Journals / KONURALP TIP DERGİSİ / 2021 / Cilt: 13 - Sayı: 1

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

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

Pages
36–44
Publication Date
2022-10-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The mean age of the non-survivors was 71.7±11.2 SD ( P

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data:A mathematical model was created from the data of both groups.Results: While 863 patients survived, 78 were non-survivors.
💡
Conclusion & Impact:The mean age of the non-survivors was 71.7±11.2 SD ( P
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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

Özet

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

🔖

Cite This Article

ÖĞÜTLÜ, A., ÇOKLUK, E., YAYLACI, S., DHEIR, H., TOMAK, Y., İNCİ, M. B., EKERBİÇER, H. Ç., GÜÇLÜ, E., KARABAY, O., KÖROĞLU, M., KARABAY, M., GÜNER, N. G., KARACAN, A. (2022). 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, Cilt: 13 - Sayı: 1, 36–44. https://ajindex.com/articles/575499

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  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.
  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.
  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.
  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.
  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.
  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.
  12. Bourgonje AR, Abdulle AE, Timens W, Hillebrands J-L, Navis GJ, Gordijn SJ, et al. An