:</b> The SEPSI Score model accurately predicted the early onset of sepsis, with performance exceeding existing scoring systems. It could be a valuable predictive tool in all hospital departments, allowing early management of sepsis patients. Its impact on associated morbidity-mortality needs to be further assessed. |
| format | Article |
| id | doaj-art-cc5187a0ff9f4a2e814dd852560bd696 |
| institution | OA Journals |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Diagnostics |
| spelling | doaj-art-cc5187a0ff9f4a2e814dd852560bd6962025-08-20T02:12:25ZengMDPI AGDiagnostics2075-44182025-01-0115330210.3390/diagnostics15030302Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All DepartmentsPierre-Elliott Thiboud0Quentin François1Cécile Faure2Gilles Chaufferin3Barthélémy Arribe4Nicolas Ettahar5PREVIA MEDICAL, 69007 Lyon, FrancePREVIA MEDICAL, 69007 Lyon, FrancePREVIA MEDICAL, 69007 Lyon, FranceALLYANE, 69004 Lyon, FrancePREVIA MEDICAL, 69007 Lyon, FranceService de Maladies Infectieuses et Tropicales, Centre Hospitalier de Valenciennes, 59300 Valenciennes, France<b>Background:</b> With 11 million sepsis-related deaths worldwide, the development of tools for early prediction of sepsis onset in hospitalized patients is a global health priority. We developed a machine learning algorithm, capable of detecting the early onset of sepsis in all hospital departments. <b>Methods:</b> Predictors of sepsis from 45,127 patients from all departments of Valenciennes Hospital (France) were retrospectively collected for training. The binary classifier SEPSI Score for sepsis prediction was constructed using a gradient boosted trees approach, and assessed on the study dataset of 5270 patient stays, including 121 sepsis cases (2.3%). Finally, the performance of the model and its ability to detect early sepsis onset were evaluated and compared with existing sepsis scoring systems. <b>Results:</b> The mean positive predictive value of the SEPSI Score was 0.610 compared to 0.174 for the SOFA (Sepsis-related Organ Failure Assessment) score. The mean area under the precision–recall curve was 0.738 for SEPSI Score versus 0.174 for the most efficient score (SOFA). High sensitivity (0.845) and specificity (0.987) were also reported for SEPSI Score. The model was more accurate than all tested scores, up to 3 h before sepsis onset. Half of sepsis cases were detected by the model at least 48 h before their medically confirmed onset. <b>Conclusions:</b> The SEPSI Score model accurately predicted the early onset of sepsis, with performance exceeding existing scoring systems. It could be a valuable predictive tool in all hospital departments, allowing early management of sepsis patients. Its impact on associated morbidity-mortality needs to be further assessed.https://www.mdpi.com/2075-4418/15/3/302sepsismachine learningalgorithmearly prediction |
| spellingShingle | Pierre-Elliott Thiboud Quentin François Cécile Faure Gilles Chaufferin Barthélémy Arribe Nicolas Ettahar Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments Diagnostics sepsis machine learning algorithm early prediction |
| title | Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments |
| title_full | Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments |
| title_fullStr | Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments |
| title_full_unstemmed | Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments |
| title_short | Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments |
| title_sort | development and validation of a machine learning model for early prediction of sepsis onset in hospital inpatients from all departments |
| topic | sepsis machine learning algorithm early prediction |
| url | https://www.mdpi.com/2075-4418/15/3/302 |
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