Abstract
BACKGROUND: Bedside monitors in the ICU routinely measure and collect patients' physiologic data in real time to continuously assess the health status of patients who are critically ill. With the advent of increased computational power and the ability to store and rapidly process big data sets in recent years, these physiologic data show promise in identifying specific outcomes and/or events during patients' ICU hospitalization.
METHODS: We introduced a methodology designed to automatically extract information from continuous-in-time vital sign data collected from bedside monitors to predict if a patient will experience a prolonged stay (length of stay) on mechanical ventilation, defined as >4 d, in a pediatric ICU.
RESULTS: Continuous-in-time vital signs information and clinical history data were retrospectively collected for 284 ICU subjects from their first 24 h on mechanical ventilation from a medical-surgical pediatric ICU at Boston Children's Hospital. Multiple machine learning models were trained on multiple subsets of these subjects to predict the likelihood that each of these subjects would experience a long stay. We evaluated the predictive power of our models strictly on unseen hold-out validation sets of subjects. Our methodology achieved model performance of >83% (area under the curve) by using only vital sign information as input, and performances of 90% (area under the curve) by combining vital sign information with subjects' static clinical data readily available in electronic health records. We implemented this approach on 300 independently trained experiments with different choices of training and hold-out validation sets to ensure the consistency and robustness of our results in our study sample. The predictive power of our approach outperformed recent efforts that used deep learning to predict a similar task.
CONCLUSIONS: Our proposed workflow may prove useful in the design of scalable approaches for real-time predictive systems in ICU environments, exploiting real-time vital sign information from bedside monitors. (ClinicalTrials.gov registration NCT02184208.)
- mechanical ventilation
- pediatrics
- machine learning
- length of stay
- prediction
- predictive analytics
- intensive care
- critical care
- biomedical and health data science
- data driven machine learning
- decision support systems
- length of stay estimation
- clinical decision making
- precision medicine
- big data in medicine
Footnotes
- Correspondence: David Castiñeira PhD, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Building 48 Cambridge, MA 02139. E-mail: davidcastineira{at}outlook.com; Mauricio Santillana PhD, Com-putational Health Informatics Program, Boston Children's Hospital, 401 Park Dr, Boston, MA 02215. E-mail: msantill{at}g.harvard.edu
Arnold and Santillana jointly supervised this work.
Supplementary material related to this paper is available at http://www.rcjournal.com.
The data sets generated and analyzed during the current study are not publicly available to protect the privacy of individually identifiable health information, but the de-identified features used for the predictive algorithms are available from the corresponding authors on reasonable request.
The authors have disclosed no conflicts of interest.
The study was performed at Boston Children's Hospital, Boston, Massachusetts.
Dr Geva was funded by NICHD T32 HD040128 and NICHD K12 HD047349.
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