Analysis of Sleep Quality and TCM Constitution Characteristics in 258 Outpatients: A Cross-Sectional Study Based on Outpatient Cases

Liu, Chang; Qu, Jing; Chen, Luquan; Liu, Ruijie

doi:10.1155/2022/2952531

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…Another study has shown that Qi-deficiency was significantly associated with emotional, pain, and fatigue in SLE patients [ 46 ]. Moreover, Qi-deficiency constitution, Qi-depression constitution, and Blood-stasis constitution were all significantly associated with insomnia [ 47 ]. In addition, other studies have shown that Blood-stasis constitution was a risk factor for cognitive dysfunction [ 48 ], was strongly associated with the development of peripheral arterial disease in patients with type 2 diabetes mellitus [ 49 ], and may also be used as an early predictive diagnostic indicator for the development of coronary artery disease in patients with chest pain [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Development and validation of non-invasive prediction models for migraine in Chinese adults

Duan,

Xia,

Zheng

et al. 2023

J Headache Pain

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Background Migraine is a common disabling neurological disorder with severe physical and psychological damage, but there is a lack of convenient and effective non-invasive early prediction methods. This study aimed to develop a new series of non-invasive prediction models for migraine with external validation. Methods A total of 188 and 94 subjects were included in the training and validation sets, respectively. A standardized professional questionnaire was used to collect the subjects' 9-item traditional Chinese medicine constitution (TCMC) scores, Pittsburgh Sleep Quality Index (PSQI) score, Zung's Self-rating Anxiety Scale and Self-rating Depression Scale scores. Logistic regression was used to analyze the risk predictors of migraine, and a series of prediction models for migraine were developed. Receiver operating characteristic (ROC) curve and calibration curve were used to assess the discrimination and calibration of the models. The predictive performance of the models were further validated using external datasets and subgroup analyses were conducted. Results PSQI score and Qi-depression score were significantly and positively associated with the risk of migraine, with the area of the ROC curves (AUCs) predicting migraine of 0.83 (95% CI:0.77–0.89) and 0.76 (95% CI:0.68–0.84), respectively. Eight non-invasive predictive models for migraine containing one to eight variables were developed using logistic regression, with AUCs ranging from 0.83 (95% CI: 0.77–0.89) to 0.92 (95% CI: 0.89–0.96) for the training set and from 0.76 (95% CI: 0.66–0.85) to 0.83 (95% CI: 0.75–0.91) for the validation set. Subgroup analyses showed that the AUCs of the eight prediction models for predicting migraine in the training and validation sets of different gender and age subgroups ranged from 0.80 (95% CI: 0.63–0.97) to 0.95 (95% CI: 0.91–1.00) and 0.73 (95% CI: 0.64–0.84) to 0.93 (95% CI: 0.82–1.00), respectively. Conclusions This study developed and validated a series of convenient and novel non-invasive prediction models for migraine, which have good predictive ability for migraine in Chinese adults of different genders and ages. It is of great significance for the early prevention, screening, and diagnosis of migraine.

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Section: Discussionmentioning

confidence: 99%

Development and validation of non-invasive prediction models for migraine in Chinese adults

Duan,

Xia,

Zheng

et al. 2023

J Headache Pain

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“…This study underscores the significant influence of TCM constitution on the prevention and prognosis of insomnia, suggesting that the CCM scale score could act as a crucial predictive tool for determining the severity of insomnia. The uniquely powerful role of an individual’s constitution in TCM highlights its impact on overall physical and mental health, with a marked effect on susceptibility and severity of illnesses, including insomnia ( 17 ). By exploring the predictive capabilities of the CCM scale in relation to insomnia severity, we are not only poised to forge innovative links between TCM and modern sleep medicine, but also lay the foundation for the development of more comprehensive, personalized therapeutic strategies that emphasize the remarkable contributions of TCM constitution in managing and predicting insomnia, thus shaping its treatment outcomes.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning models in predicting insomnia severity: an integrative approach with constitution of traditional Chinese medicine

Li,

Zhu,

Cai

et al. 2023

Front. Med.

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ObjectiveThis study sought to explore the utility of machine learning models in predicting insomnia severity based on Traditional Chinese Medicine (TCM) constitution classifications, with an aim to discuss the potential applications of such models in the treatment and prevention of insomnia.MethodsWe analyzed a dataset of 165 insomnia patients from the Shanghai Minhang District Integrated Traditional Chinese and Western Medicine Hospital. TCM constitution was assessed using a standardized Constitution in Chinese Medicine (CCM) scale. Sleep quality, or insomnia severity, was evaluated using the Spiegel Sleep Questionnaire (SSQ). Machine learning models, including Random Forest Classifier (RFC), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN), were utilized. These models were optimized using Grid Search algorithm and were trained and tested on stratified patient data, with the TCM constitution classifications serving as primary predictors.ResultsThe RFC outperformed others, achieving a weighted average accuracy, precision, recall, and F1-score of 0.91, 0.94, 0.92, and 0.92 respectively, it also effectively classified the severity of insomnia with high area under receiver operating characteristic curve (AUC-ROC) values. Feature importance analysis demonstrated the Damp-heat constitution as the most influential predictor, followed by Yang-deficiency, Qi-depression, Qi-deficiency, and Blood-stasis constitutions.ConclusionThe results demonstrate the potent utility of machine learning, specifically RFC, coupled with TCM constitution classifications in predicting insomnia severity. Notably, the constitution classifications such as Damp-heat and Yang-deficiency emerged as crucial determinants, emphasizing its potential in guiding targeted insomnia treatments. This approach enables the development of more personalized and efficient interventions, thereby enhancing patient outcomes.

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Manipulated or compromised peer review …”

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confidence: 99%

mentioning

confidence: 99%

Retracted: Analysis of Sleep Quality and TCM Constitution Characteristics in 258 Outpatients: A Cross-Sectional Study Based on Outpatient Cases

2023

Applied Bionics and Biomechanics

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Analysis of Sleep Quality and TCM Constitution Characteristics in 258 Outpatients: A Cross-Sectional Study Based on Outpatient Cases

Cited by 6 publications

References 8 publications

Development and validation of non-invasive prediction models for migraine in Chinese adults

Development and validation of non-invasive prediction models for migraine in Chinese adults

Application of machine learning models in predicting insomnia severity: an integrative approach with constitution of traditional Chinese medicine

Retracted: Analysis of Sleep Quality and TCM Constitution Characteristics in 258 Outpatients: A Cross-Sectional Study Based on Outpatient Cases

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