MuLBSTA Skorunun Şiddetli Akut Solunum Sendromu Koronavirüs 2019 Pnömonili Hospitalize Hastalarda Kritik Klinik Sonuçları Öngörmedeki Prediktif Değerinin Incelenmesi
Anadolu Kliniği Tıp Bilimleri Dergisi(2022)
摘要
Aim: Multilobar infiltration, lymphocytopenia, bacterial co-infection, smoking history, hypertension, and age>65 (MuLBSTA) score is a clinical prediction rule used to classify patients with viral pneumonia by expected mortality. We compared the predictive performance of MuLBSTA with PSI, CURB-65, and qSOFA for poor clinical outcomes in hospitalized severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients.Methods: A retrospective study was conducted on patients with SARS-CoV-2 who were hospitalized in a tertiary medical center between March 11, 2020, and May 31, 2020. 271 out of 900 patients who tested positive for SARS-CoV-2 were included in the study. The MuLBSTA, PSI, CURB-65, and qSOFA scores were used to assess thirty-day mortality, need for intensive care unit (ICU), mechanical ventilation (MV) requirement, and development of acute respiratory distress syndrome (ARDS) in all patients. Prognostic factors were also analyzed for thirty-day mortality.Results: Among all 271 hospitalized patients, 150 males (55.3%) were included. The mean age was 54.2±15.4 years. The 30-day mortality rate was 10.7%. Of the patients included in the study; 39 patients (14.3%) were admitted to the intensive care unit, 32 patients (11.8%) received mechanical ventilator support, and 23 patients (8.4%) were diagnosed with ARDS. In predicting mortality, the area under the curve (AUC) of the MuLBSTA, PSI, CURB-65 and qSOFA scores were 0.877 (95% CI 0,832 0,914), 0.853 (95% CI 0,806-0,893), 0.769 (95% CI 0,714-0,817) and 0.769 (95% CI 0,715-0,818), respectively. The MuLBSTA score showed a higher AUC value compared to other prediction scores. The MuLBSTA and PSI scores performed better than CURB-65 and qSOFA scores in determining patients’ need for ICU, MV requirement, and ARDS development.Conclusion: The MuLBSTA score is an efficient tool to predict poor clinical outcomes in hospitalized patients with SARS-CoV-2. Further studies are warranted to validate its use.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要