TY  - JOUR
AU  - Ueno, R
AU  - Xu, L
AU  - Uegami, W
AU  - Matsui, H
AU  - Okui, J
AU  - Hayashi, H
AU  - Miyajima, T
AU  - Hayashi, Y
AU  - Pilcher, D
AU  - Jones, D
Y2  - 2020/11/17
Y1  - 2020/07/13
SN  - 1932-6203
UR  - http://hdl.handle.net/11343/251520
AB  - BACKGROUND: 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.
LA  - English
PB  - PUBLIC LIBRARY SCIENCE
T1  - 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
DO  - 10.1371/journal.pone.0235835
IS  - PLoS One
VL  - 15
IS  - 7
L1  - /bitstream/handle/11343/251520/PMC7357766.pdf?sequence=1&isAllowed=y
ER  -