摘要
目的：
本研究应用实效比较研究(CER)策略,针对针灸临床研究中普遍存在的数据的不一致性和样本含量偏小等问题,引入数据挖掘技术对针灸治疗颈椎病颈痛诊疗数据进行分析,以大样本临床数据样本作为训练数据集建立疾病疗效评估模型,并对小样本数据进行疗效分析,以探讨数据挖掘技术在CER框架下对针灸治疗颈椎病颈痛临床数据的应用方式,探索针灸CER临床研究的数据挖掘关键技术。
方法：
采用临床随机对照试验和数据挖掘计算机实验相结合的研究模式。前期通过进行一项临床多中心随机对照试验,以穴位浅刺和非穴区假针刺为对照,采用NPQ颈痛量表为主要疗效指标,McGill疼痛量表和和SF-36健康状况调查问卷为补充疗效指标,分别从西医诊断、中医辨证、患者病程、疼痛程度等4个角度综合评价针刺联合皮内针优化方案治疗颈椎病颈痛的临床疗效。采用SPSS17.0统计软件,按照意向性治疗原则(ITT)进行统计分析,包括符合方案集和全分析集的疗效分析、盲法效果分析、安全性分析、依从性分析、脱落病例分析等。数据挖掘实验部分应用人工神经网络模型和决策树模型对从随机对照试验获取的数据进行挖掘分析,通过构建综合疗效指标OPROO作为疗效评价的主要参数,具体包括应用核相似关系算法探讨不同因素之间的联系,应用核决策树学习器建立疗效评估模型,应用基于近邻病例的本地疗效评估模型评价不同病例在欧氏距离意义下的本地相似性,使用k近邻方法构建小规模的本地训练集以筛选具有代表显著性的病例等。在此基础上在引入小样本颈椎病颈痛病例疗效数据,应用以大样本病例数据作为训练集的数据挖掘模型,对小样本临床试验数据进行疗效分析判断。通过比较RCT研究和数据挖掘研究两种数据分析模式,探索数据挖掘技术应用于CER临床研究的可行性和实用性。
结果：
本研究进行的多中心临床随机对照试验共纳入病例896例,其中试验过程中脱落病例103例,最终完成793例,服从方案率为88.5%。对不同证候类型颈椎病颈痛的疗效评价提示,针刺联合皮内针优化方案对风寒湿型和气滞血瘀型两个证型的疗效最佳；对不同西医诊断亚型的疗效评价提示,该优化方案对西医诊断为颈型颈椎病和神经根型颈椎病两个亚型疗效最佳；对不同病程患者的疗效评价提示,该优化方案对病程在3年内的颈痛疗效更佳；对不同疼痛程度患者的疗效评价提示,该优化方案对颈痛程度为中度(VAS评分为5～7分)和重度(VAS评分大于7分)患者的疗效优于轻度患者(VAS评分为3～5分)。在治疗有效性评价中,对治疗结束后、随访1个月后、随访3个月后共3个时点进行疗效评定,提示三组患者在治疗结束时的临床有效率分别为90.4%、78.7%、67.5%,经卡方检验组间差异有统计学意义(χ2=40.995,P<0.001)；在随访一个月后的临床有效率分别为87.7%、73.5%、64.5%,经卡方检验组间差异有统计学意义(χ2=38.306,P<0.001)；在随访三个月后的临床有效率分别为85.4%、70.1%、63.4%,经卡方检验组间差异有统计学意义(χ2=33.645,P<0.001)。对补充疗效指标的评价提示,在第五次治疗后、治疗结束、随访1个月及3个月四个时点三组患者的McGill评分均较干预前下降,针灸优化方案组McGill疼痛量表评分改善情况不劣于穴位浅刺组和安慰针组,且组间疗效存在差异；在SF—36生存质量评价方面,针刺优化方案组在生理机能、生理职能、躯体疼痛、一般健康、精力、社会功能、情感职能和精神健康8个维度的生存质量改善情况均优于安慰针组,其中生理职能、躯体疼痛、社会功能、情感职能4个维度的生存质量改善情况优于浅刺组。综合上述分析,可认为针灸优化方案组的近期和中远期临床有效率均优于穴位浅刺组和安慰针组。
数据挖掘实验提示,应用局部近邻学习算法后,数据挖掘模型对数据的判断能力较未使用该算法的模型高,且判断正确率受学习样本量大小的影响不大。将运用了多目标排序算法进行排序筛选的数据引入核决策树模型进行数据判断的实验提示,其判断正确率不会随学习数据样本量的增加而增加,而是与数据的一致性相关。在应用相似性学习算法后,核决策树模型在学习机样本抽取率为30%时判断正确率达到72.45%±3.47%,而未应用相似性学习算法的核决策树模型,在样本抽取率为10%-90%时的判断准确率一直低于前者。当集成学习器的数量达到一定时,数据挖掘模型的判断正确率则不会再增长,提示数据挖掘模型的判断正确率并非单纯依赖集成学习器的数量而增长。在使用164例小样本数据集的数据分析提示,数据挖掘模型经大样本数据集训练,再经SVM或kNN算法优化后,其疗效判断正确率从原来的65%-75%提高至75%-80%左右,效果明显,能够较准确判断针灸治疗的疗效。结论：
本研究通过进行多中心随机对照试验,揭示了针刺联合皮内针优化治疗方案对于颈型和神经根型颈椎病颈痛；证候为风寒湿型及气滞血瘀型；病程在3年以内的；疼痛程度中度以上(VAS>5分)的颈痛患者；以针刺联合皮内针治疗,每周治疗2次,两次治疗期间相隔≥1天,四周内完成8-10次,疗效最佳。
数据挖掘研究提示,影响数据挖掘模型对疗效判断正确率的主要因素不是样本量,而是病例数据的整体质量(如数据完整性、评价准确性等),通过建立数据挖掘模型,以高质量的临床数据作为数据训练集,可较准确估算和评价总体疗效数据的特征规律,从而估计针灸治疗的总体综合疗效。在经大样本疗效数据集训练后,数据挖掘模型对疗效判别能力得到提高,能够在根据临床实际情况设定的疗效评价标准,较准确地判断小样本临床试验病例的疗效,同时可以对大型临床试验的疗效数据进行期中分析,在一定程度上预测总体试验结局,以提高临床试验的效率,并降低临床试验难度,节约研究成本。
本研究初步验证了将基于CER策略的数据挖掘方法应用于针灸临床研究的可行性。在中医药临床研究中应用CER策略,将有助于探讨中医临床疗效,利用调整综合疗效参数和数据挖掘模型等技术,能够对疗效数据有效进行综合和自动实现亚组化分析,体现了CER揭示中医辨证论治的规律与精髓。
Object ive
In order to solve the common demerits of data inconsistency and limitation of sample size in acupuncture clinical researches, the data mining methods was introduced under the strategy of comparative effectiveness research (CER) in this study as a new analytic approach for the clinical data of acupuncture as treatment for neck pain caused by cervical spondylosis. A treatment effect evaluation model was built based on a training data set origninating from a large clinical data sample. And after data training, the data mining model was used to differentiate the efficacy of acupuncture treatment in a small data set of another clinical trail. The objective is to explore the key data mining technology for CER research on acupuncture.
Methods
The research applied a combined research strategy of a randomized control trial (RCT) and data mining computer experiment. At first, a multicenter RCT was conducted to assess the effect of an optimized acupuncture treatment combined with intradermal needling. An acupoint shallow needling group and a non-acupoint placebo acupuncture group were set up as controls. The Northwick Park Neck Pain Questionnaire (NPQ) was used as primary outcome for treatment effect. The McGill Pain Questionnaire (MPQ) and the Short Form (36) Health Survey (SF-36) are applied as secondary outcome measures. The evaluation was performed from four aspects, i.e. evaluation based on western medical diagonosis, evaluation based on syndrome classification of Chinese medicine, evaluation based on morbid course, and evaluation based on pain intensity, thus the comprehensive effectiveness of the optimized treatment can be concluded. The SPSS version17.0software was used for data analysis for conventional statistic methods. The interntion to treat (ITT) principle was followed in our data processing, which included analysis of the Pro-protocol set (PPS) and the full analytic set (FAS) for interventional effect, analysis for blinding effect, safety analysis, compliance analysis and analysis for drop-out cases. In the computer experiment of data mining, the artificial neural network model and decision tree model were applied to the analysis of the data set acquired from the previous RCT. A comprehensive parameter of over patient-reported outcome (OPROO) was constructed as the primary efficacy variable for efficacy assessment. The detailed tasks included the application of the Kernel-as-similarity algorithm for correlations of different influencial factors, the application of Kernel Decision Tree algorithm to build the model for effect evaluation, the application of Local Learning algorithm to build a local model for effect evaluation to measure the similarity of different observed cases by their Euclidean distance, and the application of k Nearest Neighbour (kNN) algorithm to train the local set for later selection of signif ical representative cases. After the learning machine was trained by the data set of the above large sample RCT, a new small data set from another small sample RCT on acupuncture for neck pain caused by cerical spondylosis (CS neck pain) was introduced to the trained data mining model for efficacy judgement. The analytic strategy of conventional RCT and data mining was compared, and the feasibility and practicality of data mining technology for CER studies was explored in the study.
Results
The multicenter RCT has totally recruited896patients when it was finished. There were103cases dropped out during the trial.793cases finished the intervention and follow-up survey. Thus the total protocol compliance rate was88.5%in this trial. In the effect evaluation based on Chinese medical syndrome classification, the optimized acupuncture treatment combined with intradermal needling had superior effect for the patients belonging to the Wind, Cold and Dampness syndrome and the Qi Insufficiency combined with Blood Stagnation syndrome. In the effect evaluation based on western diagnosis, the optimized treatment had superior effect for the patients diagnosed with Cervical Spondylosis (ICD-10code:M47.8) and Cervical Spondylosis with Radiculopathy (ICD-10code:M47.2). In the effect evaluation based on disease history, the optimized treatment had superior effect for the patients with a morbid course not longer than3years. In the effect evaluation based on neck pain intensity, the optimized treatment had superior effect for the patients with intermediate (VAS score from5to7) pain and severe pain (VAS score over7) compared to those with mild pain (VAS score from3to5). In the evaluation for efficacy, the patients were evaluated respectively in the end of their intervention, after one month follow-up, and after three months follow-up. The clinical effective rates in the endpoint of the intervention were90.4%in the treatment group,78.7%in the shallow needling group and67.5%in the placebo group (x2=40.995, P<0.001); the clinical effective rates after one month follow-up were87.7%in the treatment group,73.5%in the shallow needling group and64.5%in the placebo group (x2=38.306, P<0.001); the clinical effective rates after one month follow-up were85.4%in the treatment group,70.1%in the shallow needling group and63.4%in the the placebo group (x2=38.306, P<0.001). Therefore, we can conclude that the short, middle and long term clinical effective rates of the optimized acupuncture treatment were superior to the shallow needling group and the placebo group. In the evaluation of secondary outcomes, the score of McGill Pain Questionnaire declined in all observation points after the intervention and the follow-up survey. The improvement of MPQ score in the optimized acupuncture group was non-inferior to the shallow needling group or the placebo group with inter-group statistical significance. In the assessment of quality of life with SF-36, the patients in the optimized acupuncture group had superior improvement in all eight domains (i.e. Physical Functioning, Role Physical, Body Pain, General Health, Vitality, Social Functioning, Role Emotion, Mental Health), and they have superior improvement in four domains (i. e. Role Physical, Body Pain, Social Functioning, Role Emotion) of quality of life compared to the shallow needling group. Therefore, we can conclude the optimized acupuncture group has superior treatment effect over the shallow needling group and the placebo group in both short term and long term.
The computer experiment of data mining indicated that the judgement ability of data mining model was improved after the local learning algorithm was applied. And the judgement accuracy was not significantly affected by the size of the learning set. The experiemtn of the Kernel Decision Tree model showed its accuracy was not affected by the size of the learning set after the data were preprocessed by Non-Dominant Sort algorithm, but it was correlated with the consistency of the data. In the experiment of the Kernel-as-Similarity algorithm, the judgement accuracy of the Kernal Decision Tree model reached72.45%±3.47%after the Kernel-as-Similarity algorithm was applied at a sampling rate of30%, and the its judgement accuracy was superior to other Kernel Decision Tree models with the sampling rate ranging from10%to90%. When the amount of learning machine reached certain extend, the judgement accuracy of the data mining model remained and became stable, and it implied that the increase of judgement accuary is not depended on the amount of learning machine. In the data mining study with the164-case small data set, the judgement accuracy was increased from65%-75%to75%-80%when the data mining model was trained by the896-case large data set and later optimized by algorithms such as support vector machine (SVM) or kNN. Thus it reflects the data mining approach is able to accurately evaluate the efficacy of acupuncture for CS neck pain.
Conclusion
The multicenter RCT of this study reveals the clinical effect characteristics of the optimized acupuncture treatment combined with intradermal needling:it has best treatment effect to patients diagnosed with cervical spondylosis or cervical spondylosis with radiculopathy; the Chinese medical syndrome should be Wind, Cold and Dampness or Qi Insufficiency combined with Blood Stagnation syndrome; the morbid course should be within3years; the pain intensity should be over intermediate (VAS score over5); the treatment frequency should be twice a week with an interval more the1day; and the regimen should be8to10treatment session within in4weeks.
The computer experiment showed the key influencial factor to the judgement accuracy of the data mining model is not the sample size, but the overall quanlity of the clinical data (e.g. data integratity and preciseness). The data mining model can efficiently assess the effectiveness and charateristics of the full clinical data set if a high quality training set is available. After trained by data set originating from a massive sample database, the judgement capacity of the learning machine was significantly improved for efficacy judgement and differentiation when it was applied to small clinical data sets for evulation. Furthermore, it is feasible to apply data mining methods to perform interim analysis of large clinical study, and or predict the endpoint outcomes to some extend before the trial is finished. Therefore, the data mining method can improve the efficiency of clinical studies, lower the difficulty of protocol implementation and save the unnecessary cost of large sample clinical trials which is common for the efficacy evaluation for new interventions.
This study initially proved the feasibility of applying CER based data mining methods to the clinical research of acupuncture. The application of CER is helpful to explore and evaluate the efficacy of Chinese medicine. As the parameters of the overall efficacy variable is adjustable in accordance with the clinical and research needs, the sub-group analysis and inter-study analysis are earliy implemented under the framework of data mining analysis, and thus it is hopeful to reveal the clinical secrets of Chinese medicine.
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