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Diagnostic Model of COVID-19 Infection Based on the Combination of Clinical Symptoms, Chest Radiography and Laboratory Test

ACTA MEDICA INDONESIANA · Vol. 54 No. 3 (2022)

ISSN
2338-2732, 0125-9326
Published
2022-09-24
Publisher
The Indonesian Society of Internal Medicine
Language
eng

Abstract

Background: COVID-19 is an infection caused by SARS-COV 2.For screening the patient, Rapid antigen for COVID-19 is used with a high diagnostic value. However, there are still some cases of false-negative even with clinical symptoms suggesting COVID-19. Undetected COVID-19 patients certainly will increase transmission. A simple and practical diagnostic model, using determining factors, is required to guide physicians through a quicker decision making process, especially when deciding the need for the isolation rooms for patients with COVID-like symptoms. Methods: This study is a cross-sectional study. The study was conducted at CiptoMangunkusumo Hospital, Jakarta.History of contact with COVID-19, clinical symptoms, laboratory examination, and chest radiograph data were taken from medical records. Bivariate and multivariate analyses were conducted to assess the effect sizes of patient factors on the diagnostic results.ROCcurve and Hosmer-Lemeshow calibration was used to make the scoring. Results: There were 187 patients with the majority of subjects in the age group 20 breaths/minute, leucocyte ≤ 10.000 cells/mLand typical chest radiography. The area under the curve for this model was 0,777 (CI95% (0,706-0,847), P<0,001). The probability was 82% with a cut-off point ≥ 4. Conclusion: Determinant models based on the combination of contact history, presence or history of fever, dyspnea, leucocyte count ≤ 10.000 cells/mL and typical chest radiography provides good accuracy to aid physicians in managing isolation room needs for patients with suspected COVID-19.

Keywords

Collaboration network of the authors

40 authors · 271 co-authorship links · showing the 40 most connected

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  • University of Indonesia
  • Hasanuddin University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Hamzah ShatriAffiliation unknown14
Kuntjoro HarimurtiAffiliation unknown13
Mira YuliantiAffiliation unknown10
Ikhwan RinaldiAffiliation unknown9
Muhammad Khifzhon AzwarAffiliation unknown7
Rahmi IstantiAffiliation unknown7
Rudi PutrantoAffiliation unknown7
Noto DwimartutieAffiliation unknown6
Jessica MarsigitAffiliation unknown6
Eric Daniel TendaAffiliation unknown6
Roza MulyanaAffiliation unknown5
Rensa RensaAffiliation unknown5
Aulia RizkaAffiliation unknown5
Eka Dian SafitriAffiliation unknown5
Siti Rizny F SaldiAffiliation unknown5
Ari Fahrial SyamAffiliation unknown5
Sally Aman NasutionAffiliation unknown5
Imam SubektiAffiliation unknown5
Murdani AbdullahAffiliation unknown5
Em YunirUniversity of Indonesia5
Nur RiviatiAffiliation unknown4
Dyah PurnamasariAffiliation unknown4
Muhammad K AzwarAffiliation unknown4
Robert SintoAffiliation unknown4
Iris RengganisAffiliation unknown4
Juferdy KurniawanAffiliation unknown4
Purwita W LaksmiAffiliation unknown3
Tirza Z TaminAffiliation unknown3
Rudy HidayatAffiliation unknown3
Esthika DewiastyAffiliation unknown3
Muhammad YaminAffiliation unknown3
Simon SalimAffiliation unknown3
Ika FitrianaHasanuddin University3
Cleopas M RumendeAffiliation unknown3
Dadang MakmunAffiliation unknown3

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://www.actamedindones.org/index.php/ijim/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Diagnostic Model of COVID-19 Infection Based on the Combination of Clinical Symptoms, Chest Radiography and Laboratory Test</dc:title>
	<dc:creator>Herikurniawan, Herikurniawan</dc:creator>
	<dc:creator>Rumende, Cleopas Martin</dc:creator>
	<dc:creator>Setiati, Siti</dc:creator>
	<dc:creator>Kumalawati, July</dc:creator>
	<dc:creator>Deslanthy, Belinda Thania</dc:creator>
	<dc:subject xml:lang="en-US">COVID-19</dc:subject>
	<dc:subject xml:lang="en-US">SARS-CoV 2 diagnostic model</dc:subject>
	<dc:subject xml:lang="en-US">scoring</dc:subject>
	<dc:subject xml:lang="en-US">internal medicine</dc:subject>
	<dc:subject xml:lang="en-US">pulmonology</dc:subject>
	<dc:subject xml:lang="en-US">infection</dc:subject>
	<dc:description xml:lang="en-US">Background: COVID-19 is an infection caused by SARS-COV 2.For screening the patient, Rapid antigen for COVID-19 is used with a high diagnostic value. However, there are still some cases of false-negative even with clinical symptoms suggesting COVID-19. Undetected COVID-19 patients certainly will increase  transmission. A simple and practical diagnostic model, using determining factors, is required to guide physicians through a quicker decision making process, especially when deciding the need for the isolation rooms for patients with COVID-like symptoms. Methods: This study is a cross-sectional study. The study was conducted at CiptoMangunkusumo Hospital, Jakarta.History of contact with COVID-19, clinical symptoms, laboratory examination, and chest radiograph data were taken from medical records. Bivariate and multivariate analyses were conducted to assess the effect sizes of patient factors on the diagnostic results.ROCcurve and Hosmer-Lemeshow calibration was used to make the scoring. Results: There were 187 patients with the majority of subjects in the age group &amp;lt; 60 years old. The selected variables in this scoring systemwere contact history,fever/history of fever, dyspnea with respiratory rate &amp;gt;20 breaths/minute, leucocyte ≤ 10.000 cells/mLand typical chest radiography. The area under the curve for this model was 0,777 (CI95% (0,706-0,847), P&amp;lt;0,001). The probability was 82% with a cut-off point ≥ 4. Conclusion: Determinant models based on the combination of contact history, presence or history of fever, dyspnea, leucocyte count ≤ 10.000 cells/mL and typical chest radiography provides good accuracy to aid physicians in managing isolation room needs for patients with suspected COVID-19.</dc:description>
	<dc:publisher xml:lang="en-US">The Indonesian Society of Internal Medicine</dc:publisher>
	<dc:date>2022-09-24</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">cross sectional; consecutive sampling</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>http://www.actamedindones.org/index.php/ijim/article/view/2094</dc:identifier>
	<dc:source xml:lang="en-US">Acta Medica Indonesiana; Vol. 54 No. 3 (2022): Acta Medica Indonesiana; 419</dc:source>
	<dc:source>2338-2732</dc:source>
	<dc:source>0125-9326</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>http://www.actamedindones.org/index.php/ijim/article/view/2094/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Herikurniawan Herikurniawan, Cleopas Martin Rumende, Siti Setiati, July Kumalawati, Belinda Thania Deslanthy</dc:rights>
</oai_dc:dc>