Selected article for: "high number and severe model"
Author: Zou, Zhengsheng; Yang, Yongping; Chen, Jumei; Xin, Shaojie; Zhang, Wei; Zhou, Xianzhi; Mao, Yuanli; Hu, Liangping; Liu, Daojian; Chang, Binxia; Chang, Weihua; Liu, Yanping; Ma, Xuemei; Wang, Yedong; Liu, Xiqing
Title: Prognostic Factors for Severe Acute Respiratory Syndrome: A Clinical Analysis of 165 Cases
  • Cord-id: my6fjabj
  • Document date: 2004_2_15
    • ID: my6fjabj
    Snippet: This study analyzes single factors that affect the prognosis of severe acute respiratory syndrome (SARS) and establishes a prognosis model by multivariate analysis. We retrospectively analyzed the clinical features of SARS in 165 clinically confirmed severe cases. Both age and existence of other diseases before SARS were significantly correlated with prognosis (r = 0.506 and r = 0.457, respectively; P < .001). During the acute phase of SARS, lactate dehydrogenase level, degree of hypoxemia, resp
    Document: This study analyzes single factors that affect the prognosis of severe acute respiratory syndrome (SARS) and establishes a prognosis model by multivariate analysis. We retrospectively analyzed the clinical features of SARS in 165 clinically confirmed severe cases. Both age and existence of other diseases before SARS were significantly correlated with prognosis (r = 0.506 and r = 0.457, respectively; P < .001). During the acute phase of SARS, lactate dehydrogenase level, degree of hypoxemia, respiratory rate, α-hydroxybutyric dehydrogenase level, creatine kinase isoenzyme—MB, platelet count, and number of involved lobes noted on chest radiographs, and so on, correlated markedly with the prognosis (r = 0.257–0.788; P < .05). The multivariate prognosis regression model was associated with degree of hypoxemia and platelet count. The model was defined by the formula P(y=1) = e(s)/(1 + e(s)), where S is [2.490 × degree of hypoxemia]-[0.050 × number of platelets], and it had a high sensitivity (91.67%), specificity (98.33%), and accuracy (96.42%). The model could be used to effectively judge the state of illness and the prognosis.

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