Intensive care unit: mortality score in early prediction of mortality in critical COVID-19 patients

Year 2023, Volume: 4 Issue: 5, 572 - 578, 27.10.2023

Emel Sağlam , Arif Savaş , Deniz Öke , Can Özlü , Begüm Koçar , Kerem Erkalp

https://doi.org/10.47582/jompac.1346978

Abstract

Abstract
Aim: The mortality data available in the literature with regard to patients infected with SARS-COV-2, thus requiring hospitalization in the Intensive Care Unit (ICU) are not sufficient. This research aims to compare the correlation between COVID-19 Mortality Ratios (CMR), AST/ALT and neutrophil/lymphocyte (N/L) ratios of non-smoker COVID-19 patients hospitalized in the ICU and their mortality rates.
Methods: This cross-sectional study was conducted on 77 patients hospitalized in the ICU. Female participants constituted 64.9% (n = 50) of the study group while male made up 35.1% (n = 27); the mean age was 61.3±14.3 and 66.2% (n = 51) of the patients died. To exclude the adverse effect of smoking on mortality, patients were confirmed to be non-smokers by analyzing the cotinine levels in urine samples. For this purpose, patients' age, gender, comorbidities, fever, pulse, blood pressure, saturation values, APACHE scores and biochemical parameters were evaluated.
Results: In the study, 66.2% (n=51) of the patients died during follow-up. Age, urea, creatinine, AST/ALT, N/L ratio and CMR values of the nonsurvivors were significantly higher than those of the survivors. The systolic blood pressure and lymphocyte values of non-survivors were lower than survivors.
Conclusions: The conclusion of the study revealed that CMR scores, AST/ALT levels and the N/L ratio can effectively be utilized in early period to project the mortality rates of non (active) smoking patients with critical COVID-19 infection hospitalized in the ICU.

Keywords

SARS-COV-2,, intensive care unit,, COVID-19 mortality ratio,, cotinine,, smokers and non-smokers

Project Number

2020.05.2.02.058

Yoğun bakım ünitesi: kritik COVID-19 hastalarında mortalitenin erken tahmininde Mortality Score (CMR)

Abstract

Amaç: SARS-COV-2 ile enfekte olup yoğun bakım ünitesinde (YBÜ) yatış gerektiren hastalara ilişkin literatürde yer alan mortalite verileri yeterli değildir. Bu araştırma, yoğun bakımda yatan sigara içmeyen COVID-19 hastalarının COVID-19 Mortalite Oranları (CMR), AST/ALT ve nötrofil/lenfosit (N/L) oranları ile onların mortalite oranları arasındaki korelasyonu karşılaştırmayı amaçlamaktadır.
Yöntemler: Bu kesitsel çalışma YBÜ’de yatan 77 hasta üzerinde yapıldı. Çalışma grubunun %64.9'unu (n=50) kadın katılımcılar, %35.1'ini (n=27) erkekler oluşturmuştur; yaş ortalaması 61.3±14.3 olup, hastaların %66.2'si (n=51) ölmüştür. Sigara içmenin mortalite üzerindeki olumsuz etkisini dışlamak için, idrar numunelerindeki kotinin seviyeleri analiz edilerek hastaların sigara içmediği doğrulandı. Bu amaçla hastaların yaşı, cinsiyeti, ek hastalıkları, ateş, nabız, kan basıncı, satürasyon değerleri, APACHE skorları ve biyokimyasal parametreleri değerlendirildi.
Bulgular: Çalışmada, hastaların %66.2'si (n=51) takip sırasında öldü. Ölenlerin yaş, üre, kreatinin, AST/ALT, N/L oranı ve CMR değerleri ölmeyenlerden anlamlı olarak yüksekti. Ölenlerin sistolik kan basıncı ve lenfosit değerleri ölmeyenlerden daha düşüktü.
Sonuç: Çalışmanın sonucu; YBÜ’de yatan kritik COVID-19 enfeksiyonu olan ve (aktif) sigara içmeyen hastaların ölüm oranlarını tahmin etmek için CMR skorları, AST/ALT seviyeleri ve N/L oranının, erken dönemde etkili bir şekilde kullanılabileceğini ortaya koymuştur.

Keywords

SARS-COV-2;, yoğun bakım ünitesi,, COVID-19 ölüm oranı,, kotinin,, sigara içenler ve içmeyenler

Supporting Institution

Bagcilar Egitim ve Arastirma Hastanesi Klinik Arastirmalar Etik Kurulu

Project Number

2020.05.2.02.058

References

• Salah HM, Sharma T, Mehta J. Smoking doubles the mortality risk in COVID-19: a meta-analysis of recent reports and potential mechanisms. Cureus. 2020;12(10):e10837. doi: 10.7759/cureus.10837
• Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for disease control and prevention. JAMA. 2020;323(13):1239-1242. doi: 10.1001/jama.2020.2648
• Dong Y, Mo X, Hu Y, et al. Epidemiology of COVID-19 among children in China. Pediatrics. 2020;145(6):e20200702. doi: 10.1542/peds.2020-0702
• Barda N, Riesel D, Akriv A, et al. Developing a COVID-19 mortality risk prediction model when individual-level data are not available. Nat Commun. 2020;11(1):4439. doi: 10.1038/s41467-020-18297-9
• Imran A, Posokhova I, Qureshi HN, et al. AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inform Med Unlocked. 2020;20:100378. doi: 10.1016/j.imu.2020.100378
• Abdulaal A, Patel A, Charani E, Denny S, Mughal N, Moore L. Prognostic modeling of COVID-19 using artificial intelligence in the United Kingdom: model development and validation. J Med Internet Res. 2020;22(8):e20259. doi: 10.2196/20259
• Bertsimas D, Lukin G, Mingardi L, et al. COVID-19 mortality risk assessment: An international multi-center study. PLoS One. 2020;15(12):e0243262. doi: 10.1371/journal.pone.0243262
• Chen T, Guestrin C. XGBoost: a scalable tree boosting system. proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. 2016.pp.785-794.
• Epidemiology Working Group for NCIP Epidemic Response, Chinese Center for Disease Control and Prevention. [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China]. Zhonghua Liu Xing Bing XueZaZhi. 2020;41(2):145-151. Chinese.
• Parascandola M, Xiao L. Tobacco and the lung cancer epidemic in China. Transl Lung Cancer Res. 2019;8(Suppl 1):S21-30.
• Shastri MD, Shukla SD, Chong WC, et al. Smoking and COVID-19: What we know so far. Respir Med. 2021;176:106237. doi: 10.1016/j.rmed.2020.106237
• Zhao Q, Meng M, Kumar R, et al. The impact of COPD and smoking history on the severity of COVID-19: a systemic review and meta-analysis. J Med Virol. 2020;92(10):1915-1921. doi: 10.1002/jmv.25889
• COVID Analytics. Analytics can calculate the risk of mortality. 2023 Available at: https://www.covidanalytics.io/mortality_calculator
• Hippisley-Cox J, Young D, Coupland C, et al. Risk of severe COVID-19 disease with ACE inhibitors and angiotensin receptor blockers: cohort study including 8.3 million people. Heart. 2020;106(19):1503-1511. doi: 10.1136/heartjnl-2020-317393
• Saadatian-Elahi M, Amour S, Elias C, Henaff L, Dananché C, Vanhems P. Tobacco smoking and severity of COVID-19: experience from a hospital-based prospective cohort study in Lyon, France. J Med Virol. 2021;93(12):6822-6827. doi: 10.1002/jmv.27233
• Jackson SE, Brown J, Shahab L, Steptoe A, Fancourt D. COVID-19, smoking and inequalities: a study of 53 002 adults in the UK. Tob Control. 2021;30(e2):e111-e121. doi: 10.1136/tobaccocontrol-2020-055933
• Patanavanich R, Glantz SA. Smoking is associated with COVID-19 progression: a meta-analysis. Nicotine Tob Res. 2020; 22(9):1653-1656
• Clift AK, von Ende A, Tan PS, et al. Smoking and COVID-19 outcomes: an observational and Mendelian randomisation study using the UK Biobank cohort. Thorax. 2022;77(1):65-73. doi: 10.1136/thoraxjnl-2021-217080
• Choi JW. Association between smoking status and death from COVID-19 in South Korea: a nationwide cohort study. Tob Induc Dis. 2023;21:97. doi: 10.18332/tid/168672
• Zhang H, Ma S, Han T, et al. Association of smoking history with severe and critical outcomes in COVID-19 patients: A systemic review and meta-analysis. Eur J Integr Med. 2021;43:101313. doi: 10.1016/j.eujim.2021.101313
• Gallus S, Scala M, Possenti I, et al. The role of smoking in COVID-19 progression: a comprehensive meta-analysis. Eur Respir Rev. 2023;32(167):220191. doi: 10.1183/16000617.0191-2022
• Elliott J, Bodinier B, Whitaker M, et al. COVID-19 mortality in the UK Biobank cohort: revisiting and evaluating risk factors. Eur J Epidemiol. 2021;36(3):299-309. doi: 10.1007/s10654-021-00722-y
• Sun B, Wang H, Lv J, Pei H, Bai Z. Predictors of mortality in hospitalized COVID-19 patients complicated with hypotension and hypoxemia: a retrospective cohort study. Front Med (Lausanne). 2021;8:753035. doi: 10.3389/fmed.2021.753035
• Lacedonia D, Scioscia G, Santomasi C, et al. Impact of smoking, COPD and comorbidities on the mortality of COVID-19 patients. Sci Rep. 2021;11(1):19251. doi: 10.1038/s41598-021-98749-4
• Taramasso L, Vena A, Bovis F, et al. Higher mortality and intensive care unit admissions in COVID-19 patients with liver enzyme elevations. Microorganisms. 2020;8(12):2010. doi: 10.3390/microorganisms8122010
• Lei F, Liu YM, Zhou F, et al. Longitudinal association between markers of liver injury and mortality in COVID-19 in China. Hepatology. 2020;72(2):389-398. doi: 10.1002/hep.31301
• Fu J, Kong J, Wang W, et al. The clinical implication of dynamic neutrophil to lymphocyte ratio and D-dimer in COVID-19: a retrospective study in Suzhou China. Thromb Res. 2020;192:3-8. doi: 10.1016/j.thromres.2020.05.006
• Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506. doi: 10.1016/S0140-6736(20)30183-5
• Uğur Chousein EG, Çörtük M, Cınarka H, et al. Is there any effect of smoking status on severity and mortality of hospitalized patients with COVID-19 pneumonia? Tuberk Toraks. 2020;68(4):371-378. doi: 10.5578/tt.70352
• Taylor EH, Marson EJ, Elhadi M, et al. Factors associated with mortality in patients with COVID-19 admitted to intensive care: a systematic review and meta-analysis. Anaesthesia. 2021;76(9):1224-1232. doi: 10.1111/anae.15532
• Mahabee-Gittens EM, Mendy A, Merianos AL. Assessment of Severe COVID-19 Outcomes Using Measures of Smoking Status and Smoking Intensity. Int J Environ Res Public Health. 2021;18(17):8939
• Reddy RK, Charles WN, Sklavounos A, Dutt A, Seed PT, Khajuria A. The effect of smoking on COVID-19 severity: A systematic review and meta-analysis. J Med Virol. 2021;93(2):1045-1056. doi: 10.1002/jmv.26389