Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19

Yıl: 2021 Cilt: 13 Sayı: 1 Sayfa Aralığı: 36 - 44 Metin Dili: İngilizce DOI: 10.18521/ktd.841884 İndeks Tarihi: 27-06-2021

Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19

Öz:
Objective: In this study, we aimed to determine the factors that contribute to the early determination of mortality risk in patients hospitalized with COVID-19.Methods: We included 941 adult inpatients (474 male [50.4%], mean age, 53.5±17.0. The patients were divided into two groups: the discharge group and the death group. Epidemiological data, medical history, underlying comorbidities, laboratory findings, chest computed tomographic scans, real-time reverse transcription polymerase chain reaction detection results, and survival data were obtained with retrospective recordings on admission and follow-up. The statistical relationship between survival data and parameters was analyzed. A mathematical model was created from the data of both groups.Results: While 863 patients survived, 78 were non-survivors. During the study period, the preliminary case fatality rate of the inpatients was 8.3%. The mean age of the non-survivors was 71.7±11.2 SD ( P <0.001). Laboratory findings showed that mortality was high in those with high D-dimer, sodium, lactate dehydrogenase (LDH), troponin, creatine kinase-myocardial band (CK-MB), ferritin, blood lactate, activated partial thromboplastin time, and high blood glucose levels ( P <0.05). Furthermore, mortality was high in patients with low albumin, lymphocyte, and platelet levels ( P <0.05). The logistic regression model showed that advanced age, hypertension, high D-Dimer (>1000 ng/ml), high C-reactive protein (CRP), CK-MB, and LDH, and low lymphocyte count were associated with poor prognosis.Conclusions: According to week 1 data of patients with COVID-19, advanced age, hypertension, D-Dimer, CRP, CK-MB, high LDH, and low lymphocyte were associated with poor prognosis. We believe that this model will be useful in predicting patient mortality.
Anahtar Kelime:

COVID-19 Tanısıyla Hastaneye Yatırılan Yetişkin Hastaların İlk Verilerini Kullanarak Ölüm Oranını Tahmin Etmek Mümkün Müdür? COVID-19'un Erken Evresinde Bir Ölüm Tahmin Modeli

Öz:
Amaç: Bu çalışmada COVID-19 tanısıyla hastaneye yatırılan hastalarda mortalite riskininerken dönemde belirlenmesine katkıda bulunan faktörleri belirlemeyi amaçladık. Gereç ve Yöntem: Hastanede yatan 941 COVID-19 tanılı erişkin hasta (474 erkek [% 50.4],yaş ortalaması 53.5 ± 17 çalışmaya dahil edildi. Hastalar taburcu edilenler ve mortalseyredenler olarak iki gruba ayrıldı. Epidemiyolojik veriler, tıbbi öykü, altta yatankomorbiditeler, laboratuvar sonuçları, akciğer bilgisayarlı tomografi görüntüleri, PCRsonuçları, sağkalım verileri, başvuru ve takipte geriye dönük olarak kaydedildi. Sağkalımverileri ile parametreler arasındaki istatistiksel ilişki incelendi.Her iki grup verilerindenmatematiksel bir model oluşturuldu.Bulgular: 863 hasta hayatta kalırken, 78 hasta mortal seyretti. Çalışma süresi boyunca, yatanhastaların ilk vaka ölüm oranı % 8.3 idi. Mortal grupta hastaların ortalama yaşı 71.7 ± 11.2SD idi (P <0.001). Laboratuvar bulgularında, D-Dimer, sodyum, laktat dehidrojenaz (LDH),troponin, kreatin kinaz-miyokardiyal bant (CK-MB), ferritin, kan laktat, aktive parsiyeltromboplastin zamanı ve kan şekeri düzeyleri yüksek olanlarda ölüm oranının yüksek olduğutespit edilmiştir (P <0.05). Ayrıca; albümin, lenfosit ve trombosit düzeyi düşük hastalarda damortalite yükse saptandı (P <0.05). Lojistik regresyon modeli, ileri yaş, hipertansiyon, yüksekD-Dimer (> 1000 ng / ml), yüksek C-reaktif protein (CRP), CK-MB ve LDH ve düşük lenfositsayısının kötü prognozla ilişkili olduğunu gösterdi. Sonuç: COVID-19 hastalarının 1. hafta verilerine göre ileri yaş, hipertansiyon, yüksek D-Dimer, CRP, CK-MB, LDH ve düşük lenfosit kötü prognozla ilişkilendirildi. Bu modelinhasta ölümlerini tahmin etmede faydalı olacağına inanıyoruz.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • 1. Kobayashi T, Jung S-M, Linton NM, Kinoshita R, Hayashi K, Miyama T, et al. Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19). J Clin Med Res 2020;9. https://doi.org/10.3390/jcm9020580.
  • 2. Jin H, Liu J, Cui M, Lu L. Novel coronavirus pneumonia emergency in Zhuhai: impact and challenges. Journal of Hospital Infection 2020;104:452–3. https://doi.org/10.1016/j.jhin.2020.02.005.
  • 3. Website n.d. https://www.epicentro.iss.it/corona virus/bollettino/Bollettinosorveglianza-integrata-COVID19_16-aprile-2020.pdf. (accessed May 19, 2020).
  • 4. Website n.d. https://www.isciii.es/QueHacemos/ Servicios/VigilanciaSaludPublicaR ENAVE/EnfermedadesTransmisibl es/Documents/INFORMES/Inform es%20COVID-19/Informe%20n% C2%BA%2023.%20Situaci%C3% B3n%20de%20COVID- 19%20en%20Espa%C3%B1a%20 a%2016%20de%20abril%20de%2 02020.pdf (accessed May 19, 2020).
  • 5. Giorgi Rossi P, Emilia-Romagna COVID-19 working group, Broccoli S, Angelini P. Case fatality rate in patients with COVID-19 infection and its relationship with length of follow up. J Clin Virol 2020;128:104415.
  • 6. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507– 13.
  • 7. Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. Wiley Series in Probability and Statistics 2013. https://doi.org/10.1002/9781118548387.
  • 8. Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis 2020. https://doi.org/10.1016/S1473- 3099(20)30243-7.
  • 9. Volpato S, Landi F, Incalzi RA. A Frail Health Care System for an Old Population: Lesson form the COVID-19 Outbreak in Italy. The Journals of Gerontology: Series A 2020. https://doi.org/10.1093/gerona/glaa087.
  • 10. Nguyen NP, Vinh-Hung V, Baumert B, Zamagni A, Arenas M, Motta M, et al. Older Cancer Patients during the COVID-19 Epidemic: Practice Proposal of the International Geriatric Radiotherapy Group. Cancers 2020;12. https://doi.org/10.3390/cancers12051287.
  • 11. Magrone T, Magrone M, Jirillo E. Focus on Receptors for Coronaviruses with Special Reference to Angiotensin-converting Enzyme 2 as a Potential Drug Target - A Perspective. Endocr Metab Immune Disord Drug Targets 2020. https://doi.org/10.2174/1871530320666200427112902.
  • 12. Bourgonje AR, Abdulle AE, Timens W, Hillebrands J-L, Navis GJ, Gordijn SJ, et al. Angiotensinconverting enzyme-2 (ACE2), SARS-CoV-2 and pathophysiology of coronavirus disease 2019 (COVID19). J Pathol 2020. https://doi.org/10.1002/path.5471.
  • 13. Zhao Q, Meng M, Kumar R, Wu Y, Huang J, Deng Y, et al. Lymphopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A systemic review and meta-analysis. Int J Infect Dis 2020;96:131–5.
  • 14. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. New England Journal of Medicine 2020;382:727–33. https://doi.org/10.1056/nejmoa2001017.
  • 15. Conti P, Ronconi G, Caraffa A, Gallenga C, Ross R, Frydas I, et al. Induction of pro-inflammatory cytokines (IL-1 and IL-6) and lung inflammation by Coronavirus-19 (COVI-19 or SARS-CoV-2): antiinflammatory strategies. J Biol Regul Homeost Agents 2020;34. https://doi.org/10.23812/CONTI-E.
  • 16. Liao Y-C, Liang W-G, Chen F-W, Hsu J-H, Yang J-J, Chang M-S. IL-19 Induces Production of IL-6 and TNF-α and Results in Cell Apoptosis Through TNF-α. The Journal of Immunology 2002;169:4288–97. https://doi.org/10.4049/jimmunol.169.8.4288.
  • 17. Tan L, Wang Q, Zhang D, Ding J, Huang Q, Tang Y-Q, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduct Target Ther 2020;5:33.
  • 18. Fischer K, Hoffmann P, Voelkl S, Meidenbauer N, Ammer J, Edinger M, et al. Inhibitory effect of tumor cell–derived lactic acid on human T cells. Blood 2007;109:3812–9. https://doi.org/10.1182/blood-2006-07- 035972.
  • 19. Arachchillage DRJ, Laffan M. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost 2020;18:1233–4.
  • 20. Lee KS. Pneumonia Associated with 2019 Novel Coronavirus: Can Computed Tomographic Findings Help Predict the Prognosis of the Disease? Korean Journal of Radiology 2020;21:257. https://doi.org/10.3348/kjr.2020.0096.
  • 21. Wong JP, Viswanathan S, Wang M, Sun L-Q, Clark GC, D’Elia RV. Current and future developments in the treatment of virus-induced hypercytokinemia. Future Med Chem 2017;9:169–78.
  • 22. Fakültesi ZÜT. COVID-19 Önleme ve Tedavi El Kitabı. Sistematik; 2020.
  • 23. Porfidia A, Pola R. Venous thromboembolism in COVID-19 patients. J Thromb Haemost 2020. https://doi.org/10.1111/jth.14842.
  • 24. Klok FA, Kruip MJHA, van der Meer NJM, Arbous MS, Gommers D, Kant KM, et al. Confirmation of the high cumulative incidence of thrombotic complications in critically ill ICU patients with COVID-19: An updated analysis. Thromb Res 2020. https://doi.org/10.1016/j.thromres.2020.04.041.
  • 25. Deng Y, Liu W, Liu K, Fang Y-Y, Shang J, Zhou L, et al. Clinical characteristics of fatal and recovered cases of coronavirus disease 2019 (COVID-19) in Wuhan, China: a retrospective study. Chin Med J 2020. https://doi.org/10.1097/CM9.0000000000000824.
  • 26. Pepys MB, Hirschfield GM. C-reactive protein: a critical update. Journal of Clinical Investigation 2003;111:1805–12. https://doi.org/10.1172/jci200318921.
  • 27. Wang G, Wu C, Zhang Q, Wu F, Yu B, Lv J, et al. C-Reactive Protein Level May Predict the Risk of COVID-19 Aggravation. Open Forum Infect Dis 2020;7:ofaa153.
  • 28. Bolten MP. C. Janzing, A. van den Berg en F. Kruisdijk (2003). Handboek voor milieutherapie. Theorie en praktijk van de klinische psychotherapie (deel 2). Assen: Van Gorcum. 244 pp., € 36,–. Tijdschrift Voor Psychotherapie 2005;31:93–6. https://doi.org/10.1007/bf03062133.
  • 29. Terpos E, Ntanasis-Stathopoulos I, Elalamy I, Kastritis E, Sergentanis TN, Politou M, et al. Hematological findings and complications of COVID-19. Am J Hematol 2020. https://doi.org/10.1002/ajh.25829.
APA KARABAY O, inci m, Öğütlü A, Ekerbiçer H, Guclu E, dheir h, YAYLACI S, Karabay M, GÜNER N, koroglu m, karacan a, ÇOKLUK e, tomak y (2021). Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19. , 36 - 44. 10.18521/ktd.841884
Chicago KARABAY OĞUZ,inci mustafa baran,Öğütlü Aziz,Ekerbiçer Hasan Çetin,Guclu Ertugrul,dheir hamad,YAYLACI SELÇUK,Karabay Meltem,GÜNER Necip Gökhan,koroglu mehmet,karacan alper,ÇOKLUK erdem,tomak yakup Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19. (2021): 36 - 44. 10.18521/ktd.841884
MLA KARABAY OĞUZ,inci mustafa baran,Öğütlü Aziz,Ekerbiçer Hasan Çetin,Guclu Ertugrul,dheir hamad,YAYLACI SELÇUK,Karabay Meltem,GÜNER Necip Gökhan,koroglu mehmet,karacan alper,ÇOKLUK erdem,tomak yakup Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19. , 2021, ss.36 - 44. 10.18521/ktd.841884
AMA KARABAY O,inci m,Öğütlü A,Ekerbiçer H,Guclu E,dheir h,YAYLACI S,Karabay M,GÜNER N,koroglu m,karacan a,ÇOKLUK e,tomak y Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19. . 2021; 36 - 44. 10.18521/ktd.841884
Vancouver KARABAY O,inci m,Öğütlü A,Ekerbiçer H,Guclu E,dheir h,YAYLACI S,Karabay M,GÜNER N,koroglu m,karacan a,ÇOKLUK e,tomak y Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19. . 2021; 36 - 44. 10.18521/ktd.841884
IEEE KARABAY O,inci m,Öğütlü A,Ekerbiçer H,Guclu E,dheir h,YAYLACI S,Karabay M,GÜNER N,koroglu m,karacan a,ÇOKLUK e,tomak y "Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19." , ss.36 - 44, 2021. 10.18521/ktd.841884
ISNAD KARABAY, OĞUZ vd. "Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19". (2021), 36-44. https://doi.org/10.18521/ktd.841884
APA KARABAY O, inci m, Öğütlü A, Ekerbiçer H, Guclu E, dheir h, YAYLACI S, Karabay M, GÜNER N, koroglu m, karacan a, ÇOKLUK e, tomak y (2021). Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19. KONURALP TIP DERGİSİ, 13(1), 36 - 44. 10.18521/ktd.841884
Chicago KARABAY OĞUZ,inci mustafa baran,Öğütlü Aziz,Ekerbiçer Hasan Çetin,Guclu Ertugrul,dheir hamad,YAYLACI SELÇUK,Karabay Meltem,GÜNER Necip Gökhan,koroglu mehmet,karacan alper,ÇOKLUK erdem,tomak yakup Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19. KONURALP TIP DERGİSİ 13, no.1 (2021): 36 - 44. 10.18521/ktd.841884
MLA KARABAY OĞUZ,inci mustafa baran,Öğütlü Aziz,Ekerbiçer Hasan Çetin,Guclu Ertugrul,dheir hamad,YAYLACI SELÇUK,Karabay Meltem,GÜNER Necip Gökhan,koroglu mehmet,karacan alper,ÇOKLUK erdem,tomak yakup Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19. KONURALP TIP DERGİSİ, vol.13, no.1, 2021, ss.36 - 44. 10.18521/ktd.841884
AMA KARABAY O,inci m,Öğütlü A,Ekerbiçer H,Guclu E,dheir h,YAYLACI S,Karabay M,GÜNER N,koroglu m,karacan a,ÇOKLUK e,tomak y Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19. KONURALP TIP DERGİSİ. 2021; 13(1): 36 - 44. 10.18521/ktd.841884
Vancouver KARABAY O,inci m,Öğütlü A,Ekerbiçer H,Guclu E,dheir h,YAYLACI S,Karabay M,GÜNER N,koroglu m,karacan a,ÇOKLUK e,tomak y Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19. KONURALP TIP DERGİSİ. 2021; 13(1): 36 - 44. 10.18521/ktd.841884
IEEE KARABAY O,inci m,Öğütlü A,Ekerbiçer H,Guclu E,dheir h,YAYLACI S,Karabay M,GÜNER N,koroglu m,karacan a,ÇOKLUK e,tomak y "Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19." KONURALP TIP DERGİSİ, 13, ss.36 - 44, 2021. 10.18521/ktd.841884
ISNAD KARABAY, OĞUZ vd. "Is It Possible To Predict Mortality Using Initial Data Of Adult Patients Hospitalized with COVID-19? A Mortality Prediction Model in the Early Phase of COVID-19". KONURALP TIP DERGİSİ 13/1 (2021), 36-44. https://doi.org/10.18521/ktd.841884