Personality, Schizophrenia, and Violence: A Longitudinal Study: The Second Wave of the VIORMED Project

Bottesi, Gioia; Candini, Valentina; Ghisi, Marta; Bava, Mattia; Bianconi, Giorgio; Bulgari, Viola; Carrà, Giuseppe; Cavalera, Cesare; Conte, Giovanni; Cricelli, Marta; Ferla, Maria Teresa; Iozzino, Laura; Macis, Ambra; Stefana, Alberto; Girolamo, Giovanni de

doi:10.1521/pedi_2019_33_436

Cited by 7 publications

(4 citation statements)

References 47 publications

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“…Given only a minority of patients with schizophrenia possess violent tendencies, researchers have attempted to find factors that increase the risk of violent behaviors. There are many risk factors identified by conventional statistical methods (hypothesis testing) including abnormal brain cortical characteristics ( 8 ) substance use disorder ( 9 ) personality disorders ( 10 , 11 ) and childhood victimization ( 12 ), but it is difficult to integrate these risk factors into a model to subsequently predict an individual's probability of committing violent behavior.…”

Section: Introductionmentioning

confidence: 99%

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

Zhang

Liu

et al. 2022

Front. Psychiatry

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BackgroundEarly to identify male schizophrenia patients with violence is important for the performance of targeted measures and closer monitoring, but it is difficult to use conventional risk factors. This study is aimed to employ machine learning (ML) algorithms combined with routine data to predict violent behavior among male schizophrenia patients. Moreover, the identified best model might be utilized to calculate the probability of an individual committing violence.MethodWe enrolled a total of 397 male schizophrenia patients and randomly stratified them into the training set and the testing set, in a 7:3 ratio. We used eight ML algorithms to develop the predictive models. The main variables as input features selected by the least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were integrated into prediction models for violence among male schizophrenia patients. In the training set, 10 × 10-fold cross-validation was conducted to adjust the parameters. In the testing set, we evaluated and compared the predictive performance of eight ML algorithms in terms of area under the curve (AUC) for the receiver operating characteristic curve.ResultOur results showed the prevalence of violence among male schizophrenia patients was 36.8%. The LASSO and LR identified main risk factors for violent behavior in patients with schizophrenia integrated into the predictive models, including lower education level [0.556 (0.378–0.816)], having cigarette smoking [2.121 (1.191–3.779)], higher positive syndrome [1.016 (1.002–1.031)] and higher social disability screening schedule (SDSS) [1.081 (1.026–1.139)]. The Neural Net (nnet) with an AUC of 0.6673 (0.5599–0.7748) had better prediction ability than that of other algorithms.ConclusionML algorithms are useful in early identifying male schizophrenia patients with violence and helping clinicians take preventive measures.

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Section: Introductionmentioning

confidence: 99%

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

Zhang

Liu

et al. 2022

Front. Psychiatry

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“…In a sample of patients living in residential facilities, Candini and colleagues [14] found that antisocial personality traits were strong predictors of aggressive behavior. In the same study, in an outpatient sample, several personality traits, including depressive, sadistic, passive-aggressive, schizotypal, borderline, and compulsive traits, were predictors of aggressive and violent behavior (Bottesi et al [11]).…”

Section: Predictors Of Aggressive and Violent Behavior And The Role Omentioning

confidence: 93%

Aggressive behavior and metacognitive functions: a longitudinal study on patients with mental disorders

Candini

Ghisi

Bianconi³

et al. 2020

Ann Gen Psychiatry

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Background: Metacognitive functions play a key role in understanding which psychological variables underlying the personality might lead a person with a severe mental disorder to commit violent acts against others. The aims of this study were to: (a) investigate the differences between patients with poor metacognitive functioning (PM group) and patients with good metacognitive functioning (GM group) in relation to a history of violence; (b) investigate the differences between the two groups in relation to aggressive behavior during a 1-year follow-up; and (c) analyze the predictors of aggressive behavior. Methods: In a prospective cohort study, patients with severe mental disorders with and without a lifetime history of serious violence were assessed with a large set of standardized instruments and were evaluated bimonthly with MOAS in order to monitor any aggressive behavior. The total sample included 180 patients: 56% outpatients and 44% inpatients, and the majority were male (75%) with a mean age of 44 (± 9.8) years, and half of them had a history of violence. The sample was split into two groups: poor metacognition (PM) group and good metacognition (GM) group, according to MAI evaluation scores. Results: The PM patients reported a history of violence more frequently than GM patients, during the 1-year followup, but no differences between groups in aggressive and violent behavior were found. The strongest predictors of aggressive behavior were: borderline and passive-aggressive personality traits and a history of violence, anger, and hostility. The metacognitive functions alone did not predict aggressive behavior, but metacognitive functions interacted with hostility and angry reactions in predicting aggressive behavior. Conclusions: This study led to some important conclusions: (a) some aspects closely related to violence are predictive of aggressive behavior only in patients with poor metacognition, thus good metacognition is a protective factor; (b) poor metacognition is associated with a history of violence, which in turn increases the risk of committing aggressive behavior.

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“…The evaluation process includes many tools and will take some time to be completed, especially for the participants who will be enrolled in all three studies. Although this may lead to high refusal rates, previous experience with health services research carried out in Italy has shown that, due to the high degree of collaboration between staff and patients, refusal rates can be kept very low [43,44,89,90].…”

Section: Limitationsmentioning

confidence: 99%

DAily time use, Physical Activity, quality of care and interpersonal relationships in patients with Schizophrenia spectrum disorders (DiAPASon): an Italian multicentre study

et al. 2020

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Background Schizophrenia spectrum disorders (SSD) are ranked among the leading causes of disabilities worldwide. Many people with SSD spend most of their daily time being inactive, and this is related to the severity of negative symptoms. Here, we present the 3-year DiAPAson project aimed at (1) evaluating the daily time use among patients with SSD living in Residential Facilities (RFs) compared to outpatients with SSD and to the general population (Study 1); (2) evaluating the quality of staff-patient relationships, its association with specific patient outcomes and the quality of care provided in RFs (Study 2); and (3) assessing daily activity patterns in residential patients, outpatients with SSD and healthy controls using real-time methodologies (Study 3). Methods Study 1 will include 300 patients with SSD living in RFs and 300 outpatients; data obtained in these clinical populations will be compared with normative data obtained by the National Institute of Statistics (ISTAT) in the national survey on daily time use. Time use assessments will consist of daily diaries asking participants to retrospectively report time spent in different activities. In Study 2, a series of questionnaires will be administered to 300 residential patients (recruited for Study 1) to evaluate the quality of care and staff-patient relationships, level of well-being and burnout of RFs’ staff, and quality of RFs using a European standardized questionnaire (QuIRC-SA). In Study 3, the daily time use will be evaluated in a subgroup of 50 residential patients, 50 outpatients and 50 healthy controls using the Experience Sampling Method approach (participants will complete a brief questionnaire -about time use, mood and perceived energy- on a smartphone 8 times a day for 1 week) to compare retrospective and real-time reports. Moreover, their level of physical activity, sleep patterns, and energy expenditure will be monitored through a multi-sensor device. Discussion This project is highly innovative because it combines different types of assessments (i.e., retrospective and real-time reports; multi-sensor monitoring) to trace an accurate picture of daily time use and levels of physical activity that will help identify the best therapeutic options promoting daily activities and physical exercise in patients with SSD. Trial registration ISRCTN registry ID ISRCTN21141466.

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Personality, Schizophrenia, and Violence: A Longitudinal Study: The Second Wave of the VIORMED Project

Cited by 7 publications

References 47 publications

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

Aggressive behavior and metacognitive functions: a longitudinal study on patients with mental disorders

DAily time use, Physical Activity, quality of care and interpersonal relationships in patients with Schizophrenia spectrum disorders (DiAPASon): an Italian multicentre study

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