Early detection of ICU-acquired infections using high-frequency electronic health record data
Abstract Background Nosocomial infections are a major cause of morbidity and mortality in the ICU. Earlier identification of these complications may facilitate better clinical management and improve outcomes. We developed a dynamic prediction model that leveraged high-frequency longitudinal data to...
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BMC
2025-07-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-03031-6 |
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| author | Meri R. J. Varkila Giacomo Lancia Maarten van Smeden Marc J. M. Bonten Cristian Spitoni Olaf L. Cremer |
| author_facet | Meri R. J. Varkila Giacomo Lancia Maarten van Smeden Marc J. M. Bonten Cristian Spitoni Olaf L. Cremer |
| author_sort | Meri R. J. Varkila |
| collection | DOAJ |
| description | Abstract Background Nosocomial infections are a major cause of morbidity and mortality in the ICU. Earlier identification of these complications may facilitate better clinical management and improve outcomes. We developed a dynamic prediction model that leveraged high-frequency longitudinal data to estimate infection risk 48 h ahead of clinically overt deterioration. Methods We used electronic health record data from consecutive adults who had been treated for > 48 h in a mixed tertiary ICU in the Netherlands enrolled in the Molecular Diagnosis and Risk Stratification of Sepsis (MARS) cohort from 2011 to 2018. All infectious episodes were prospectively adjudicated. ICU-acquired infection (ICU-AI) risk was estimated using a Cox landmark model with high-resolution vital sign data processed via a convolutional neural network (CNN). Results We studied 32,178 observation days in 4444 patients and observed 1197 infections, yielding an overall infection risk of 3.5% per ICU day. Discrimination of the composite model was moderate with c-index values varying between 0.64 (95%CI: 0.58–0.69) and 0.72 (95%CI: 0.66–0.78) across timepoints, with some overestimation of ICU-AI risk overall (mean calibration slope 0.58). Compared to 38 common features of infection, a CNN risk score derived from five vital sign signals consistently ranked as a strong predictor of ICU-AI across all time points but did not substantially change risk prediction of ICU-AI. Conclusion A dynamic modelling approach that incorporates machine learning of high-frequency vital sign data shows promise as a continuous bedside index of infection risk. Further validation is needed to weigh added complexity and interpretability of the deep learning model against potential benefits for clinical decision support in the ICU. |
| format | Article |
| id | doaj-art-0e98f0f845b44c17891dfed796855d5c |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-0e98f0f845b44c17891dfed796855d5c2025-08-20T03:46:00ZengBMCBMC Medical Informatics and Decision Making1472-69472025-07-012511910.1186/s12911-025-03031-6Early detection of ICU-acquired infections using high-frequency electronic health record dataMeri R. J. Varkila0Giacomo Lancia1Maarten van Smeden2Marc J. M. Bonten3Cristian Spitoni4Olaf L. Cremer5Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht UniversityDepartment of Mathematics, University UtrechtJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht UniversityJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht UniversityDepartment of Mathematics, University UtrechtDepartment of Intensive Care Medicine, University Medical Center UtrechtAbstract Background Nosocomial infections are a major cause of morbidity and mortality in the ICU. Earlier identification of these complications may facilitate better clinical management and improve outcomes. We developed a dynamic prediction model that leveraged high-frequency longitudinal data to estimate infection risk 48 h ahead of clinically overt deterioration. Methods We used electronic health record data from consecutive adults who had been treated for > 48 h in a mixed tertiary ICU in the Netherlands enrolled in the Molecular Diagnosis and Risk Stratification of Sepsis (MARS) cohort from 2011 to 2018. All infectious episodes were prospectively adjudicated. ICU-acquired infection (ICU-AI) risk was estimated using a Cox landmark model with high-resolution vital sign data processed via a convolutional neural network (CNN). Results We studied 32,178 observation days in 4444 patients and observed 1197 infections, yielding an overall infection risk of 3.5% per ICU day. Discrimination of the composite model was moderate with c-index values varying between 0.64 (95%CI: 0.58–0.69) and 0.72 (95%CI: 0.66–0.78) across timepoints, with some overestimation of ICU-AI risk overall (mean calibration slope 0.58). Compared to 38 common features of infection, a CNN risk score derived from five vital sign signals consistently ranked as a strong predictor of ICU-AI across all time points but did not substantially change risk prediction of ICU-AI. Conclusion A dynamic modelling approach that incorporates machine learning of high-frequency vital sign data shows promise as a continuous bedside index of infection risk. Further validation is needed to weigh added complexity and interpretability of the deep learning model against potential benefits for clinical decision support in the ICU.https://doi.org/10.1186/s12911-025-03031-6Critical illnessICUInfectionNosocomialMachine learning |
| spellingShingle | Meri R. J. Varkila Giacomo Lancia Maarten van Smeden Marc J. M. Bonten Cristian Spitoni Olaf L. Cremer Early detection of ICU-acquired infections using high-frequency electronic health record data BMC Medical Informatics and Decision Making Critical illness ICU Infection Nosocomial Machine learning |
| title | Early detection of ICU-acquired infections using high-frequency electronic health record data |
| title_full | Early detection of ICU-acquired infections using high-frequency electronic health record data |
| title_fullStr | Early detection of ICU-acquired infections using high-frequency electronic health record data |
| title_full_unstemmed | Early detection of ICU-acquired infections using high-frequency electronic health record data |
| title_short | Early detection of ICU-acquired infections using high-frequency electronic health record data |
| title_sort | early detection of icu acquired infections using high frequency electronic health record data |
| topic | Critical illness ICU Infection Nosocomial Machine learning |
| url | https://doi.org/10.1186/s12911-025-03031-6 |
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