ArticlePloS one2020
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.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.Annals of laboratory medicine · 2025Article
- Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.BMC medical informatics and decision making · 2025Article
- Integrative machine learning and Mendelian randomization identify causal laboratory biomarkers for coronary artery lesions in Kawasaki disease: a prospective study.Frontiers in genetics · 2025Article
- Early prediction cardiac arrest in intensive care units: the value of laboratory indicator trends.World journal of emergency medicine · 2025Article
- Temporal variations in and predictive values of ABG results prior to in-hospital cardiac arrest.Journal of medicine, surgery, and public health · 2024Article
- A big data analysis of fever threshold and vital sign characteristics using tympanic temperature in hospitalized patients.Scientific reports · 2024Article
- Prediction model of in-hospital cardiac arrest using machine learning in the early phase of hospitalization.The Kaohsiung journal of medical sciences · 2024Article
- Prediction of In-Hospital Cardiac Arrest in the Intensive Care Unit: Machine Learning-Based Multimodal Approach.JMIR medical informatics · 2024Article
- Short-term All-cause In-hospital Mortality Prediction by Machine Learning Using Numeric Laboratory Results.JMA journal · 2023Article
- Artificial Intelligence in Ventricular Arrhythmias and Sudden Death.Arrhythmia & electrophysiology review · 2023Review
- Variation Across Hospitals in In-Hospital Cardiac Arrest Incidence Among Medicare Beneficiaries.JAMA network open · 2022Observational
- Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review.JMIR medical informatics · 2021Article
- Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning.Diagnostics (Basel, Switzerland) · 2021Article
- Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit.Healthcare informatics research · 2021Article
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10 authors.
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Abstract
backgroundAlthough 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).
methodsAll 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.
resultsOf 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.
conclusionsIn 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.
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