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  • 标题:Development and Validation of a Disease Severity Scoring Model for Pediatric Sepsis
  • 作者:Li HU ; Yimin ZHU ; Mengshi CHEN
  • 期刊名称:Iranian Journal of Public Health
  • 印刷版ISSN:2251-6085
  • 电子版ISSN:2251-6093
  • 出版年度:2016
  • 卷号:45
  • 期号:7
  • 页码:875-884
  • 语种:English
  • 出版社:THE SCHOOL OF PUBLIC HEALTH, TEHRAN UNIVERSITY OF MEDICAL SCIENCES
  • 摘要:Background: Multiple severity scoring systems have been devised and evaluated in adult sepsis, but a simplified scoring model for pediatric sepsis has not yet been developed. This study aimed to develop and validate a new scoring model to stratify the severity of pediatric sepsis, thus assisting the treatment of sepsis in children. Methods: Data from 634 consecutive patients who presented with sepsis at Children’s hospital of Hunan province in China in 2011–2013 were analyzed, with 476 patients placed in training group and 158 patients in validation group. Stepwise discriminant analysis was used to develop the accurate discriminate model. A simplified scoring model was generated using weightings defined by the discriminate coefficients. The discriminant ability of the model was tested by receiver operating characteristic curves (ROC). Results: The discriminant analysis showed that prothrombin time, D-dimer, total bilirubin, serum total protein, uric acid, PaO2/FiO2 ratio, myoglobin were associated with severity of sepsis. These seven variables were assigned with values of 4, 3, 3, 4, 3, 3, 3 respectively based on the standardized discriminant coefficients. Patients with higher scores had higher risk of severe sepsis. The areas under ROC (AROC) were 0.836 for accurate discriminate model, and 0.825 for simplified scoring model in validation group. Conclusions: The proposed disease severity scoring model for pediatric sepsis showed adequate discriminatory capacity and sufficient accuracy, which has important clinical significance in evaluating the severity of pediatric sepsis and predicting its progress.
  • 关键词:Sepsis; Disease severity scoring model; Pediatrics; Discriminant analysis
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