Prediction and associated factors of non-steroidal anti-inflammatory drugs efficacy in migraine treatment

Lu, Zhaoxuan; Dong, Bing-Qing; Wei, Heng-Le; Chen, Liang

doi:10.3389/fphar.2022.1002080

Cited by 6 publications

(2 citation statements)

References 54 publications

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“…A study by Amanzio et al (2009) evaluated the frequencies of different adverse events across clinical trials for three migraine preventive pills. Adverse events of non-steroidal anti-inflammatory drugs, frequently used to treat mild-to-moderate migraines, included mild gastrointestinal symptoms such as an upset stomach or indigestion (Bindu et al, 2020;Lu et al, 2022). Adverse events of triptans, a specific class of drugs used for acute migraine treatment, included mild headache, fatigue, nausea, and dizziness (De Boer et al, 2023;Juhasz et al, 2023;Naghibi et al, 2019).…”

Section: Further Insights Into Nocebo -Placebo Effectmentioning

confidence: 99%

Placebo–Placebo, Nocebo–Nocebo, Placebo–Nocebo, and Nocebo–Placebo Effects: A Review of Eastern and Western Perspectives

JARALI

2024

Preprint

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This study examined placebo and nocebo effects, characterized by classical conditioning, operant conditioning, and expectancy effects. The placebo–placebo and nocebo–nocebo effects can be categorized to function independently, including the influence of various factors such as pill color, size, genetics (including other biological factors), branding, the demeanor of healthcare providers, and treatment settings and also considering beyond science understandings (spiritual perspective). To enhance understanding and optimize treatment outcomes, these effects can be categorized under classical conditioning, operant conditioning, and expectancy effect as follows in dominating pre-dominants of nature and beliefs ranging in the respective involuted evolutionary probability of occurrence: 1) placebo–placebo effect, driven by optimistic nature and belief leading to pure placebo effect which can be divided into double placebo and yogic placebo effect; 2) nocebo–nocebo effect, influenced by a pessimistic nature and belief leading to pure nocebo effect which can be divided into double nocebo and vamamarya nocebo effect; 3) placebo–nocebo effect, where optimism nature counters pessimistic belief, resulting in a impure placebo effect; and 4) nocebo–placebo effect, where pessimistic nature undermines optimistic belief, leading to a impure nocebo effect; understanding that not an individual will go through only one of the categories discussed here. It is recommended that future studies adopt this categorization and consider adjusting the definitions of placebo and nocebo to accommodate Eastern and Western (Universal) perspectives.

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Section: Further Insights Into Nocebo -Placebo Effectmentioning

confidence: 99%

Placebo–Placebo, Nocebo–Nocebo, Placebo–Nocebo, and Nocebo–Placebo Effects: A Review of Eastern and Western Perspectives

JARALI

2024

Preprint

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show abstract

“…Machine learning models based on clinical features or imaging data have been developed for predicting drug responsiveness [ [113] , [114] , [115] , [116] , [117] , [118] , [119] , [120] , [121] ] ( Table 4 ). Gonzalez-Martinez et al [ 113 ] demonstrated a model for predicting patient response to anti-CGRP therapies at 6, 9, and 12 months based on variables such as headache days per month, migraine days per month, change in the number of headaches or migraine days per month from baseline to 3 and 6 months, and Headache Impact Test score.…”

Section: Personalized Strategymentioning

confidence: 99%

New management strategies for primary headache disorders: Insights from P4 medicine

Zhang,

Huang,

Pan

et al. 2023

Heliyon

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Artificial intelligence and headache

Stubberud,

Langseth,

Nachev

et al. 2024

Cephalalgia

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Background and methods In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a comprehensive literature search across PubMed, Embase and IEEExplore. Finally, we discuss limitations, as well as ethical and political perspectives. Results We identified six main research topics. First, natural language processing can be used to effectively extract and systematize unstructured headache research data, such as from electronic health records. Second, the most common application of ML is for classification of headache disorders, typically based on clinical record data, or neuroimaging data, with accuracies ranging from around 60% to well over 90%. Third, ML is used for prediction of headache disease trajectories. Fourth, ML shows promise in forecasting of headaches using self-reported data such as triggers and premonitory symptoms, data from wearable sensors and external data. Fifth and sixth, ML can be used for prediction of treatment responses and inference of treatment effects, respectively, aiming to optimize and individualize headache management. Conclusions The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.

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Prediction and associated factors of non-steroidal anti-inflammatory drugs efficacy in migraine treatment

Cited by 6 publications

References 54 publications

Placebo–Placebo, Nocebo–Nocebo, Placebo–Nocebo, and Nocebo–Placebo Effects: A Review of Eastern and Western Perspectives

Placebo–Placebo, Nocebo–Nocebo, Placebo–Nocebo, and Nocebo–Placebo Effects: A Review of Eastern and Western Perspectives

New management strategies for primary headache disorders: Insights from P4 medicine

Artificial intelligence and headache

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