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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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ROC curves of machine learning scores, TIMI and MEWS scores in predicting MACE within 72 h.
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Figure 4: ROC curves of machine learning scores, TIMI and MEWS scores in predicting MACE within 72 h.

Mentions: The performance of the intelligent scoring system with different numbers of selected variables is summarized in Table 5. The order of variables was determined from Figure 3 where each variable received its ranking with the proposed variable selection algorithm as shown in Figure 1. The combination of these variables therefore may not reflect any clinical meanings. The ML score with the top three selected variables was able to achieve the highest AUC among all other variable combinations. Figure 4 illustrates ROC curves produced by the ML score with the top three variables and the ML score with all 23 variables, TIMI score and MEWS score. A cut-off score was determined by the point that was nearest to the upper-left corner of the ROC curve. The ML score with top three variables produced an AUC of 0.812 (95% CI: 0.716 - 0.908) and a cutoff score of 43 gave a sensitivity of 82.8% (95% CI: 69.0% - 96.5%) and specificity of 63.4% (95% CI: 59.8% - 67.0%), while the ML score with all 23 variables had an AUC of 0.736 (95% CI: 0.630 - 0.841) and a cutoff score of 49 gave a sensitivity of 72.4% (95% CI: 56.1% - 88.7%) and specificity of 63.0% (95% CI: 59.3% - 66.6%). The TIMI score and the MEWS score achieved AUC values of 0.637 (95% CI: 0.526 - 0.747) and 0.622 (95% CI: 0.511 - 0.733), respectively.


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)

ROC curves of machine learning scores, TIMI and MEWS scores in predicting MACE within 72 h.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: ROC curves of machine learning scores, TIMI and MEWS scores in predicting MACE within 72 h.
Mentions: The performance of the intelligent scoring system with different numbers of selected variables is summarized in Table 5. The order of variables was determined from Figure 3 where each variable received its ranking with the proposed variable selection algorithm as shown in Figure 1. The combination of these variables therefore may not reflect any clinical meanings. The ML score with the top three selected variables was able to achieve the highest AUC among all other variable combinations. Figure 4 illustrates ROC curves produced by the ML score with the top three variables and the ML score with all 23 variables, TIMI score and MEWS score. A cut-off score was determined by the point that was nearest to the upper-left corner of the ROC curve. The ML score with top three variables produced an AUC of 0.812 (95% CI: 0.716 - 0.908) and a cutoff score of 43 gave a sensitivity of 82.8% (95% CI: 69.0% - 96.5%) and specificity of 63.4% (95% CI: 59.8% - 67.0%), while the ML score with all 23 variables had an AUC of 0.736 (95% CI: 0.630 - 0.841) and a cutoff score of 49 gave a sensitivity of 72.4% (95% CI: 56.1% - 88.7%) and specificity of 63.0% (95% CI: 59.3% - 66.6%). The TIMI score and the MEWS score achieved AUC values of 0.637 (95% CI: 0.526 - 0.747) and 0.622 (95% CI: 0.511 - 0.733), respectively.

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