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Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection.

Liu N, Koh ZX, Goh J, Lin Z, Haaland B, Ting BP, Ong ME - BMC Med Inform Decis Mak (2014)

Bottom Line: Out of 702 patients, 29 (4.1%) met the primary outcome.We conclude that more predictors do not necessarily guarantee better prediction results.Furthermore, machine learning-based variable selection seems promising in discovering a few relevant and significant measures as predictors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Emergency Medicine, Singapore General Hospital, Outram Road, Singapore 169608, Singapore. marcus.ong.e.h@sgh.com.sg.

ABSTRACT

Background: The key aim of triage in chest pain patients is to identify those with high risk of adverse cardiac events as they require intensive monitoring and early intervention. In this study, we aim to discover the most relevant variables for risk prediction of major adverse cardiac events (MACE) using clinical signs and heart rate variability.

Methods: A total of 702 chest pain patients at the Emergency Department (ED) of a tertiary hospital in Singapore were included in this study. The recruited patients were at least 30 years of age and who presented to the ED with a primary complaint of non-traumatic chest pain. The primary outcome was a composite of MACE such as death and cardiac arrest within 72 h of arrival at the ED. For each patient, eight clinical signs such as blood pressure and temperature were measured, and a 5-min ECG was recorded to derive heart rate variability parameters. A random forest-based novel method was developed to select the most relevant variables. A geometric distance-based machine learning scoring system was then implemented to derive a risk score from 0 to 100.

Results: Out of 702 patients, 29 (4.1%) met the primary outcome. We selected the 3 most relevant variables for predicting MACE, which were systolic blood pressure, the mean RR interval and the mean instantaneous heart rate. The scoring system with these 3 variables produced an area under the curve (AUC) of 0.812, and a cutoff score of 43 gave a sensitivity of 82.8% and specificity of 63.4%, while the scoring system with all the 23 variables had an AUC of 0.736, and a cutoff score of 49 gave a sensitivity of 72.4% and specificity of 63.0%. Conventional thrombolysis in myocardial infarction score and the modified early warning score achieved AUC values of 0.637 and 0.622, respectively.

Conclusions: It is observed that a few predictors outperformed the whole set of variables in predicting MACE within 72 h. We conclude that more predictors do not necessarily guarantee better prediction results. Furthermore, machine learning-based variable selection seems promising in discovering a few relevant and significant measures as predictors.

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Flowchart of the machine learning-based risk scoring method.
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Figure 2: Flowchart of the machine learning-based risk scoring method.

Mentions: Variable selection was the process of choosing a set of variables for the subsequent risk prediction. In this study, a machine learning (ML) based intelligent scoring system[17] (subsequently referred to here as the ML score) was implemented to build prediction models. The ML method examines geometric distances in Euclidean space between a testing sample and the training samples and produces a score on the possibility that the outcome of the testing sample approximates to the primary outcome. The ML method is illustrated in FigureĀ 2 and is briefly described as follows: Firstly, the selected variables were converted into interval [-1, 1] with min-max normalization[30]. Secondly, cluster centers for both positive samples (patients with MACE) and negative samples (patients without MACE) were calculated based on Euclidean distance, and an initial score for a testing sample was derived by measuring distances between the testing sample and two cluster centers. Lastly, the support vector machine (SVM)[31] was implemented to fine-tune the risk score. Details of the ML method are described in[17].


Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection.

Liu N, Koh ZX, Goh J, Lin Z, Haaland B, Ting BP, Ong ME - BMC Med Inform Decis Mak (2014)

Flowchart of the machine learning-based risk scoring method.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4150554&req=5

Figure 2: Flowchart of the machine learning-based risk scoring method.
Mentions: Variable selection was the process of choosing a set of variables for the subsequent risk prediction. In this study, a machine learning (ML) based intelligent scoring system[17] (subsequently referred to here as the ML score) was implemented to build prediction models. The ML method examines geometric distances in Euclidean space between a testing sample and the training samples and produces a score on the possibility that the outcome of the testing sample approximates to the primary outcome. The ML method is illustrated in FigureĀ 2 and is briefly described as follows: Firstly, the selected variables were converted into interval [-1, 1] with min-max normalization[30]. Secondly, cluster centers for both positive samples (patients with MACE) and negative samples (patients without MACE) were calculated based on Euclidean distance, and an initial score for a testing sample was derived by measuring distances between the testing sample and two cluster centers. Lastly, the support vector machine (SVM)[31] was implemented to fine-tune the risk score. Details of the ML method are described in[17].

Bottom Line: Out of 702 patients, 29 (4.1%) met the primary outcome.We conclude that more predictors do not necessarily guarantee better prediction results.Furthermore, machine learning-based variable selection seems promising in discovering a few relevant and significant measures as predictors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Emergency Medicine, Singapore General Hospital, Outram Road, Singapore 169608, Singapore. marcus.ong.e.h@sgh.com.sg.

ABSTRACT

Background: The key aim of triage in chest pain patients is to identify those with high risk of adverse cardiac events as they require intensive monitoring and early intervention. In this study, we aim to discover the most relevant variables for risk prediction of major adverse cardiac events (MACE) using clinical signs and heart rate variability.

Methods: A total of 702 chest pain patients at the Emergency Department (ED) of a tertiary hospital in Singapore were included in this study. The recruited patients were at least 30 years of age and who presented to the ED with a primary complaint of non-traumatic chest pain. The primary outcome was a composite of MACE such as death and cardiac arrest within 72 h of arrival at the ED. For each patient, eight clinical signs such as blood pressure and temperature were measured, and a 5-min ECG was recorded to derive heart rate variability parameters. A random forest-based novel method was developed to select the most relevant variables. A geometric distance-based machine learning scoring system was then implemented to derive a risk score from 0 to 100.

Results: Out of 702 patients, 29 (4.1%) met the primary outcome. We selected the 3 most relevant variables for predicting MACE, which were systolic blood pressure, the mean RR interval and the mean instantaneous heart rate. The scoring system with these 3 variables produced an area under the curve (AUC) of 0.812, and a cutoff score of 43 gave a sensitivity of 82.8% and specificity of 63.4%, while the scoring system with all the 23 variables had an AUC of 0.736, and a cutoff score of 49 gave a sensitivity of 72.4% and specificity of 63.0%. Conventional thrombolysis in myocardial infarction score and the modified early warning score achieved AUC values of 0.637 and 0.622, respectively.

Conclusions: It is observed that a few predictors outperformed the whole set of variables in predicting MACE within 72 h. We conclude that more predictors do not necessarily guarantee better prediction results. Furthermore, machine learning-based variable selection seems promising in discovering a few relevant and significant measures as predictors.

Show MeSH
Related in: MedlinePlus