Comparative Study on the Effectiveness of the CROW-65, MEWS, and 4C Mortality Score in Predicting In-Hospital Mortality among COVID-19 Patients Treated with High-Flow Nasal Cannula
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Abstract
The purpose of this retrospective cross-sectional study was to compare the effectiveness of the CROW-65, Modified Early Warning Score (MEWS), and 4C Mortality Score in predicting in-hospital mortality among COVID-19 patients treated with high-flow nasal cannula (HFNC). Data were collected from medical records of 144 COVID-19 patients with acute hypoxemic respiratory failure who were treated with HFNC at Somdech Phra Pinklao Hospital between November 1, 2020, and October 31, 2021. The research instruments included a demographic and clinical data record form, pre-HFNC treatment data, and HFNC treatment data. Descriptive statistics were used to analyze general patient characteristics. The effectiveness of the CROW-65, MEWS, and 4C Mortality Score in predicting in-hospital mortality was compared using area under curve (AUC), sensitivity, specificity, and accuracy.
Results showed that the 4C Mortality Score was highly suitable for predicting the mortality rate of COVID-19 patients (AUC = 0.862), followed by CROW-65 at a good level (AUC = 0.748), and MEWS at a satisfactory level (AUC = 0.694). When considering the appropriate cut-off point, the 4C Mortality Score with a cut-off value of 13 demonstrated the highest Youden's index (0.551), with a sensitivity of 76.3, specificity of 78.8, and accuracy of 77.8. The CROW-65 Score, with a cut-off value of 11 and 12, showed the highest Youden’s index (0.381), with a sensitivity of 74.6, specificity of 63.5, and accuracy of 68.1. The MEWS Score, with a cut-off value of 4, yielded the highest Youden's index (0.293), with a sensitivity of 45.8, specificity of 83.5, and accuracy of 68.1.
The results of this study indicate that the 4C Mortality Score is the most appropriate tool for predicting in-hospital mortality among COVID-19 patients treated with HFNC. It can be used by healthcare professionals as a guideline for clinical decision-making to enhance efficiency in treatment prioritization and optimal allocation of medical resources.
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