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dc.contributor.authorUeno, R
dc.contributor.authorXu, L
dc.contributor.authorUegami, W
dc.contributor.authorMatsui, H
dc.contributor.authorOkui, J
dc.contributor.authorHayashi, H
dc.contributor.authorMiyajima, T
dc.contributor.authorHayashi, Y
dc.contributor.authorPilcher, D
dc.contributor.authorJones, D
dc.date.accessioned2020-11-17T03:29:58Z
dc.date.available2020-11-17T03:29:58Z
dc.date.issued2020-07-13
dc.identifierpii: PONE-D-20-03952
dc.identifier.citationUeno, R., Xu, L., Uegami, W., Matsui, H., Okui, J., Hayashi, H., Miyajima, T., Hayashi, Y., Pilcher, D. & Jones, D. (2020). Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study. PLOS ONE, 15 (7), https://doi.org/10.1371/journal.pone.0235835.
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11343/251520
dc.description.abstractBACKGROUND: Although machine learning-based prediction models for in-hospital cardiac arrest (IHCA) have been widely investigated, it is unknown whether a model based on vital signs alone (Vitals-Only model) can perform similarly to a model that considers both vital signs and laboratory results (Vitals+Labs model). METHODS: All adult patients hospitalized in a tertiary care hospital in Japan between October 2011 and October 2018 were included in this study. Random forest models with/without laboratory results (Vitals+Labs model and Vitals-Only model, respectively) were trained and tested using chronologically divided datasets. Both models use patient demographics and eight-hourly vital signs collected within the previous 48 hours. The primary and secondary outcomes were the occurrence of IHCA in the next 8 and 24 hours, respectively. The area under the receiver operating characteristic curve (AUC) was used as a comparative measure. Sensitivity analyses were performed under multiple statistical assumptions. RESULTS: Of 141,111 admitted patients (training data: 83,064, test data: 58,047), 338 had an IHCA (training data: 217, test data: 121) during the study period. The Vitals-Only model and Vitals+Labs model performed comparably when predicting IHCA within the next 8 hours (Vitals-Only model vs Vitals+Labs model, AUC = 0.862 [95% confidence interval (CI): 0.855-0.868] vs 0.872 [95% CI: 0.867-0.878]) and 24 hours (Vitals-Only model vs Vitals+Labs model, AUC = 0.830 [95% CI: 0.825-0.835] vs 0.837 [95% CI: 0.830-0.844]). Both models performed similarly well on medical, surgical, and ward patient data, but did not perform well for intensive care unit patients. CONCLUSIONS: In this single-center study, the machine learning model predicted IHCAs with good discrimination. The addition of laboratory values to vital signs did not significantly improve its overall performance.
dc.languageEnglish
dc.publisherPUBLIC LIBRARY SCIENCE
dc.titleValue of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pone.0235835
melbourne.affiliation.departmentSurgery (Austin & Northern Health)
melbourne.source.titlePLoS One
melbourne.source.volume15
melbourne.source.issue7
dc.rights.licenseCC BY
melbourne.elementsid1459257
melbourne.contributor.authorJones, Daryl
melbourne.contributor.authorPIlcher, David
dc.identifier.eissn1932-6203
melbourne.accessrightsOpen Access


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