Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Sep;25(9):987-991.
doi: 10.5005/jp-journals-10071-23946.

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

Affiliations

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 et al. Indian J Crit Care Med. 2021 Sep.

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).

How to cite this article: Shanbhag V, Arjun NR, 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.

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

PubMed Disclaimer

Conflict of interest statement

Source of support: Nil Conflict of interest: None

Figures

Fig. 1
Fig. 1
ROC curve depicting the methodology of the study
Fig. 2
Fig. 2
ROC curve depicting the age-adjusted CCI for predicting the mortality (AUC of 0.709, p = 0.001, and sensitivity 68% and specificity 62%, for a cutoff score >4)
Fig. 3
Fig. 3
ROC of age-adjusted CCI for predicting the need for IMV (AUC of 0.696, p = 0.001, and sensitivity 67% and specificity 63%, for a cutoff score >4)
Flowchart 1
Flowchart 1
ROC curve of age-adjusted CCI for predicting the LOS (AUC of 0.448, p = 0.319)

References

    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. DOI: - DOI - PMC - PubMed
    1. 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. DOI: - DOI - PMC - PubMed
    1. 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. DOI: - DOI - PMC - PubMed
    1. 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. DOI: - DOI - PMC - PubMed
    1. 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. DOI: - DOI - PMC - PubMed

LinkOut - more resources