范馨月, 崔雷
中国医科大学医学信息学院 沈阳 110122
Fan Xinyue, Cui Lei
中图分类号: TP391 G353
通讯作者:
收稿日期: 2017-10-20
修回日期: 2018-01-22
网络出版日期: 2018-03-25
版权声明: 2018 《数据分析与知识发现》编辑部 《数据分析与知识发现》编辑部
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摘要
【目的】利用文本挖掘方法发现潜在的药物-副作用关系, 为完善现有药物-副作用数据库及药物副作用早期预测提供有效途径。【方法】从PubMed数据库获取2011年 - 2016年间与人类药物治疗和副作用相关文献共100 873篇, 对文献集进行Perl语言切分处理、基于词典的命名实体识别、R语言生成药物-副作用共现矩阵、gCLUTO双聚类分析等一系列研究。【结果】以聚类结果中一类为例, 计算得到本方法提取药物-副作用的准确率达75.65%, 其中发现潜在的药物-副作用关系比例达13.91%。【局限】仅使用基于词典的命名实体识别方法, 并未考虑语法、词法等因素, 造成较高的假阳性率。【结论】本研究可用于发现数据库中尚无记载的药物副作用, 为药物副作用的早期发现提供参考, 为进一步运用自动学习的方法更加准确地提取药物-副作用提供可行的方案。
关键词：
Abstract
[Objective] This paper finds the potential side effects of drugs with the help of text mining, aiming to improve the contents of existing databases and early prediction of drug side effects. [Methods] A total of 100, 873 articles were retrieved from the PubMed database for about five years (2011-2016). We generated the drug side effects co-occurrence matrix and conducted gCLUTO bi-clustering analysis with Perl’s segmentation technique, named entity recognition method based on the dictionary, as well as the R language. [Results] For one category of results, we found the precision rate of the proposed method reached 75.65%, and identified 13.91% potential side effects. [Limitations] Only used the dictionary-based named entity recognition method and did not consider grammatical or lexis factors, which yielded high false positive rates. [Conclusions] This paper proposes a new approach to detect the unannounced side effects of drugs automatically and effectively.
Keywords：
随着生物医学技术的迅猛发展, 治疗各种疾病的药物种类也随之增多。在疾病得到更好治疗的同时, 药物不良反应随之增多, 造成的后果也日益严重, 药物安全问题已经引起了社会的广泛关注[1,2]。目前, 临床上大多采用药物不良反应(Adverse Drug Reactions, ADRs)来评价药品的安全性。
药物不良反应(ADRs)是指在疾病的预防、诊断、治疗或者人体机能的恢复时期, 使用正常剂量的药物时, 使病人出现的对身体有害的、非期望的反应[3], 而不包括因用药错误或用药剂量不当引起的作用及病人的不合作而导致的意外事故等[4,5]。药物不良反应包括药物副作用、不良反应、后遗效应、变态反应、继发反应、特异质反应、过敏反应等[6,7], 其中药物副作用是其主要组成部分[8]。
药物副作用已成为评价药品安全性的重要指标, 药物安全问题频繁出现主要有两方面的因素: 一方面,
临床上对药物副作用的认识不足, 导致药物不良反应频繁出现; 另一方面, 由于临床试验的长期性、复杂性, 批准上市药物的副作用反馈有延迟, 导致早期难以预测药物副作用[9]。基于此现状, 越来越多的国内外研究人员转而采用机器学习或数据挖掘的方法识别及预测药物潜在副作用: Liu 等整合SIDER、PubCheme、DrugBank和KEGG中药物及其副作用信息, 采用支持向量机方法预测药物潜在副作用, 并将其与Logistic回归、随机森林、朴素贝叶斯及K最近邻进行性能比较[10]; Pauwels等从SIDER中提取副作用和化学亚结构, 提出一种基于化学结构预测药物潜在副作用的新方法[11]; Vilar等建立3D药物相似性矩阵预测未知的副作用[12], 上述研究存在一个主要问题, 即研究数据均来自已有的数据库, 但目前可用的药物副作用数据库都存在一定的局限性, 如SRS、FAERS等数据来源缺乏权威性, 收集数据不够全面, 存在反复申报等问题; SIDER中药物仅仅局限于FDA批准的药物; EMA等规模较大的数据库内容错综复杂, 没有专门的药物副作用的检索[9], 这都为数据使用造成诸多不便。
考虑到现有数据库的局限性, 部分研究人员将数据来源转向文本: Wang等在PubMed中查找与治疗中性粒细胞减少症的25种药物相关的文章, 提取药物-药物不良反应关系, 达到预测潜在副作用的目的[13]; 刘晓倩等在中国知网、万方、维普和PubMed等数据库获得中药减重文献集, 采用分类及统计方法探索中医治疗肥胖病的用药规律[14]; 郭佳栋等运用数据挖掘Apriori算法构建关联模型, 研究胃癌相关化疗药物的不良反应等[15], 这些研究虽在一定程度上克服了数据库的局限性, 但研究均是针对某一特定疾病相关的药物不良反应展开的。
本研究综合考虑上述药物副作用数据库存在的局限性及研究方向侧重于单一疾病等问题, 结合文本挖掘技术发展的现状[16,17], 将PubMed中近5年涉及的与药物及其副作用有关的所有文献作为研究样本, 借鉴生物医学领域基因和蛋白质的命名实体识别研究, 采用基于词典的命名实体识别方法[18,19,20]识别文本中的药物名称和副作用名称, 从大量生物文献中抽取药物-副作用对, 对药物所能导致的副作用进行客观描述, 预测潜在的药物-副作用关系, 以达到提高当前药物-副作用资源完整性, 为药物开发提供参考的目的。
本文研究路线如图1所示。
本研究从文本出发, 采用文本挖掘的方法, 经Perl语言[21]切分文摘、基于词典的命名实体识别、R语言生成共现矩阵一系列操作, 从文摘中提取出药物-副作用关联, 通过聚类分析及将聚类结果与SIDER对比, 推断药物潜在的副作用, 达到知识发现的目的。
在PubMed数据库中使用检索策略: "drug therapy" [Subheading] AND "adverse effects"[Subheading] AND "humans"[MeSH Terms], 查找2011-2016年间与人类药物治疗和副作用有关的全部文献, 共100 873篇, 输出MEDLINE格式的检索结果, 作为研究样本。
使用Perl语言切分处理文档[22,23,24], 将文章的PMID、标题(TI)、摘要(AB)字段提出, 其中AB字段以“.”为分隔符, 切分成句子AB1, AB2, …, ABn, 处理后文件格式如图2所示。
(1) 副作用词典
副作用词典由SIDER[25]中记载的副作用构成, 其中包含市场上销售药物及从生物医学文献中获取的药物名称、副作用出现频率、药物和副作用分类以及药物-靶点相互作用等其他链接信息, 将其与MeSH主题词相对应, 并指定统一的表达方式(MeSH ID), 从而保证词典的全面性, 以达到尽可能多地识别文献中提及的副作用及结果规范化的效果。
(2) 药物词典
本研究药物词表来自药物研究领域权威数据库DrugBank[26]。该数据库包含10 505个药物条目, 其中有1 739种FDA批准的小分子药物, 871种FDA批准的生物技术药物(蛋白质/肽), 105种营养药物和5 025种实验药物。除此之外, 还有4 772个与这些药物相链接的非冗余的蛋白质序列, 经同义词扩充处理后本研究药物词典共包含35 463种药物名称。
(3) 副作用及药物词典的标准化
在PubMed MeSH词表中C大类和D大类分别对应化学物质与药物大类(Chemicals and Drug Category)和疾病大类(Diseases Category), 提取MeSH词表中C、D两大类对应的主题词和款目词(如表1所示), 将以上获得的药物和副作用分别与MeSH ID匹配, 并统一规定使用MeSH词表示, 对于没有对应MeSH词的药物或副作用, 则保留其原始表达方式。
表1 MeSH ID、主题词、款目词对应格式(局部)
| MeSH ID | 药物MeSH词 | 药物款目词 |
|---|---|---|
| D000935 | Antifungal Agents | Agents, Antifungal |
| Therapeutic Fungicides | ||
| Fungicides, Therapeutic | ||
| Antibiotics, Antifungal | ||
| Antifungal Antibiotics | ||
| D001569 | Benzodiazepines | Benzodiazepine Compounds |
| Benzodiazepine | ||
| D006493 | Heparin | Unfractionated Heparin |
| Heparin, Unfractionated | ||
| Heparinic Acid | ||
| Liquaemin | ||
| Sodium Heparin | ||
| Heparin, Sodium | ||
| Heparin Sodium | ||
| alpha-Heparin | ||
| alpha Heparin | ||
| … | … | … |
采用本研究构建的药物词典和副作用词典, 基于Perl语言模式匹配方法, 实行基于词典的命名实体识别, 抽取文摘中包含的药物及副作用名称。以单篇文章的摘要为例, 处理结果格式如表2和表3所示, 得到同一文摘中在句子水平上共现的药物及副作用名称。
表2 副作用词典匹配结果(局部)
| PubMed MEDLINE | SIDER |
|---|---|
| PMID: 24739449 | |
| TI | |
| AB 1 | Depression |
| AB 2 | |
| AB 3 | |
| AB 4 | |
| AB 5 | Epilepsy |
| AB 6 | |
| AB 7 | |
| AB 8 | |
| AB 9 | |
| AB 10 | |
| AB 11 | |
| AB 12 |
表3 药物词典匹配结果(局部)
| PubMed MEDLINE | Drug |
|---|---|
| PMID: 24739449 | |
| TI | tianeptine |
| AB 1 | |
| AB 2 | |
| AB 3 | tianeptine |
| AB 4 | tianeptine |
| AB 5 | tianeptine |
| AB 6 | tianeptine |
| AB 7 | |
| AB 8 | |
| AB 9 | tianeptine |
| AB 10 | tianeptine |
| AB 11 | tianeptine |
| AB 12 | tianeptine |
采用R语言, 基于命名实体识别过程得到的药物及副作用匹配结果构建同一篇文摘的药物-副作用共现矩阵[1,27], 若药物和副作用在一篇文摘中同时出现, 则记为1, 否则记为0, 得到的矩阵格式如表4所示。
表4 药物-副作用共现矩阵(局部)
| Lyme disease | Polyps | General Surgery | Hypothermia | ||
|---|---|---|---|---|---|
| clarithromycin | 1 | 0 | 0 | 0 | … |
| ceftriaxone | 1 | 0 | 0 | 0 | … |
| doxycycline | 1 | 0 | 0 | 0 | … |
| erlotinib | 0 | 1 | 0 | 0 | … |
| thyroid | 0 | 0 | 1 | 0 | … |
| tyrosine | 0 | 0 | 1 | 0 | … |
| cabozantinib | 0 | 0 | 1 | 0 | … |
| morphine | 0 | 0 | 1 | 1 |
运用gCLUTO双聚类软件[28], 采用重复二分法对药物-副作用共现矩阵进行聚类分析[29], 其基本原理是将药物通过共有副作用的数量聚集在一起, 虽然每类中包含的药物其化学基本性质或副作用都不尽相同, 但在副作用层面上提供了一定程度的药物相似信息。其中, 相似函数为cosine, 判别函数为I2, 其余选项均为默认值。以类内平均相似度(Isim)越高, 类间平均相似度(Esim)越低的聚类结果为最佳结果[30,31]。对结果中的1 287种药物聚类, 分别比较聚类类别为10、13、15、20、25时, Isim和Esim的取值, 并结合每类中包含药物数目、药物功能等信息, 最终确定25类时聚类结果最佳, 获得此时相应的可视化矩阵和山丘图。
将药物-副作用矩阵中1 287种药物分为25类, 其中涉及到药物词典中的副作用1 218种(山丘图如图3所示), 将聚类结果作为依据, 与SIDER数据库中已有记录的药物副作用比较, 推断药物的潜在副作用, 达到知识发现的目的。
由图3可见, 0、1、2、3、4、5类的类内相似度较高, 其余20类的类内相似度略低。综合聚类结果和山丘图, 可知0类的类内相似度最高, 且 0类描述度最高的副作用分别为Pain、Postoperative pain、Rheumatism disease、Headache。
以Cluster 0类为例, 对聚类结果进行分析。在SIDER中分别查询Cluster0类115种药物副作用, 将该数据库记载的药物副作用与本文结果比对, SIDER中有记录的副作用记为(+), 无记录的记为(-)(注: SIDER中无记载的药物记为“—”), 所得结果如表5所示。
表5 SIDER数据库对比结果(局部)
| Cluster 0 | Pain | Postoperative pain | Rheumatism disease | Headache |
|---|---|---|---|---|
| ceftriaxone | (+) | (-) | (-) | (+) |
| hyaluronic acid | — | — | — | — |
| naloxone | (-) | (-) | (-) | (-) |
| fluconazole | (+) | (-) | (-) | (+) |
| ciclosporin | (+) | (-) | (+) | (+) |
| palonosetron | (+) | (-) | (-) | (+) |
| … | … | … | … | … |
| dinoprostone | (+) | (-) | (-) | (-) |
根据比对结果可以发现:
(1) Cluster0 类的115种药物中, 有33类药物在SIDER 数据库中无记载;
(2) 本研究提取的药物种类与SIDER数据库中记载的药物相比增加了40.24%, 提取的药物对应副作用数提高了53.33%;
$\frac{本研究药物提取总量}{SIDER中有记载药物数}=115/82=140.24\text{ }\!\!%\!\!\text{ }$
$\frac{本研究提取具有上述副作用药物总数}{SIDER中记载有上述副作用药物总数}=115/75=153.33\text{ }\!\!%\!\!\text{ }$
(3) SIDER中有记载的82类药物, 有7种药物没有表格中4种副作用(疼痛、术后疼痛、风湿、头痛)的记载, 其余75种药物均至少有4种副作用中的一种。
对于SIDER中无记载的33种药物, 使用检索策略("drug therapy"[Subheading] AND "adverse effects" [Subheading] AND(SIDER中无记载药物名称) AND (上述4种副作用任一种))在PubMed数据库检索, 发现部分文献中提到这些药物有上述副作用, 如药物levobupivacaine在文献中记载其能够引起Pain、Postoperative pain和Headache[32]; 药物ramosetron可引起Postoperative pain[33]; 药物cefazoline可引起Pain[34,35]等。但在检索过程中也发现一些与此相反的结果, 如在SIDER数据库无记载的药物Sucrose[36]、 Naloxegol[37]、Metamizole[38], 在本研究中得到它们和Pain等词同时出现, 但检索结果显示它们同时出现的情况并不是上述药物能够引起副作用Pain, 而是治疗Pain, 这也正是本研究仅使用基于词典的命名实体识别, 而没有进行句法分析的弊端。
为验证本研究使用方法的准确性, 分别在PubMed对剩余40种药物(包括SIDER数据库中未收录的药物33种和SIDER中无副作用记载的药物7种)查找文献验证, 其中有12种药物在文献中明确被指出其治疗效用而不是引起副作用, 16种药物在文献及数据库中均为有上述副作用记载, 剩余的12种药物虽未直接提及Pain等副作用, 但都可引起一定程度的腹痛、胸痛、背痛等副作用, 也将其统一归为疼痛, 由此计算得到本研究的错误率、潜在药物-副作用关系比例及准确率, 对于本研究发现的潜在药物-副作用关系, 还有待进一步实验进行验证。
$错误路=\frac{明确指出药物治疗而并非引起该种副作用文献数量}{0类药物总量}=12/115=10.43\%$$潜在药物副作用关系比例=\frac{文献及数据库中未涉及上述副作用药物数量}{0类药物总量}=16/115=13.91\%$$准确率=\frac{SIDER中有副作用记载药物数量+引起腹痛等副作用药物数量}{0类药物总量}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ =\frac{75+12}{115}=75.65\%$
使用Perl语言处理文本时, 以文摘中单个句子为基本单位, 但命名实体识别时发现基于文摘的药物-副作用矩阵疏密程度适中, 如果要完成在句子层面的共现反而会使矩阵变得异常稀疏, 故在处理文本时可不进行句子切割和计数, 直接将摘要作为一个整体处理, 但本文并没有考虑到在摘要中同时出现多个药物和副作用时, 如何确定二者之间的对应关系, 还有待进一步改进; 另外, 在文本处理上, 可加入语义关系的提取, 会使获得的药物-副作用关系更准确。
R语言生成共现矩阵时, 发现药物中会有His、M、I、P、G等字母, 并且其与多种副作用共现, 回归原文发现, 例如His在原文中并不代表药物缩写, 而是把英文“his(他)”全部当成药物, P则是在统计中经常使用的P值, G多数表示重量单位“克”, 这种现象造成一定的误差, 故在矩阵处理时要引起注意并及时删除和修正。
(1) 使用的样本量大, 虽不及大数据的规模, 但是研究中秉承着大数据的思想, 不同于统计研究中的抽样调查, 而是将所有数据作为研究样本, 因而数据涉及范围更广、药物种类更全、研究结果可信度更高;
(2) 本研究命名实体识别使用基于词典的方法, 直接从文献中获取药物、副作用名称, 并且采用的副作用词典和药物词典是独立编撰的, 集成多个数据库的药物及副作用名称, 词典规模较大, 关于药物和副作用的记载较全面。
(3) 从大量生物医学文献中提取药物及副作用, 通过聚类分析、结果比对、查阅文献等一系列过程进行知识发现, 预测尚无记载但可能存在的药物副作用, 直接面向临床应用, 为临床数据挖掘提供参考, 同时为生物医学其他各个领域的研究提供了一种可行的自动学习方法。
本研究使用文本挖掘方法, 对PubMed数据库2011-2016年间与人类药物治疗和副作用有关的文献进行基于摘要的分析, 从中提取出药物副作用, 与已有研究相比, 研究范围从单一疾病药物副作用扩展到全部药物副作用, 并且数据集来源于文献, 克服了已知数据库的不足, 且可以发现大量数据库中不包含而在文献中出现的药物-副作用关系。
本研究尚不完善, 后期拟在此基础上增加自然语言处理的词法、句法分析及关系提取算法, 使药物-副作用关系更加准确, 而不仅是依靠共现关系, 且进一步研究可扩展到药物-靶点关系提取, 发现潜在的药物靶点信息, 为药物开发提供参考, 从而有效缩短药物开发周期, 提升药物安全性。
除此之外, 本研究使用SIDER为金标准, 对研究方法的可行性和准确性进行评估, 发现了一部分数据库中尚未记载的药物副作用, 可为药物副作用的早期发现提供参考, 同时也为知识发现提供可能, 为进一步运用自动学习方法更加准确地提取药物-副作用及文本中生物医学实体间的关系提供了一种可行的途径。
崔雷: 提出研究思路, 论文最终版本修订;
范馨月: 设计研究方案, 进行实验, 采集、清洗和分析数据, 论文起草。
所有作者声明不存在利益冲突关系。
支撑数据由作者自存储, E-mail: 875763928@qq.com。
[1] 范馨月. PubMed_result.txt. PubMed下载的文献集.
[2] 范馨月. Drug.txt; Side Effect.txt. 药物词典和副作用词典.
[3] 范馨月. cut literature.pl;ner.pl. 文献切分和命名实体识别算法.
[4] 范馨月. matrix.csv. 药物—副作用共现矩阵.
[5] 范馨月. solution.xml. 聚类结果.
| [1] |
基于评论挖掘的药物副作用发现机制 [J].
从医疗社交网站的用户评论中挖掘药物副作用时,由于人们可能采用不同的表述方式来描述副作用,而新药的上市与用药者的差异性也会造成新的副作用出现,因此从评论中识别新的副作用名称并进行标准化十分重要。该文利用条件随机场模型识别评论中的副作用,对识别出的副作用名称进行标准化,最后得到药物的副作用。通过将挖掘出的药物已知的副作用与数据库记录进行对比验证了本文方法的有效性,同时得到一个按评论中的发生频率排序的药物潜在副作用列表。实验结果显示,条件随机场模型可以识别出已知的与新的副作用名称,而标准化技术将副作用名称进行聚合与归并,有利于药物副作用的发现。<br/>
Detection of Adverse Drug Reactions Based on Comment Mining [J].
从医疗社交网站的用户评论中挖掘药物副作用时,由于人们可能采用不同的表述方式来描述副作用,而新药的上市与用药者的差异性也会造成新的副作用出现,因此从评论中识别新的副作用名称并进行标准化十分重要。该文利用条件随机场模型识别评论中的副作用,对识别出的副作用名称进行标准化,最后得到药物的副作用。通过将挖掘出的药物已知的副作用与数据库记录进行对比验证了本文方法的有效性,同时得到一个按评论中的发生频率排序的药物潜在副作用列表。实验结果显示,条件随机场模型可以识别出已知的与新的副作用名称,而标准化技术将副作用名称进行聚合与归并,有利于药物副作用的发现。<br/>
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| [2] |
基于信息整合的药物副作用预测方法研究 [D].Method Research for the Prediction of Drug’s Side Effect Based on Information Integration [D]. |
| [3] |
药物不良反应与药物不良反应事件 [J].https://doi.org/10.3870/j.issn.1004-0781.2004.08.062 URL [本文引用: 1] 摘要
药物不良反应(ADRs)和药物不良反应事件(ADEs)是ADRs监测和临床医疗实践涉及的两个重要基本概念,正确区分和识别意义重大.该文对ADRs和ADEs概念、特征界定、药品-ADRs因果关系判定及相关重大问题进行了介绍.
Adverse Drug Reactions and Adverse Drug Events [J].https://doi.org/10.3870/j.issn.1004-0781.2004.08.062 URL [本文引用: 1] 摘要
药物不良反应(ADRs)和药物不良反应事件(ADEs)是ADRs监测和临床医疗实践涉及的两个重要基本概念,正确区分和识别意义重大.该文对ADRs和ADEs概念、特征界定、药品-ADRs因果关系判定及相关重大问题进行了介绍.
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| [4] |
Data-driven Approach to Detect and Predict Adverse Drug Reactions [J].https://doi.org/10.2174/1381612822666160509125047 URL PMID: 27157416 [本文引用: 1] 摘要
Background: Many factors that directly or indirectly cause adverse drug reaction (ADRs) varying from pharmacological, immunological and genetic factors to ethnic, age, gender, social factors as well as drug and disease related ones. On the other hand, advanced methods of statistics, machine learning and data mining allow the users to more effectively analyze the data for descriptive and predictive purposes. The fast changes in this field make it difficult to follow the research progress and context on ADR detection and prediction. Methods: A large amount of articles on ADRs in the last twenty years is collected. These articles are grouped by recent data types used to study ADRs: omics, social media and electronic medical records (EMRs), and reviewed in terms of the problem addressed, the datasets used and methods. Results: Corresponding three tables are established providing brief information on the research for ADRs detection and prediction. Conclusion: The data-driven approach has shown to be powerful in ADRs detection and prediction. The review helps researchers and pharmacists to have a quick overview on the current status of ADRs detection and prediction.
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| [5] |
Text and Data Mining Techniques in Adverse Drug Reaction Detection [J].https://doi.org/10.1145/2719920 URL [本文引用: 1] 摘要
We review data mining and related computer science techniques that have been studied in the area of drug safety to identify signals of adverse drug reactions from different data sources, such as spontaneous reporting databases, electronic health records, and medical literature. Development of such techniques has become more crucial for public heath, especially with the growth of data repositories that include either reports of adverse drug reactions, which require fast processing for discovering signals of adverse reactions, or data sources that may contain such signals but require data or text mining techniques to discover them. In order to highlight the importance of contributions made by computer scientists in this area so far, we categorize and review the existing approaches, and most importantly, we identify areas where more research should be undertaken.
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| [6] |
正确区分药物不良反应杜绝药物不良反应事件发生 [J].The Correct Distinction Between Adverse Drug Reactions to Eliminate Adverse Drug Reactions Occured [J]. |
| [7] |
浅谈药品不良反应发生的原因及预防措施 [J].https://doi.org/10.3969/j.issn.0253-9926.2010.05.028 URL [本文引用: 1] 摘要
篇首： 在我国中药品不良反应定义为合格药品在正常用法用量下出现的与用药目的无关的或意外的有害反 应.1998-2005年间美国食品和药物管理局(FDA)报告的不良反应事件数量从34 966例上升到89 842例,死亡数从5 519例上升到15 105例.期间,处方量每年以25％的速度增加.
Talking about the Reasons and Preventive Measures of Adverse Drug Reactions [J].https://doi.org/10.3969/j.issn.0253-9926.2010.05.028 URL [本文引用: 1] 摘要
篇首： 在我国中药品不良反应定义为合格药品在正常用法用量下出现的与用药目的无关的或意外的有害反 应.1998-2005年间美国食品和药物管理局(FDA)报告的不良反应事件数量从34 966例上升到89 842例,死亡数从5 519例上升到15 105例.期间,处方量每年以25％的速度增加.
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| [8] |
临床常用药物副作用概述 [J].
目的:药物种类众多,作用复杂,药物都有一定的副作用,而目前药源性疾病日益增多,通过了解掌握临床常用药物的副作用,可以提高临床各科诊断、治疗的正确率及时率,减少诊断治疗的困难和错误,同时最大限度避免药源性疾病发生.方法:笔者根据本院发生的药源性疾病及多年的临床工作实践经验,并着重收集国内外所报告的药源性疾病病例,并参考关于药物副作用的有关书籍等,并加以整理分析总结.结果:通过临床实践证明,提高了临床诊断治疗的正确率及时率,减少了诊断治疗的困难和错误和不良后果,同时最大限度地避免了药源性疾病的发生.结论:临床医护人员必须高度重视临床常用药物的副作用,了解掌握临床常用药物的副作用,在临床诊疗过程中一定要询问患者用药情况,始终想到药源性疾病的情况.
Common Clinical Side Effects of Drugs Outlined [J].
目的:药物种类众多,作用复杂,药物都有一定的副作用,而目前药源性疾病日益增多,通过了解掌握临床常用药物的副作用,可以提高临床各科诊断、治疗的正确率及时率,减少诊断治疗的困难和错误,同时最大限度避免药源性疾病发生.方法:笔者根据本院发生的药源性疾病及多年的临床工作实践经验,并着重收集国内外所报告的药源性疾病病例,并参考关于药物副作用的有关书籍等,并加以整理分析总结.结果:通过临床实践证明,提高了临床诊断治疗的正确率及时率,减少了诊断治疗的困难和错误和不良后果,同时最大限度地避免了药源性疾病的发生.结论:临床医护人员必须高度重视临床常用药物的副作用,了解掌握临床常用药物的副作用,在临床诊疗过程中一定要询问患者用药情况,始终想到药源性疾病的情况.
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| [9] |
用文本挖掘方法发现药物的副作用 [J].https://doi.org/10.3969/j.issn.1671-3982.2015.11.016 URL [本文引用: 2] 摘要
分析了用文本挖掘方法探测药物副作用的必要性及可行性,从挖掘流程、挖掘/提取方法、结果评价和现有工具软件4个方面总结了用文本挖掘技术提取药物副作用的研究现状及尚未解决的问题和未来发展趋势。
Detection of Drug Adverse Effects by Text-Mining [J].https://doi.org/10.3969/j.issn.1671-3982.2015.11.016 URL [本文引用: 2] 摘要
分析了用文本挖掘方法探测药物副作用的必要性及可行性,从挖掘流程、挖掘/提取方法、结果评价和现有工具软件4个方面总结了用文本挖掘技术提取药物副作用的研究现状及尚未解决的问题和未来发展趋势。
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| [10] |
Large-scale Prediction of Adverse Drug Reactions Using Chemical, Biological, and Phenotypic Properties of Drugs [J].https://doi.org/10.1136/amiajnl-2011-000699 URL [本文引用: 1] 摘要
Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.
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| [11] |
Predicting Drug Side-effect Profiles: A Chemical Fragment-based Approach [J].https://doi.org/10.1186/1471-2105-12-169 URL PMID: 3125260 [本文引用: 1] 摘要
Background Drug side-effects, or adverse drug reactions, have become a major public health concern. It is one of the main causes of failure in the process of drug development, and of drug withdrawal once they have reached the market. Therefore, in silico prediction of potential side-effects early in the drug discovery process, before reaching the clinical stages, is of great interest to improve this long and expensive process and to provide new efficient and safe therapies for patients. Results In the present work, we propose a new method to predict potential side-effects of drug candidate molecules based on their chemical structures, applicable on large molecular databanks. A unique feature of the proposed method is its ability to extract correlated sets of chemical substructures (or chemical fragments) and side-effects. This is made possible using sparse canonical correlation analysis (SCCA). In the results, we show the usefulness of the proposed method by predicting 1385 side-effects in the SIDER database from the chemical structures of 888 approved drugs. These predictions are performed with simultaneous extraction of correlated ensembles formed by a set of chemical substructures shared by drugs that are likely to have a set of side-effects. We also conduct a comprehensive side-effect prediction for many uncharacterized drug molecules stored in DrugBank, and were able to confirm interesting predictions using independent source of information. Conclusions The proposed method is expected to be useful in various stages of the drug development process.
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| [12] |
3D Pharmacophoric Similarity Improves Multi Adverse Drug Event Identification in Pharmacovigilance [J].https://doi.org/10.1038/srep08809 URL PMID: 25744369 [本文引用: 1] 摘要
Abstract Adverse drugs events (ADEs) detection constitutes a considerable concern in patient safety and public health care. For this reason, it is important to develop methods that improve ADE signal detection in pharmacovigilance databases. Our objective is to apply 3D pharmacophoric similarity models to enhance ADE recognition in Offsides, a pharmacovigilance resource with drug-ADE associations extracted from the FDA Adverse Event Reporting System (FAERS). We developed a multi-ADE predictor implementing 3D drug similarity based on a pharmacophoric approach, with an ADE reference standard extracted from the SIDER database. The results showed that the application of our 3D multi-type ADE predictor to the pharmacovigilance data in Offsides improved ADE identification and generated enriched sets of drug-ADE signals. The global ROC curve for the Offsides ADE candidates ranked with the 3D similarity score showed an area of 0.7. The 3D predictor also allows the identification of the most similar drug that causes the ADE under study, which could provide hypotheses about mechanisms of action and ADE etiology. Our method is useful in drug development, screening potential adverse effects in experimental drugs, and in drug safety, applicable to the evaluation of ADE signals selected through pharmacovigilance data mining.
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| [13] |
A Drug-Adverse Event Extraction Algorithm to Support Pharmacovigilance Knowledge Mining from PubMed Citations [C]// |
| [14] |
基于文本挖掘方法探索中医治疗肥胖病的用药规律 [J].Exploration of the Medication Regularity of Traditional Chinese Medicine for Obesity Based on Text Mining Techniques [J]. |
| [15] |
基于数据挖掘技术对胃癌化疗药物不良反应关联性研究 [J].
目的:寻找胃癌化疗药物、用药人群与药品不良反应间潜在的关联性,为临床合理用药提供参考。方法:收集109家药品生产企业2010~2014年生产的8种胃癌化疗药物不良反应报告数据,运用数据挖掘Apriori算法构建关联模型,使用Clementine 14.1进行统计分析。结果:共收集有效报告10 572份,男女比例为2.11∶1。8种抗肿瘤药物的不良反应主要为恶心、呕吐、腹泻、局部麻木和骨髓抑制,主要发生于40~65岁患者。男性与恶心、呕吐、骨髓抑制、局部麻木、腹泻和白细胞减少的关联性较强,女性与胸闷、恶心和呕吐的关联性较强。奥沙利铂与恶心、呕吐、局部麻木、骨髓抑制、白细胞减少、腹泻的关联性较强。多西他赛与胸闷、腹泻的关联性较强。氟尿嘧啶出现骨髓抑制、血小板减少的可能性较大。卡培他滨与乏力、腹泻的关联性较强。顺铂与呕吐、血小板减少,紫杉醇与肝功能异常、呕吐、乏力的关联性较大。结论:临床工作者在患者用药过程中应关注联合用药、用药人群与药品不良反应间的关联性。
Correlation Analysis of Gastric Cancer Chemotherapy Drugs Adverse Drug Reaction Based on Data Mining Technology [J].
目的:寻找胃癌化疗药物、用药人群与药品不良反应间潜在的关联性,为临床合理用药提供参考。方法:收集109家药品生产企业2010~2014年生产的8种胃癌化疗药物不良反应报告数据,运用数据挖掘Apriori算法构建关联模型,使用Clementine 14.1进行统计分析。结果:共收集有效报告10 572份,男女比例为2.11∶1。8种抗肿瘤药物的不良反应主要为恶心、呕吐、腹泻、局部麻木和骨髓抑制,主要发生于40~65岁患者。男性与恶心、呕吐、骨髓抑制、局部麻木、腹泻和白细胞减少的关联性较强,女性与胸闷、恶心和呕吐的关联性较强。奥沙利铂与恶心、呕吐、局部麻木、骨髓抑制、白细胞减少、腹泻的关联性较强。多西他赛与胸闷、腹泻的关联性较强。氟尿嘧啶出现骨髓抑制、血小板减少的可能性较大。卡培他滨与乏力、腹泻的关联性较强。顺铂与呕吐、血小板减少,紫杉醇与肝功能异常、呕吐、乏力的关联性较大。结论:临床工作者在患者用药过程中应关注联合用药、用药人群与药品不良反应间的关联性。
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| [16] |
Text Mining in Practice with R [M]. |
| [17] |
Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques [OL].
Abstract: The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.
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| [18] |
命名实体识别综述 [J].Survey of Named Entity Recognition [J]. |
| [19] |
生物医学领域的命名实体识别和标准化 [D].Named Entities Recognition and Normalization in Biomedical Literatures [D]. |
| [20] |
基于机器学习的蛋白质命名实体识别和相互作用关系抽取的研究 [D].Research on Machine Learning Algorithms of Protein Named Entity Recognition and Protein Interaction Relation Extraction [D]. |
| [21] |
Perl程序设计语言综述 [J].Perl Programming Language Summary [J]. |
| [22] |
Opposing Actions of Perl and Cry2 in the Regulation of Perl Target Gene Expression in the Liver and Kidney [J]. |
| [23] |
运用perl轻松处理字符串 [J].Using Perl Easy Processing String [J]. |
| [24] |
基于Perl的汉语自动分词算法研究 [J].https://doi.org/10.3969/j.issn.1008-3715.2007.01.041 URL [本文引用: 1] 摘要
汉语的自动分词是汉语语言处理的基础,本文讨论了汉语自动分词歧义问题的产生与分类,重点研究了交集型歧义字段的排歧策略,实现了一种基于Perl的汉语自动分词方法,在比较同种算法的VC++实现上,结果显示基于Perl的实现具有简洁和高效的优点。
Algorithmic Study on Perl-based Automatic Segmentation of Chinese Words [J].https://doi.org/10.3969/j.issn.1008-3715.2007.01.041 URL [本文引用: 1] 摘要
汉语的自动分词是汉语语言处理的基础,本文讨论了汉语自动分词歧义问题的产生与分类,重点研究了交集型歧义字段的排歧策略,实现了一种基于Perl的汉语自动分词方法,在比较同种算法的VC++实现上,结果显示基于Perl的实现具有简洁和高效的优点。
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| [25] |
The SIDER Database of Drugs and Side Effects [J].https://doi.org/10.1093/nar/gkv1075 URL PMID: 26481350 [本文引用: 1] 摘要
Abstract Unwanted side effects of drugs are a burden on patients and a severe impediment in the development of new drugs. At the same time, adverse drug reactions (ADRs) recorded during clinical trials are an important source of human phenotypic data. It is therefore essential to combine data on drugs, targets and side effects into a more complete picture of the therapeutic mechanism of actions of drugs and the ways in which they cause adverse reactions. To this end, we have created the SIDER ('Side Effect Resource', http://sideeffects.embl.de) database of drugs and ADRs. The current release, SIDER 4, contains data on 1430 drugs, 5880 ADRs and 140 064 drug-ADR pairs, which is an increase of 40% compared to the previous version. For more fine-grained analyses, we extracted the frequency with which side effects occur from the package inserts. This information is available for 39% of drug-ADR pairs, 19% of which can be compared to the frequency under placebo treatment. SIDER furthermore contains a data set of drug indications, extracted from the package inserts using Natural Language Processing. These drug indications are used to reduce the rate of false positives by identifying medical terms that do not correspond to ADRs. The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
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| [26] |
DrugBank: A Knowledgebase for Drugs, Drug Actions and Drug Targets [J].https://doi.org/10.1093/nar/gkm958 URL PMID: 2238889 [本文引用: 1] 摘要
DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With approximately 4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more 'experimental' drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food-drug interactions, drug-drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca.
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| [27] |
基于主题词关联规则的实体间语义关系抽取——以药物副作用引起疾病为例 [D].Semantic Relations Extraction Based on MeSH Term Association Rules: A Case Study of Drug Side Effects Causing Disease [D]. |
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gCLUTO-An Interactive Clustering, Visualization, and Analysis System [R]. |
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同被引双聚类方法在情报分析中应用研究 [C]//Applied Research of Cited Biclustering Method in Intelligence Analysis [C]// |
| [30] |
基于双聚类方法的生物医学信息学文本数据挖掘研究 [J].
运用TDA和gCluto软件对SCI数据库中近5年的相关文献进行处理,获得双聚类矩阵图,经分析后得到该学科领域近年来的期刊研究主题方向与热点。结论认为,双聚类方法能够很好地反映学科发展状况及研究热点,从而为医学人员提供有价值的信息与知识,值得进一步研究与推广。
Text Data Mining in Biomedical Informatics Based on Biclustering Method [J].
运用TDA和gCluto软件对SCI数据库中近5年的相关文献进行处理,获得双聚类矩阵图,经分析后得到该学科领域近年来的期刊研究主题方向与热点。结论认为,双聚类方法能够很好地反映学科发展状况及研究热点,从而为医学人员提供有价值的信息与知识,值得进一步研究与推广。
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| [31] |
利用双聚类算法探测学科前沿及知识基础——以h指数研究领域为例 [J].
双聚类算法在探测学科前沿和知识基础方面体现出了一定的可行性。文章以h指数研究领域为例, 利用gCLUTO软件对数据进行双聚类分析,获取h指数研究领域知识基础和学科前沿。结果表明,在探测h指数研究领域知识基础方面,双聚类对高被引文献的 聚类比较结构化和清晰,在探测h指数学科前沿方面,双聚类获得的前沿与知识基础对应关系明显,但对时间不敏感。研究结果为以后学科前沿和知识基础的探测提 供了有益参考,并提出观测科学活动特征的新角度,将双聚类分析推广到各领域的学科发展评价中。
Detection of Frontier and Knowledge Base Using Biclustering Algorithm-A Case Study of h Index [J].
双聚类算法在探测学科前沿和知识基础方面体现出了一定的可行性。文章以h指数研究领域为例, 利用gCLUTO软件对数据进行双聚类分析,获取h指数研究领域知识基础和学科前沿。结果表明,在探测h指数研究领域知识基础方面,双聚类对高被引文献的 聚类比较结构化和清晰,在探测h指数学科前沿方面,双聚类获得的前沿与知识基础对应关系明显,但对时间不敏感。研究结果为以后学科前沿和知识基础的探测提 供了有益参考,并提出观测科学活动特征的新角度,将双聚类分析推广到各领域的学科发展评价中。
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| [32] |
Epidural Pain Relief in Labour: Potencies of Levobupivacaine and Racemic Bupivacaine [J].https://doi.org/10.1093/bja/81.6.899 URL PMID: 10211016 [本文引用: 1] 摘要
We have compared the minimum local analgesic concentrations (MLAC) of levobupivacaine relative to racemic bupivacaine in a prospective, randomized, double-blind, sequential allocation study. Women in labour were given a 20-ml bolus of epidural levobupivacaine or bupivacaine diluted to a concentration determined by up-down sequential allocation. The initial concentration was 0.07% w/v for both drugs. Efficacy was defined using a visual analogue pain score (VAPS) at 10 mm or less within 30 min. The MLAC of levobupivacaine was 0.083% w/v (95% CI 0.065-0.101) and the MLAC of bupivacaine 0.081% w/v (95% CI 0.055-0.108). In molar terms, the MLAC of levobupivacaine was 2.87 mmol litre-1 (95% CI 2.25-3.49) and the MLAC of bupivacaine 2.49 mmol litre-1 (95% CI 1.69-3.32). With regard to the commercial preparations, the potency ratio levobupivacaine: bupivacaine was 0.98 (95% CI 0.67-1.41), and this is unlikely to be of clinical relevance. In molar terms, the ratio was 0.87 (95% CI 0.60-1.25). With regard to toxicity, the evidence should be evaluated in the light of a possible 13% potency difference in molar concentration in favour of racemic bupivacaine.
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| [33] |
Effects of Ramosetron and Dexamethasone on Postoperative Nausea, Vomiting, Pain, and Shivering in Female Patients Undergoing Thyroid Surgery [J].https://doi.org/10.1007/s00540-012-1473-8 URL PMID: 22965329 [本文引用: 1] 摘要
Some antiemetics are effective in the treatment of postoperative pain and shivering, as well as for postoperative nausea and vomiting (PONV). The aim of this study was to investigate the effects of ramosetron and dexamethasone on PONV, pain, and shivering and to determine the correlations between nausea, pain, and shivering.For this study, 123 patients scheduled for thyroid surgery were randomly allocated to one of three groups: the control group (group C, n = 41), dexamethasone group (group D, n = 41), or the ramosetron group (group R, n = 41). The patients were treated intravenously with 2 mL of 0.9 % NaCl, 2 mL of 5 mg/mL dexamethasone, or 2 mL of 0.15 mg/mL ramosetron immediately after anesthesia.The overall incidence and severity of postoperative nausea and the level of antiemetic consumption were significantly lower in group R compared with group D, and these parameters were significantly lower in groups R and D than in group C. There were significant differences in the incidence and severity of shivering, severity of pain, and analgesic consumption between group C and group R or D, but the incidence of shivering, pain severity, and analgesic consumption did not differ between groups R and D. The severity of shivering was significantly lower in group R than in group D. The correlation coefficients for shivering and pain, shivering and nausea, and pain and nausea were 0.210 (P = 0.010), 0.106 (P = 0.198), and 0.190 (P = 0.035), respectively, in group C.Two antiemetic drugs, ramosetron and dexamethasone, significantly reduced the incidence and severity of postoperative nausea and the need for administration of rescue antiemetic drugs. Furthermore, both drugs effectively decreased the severity of pain and shivering. Ramosetron was superior to dexamethasone for reducing nausea, antiemetic consumption, and the severity of nausea, but not for reducing the incidence of shivering. Further studies are required to elucidate the correlations between postoperative nausea, pain, and shivering, as a statistically significant but weak correlation was shown in the present study.
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| [34] |
头孢唑林钠引起腹痛1例 [J].https://doi.org/10.3969/j.issn.1009-6647.2006.19.223 URL [本文引用: 1] 摘要
篇首： 1 病例报告 女,48岁.因高位截瘫于2005-09-10转我院针灸科行康复治疗,入院后2 d,患者出现发热、气喘,T 38.6℃,P 84次/min,R 20次/min,BP 120/80 mm Hg,听诊肺部出现湿罗音及少量哮鸣音,诊断为肺部感染,给予静脉滴注头孢唑林钠4 g,加入生理盐水250 ml中,皮试(-),静脉穿刺成功后,滴注4 min,患者自感腹部疼痛,能忍受,遂减慢滴速继续观察,约8 min,上腹部剧痛,全身出冷汗,面色苍白,恶心欲吐,脉搏细弱,HR 68次/min,BP 100/70 mm Hg,全腹部紧张压痛,以剑突为重,有反跳痛,考虑为过敏反应.立即停止用药,给予肾上腺素0.5 mg肌肉注射,异丙嗪25 mg肌肉注射,1～6 min后,患者腹痛完全消失,出汗停止,面色转红润,精神好.
Cefazolin Sodium Caused Abdominal Pain in 1 Case [J].https://doi.org/10.3969/j.issn.1009-6647.2006.19.223 URL [本文引用: 1] 摘要
篇首： 1 病例报告 女,48岁.因高位截瘫于2005-09-10转我院针灸科行康复治疗,入院后2 d,患者出现发热、气喘,T 38.6℃,P 84次/min,R 20次/min,BP 120/80 mm Hg,听诊肺部出现湿罗音及少量哮鸣音,诊断为肺部感染,给予静脉滴注头孢唑林钠4 g,加入生理盐水250 ml中,皮试(-),静脉穿刺成功后,滴注4 min,患者自感腹部疼痛,能忍受,遂减慢滴速继续观察,约8 min,上腹部剧痛,全身出冷汗,面色苍白,恶心欲吐,脉搏细弱,HR 68次/min,BP 100/70 mm Hg,全腹部紧张压痛,以剑突为重,有反跳痛,考虑为过敏反应.立即停止用药,给予肾上腺素0.5 mg肌肉注射,异丙嗪25 mg肌肉注射,1～6 min后,患者腹痛完全消失,出汗停止,面色转红润,精神好.
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| [35] |
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| [36] |
Sucrose for Analgesia in Newborn Infants Undergoing Painful Procedures [J].https://doi.org/10.1002/14651858.CD001069.pub4 URL PMID: 23440783 [本文引用: 1] 摘要
Abstract BACKGROUND: Administration of oral sucrose with and without non-nutritive sucking is the most frequently studied non-pharmacological intervention for procedural pain relief in neonates. OBJECTIVES: To determine the efficacy, effect of dose and safety of oral sucrose for relieving procedural pain in neonates. SEARCH METHODS: We used the standard methods of the Cochrane Neonatal Review Group. Electronic and manual searches were performed in November 2011 for published randomised controlled trials (RCTs) in MEDLINE (1950 to November 2011), EMBASE (1980 to 2011), CINAHL (1982 to November 2011) and the Cochrane Central Register of Controlled Trials (The Cochrane Library). We did not impose language restrictions. SELECTION CRITERIA: RCTs in which term, preterm, or both term and preterm neonates (postnatal age maximum of 28 days after reaching 40 weeks' postmenstrual age) received sucrose for procedural pain. Control conditions included no treatment, water, pacifier, positioning/containing or breastfeeding. DATA COLLECTION AND ANALYSIS: Main outcome measures were physiological, behavioural, or both pain indicators with or without composite pain scores. A mean difference (MD) with 95% confidence intervals (CI) using the fixed-effect model was reported for continuous outcome measures. Trial quality was assessed as per The Cochrane Collaboration MAIN RESULTS: Fifty-seven studies enrolling 4730 infants were included. Results from only a few studies could be combined in meta-analyses. When Premature Infant Pain Profile (PIPP) scores were pooled, sucrose groups had significantly lower scores at 30 seconds (weighted mean difference (WMD) -1.76; 95% CI -2.54 to - 0.97; 4 trials; 264 neonates] and 60 seconds (WMD -2.05; 95% CI -3.08 to -1.02; 3 trials' 195 neonates) post-heel lance. For retinopathy of prematurity (ROP) examinations, sucrose did not significantly reduce PIPP scores (WMD -0.65; 95% CI -1.88 to 0.59; 3 trials; 82 neonates). There were no differences in adverse effects between sucrose and control groups. Sucrose significantly reduced duration of total crying time (WMD -39 seconds; 95% CI -44 to -34; 2 trials; 88 neonates), but did not reduce duration of first cry during heel lance (WMD -9 seconds; 95% CI -20 to 2; 3 trials; 192 neonates). Oxygen saturation (%) was significantly lower in infants given sucrose during ROP examination compared to controls (WMD -2.6; 95% CI -4.9 to - 0.2; 2 trials; 62 neonates). Results of individual trials that could not be incorporated in meta-analyses supported these findings. The effects of sucrose on long-term neurodevelopmental outcomes are unknown. AUTHORS' CONCLUSIONS: Sucrose is safe and effective for reducing procedural pain from single events. An optimal dose could not be identified due to inconsistency in effective sucrose dosage among studies. Further investigation on repeated administration of sucrose in neonates and the use of sucrose in combination with other non-pharmacological and pharmacological interventions is needed. Sucrose use in extremely preterm, unstable, ventilated (or a combination of these) neonates needs to be addressed. Additional research is needed to determine the minimally effective dose of sucrose during a single painful procedure and the effect of repeated sucrose administration on immediate (pain intensity) and long-term (neurodevelopmental) outcomes.
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| [37] |
Randomised Clinical Trial: The Long-term Safety and Tolerability of Naloxegol in Patients with Pain and Opioid-induced Constipation [J]. |
| [38] |
Efficacy and Tolerance of Metamizole versus Morphine for Acute Pancreatitis Pain [J].https://doi.org/10.1159/000114852 URL PMID: 18235213 [本文引用: 1] 摘要
Background/Aims: Morphine has been contraindicated for pain treatment in acute pancreatitis because of its presumed opioid-induced sphincter of Oddi dysfunction. However, scientific evidence supporting a deleterious influence on the clinical course is absent. This pilot study was undertaken to evaluate the efficacy and adverse events of metamizole versus morphine in acute pancreatitis. Methods: 16 patients with acute pancreatitis were randomized to receive 10 mg/ 4 h s.c. (n = 8) morphine or 2 g/8 h i.v. (n = 8) metamizole. Pain scores were recorded every 4 h during 48 h after admission by a Visual Analogue Scale. Pethidine was additionally administered as a rescue therapy. Results: 75% of patients achieved pain relief in the metamizole group versus 37.5% in the morphine group within 24 h of hospitalization (6/8 vs. 3/8; p: n.s.).The mean time to achieve pain relief was shorter in the metamizole group (10 卤 6.6 vs. 17 卤 18.3 h; p: n.s.). At the end of the study, 75% of patients achieved pain relief in the metamizole group versus 50% in the morphine group. Three patients in each group needed pethidine: 2 out of 3 achieved pain control in the metamizole group vs. 0 out of 3 in the morphine group. Conclusions: Intravenous metamizole shows a non-significant association with a quicker pain relief than morphine s.c. in acute pancreatitis. A larger randomized controlled trial should be desirable to confirm this result.
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