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

Ueno, Ryohei; Xu, Liyuan; Uegami, Wataru; Matsui, Hiroki; Okui, Jun; Hayashi, Hiroshi; Miyajima, Toru; Hayashi, Yoshihiko; Pilcher, David; Jones, Daryl

doi:10.1371/journal.pone.0235835

Cited by 24 publications

(24 citation statements)

References 36 publications

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“…In many of these studies the predicted outcome was a defined event that could be easily detected, such as mortality, cardiac arrest, or ICU transfer. In other cases, the predicted end-point was a more complex condition, such as renal failure or sepsis, that required a rule-based algorithm for event tagging [16][17][18][19][20][21]. In most studies, although the rules for event tagging were specified, no details as to the reliability of the tagging, manual or automatic, were provided.…”

Section: Discussionmentioning

confidence: 99%

Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit

Jeddah

Chen²,

Lipsky³

et al. 2021

Healthc Inform Res

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Objectives: Predictive models for critical events in the intensive care unit (ICU) might help providers anticipate patient deterioration. At the heart of predictive model development lies the ability to accurately label significant events, thereby facilitating the use of machine learning and similar strategies. We conducted this study to establish the validity of an automated system for tagging respiratory and hemodynamic deterioration by comparing automatic tags to tagging by expert reviewers.Methods: This retrospective cohort study included 72,650 unique patient stays collected from Electronic Medical Records of the University of Massachusetts’ eICU. An enriched subgroup of stays was manually tagged by expert reviewers. The tags generated by the reviewers were compared to those generated by an automated system.Results: The automated system was able to rapidly and efficiently tag the complete database utilizing available clinical data. The overall agreement rate between the automated system and the clinicians for respiratory and hemodynamic deterioration tags was 89.4% and 87.1%, respectively. The automatic system did not add substantial variability beyond that seen among the reviewers.Conclusions: We demonstrated that a simple rule-based tagging system could provide a rapid and accurate tool for mass tagging of a compound database. These types of tagging systems may replace human reviewers and save considerable resources when trying to create a validated, labeled database used to train artificial intelligence algorithms. The ability to harness the power of artificial intelligence depends on efficient clinical validation of targeted conditions; hence, these systems and the methodology used to validate them are crucial.

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Section: Discussionmentioning

confidence: 99%

Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit

Jeddah

Chen²,

Lipsky³

et al. 2021

Healthc Inform Res

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“…The system predicted cardiac arrest using abnormal body temperature or heart rate [ 23 ]. Ueno Ryo et al developed algorithms to predict cardiac arrest based on RF in patients [ 24 ]. They collected 8-hourly vital signs and laboratory data for two days to obtain 24-h of data [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…Ueno Ryo et al developed algorithms to predict cardiac arrest based on RF in patients [ 24 ]. They collected 8-hourly vital signs and laboratory data for two days to obtain 24-h of data [ 24 ]. Sensitivity was higher when only vital signs were used, but the use of vital signs and laboratory data gave a higher positive predictive value (PPV) [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…They collected 8-hourly vital signs and laboratory data for two days to obtain 24-h of data [ 24 ]. Sensitivity was higher when only vital signs were used, but the use of vital signs and laboratory data gave a higher positive predictive value (PPV) [ 24 ]. Hardt et al investigated predicted risk for clinical alerts based on deep learning using time series data [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning

et al. 2021

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Sudden cardiac arrest can leave serious brain damage or lead to death, so it is very important to predict before a cardiac arrest occurs. However, early warning score systems including the National Early Warning Score, are associated with low sensitivity and false positives. We applied shallow and deep learning to predict cardiac arrest to overcome these limitations. We evaluated the performance of the Synthetic Minority Oversampling Technique Ratio. We evaluated the performance using a Decision Tree, a Random Forest, Logistic Regression, Long Short-Term Memory model, Gated Recurrent Unit model, and LSTM–GRU hybrid models. Our proposed Logistic Regression demonstrated a higher positive predictive value and sensitivity than traditional early warning systems.

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“…Compared to vital signs alone, laboratory indicators increase the ability to identify patients at risk of deterioration [14]. In spite of that, the di culty in obtaining laboratory indicators limits the application of the prediction [15].…”

Section: Introductionmentioning

confidence: 99%

Factors Predicting Cardiac Arrest in the Intensive Care Unit: A Matched Case-Control Study

Sang

Pan

Zhao

et al. 2021

Preprint

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Background: Early detection and intervention of disease deterioration are the keys to reducing the incidence of preventable intensive care unit cardiac arrest (ICU-CA). We aimed to investigate the ICU-CA predictive factors, including vital signs and laboratory indicators, and to analyze the performance of trends value of those factors on predicting ICU-CA. Methods: We conducted a matched case-control study at Qilu Hospital of Shandong University. Data on adult patients in ICU who suffered a cardiac arrest (CA) were retrospectively collected from 2016 to 2019, including vital signs and laboratory indicators at 48, 36, 24, 12, and 8 hours before ICU-CA. These cases were matched (ward, sex, and admission data) with controls (no ICU-CA) at a 1:2 ratio. Univariable logistic regression was used for statistical comparisons between cases and controls, and multivariate logistic regression was used to investigate the independent associations of indicators and their tendency with ICU-CA at given time points. The area under receiver operating characteristic (AUROC) was used to evaluate the predictive performance on ICU-CA.Results: Of 6164 ICU patients, 1042 patients suffered an ICU-CA during the 3 years. After careful screening, a total of 427 patients were included as the cases in the study, and 790 patients were included as controls. The vital signs and laboratory indicators at 8h before cardiac arrest, such as heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), oxygen saturation (SaO2), hemoglobin (HGB), potassium (K+), sodium (Na+), lactic acid (Lac), and pH all can predict the ICU-CA. The mean value, maximum value, minimum value, and range of these indicators were related to the occurrence of ICU-CA, and the trend values were more accurate than the current value for the variability in laboratory indicators. Conclusions: The ability of trends value of laboratory indicators for predicting ICU-CA was more accurate than the value at given time points for the variability in laboratory indicators. Adding trends of laboratory indicators may increase the accuracy of models designed to detect critical illness in ICU. Trial registration: ClinicalTrials.gov Identifier: NCT04670458.

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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

Cited by 24 publications

References 36 publications

Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit

Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit

Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning

Factors Predicting Cardiac Arrest in the Intensive Care Unit: A Matched Case-Control Study

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