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Volume 2021, Issue 2
  • ISSN: 0253-8253
  • EISSN: 2227-0426

Abstract

Introduction: The presence of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and its associated disease, COVID-19 has had an enormous impact on the operations of the emergency department (ED), particularly the triage area. The aim of the study was to derive and validate a prediction rule that would be applicable to Qatar’s adult ED population to predict COVID-19-positive patients.

Methods: This is a retrospective study including adult patients. The data were obtained from the electronic medical records (EMR) of the Hamad Medical Corporation (HMC) for three EDs. Data from the Hamad General Hospital ED were used to derive and internally validate a prediction rule (Q-PREDICT). The Al Wakra Hospital ED and Al Khor Hospital ED data formed an external validation set consisting of the same time frame. The variables in the model included the weekly ED COVID-19-positivity rate and the following patient characteristics: region (nationality), age, acuity, cough, fever, tachypnea, hypoxemia, and hypotension. All statistical analyses were executed with Stata 16.1 (Stata Corp). The study team obtained appropriate institutional approval.

Results: The study included 45,663 adult patients who were tested for COVID-19. Out of these, 47% (n = 21461) were COVID-19 positive. The derivation-set model had very good discrimination (c = 0.855, 95% Confidence intervals (CI) 0.847–0.861). Cross-validation of the model demonstrated that the validation-set model (c = 0.857, 95% CI 0.849–0.863) retained high discrimination.

A high Q-PREDICT score ( ≥ 13) is associated with a nearly 6-fold increase in the likelihood of being COVID-19 positive (likelihood ratio 5.9, 95% CI 5.6–6.2), with a sensitivity of 84.7% (95% CI, 84.0%–85.4%). A low Q-PREDICT ( ≤ 6) is associated with a nearly 20-fold increase in the likelihood of being COVID-19 negative (likelihood ratio 19.3, 95% CI 16.7–22.1), with a specificity of 98.7% (95% CI 98.5%–98.9%).

Conclusion: The Q-PREDICT is a simple scoring system based on information readily collected from patients at the front desk of the ED and helps to predict COVID-19 status at triage. The scoring system performed well in the internal and external validation on datasets obtained from the state of Qatar.

© 2021 Pathan, Thomas, Bhutta, Qureshi, Thomas, Moinudheen, Thomas, licensee HBKU Press.
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2021-08-11
2024-11-08
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References

  1. Kashir J, Yaqinuddin A, . Loop mediated isothermal amplification (LAMP) assays as a rapid diagnostic for COVID-19. Med Hypotheses. 2020;141:109786.
     [Google Scholar]
  2. Diaz-Quijano FA. A simple method for estimating relative risk using logistic regression. BMC Med Res Methodol. 2012;12:14.
     [Google Scholar]
  3. Cummings P, . The relative merits of risk ratios and odds ratios. Arch Pediatr Adolesc Med. 2009; 163:(5):438–45.
     [Google Scholar]
  4. Cobb JS, Seale MA, . Examining the effect of social distancing on the compound growth rate of COVID-19 at the county level (United States) using statistical analyses and a random forest machine learning model. Public Health. 2020;185:27–9.
     [Google Scholar]
  5. Schonlau M, Zou RY, . The random forest algorithm for statistical learning. Stata Journal 2020; 20:(1):3–29.
     [Google Scholar]
  6. Uddin S, Khan A, Hossain ME, Moni MA, . Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak. 2019; 19:(1):281.
     [Google Scholar]
  7. Ye SCM, . Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos Solitons Fractals. 2020;140:110210.
     [Google Scholar]
  8. Hosmer DW, Lemeshow S, . Applied logistic regression. New York: Wiley; 1989.
     [Google Scholar]
  9. Huang Y, Li W, Macheret F, Gabriel RA, Ohno-Machado L. A tutorial on calibration measurements and calibration models for clinical prediction models. J Am Med Inform Assoc. 2020; 27:(4):621–33.
     [Google Scholar]
  10. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010; 21:(1):128–38.
     [Google Scholar]
  11. Royston P, Altman DG, . Visualizing and assessing discrimination in the logistic regression model. Stat Med. 2010; 29:(24):2508–20.
     [Google Scholar]
  12. Zhang J, Yu KF, . What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA. 1998; 280:(19):1690–1.
     [Google Scholar]
  13. McNutt LA, Wu C, Xue X, Hafner JP, . Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol. 2003; 157:(10):940–3.
     [Google Scholar]
  14. Luque-Fernandez MA, Redondo-Sanchez D, Maringe C. cvauroc: command to compute cross-validated area under the curve for ROC analysis after predictive modeling for binary outcomes. Stata Journal. 2019; 19:(3):615–25.
     [Google Scholar]
  15. Horby P, Lim WS, Emberson JR, et al.. Dexamethasone in hospitalized patients with Covid-19 - preliminary report. N Engl J Med. 2020.
     [Google Scholar]
  16. Li Z, Yi Y, Luo X, Xiong N, Liu Y, Li S, et al.. Development and clinical application of a rapid IgM-IgG combined antibody test for SARS-CoV-2 infection diagnosis. J Med Virol. 2020; 92:(9):1518–24.
     [Google Scholar]
  17. Dohla M, Boesecke C, Schulte B, Diegmann C, Sib E, Richter E, et al.. Rapid point-of-care testing for SARS-CoV-2 in a community screening setting shows low sensitivity. Public Health. 2020;182:170–2.
     [Google Scholar]
  18. Khan AA, Ata F, Munir W, Yousaf Z, . Fluid replacement versus fluid restriction in COVID-19 associated hyponatremia. Cureus. 2020; 12:(7):e9059.
     [Google Scholar]
  19. Zhang W, Lu S, Zhang M, Zheng H, Huang Y, Chen S, et al.. Correlation between hyponatremia and the severity of coronavirus disease 2019. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 2020; 32:(7):774–8.
     [Google Scholar]
  20. Ghahramani S, Tabrizi R, Lankarani KB, Kashani SMA, Rezaei S, Zeidi N, et al.. Laboratory features of severe vs. nonsevere COVID-19 patients in Asian populations: a systematic review and meta-analysis. Eur J Med Res. 2020; 25:(1):30.
     [Google Scholar]
  21. Ata F, Almasri H, Sajid J, Yousaf Z, . COVID-19 presenting with diarrhoea and hyponatraemia. BMJ Case Rep. 2020; 13:(6).
     [Google Scholar]
  22. Cai X, Ma Y, Li S, Chen Y, Rong Z, Li W, . Clinical characteristics of 5 COVID-19 cases With nonrespiratory symptoms as the first manifestation in children. Front Pediatr. 2020;8:258.
     [Google Scholar]
  23. Ferrari D, Motta A, Strollo M, Banfi G, Locatelli M, . Routine blood tests as a potential diagnostic tool for COVID-19. Clin Chem Lab Med. 2020; 58:(7):1095–9.
     [Google Scholar]
  24. Fu J, Kong J, Wang W, Wu M, Yao L, Wang Z, et al.. The clinical implication of dynamic neutrophil to lymphocyte ratio and D-dimer in COVID-19: a retrospective study in Suzhou China. Thromb Res. 2020;192:3–8.
     [Google Scholar]
  25. Akbari H, Tabrizi R, Lankarani KB, Aria H, Vakili S, Asadian F, et al.. The role of cytokine profile and lymphocyte subsets in the severity of coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis. Life Sci. 2020;258:118167.
     [Google Scholar]
  26. Huang G, Kovalic AJ, Graber CJ, . Prognostic value of leukocytosis and lymphopenia for coronavirus disease severity. Emerg Infect Dis. 2020; 26:(8):1839–41.
     [Google Scholar]
  27. Wang M, Zhang J, Ye D, Wang Z, Liu J, He Hm, et al. Time-dependent changes in the clinical characteristics and prognosis of hospitalized COVID-19 patients in Wuhan, China: a retrospective study. Clin Chim Acta. 2020;510:220–7.
     [Google Scholar]
  28. Blomme S, Smets L, Van Ranst M, Boeckx N, Van Laer C. The influence of COVID-19 on routine hematological parameters of hospitalized patients. Acta Clin Belg. 2020:1–6.
     [Google Scholar]
  29. Mo J, Liu J, Wu S, Lü A, Xiao L, Chen D, et al. Predictive role of clinical features in patients with coronavirus disease 2019 for severe disease. Zhong Nan Da Xue Xue Bao Yi Xue Ban 2020; 45:(5):536–41.
     [Google Scholar]
  30. Sinha P, Calfee CS, Cherian S, Brealey D, Cutler S, King C, et al.. Prevalence of phenotypes of acute respiratory distress syndrome in critically ill patients with COVID-19: a prospective observational study. Lancet Respir Med. 2020; 8:(12):1209–18.
     [Google Scholar]
  31. Chen J, Ye J, Li H, Xia Z, Yan H, . Changes in the clinical characteristics of 62 patients who died from coronavirus disease 2019. Biomed Res Int. 2020;2020:3280908.
     [Google Scholar]
  32. Han R, Huang L, Jiang H, Dong J, Peng H, Zhang D, . Early clinical and CT manifestations of coronavirus disease 2019 (COVID-19) pneumonia. AJR Am J Roentgenol. 2020; 215:(2):338–43.
     [Google Scholar]
  33. Ciceri F, Castagna A, Rovere-Querini P, De Cobelli F, Ruggeri A, Galli L, et al. Early predictors of clinical outcomes of COVID-19 outbreak in Milan, Italy. Clin Immunol. 2020;217:108509.
     [Google Scholar]
  34. Aggarwal S, Garcia-Telles N, Aggarwal G, Lavie C, Lippi G, Henry BM. Clinical features, laboratory characteristics, and outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19): early report from the United States. Diagnosis (Berl). 2020; 7:(2):91–6.
     [Google Scholar]
  35. Kuzan TY, Murzoglu Altintoprak K, Ciftci HOErgul UUnal Ozdemir NBBulut M et al. A comparison of clinical, laboratory and chest CT findings of laboratory-confirmed and clinically diagnosed COVID-19 patients at first admission. Diagn Interv Radiol. 2020.
     [Google Scholar]
  36. Ghayda RA, Lee J, Lee JY, Kim DK, Lee KH, Hong SH, et al.. Correlations of clinical and laboratory characteristics of cOVID-19: a systematic review and meta-analysis. Int J Environ Res Public Health. 2020; 17:(14).
     [Google Scholar]
  37. Song L, Langfelder P, Horvath S, . Random generalized linear model: a highly accurate and interpretable ensemble predictor. BMC Bioinformatics. 2013;14:5.
     [Google Scholar]
  38. Mashayekhi M, Gras R, . Rule extraction from random forest. 28th Canadian Conference on Artificial Intelligence; 2015 June 2015; Halifax: Springer International Publishing. p. 223–37.
     [Google Scholar]
  39. Patel R, Babady E, Theel ES, Storch GA, Pinsky BA, St George K, et al.. Report from the American Society for Microbiology COVID-19 International Summit, 23 March 2020: value of diagnostic testing for SARS-CoV-2/COVID-19. mBio. 2020; 11:(2).
     [Google Scholar]
  40. Haydel MJ, Preston CA, Mills TJ, Luber S, Blaudeau E, DeBlieux PM, . Indications for computed tomography in patients with minor head injury. N Engl J Med. 2000; 343:(2):100–5.
     [Google Scholar]
/content/journals/10.5339/qmj.2021.18
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Qatar Prediction Rule Using ED Indicators of COVID-19 at Triage
Qatar Medical Journal 2021, 18 (2021); https://doi.org/10.5339/qmj.2021.18
/content/journals/10.5339/qmj.2021.18
/content/journals/10.5339/qmj.2021.18
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  • Article Type: Research Article
Keyword(s): COVID positive, COVID-19, emergency department, predict, Qatar and triage

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