A decision-tree approach to the assessment of posttraumatic stress disorder: Engineering empirically rigorous and ecologically valid assessment measures

Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19

Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19 pandemic. Therefore, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. Here, we propose a computer-based method to better understand stress in healthcare workers facing COVID-19 at the beginning of the pandemic. We based our study on a representative sample of healthcare professionals attending to COVID-19 patients in the northeast region of Mexico, at the beginning of the pandemic. We used a machine learning classification algorithm to obtain a visualization model to analyze perceived stress. The C5.0 decision tree algorithm was used to study datasets. We carried out an initial preprocessing statistical analysis for a group of 101 participants. We performed chi-square tests for all questions, individually, in order to validate stress level calculation (p < 0.05) and a calculated Cronbach's alpha of 0.94 and McDonald's omega of 0.95, demonstrating good internal consistency in the dataset. The obtained model failed to classify only 6 out of the 101, missing two cases for mild, three for moderate and one for severe (accuracy of 94.1%). We performed statistical correlation analysis to ensure integrity of the method. In addition, based on the decision tree model, we concluded that severe stress cases can be related mostly to high levels of xenophobia and compulsive stress. Thus, showing that applied machine learning algorithms represent valuable tools in the assessment of perceived stress, which can potentially be adapted to other areas of the medical field.

Psychological Flexibility Is Key for Reducing the Severity and Impact of Fibromyalgia

Fibromyalgia has a significant impact on the lives of patients; symptoms are influenced by psychological factors, such as psychological flexibility and catastrophizing. The objective of this study was to determine the importance of these variables in moderating the association between the severity and impact of fibromyalgia symptoms. A total of 187 patients from a general hospital population were evaluated using the Combined Index of Severity of Fibromyalgia (ICAF), the Fibromyalgia Impact Questionnaire (FIQ), the Acceptance and Action Questionnaire-II (AAQ-II), and the Pain Catastrophizing Scale (PCS). A series of multiple regression analyses were carried out using the PROCESS macro and decision tree analysis. The results show that psychological flexibility modulates the relation between severity and the impact of fibromyalgia symptoms. Catastrophism has residual importance and depends on the interaction with psychological flexibility. Interaction occurs if the severity of the disease is in transition from a mild to a moderate level and accounts for 40.1% of the variance in the sample. These aspects should be considered for evaluation and early intervention in fibromyalgia patients.

Empirical Grouping of Pain Zones in Fibromyalgia: A Preliminary Study

OBJECTIVES

Widespread pain is important for the diagnosis of fibromyalgia (FM). For this purpose, the sum of pain regions obtained from a topographical distribution has been used to compute a Widespread Pain Index (WPI), but there is no empirical basis for choosing the regions. The aim of this study was to find an empirical distribution of the pain regions.

MATERIALS AND METHODS

We evaluated 228 female patients with FM. They completed the Fibromyalgia Survey Questionnaire, Fibromyalgia Impact Questionnaire (FIQ), Combined Index of Severity in Fibromyalgia (ICAF), and Short Form-36 Health Survey. The pain regions of the WPI were grouped by the topographical distribution (WPIR) and compared with a new empirical distribution (WPIE) obtained through exploratory factor analysis. A decision- tree analysis was conducted to identify the optimal algorithm for selecting pain regions related to the severity of FM.

RESULTS

The WPIE has a normal distribution compared with the WPIR. It also shows higher correlations with FM severity. From the factor analysis, 4 factors explain 48.5% of the variance. Two factors (emotional and physical) can conform to the decision-tree analysis using the dependent variables FIQ and ICAF. These factors are very congruent with the cutoff points previously proposed for FIQ and ICAF. The emotional factor is the first in the decision-tree.

DISCUSSION

WPIE has a normal distribution and shows better predictive qualities than WPIR. The emotional factor is conceptualized as emotional because of the relative importance of the right hemisphere in negative emotions and pain. The physical factor could be responsible for the decreased ability to coordinate left-right stepping.

PTSD in Court III: Malingering, assessment, and the law

This journal's third article on PTSD in Court focuses especially on the topic's "court" component. It first considers the topic of malingering, including in terms of its definition, certainties, and uncertainties. As with other areas of the study of psychological injury and law, generally, and PTSD (posttraumatic stress disorder), specifically, malingering is a contentious area not only definitionally but also empirically, in terms of establishing its base rate in the index populations assessed in the field. Both current research and re-analysis of past research indicates that the malingering prevalence rate at issue is more like 15±15% as opposed to 40±10%. As for psychological tests used to assess PTSD, some of the better ones include the TSI-2 (Trauma Symptom Inventory, Second Edition; Briere, 2011), the MMPI-2-RF (Minnesota Multiphasic Personality Inventory, Second Edition, Restructured Form; Ben-Porath & Tellegen, 2008/2011), and the CAPS-5 (The Clinician-Administered PTSD Scale for DSM-5; Weathers, Blake, Schnurr, Kaloupek, Marx, & Keane, 2013b). Assessors need to know their own possible biases, the applicable laws (e.g., the Daubert trilogy), and how to write court-admissible reports. Overall conclusions reflect a moderate approach that navigates the territory between the extreme plaintiff or defense allegiances one frequently encounters in this area of forensic practice.

A risk prediction model for post-stroke depression in Chinese stroke survivors based on clinical and socio-psychological features

Background

Post-stroke depression (PSD) is a frequent complication that worsens rehabilitation outcomes and patient quality of life. This study developed a risk prediction model for PSD based on patient clinical and socio-psychology features for the early detection of high risk PSD patients.

Results

Risk predictors included a history of brain cerebral infarction (odds ratio [OR], 3.84; 95% confidence interval [CI], 2.22-6.70; P < 0.0001) and four socio-psychological factors including Eysenck Personality Questionnaire with Neuroticism/Stability (OR, 1.18; 95% CI, 1.12-1.20; P < 0.0001), life event scale (OR, 0.99; 95% CI, 0.98-0.99; P = 0.0007), 20 items Toronto Alexithymia Scale (OR, 1.06; 95% CI, 1.02-1.10; P = 0.002) and Social Support Rating Scale (OR, 0.91; 95% CI, 0.87-0.90; P < 0.001) in the logistic model. In addition, 11 rules were generated in the tree model. The areas under the curve of the ROC and the accuracy for the tree model were 0.85 and 0.86, respectively.

Methods

This study recruited 562 stroke patients in China who were assessed for demographic data, medical history, vascular risk factors, functional status post-stroke, and socio-psychological factors. Multivariate backward logistic regression was used to extract risk factors for depression in 1-month after stroke. We converted the logistic model to a visible tree model using the decision tree method. Receiver operating characteristic (ROC) was used to evaluate the performance of the model.

Conclusion

This study provided an effective risk model for PSD and indicated that the socio-psychological factors were important risk factors of PSD.