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Application of critical condition scoring scale of traditional Chinese medicine in predicting operative risks for elder patients with orthopedic surgery

机译:中医危重评分量表在老年骨科手术患者手术风险预测中的应用

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Objective: To evaluate the capability of Critical Condition Scoring Scale of Traditional Chinese Medicine (CSSTCM) in predicting complication morbidity and mortality among elder patients who received orthopedic surgery. Methods: Evaluate patients in second orthopedic department of Guangdong Provincial Hospital of Chinese Medicine from September 2012 to March 2013 by CSSTCM and POSSUM. All data were analyzed in SPSS 19.0. Results: Logistic regression analysis yielded a statistically significant equation for complication morbidity: In R/(1 — R) = − 7.821+0.495×FS+0.335×CS+0.625×QS (FS: four examinations and eight principles score; CS: constitution score; QS: qi & blood syndrome differentiation score). The cutting point of the model was 48.32%, sensitivity 78.13%, specificity 91.30%, misclassification rate 8.70%, omission classification rate 21.87%, Youden index 0.69 and coincidence rate 85.1% The data did not produce an effective Logistic regression equation for mortality. CSSTCM model had better C-Index (0.902 to 0.757), sensitivity (0.781 to 0.656), specificity (0.913 to 0.812) and coincidence rate (0.851 to 0.762) than POSSUM. Conclusion: CSSTCM can generate a significant and effective regression model for complication morbidity, but fails to establish a significant equation for mortality due to limited sample volume. CSSTCM model in predicting complication morbidity has better fitness for the observed sample than POSSUM does.
机译:目的:评估中医危重病评分量表(CSSTCM)预测骨科手术老年患者并发症发生率和死亡率的能力。方法:采用CSSTCM和POSSUM对2012年9月至2013年3月广东省中医院第二骨科的患者进行评估。所有数据均在SPSS 19.0中进行了分析。结果:Logistic回归分析得出了并发症发生率的统计学显着方程:In R /(1- R)= − 7.821 + 0.495×FS + 0.335×CS + 0.625×QS(FS:四项检查和八项原则评分; CS:构成分数; QS:气血证候分化分数)。该模型的临界点为48.32%,敏感性为78.13%,特异性为91.30%,分类错误率为8.70%,遗漏分类率为21.87%,Youden指数为0.69,符合率为85.1%。该数据未能产生有效的死亡率Logistic回归方程。 CSSTCM模型具有比POSSUM更好的C指数(0.902至0.757),敏感性(0.781至0.656),特异性(0.913至0.812)和重合率(0.851至0.762)。结论:CSSTCM可以为并发症的发病率提供有效的回归模型,但由于样本量有限,未能建立重要的死亡率方程。与POSSUM相比,用于预测并发症发生率的CSSTCM模型对观察样本的适应性更好。

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