Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19

Skakun Oleksiy; Seredyuk Nestor

doi:doi:10.11648/j.ijidt.20230803.13

| Peer-Reviewed

Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19

Skakun Oleksiy, Seredyuk Nestor

Published in International Journal of Infectious Diseases and Therapy (Volume 8, Issue 3)

Received: 4 July 2023 Accepted: 18 July 2023 Published: 27 July 2023

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Abstract

Background: Despite the presence of many scores for the prediction of severity and mortality in COVID-19 patients, predictive accuracies of them are not high enough. Aim: Development of a scale for the prediction of severe condition and in-hospital mortality in hospitalized patients with COVID-19-associated pneumonia. Methods: The study included 135 adult patients hospitalized for COVID-19-associated pneumonia. Risk factors and optimal cut-off criteria for severe/critical condition and in-hospital mortality was established. Results: body mass index (BMI), scales CURB-65 and PSI, history of diabetes mellitus, SpO₂ level at admission, leukocyte count, lymphocyte percentage, levels of fasting glucose, alanine aminotransferase, ferritin, soluble IL-2 receptors, IL-6, and ferritin-hemoglobin ratio (FHR) were risk factors for disease progression to severe/critical condition. Logistic regression showed that only SpO₂, creatinine, and blood urea nitrogen were independent risk factors of severe/critical condition. Risk factors for in-hospital mortality included age, BMI, scales CURB-65 and PSI, SpO₂ level at admission, hemoglobin level, leukocyte count, levels of fasting glucose, creatinine, blood urea nitrogen, ferritin, IL-6, and FHR. However, logistic regression showed no relevant independent risk factor of in-hospital mortality. The novel score has been developed; it included the following parameters: blood pressure, BMI, ferritin level, SpO₂, creatinine level, history of arterial hypertension/ prior myocardial infarction / stroke, leukocyte count, elderly, history of diabetes mellitus (acronym “BIFOCALED”). There was good discriminative power of the novel score for severe/critical condition (AUC, 0.806, p<0.001) and in-hospital mortality (AUC, 0.804, p<0.001). The Youden index was 0.47 at the value of >2 points (sensitivity of 84.72%; specificity of 61.90%) for the prediction of severe/critical condition and 0.58 at the value of >5 points (sensitivity of 64.29%; specificity of 93.39%) for prediction of in-hospital mortality. Patients who scored >2 points had a far much higher risk of severe/critical condition (OR, 9.01; 95%CI, 3.97–20.44; p<0.001). In-hospital mortality was significantly higher in patients with >5 points according to the novel score (OR, 25.43; 95%CI, 6.88–93.99; p<0.001). Also, the probability of severe/critical condition and in-hospital mortality depending on the novel score was assessed. Conclusion: The BIFOCALED score may be used for predicting severe/critical condition and in-hospital mortality. The disease progression to severe/critical condition should be suspected in patients who scored >2 points; however, a score of >5 points is associated with high in-hospital mortality.

Published in	International Journal of Infectious Diseases and Therapy (Volume 8, Issue 3)
DOI	10.11648/j.ijidt.20230803.13
Page(s)	91-100
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

COVID-19, Score, BIFOCALED, Severity, Mortality

References

[1]	WHO. WHO COVID-19 dashboard [Internet]. World Health Organization. 2023. Available from: https://covid19.who.int/ [Accessed 2023 June 29].
[2]	Wang, H., Paulson, K. R., Pease, S. A., Watson, S., Comfort, H., Zheng, P., Aravkin, A. Y., Bisignano, C., Barber, R. M., Alam, T., Fuller, J. E., May, E. A., Jones, D. P., Frisch, M. E., Abbafati, C., Adolph, C., Allorant, A., Amlag, J. O., Bang-Jensen, B., & Bertolacci, G. J. (2022). Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21. The Lancet, 399 (10334), 1513–1536. https://doi.org/10.1016/s0140-6736(21)02796-3.
[3]	Panneer, S., Kantamaneni, K., Palaniswamy, U., Bhat, L., Pushparaj, R. R. B., Nayar, K. R., Soundari Manuel, H., Flower, F. X. L. L., & Rice, L. (2022). Health, Economic and Social Development Challenges of the COVID-19 Pandemic: Strategies for Multiple and Interconnected Issues. Healthcare, 10 (5), 770. https://doi.org/10.3390/healthcare10050770
[4]	Hu, Y., Sun, J., Dai, Z., Deng, H., Li, X., Huang, Q., Wu, Y., Sun, L., & Xu, Y. (2020). Prevalence and severity of corona virus disease 2019 (COVID-19): A systematic review and meta-analysis. Journal of Clinical Virology, 127, 104371. https://doi.org/10.1016/j.jcv.2020.104371.
[5]	Hebatallah Hany Assal, Hoda Abdel-Hamid, Magdy, S., Salah, M., Ali, A., Rasha Elkaffas, & Sabry, I. M. (2022). Predictors of severity and mortality in COVID-19 patients. 16 (1). https://doi.org/10.1186/s43168-022-00122-0.
[6]	Sitasuwan, T., Phisalprapa, P., Srivanichakorn, W., Washirasaksiri, C., Auesomwang, C., Tinmanee, R., Sayabovorn, N., Chayakulkeeree, M., Phoompoung, P., Mayurasakorn, K., Sookrung, N., Tungtrongchitr, A., Wanitphakdeedecha, R., Muangman, S., Senawong, S., Tangjittipokin, W., Sanpawitayakul, G., Woradetsittichai, D., Nimitpunya, P., Kositamongkol, C., … Chaisathaphol, T. (2022). Early antiviral and supervisory dexamethasone treatment improve clinical outcomes of nonsevere COVID-19 patients. Medicine, 101 (45), e31681. https://doi.org/10.1097/MD.0000000000031681.
[7]	Atluri, K., Aimlin, I., & Arora, S. (2022). Current Effective Therapeutics in Management of COVID-19. Journal of Clinical Medicine, 11 (13), 3838. https://doi.org/10.3390/jcm11133838.
[8]	Rajme-López, S., Martinez-Guerra, B. A., Zalapa-Soto, J., Román-Montes, C. M., Tamez-Torres, K. M., González-Lara, M. F., Hernandez-Gilosul, T., Kershenobich-Stalnikowitz, D., Sifuentes-Osornio, J., Ponce-de-León, A., & Ruíz-Palacios, G. M. (2022). Early Outpatient Treatment With Remdesivir in Patients at High Risk for Severe COVID-19: A Prospective Cohort Study. Open forum infectious diseases, 9 (10), ofac502. https://doi.org/10.1093/ofid/ofac502.
[9]	Dadras, O., SeyedAlinaghi, S., Karimi, A., Shamsabadi, A., Qaderi, K., Ramezani, M., Mirghaderi, S. P., Mahdiabadi, S., Vahedi, F., Saeidi, S., Shojaei, A., Mehrtak, M., Azar, S. A., Mehraeen, E., & Voltarelli, F. A. (2022). COVID-19 mortality and its predictors in the elderly: A systematic review. Health science reports, 5 (3), e657. https://doi.org/10.1002/hsr2.657.
[10]	Yen, Y. F., Chan, S. Y., Chen, C. C., & Deng, C. Y. (2022). Predictors for Early and Late Death in Adult Patients with COVID-19: A Cohort Study. International journal of environmental research and public health, 19 (6), 3357. https://doi.org/10.3390/ijerph19063357.
[11]	Nguyen, N. T., Chinn, J., De Ferrante, M., Kirby, K. A., Hohmann, S. F., & Amin, A. (2021). Male gender is a predictor of higher mortality in hospitalized adults with COVID-19. PloS one, 16 (7), e0254066. https://doi.org/10.1371/journal.pone.0254066.
[12]	Espejo-Paeres, C., Núñez-Gil, I. J., Estrada, V., Fernández-Pérez, C., Uribe-Heredia, G., Cabré-Verdiell, C., Uribarri, A., Romero, R., García-Aguado, M., Fernández-Rozas, I., Becerra-Muñoz, V., Pepe, M., Cerrato, E., Raposeiras-Roubín, S., Barrionuevo-Ramos, M., Aveiga-Ligua, F., Aguilar-Andrea, C., Alfonso-Rodríguez, E., Ugo, F., García-Prieto, J. F. …& Signes-Costa, J. (2021). Impact of smoking on COVID-19 outcomes: a HOPE Registry subanalysis. BMJ nutrition, prevention & health, 4 (1), 285–292. https://doi.org/10.1136/bmjnph-2021-000269.
[13]	Deng, M., Qi, Y., Deng, L., Wang, H., Xu, Y., Li, Z., Meng, Z., Tang, J., & Dai, Z. (2020). Obesity as a Potential Predictor of Disease Severity in Young COVID-19 Patients: A Retrospective Study. Obesity (Silver Spring, Md.), 28 (10), 1815–1825. https://doi.org/10.1002/oby.22943.
[14]	Tamara, A., & Tahapary, D. L. (2020). Obesity as a predictor for a poor prognosis of COVID-19: A systematic review. Diabetes & metabolic syndrome, 14 (4), 655–659. https://doi.org/10.1016/j.dsx.2020.05.020.
[15]	Moftakhar, L., Piraee, E., Mohammadi Abnavi, M., Moftakhar, P., Azarbakhsh, H., & Valipour, A. (2021). Epidemiological Features and Predictors of Mortality in Patients with COVID-19 with and without Underlying Hypertension. International journal of hypertension, 2021, 7427500. https://doi.org/10.1155/2021/7427500.
[16]	John, K. J., Mishra, A. K., Ramasamy, C., George, A. A., Selvaraj, V., & Lal, A. (2022). Heart failure in COVID-19 patients: Critical care experience. World journal of virology, 11 (1), 1–19. https://doi.org/10.5501/wjv.v11.i1.1.
[17]	Atmosudigdo, I. S., Lim, M. A., Radi, B., Henrina, J., Yonas, E., Vania, R., & Pranata, R. (2021). Dyslipidemia Increases the Risk of Severe COVID-19: A Systematic Review, Meta-analysis, and Meta-regression. Clinical medicine insights. Endocrinology and diabetes, 14, 1179551421990675. https://doi.org/10.1177/1179551421990675.
[18]	Schwerzmann, M., Francisco Javier Ruperti-Repilado, Baumgartner, H., Bouma, B. J., Bouchardy, J., Budts, W., Campens, L., Massimo Chessa, Cerro, del, Gabriel, H., Gallego, P., Garcia-Orta, R., Hector Alejandro Keller, Annette Bruun Jensen, Ladouceur, M., Miranda-Barrio, B., Morissens, M., Pasquet, A., Rueda, J., & van. (2021). Clinical outcome of COVID-19 in patients with adult congenital heart disease. 107 (15), 1226–1232. https://doi.org/10.1136/heartjnl-2020-318467
[19]	Hariyanto, T. I., Putri, C., Situmeang, R. F. V., & Kurniawan, A. (2021). Dementia is a predictor for mortality outcome from coronavirus disease 2019 (COVID-19) infection. European archives of psychiatry and clinical neuroscience, 271 (2), 393–395. https://doi.org/10.1007/s00406-020-01205-z.
[20]	Al-Hussain O. H. (2022). Complications and Comorbidities in COVID-19 Patients: A Comparative study. Cureus, 14 (8), e28614. https://doi.org/10.7759/cureus.28614.
[21]	Gansevoort, R. T., Hilbrands, L. B. CKD is a key risk factor for COVID-19 mortality. Nat Rev Nephrol 16, 705–706 (2020). https://doi.org/10.1038/s41581-020-00349-4.
[22]	Vujčić I. (2023). Outcomes of COVID-19 among patients with liver disease. World journal of gastroenterology, 29 (5), 815–824. https://doi.org/10.3748/wjg.v29.i5.815.
[23]	Xu, J., Xiao, W., Shi, L., Wang, Y., & Yang, H. (2021). Is Cancer an Independent Risk Factor for Fatal Outcomes of Coronavirus Disease 2019 Patients?. Archives of medical research, 52 (7), 755–760. https://doi.org/10.1016/j.arcmed.2021.05.003.
[24]	Gerayeli, F. V., Milne, S., Cheung, C., Li, X., Yang, C. W. T., Tam, A., Choi, L. H., Bae, A., & Sin, D. D. (2021). COPD and the risk of poor outcomes in COVID-19: A systematic review and meta-analysis. EClinicalMedicine, 33, 100789. https://doi.org/10.1016/j.eclinm.2021.100789.
[25]	Zheng, Z., Peng, F., Xu, B., Zhao, J., Liu, H., Peng, J., Li, Q., Jiang, C., Zhou, Y., Liu, S., Ye, C., Zhang, P., Xing, Y., Guo, H., & Tang, W. (2020). Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. The Journal of infection, 81 (2), e16–e25. https://doi.org/10.1016/j.jinf.2020.04.021.
[26]	Liu, W., Yang, C., Liao, Y. G., Wan, F., Lin, L., Huang, X., Zhang, B. H., Yuan, Y., Zhang, P., Zhang, X. J., She, Z. G., Wang, L., & Li, H. (2022). Risk factors for COVID-19 progression and mortality in hospitalized patients without pre-existing comorbidities. Journal of infection and public health, 15 (1), 13–20. https://doi.org/10.1016/j.jiph.2021.11.012.
[27]	Kilercik, M., Demirelce, Ö., Serdar, M. A., Mikailova, P., & Serteser, M. (2021). A new haematocytometric index: Predicting severity and mortality risk value in COVID-19 patients. PloS one, 16 (8), e0254073. https://doi.org/10.1371/journal.pone.0254073
[28]	Banchini, F., Cattaneo, G. M., & Capelli, P. (2021). Serum ferritin levels in inflammation: a retrospective comparative analysis between COVID-19 and emergency surgical non-COVID-19 patients. World Journal of Emergency Surgery, 16 (1). https://doi.org/10.1186/s13017-021-00354-3.
[29]	Sabaka, P., Koščálová, A., Straka, I., Hodosy, J., Lipták, R., Kmotorková, B., Kachlíková, M., & Kušnírová, A. (2021). Role of interleukin 6 as a predictive factor for a severe course of Covid-19: retrospective data analysis of patients from a long-term care facility during Covid-19 outbreak. BMC infectious diseases, 21 (1), 308. https://doi.org/10.1186/s12879-021-05945-8.
[30]	Karimi Shahri, M., Niazkar, H. R., & Rad, F. (2021). COVID-19 and hematology findings based on the current evidences: A puzzle with many missing pieces. International journal of laboratory hematology, 43 (2), 160–168. https://doi.org/10.1111/ijlh.13412.
[31]	Lee, J., Park, S. S., Kim, T. Y., Lee, D. G., & Kim, D. W. (2021). Lymphopenia as a Biological Predictor of Outcomes in COVID-19 Patients: A Nationwide Cohort Study. Cancers, 13 (3), 471. https://doi.org/10.3390/cancers13030471.
[32]	Khraise, W. N., Khraise, T. W., Starling Emerald, B., & Allouh, M. Z. (2020). Epidemiologic and Clinical Characteristics of COVID-19 Patients from a Quarantine Center in a Developing Community: A Retrospective Study. International journal of general medicine, 13, 937–944. https://doi.org/10.2147/IJGM.S276742.
[33]	Smilowitz, N. R., Kunichoff, D., Garshick, M., Shah, B., Pillinger, M., Hochman, J. S., & Berger, J. S. (2021). C-reactive protein and clinical outcomes in patients with COVID-19. European heart journal, 42 (23), 2270–2279. https://doi.org/10.1093/eurheartj/ehaa1103.
[34]	Fawzy, S., Ahmed, M. M., Alsayed, B. A., Mir, R., & Amle, D. (2022). IL-2 and IL-1β Patient Immune Responses Are Critical Factors in SARS-CoV-2 Infection Outcomes. Journal of personalized medicine, 12 (10), 1729. https://doi.org/10.3390/jpm12101729.
[35]	Mohd Zawawi, Z., Kalyanasundram, J., Mohd Zain, R., Thayan, R., Basri, D. F., & Yap, W. B. (2023). Prospective Roles of Tumor Necrosis Factor-Alpha (TNF-α) in COVID-19: Prognosis, Therapeutic and Management. International journal of molecular sciences, 24 (7), 6142. https://doi.org/10.3390/ijms24076142.
[36]	Almayahi, Z. K., Raveendran, A. V., Al Malki, R., Safwat, A., Al Baloshi, M., Abbas, A., Al Salami, A. S., Al Mujaini, S. M., Al Dhuhli, K., & Al Mandhari, S. (2022). Clinical features, laboratory characteristics and risk factors for mortality of COVID-19 patients in a secondary hospital in Oman during the first wave of the SARS-CoV-2 pandemic. Bulletin of the National Research Centre, 46 (1), 139. https://doi.org/10.1186/s42269-022-00825-w.
[37]	Jang, H. J., Leem, A. Y., Chung, K. S., Ahn, J. Y., Jung, J. Y., Kang, Y. A., Park, M. S., Kim, Y. S., & Lee, S. H. (2021). Soluble IL-2R Levels Predict in-Hospital Mortality in COVID-19 Patients with Respiratory Failure. Journal of clinical medicine, 10 (18), 4242. https://doi.org/10.3390/jcm10184242.
[38]	Kaya, H., Kaji, M., & Usuda, D. (2021). Soluble interleukin-2 receptor levels on admission associated with mortality in coronavirus disease 2019. International journal of infectious diseases: IJID: official publication of the International Society for Infectious Diseases, 105, 522–524. https://doi.org/10.1016/j.ijid.2021.03.011.
[39]	Raman, N., Kv, P., Ashta, K. K., Vardhan, V., Thareja, S., J, M., Kumar, A., & Basavaraj (2021). Ferritin and Hemoglobin as Predictors of Fatal Outcome in COVID-19: Two Sides of the Same Coin. The Journal of the Association of Physicians of India, 69 (8), 11–12.
[40]	Bradley, J., Sbaih, N., Chandler, T. R., Furmanek, S., Ramirez, J. A., & Cavallazzi, R. (2022). Pneumonia Severity Index and CURB-65 Score Are Good Predictors of Mortality in Hospitalized Patients With SARS-CoV-2 Community-Acquired Pneumonia. Chest, 161 (4), 927–936. https://doi.org/10.1016/j.chest.2021.10.031.
[41]	Lombardi, Y., Azoyan, L., Szychowiak, P., Bellamine, A., Lemaitre, G., Bernaux, M., Daniel, C., Leblanc, J., Riller, Q., Steichen, O., & AP-HP/Universities/INSERM COVID-19 Research Collaboration AP-HP COVID CDR Initiative (2021). External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals. Intensive care medicine, 47 (12), 1426–1439. https://doi.org/10.1007/s00134-021-06524-w.
[42]	Ministerstvo Okhorony Zdorovia Ukrainy. Derzhavnyi Ekspertnyi Tsentr [Internet]. Protokol «Nadannia medychnoi dopomohy dlia likuvannia koronavirusnoi khvoroby (COVID-19)» [cited 2022 Jul 1]. Available from: https://www.dec.gov.ua/wp-content/uploads/2021/01/2021_01_kn_covid-19.pdf.
[43]	Rahman, A., & Sathi, N. J. (2021). Risk factors of the severity of COVID-19: A meta-analysis. International journal of clinical practice, 75 (7), e13916. https://doi.org/10.1111/ijcp.13916.
[44]	Ou, M., Zhu, J., Ji, P., Li, H., Zhong, Z., Li, B., Pang, J., Zhang, J., & Zheng, X. (2020). Risk factors of severe cases with COVID-19: a meta-analysis. Epidemiology and infection, 148, e175. https://doi.org/10.1017/S095026882000179X.
[45]	Booth, A., Reed, A. B., Ponzo, S., Yassaee, A., Aral, M., Plans, D., Labrique, A., & Mohan, D. (2021). Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis. PloS one, 16 (3), e0247461. https://doi.org/10.1371/journal.pone.0247461.
[46]	Kaeuffer, C., Le Hyaric, C., Fabacher, T., Mootien, J., Dervieux, B., Ruch, Y., Hugerot, A., Zhu, Y. J., Pointurier, V., Clere-Jehl, R., Greigert, V., Kassegne, L., Lefebvre, N., Gallais, F., Covid Alsace Study Group, Meyer, N., Hansmann, Y., Hinschberger, O., Danion, F., & COVID Alsace Study Group (2020). Clinical characteristics and risk factors associated with severe COVID-19: prospective analysis of 1,045 hospitalised cases in North-Eastern France, March 2020. Euro surveillance: bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin, 25 (48), 2000895. https://doi.org/10.2807/1560-7917.ES.2020.25.48.2000895.
[47]	Li, X., Zhong, X., Wang, Y., Zeng, X., Luo, T., & Liu, Q. (2021). Clinical determinants of the severity of COVID-19: A systematic review and meta-analysis. PloS one, 16 (5), e0250602. https://doi.org/10.1371/journal.pone.0250602.
[48]	Jiang, N., Liu, Y. N., Bao, J., Li, R., Ni, W. T., Tan, X. Y., Xu, Y., Peng, L. P., Wang, X. R., Zeng, Y. M., Liu, D. S., Xue, Q., Li, J. S., Hu, K., Zheng, Y. L., & Gao, Z. C. (2021). Clinical features and risk factors associated with severe COVID-19 patients in China. Chinese medical journal, 134 (8), 944–953. https://doi.org/10.1097/CM9.0000000000001466.
[49]	Utulu, R., Ajayi, I. O., Bello, S., Balogun, M. S., Madubueze, U. C., Adeyemi, I. T., Omoju, O. T., Adeke, A. S., Adenekan, A. O., & Iyare, O. (2022). Risk factors for COVID-19 infection and disease severity in Nigeria: a case-control study. The Pan African medical journal, 41, 317. https://doi.org/10.11604/pamj.2022.41.317.34307.
[50]	Talebi, S. S., Hosseinzadeh, A., Zare, F., Daliri, S., Jamali Atergeleh, H., Khosravi, A., Goli, S., & Rohani-Rasaf, M. (2022). Risk Factors Associated with Mortality in COVID-19 Patient's: Survival Analysis. Iranian journal of public health, 51 (3), 652–658. https://doi.org/10.18502/ijph.v51i3.8942.
[51]	Ayón-Aguilar, J., Méndez-Martínez, S., Toledo-Tapia, R., García-Flores, M. A., Mayoral-Ortiz, A., Tlecuitl-Mendoza, N., Toledo-Tapia, M., Ortega-Aguirre, M., & Amaro-Balderas, E. (2022). Influencia de factores de riesgo sobre mortalidad por COVID-19 [Influence of risk factors on mortality from COVID-19]. Revista medica del Instituto Mexicano del Seguro Social, 60 (4), 433–439.
[52]	Sepandi, M., Taghdir, M., Alimohamadi, Y., Afrashteh, S., & Hosamirudsari, H. (2020). Factors Associated with Mortality in COVID-19 Patients: A Systematic Review and Meta-Analysis. Iranian journal of public health, 49 (7), 1211–1221. https://doi.org/10.18502/ijph.v49i7.3574.
[53]	Philip, C., David, A., Mathew, S. K., Sunny, S., Kumar K, V., Jacob, L., Mathew, L., Kumar, S., & Chandy, G. (2022). The Predictive Score for Patients Hospitalized With COVID-19 in Resource-Limited Settings. Cureus, 14 (10), e30373. https://doi.org/10.7759/cureus.30373.
[54]	Subhani, F., Chhotani, A. A., Waheed, S., Zahid, R. O., Azizi, K., & Buksh, A. R. (2022). Development of COVID-19 severity assessment score in adults presenting with COVID-19 to the emergency department. BMC infectious diseases, 22 (1), 576. https://doi.org/10.1186/s12879-022-07535-8.
[55]	Sebastian, A., Madziarski, M., Madej, M., Proc, K., Szymala-Pędzik, M., Żórawska, J., Gronek, M., Morgiel, E., Kujawa, K., Skarupski, M., Trocha, M., Rola, P., Gawryś, J., Letachowicz, K., Doroszko, A., Adamik, B., Kaliszewski, K., Kiliś-Pstrusińska, K., Matera-Witkiewicz, A., Pomorski, M. …& Madziarska, K. (2022). The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19. International journal of environmental research and public health, 19 (19), 12537. https://doi.org/10.3390/ijerph191912537.
[56]	Wang, P., Sha, J., Meng, M., Wang, C., Yao, Q., Zhang, Z., Sun, W., Wang, X., Qie, G., Bai, X., Liu, K., & Chu, Y. (2020). Risk factors for severe COVID-19 in middle-aged patients without comorbidities: a multicentre retrospective study. Journal of translational medicine, 18 (1), 461. https://doi.org/10.1186/s12967-020-02655-8.
[57]	Wang, M., Wu, D., Liu, C. H., Li, Y., Hu, J., Wang, W., Jiang, W., Zhang, Q., Huang, Z., Bai, L., & Tang, H. (2022). Predicting progression to severe COVID-19 using the PAINT score. BMC infectious diseases, 22 (1), 498. https://doi.org/10.1186/s12879-022-07466-4.
[58]	Martín-Rodríguez, F., Sanz-García, A., Ortega, G. J., Delgado-Benito, J. F., García Villena, E., Mazas Pérez-Oleaga, C., López-Izquierdo, R., & Castro Villamor, M. A. (2022). One-on-one comparison between qCSI and NEWS scores for mortality risk assessment in patients with COVID-19. Annals of medicine, 54 (1), 646–654. https://doi.org/10.1080/07853890.2022.2042590.
[59]	Giamarellos-Bourboulis, E. J., Poulakou, G., de Nooijer, A., Milionis, H., Metallidis, S., Ploumidis, M., Grigoropoulou, P., Rapti, A., Segala, F. V., Balis, E., Giannitsioti, E., Rodari, P., Kainis, I., Alexiou, Z., Focà, E., Lucio, B., Rovina, N., Scorzolini, L., Dafni, M., Ioannou, S., … Netea, M. G. (2022). Development and validation of SCOPE score: A clinical score to predict COVID-19 pneumonia progression to severe respiratory failure. Cell reports. Medicine, 3 (3), 100560. https://doi.org/10.1016/j.xcrm.2022.100560.
[60]	Cr, P., Vanidassane, I., Pownraj, D., Kandasamy, R., & Basheer, A. (2021). National Early Warning Score 2 (NEWS2) to predict poor outcome in hospitalised COVID-19 patients in India. PloS one, 16 (12), e0261376. https://doi.org/10.1371/journal.pone.0261376.

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Skakun Oleksiy, Seredyuk Nestor. (2023). Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19. International Journal of Infectious Diseases and Therapy, 8(3), 91-100. https://doi.org/10.11648/j.ijidt.20230803.13

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ACS Style

Skakun Oleksiy; Seredyuk Nestor. Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19. Int. J. Infect. Dis. Ther. 2023, 8(3), 91-100. doi: 10.11648/j.ijidt.20230803.13

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AMA Style

Skakun Oleksiy, Seredyuk Nestor. Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19. Int J Infect Dis Ther. 2023;8(3):91-100. doi: 10.11648/j.ijidt.20230803.13

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@article{10.11648/j.ijidt.20230803.13,
  author = {Skakun Oleksiy and Seredyuk Nestor},
  title = {Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19},
  journal = {International Journal of Infectious Diseases and Therapy},
  volume = {8},
  number = {3},
  pages = {91-100},
  doi = {10.11648/j.ijidt.20230803.13},
  url = {https://doi.org/10.11648/j.ijidt.20230803.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijidt.20230803.13},
  abstract = {Background: Despite the presence of many scores for the prediction of severity and mortality in COVID-19 patients, predictive accuracies of them are not high enough. Aim: Development of a scale for the prediction of severe condition and in-hospital mortality in hospitalized patients with COVID-19-associated pneumonia. Methods: The study included 135 adult patients hospitalized for COVID-19-associated pneumonia. Risk factors and optimal cut-off criteria for severe/critical condition and in-hospital mortality was established. Results: body mass index (BMI), scales CURB-65 and PSI, history of diabetes mellitus, SpO2 level at admission, leukocyte count, lymphocyte percentage, levels of fasting glucose, alanine aminotransferase, ferritin, soluble IL-2 receptors, IL-6, and ferritin-hemoglobin ratio (FHR) were risk factors for disease progression to severe/critical condition. Logistic regression showed that only SpO2, creatinine, and blood urea nitrogen were independent risk factors of severe/critical condition. Risk factors for in-hospital mortality included age, BMI, scales CURB-65 and PSI, SpO2 level at admission, hemoglobin level, leukocyte count, levels of fasting glucose, creatinine, blood urea nitrogen, ferritin, IL-6, and FHR. However, logistic regression showed no relevant independent risk factor of in-hospital mortality. The novel score has been developed; it included the following parameters: blood pressure, BMI, ferritin level, SpO2, creatinine level, history of arterial hypertension/ prior myocardial infarction / stroke, leukocyte count, elderly, history of diabetes mellitus (acronym “BIFOCALED”). There was good discriminative power of the novel score for severe/critical condition (AUC, 0.806, p2 points (sensitivity of 84.72%; specificity of 61.90%) for the prediction of severe/critical condition and 0.58 at the value of >5 points (sensitivity of 64.29%; specificity of 93.39%) for prediction of in-hospital mortality. Patients who scored >2 points had a far much higher risk of severe/critical condition (OR, 9.01; 95%CI, 3.97–20.44; p5 points according to the novel score (OR, 25.43; 95%CI, 6.88–93.99; p2 points; however, a score of >5 points is associated with high in-hospital mortality.},
 year = {2023}
}

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TY  - JOUR
T1  - Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19
AU  - Skakun Oleksiy
AU  - Seredyuk Nestor
Y1  - 2023/07/27
PY  - 2023
N1  - https://doi.org/10.11648/j.ijidt.20230803.13
DO  - 10.11648/j.ijidt.20230803.13
T2  - International Journal of Infectious Diseases and Therapy
JF  - International Journal of Infectious Diseases and Therapy
JO  - International Journal of Infectious Diseases and Therapy
SP  - 91
EP  - 100
PB  - Science Publishing Group
SN  - 2578-966X
UR  - https://doi.org/10.11648/j.ijidt.20230803.13
AB  - Background: Despite the presence of many scores for the prediction of severity and mortality in COVID-19 patients, predictive accuracies of them are not high enough. Aim: Development of a scale for the prediction of severe condition and in-hospital mortality in hospitalized patients with COVID-19-associated pneumonia. Methods: The study included 135 adult patients hospitalized for COVID-19-associated pneumonia. Risk factors and optimal cut-off criteria for severe/critical condition and in-hospital mortality was established. Results: body mass index (BMI), scales CURB-65 and PSI, history of diabetes mellitus, SpO2 level at admission, leukocyte count, lymphocyte percentage, levels of fasting glucose, alanine aminotransferase, ferritin, soluble IL-2 receptors, IL-6, and ferritin-hemoglobin ratio (FHR) were risk factors for disease progression to severe/critical condition. Logistic regression showed that only SpO2, creatinine, and blood urea nitrogen were independent risk factors of severe/critical condition. Risk factors for in-hospital mortality included age, BMI, scales CURB-65 and PSI, SpO2 level at admission, hemoglobin level, leukocyte count, levels of fasting glucose, creatinine, blood urea nitrogen, ferritin, IL-6, and FHR. However, logistic regression showed no relevant independent risk factor of in-hospital mortality. The novel score has been developed; it included the following parameters: blood pressure, BMI, ferritin level, SpO2, creatinine level, history of arterial hypertension/ prior myocardial infarction / stroke, leukocyte count, elderly, history of diabetes mellitus (acronym “BIFOCALED”). There was good discriminative power of the novel score for severe/critical condition (AUC, 0.806, p2 points (sensitivity of 84.72%; specificity of 61.90%) for the prediction of severe/critical condition and 0.58 at the value of >5 points (sensitivity of 64.29%; specificity of 93.39%) for prediction of in-hospital mortality. Patients who scored >2 points had a far much higher risk of severe/critical condition (OR, 9.01; 95%CI, 3.97–20.44; p5 points according to the novel score (OR, 25.43; 95%CI, 6.88–93.99; p2 points; however, a score of >5 points is associated with high in-hospital mortality.
VL  - 8
IS  - 3
ER  -

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Author Information

Skakun Oleksiy

Department of Internal Medicine No. 2 and Nursing, Ivano-Frankivsk National Medical University, Ivano-Frankivsk, Ukraine
Seredyuk Nestor

Department of Internal Medicine No. 2 and Nursing, Ivano-Frankivsk National Medical University, Ivano-Frankivsk, Ukraine

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Plain Text BibTeX RIS

APA Style

Skakun Oleksiy, Seredyuk Nestor. (2023). Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19. International Journal of Infectious Diseases and Therapy, 8(3), 91-100. https://doi.org/10.11648/j.ijidt.20230803.13

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ACS Style

Skakun Oleksiy; Seredyuk Nestor. Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19. Int. J. Infect. Dis. Ther. 2023, 8(3), 91-100. doi: 10.11648/j.ijidt.20230803.13

Copy | Download

AMA Style

Skakun Oleksiy, Seredyuk Nestor. Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19. Int J Infect Dis Ther. 2023;8(3):91-100. doi: 10.11648/j.ijidt.20230803.13

Copy | Download

@article{10.11648/j.ijidt.20230803.13,
  author = {Skakun Oleksiy and Seredyuk Nestor},
  title = {Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19},
  journal = {International Journal of Infectious Diseases and Therapy},
  volume = {8},
  number = {3},
  pages = {91-100},
  doi = {10.11648/j.ijidt.20230803.13},
  url = {https://doi.org/10.11648/j.ijidt.20230803.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijidt.20230803.13},
  abstract = {Background: Despite the presence of many scores for the prediction of severity and mortality in COVID-19 patients, predictive accuracies of them are not high enough. Aim: Development of a scale for the prediction of severe condition and in-hospital mortality in hospitalized patients with COVID-19-associated pneumonia. Methods: The study included 135 adult patients hospitalized for COVID-19-associated pneumonia. Risk factors and optimal cut-off criteria for severe/critical condition and in-hospital mortality was established. Results: body mass index (BMI), scales CURB-65 and PSI, history of diabetes mellitus, SpO2 level at admission, leukocyte count, lymphocyte percentage, levels of fasting glucose, alanine aminotransferase, ferritin, soluble IL-2 receptors, IL-6, and ferritin-hemoglobin ratio (FHR) were risk factors for disease progression to severe/critical condition. Logistic regression showed that only SpO2, creatinine, and blood urea nitrogen were independent risk factors of severe/critical condition. Risk factors for in-hospital mortality included age, BMI, scales CURB-65 and PSI, SpO2 level at admission, hemoglobin level, leukocyte count, levels of fasting glucose, creatinine, blood urea nitrogen, ferritin, IL-6, and FHR. However, logistic regression showed no relevant independent risk factor of in-hospital mortality. The novel score has been developed; it included the following parameters: blood pressure, BMI, ferritin level, SpO2, creatinine level, history of arterial hypertension/ prior myocardial infarction / stroke, leukocyte count, elderly, history of diabetes mellitus (acronym “BIFOCALED”). There was good discriminative power of the novel score for severe/critical condition (AUC, 0.806, p2 points (sensitivity of 84.72%; specificity of 61.90%) for the prediction of severe/critical condition and 0.58 at the value of >5 points (sensitivity of 64.29%; specificity of 93.39%) for prediction of in-hospital mortality. Patients who scored >2 points had a far much higher risk of severe/critical condition (OR, 9.01; 95%CI, 3.97–20.44; p5 points according to the novel score (OR, 25.43; 95%CI, 6.88–93.99; p2 points; however, a score of >5 points is associated with high in-hospital mortality.},
 year = {2023}
}

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TY  - JOUR
T1  - Novel Score for Prediction of Severity and Mortality in Hospitalized Patients with COVID-19
AU  - Skakun Oleksiy
AU  - Seredyuk Nestor
Y1  - 2023/07/27
PY  - 2023
N1  - https://doi.org/10.11648/j.ijidt.20230803.13
DO  - 10.11648/j.ijidt.20230803.13
T2  - International Journal of Infectious Diseases and Therapy
JF  - International Journal of Infectious Diseases and Therapy
JO  - International Journal of Infectious Diseases and Therapy
SP  - 91
EP  - 100
PB  - Science Publishing Group
SN  - 2578-966X
UR  - https://doi.org/10.11648/j.ijidt.20230803.13
AB  - Background: Despite the presence of many scores for the prediction of severity and mortality in COVID-19 patients, predictive accuracies of them are not high enough. Aim: Development of a scale for the prediction of severe condition and in-hospital mortality in hospitalized patients with COVID-19-associated pneumonia. Methods: The study included 135 adult patients hospitalized for COVID-19-associated pneumonia. Risk factors and optimal cut-off criteria for severe/critical condition and in-hospital mortality was established. Results: body mass index (BMI), scales CURB-65 and PSI, history of diabetes mellitus, SpO2 level at admission, leukocyte count, lymphocyte percentage, levels of fasting glucose, alanine aminotransferase, ferritin, soluble IL-2 receptors, IL-6, and ferritin-hemoglobin ratio (FHR) were risk factors for disease progression to severe/critical condition. Logistic regression showed that only SpO2, creatinine, and blood urea nitrogen were independent risk factors of severe/critical condition. Risk factors for in-hospital mortality included age, BMI, scales CURB-65 and PSI, SpO2 level at admission, hemoglobin level, leukocyte count, levels of fasting glucose, creatinine, blood urea nitrogen, ferritin, IL-6, and FHR. However, logistic regression showed no relevant independent risk factor of in-hospital mortality. The novel score has been developed; it included the following parameters: blood pressure, BMI, ferritin level, SpO2, creatinine level, history of arterial hypertension/ prior myocardial infarction / stroke, leukocyte count, elderly, history of diabetes mellitus (acronym “BIFOCALED”). There was good discriminative power of the novel score for severe/critical condition (AUC, 0.806, p2 points (sensitivity of 84.72%; specificity of 61.90%) for the prediction of severe/critical condition and 0.58 at the value of >5 points (sensitivity of 64.29%; specificity of 93.39%) for prediction of in-hospital mortality. Patients who scored >2 points had a far much higher risk of severe/critical condition (OR, 9.01; 95%CI, 3.97–20.44; p5 points according to the novel score (OR, 25.43; 95%CI, 6.88–93.99; p2 points; however, a score of >5 points is associated with high in-hospital mortality.
VL  - 8
IS  - 3
ER  -

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