Development of a clinical risk score to predict death in patients with COVID-19

Ghadeer Alhamar 1 2, Ernesto Maddaloni 3, Abdullah Al Shukry 4, Salman Al-Sabah 5 6, Mohannad Al-Haddad 5, Sarah Al-Youha 5, Mohammed Jamal 5 6, Sulaiman Almazeedi 5, Abdullah A Al-Shammari 2 7, Mohamed Abu-Farha 2, Jehad Abubaker 2, Abdulnabi T Alattar 2 8, Ebaa AlOzairi 2, Francesco Alessandri 9, Luca D'Onofrio 9, Gaetano Leto 10, Carlo Maria Mastroianni 9, Carmen Mignogna 9, Giuseppe Pascarella 11, Francesco Pugliese 9, Hamad Ali 2 12, Fahd Al Mulla 2, Raffaella Buzzetti 3, Paolo Pozzilli 1

Affiliations

01 July 2022

-

doi: 10.1002/dmrr.3526

Abstract

Objective: To build a clinical risk score to aid risk stratification among hospitalised COVID-19 patients.

Methods: The score was built using data of 417 consecutive COVID-19 in patients from Kuwait. Risk factors for COVID-19 mortality were identified by multivariate logistic regressions and assigned weighted points proportional to their beta coefficient values. A final score was obtained for each patient and tested against death to calculate an Receiver-operating characteristic curve. Youden's index was used to determine the cut-off value for death prediction risk. The score was internally validated using another COVID-19 Kuwaiti-patient cohort of 923 patients. External validation was carried out using 178 patients from the Italian CoViDiab cohort.

Results: Deceased COVID-19 patients more likely showed glucose levels of 7.0-11.1 mmol/L (34.4%, p < 0.0001) or >11.1 mmol/L (44.3%, p < 0.0001), and comorbidities such as diabetes and hypertension compared to those who survived (39.3% vs. 20.4% [p = 0.0027] and 45.9% vs. 26.6% [p = 0.0036], respectively). The risk factors for in-hospital mortality in the final model were gender, nationality, asthma, and glucose categories (<5.0, 5.5-6.9, 7.0-11.1, or 11.1 > mmol/L). A score of ≥5.5 points predicted death with 75% sensitivity and 86.3% specificity (area under the curve (AUC) 0.901). Internal validation resulted in an AUC of 0.826, and external validation showed an AUC of 0.687.

Conclusion: This clinical risk score was built with easy-to-collect data and had good probability of predicting in-hospital death among COVID-19 patients.

Keywords: COVID-19; clinical risk score; comorbidities; glucose control; hyperglycemia; intensive care.

Conflict of interest statement

The authors declare no competing interests.

Similar articles

[Combined prognostic value of serum lactic acid, procalcitonin and severity score for short-term prognosis of septic shock patients].

Hao C, Hu Q, Zhu L, Xu H, Zhang Y.Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2021 Mar;33(3):281-285. doi: 10.3760/cma.j.cn121430-20201113-00715.PMID: 33834968 Chinese.

[Risk factors for death in elderly patients admitted to intensive care unit after elective abdominal surgery: a consecutive 5-year retrospective study].

Li S, He T, Shen F, Wang D, Liu X, Qin J, Xiao C, Li W, Li Q, Gao D.Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2021 Dec;33(12):1453-1458. doi: 10.3760/cma.j.cn121430-20210804-00118.PMID: 35131012 Chinese.

[Combined predictive value of the risk factors influencing the short-term prognosis of sepsis].

Chen R, Zhou X, Rui Q, Wang X.Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2020 Mar;32(3):307-312. doi: 10.3760/cma.j.cn121430-20200306-00218.PMID: 32385994 Chinese.

[Clinical predictive value of short-term dynamic changes in platelet counts for prognosis of sepsis patients in intensive care unit: a retrospective cohort study in adults].

Zhou Z, Xie Y, Feng T, Zhang X, Zhang Y, Jin W, Tian R, Wang R.Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2020 Mar;32(3):301-306. doi: 10.3760/cma.j.cn121430-20190909-00069.PMID: 32385993 Chinese.

[Logistic regression analysis of death risk factors of patients with severe and critical coronavirus disease 2019 and their predictive value].

Hu K, Li B.Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2020 May;32(5):544-547. doi: 10.3760/cma.j.cn121430-20200507-00364.PMID: 32576344 Chinese.

Cited by

A wastewater-based risk index for SARS-CoV-2 infections among three cities on the Canadian Prairie.

Asadi M, Oloye FF, Xie Y, Cantin J, Challis JK, McPhedran KN, Yusuf W, Champredon D, Xia P, De Lange C, El-Baroudy S, Servos MR, Jones PD, Giesy JP, Brinkmann M.Sci Total Environ. 2023 Jun 10;876:162800. doi: 10.1016/j.scitotenv.2023.162800. Epub 2023 Mar 11.PMID: 36914129 Free PMC article.

Prognostic models in COVID-19 infection that predict severity: a systematic review.

Buttia C, Llanaj E, Raeisi-Dehkordi H, Kastrati L, Amiri M, Meçani R, Taneri PE, Ochoa SAG, Raguindin PF, Wehrli F, Khatami F, Espínola OP, Rojas LZ, de Mortanges AP, Macharia-Nimietz EF, Alijla F, Minder B, Leichtle AB, Lüthi N, Ehrhard S, Que YA, Fernandes LK, Hautz W, Muka T.Eur J Epidemiol. 2023 Apr;38(4):355-372. doi: 10.1007/s10654-023-00973-x. Epub 2023 Feb 25.PMID: 36840867 Free PMC article. Review.

Comorbid Asthma Increased the Risk for COVID-19 Mortality in Asia: A Meta-Analysis.

Shi L, Ren J, Wang Y, Feng H, Liu F, Yang H.Vaccines (Basel). 2022 Dec 30;11(1):89. doi: 10.3390/vaccines11010089.PMID: 36679934 Free PMC article. Review.

Biochemical and hematological factors associated with COVID-19 severity among Gabonese patients: A retrospective cohort study.

N'dilimabaka N, Mounguegui DM, Lekana-Douki SE, Yattara MK, Obame-Nkoghe J, Longo-Pendy NM, Koumba IPK, Mve-Ella OLB, Moukouama SK, Dzembo CE, Bolo LY, Biyie-Bi-Ngoghe P, Mangouka GL, Nzenze JR, Lekana-Douki JB.Front Cell Infect Microbiol. 2022 Dec 23;12:975712. doi: 10.3389/fcimb.2022.975712. eCollection 2022.PMID: 36619758 Free PMC article.

Bedside Scoring System for Predicting Adverse Outcomes Among Patients Suffering From SARS-CoV-2 Infection.

Woodayagiri S, Moorthy S, Bhaskar E, Marappa L.Cureus. 2022 Nov 29;14(11):e32009. doi: 10.7759/cureus.32009. eCollection 2022 Nov.PMID: 36589201 Free PMC article.

KMEL References

https://pubmed.ncbi.nlm.nih.gov/

References

  1.  
    1. Lai CC, Shih TP, Ko WC. Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and coronavirus disease‐2019 (COVID‐19): the epidemic and the challenges. Int J Antimicrob Agents. 2020;55(3):105924. 10.1016/j.ijantimicag.2020.105924 - DOI - PMC - PubMed
  2.  
    1. Yuki K, Fujiogi M, Koutsogiannaki S. COVID‐19 pathophysiology: a review. Clin Immunol. 2020;215:108427. 10.1016/j.clim.2020.108427 - DOI - PMC - PubMed
  3.  
    1. Shi Y, Wang G, Cai Xp, et al. An overview of COVID‐19. J Zhejiang Univ ‐ Sci B. 2020;21(5):343‐360. 10.1631/jzus.b2000083 - DOI - PMC - PubMed
  4.  
    1. Zhang X, Li S, Niu S. ACE2 and COVID‐19 and the resulting ARDS. Postgrad Med. 2020;96(1137):403‐407. 10.1136/postgradmedj-2020-137935 - DOI - PubMed
  5.  
    1. Asselah T, Durantel D, Pasmant E. COVID‐19: discovery, diagnostics and drug development. J Hepatol. 2021;74(1):168‐184. 10.1016/j.jhep.2020.09.031 - DOI - PMC - PubMed
  6.  
    1. Al‐Shammari AA, Ali H, Alahmad B, et al. The impact of strict public health measures on COVID‐19 transmission in developing countries: the case of Kuwait. Front Public Health. 2021;9:757419. 10.3389/fpubh.2021.757419 - DOI - PMC - PubMed
  7.  
    1. Ragab D, Salah Eldin H, Taeimah M. The COVID‐19 cytokine storm; what we know so far. Front Immunol. 2020;11:1446. 10.3389/fimmu.2020.01446 - DOI - PMC - PubMed
  8.  
    1. Alshukry A, Ali H, Ali Y, et al. Clinical characteristics of coronavirus disease 2019 (COVID‐19) patients in Kuwait. PLoS One. 2020;15(11):e0242768. 10.1371/journal.pone.0242768 - DOI - PMC - PubMed
  9.  
    1. Xie J, Tong Z, Guan X. Clinical characteristics of patients who died of coronavirus disease 2019 in China. JAMA Netw Open. 2020;3(4):e205619. 10.1001/jamanetworkopen.2020.5619 - DOI - PMC - PubMed
  10.  
    1. Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID‐19): A Review. JAMA; 2020. - PubMed
  11.  
    1. Alahmad B, Al‐Shammari AA, Bennakhi A. Fasting blood glucose and COVID‐19 severity: nonlinearity matters. Diabetes Care. 2020;43(12):3113‐3116. 10.2337/dc20-1941 - DOI - PMC - PubMed
  12.  
    1. Abu‐Farha M, Al‐Mulla F, Thanaraj TA, et al. Impact of diabetes in patients diagnosed with COVID‐19. Front Immunol. 2020;11:576818. 10.3389/fimmu.2020.576818 - DOI - PMC - PubMed
  13.  
    1. Ali H, Alshukry A, Marafie SK, et al. Outcomes of COVID‐19: disparities by ethnicity. Infect Genet Evol. 2021;87:104639. 10.1016/j.meegid.2020.104639 - DOI - PMC - PubMed
  14.  
    1. Bourgonje AR, Abdulle AE, Timens W, et al. Angiotensin‐converting enzyme 2 (ACE2), SARS‐CoV‐2 and the pathophysiology of coronavirus disease 2019 (COVID‐19). J Pathol. 2020;251(3):228‐248. 10.1002/path.5471 - DOI - PMC - PubMed
  15.  
    1. Al‐Mulla F, Mohammad A, Al Madhoun A, et al. ACE2 and FURIN variants are potential predictors of SARS‐CoV‐2 outcome: a time to implement precision medicine against COVID‐19. Heliyon. 2021;7(2):e06133. 10.1016/j.heliyon.2021.e06133 - DOI - PMC - PubMed
  16.  
    1. Haddad D, John SE, Mohammad A, et al. SARS‐CoV‐2: possible recombination and emergence of potentially more virulent strains. PLoS One. 2021;16(5):e0251368. 10.1371/journal.pone.0251368 - DOI - PMC - PubMed
  17.  
    1. Ali H, Alterki A, Sindhu S, et al. Robust antibody levels in both diabetic and non‐diabetic individuals after BNT162b2 mRNA COVID‐19 vaccination. Front Immunol. 2021;12:752233. 10.3389/fimmu.2021.752233 - DOI - PMC - PubMed
  18.  
    1. Ali H, Alahmad B, Al‐Shammari AA, et al. Previous COVID‐19 infection and antibody levels after vaccination. Front Public Health. 1964;9:2021. 10.3389/fpubh.2021.778243 - DOI - PMC - PubMed
  19.  
    1. Shehab M, Abu‐Farha M, Alrashed F, et al. Immunogenicity of BNT162b2 vaccine in patients with inflammatory bowel disease on infliximab combination therapy: a multicenter prospective study. J Clin Med. 2021;10(22):5362. 10.3390/jcm10225362 - DOI - PMC - PubMed
  20.  
    1. Shehab M, Alrashed F, Alfadhli A, et al. Serological response to BNT162b2 and ChAdOx1 nCoV‐19 vaccines in patients with inflammatory bowel disease on biologic therapies. Vaccines. 2021;9(12):1471. 10.3390/vaccines9121471 - DOI - PMC - PubMed
  21.  
    1. Remap‐Cap Investigators, Gordon AC, Mouncey PR , et al. Interleukin‐6 Receptor Antagonists in Critically Ill Patients with Covid‐19. N Engl J Med; 2021. - PMC - PubMed
  22.  
    1. Alshukry A, Bu Abbas M, Ali Y, et al. Clinical characteristics and outcomes of COVID‐19 patients with diabetes mellitus in Kuwait. Heliyon. 2021;7(4):e06706. 10.1016/j.heliyon.2021.e06706 - DOI - PMC - PubMed
  23.  
    1. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315‐1316. 10.1097/jto.0b013e3181ec173d - DOI - PubMed
  24.  
    1. Maddaloni E, D’Onofrio L, Alessandri F, et al. Cardiometabolic multimorbidity is associated with a worse Covid‐19 prognosis than individual cardiometabolic risk factors: a multicentre retrospective study (CoViDiab II). Cardiovasc Diabetol. 2020;19(1):164. 10.1186/s12933-020-01140-2 - DOI - PMC - PubMed
  25.  
    1. Almazeedi S, Al‐Youha S, Jamal MH, et al. Characteristics, risk factors and outcomes among the first consecutive 1096 patients diagnosed with COVID‐19 in Kuwait. EClinicalMedicine. 2020;24:100448. 10.1016/j.eclinm.2020.100448 - DOI - PMC - PubMed
  26.  
    1. Liang W, Liang H, Ou L, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID‐19. JAMA Intern Med. 2020;180(8):1081‐1089. 10.1001/jamainternmed.2020.2033 - DOI - PMC - PubMed
  27.  
    1. Fumagalli C, Rozzini R, Vannini M, et al. Clinical risk score to predict in‐hospital mortality in COVID‐19 patients: a retrospective cohort study. BMJ Open. 2020;10(9):e040729. 10.1136/bmjopen-2020-040729 - DOI - PMC - PubMed
  28.  
    1. Ali H, Al‐Shammari AA, Alahmad B. Advancing risk analysis of COVID‐19 clinical predictors: the case of fasting blood glucose. Front Med. 2021;8:636065. 10.3389/fmed.2021.636065 - DOI - PMC - PubMed
  29.  
    1. Alzaman N, Ali A. Obesity and diabetes mellitus in the Arab world. Journal of Taibah University Medical Sciences. 2016;11(4):301‐309. 10.1016/j.jtumed.2016.03.009 - DOI
  30.  
    1. Jackson R, Al‐Mousa Z, Al‐Raqua M, Prakash P, Ali Muhanna A . Prevalence of coronary risk factors in healthy adult Kuwaitis. Int J Food Sci Nutr. 2001;52(4):301‐311. 10.1080/09637480120057558 - DOI - PubMed
  31.  
    1. Ejaz H, Alsrhani A, Zafar A, et al. COVID‐19 and comorbidities: deleterious impact on infected patients. J Infect Public Health. 2020;13(12):1833‐1839. 10.1016/j.jiph.2020.07.014 - DOI - PMC - PubMed
  32.  
    1. Cuschieri S, Grech S. COVID‐19 and diabetes: the why, the what and the how. J Diabet Complicat. 2020;34(9):107637. 10.1016/j.jdiacomp.2020.107637 - DOI - PMC - PubMed
  33.  
    1. Gianchandani R, Esfandiari NH, Ang L, et al. Managing hyperglycemia in the COVID‐19 inflammatory storm. Diabetes. 2020;69(10):2048‐2053. 10.2337/dbi20-0022 - DOI - PubMed
  34.  
    1. Sachdeva S, Desai R, Gupta U, Prakash A, Jain A, Aggarwal A. Admission hyperglycemia in non‐diabetics predicts mortality and disease severity in COVID‐19: a pooled analysis and meta‐summary of literature. SN Compr Clin Med. 2020;2(11):1‐6. 10.1007/s42399-020-00575-8 - DOI - PMC - PubMed
  35.  
    1. Parikh R, Mathai A, Parikh S, Chandra Sekhar G, Thomas R. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol. 2008;56(1):45‐50. 10.4103/0301-4738.41424 - DOI - PMC - PubMed

Page Navigation