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Validation of Self-Assessment-Based Chest Pain Algorithm (DETAK) as An Early Identification Tool for Acute Coronary Syndrome

Heart Science Journal · Vol. 4 No. 4 (2023) · hlm. 18–23

DOI
10.21776/ub.hsj.2023.004.04.5
ISSN
2721-9984, 2721-9976
Terbit
2023-10-01
Penerbit
Universitas Brawijaya
Bahasa
en

Abstrak

BackgroundThe most common reason of prehospital delay in ACS patients is inability to pay attention to symptoms in order to act fast and effectively. Patient oriented machine learning algorithms has the opportunity to reduce the total ischemic time, that determines the clinical outcome of ACS patients.AimAssessing the accuracy of the chest pain self-assessment algorithm (DETAK) in identifying ACS.MethodThis study included seven hospitals, five PCI capable hospitals and two of non-PCI capable hospitals. The study was conducted from August 2021 to June 2022. The study included all patients with chest pain who visited the hospital and used the DETAK algorithm. Patients were interviewed after being confirmed hemodynamically stable. Patients with UAP, as well as those who died or declined to participate in this study were excluded. The area under the curve receiver operating characteristic (AUROC) was used to verify DETAK's performance in identifying SKA. We compare the DETAK algorithm's diagnosis with the definitive diagnostic based on ECG and/or troponin results.ResultsA total of 539 patients (mean age 58 years) with a higher proportion of male patients (n=424). An AUC value of 0.854 was obtained, where the cut of point accuracy of DETAK in identifying ACS for the entire sample had a sensitivity of 89.5% and a specificity of 81.2%. The algorithm's specificity decreased in certain subgroups, including type 2 diabetes (79.4%), women (77.3%), and hypertensive patients (80.9%). Algorithm reliability test obtained moderate to strong level of agreement values.ConclusionDETAK's self-assessment-based chest pain algorithm offers an excellent diagnostic performance in early identification of ACS.

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Rekan penulisOrganisasiArtikel bersama
Indra PrasetyaUniversity of Brawijaya54
Cholid Tri TjahjonoUniversity of Brawijaya41
Novi KurnianingsihUniversity of Brawijaya35
Budi SatrijoUniversity of Brawijaya34
Heny MartiniUniversity of Brawijaya28
Djanggan SargowoUniversity of Brawijaya27
Sasmojo WiditoUniversity of Brawijaya25
Yoga WaranugrahaUniversity of Brawijaya16
Wella KarolinaUniversity of Brawijaya14
Adhika Prastya WikanandaUniversity of Brawijaya11
Valerinna YogibuanaAfiliasi tidak diketahui11
Muhammad Rizki FadlanUniversity of Brawijaya9
Nikhen NoverikeUniversity of Brawijaya8
Valerinna Yogibuana Swastika PutriUniversity of Brawijaya8
Putri Annisa KamilaUniversity of Brawijaya7
Fandy HazzyUniversity of Brawijaya7
Olivia HandayaniUniversity of Brawijaya6
Fahmy RusnantaUniversity of Brawijaya6
Ratna PancasariUniversity of Brawijaya6
Ayu Asri Devi AdityawatiUniversity of Brawijaya6
Oktafin Srywati PamunaUniversity of Brawijaya5
Imelda KrisnasariUniversity of Brawijaya5
Teguh AryanugrahaUniversity of Brawijaya5
Lukitasari Ayu Galuh ArdhiUniversity of Brawijaya4
Icmi Dian RochmawatiUniversity of Brawijaya4
Anna Fuji RahimahAfiliasi tidak diketahui4
Seprian WidasmaraUniversity of Brawijaya4
Lutfi Hafiz ZunardiUniversity of Brawijaya4
Imam Mi'raj SuprayogaUniversity of Brawijaya4
Dadang HendrawanUniversity of Brawijaya3
Hayla Iqda MillisaniUniversity of Brawijaya3
Tria Yudha NugrahaUniversity of Brawijaya3

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Sumber metadata: OAI-PMH (metadata jurnal) https://heartscience.ub.ac.id/index.php/heartscience/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Validation of Self-Assessment-Based Chest Pain Algorithm (DETAK) as An Early Identification Tool for Acute Coronary Syndrome</dc:title>
	<dc:creator xml:lang="en">Nugraha, Krishna Ari</dc:creator>
	<dc:creator xml:lang="en">Rohman, Mohammad Saifur</dc:creator>
	<dc:creator xml:lang="en">Rahimah, Anna Fuji</dc:creator>
	<dc:creator xml:lang="en">Anjarwani, Setyasih</dc:creator>
	<dc:creator xml:lang="en">Rizal, Ardian</dc:creator>
	<dc:creator xml:lang="en">Astiawati, Tri</dc:creator>
	<dc:creator xml:lang="en">Adi, Andi Wahjono</dc:creator>
	<dc:creator xml:lang="en">Haryati, Lina</dc:creator>
	<dc:subject xml:lang="en">Acute coronary syndrome</dc:subject>
	<dc:subject xml:lang="en">algorithm</dc:subject>
	<dc:subject xml:lang="en">chest pain</dc:subject>
	<dc:description xml:lang="en">BackgroundThe most common reason of prehospital delay in ACS patients is inability to pay attention to symptoms in order to act fast and effectively. Patient oriented machine learning algorithms has the opportunity to reduce the total ischemic time, that determines the clinical outcome of ACS patients.AimAssessing the accuracy of the chest pain self-assessment algorithm (DETAK) in identifying ACS.MethodThis study included seven hospitals, five PCI capable hospitals and two of non-PCI capable hospitals. The study was conducted from August 2021 to June 2022. The study included all patients with chest pain who visited the hospital and used the DETAK algorithm. Patients were interviewed after being confirmed hemodynamically stable. Patients with UAP, as well as those who died or declined to participate in this study were excluded. The area under the curve receiver operating characteristic (AUROC) was used to verify DETAK's performance in identifying SKA. We compare the DETAK algorithm's diagnosis with the definitive diagnostic based on ECG and/or troponin results.ResultsA total of 539 patients (mean age 58 years) with a higher proportion of male patients (n=424). An AUC value of 0.854 was obtained, where the cut of point accuracy of DETAK in identifying ACS for the entire sample had a sensitivity of 89.5% and a specificity of 81.2%. The algorithm's specificity decreased in certain subgroups, including type 2 diabetes (79.4%), women (77.3%), and hypertensive patients (80.9%). Algorithm reliability test obtained moderate to strong level of agreement values.ConclusionDETAK's self-assessment-based chest pain algorithm offers an excellent diagnostic performance in early identification of ACS.</dc:description>
	<dc:publisher xml:lang="en">Universitas Brawijaya</dc:publisher>
	<dc:date>2023-10-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://heartscience.ub.ac.id/index.php/heartscience/article/view/6498</dc:identifier>
	<dc:identifier>10.21776/ub.hsj.2023.004.04.5</dc:identifier>
	<dc:source xml:lang="en">Heart Science Journal; Vol. 4 No. 4 (2023): The Science and Art of Caring for Critically III Patients in Intensive Cardiac Care Unit ICCU; 18-23</dc:source>
	<dc:source>2721-9984</dc:source>
	<dc:source>2721-9976</dc:source>
	<dc:source>10.21776/ub.hsj.2023.004.04</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://heartscience.ub.ac.id/index.php/heartscience/article/view/6498/376</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Krishna Ari Nugraha, Mohammad Saifur Rohman, Anna Fuji Rahimah, Setyasih Anjarwani, Ardian Rizal, Andi Wahjono Adi, Lina Haryati</dc:rights>
</oai_dc:dc>