• Yonsei medical journal · Jul 2013

    Establishment and evaluation of a simplified evaluation system of acute respiratory distress syndrome.

    • Shubiao Lu, Shaoxi Cai, Chunquan Ou, and Haijin Zhao.
    • Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
    • Yonsei Med. J. 2013 Jul 1; 54 (4): 935-41.

    PurposeIn recent years, a variety of acute respiratory distress syndrome (ARDS) evaluation systems have been developed worldwide; however, they are not so convenient for the doctors clinically, we decided to establish and evaluate a simplified evaluation system of ARDS (SESARDS).Materials And MethodsData from 140 ARDS patients (derivation data set) were collected to screen for prognostic factors affecting outcomes in ARDS patients. By logistic regression analysis, scores were allocated to corresponding intervals of the variables, respectively, by means of analysis of the frequency distribution to establish a preliminary scoring system. Based on this primary scoring system, a definitive evaluation scheme was created through consultation with a panel of experts. The scores for the validation data set (92 cases) were assigned and calculated by their predictive mortality with the SESARDS and acute physiology and chronic health evaluation II (APACHE II). The performance of SESARDS was compared with that of APACHE II by means of statistical analysis.ResultsThe factors of age, pH, Glasgow coma scale (GCS), oxygenation index (OI), and the lobes of lung were associated with prognosis of ARDS respectively. The sensitivity and specificity of SESARDS for the validation data set were 96.43% and 58.33%, respectively. On the AUC, no significant difference between APACHE II and SESARDS was detected. There were no significant differences between the prediction and the actuality obtained by SESARDS for the validation data set the SESARDS scores were positively correlated with the actual mortality.ConclusionSESARDS was shown to be simple, accurate and effective in predicting ARDS progression.

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