A biomarker based severity progression indicator for COVID-19: the Kuwait prognosis indicator score

Mohammad H Jamal 1 2, Suhail A Doi 3 4, Sarah AlYouha 1, Sulaiman Almazeedi 1, Mohannad Al-Haddad 1, Ali Al-Muhaini 1, Fahad Al-Ghimlas 5, Muhammad E Chowdhury 6, Salman Al-Sabah 1 2

Affiliations

01 December 2020

-

doi: 10.1080/1354750X.2020.1841296

Abstract

Background: COVID-19 is a worldwide pandemic that is mild in most patients but can result in a pneumonia like illness with progression to acute respiratory distress syndrome and death. Predicting the disease severity at time of diagnosis can be helpful in prioritizing hospital admission and resources.

Methods: We prospectively recruited 1096 consecutive patients of whom 643 met the inclusion criterion with COVID-19 from Jaber Hospital, a COVID-19 facility in Kuwait, between 24 February and 20 April 2020. The primary endpoint of interest was disease severity defined algorithmically. Predefined risk variables were collected at the time of PCR based diagnosis of the infection. Prognostic model development used 5-fold cross-validated regularized logit regression. The model was externally validated against data from Wuhan, China.

Results: There were 643 patients with clinical course data of whom 94 developed severe COVID-19. In the final model, age, CRP, procalcitonin, lymphocyte percentage, monocyte percentages and serum albumin were independent predictors of a more severe illness course. The final prognostic model demonstrated good discrimination, and both discrimination and calibration were confirmed with an external dataset.

Conclusion: We developed and validated a simple score calculated at time of diagnosis that can predict patients with severe COVID-19 disease reliably and that has been validated externally. The KPI score calculator is now available online at covidkscore.com.

Keywords: COVID-19; adverse outcome; health policy; mortality; procalcitonin; prognosis.

Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1. Patient flow diagram.

Figure 1. Patient flow diagram.

Figure 2. Area under the receiver operating characteristic curve for the severity score in the training (top left), internal validation (top right) and external validation (bottom) cohorts.

Figure 2. Area under the receiver operating characteristic curve for the severity score in the training (top left), internal validation (top right) and external validation (bottom) cohorts.

Figure 3. Calibration plots for the severity score in the internal (left) and external (right) validation cohorts.

Figure 3. Calibration plots for the severity score in the internal (left) and external (right) validation cohorts.

Similar articles

Diagnostic yield of bacteriological tests and predictors of severe outcome in adult patients with COVID-19 presenting to the emergency department.

Kaal A, Snel L, Dane M, van Burgel N, Ottens T, Broekman W, El Bouazzaoui L, Kolfschoten N, Schippers E, Steyerberg E, Meziyerh S, van Nieuwkoop C.Emerg Med J. 2021 Sep;38(9):685-691. doi: 10.1136/emermed-2020-211027. Epub 2021 Jul 21.PMID: 34289966 Free PMC article.

Interleukin-6, procalcitonin and neutrophil-to-lymphocyte ratio: Potential immune-inflammatory parameters to identify severe and fatal forms of COVID-19.

Sayah W, Berkane I, Guermache I, Sabri M, Lakhal FZ, Yasmine Rahali S, Djidjeli A, Lamara Mahammed L, Merah F, Belaid B, Berkani L, Lazli NZ, Kheddouci L, Kadi A, Ouali M, Khellafi R, Mekideche D, Kheliouen A, Hamidi RM, Ayoub S, Raaf NB, Derrar F, Gharnaout M, Allam I, Djidjik R.Cytokine. 2021 May;141:155428. doi: 10.1016/j.cyto.2021.155428. Epub 2021 Jan 15.PMID: 33550165 Free PMC article.

Clinical Characteristics of Patients with Severe Pneumonia Caused by the SARS-CoV-2 in Wuhan, China.

Wang Y, Zhou Y, Yang Z, Xia D, Hu Y, Geng S.Respiration. 2020;99(8):649-657. doi: 10.1159/000507940. Epub 2020 Aug 25.PMID: 32841948 Free PMC article.

Laboratory abnormalities in children with mild and severe coronavirus disease 2019 (COVID-19): A pooled analysis and review.

Henry BM, Benoit SW, de Oliveira MHS, Hsieh WC, Benoit J, Ballout RA, Plebani M, Lippi G.Clin Biochem. 2020 Jul;81:1-8. doi: 10.1016/j.clinbiochem.2020.05.012. Epub 2020 May 27.PMID: 32473151 Free PMC article. Review.

Association of elevated inflammatory markers and severe COVID-19: A meta-analysis.

Ji P, Zhu J, Zhong Z, Li H, Pang J, Li B, Zhang J.Medicine (Baltimore). 2020 Nov 20;99(47):e23315. doi: 10.1097/MD.0000000000023315.PMID: 33217868 Free PMC article. Review.

References

  1.  
    1. Ahrens, A., Hansen, C.B., and Schaffer, M.E., 2020. Lassopack: model selection and prediction with regularized regression in Stata. The stata journal: promoting communications on statistics and stata, 20 (1), 176–235.
  2.  
    1. Chen, J., et al. , 2020. Clinical progression of patients with COVID-19 in Shanghai, China. Journal of infection, 80 (5), e1–e6. - PMC - PubMed
  3.  
    1. Dong, E., Du, H., and Gardner, L., 2020. An interactive web-based dashboard to track COVID-19 in real time. The lancet. Infectious diseases, 20 (5), 533–534. - PMC - PubMed
  4.  
    1. Dudel, C., et al. , 2020. Monitoring trends and differences in COVID-19 case fatality rates using decomposition methods: contributions of age structure and age-specific fatality. PLOS one, 15 (9), e0238904. doi:10.1371/journal.pone.0238904 - DOI - PMC - PubMed
  5.  
    1. Ensor, J., Snell, K.I.E., and Martin, E.C., 2018. “PMCALPLOT: Stata module to produce calibration plot of prediction model performance,” Statistical Software Components S458486, Boston College Department of Economics, revised 04 Jan 2020.
  6.  
    1. Gong, J., et al. , 2020. A tool to early predict severe corona virus disease 2019 (COVID-19): a multicenter study using the risk nomogram in Wuhan and Guangdong, China. Clinical infectious diseases, 71 (15), 833–840. - PMC - PubMed
  7.  
    1. Hu, R., et al. , 2020. Procalcitonin levels in COVID-19 patients. International journal of antimicrobial agents, 56 (2), 106051. - PMC - PubMed
  8.  
    1. Mehta, P., et al. , 2020. COVID-19: consider cytokine storm syndromes and immunosuppression. The lancet, 395 (10229), 1033–1034. - PMC - PubMed
  9.  
    1. Richardson, S., the Northwell COVID-19 Research Consortium ., et al., 2020. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA, 323 (20), 2052–2059. - PMC - PubMed
  10.  
    1. Schuetz, P., Albrich, W., and Mueller, B., 2011. Procalcitonin for diagnosis of infection and guide to antibiotic decisions: past, present and future. BMC medicine, 9, 107. - PMC - PubMed
  11.  
    1. Shi, Y., et al. , 2020. Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan. Critical care, 24 (1), 108. - PMC - PubMed
  12.  
    1. Tan, L., et al. , 2020. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal transduction and targeted therapy, 5 (1), 33. - PMC - PubMed
  13.  
    1. Violi,F., et al. , 2020. Is albumin predictor of mortality in COVID-19? Antioxidants & redox signaling. doi:10.1089/ars.2020.8142 - DOI - PubMed
  14.  
    1. Wang, L., 2020. C-reactive protein levels in the early stage of COVID-19. Medecine et maladies infectieuses, 50 (4), 332–334. - PMC - PubMed
  15.  
    1. Wynants, L., et al. , 2020. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ, 369, m1328. - PMC - PubMed
  16.  
    1. Yan, L., et al. , 2020. An interpretable mortality prediction model for COVID-19 patients. Nature machine intelligence, 1–6.doi:10.1038/s42256-020-0180-7 - DOI
  17.  
    1. Zhu, N., et al., 2020. A novel coronavirus from patients with pneumonia in China, 2019. The new England journal of medicine, 382 (8), 727–733. - PMC - PubMed

Page Navigation