Manifold ranking based scoring system with its application to cardiac arrest prediction: A retrospective study in emergency department patients

Liu, Tianchi; Lin, Zhiping; Ong, Marcus Eng Hock; Koh, Zhi Xiong; Pek, Pin Pin; Yeo, Yong Kiang; Oh, Beom-Seok; Ho, Andrew Fu Wah; Liu, Nan

doi:10.1016/j.compbiomed.2015.10.001

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…were able to predict in‐hospital mortality with an area under the receiver operating characteristic curve of 0.93–0.94, validated across two sites . ML has been applied to vital sign and medical record data to develop a scoring system for predicting cardiac arrest within 72 h, and has also been used to retrospectively predict defibrillation success for out‐of‐hospital cardiac arrest . ML based ‘ E ‐triage’ has demonstrated equivalent or improved identification of patient outcomes compared with the Emergency Severity Index .…”

Section: Clinical Outcome Predictionsmentioning

confidence: 99%

“…31 ML has been applied to vital sign and medical record data to develop a scoring system for predicting cardiac arrest within 72 h, and has also been used to retrospectively predict defibrillation success for out-of-hospital cardiac arrest. 32,33 ML based 'E-triage' has demonstrated equivalent or improved identification of patient outcomes compared with the Emergency Severity Index. 34 ML models have been developed that outperform 'TIMI' and 'GRACE' scores for cardiovascular risk, even when trained on medical records with significant missing and noisy data.…”

Section: Clinical Monitoringmentioning

confidence: 99%

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Artificial intelligence and machine learning in emergency medicine

Stewart

Sprivulis

Dwivedi

2018

Emerg Medicine Australasia

121

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Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.

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Section: Clinical Outcome Predictionsmentioning

confidence: 99%

Section: Clinical Monitoringmentioning

confidence: 99%

Artificial intelligence and machine learning in emergency medicine

Stewart

Sprivulis

Dwivedi

2018

Emerg Medicine Australasia

121

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“…Heart rate variability (HRV), characterized by fluctuations in the time interval between successive heart beats, was first suggested as a marker of fetal distress in 1965, and it subsequently gained recognition as a noninvasive marker of autonomic activity [ 1 ]. HRV is considered a promising tool for predicting mortality in emergency patients transported via ambulance or trauma victims [ 2 3 ], predicting sudden cardiac arrests [ 4 ], and diagnosing poisoning or overdose [ 5 6 ]. The optimal electrocardiogram (ECG) sampling frequency required to ensure sufficient precision of R–R intervals for HRV analysis is yet to be determined.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Sampling Frequency Range Acceptable for Heart Rate Variability Analysis

et al. 2018

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ObjectivesHeart rate variability (HRV) has gained recognition as a noninvasive marker of autonomic activity. HRV is considered a promising tool in various clinical scenarios. The optimal electrocardiogram (ECG) sampling frequency required to ensure sufficient precision of R–R intervals for HRV analysis has not yet been determined. Here, we aimed to determine the acceptable ECG sampling frequency range by analyzing ECG signals from patients who visited an emergency department with the chief complaint of acute intoxication or overdose.MethodsThe study included 83 adult patients who visited an emergency department with the chief complaint of acute poisoning. The original 1,000-Hz ECG signals were down-sampled to 500-, 250-, 100-, and 50-Hz sampling frequencies with linear interpolation. R–R interval data were analyzed for time-domain, frequency-domain, and nonlinear HRV parameters. Parameters derived from the data on down-sampled frequencies were compared with those derived from the data on 1,000-Hz signals, and Lin's concordance correlation coefficients were calculated.ResultsDown-sampling to 500 or 250 Hz resulted in excellent concordance. Signals down-sampled to 100 Hz produced acceptable results for time-domain analysis and Poincaré plots, but not for frequency-domain analysis. Down-sampling to 50 Hz proved to be unacceptable for both time- and frequency-domain analyses. At 50 Hz, the root-mean-squared successive differences and the power of high frequency tended to have high values and random errors.ConclusionsA 250-Hz sampling frequency would be acceptable for HRV analysis. When frequency-domain analysis is not required, a 100-Hz sampling frequency would also be acceptable.

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“…Risk factors in CA are associated with a multitude of variables, including age, heart rate, blood pressure, laboratory data, ST‐T abnormalities, heart rate variability, and Killip class . These risk factors could possibly help in guiding decision‐making and risk assessment for individual patients, but remain controversial.…”

Section: Introductionmentioning

confidence: 99%

Decision tree model for predicting in‐hospital cardiac arrest among patients admitted with acute coronary syndrome

Wu²,

Yang³

et al. 2019

Clinical Cardiology

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Background In‐hospital cardiac arrest (IHCA) may be preventable, with patients often showing signs of physiological deterioration before an event. Our objective was to develop and validate a simple clinical prediction model to identify the IHCA risk among cardiac arrest (CA) patients hospitalized with acute coronary syndrome (ACS). Hypothesis A predicting model could help to identify the risk of IHCA among patients admitted with ACS. Methods We conducted a case‐control study and analyzed 21 337 adult ACS patients, of whom 164 had experienced CA. Vital signs, demographic, and laboratory data were extracted from the electronic health record. Decision tree analysis was applied with 10‐fold cross‐validation to predict the risk of IHCA. Results The decision tree analysis detected seven explanatory variables, and the variables' importance is as follows: VitalPAC Early Warning Score (ViEWS), fatal arrhythmia, Killip class, cardiac troponin I, blood urea nitrogen, age, and diabetes. The development decision tree model demonstrated a sensitivity of 0.762, a specificity of 0.882, and an area under the receiver operating characteristic curve (AUC) of 0.844 (95% CI, 0.805 to 0.849). A 10‐fold cross‐validated risk estimate was 0.198, while the optimism‐corrected AUC was 0.823 (95% CI, 0.786 to 0.860). Conclusions We have developed and internally validated a good discrimination decision tree model to predict the risk of IHCA. This simple prediction model may provide healthcare workers with a practical bedside tool and could positively impact decision‐making with regard to deteriorating patients with ACS.

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Manifold ranking based scoring system with its application to cardiac arrest prediction: A retrospective study in emergency department patients

Cited by 13 publications

References 22 publications

Artificial intelligence and machine learning in emergency medicine

Artificial intelligence and machine learning in emergency medicine

Electrocardiogram Sampling Frequency Range Acceptable for Heart Rate Variability Analysis

Decision tree model for predicting in‐hospital cardiac arrest among patients admitted with acute coronary syndrome

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