Document Type : Original Article
Instructor, Faculty member of the Nursing Department, School of Nursing and Midwifery, Islamic Azad University, Birjand, Iran
M.Sc in Emergency Nursing, Mashhad University of Medical Sciences, Mashhad, Iran
Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Department of Nursing, School of Nursing and Midwifery, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
Associated Professor, Department of Cardiac Surgery, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
M.Sc in Perfusion Technology, Department of Extra-Corporeal Circulation (ECC), Razavi Hospital, Mashhad, Iran
Background: MuLBSTA is a scale designed for easy clinical assessment of the mortality risk of viral pneumonia patients.
Objectives: The overall purpose of conducting this research is to investigate the effectiveness of MuLBSTA in estimating the mortality risk of COVID-19 patients.
Methods: A cross-sectional study was performed on 99 COVID-19 patients from December 2020 to February 2021. The MuLBSTA scores of patients were calculated, and their survival and risk rates were estimated by the Kaplan-Meier method. The ROC diagram was used for the logistic model assessment to determine the best mortality prediction cut-off point. Data were analyzed in SPSS version 21 at the 0.05 significance level.
Results: Of the 99 monitored patients, 69 (69.69%) recovered, and 30 (30.31%) died during the study period. The mean MuLBSTA scores of patients who recovered and died were 10.51±3.99 and 16.53±3.02, respectively. A statistically significant positive relationship was found between MuLBSTA scores and mortality (p<0.001). The area under the ROC curve (AUC) of MuLBSTA in predicting mortality during hospitalization was calculated to be 0.88 (95%CI=0.82-0.95, SE=1.55).
Conclusion: MuLBSTA scores are highly correlated with the severity of COVID-19. Therefore, MuLBSTA can serve as a tool for rapid situation assessment and swift decision-making about the treatment approach and the allocation of hospital resources to COVID-19 patients.