Understanding symptom profiles of depression with the PHQ-9 in a community sample using network analysis.

BACKGROUND: Depression is one of the most prevalent mental health conditions in the world. However, the heterogeneity of depression has presented obstacles for research concerning disease mechanisms, treatment indication, and personalization. The current study used network analysis to analyze and compare profiles of depressive symptoms present in community samples, considering the relationship between symptoms. METHODS: Cross-sectional measures of depression using the Patient Health Questionnaire - 9 items (PHQ-9) were collected from community samples using data from participants scoring above a clinical threshold of ≥10 points (N = 2,023; 73.9% female; mean age 49.87, SD = 17.40). Data analysis followed three steps. First, a profiling algorithm was implemented to identify all possible symptom profiles by dichotomizing each PHQ-9 item. Second, the most prevalent symptom profiles were identified in the sample. Third, network analysis for the most prevalent symptom profiles was carried out to identify the centrality and covariance of symptoms. RESULTS: Of 382 theoretically possible depression profiles, only 167 were present in the sample. Furthermore, 55.6% of the symptom profiles present in the sample were represented by only eight profiles. Network analysis showed that the network and symptoms' relationship varied across the profiles. CONCLUSIONS: Findings indicate that the vast number of theoretical possible ways to meet the criteria for major depressive disorder (MDD) is significantly reduced in empirical samples and that the most common profiles of symptoms have different networks and connectivity patterns. Scientific and clinical consequences of these findings are discussed in the context of the limitations of this study.

Parental personality traits and emotion regulation: Its relationship with infants' socioemotional development during the perinatal period.

Socioemotional development is central throughout life, and it unfolds in an interpersonal context in which each significant caregiver has an impact, particularly during infancy. However, only a relatively small number of studies have investigated associations between mothers and fathers' personality and emotional characteristics with their infant's socioemotional development during the perinatal period. Therefore, the present article examines the relationship between maternal and paternal personality traits and emotion regulation difficulties during the prenatal period with offspring's socioemotional development. This was a non-experimental and longitudinal study that included a community sample of 55 mother-father-baby triads. Parental assessments were carried out between the second and third trimester of pregnancy, and baby's socio-emotional development was assessed during their 2nd month after birth. Results evidenced differences between maternal and paternal personality traits and emotion regulation difficulties during the perinatal period as well as distinct contributions on infant's socioemotional development.

Evaluating the implementation of community engagement guidelines (EVALUA GPS project): a study protocol.

INTRODUCTION: The EVALUA GPS project aims to evaluate the impact of the implementation of the National Institute for Health Care and Excellence (NICE) guideline 'Community engagement: improving health and well-being and reducing health inequalities' adapted to the Spanish context. METHODS AND ANALYSIS: Phase I: A tool will be designed to evaluate the impact of implementing the recommendations of the adapted NICE guideline. The tool will be developed through a review of the literature on implementation of public health guidelines between 2000 and 2021 and an expert's panel consensus. PHASE II: The developed tool will be implemented in 16 community-based programmes, acting as intervention sites, and 4 controls through a quasi-experimental pre-post study. Phase III: A final online web tool, based on all previously collected information, will be developed to support the implementation of the adapted NICE guidelines recommendations in other contexts and programmes. DATA COLLECTION AND ANALYSIS: Data will be collected through surveys and semistructured interviews. Quantitative and qualitative data will be analysed to identify implementation scenarios, changes in community engagement approaches, and barriers and facilitators to the implementation of the recommendations. All this information will be further synthesised to develop the online tool. ETHICS AND DISSEMINATION: The proposed research has been approved by the Clinical Research Ethics Committee of Aragon. Results will be presented at national and international conferences and published in peer-reviewed open access journals. The interactive online tool (phase III) will include examples of its application from the fieldwork.

[Performance of EuroSCORE II in Latin America: a systematic review]. / Rendimiento de EuroSCORE II en Latinoamérica: una revisión sistemática.

BACKGROUND: EuroSCORE II is a mortality risk score for cardiac surgery in adults. This version is widely validated and compared with other scores in Europe, North America, and Asia. AIM: To determine the performance of the EuroSCORE II for the prediction of mortality in cardiac surgeries in Latin America. MATERIAL AND METHODS: A systematic review was carried out of studies from Latin American countries evaluating the performance of EuroSCORE II in cardiac surgery. The inclusion criteria were patients older than 18 years, from Latin America, published in English, Spanish and/or Portuguese, between the years 2012 to 2020, with the term "EuroSCORE II" in the title. Observed mortality and estimated mortality data by EuroSCORE II were extracted. The calibration was determined by the observed/estimated mortality ratio and the discrimination was evaluated using receiver operating characteristic (ROC) curves. RESULTS: Four articles met the inclusion criteria, including 8372 patients. The average patients' age was 62 years and 34% were women. The observed and Euroscore II estimated mortality figures were 7.08 and 3.89%, respectively. The average area under the curve of ROC curves was 0.77 and the observed/ estimated mortality ratio was 2.04. CONCLUSIONS: In these studies, EuroSCORE II underestimated mortality in cardiac surgery.

Rendimiento de EuroSCORE II en Latinoamérica: una revisión sistemática / Performance of EuroSCORE II in Latin America: a systematic review

BACKGROUND: EuroSCORE II is a mortality risk score for cardiac surgery in adults. This version is widely validated and compared with other scores in Europe, North America, and Asia. AIM: To determine the performance of the EuroSCORE II for the prediction of mortality in cardiac surgeries in Latin America. MATERIAL AND METHODS: A systematic review was carried out of studies from Latin American countries evaluating the performance of EuroSCORE II in cardiac surgery. The inclusion criteria were patients older than 18 years, from Latin America, published in English, Spanish and/or Portuguese, between the years 2012 to 2020, with the term "EuroSCORE II" in the title. Observed mortality and estimated mortality data by EuroSCORE II were extracted. The calibration was determined by the observed/estimated mortality ratio and the discrimination was evaluated using receiver operating characteristic (ROC) curves. RESULTS: Four articles met the inclusion criteria, including 8372 patients. The average patients' age was 62 years and 34% were women. The observed and Euroscore II estimated mortality figures were 7.08 and 3.89%, respectively. The average area under the curve of ROC curves was 0.77 and the observed/ estimated mortality ratio was 2.04. CONCLUSIONS: In these studies, EuroSCORE II underestimated mortality in cardiac surgery.

"What If We Get Sick?": Spanish Adaptation and Validation of the Fear of Illness and Virus Evaluation Scale in a Non-clinical Sample Exposed to the COVID-19 Pandemic.

Distinct sources of stress have emerged during the COVID-19 pandemic. Particularly, fear is expected to generate significant psychological burden on individuals and influence on either unsafe behavior that may hinder recovery efforts or virus-mitigating behaviors. However, little is known about the properties of measures to capture them in research and clinical settings. To resolve this gap, we evaluated the psychometric properties of a novel measure of fear of illness and viruses and tested its predictive value for future development of distress. We extracted a random sample of 450 Chilean adult participants from a large cross-sectional survey panel and invited to participate in this intensive longitudinal study for 35 days. Of these, 163 ended up enrolling in the study after the demanding nature of the measurement schedule was clearly explained to them. For this final sample, we calculated different Confirmatory Factor Analyses (CFA) to evaluate the preliminary proposed structure for the instrument. Complementarily, we conducted a content analysis of the items to qualitatively extract its latent structure, which was also subject to empirical test via CFA. Results indicated that the original structure did not fit the data well; however, the new proposed structure based on the content analysis did. Overall, the modified instrument showed good reliability through all subscales both by its internal consistency with Cronbach's alphas ranging from 0.814 to 0.913, and with test-retest correlations ranging from 0.715 to 0.804. Regarding its convergent validity, individuals who scored higher in fears tended to also score higher in depressive and posttraumatic stress symptoms at baseline. Furthermore, higher fears at baseline predicted a higher score in posttraumatic stress symptomatology 7 days later. These results provide evidence for the validity, reliability, and predictive performance of the scale. As the scale is free and multidimensional potentially not circumscribed to COVID-19, it might work as a step toward understanding the psychological impact of current and future pandemics, or further life-threatening health situations of similar characteristics. Limitations, practical implications, and future directions for research are discussed.

Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.

BACKGROUND: This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional dependence relationships among variables for each individual subject studied. These conditional dependencies represented the different states that patients could experience in relation to suicidal behavior (SB). The clinical sample included 650 mental health patients with mood and anxiety symptomatology. RESULTS: Mainly indicated that variables within the Bayesian network are part of each patient's state of psychological vulnerability and have the potential to impact such states and that these variables coexist and are relatively stable over time. These results have enabled us to offer a tool to detect states of psychological vulnerability associated with suicide risk. CONCLUSION: If we accept that suicidal behaviors (vulnerability, ideation, and suicidal attempts) exist in constant change and are unstable, we can investigate what individuals experience at specific moments to become better able to intervene in a timely manner to prevent such behaviors. Future testing of the tool developed in this study is needed, not only in specialized mental health environments but also in other environments with high rates of mental illness, such as primary healthcare facilities and educational institutions.

Suicide risk configuration system in a clustered clinical sample: a generalized linear model obtained through the LASSO technique

Objective: To identify clinical and sociodemographic factors that increase or decrease suicidal risk in a clinical sample of subjects seeking mental health care. Method: A cross-sectional study was performed at three health centers in Santiago, Chile. The Parental Bonding Instrument (PBI), Depressive Experience Questionnaire (DEQ), Outcome Questionnaire (OQ-45.2), Reasons for Living Inventory (RFL), and State Trait Anger Expression Inventory (STAXI-2), in addition to a sociodemographic survey, were applied to 544 participants (333 with suicidal behavior and 211 without current suicidal behavior). Through hierarchical clustering analysis, participants were grouped by similarity regarding suicidal risk. Then, a regression analysis was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) technique, and factors that decrease or increase suicide risk (SR) were identified for each cluster. Results: The resultant clusters were grouped mainly by the age of participants. The most important protective factor was having confidence in one's own coping skills in difficult situations. Relevant risk factors were major depressive disorder (MDD), poor anger management, and difficulties in interpersonal relationships. Conclusions: Suicidal risk manifests differently throughout the life cycle, and different types of bonds may protect from or increase risk of suicide.

Suicide risk configuration system in a clustered clinical sample: a generalized linear model obtained through the LASSO technique.

OBJECTIVE: To identify clinical and sociodemographic factors that increase or decrease suicidal risk in a clinical sample of subjects seeking mental health care. METHOD: A cross-sectional study was performed at three health centers in Santiago, Chile. The Parental Bonding Instrument (PBI), Depressive Experience Questionnaire (DEQ), Outcome Questionnaire (OQ-45.2), Reasons for Living Inventory (RFL), and State Trait Anger Expression Inventory (STAXI-2), in addition to a sociodemographic survey, were applied to 544 participants (333 with suicidal behavior and 211 without current suicidal behavior). Through hierarchical clustering analysis, participants were grouped by similarity regarding suicidal risk. Then, a regression analysis was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) technique, and factors that decrease or increase suicide risk (SR) were identified for each cluster. RESULTS: The resultant clusters were grouped mainly by the age of participants. The most important protective factor was having confidence in one's own coping skills in difficult situations. Relevant risk factors were major depressive disorder (MDD), poor anger management, and difficulties in interpersonal relationships. CONCLUSIONS: Suicidal risk manifests differently throughout the life cycle, and different types of bonds may protect from or increase risk of suicide.

Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders

Objective: To analyze suicidal behavior and build a predictive model for suicide risk using data mining (DM) analysis. Methods: A study of 707 Chilean mental health patients (with and without suicide risk) was carried out across three healthcare centers in the Metropolitan Region of Santiago, Chile. Three hundred forty-three variables were studied using five questionnaires. DM and machine-learning tools were used via the support vector machine technique. Results: The model selected 22 variables that, depending on the circumstances in which they all occur, define whether a person belongs in a suicide risk zone (accuracy = 0.78, sensitivity = 0.77, and specificity = 0.79). Being in a suicide risk zone means patients are more vulnerable to suicide attempts or are thinking about suicide. The interrelationship between these variables is highly nonlinear, and it is interesting to note the particular ways in which they are configured for each case. The model shows that the variables of a suicide risk zone are related to individual unrest, personal satisfaction, and reasons for living, particularly those related to beliefs in one’s own capacities and coping abilities. Conclusion: These variables can be used to create an assessment tool and enables us to identify individual risk and protective factors. This may also contribute to therapeutic intervention by strengthening feelings of personal well-being and reasons for staying alive. Our results prompted the design of a new clinical tool, which is fast and easy to use and aids in evaluating the trajectory of suicide risk at a given moment.

Acute Mental Discomfort Associated with Suicide Behavior in a Clinical Sample of Patients with Affective Disorders: Ascertaining Critical Variables Using Artificial Intelligence Tools.

AIM: In efforts to develop reliable methods to detect the likelihood of impending suicidal behaviors, we have proposed the following. OBJECTIVE: To gain a deeper understanding of the state of suicide risk by determining the combination of variables that distinguishes between groups with and without suicide risk. METHOD: A study involving 707 patients consulting for mental health issues in three health centers in Greater Santiago, Chile. Using 345 variables, an analysis was carried out with artificial intelligence tools, Cross Industry Standard Process for Data Mining processes, and decision tree techniques. The basic algorithm was top-down, and the most suitable division produced by the tree was selected by using the lowest Gini index as a criterion and by looping it until the condition of belonging to the group with suicidal behavior was fulfilled. RESULTS: Four trees distinguishing the groups were obtained, of which the elements of one were analyzed in greater detail, since this tree included both clinical and personality variables. This specific tree consists of six nodes without suicide risk and eight nodes with suicide risk (tree decision 01, accuracy 0.674, precision 0.652, recall 0.678, specificity 0.670, F measure 0.665, receiver operating characteristic (ROC) area under the curve (AUC) 73.35%; tree decision 02, accuracy 0.669, precision 0.642, recall 0.694, specificity 0.647, F measure 0.667, ROC AUC 68.91%; tree decision 03, accuracy 0.681, precision 0.675, recall 0.638, specificity 0.721, F measure, 0.656, ROC AUC 65.86%; tree decision 04, accuracy 0.714, precision 0.734, recall 0.628, specificity 0.792, F measure 0.677, ROC AUC 58.85%). CONCLUSION: This study defines the interactions among a group of variables associated with suicidal ideation and behavior. By using these variables, it may be possible to create a quick and easy-to-use tool. As such, psychotherapeutic interventions could be designed to mitigate the impact of these variables on the emotional state of individuals, thereby reducing eventual risk of suicide. Such interventions may reinforce psychological well-being, feelings of self-worth, and reasons for living, for each individual in certain groups of patients.

Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders.

OBJECTIVE:: To analyze suicidal behavior and build a predictive model for suicide risk using data mining (DM) analysis. METHODS:: A study of 707 Chilean mental health patients (with and without suicide risk) was carried out across three healthcare centers in the Metropolitan Region of Santiago, Chile. Three hundred forty-three variables were studied using five questionnaires. DM and machine-learning tools were used via the support vector machine technique. RESULTS:: The model selected 22 variables that, depending on the circumstances in which they all occur, define whether a person belongs in a suicide risk zone (accuracy = 0.78, sensitivity = 0.77, and specificity = 0.79). Being in a suicide risk zone means patients are more vulnerable to suicide attempts or are thinking about suicide. The interrelationship between these variables is highly nonlinear, and it is interesting to note the particular ways in which they are configured for each case. The model shows that the variables of a suicide risk zone are related to individual unrest, personal satisfaction, and reasons for living, particularly those related to beliefs in one's own capacities and coping abilities. CONCLUSION:: These variables can be used to create an assessment tool and enables us to identify individual risk and protective factors. This may also contribute to therapeutic intervention by strengthening feelings of personal well-being and reasons for staying alive. Our results prompted the design of a new clinical tool, which is fast and easy to use and aids in evaluating the trajectory of suicide risk at a given moment.

Synthesis, 5-hydroxytryptamine1A receptor affinity and docking studies of 3-[3-(4-aryl-1-piperazinyl)-propyl]-1H-indole derivatives.

A series of 3-[3-(4-aryl-1-piperazinyl)-propyl]-1H-indole derivatives (12a-h) was synthesized and evaluated for binding affinity at the human 5-hydroxytryptamine(1A) receptor (5-HT(1A)R) compounds (12b) and (12h) showed the highest 5-HT(1A) receptor affinity (IC(50)=15 nM). Molecular docking studies with all the compounds in a homology model of 5-HT(1A) showed that the main interaction anchoring the ligand in the receptor was a charge-reinforced bond between the protonated nitrogen atom (N-4) of the piperazine ring and Aspartate(3.32).