A simple diagnostic scoring system for COVID-19 screening
Background: The COVID‐19 pandemic has prompted the world to make various efforts to control its spread by finding ways to diagnose COVID‐19 quickly and accurately. Early identification of COVID‐19 infection is essential, especially in hospitals with limited resources. We aimed to generate two scores...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Pakistan Medical Commission (PMC)
2022
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| Online Access: | https://repository.ugm.ac.id/278626/1/Widyastuti_KKMK.pdf |
| _version_ | 1826050291497172992 |
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| author | Widyastuti, Yunita Sari, Djayanti Kurniawaty, Juni Jufan, Akhmad Yun Sutaendy, Ketut Jaya, Purnama Wisudarti, Calcarina FR Widodo, Untung Ulfa, Dinda |
| author_facet | Widyastuti, Yunita Sari, Djayanti Kurniawaty, Juni Jufan, Akhmad Yun Sutaendy, Ketut Jaya, Purnama Wisudarti, Calcarina FR Widodo, Untung Ulfa, Dinda |
| author_sort | Widyastuti, Yunita |
| collection | UGM |
| description | Background: The COVID‐19 pandemic has prompted the world to make various efforts to control its spread by finding ways to diagnose COVID‐19 quickly and accurately. Early identification of COVID‐19 infection is essential, especially in hospitals with limited resources. We aimed to generate two scores based upon clinical and laboratory findings in patients screen for COVID-19 infection.
Methodology: This study used a retrospective cohort design that involved 705 adults (≥ 18 y old) admitted in Dr.
Sardjito Hospital and Dr. S. Hardjolukito Hospital. The patients' data collected included demographic characteristics, anamnesis on signs and symptoms, history of contact with COVID-19 patients, history of staying or visiting an endemic area, comorbidities, and laboratory and radiologic indicators. All variables with a P < 0.25 on the bivariate
test were included in a univariable logistic regression. If the P < 0.05, the variable was included in the multivariable
logistic regression with a P < 0.05 considered significant. Receiver Operating Characteristic (ROC) producing an area
under the curve (AUC) with 95% confidence intervals (CIs) was used to assess discrimination power.
Results: Two scores were generated; score in Model 1 consisted of clinical signs, basic laboratory indicators, and
chest X-ray, and in Model 2 consisted of clinical signs, chest X-ray, basic and advanced laboratory indicators, including
C-reactive protein (CRP), lactate dehydrogenase (LDH), albumin, and D-dimer. The ROC score of Model 1 was 0.801
(0.764−0. 838), which is considered good discrimination, and of Model 2 had excellent discrimination with a ROC of
0.858 (0.826−0. 891); the differences in the ROC of the two models was statistically significant (P = 0.03). The score
of Model 1 more than 5 had 85% sensitivity and 61% specificity of positive COVID-19. A score of Model 2 more than
4 had 83% sensitivity and 72% specificity for diagnosing positive COVID-19.
Conclusions: A simple score consisting of clinical symptoms and signs, and simple laboratory indicators can be used
to screen for COVID-19 infection. |
| first_indexed | 2024-03-14T00:01:34Z |
| format | Article |
| id | oai:generic.eprints.org:278626 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:01:34Z |
| publishDate | 2022 |
| publisher | Pakistan Medical Commission (PMC) |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2786262023-11-02T01:57:27Z https://repository.ugm.ac.id/278626/ A simple diagnostic scoring system for COVID-19 screening Widyastuti, Yunita Sari, Djayanti Kurniawaty, Juni Jufan, Akhmad Yun Sutaendy, Ketut Jaya, Purnama Wisudarti, Calcarina FR Widodo, Untung Ulfa, Dinda Clinical Sciences Background: The COVID‐19 pandemic has prompted the world to make various efforts to control its spread by finding ways to diagnose COVID‐19 quickly and accurately. Early identification of COVID‐19 infection is essential, especially in hospitals with limited resources. We aimed to generate two scores based upon clinical and laboratory findings in patients screen for COVID-19 infection. Methodology: This study used a retrospective cohort design that involved 705 adults (≥ 18 y old) admitted in Dr. Sardjito Hospital and Dr. S. Hardjolukito Hospital. The patients' data collected included demographic characteristics, anamnesis on signs and symptoms, history of contact with COVID-19 patients, history of staying or visiting an endemic area, comorbidities, and laboratory and radiologic indicators. All variables with a P < 0.25 on the bivariate test were included in a univariable logistic regression. If the P < 0.05, the variable was included in the multivariable logistic regression with a P < 0.05 considered significant. Receiver Operating Characteristic (ROC) producing an area under the curve (AUC) with 95% confidence intervals (CIs) was used to assess discrimination power. Results: Two scores were generated; score in Model 1 consisted of clinical signs, basic laboratory indicators, and chest X-ray, and in Model 2 consisted of clinical signs, chest X-ray, basic and advanced laboratory indicators, including C-reactive protein (CRP), lactate dehydrogenase (LDH), albumin, and D-dimer. The ROC score of Model 1 was 0.801 (0.764−0. 838), which is considered good discrimination, and of Model 2 had excellent discrimination with a ROC of 0.858 (0.826−0. 891); the differences in the ROC of the two models was statistically significant (P = 0.03). The score of Model 1 more than 5 had 85% sensitivity and 61% specificity of positive COVID-19. A score of Model 2 more than 4 had 83% sensitivity and 72% specificity for diagnosing positive COVID-19. Conclusions: A simple score consisting of clinical symptoms and signs, and simple laboratory indicators can be used to screen for COVID-19 infection. Pakistan Medical Commission (PMC) 2022-12 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/278626/1/Widyastuti_KKMK.pdf Widyastuti, Yunita and Sari, Djayanti and Kurniawaty, Juni and Jufan, Akhmad Yun and Sutaendy, Ketut and Jaya, Purnama and Wisudarti, Calcarina FR and Widodo, Untung and Ulfa, Dinda (2022) A simple diagnostic scoring system for COVID-19 screening. Anaesthesia, Pain & Intensive Care, 26 (6). pp. 785-793. ISSN 2220-5799 http://www.apicareonline.com/ 10.35975/apic.v26i6.2076 |
| spellingShingle | Clinical Sciences Widyastuti, Yunita Sari, Djayanti Kurniawaty, Juni Jufan, Akhmad Yun Sutaendy, Ketut Jaya, Purnama Wisudarti, Calcarina FR Widodo, Untung Ulfa, Dinda A simple diagnostic scoring system for COVID-19 screening |
| title | A simple diagnostic scoring system for COVID-19 screening |
| title_full | A simple diagnostic scoring system for COVID-19 screening |
| title_fullStr | A simple diagnostic scoring system for COVID-19 screening |
| title_full_unstemmed | A simple diagnostic scoring system for COVID-19 screening |
| title_short | A simple diagnostic scoring system for COVID-19 screening |
| title_sort | simple diagnostic scoring system for covid 19 screening |
| topic | Clinical Sciences |
| url | https://repository.ugm.ac.id/278626/1/Widyastuti_KKMK.pdf |
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