No relationship between generalised anxiety symptoms and cardiovascular autonomic dysfunction

Kupats, Einars; Noviks, Iļja; Vrubļevska, Jeļena; Ķēniņa, Viktorija; Kojalo, Una; Logina, Ināra

doi:10.1016/j.npbr.2018.07.001

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…A recent study by Kupats et al, reported that there was no relationship between the symptoms of generalized anxiety and cardiovascular ADy [4]. However, in our results, we observed that 11 out of 23 features estimated from ECG and respiratory signal have shown a significant statistical difference in both anxious and non-anxious groups.…”

Section: Discussioncontrasting

confidence: 82%

“…However, the detection of ADy in anxiety disorder is difficult with the existing traditional methods (questionnaires). There are very few articles in the literature that directly studied the association between anxiety and its effect on the autonomic nervous system by measuring the variability in heart rate and respiratory rate during a sleep recording [4], [5]. Therefore, the current study aimed to develop computational tools that can be used for the detection of anxiety disorder by screening ADys from electrocardiogram (ECG) and respiratory signals.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Autonomic Dysfunction in Anxiety Disorder from ECG and Respiratory Signals Using Machine Learning Models

et al. 2021

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<p class="0abstract">Anxiety is a cognitive, behavioural, and biological response that prepares the individual to handle the stresses and conflicts of everyday life. The excessive appearance of this biological response is diagnosed as an anxiety disorder, which is often associated with Autonomic dysfunction (ADy). ADy is difficult to study in clinics with very few parameters available. Detection of ADy may not be possible/difficult in anxiety disorder with the existing method. In this study, we built machine learning models to identify ADy in subjects with anxiety using properties extracted from ECG and respiratory signals. For each dataset, statistical and frequency domain features were estimated from ECG and respiratory signals. Supervised machine learning (ML) algorithms were used to classify the subjects. Out of 23 features estimated, 11 were found to be statistically significant for the classification. We segmented the signals into 5, 10, and 30 minutes intervals to build generalized models. To overcome data imbalance, ensemble techniques like boosting was used. The highest accuracy was obtained in the SVM, Random forest and Gradient Boosting classifiers (cross-validation accuracy of 82.2%, 81.64% and 79.06% and; AUC of 0.81, 0.76 and 0.84) for 10 and 30 minutes segmented datasets. Our results showed that the features extracted from the ECG signal are a good marker for diagnosing ADy in patients with anxiety disorder. Further, a deep neural network-based model can be implemented that may achieve better accuracy for classification provided with the cost of a large number of datasets and computation time.</p>

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Section: Discussioncontrasting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Predicting Autonomic Dysfunction in Anxiety Disorder from ECG and Respiratory Signals Using Machine Learning Models

et al. 2021

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“…However, earlier studies could not demonstrate the definitive role of ANS dysfunction as the link to this association. [ 25 ] Some of our patients did have clinically significant pain and anxiety; however, the representation was small, and this could be due to the routine anti-anxiety (tablet alprazolam 0.5 mg) and analgesic medications (tablet paracetamol 500 mg) standardly prescribed in the preoperative period to all patients in addition to patient counselling.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and predictors of preoperative cardiac autonomic dysfunction among elective neurosurgical patients: A prospective observational study

Sangeetha,

Chakrabarti,

Sriganesh

et al. 2024

Indian Journal of Anaesthesia

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Background and Aims: The autonomic nervous system (ANS) is cardinal for systemic homeostasis. Autonomic dysfunction is prevalent in as high as 65% of patients presenting for cardiac surgery in the Indian scenario. Pre-existing cardiac autonomic dysfunction (CAD) in surgical patients can accentuate perioperative haemodynamic fluctuations during stressful intraoperative events, predispose to adverse cardiac events, and contribute to morbidity and mortality. The prevalence and predictors of CAD in the elective neurosurgical population are unknown in the Indian scenario. The current study was conducted to bridge this knowledge gap. Methods: In this single-centre prospective observational study conducted at a tertiary care neurosciences centre, among 400 consenting adult patients of either gender, between 18 and 80 years of age, undergoing elective neurosurgery, the preoperative ANS function at the bedside was assessed as the primary outcome measure. The ANS status was evaluated using ANSiscope™-derived indices of heart rate variability. The diagnosis of CAD was made when the ANS index exceeded a threshold of 13.5. Data regarding predictors of CAD were collected from patient records as the secondary outcome measure. Statistical analysis was done using the R software. A P-value of <0.05 was considered statistically significant. Results: The prevalence of preoperative CAD in our study population was 79.7% (319/400 patients). None of the demographic and baseline clinical characteristics we studied predicted CAD in our study. Conclusion: We observed a significant prevalence of preoperative CAD among elective neurosurgical patients. None of the parameters we evaluated predicted CAD in our study.

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“…To illustrate the equivalence interval conceptually, let us consider an example from the literature borrowed from Quintana (2018): Kupats et al (2018) examined the lack of relationship between symptoms of generalized anxiety and cardiovascular autonomic dysfunction, which is measured by heart-rate variability (HRV). According to Quintana (2016), about 75% of HRV effect sizes in anxiety studies are at d = 0.26 or above.…”

Section: Defining Negligible Associationmentioning

confidence: 99%

Determining Negligible Associations in Regression

Alter¹,

Counsell²

2023

TQMP

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Psychological research is rife with inappropriately concluding "no effect" between predictors and outcome in regression models following statistically nonsignificant results. However, this approach is methodologically flawed because failing to reject the null hypothesis using traditional, difference-based tests does not mean the null is true. Using this approach leads to high rates of incorrect conclusions that flood psychological literature. This paper introduces a novel, methodologically sound alternative. In this paper, we demonstrate how an equivalence testing approach can be applied to multiple regression (which we refer to here as "negligible effect testing") to evaluate whether a predictor (measured in standardized or unstandardized units) has a negligible association with the outcome. In the first part of the paper, we evaluate the performance of two equivalence-based techniques and compare them to the traditional, difference-based test via a Monte Carlo simulation study. In the second part of the paper, we use examples from the literature to illustrate how researchers can implement the recommended negligible effect testing methods in their own work using open-access and user-friendly tools (negligible R package and Shiny app). Finally, we discuss how to report and interpret results from negligible effect testing and provide practical recommendations for best research practices based on the simulation results. All materials, including R code, results, and additional resources, are publicly available on the Open Science Framework (OSF): osf.io/w96xe/.

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No relationship between generalised anxiety symptoms and cardiovascular autonomic dysfunction

Cited by 4 publications

References 26 publications

Predicting Autonomic Dysfunction in Anxiety Disorder from ECG and Respiratory Signals Using Machine Learning Models

Predicting Autonomic Dysfunction in Anxiety Disorder from ECG and Respiratory Signals Using Machine Learning Models

Prevalence and predictors of preoperative cardiac autonomic dysfunction among elective neurosurgical patients: A prospective observational study

Determining Negligible Associations in Regression

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