Computerized migraine diagnostic tools: a systematic review.
Computerized migraine diagnostic tools: a systematic review.
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DOI：
10.1177/20406223211065235
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发表时间：
2022
影响因子：
3.5
通讯作者：
Cowan RP
中科院分区：
文献类型：
--
作者：
Woldeamanuel YW;Cowan RP
自1960年以来，计算机化的偏头痛诊断工具已经开发和验证。我们进行了一项系统回顾，总结并严格评价所有已发表的涉及计算机化偏头痛诊断工具的研究的质量。我们使用PubMed、Web of Science、Scopus、滚雪球和引文检索进行了系统的文献检索。检索的截止日期为2021年6月1日。纳入了评估计算机化/自动化偏头痛诊断工具的英文已发表文章。以下总结了每项研究：发表年份、数字工具名称、开发基础、样本量、灵敏度、特异性、参考诊断、强度和局限性。应用诊断准确性研究质量评估（QUADAS）工具评价纳入研究的偏倚风险和适用性问题。共纳入41项研究（中位样本量：288名参与者，中位年龄= 43岁; 77%为女性）。大多数（60%）工具是根据头痛疾病国际分类标准开发的，一半是自我管理的，82%是使用面对面访谈作为参考诊断进行评估的。一些自动化算法和机器学习程序涉及基于案例的推理、深度学习、分类器集成、蚁群、人工免疫、随机森林、白色和黑箱组合以及混合模糊专家系统。中位诊断准确性为一致性= 89% [四分位距（IQR）= 76-93%;范围= 45-100%]，灵敏度= 87%（IQR = 80-95%;范围= 14-100%），特异性= 90%（IQR = 77-96%;范围= 65-100%）。在95%的研究中观察到缺乏随机患者抽样。所有研究均避免病例对照设计。大多数（76%）参考测试显示偏倚风险低，适用性低。患者流量和时间显示83%的偏倚风险较低。不同的计算机化和自动化偏头痛诊断工具具有不同的准确性。随机患者抽样，工具之间的头对头比较，以及对其他头痛诊断的普遍性可能会提高其效用。
Computerized migraine diagnostic tools have been developed and validated since 1960. We conducted a systematic review to summarize and critically appraise the quality of all published studies involving computerized migraine diagnostic tools. We performed a systematic literature search using PubMed, Web of Science, Scopus, snowballing, and citation searching. Cutoff date for search was 1 June 2021. Published articles in English that evaluated a computerized/automated migraine diagnostic tool were included. The following summarized each study: publication year, digital tool name, development basis, sample size, sensitivity, specificity, reference diagnosis, strength, and limitations. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool was applied to evaluate the quality of included studies in terms of risk of bias and concern of applicability. A total of 41 studies (median sample size: 288 participants, median age = 43 years; 77% women) were included. Most (60%) tools were developed based on International Classification of Headache Disorders criteria, half were self-administered, and 82% were evaluated using face-to-face interviews as reference diagnosis. Some of the automated algorithms and machine learning programs involved case-based reasoning, deep learning, classifier ensemble, ant-colony, artificial immune, random forest, white and black box combinations, and hybrid fuzzy expert systems. The median diagnostic accuracy was concordance = 89% [interquartile range (IQR) = 76–93%; range = 45–100%], sensitivity = 87% (IQR = 80–95%; range = 14–100%), and specificity = 90% (IQR = 77–96%; range = 65–100%). Lack of random patient sampling was observed in 95% of studies. Case–control designs were avoided in all studies. Most (76%) reference tests exhibited low risk of bias and low concern of applicability. Patient flow and timing showed low risk of bias in 83%. Different computerized and automated migraine diagnostic tools are available with varying accuracies. Random patient sampling, head-to-head comparison among tools, and generalizability to other headache diagnoses may improve their utility.
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影响因子：
4.4
作者：
Bowling, A
通讯作者：
Bowling, A
影响因子：
5
作者：
Crawford, Megan R.;Luik, Annemarie I.;Ong, Jason C.
通讯作者：
Ong, Jason C.
影响因子：
5
作者：
BANA, DS;LEVITON, A;GRAHAM, JR
通讯作者：
GRAHAM, JR
影响因子：
1.4
作者：
Bembalgi, Veena;Naik, Karkal Ravishankar
通讯作者：
Naik, Karkal Ravishankar
DOI：
10.1016/j.nurpra.2021.01.014
发表时间：
2021-04-21
期刊：
JNP-JOURNAL FOR NURSE PRACTITIONERS
影响因子：
--
作者：
Bensink, Mark;Shah, Shweta;Quillen, Apryl
通讯作者：
Quillen, Apryl