2022, Volume 8, Issue 1
Engineering >> 2022, Volume 8, Issue 1 doi: 10.1016/j.eng.2020.05.014
A Predictive Nomogram for Predicting Improved Clinical Outcome Probability in Patients with COVID-19 in Zhejiang Province, China
a State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
b State Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, China
c Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Key Lab of Combined Multi-organ Transplantation of the Ministry of Health, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
d Division of of Endocrinology and Metabolism, Department of Internal Medicine System, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
# These authors contributed equally to this work.
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Abstract
The aim of this research was to develop a quantitative method for clinicians to predict the probability of improved prognosis in patients with coronavirus disease 2019 (COVID-19). Data on 104 patients admitted to hospital with laboratory-confirmed COVID-19 infection from 10 January 2020 to 26 February 2020 were collected. Clinical information and laboratory findings were collected and compared between the outcomes of improved patients and non-improved patients. The least absolute shrinkage and selection operator (LASSO) logistics regression model and two-way stepwise strategy in the multivariate logistics regression model were used to select prognostic factors for predicting clinical outcomes in COVID-19 patients. The concordance index (C-index) was used to assess the discrimination of the model, and internal validation was performed through bootstrap resampling. A novel predictive nomogram was constructed by incorporating these features. Of the 104 patients included in the study (median age 55 years), 75 (72.1%) had improved short-term outcomes, while 29 (27.9%) showed no signs of improvement. There were numerous differences in clinical characteristics and laboratory findings between patients with improved outcomes and patients without improved outcomes. After a multi-step screening process, prognostic factors were selected and incorporated into the nomogram construction, including immunoglobulin A (IgA), C-reactive protein (CRP), creatine kinase (CK), Acute Physiology and Chronic Health Evaluation II (APACHE II), and interaction between CK and APACHE II. The C-index of our model was 0.962 (95% confidence interval (CI), 0.931–0.993) and still reached a high value of 0.948 through bootstrapping validation. A predictive nomogram we further established showed close performance compared with the ideal model on the calibration plot and was clinically practical according to the decision curve and clinical impact curve. The nomogram we constructed is useful for clinicians to predict improved clinical outcome probability for each COVID-19 patient, which may facilitate personalized counselling and treatment.
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References
[ 1 ] Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395 (10223):497–506. link1
[ 2 ] Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395(10223):507–13. link1
[ 3 ] World Health Organization. Naming the coronavirus disease (COVID-19) and the virus that causes it [Internet]. Geneva: Word Health Organization; c2020 [cited 2020 Feb 11]. Available from: https://www.who.int/emergencies/ diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirusdisease-(covid-2019)-and-the-virus-that-causes-it. link1
[ 4 ] Holshue ML, DeBolt C, Lindquist S, Lofy KH,Wiesman J, Bruce H, et al. First case of 2019 novel coronavirus in the United States. N Engl J Med 2020;382(10):929–36. link1
[ 5 ] Xu X, Wu X, Jiang X, Xu K, Ying L, Ma C, et al. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. BMJ 2020;368:m606. link1
[ 6 ] Zaki AM, van Boheemen S, Bestebroer TM, Osterhaus AD, Fouchier RA. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N Engl J Med 2012;367:1814–20. link1
[ 7 ] Zhong N, Zheng B, Li Y, Poon, Xie Z, Chan K, et al. Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People’s Republic of China, in February, 2003. Lancet 2003;362(9393):1353–8. link1
[ 8 ] Guan W, Ni Z, Hu Y, Liang W, Qu C, He J, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2019;2020(382):1708–20. link1
[ 9 ] nhc.gov.cn/[Internet]. Beijing: National Health Commission of the People’s Republic of China; [cited 2020 Mar 3]. Available from: http://www.nhc.gov.cn. Chinese. link1
[10] Coronavirus Disease (COVID-19) Dashboard [Internet]. Geneva: Word Health Orgainization; c2020 [cited 2020 Mar 2]. Available from: https://covid19.who.int. link1
[11] Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020;323(11):1061–9. link1
[12] Peng J, Wang M, Ang IYH, Tan SHX, Lewis RF, Chen J, et al. Potential rapid diagnostics, vaccine and therapeutics for 2019 novel coronavirus (2019- nCoV): a systematic review. J Clin Med 2020;9(3):623. link1
[13] Clinical management of severe acute respiratory infection when COVID-19 is suspected: interim guidance [Internet]. Geneva: World Health Organization; c2020 [cited 2020 Mar 13]. Available from: https://www.who.int/publicationsdetail/clinical-management-of-severe-acute-respiratory-infection-when-novelcoronavirus-(ncov)-infection-is-suspected. link1
[14] Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med 1985;13(10):818–29. link1
[15] National Health Commission of the People’s Republic of China; National Administration of Traditional Chinese Medicine. Guidelines for diagnosis and treatment of novel coronavirus pneumonia (trial version 7) [Internet]. Beijing: The State Council of the People’s Republic of China; 2020 Mar 3 [cited 2020 Mar 4]. Available from: http://www.gov.cn/zhengce/zhengceku/2020-03/04/ 5486705/files/ae61004f930d47598711a0d4cbf874a9.pdf. Chinese. link1
[16] Al Ghamdi M, Alghamdi KM, Ghandoora Y, Alzahrani A, Salah F, Alsulami A, et al. Treatment outcomes for patients with Middle Eastern respiratory syndrome coronavirus (MERS CoV) infection at a coronavirus referral center in the Kingdom of Saudi Arabia. BMC Infect Dis 2016;16:174. link1
[17] Wu J, Xu F, Zhou W, Feikin DR, Lin C, He X, et al. Risk factors for SARS among persons without known contact with SARS patients, Beijing, China. Emerg Infect Dis 2004;10(2):210–6. link1
[18] Saad M, Omrani AS, Baig K, Bahloul A, Elzein F, Matin MA, et al. Clinical aspects and outcomes of 70 patients with Middle East respiratory syndrome coronavirus infection: a single-center experience in Saudi Arabia. Int J Infect Dis 2014;29:301–6. link1
[19] Feikin DR, Alraddadi B, Qutub M, Shabouni O, Curns A, Oboho IK, et al. Association of higher MERS-CoV virus load with severe disease and death, Saudi Arabia, 2014. Emerg Infect Dis 2015;21:2029–35. link1
[20] Majumder MS, Kluberg SA, Mekaru SR, Brownstein JS. Mortality risk factors for Middle East respiratory syndrome outbreak, South Korea, 2015. Emerg Infect Dis 2015;21(11):2088–90. link1
[21] Ahmed AE. The predictors of 3- and 30-day mortality in 660 MERS-CoV patients. BMC Infect Dis 2017;17:615. link1
[22] Nam HS, Park JW, Ki M, Yeon MY, Kim J, Kim SW. High fatality rates and associated factors in two hospital outbreaks of MERS in Daejeon, the Republic of Korea. Int J Infect Dis 2017;58:37–42. link1
[23] Leung TW, Wong KS, Hui AC, To KF, Lai ST, Ng WF, et al. Myopathic changes associated with severe acute respiratory syndrome: a postmortem case series. Arch Neurol 2005;62(7):1113–7. link1
[24] Ko JH, Park GE, Lee JY, Lee JY, Cho SY, Ha YE, et al. Predictive factors for pneumonia development and progression to respiratory failure in MERS-CoV infected patients. J Infect 2016;73(5):468–75. link1
[25] Liew FY, Russell SM, Appleyard G, Brand CM, Beale J. Cross-protection in mice infected with influenza A virus by the respiratory route is correlated with local IgA antibody rather than serum antibody or cytotoxic T cell reactivity. Eur J Immunol 1984;14(4):350–6. link1
[26] Otten MA, van Egmond M. The Fc receptor for IgA (FcalphaRI, CD89). Immunol Lett 2004;92(1–2):23–31. link1
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