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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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Related in: MedlinePlus

Individual variables and their corresponding occurrences in the ensemble for variable selection. The occurrence indicates the total number of appearance for a single variable in 500 random forest-based variable selectors. Therefore, the upper bound of the occurrence is 500.
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Figure 3: Individual variables and their corresponding occurrences in the ensemble for variable selection. The occurrence indicates the total number of appearance for a single variable in 500 random forest-based variable selectors. Therefore, the upper bound of the occurrence is 500.

Mentions: Figure 3 presents the 23 individual variables and their corresponding occurrences in the ensemble for variable selection. Systolic BP (SBP), avHR, aRR, diastolic BP (DBP), triangular index (TI), LF/HF, HF power norm, and LF power norm were the eight top-ranked predictors associated with the primary outcome. These selected variables were fed into the intelligent scoring system[17] for risk prediction. Twenty-three variables consisting of 15 HRV parameters and 8 clinical signs are shown in Table 4. A predictor is considered significant if it has a p-value of <0.05. Temperature, oxygen saturation and pain score (clinical signs), and STD, sdHR, RMSSD, pNN50, NN50, TINN, HF power and Total power of HRV parameters were not considered statistically significant. As observed in Figure 3 and Table 4, all eight top-ranked variables were significant in terms of p-value. Ultimately, these eight variables were chosen for model building and analysis.


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)

Individual variables and their corresponding occurrences in the ensemble for variable selection. The occurrence indicates the total number of appearance for a single variable in 500 random forest-based variable selectors. Therefore, the upper bound of the occurrence is 500.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Individual variables and their corresponding occurrences in the ensemble for variable selection. The occurrence indicates the total number of appearance for a single variable in 500 random forest-based variable selectors. Therefore, the upper bound of the occurrence is 500.
Mentions: Figure 3 presents the 23 individual variables and their corresponding occurrences in the ensemble for variable selection. Systolic BP (SBP), avHR, aRR, diastolic BP (DBP), triangular index (TI), LF/HF, HF power norm, and LF power norm were the eight top-ranked predictors associated with the primary outcome. These selected variables were fed into the intelligent scoring system[17] for risk prediction. Twenty-three variables consisting of 15 HRV parameters and 8 clinical signs are shown in Table 4. A predictor is considered significant if it has a p-value of <0.05. Temperature, oxygen saturation and pain score (clinical signs), and STD, sdHR, RMSSD, pNN50, NN50, TINN, HF power and Total power of HRV parameters were not considered statistically significant. As observed in Figure 3 and Table 4, all eight top-ranked variables were significant in terms of p-value. Ultimately, these eight variables were chosen for model building and analysis.

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