Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach.
Background. Bedside monitors in intensive care units (ICUs) routinely measure and collect patients' physiologic data in real time in order to continuously assess the health status of critically ill patients. With the advent of increased computational power and the ability to store and rapidly proces...
| Publicado en: | Respiratory Care Vol. 65; no. 9; pp. 1367 - 1378 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Mary Ann Liebert, Inc.
Sep2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145308248&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145308248 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00201324 4GG jtl: Respiratory Care issn: 00201324 maglogo: N pubinfo: dt: Sep2020 vid: 65 iid: 9 pid: 1365 pub: Mary Ann Liebert, Inc. place: New Rochelle, New York artinfo: ui: 145308248 145308248 145308248 10.4187/respcare.07561 145308248 ppf: 1367 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach. aug: au: Castiñeira, David Schlosser, Katherine R. Geva, Alon Rahmani, Amir R. Fiore, Gaston Walsh, Brian K. Smallwood, Craig D. Arnold, John H. Santillana, Mauricio affil: Massachusetts Institute of Technology, Cambridge, Massachusetts sug: subj: Vital Signs Electronic Health Records Length of Stay Respiration, Artificial Machine Learning Intensive Care Units, Pediatric Decision Support Systems, Clinical Quality Improvement Human Retrospective Design Descriptive Statistics Hospitals, Pediatric Predictive Validity Critical Care Decision Making, Clinical Data Analytics Inpatients Critically Ill Patients Hospital Mortality Clinical Information Systems Sensitivity and Specificity ab: Background. Bedside monitors in intensive care units (ICUs) routinely measure and collect patients' physiologic data in real time in order to continuously assess the health status of critically ill patients. With the advent of increased computational power and the ability to store and rapidly process big datasets in recent years, this physiologic data show promise in identifying specific outcomes and/or events during patients' ICU hospitalization. Methods. We introduce a methodological workflow capable of automatically extracting meaningful information from continuous-in-time vital signs to predict patient outcomes. Our prediction algorithms are based on robust and scalable machine-learning techniques. We demonstrate the feasibility and applicability of this approach to a pilot study aimed at predicting whether or not a patient will experience a prolonged ICU length of stay (defined as longer than 4 days) in a cohort of 284 mechanically ventilated patients collected from a pediatric medical and surgical ICU at Boston Children's Hospital, using only information collected or available during the first 24 hours of mechanical ventilation. Results. Our methodology achieves predictive accuracies above 83% (AUC) by using only vital sign information collected from bedside physiologic monitors. In addition, we show that combining vital sign information with patients' demographic and clinical history data contained in electronic health records, improves the accuracy of our approach to accuracies of 90% (AUC). The predictive power of our methodology is assessed strictly on an unseen hold-out validation set of patients. 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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