The impact of objective nutritional indexes on in-hospital mortality in Covid-19 infection

Year 2021, , 724 - 731, 30.06.2021

Arafat Yıldırım , Ozge Ozcan Abacıoglu , Mehmet Cenk Belibağlı

https://doi.org/10.17826/cumj.866208

Cited By: 1

Abstract

Purpose: The effect of nutritional status on in-hospital mortality in patients with coronavirus disease 2019 (COVID-19) is unclear. We aimed to analyze the relationship between nutritional status score (CONUT) and prognostic nutritional index (PNI) and in-hospital mortality in COVID-19 infection.
Materials and Methods: We included 187 patients with COVID-19 between 11 March-10 May 2020. The CONUT and PNI scores were calculated using the laboratory results. The groups were divided into survival and in-hospital mortality
Results: The mean age of the patients was 66.2±15.8 years (55.3% male). The mortality rate was 20.8% (n=39). The median PNI scores was significantly lower and the median CONUT score was significantly higher in the mortality group. Multivariate regression analysis showed that PNI and CONUT were independent predictors of mortality. ROC curve analyses showed that CONUT had a better performance than PNI to predict in-hospital mortality.
Conclusion: In COVID-19 patients, CONUT and PNI scores were independently associated with in-hospital mortality, with CONUT presenting a better performance than PNI.

Keywords

coronavirus disease 2019, CONUT, PNI

Covid-19 enfeksiyonunda objektif nutrisyonel indekslerin hastane içi mortaliteye etkisi

Abstract

Amaç: Koronavirüs hastalığı 2019 (COVID-19) tanısı nedeniyle yoğun bakım ünitelerinde yatan hastaların beslenme durumunun hastane içi mortaliteye etkisi belirsizdir. Çalışma, COVID-19 enfeksiyonu hastalarında iki objektif nutrisyonel indeksler olan controlling nutritional status score (CONUT) ve prognostic nutritional indeksi (PNI) nin hastane içi mortalite ilişkisini analiz etmeyi amaçladı.
Gereç ve Yöntem: Çalışmaya 11 Mart-10 Mayıs 2020 tarihleri arasında yoğun bakım ünitelerinde yatan 187 COVID-19 hastası dahil edildi. Demografik ve klinik verileri kaydedildi. CONUT ve PNI skorları laboratuvar sonuçlarından hesaplandı. Hastalar mortalite grup ve ve non-mortalite grup olmak üzere iki gruba ayrıldı.
Bulgular: Hastaların ortalama yaşı 66.2±15.8 yıl (% 55,3 erkek), ölüm oranı % 20,8 (n = 39) idi. Mortalite grubunda Medyan PNI skorları anlamlı olarak daha düşük ve medyan CONUT skorları anlamlı olarak daha yüksek belirlendi. Çok değişkenli regresyon analizinde, PNI ve CONUT un mortalite ile bağımsız olarak ilişkili olduğunu belirlendi. Ayrıca ROC eğrisi analizlerinde, CONUT un hastane içi mortaliteyi tahmin etmede, PNI'den daha iyi bir performansa sahip olduğunu belirlendi.
Sonuç: COVID-19 hastalarında, CONUT ve PNI skorlarının hastane içi mortalitede bağımsız prediktörler olduğunu ve CONUT'un PNI'dan daha iyi bir performansa sahip olduğu belirlenmiştir.

Keywords

Coronavirüs hastalığı 2019, CONUT, PNI

References

• 1. Lai C-C, Shih T-P, Ko W-C, Tang H-J, Hsueh P-R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019(COVID-19): the epidemic and the challenges. International journal of antimicrobial agents.2020:105924.
• 2. 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;323:1061-9.
• 3. Cascella M, Rajnik M, Cuomo A, Dulebohn SC, Di Napoli R. Features, evaluation and treatment coronavirus(COVID-19). Statpearls [internet]:StatPearls Publishing;2020.
• 4. Guan W-j, Ni Z-y, Hu Y, Liang W-h, Ou C-q, He J-x, et al. Clinical characteristics of coronavirus disease 2019 in China. New England journal of medicine.2020;382:1708-20.
• 5. Kinross P, Suetens C, Dias JG, Alexakis L, Wijermans A, Colzani E, et al. Rapidly increasing cumulative incidence of coronavirus disease(COVID-19) in the European Union/European Economic Area and the United Kingdom, 1 January-15 March 2020. Eurosurveillance.2020;25:2000285.
• 6. Mardani R, Vasmehjani AA, Zali F, Gholami A, Nasab SDM, Kaghazian H, et al. Laboratory parameters in detection of COVID-19 patients with positive RT-PCR; a diagnostic accuracy study. Archives of Academic Emergency Medicine.2020 Apr 4;8(1):e43.eCollection 2020.
• 7. Wu T, Zuo Z, Kang S, Jiang L, Luo X, Xia Z, et al. Multi-organ Dysfunction in Patients with COVID-19: A Systematic Review and Meta-analysis.Aging and disease.2020;11:874.
• 8. Thachil J, Tang N, Gando S, Falanga A, Cattaneo M, Levi M, et al. ISTH interim guidance on recognition and management of coagulopathy in COVID-19.Journal of Thrombosis and Haemostasis.2020;18:1023-6.
• 9. Abou-Ismail MY, Diamond A, Kapoor S, Arafah Y, Nayak L. The hypercoagulable state in COVID-19:Incidence, pathophysiology, and management. Thrombosis Research.2020-Jun-20;194:101-115.
• 10. Sun G, Li Y, Peng Y, Lu D, Zhang F, Cui X, et al. Impact of the preoperative prognostic nutritional index on postoperative and survival outcomes in colorectal cancer patients who underwent primary tumor resection: a systematic review and meta-analysis.International journal of colorectal disease.2019;34:681-9.
• 11. Li J, Xu R, Hu D-M, Zhang Y, Gong T-P, Wu X-L. Prognostic nutritional index predicts outcomes of patients after gastrectomy for cancer: a systematic review and meta-analysis of nonrandomized studies.Nutrition and cancer.2019;71:557-68.
• 12. Ai Tao, Yang Z, Hou H, Zhan C, Chen C, Lv W. et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019(COVID-19) in China:A Report of 1014 Cases. Radiology.2020.doi:10.1148/radiol.2020200642.
• 13. Sun X, Luo L, Zhao X, Ye P. Controlling Nutritional Status (CONUT) score as a predictor of all-cause mortality in elderly hypertensive patients: a prospective follow-up study.BMJ open.2017;7:e015649.
• 14. DeLong, E.R., D.M. DeLong, D.L. Clarke-Pearson, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.Biometrics,1988.44:837-45
• 15. Lew CCH, Yandell R, Fraser RJ, Chua AP, Chong MFF, Miller M. Association between malnutrition and clinical outcomes in the intensive care unit: a systematic review. Journal of Parenteral and Enteral Nutrition.2017;41:744-58.
• 16. Barker LA, Gout BS, Crowe TC. Hospital malnutrition: prevalence, identification and impact on patients and the healthcare system. International journal of environmental research and public health.2011;8:514-27.
• 17. Narumi T, Arimoto T, Funayama A, Kadowaki S, Otaki Y, Nishiyama S, et al. The prognostic importance of objective nutritional indexes in patients with chronic heart failure.Journal of cardiology.013;62:307-13.
• 18. Huang W, Li C, Wang Z, Wang H, Zhou N, Jiang J, et al. Decreased serum albumin level indicates poor prognosis of COVID-19 patients: hepatic injury analysis from 2,623 hospitalized cases.Science China Life Sciences.2020:1-10.
• 19. Hu X, Chen D, Wu L, He G, Ye W. Low Serum Cholesterol Level Among Patients with COVID-19 Infection in Wenzhou, China. China.2020.lancet 20-02111
• 20. Aparisi A, Echeverría CI, Falcón CY , Cusácovich I, et all. Low-density lipoprotein cholesterol levels are associated with poor clinical outcomes in COVID-19.medRxiv preprint ,doi:https://doi.org/10.1101/2020.10.06.20207092
• 21. Mo X, Wei F, Tong Y, Ding L, Zhu Q, Du S, et al. Lactic acid downregulates viral MicroRNA to promote epstein-barr virus-immortalized B lymphoblastic cell adhesion and growth. J Virol.2018;92:e00033–18.
• 22. Calderón EJ, Gutiérrez-Rivero S, Durand-Joly I, Dei-Cas E. Pneumocystis infection in humans: diagnosis and treatment.Expert Rev Anti Infect Ther.2010;8:683–701.