Validation studies on migraine diagnostic tools for use in nonclinical settings: a systematic review / Estudos de validação de ferramentas de diagnóstico de enxaqueca (migrânea) para uso em ambientes não clínicos: uma revisão sistemática

Abstract Background Migraine underdiagnosis and undertreatment are so widespread, that hence is essential to diagnose migraine sufferers in nonclinical settings. A systematic review of validation studies on migraine diagnostic tools applicable to nonclinical settings can help researchers and practitioners in tool selection decisions. Objective To systematically review and critically assess published validation studies on migraine diagnostic tools for use in nonclinical settings, as well as to describe their diagnostic performance. Methods A multidisciplinary workgroup followed transparent and systematic procedures to collaborate on this work. PubMed, Medline, and Web of Science were searched for studies up to January 17, 2022. The QUADAS-2 was employed to assess methodological quality, and the quality thresholds adopted by the Global Burden Disease study were used to tail signaling questions. Results From 7,214 articles identified, a total of 27 studies examining 19 tools were eligible for inclusion. There has been no high-quality evidence to support any tool for use of migraine diagnosis in nonclinical settings. The diagnostic accuracy of the ID-migraine, structured headache and HARDSHIP questionnaires have been supported by moderate-quality evidence, with sensitivity and specificity above 70%. Of them, the HARDSHIP questionnaire has been the most extensively validated. The remaining 16 tools have provided poor-quality evidence for migraine diagnosis in nonclinical populations. Conclusions Up till now, the HARDSHIP questionnaire is the optimal choice for diagnosing migraine in nonclinical settings, with satisfactory diagnostic accuracy supported by moderate methodological quality. This work reveals the crucial next step, which is further high-quality validation studies in diverse nonclinical population groups.

Resumo Antecedentes O sub-diagnóstico e o subtratamento da enxaqueca são tão difundidos que, portanto, é essencial para diagnosticar os portadores de enxaqueca em ambientes não-clínicos. Uma revisão sistemática dos estudos de validação das ferramentas de diagnóstico da enxaqueca aplicáveis a ambientes não-clínicos pode ajudar os pesquisadores e profissionais nas decisões de seleção de ferramentas. Objetivo Revisar sistematicamente e avaliar criticamente estudos de validação publicados sobre ferramentas de diagnóstico da enxaqueca para uso em ambientes não-clínicos, bem como descrever seu desempenho diagnóstico. Métodos Um grupo de trabalho multidisciplinar seguiu procedimentos transparentes e sistemáticos para colaborar neste trabalho. PubMed, Medline e Web of Science foram pesquisados por estudos até 17 de janeiro de 2022. O QUADAS-2 foi empregado para avaliar a qualidade metodológica, e os limites de qualidade adotados pelo estudo da Global Burden Disease foram usados para responder a questões de sinalização. Resultados De 7.214 artigos identificados, um total de 27 estudos examinando 19 ferramentas foram elegíveis para inclusão. Não houve evidência de alta qualidade para apoiar qualquer ferramenta para o uso de diagnóstico de enxaqueca em ambientes não clínicos. A precisão diagnóstica do ID-Migraine, questionário de dor de cabeça estruturada e questionário HARDSHIP foram apoiados por evidências de qualidade moderada, com sensibilidade e especificidade acima de 70%. Deles, o questionário HARDSHIP foi o mais amplamente validado. As 16 ferramentas restantes forneceram provas de má qualidade para o diagnóstico de enxaqueca em populações não-clínicas. Conclusões Até agora, o questionário HARDSHIP é a escolha ideal para o diagnóstico da enxaqueca em ambientes não-clínicos, com precisão diagnóstica satisfatória apoiada por uma qualidade metodológica moderada. Este trabalho revela o próximo passo crucial, que é a realização de mais estudos de validação de alta qualidade em diversos grupos populacionais não-clínicos.

Association between Migraine and Workplace Social Support in the Social Context of China: Using a Validated Chinese Version of the DCSQ.

BACKGROUND: Workplace social support might have a protective function against migraine in the social context of China, as close co-worker relationships and collectivism are acknowledged as work values in Chinese society. OBJECTIVES: This paper aimed to analyse the association between migraine and workplace social support. The validity and reliability of the Chinese version of the Support scale of the Demand-Control-Support Questionnaire (DCSQ) used were also determined. METHODS: Following international guidelines, this study was carried out in two stages. Stage I involved translations and pilot testing to assess content and face validity of the Chinese version of the DCSQ Support scale. Stage II was a cross-sectional survey (N = 677 bank employees) to evaluate structural validity, internal consistency and test-retest reliability of the Support scale, as well as to examine the association between workplace social support and a migraine-positive diagnosis. RESULTS: A high level of social support in the workplace was associated with a 74% decreased likelihood of migraine (adjusted OR = 0.26, 95%CI: 0.14-0.46). Of the six aspects of workplace social support, co-worker support had the greatest protective effect (adjusted OR = 0.49, 95% CI: 0.39-0.60). The Chinese version of the DCSQ Support scale established satisfactory content and face validity (I-CVIs ≥ 0.78; S-CVIAVE ≥ 0.90). Confirmatory factor analysis verified its one-dimensional theoretical factor, with adequate internal consistency (Cronbach's α 0.98; item-total correlations ≥ 0.80) and test-retest reliability (weighted Kappa coefficients 0.81-0.87; percentages agreement 85.23-88.92%). CONCLUSIONS: In the Chinese social context, workplace social support could protect against migraine, with the strongest benefit coming from co-workers. This study also provides a Chinese-language DCSQ Support scale as a valid and reliable instrument for measuring workplace social support.

Validation studies on migraine diagnostic tools for use in nonclinical settings: a systematic review.

BACKGROUND: Migraine underdiagnosis and undertreatment are so widespread, that hence is essential to diagnose migraine sufferers in nonclinical settings. A systematic review of validation studies on migraine diagnostic tools applicable to nonclinical settings can help researchers and practitioners in tool selection decisions. OBJECTIVE: To systematically review and critically assess published validation studies on migraine diagnostic tools for use in nonclinical settings, as well as to describe their diagnostic performance. METHODS: A multidisciplinary workgroup followed transparent and systematic procedures to collaborate on this work. PubMed, Medline, and Web of Science were searched for studies up to January 17, 2022. The QUADAS-2 was employed to assess methodological quality, and the quality thresholds adopted by the Global Burden Disease study were used to tail signaling questions. RESULTS: From 7,214 articles identified, a total of 27 studies examining 19 tools were eligible for inclusion. There has been no high-quality evidence to support any tool for use of migraine diagnosis in nonclinical settings. The diagnostic accuracy of the ID-migraine, structured headache and HARDSHIP questionnaires have been supported by moderate-quality evidence, with sensitivity and specificity above 70%. Of them, the HARDSHIP questionnaire has been the most extensively validated. The remaining 16 tools have provided poor-quality evidence for migraine diagnosis in nonclinical populations. CONCLUSIONS: Up till now, the HARDSHIP questionnaire is the optimal choice for diagnosing migraine in nonclinical settings, with satisfactory diagnostic accuracy supported by moderate methodological quality. This work reveals the crucial next step, which is further high-quality validation studies in diverse nonclinical population groups.

ANTECEDENTES: O sub-diagnóstico e o subtratamento da enxaqueca são tão difundidos que, portanto, é essencial para diagnosticar os portadores de enxaqueca em ambientes não-clínicos. Uma revisão sistemática dos estudos de validação das ferramentas de diagnóstico da enxaqueca aplicáveis a ambientes não-clínicos pode ajudar os pesquisadores e profissionais nas decisões de seleção de ferramentas. OBJETIVO: Revisar sistematicamente e avaliar criticamente estudos de validação publicados sobre ferramentas de diagnóstico da enxaqueca para uso em ambientes não-clínicos, bem como descrever seu desempenho diagnóstico. MéTODOS: Um grupo de trabalho multidisciplinar seguiu procedimentos transparentes e sistemáticos para colaborar neste trabalho. PubMed, Medline e Web of Science foram pesquisados por estudos até 17 de janeiro de 2022. O QUADAS-2 foi empregado para avaliar a qualidade metodológica, e os limites de qualidade adotados pelo estudo da Global Burden Disease foram usados para responder a questões de sinalização. RESULTADOS: De 7.214 artigos identificados, um total de 27 estudos examinando 19 ferramentas foram elegíveis para inclusão. Não houve evidência de alta qualidade para apoiar qualquer ferramenta para o uso de diagnóstico de enxaqueca em ambientes não clínicos. A precisão diagnóstica do ID-Migraine, questionário de dor de cabeça estruturada e questionário HARDSHIP foram apoiados por evidências de qualidade moderada, com sensibilidade e especificidade acima de 70%. Deles, o questionário HARDSHIP foi o mais amplamente validado. As 16 ferramentas restantes forneceram provas de má qualidade para o diagnóstico de enxaqueca em populações não-clínicas. CONCLUSõES: Até agora, o questionário HARDSHIP é a escolha ideal para o diagnóstico da enxaqueca em ambientes não-clínicos, com precisão diagnóstica satisfatória apoiada por uma qualidade metodológica moderada. Este trabalho revela o próximo passo crucial, que é a realização de mais estudos de validação de alta qualidade em diversos grupos populacionais não-clínicos.

A Novel Theoretical Investigation of the Abu-Shady-Kaabar Fractional Derivative as a Modeling Tool for Science and Engineering.

A newly proposed generalized formulation of the fractional derivative, known as Abu-Shady-Kaabar fractional derivative, is investigated for solving fractional differential equations in a simple way. Novel results on this generalized definition is proposed and verified, which complete the theory introduced so far. In particular, the chain rule, some important properties derived from the mean value theorem, and the derivation of the inverse function are established in this context. Finally, we apply the results obtained to the derivation of the implicitly defined and parametrically defined functions. Likewise, we study a version of the fixed point theorem for α-differentiable functions. We include some examples that illustrate these applications. The obtained results of our proposed definition can provide a suitable modeling guide to study many problems in mathematical physics, soliton theory, nonlinear science, and engineering.

A Novel Computational Tool for the Fractional-Order Special Functions Arising from Modeling Scientific Phenomena via Abu-Shady-Kaabar Fractional Derivative.

Recently, a generalized fractional derivative formulation, known as Abu-Shady-Kaabar fractional derivative, is studied in detail which produces satisfactory results that are consistent with conventional definitions of fractional derivative such as Caputo and Riemann-Liouville. To derive the fractional forms of special functions, the generalized fractional derivative is used. The findings demonstrate that the current findings are compatible with Caputo findings. In addition, the fractional solution to the Bessel equation is found. While modeling phenomena in engineering, physical, and health sciences, special functions can be encountered in most modeling scenarios related to electromagnetic waves, hydrodynamics, and other related models. Therefore, there is a need for a computational tool for computing special functions in the sense of fractional calculus. This tool provides a straightforward technique for some fractional-order special functions while modeling these scientific phenomena in science, medicine, and engineering.

Mathematical modeling of COVID-19 pandemic in India using Caputo-Fabrizio fractional derivative.

The range of effectiveness of the novel corona virus, known as COVID-19, has been continuously spread worldwide with the severity of associated disease and effective variation in the rate of contact. This paper investigates the COVID-19 virus dynamics among the human population with the prediction of the size of epidemic and spreading time. Corona virus disease was first diagnosed on January 30, 2020 in India. From January 30, 2020 to April 21, 2020, the number of patients was continuously increased. In this scientific work, our main objective is to estimate the effectiveness of various preventive tools adopted for COVID-19. The COVID-19 dynamics is formulated in which the parameters of interactions between people, contact tracing, and average latent time are included. Experimental data are collected from April 15, 2020 to April 21, 2020 in India to investigate this virus dynamics. The Genocchi collocation technique is applied to investigate the proposed fractional mathematical model numerically via Caputo-Fabrizio fractional derivative. The effect of presence of various COVID parameters e.g. quarantine time is also presented in the work. The accuracy and efficiency of the outputs of the present work are demonstrated through the pictorial presentation by comparing it to known statistical data. The real data for COVID-19 in India is compared with the numerical results obtained from the concerned COVID-19 model. From our results, to control the expansion of this virus, various prevention measures must be adapted such as self-quarantine, social distancing, and lockdown procedures.

Optimization: Molecular Communication Networks for Viral Disease Analysis Using Deep Leaning Autoencoder.

Developing new treatments for emerging infectious diseases in infectious and noninfectious diseases has attracted a particular attention. The emergence of viral diseases is expected to accelerate; these data indicate the need for a proactive approach to develop widely active family specific and cross family therapies for future disease outbreaks. Viral disease such as pneumonia, severe acute respiratory syndrome type 2, HIV infection, and Hepatitis-C virus can cause directly and indirectly cardiovascular disease (CVD). Emphasis should be placed not only on the development of broad-spectrum molecules and antibodies but also on host factor therapy, including the reutilization of previously approved or developing drugs. Another new class of therapeutics with great antiviral therapeutic potential is molecular communication networks using deep learning autoencoder (DL-AEs). The use of DL-AEs for diagnosis and prognosis prediction of infectious and noninfectious diseases has attracted a particular attention. MCN is map to molecular signaling and communication that are found inside and outside the human body where the goal is to develop a new black box mechanism that can serve the future robust healthcare industry (HCI). MCN has the ability to characterize the signaling process between cells and infectious disease locations at various levels of the human body called point-to-point MCN through DL-AE and provide targeted drug delivery (TDD) environment. Through MCN, and DL-AE healthcare provider can remotely measure biological signals and control certain processes in the required organism for the maintenance of the patient's health state. We use biomicrodevices to promote the real-time monitoring of human health and storage of the gathered data in the cloud. In this paper, we use the DL-based AE approach to design and implement a new drug source and target for the MCN under white Gaussian noise. Simulation results show that transceiver executions for a given medium model that reduces the bit error rate which can be learned. Then, next development of molecular diagnosis such as heart sounds is classified. Furthermore, biohealth interface for the inside and outside human body mechanism is presented, comparative perspective with up-to-date current situation about MCN.

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions.

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain.

For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.