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VOLUME 25 , ISSUE 9 ( September, 2021 ) > List of Articles

Original Article

Utility of Age-adjusted Charlson Comorbidity Index as a Predictor of Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Survival in COVID-19 Patients

Vishal Shanbhag, Arjun NR, Souvik Chaudhuri, Akhilesh K Pandey

Keywords : Age-adjusted Charlson comorbidity index, Coronavirus disease 2019, Invasive mechanical ventilation, Length of hospital stay, Mortality, Remdesivir

Citation Information : Shanbhag V, NR A, Chaudhuri S, Pandey AK. Utility of Age-adjusted Charlson Comorbidity Index as a Predictor of Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Survival in COVID-19 Patients. Indian J Crit Care Med 2021; 25 (9):987-991.

DOI: 10.5005/jp-journals-10071-23946

License: CC BY-NC 4.0

Published Online: 08-09-2021

Copyright Statement:  Copyright © 2021; Jaypee Brothers Medical Publishers (P) Ltd.


Abstract

Background: Multiple parameters may be used to prognosticate coronavirus disease-2019 (COVID-19) patients, which are often expensive laboratory or radiological investigations. We evaluated the utility of age-adjusted Charlson comorbidity index (CCI) as a predictor of outcome in COVID-19 patients treated with remdesivir. Materials and methods: This was a single-center, retrospective study on 126 COVID-19 patients treated with remdesivir. The age-adjusted CCI, length of hospital stay (LOS), need for invasive mechanical ventilation (IMV), and survival were recorded. Results: The mean and standard deviation (SD) of age-adjusted CCI were 3.37 and 2.186, respectively. Eighty-six patients (70.5%) had age-adjusted CCI ≤4, and 36 (29.5%) had age-adjusted CCI >4. Among patients with age-adjusted CCI ≤4, 20 (23.3%) required IMV, whereas in those with age-adjusted CCI >4, 19 (52.8%) required IMV (p <0.05, Pearson\'s chi-square test). In those with age-adjusted CCI ≤4, the mortality was 18.6%, whereas it was 41.7% in patients with age-adjusted CCI >4 (p <0.05, Pearson\'s chi-square test). The receiver operating curve (ROC) of age-adjusted CCI for predicting the mortality had an area under the curve (AUC) of 0.709, p = 0.001, and sensitivity 68%, specificity 62%, and 95% confidence interval (CI) [0.608, 0.810], for a cutoff score >4. The ROC for age-adjusted CCI for predicting the need for IMV had an AUC of 0.696, p = 0.001, and sensitivity 67%, specificity 63%, and 95% CI [0.594, 0.797], for a cutoff score >4. ROC for age-adjusted CCI as a predictor of prolonged LOS (≥14 days) was insignificant. Conclusion: In COVID-19 patients, the age-adjusted CCI is an independent predictor of the need for IMV (score >4) and mortality (score >4) but is not useful to predict LOS (CTRI/2020/11/029266).


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  1. Yang L, Jin J, Luo W, Gan Y, Chen B, Li W. Risk factors for predicting mortality of COVID-19 patients: a systematic review and meta-analysis. PLoS One 2020;15:1–11. DOI: 10.1371/journal.pone.0243124.
  2. Hu Y, Zhan C, Chen C, Ai T, Xia L. Chest CT findings related to mortality of patients with COVID-19: a retrospective case-series study. PLoS One 2020;15:1–12. DOI: 10.1371/journal.pone.0237302.
  3. Huang I, Pranata R, Lim MA, Oehadian A, Alisjahbana B. C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis. Ther Adv Respir Dis 2020;14:1753466620937175. DOI: 10.1177/1753466620937175.
  4. Ghaffari Darab M, Keshavarz K, Sadeghi E, Shahmohamadi J, Kavosi Z. The economic burden of coronavirus disease 2019 (COVID-19): evidence from Iran. BMC Health Serv Res 2021;21(1):1–7. DOI: 10.1186/s12913-021-06126-8.
  5. Shang Y, Liu T, Wei Y, Li J, Shao L, Liu M, et al. Scoring systems for predicting mortality for severe patients with COVID-19. EClinicalMedicine 2020;24:100426. DOI: 10.1016/j.eclinm.2020.100426.
  6. Wu C-C, Hsu T-W, Chang C-M, Yu C-H, Lee C-C. Age-adjusted Charlson comorbidity index scores as predictor of survival in colorectal cancer patients who underwent surgical resection and chemoradiation. Medicine (Baltimore) 2015;94(2):e431. DOI: 10.1097/MD.0000000000000431.
  7. Simpson KJ, Porter BR. The new normal: patient-physician relationships during COVID-19. Methodist Debakey Cardiovasc J 2020;16(2):181–182. DOI: 10.14797/mdcj-16-2-181.
  8. Ferroni E, Giorgi Rossi P, Spila Alegiani S, Trifirò G, Pitter G, Leoni O, et al. Survival of hospitalized COVID-19 patients in Northern Italy: a population-based cohort study by the ITA-COVID-19 network. Clin Epidemiol 2020;12:1337–1346. DOI: 10.2147/CLEP.S271763.
  9. Guido I, Guido G, Claudio B, Claudio F, Massimo S, Massimo V. Age and multimorbidity predict death among COVID-19 patients. Hypertension 2020;76:366–372. DOI: 10.1161/HYPERTENSIONAHA.120.15324.
  10. Chen R, Liang W, Jiang M, Guan W, Zhan C, Wang T, et al. Risk factors of fatal outcome in hospitalized subjects with coronavirus disease 2019 from a nationwide analysis in China. Chest 2020;158(1):97–105. DOI: 10.1016/j.chest.2020.04.010.
  11. Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant 2013;48(3): 452–458. DOI: 10.1038/bmt.2012.244.
  12. Asghar MS, Haider Kazmi SJ, Ahmed Khan N, Akram M, Hassan M, Rasheed U, et al. Poor prognostic biochemical markers predicting fatalities caused by COVID-19: a retrospective observational study from a developing country. Cureus 2020;12(8):1–17. DOI: 10.7759/cureus.9575.
  13. Gorham J, Moreau A, Corazza F, Peluso L, Ponthieux F, Talamonti M, et al. Interleukine-6 in critically ill COVID-19 patients: a retrospective analysis. PLoS One 2020;15(12):1–11. DOI: 10.1371/journal.pone.0244628.
  14. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40(5):373–383. DOI: 10.1016/0021-9681(87)90171-8.
  15. Undurraga EA, Chowell G, Mizumoto K. COVID-19 case fatality risk by age and gender in a high testing setting in Latin America: Chile, March–August 2020. Infect Dis Poverty 2021;10(1):1–11. DOI: 10.1186/s40249-020-00785-1.
  16. Dessai SB, Fasal R, Dipin J, Adarsh D, Balasubramanian S. Age-adjusted charlson comorbidity index and 30-day morbidity in pelvic surgeries. South Asian J Cancer 2018;7(4):240–243. DOI: 10.4103/sajc.sajc_241_17. Available from: https://pubmed.ncbi.nlm.nih.gov/30430092.
  17. Tuty Kuswardhani RA, Henrina J, Pranata R, Anthonius Lim M, Lawrensia S, Suastika K. Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: a systematic review and meta-analysis. Diabetes Metab Syndr 2020;14(6):2103–2109. DOI: 10.1016/j.dsx.2020.10.022.
  18. Varol Y, Hakoglu B, Kadri Cirak A, Polat G, Komurcuoglu B, Akkol B, et al. The impact of charlson comorbidity index on mortality from SARS-CoV-2 virus infection and a novel COVID-19 mortality index: CoLACD. Int J Clin Pract 2021;75(4):e13858. DOI: 10.1111/ijcp.13858.
  19. Rees EM, Nightingale ES, Jafari Y, Waterlow NR, Clifford S, Pearson CAB, et al. COVID-19 length of hospital stay: a systematic review and data synthesis. BMC Biomed 2020. DOI: 10.1101/2020.04.30.20084780.
  20. Chuang MH, Chuang TL, Huang KY, Wang YF. Age-adjusted Charlson Comorbidity Index scores predict major adverse cardiovascular events and all-cause mortality among systemic lupus erythematosus patients. Tzu Chi Med J 2017;29(3):154–158. DOI: 10.4103/tcmj.tcmj_57_17.
  21. Yang CC, Fong Y, Lin LC, Que J, Ting WC, Chang CL, et al. The age-adjusted Charlson comorbidity index is a better predictor of survival in operated lung cancer patients than the Charlson and Elixhauser comorbidity indices. Eur J Cardio-thoracic Surg 2018;53(1):235–240. DOI: 10.1093/ejcts/ezx215.
  22. Radovanovic D, Seifert B, Urban P, Eberli FR, Rickli H, Bertel O, et al. Validity of Charlson Comorbidity Index in patients hospitalised with acute coronary syndrome. Insights from the nationwide AMIS Plus registry 2002-2012. Heart 2014;100(4):288–294. DOI: 10.1136/heartjnl-2013-304588.
  23. Kim DH, Park HC, Cho A, Kim J, Yun K, Kim J, et al. Age-adjusted Charlson comorbidity index score is the best predictor for severe clinical outcome in the hospitalized patients with COVID-19 infection: a result from nationwide database of 5,621 Korean patients. medRxiv 2020;2020.10.26.20220244. DOI: 10.1101/2020.10.26.20220244. Available from: http://medrxiv.org/content/early/2020/10/27/2020.10.26.20220244.abstract.
  24. Alnababteh M, Hashmi M, Drescher G, Vedantam K, Talish M, Desai N, et al. Predicting the need for invasive mechanical ventilation in patients with coronavirus disease 2019. Chest 2020;158(4):A2410. DOI: 10.1016/j.chest.2020.09.009.
  25. Chen F-J, Li F-R, Zheng J-Z, Zhou R, Liu H-M, Wu K-Y, et al. Factors associated with duration of hospital stay and complications in patients with COVID-19. J Public Heal Emerg 2021;5:6–6. DOI: 10.21037/jphe-20-74.
  26. Wu S, Xue L, Legido-Quigley H, Khan M, Wu H, Peng X, et al. Understanding factors influencing the length of hospital stay among non-severe COVID-19 patients: a retrospective cohort study in a Fangcang shelter hospital. PLoS One 2020;15:1–14. DOI: 10.1371/journal.pone.0240959.
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