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

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 mod...

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Vydané v:	PLOS ONE Ročník 15; číslo 7; s. e0235835
Hlavní autori:	Ueno, Ryo, Xu, Liyuan, Uegami, Wataru, Matsui, Hiroki, Okui, Jun, Hayashi, Hiroshi, Miyajima, Toru, Hayashi, Yoshiro, Pilcher, David, Jones, Daryl
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	San Francisco Public Library of Science (PLoS) 13.07.2020 Public Library of Science
Predmet:	Age Aged Algorithms Blood pressure Cardiac arrest Care and treatment Cohort analysis Computer and Information Sciences Confidence intervals Demography Diagnosis Education Female Graduate studies Health care Health risk assessment Heart Heart Arrest Hemoglobin Hospitalization Hospitals Humans Intensive care Laboratories Learning algorithms Machine Learning Male Medicine Medicine and Health Sciences Middle Aged Observations Patients Performance prediction Physical Sciences Prediction models Preventive medicine Prognosis Public health Q R Research and Analysis Methods Research Article Retrospective Studies Science Sensitivity analysis Statistical analysis Technology application Training Variables Vital Signs Japan New Zealand Melbourne Victoria Australia Australia
ISSN:	1932-6203, 1932-6203
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Shrnutí:	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). 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. 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. 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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: There are no conflicts of interest to declare.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0235835