Dergiler / İstanbul Kuzey Klinikleri / 2022 / Cilt: 9 - Sayı: 2

Does the Charlson comorbidity index help predict the risk of death in COVID-19 patients?

Sayfa
117–121
Yayın Tarihi
2022-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Increasing awareness of potential comorbidities in COVID-19 patients can provide insight into the disease and to improve outcomes by identifying and treating patients earlier and more effectively.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:OBJECTIVE: Comorbidities are diseases that coexist with a disease of interest or an index disease, which can directly affect the prognosis of the disease of interest or indirectly affect the choice of treatment.
📊
Temel Bulgular & Veri:In this study, it was aimed to examine the predictive role of the CCI score on the mortality of patients with COVID-19.
💡
Sonuç & Katkı:Increasing awareness of potential comorbidities in COVID-19 patients can provide insight into the disease and to improve outcomes by identifying and treating patients earlier and more effectively.
🔬Tür:Araştırma Makalesi
📍Kapsam:2020 Verileri
⚙️Değişkenler:Alan Parametreleri

Abstract

OBJECTIVE: Comorbidities are diseases that coexist with a disease of interest or an index disease, which can directly affect the prognosis of the disease of interest or indirectly affect the choice of treatment. The Charlson comorbidity index (CCI) is the most widely used comorbidity index. In this study, it was aimed to examine the predictive role of the CCI score on the mortality of patients with COVID-19. METHODS: We have retrospectively analyzed COVID-19 patients whose diagnosis was confirmed by PCR and who were hospitalized in two centers between April 2020 and December 2020. The severity of comorbidity of the patients was categorized into five groups according to the CCI score: CCI score 0, CCI score 1–2, CCI score 3–4, CCI score 5–6, and CCI score ≥7. Factors affecting mortality and differences between groups classified by CCI were determined by logistic regression analysis and one-way analysis of variance. RESULTS: A total of 1,559 COVID-19 patients were included in the study and 70 (4.49%) patients had deceased. Half of the study population (n=793, 50.9%) had different comorbidities. The CCI score was 3.8±2.7 in deceased patients and 1.3±1.9 in surviving individuals. There was a positive correlation between CCI scores and mortality in COVID-19 patients, with each point increase in the CCI score increasing the risk of death by 2.5%. CCI score of 4 and above predicted mortality with 87.2% sensitivity and 97.9% negative predictive value. Five (0.6%) of 766 patients with CCI scores of 0, 16 (3.6%) of 439 patients with CCI scores of 1–2, 13 (6.9%) of 189 patients with CCI scores of 3–4, and a CCI score of 5, 13 (15.7%) of 83 patients with -6 and 23 (28.0%) of 82 patients with a CCI score of ≥7 died. CONCLUSION: CCI is a simple, easily applicable, and valid method for classifying comorbidities and estimating COVID-19 mortality. The close relationship between the CCI score and mortality reveals the reality of how important vaccination is, especially in this group of patients. Increasing awareness of potential comorbidities in COVID-19 patients can provide insight into the disease and to improve outcomes by identifying and treating patients earlier and more effectively.

🔖

Bu Makaleye Atıf Yap

ÇOMOĞLU, Ş., KANT, A. (2022). Does the Charlson comorbidity index help predict the risk of death in COVID-19 patients?. İstanbul Kuzey Klinikleri, Cilt: 9 - Sayı: 2, 117–121. https://ajindex.com/articles/551214

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (10)

  1. World Health Organization. WHO Coronavirus (COVID-19) Dashboard. Available at: https://covid19.who.int/. Accessed Dec 12, 2021
  2. Moon SS, Lee K, Park J, Yun S, Lee YS, Lee DS. Clinical characteristics and mortality predictors of COVID-19 patients hospitalized at nationally-designated treatment hospitals. J Korean Med Sci 2020;35:e328
  3. 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;40:373–83
  4. Guan WJ, Liang WH, Zhao Y, Liang HR, Chen ZS, Li YM, et al; China Medical Treatment Expert Group for COVID-19. Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J 2020;55:2000547
  5. Lim MA, Huang I, Yonas E, Vania R, Pranata R. A wave of non-communicable diseases following the COVID-19 pandemic. Diabetes Metab Syndr 2020;14:979–80
  6. Pranata R, Huang I, Lim MA, Wahjoepramono EJ, July J. Impact of cerebrovascular and cardiovascular diseases on mortality and severity of COVID-19-systematic review, meta-analysis, and meta-regression. J Stroke Cerebrovasc Dis 2020;29:104949
  7. Kant A, Çomoğlu Ş, Öztürk S, Aydın E, Yılmaz G. Does Chronic Obstructive Pulmonary Disease Affect the Severity of COVID-19 Infection? KÜ Tıp Fak Derg 2020;22:440–4
  8. Imam Z, Odish F, Gill I, O’Connor D, Armstrong J, Vanood A, et al. Older age and comorbidity are independent mortality predictors in a large cohort of 1305 COVID-19 patients in Michigan, United States. J Intern Med 2020;288:469–76
  9. 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;14:2103–9
  10. 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.