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  4. Vol 7, Issue 1, 2026

The Value of Five Scoring Systems in Predicting the Prognosis of Patients with COVID-19

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DOI: 10.23977/medsc.2026.070117 | Downloads: 7 | Views: 202

Author(s)

Jiaqian Liu 1, Shuangli Wang 1, Youfeng Zhu 1

Affiliation(s)

1 Department of Intensive Care Unit, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China

Corresponding Author

Youfeng Zhu

ABSTRACT

Scoring systems are routinely used in the intensive care unit (ICU) to evaluate disease prognosis. However, the value of these scoring systems for COVID-19 patient assessment is unclear. This study was conducted to identify the optimal scoring system for predicting the prognosis of COVID-19 patients. All data were obtained from the fourth version of the Medical Information Mart for Intensive Care (MIMIC-IV) database. Patients were grouped into two groups according to survival status at 28 days after admission. Independent risk factors for death in hospitals were identified by Logistic and Cox regression analysis. The scores of five scoring systems were calculated and collected. The predictive value of each of the five scoring systems was evaluated by the area under the receiver operating characteristic curve (AUROC). Multiple subgroup analyses were performed with respect to 28-day mortality according to age and sex. A total of 4274 COVID-19 patients were included. The median patient age was 67 (57,77) years, and 2507 patients (58.7%) were men. The median SIRS, SOFA, OASIS, SAPSⅡ, and APSⅢ scores were higher in the nonsurvival group than in the survivor group. The discrimination for 28-day mortality using the SAPSⅡ (AUROC 0.774, 95% confidence interval (CI): 0.755–0.793) and APSⅢ (AUROC 0.767, 95% CI: 0.748–0.786) models was superior to that using the SOFA (AUROC 0.727, 95% CI: 0.707–0.748), OASIS (AUROC 0.740, 95% CI: 0.721–0.760), SIRS (AUROC 0.617, 95% CI: 0.595–0.640), and Charlson (AUROC 0.666, 95% CI: 0.644–0.688) models. The Youden index of the SAPSⅡ model was 0.407, which was the highest among the models. The results of subgroup analyses were similar to the overall results. The SAPSII and APSIII models was superior than other scoring systems with regards to the discrimination of 28-day mortality in COVID-19 patients.

KEYWORDS

COVID-19, Scoring system, Mortality, SOFA, SAPSII, APSIII

CITE THIS PAPER

Jiaqian Liu, Shuangli Wang, Youfeng Zhu. The Value of Five Scoring Systems in Predicting the Prognosis of Patients with COVID-19. MEDS Clinical Medicine (2026). Vol. 7, No.1, 168-182. DOI: http://dx.doi.org/10.23977/medsc.2026.070117.

REFERENCES

[1] Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., Guan, L., Wei, Y., Li, H., Wu, X., Xu, J., Tu, S., Zhang, Y., Chen, H., and Cao, B. (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet, 395:1054-1062.
[2] Yang, X., Yu, Y., Xu, J., Shu, H., Xia, J., Liu, H., Wu, Y., Zhang, L., Yu, Z., Fang, M., Yu, T., Wang, Y., Pan, S., Zou, X., Yuan, S., and Shang, Y. (2020) Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet resp med, 8:475-481.
[3] Basham, M. A., Ghumro, H. A., Syed, M. U. S., Saeed, S., Pervez, S. A., Farooque, U., Kumar, N., Imtiaz, Z., Sajjad, M., Jamal, A., Aslam Siddiqui, I., and Idris, F. (2020) Validity of Sequential Organ Failure Assessment and Quick Sequential Organ Failure Assessment in Assessing Mortality Rate in the Intensive Care Unit With or Without Sepsis. Cureus, 12:e11071. 
[4] Singh, P., Pathak, S., and Sharma, R. M. (2018) A Comparison of Acute Physiology and Chronic Health Evaluation III and Simplified Acute Physiology Score II in Predicting Sepsis Outcome in Intensive Care Unit. Anesth Essays Res, 12:592-597. 
[5] Le Gall, J. R., Lemeshow, S., and Saulnier, F. (1993) A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. Jama-j am med assoc, 270:2957-2963. 
[6] Moralez, G. M., Rabello, L., Lisboa, T. C., Lima, M., Hatum, R. M., De Marco, F. V. C., Alves, A., Pinto, J., de Araújo, H. B. N., Ramos, G. V., Silva, A. R., Fernandes, G. C., Faria, G. B. A., Mendes, C. L., Ramos Filho, R., de Souza, V. P., do Brasil, P., Bozza, F. A., Salluh, J. I. F., and Soares, M. (2017) External validation of SAPS 3 and MPM0-III scores in 48,816 patients from 72 Brazilian ICUs. Ann Intensive Care, 7:53. 
[7] Metnitz, P. G., Moreno, R. P., Almeida, E., Jordan, B., Bauer, P., Campos, R. A., Iapichino, G., Edbrooke, D., Capuzzo, M., and Le Gall, J. R. (2005) SAPS 3--From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description. Intens care med, 31:1336-1344. 
[8] Kaukonen, K. M., Bailey, M., Pilcher, D., Cooper, D. J., and Bellomo, R. (2015) Systemic inflammatory response syndrome criteria in defining severe sepsis. New engl j med, 372:1629-1638. 
[9] Chen, Q., Zhang, L., Ge, S., He, W., and Zeng, M. (2019) Prognosis predictive value of the Oxford Acute Severity of Illness Score for sepsis: a retrospective cohort study. PeerJ, 7:e7083.
[10] Charlson, M., Szatrowski, T. P., Peterson, J., and Gold, J. (1994) Validation of a combined comorbidity index. J clin epidemiol, 47:1245-1251.
[11] Fan, S., and Ma, J. (2024) The value of five scoring systems in predicting the prognosis of patients with sepsis-associated acute respiratory failure. Sci Rep, 14:4760. 
[12] George, M., Sriram, D. K., Rathakrishnan, D., Moka, M. K., Sahay, M. I., and Jagadeeshwaran, V. (2025) Evaluation of Sepsis Outcomes Using SOFA, APACHE II, and SAPS Indices: A Retrospective Study in a Quaternary Care Hospital with Implications for Enhanced Mortality Prediction Models. J Assoc Physicians India, 73:e1-e8. 
[13] Schertz, A. R., Lenoir, K. M., Bertoni, A. G., Levine, B. J., Mongraw-Chaffin, M., and Thomas, K. W. (2023) Sepsis Prediction Model for Determining Sepsis vs SIRS, qSOFA, and SOFA. JAMA Netw Open, 6:e2329729. 
[14] Khan, A. M., & Aslam, S. M. (2022) Comparison of qSOFA Score, SIRS Criteria, and SOFA Score as predictors of mortality in patients with sepsis. Ghana Med J, 56:191-197. 
[15] von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., and Vandenbroucke, J. P. (2007) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med, 147:573-577. 
[16] Zeng, Z., Ma, Y., Zeng, H., Huang, P., Liu, W., Jiang, M., Xiang, X., Deng, D., Liao, X., Chen, P., and Chen, Y. (2021) Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients: Multicenter retrospective study. J med virol, 93:434-440. 
[17] Shamsabadi, F., Assarroudi, A., Armat, M., Sarchahi, Z., and Sahebkar, M. (2024) Comparison of Performance Characteristics in Early Warning Scoring Tools for Diagnosis of Intubation and Mortality Among COVID-19 Patients. J emerg nurs, 50:786-800. 
[18] Neumann, E., Movlilishvili, A., Scherfeld, S. T., Al Haj Hossen, L., Titze, U., Addicks, J. P., Eisenblätter, M., and Höink, A. J. (2025) Visual and AI-Based Assessment of COVID-19 Pneumonia: Practicability and Reproducibility of an Established Semi-Quantitative Chest CT Scoring System. Diagnostics (Basel), 15 (16)
[19] Lin, P. C., Huang, Y. B., Kuan, F. C., Ng, C. J., and Chen, S. Y. (2025) New scoring system combining WHO ordinal scale and MASCC score in predicting mortality of COVID-19 patients with malignancy and neutropenia: a retrospective cohort study. BMC Infect Dis, 25:1304. 
[20] Shibata, J., Osawa, I., Ito, H., Soeno, S., Hara, K., Sonoo, T., Nakamura, K., and Goto, T. (2021) Risk factors of sepsis among patients with qSOFA<2 in the emergency department. Am j emerg med, 50:699-706.
[21] Raith, E. P., Udy, A. A., Bailey, M., McGloughlin, S., MacIsaac, C., Bellomo, R., and Pilcher, D. V. (2017) Prognostic Accuracy of the SOFA Score, SIRS Criteria, and qSOFA Score for In-Hospital Mortality Among Adults With Suspected Infection Admitted to the Intensive Care Unit. Jama-j am med assoc, 317:290-300
[22] Yue, S., Li, S., Huang, X., Liu, J., Hou, X., Zhao, Y., Niu, D., Wang, Y., Tan, W., and Wu, J. (2022) Machine learning for the prediction of acute kidney injury in patients with sepsis. J Transl Med, 20:215.
[23] Huang, W. C., Xie, H. J., Fan, H. T., Yan, M. H., and Hong, Y. C. (2021) Comparison of prognosis predictive value of 4 disease severity scoring systems in patients with acute respiratory failure in intensive care unit: A STROBE report. Medicine, 100:e27380.
[24] Rudd, K. E., Seymour, C. W., Aluisio, A. R., Augustin, M. E., Bagenda, D. S., Beane, A., Byiringiro, J. C., Chang, C. H., Colas, L. N., Day, N. P. J., De Silva, A. P., Dondorp, A. M., Dünser, M. W., Faiz, M. A., Grant, D. S., Haniffa, R., Van Hao, N., Kennedy, J. N., Levine, A. C.,…Angus, D. C. (2018) Association of the Quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) Score With Excess Hospital Mortality in Adults With Suspected Infection in Low- and Middle-Income Countries. Jama, 319:2202-2211.
[25] Godinjak, A., Iglica, A., Rama, A., Tančica, I., Jusufović, S., Ajanović, A., and Kukuljac, A. (2016) Predictive value of SAPS II and APACHE II scoring systems for patient outcome in a medical intensive care unit. Acta Med Acad, 45:97-103. 
[26] Gilani, M. T., Razavi, M., and Azad, A. M. (2014) A comparison of Simplified Acute Physiology Score II, Acute Physiology and Chronic Health Evaluation II and Acute Physiology and Chronic Health Evaluation III scoring system in predicting mortality and length of stay at surgical intensive care unit. Niger Med J, 55:144-147. 
[27] de Oliveira, V. L., Almeida, T. V. R., da Silva Junior, H. B., Abrahim Alves, T. L. I., Boechat, A. L., Chaves, Y. O., João, G. A. P., de Almeida E Val, F. F., Dos Reis, M. F., de Lima Ferreira, L. C., and Gonçalves, R. L. (2025) Prognostic performance of ICU scoring systems in patients with HIV/AIDS: a retrospective analysis in the western Brazilian Amazon. BMC Infect Dis, 25:1554.
[28] Ghanghoria, K. N., R K, J., and Parmar, S. (2025) Evaluation of MODS and survival outcome in infectious diseases using SOFA and SAPS scores. Bioinformation, 21:2262-2266.
[29] Zhu, Y., Zhang, R., Ye, X., Liu, H., and Wei, J. (2022) SAPS III is superior to SOFA for predicting 28-day mortality in sepsis patients based on Sepsis 3.0 criteria. Int j infect dis, 114:135-141.

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