Chinese Journal of Tissue Engineering Research ›› 2016, Vol. 20 ›› Issue (47): 7043-7050.doi: 10.3969/j.issn.2095-4344.2016.47.007
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Received:
2016-10-06
Online:
2016-11-18
Published:
2016-11-18
Contact:
Wei Xiao-chun, Professor, Chief physician, Doctoral supervisor, Department of Orthopaedics, the Second Hospital of Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
About author:
Mao Zi-mu, Studying for master’s degree, Department of Orthopaedics, the Second Hospital of Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
CLC Number:
Mao Zi-mu, Yin Kun, Wang Yu-ze, Li Peng-cui, Guo Li, Wei Xiao-chun .
2.4 两组治疗后Harris评分变化 末次随访时,单纯钽棒组、联合组的Harris评分分别为78.52±18.37、63.30±17.42,两组Harris评分均高于治疗前(P < 0.05),且单纯钽棒组Harris评分高于联合组(P < 0.05)。 2.5 出现终点事件的髋生存分析和预后因素评估 末次随访时,单纯钽棒组31髋中,有8髋出现终点事件,其中1髋转归至全髋关节置换;联合组10髋中,有6髋出现终点事件,其中3髋转归至全髋关节置换。对两组进行Kaplan-Meier生存分析,结果示末次随访时,单纯钽棒组髋累计生存率为(50.4±15.8)%,联合组累计生存率为(24.0±18.8)%,两组髋累计生存率比较差异有显著性意义(χ2=6.056,P=0.014),见表3,图2。"
根据当前所研究的数据显示,激素使用史 (χ2=0.591,P=0.441)、是否合并其他慢性系统疾病(χ2=0.818,P=0.366)、骨坏死区范围30%分界 (χ2=1.946,P=0.163)、单双侧发病(χ2=0.568,P=0.451)、ARCO分型(χ2=2.253,P=0.324)分层研究术后生存曲线差异无统计学意义。 Cox多因素分析显示,激素使用史(RR=162.593,P=0.010)、合并其他慢性系统性疾病(RR=0.319,P=0.014)、是否出现不良反应(RR=12.297,P=0.042)为转归至出现终点事件的独立预后因素。术式、年龄、ARCO分期、末次随访Harris评分等因素与出现终点事件无统计学相关性,见表4。"
2.6 数学建模 为了更好地确定钽棒置入时机和不同术式的疗效差别,作者对所有纳入研究的病例进行了股骨头坏死钽棒置入手术预后预测模型的数学建模[9],通过提取手术前后相关数据,构造数据集进行建模及预测。数学模型选取支持向量机(support vector machine,SVM),随机森林(randomForest)两种数学模型并与Logistic回归分析进行模型对比[10],所有建模过程均在R语言下完成。建模选取19个变量,分别为性别、年龄、住院时间、手术持续时间、术中出血量、单双侧发病、术式、ARCO分型、术前Harris评分、末次随访Harris评分、发病至入院时间、随访时长、致病因素、激素使用史、是否合并其他慢性系统性疾病、实施手术髋数、骨坏死区30%分界、有无不良反应、切口愈合情况,预测变量为结果事件有无。结果显示,SVM模型在训练集中预测准确率为95.8%,在测试集中预测准确率为70.6%;randomForest模型在训练集中预测准确率为100%,在测试集中预测准确率为76.5%;Logistic回归模型在训练集中预测准确率为100%,在测试集中预测准确率为58.8%。randomForest模型预测准确率最高,在小样本条件下已显示出优越的预测性能。因此作者认为randomForest模型可较好地用于预测术后情况,对于患者术后跟进具有实际价值,可作为预测该术式5年生存期的有效方法。 2.6.1 SVM模型 SVM模型在训练集中的预测效果,在训练集中预测准确率为95.8%,训练集中真实塌陷率为41.7%,预测塌陷率为45.8%,见表5。"
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