[Prevalence and factors associated with benzodiazepine use in children and adult inpatients]. / Prevalentie van en factoren geassocieerd met benzodiazepinegebruik tijdens opname.

BACKGROUND: Given the growing focus on deprescribing, it is crucial to thoroughly evaluate our benzodiazepine prescribing practices considering the potential risks. AIM: To investigate the prevalence and predictors of benzodiazepine prescriptions during psychiatric hospitalization and as discharge medication. METHOD: This retrospective electronic patient file study included psychiatric admissions at the UMC Utrecht between 12/01/01 and 21/04/01. Descriptive statistics were used to evaluate prevalence of benzodiazepine prescriptions in youth and adults. Multivariate regression analyses were performed to predict factors associated with benzodiazepine prescriptions and dosage. RESULTS: In total, we analyzed data from 856 admissions of youth and 4002 admissions of adults. 36.0% of the youth were prescribed benzodiazepines during admission and 14.8% at discharge. Associated factors were age (OR: 1.38) and bipolar disorder (OR: 3.98). In adults, 69.7% were prescribed benzodiazepines during admission and 37.6% at discharge. Associated factors were length of hospital stay (OR: 1.01) and anxiety disorders (OR: 2.53). Male sex, age (resp. higher and lower), and a longer length of stay predicted benzodiazepine dosages for both youth (B = 3.48; 95% CI: 0.83-0.07) and adults (B = 2.17; 95% CI: -0.04-0.05). CONCLUSION: Benzodiazepines are frequently prescribed during inpatient stays and at discharge in youth and adults, offering opportunities for deprescribing.

Combining automatic speech recognition with semantic natural language processing in schizophrenia.

Natural language processing (NLP) tools are increasingly used to quantify semantic anomalies in schizophrenia. Automatic speech recognition (ASR) technology, if robust enough, could significantly speed up the NLP research process. In this study, we assessed the performance of a state-of-the-art ASR tool and its impact on diagnostic classification accuracy based on a NLP model. We compared ASR to human transcripts quantitatively (Word Error Rate (WER)) and qualitatively by analyzing error type and position. Subsequently, we evaluated the impact of ASR on classification accuracy using semantic similarity measures. Two random forest classifiers were trained with similarity measures derived from automatic and manual transcriptions, and their performance was compared. The ASR tool had a mean WER of 30.4%. Pronouns and words in sentence-final position had the highest WERs. The classification accuracy was 76.7% (sensitivity 70%; specificity 86%) using automated transcriptions and 79.8% (sensitivity 75%; specificity 86%) for manual transcriptions. The difference in performance between the models was not significant. These findings demonstrate that using ASR for semantic analysis is associated with only a small decrease in accuracy in classifying schizophrenia, compared to manual transcripts. Thus, combining ASR technology with semantic NLP models qualifies as a robust and efficient method for diagnosing schizophrenia.

[Antipsychotic drugs for women: a narrative review and clinical guidelines]. / Antipsychotica voor vrouwen: overzicht van de literatuur en praktijkadviezen.

BACKGROUND: It has long been thought that women with a schizophrenia spectrum disorder have a more favorable course than men. However, this is not the case, even though they become ill later in life and are less likely to have comorbid drug abuse. Guidelines for prescribing antipsychotics are based on research with mostly male participants, and by following these guidelines we are doing our female patients a disservice. Gender and sex differences lead to differences in preferences, pharmacokinetics and pharmacodynamics. AIM: Providing an overview of antipsychotics for women with a schizophrenia spectrum disorder and discuss the consequences for practice. METHOD: A clinically oriented study of the literature. RESULTS: Women reach higher plasma levels than men when they receive the same dose of antipsychotic drugs (except for lurasidone and quetiapine). The effect of antipsychotics is also greater in women, because estrogens increase the brain’s dopamine sensitivity. This leads to higher risks of side effects. Clinical guidelines differ for women at different stages of life because estrogens greatly contribute to the sex differences seen in the efficacy and tolerability of antipsychotics. CONCLUSION: Clinicians should be aware that women should be treated differently with antipsychotics than men.

[Taal: een introductie]. / Taal: een introductie.

BACKGROUND: This special issue discusses the roles and functions of language in psychiatric practice from different perspectives. As an introduction, we discuss the phenomenon ‘language’ as an object of scientific investigation. AIM: To give a brief introduction to this theme issue. METHOD: After a terminological introduction and an outline of linguistics, we discuss some of the cognitive processes that enable humans to produce and interpret verbal utterances. RESULTS: The mental lexicon, the dictionary in our head, plays a central role in both language production and understanding. The starting point for language comprehension is recognizing basic form elements in the speech or sign stream (phonemes). Next, the perceiver must determine how words are related grammatically in order to deduce sentence meanings. We distinguish three successive steps in the production of language: conceptualizing, formulating and articulating. CONCLUSION: Production and understanding words and sentences rely on a complex interplay of cognitive processes. In communication, we use words and sentences to convey and recognize intentions. This requires close cooperation between interlocutors.

[Do doctors speak a different language? The case of 'patient' without an article]. / Spreken artsen een andere taal? Het geval van 'patiënt' zonder lidwoord.

BACKGROUND: In medical speech and writing, the article before ‘patient’ is often omitted, while this would be ungrammatical in Standard Dutch. AIM: To investigate how and when the article is omitted in medical Dutch. METHOD: An online questionnaire was distributed among individuals with a medical background. RESULTS: 85 respondents participated in this study. The participants showed a strong preference to omit the definite article (‘the’) befor the word ‘patient’ and related words. This phenomenon seemed independent of the function of the word in the sentence, or of the sentence construction. However, the indefinite article (‘a’) was not omitted in the same situations. CONCLUSION: Doctors speak a different language, namely a variety of Standard Dutch in which the omission of the definite article befor ‘patient’ is preferred. We conclude that this phenomenon can be explained by recognizing ‘patient’ as a substitute for the name of the patient.

[Automated speech analysis can distinguish frontotemporal dementia from depression: a pilot study]. / Geautomatiseerde spraakanalyse bij onderscheiden van frontotemporale dementie en depressie.

BACKGROUND: Differentiating the behavioural variant of frontotemporal dementia from a depression is challenging. Recent development of automated speech analyses might add to diagnostic. AIM: To investigate the value of automated speech analyses in differentiating bvFTD from a depressive disorder. METHOD: A semistructured interview was recorded in 15 patients with bvFTD, 15 patients with a depressive disorder and 15 healthy controls, which was transcribed and analysed. Acoustic and semantic values were extracted and classified using machine learning. RESULTS: Acoustic values showed an 80% accuracy for differentiating bvFTD from depressive disorder and semantic values showed an 70.8% accuracy. CONCLUSION: Acoustic as well as semantic values show significant differences between bvFTD and depressive disorder. In automated speech analyses researches should consider privacy matters as well as possible confounders like age, sex and ethnicity. This study should be repeated in a larger population.

[Recognizing depression through computational language analysis]. / Herkennen van depressie met computationele taalanalyse.

BACKGROUND: Currently, clinical practice lacks a usable biomarker for the detection and differentiation of depression. Such a biomarker may be found in speech, from which important information can be distilled using automated speech analysis. AIM: To provide an overview of the fast-developing field of automated speech analysis for depression. METHOD: We summarize the current literature on speech features in depression. RESULTS: Current computational models can detect depression with high accuracy, rendering them applicable for diagnostic tools based on automatic speech analysis. Such tools are developing at a fast rate. CONCLUSION: Some challenges are still in the way of clinical implementation. For example, results differ largely between studies due to much variation in methodology. Furthermore, privacy and ethical issues need to be addressed before tools can be used.

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool.

BACKGROUND: Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. METHODS: Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. RESULTS: The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. CONCLUSIONS: Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.

Assessing coherence through linguistic connectives: Analysis of speech in patients with schizophrenia-spectrum disorders.

BACKGROUND: Incoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD. METHODS: Connectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups. RESULTS: SSD-patients used less contingency (e.g., because) (p = .008) and multiclass connectives (e.g., as) (p < .001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p = .04) and temporality connectives (e.g., after) (adj-p < .001), narrower similarity-range of expansion (e.g., and) (adj-p = .002) and multiclass connectives (adj-p = .04), and lower maximum similarity of expansion connectives (adj-p = .005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85 % accuracy. DISCUSSION: Our results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD.

[Improving care for women with schizophrenia-spectrum disorders]. / Vrouwen met schizofreniespectrum-stoornissen verdienen betere zorg.

Background   Women with a schizophrenia-spectrum disorder (SSD) have a better clinical profile than men at the start of their illness but lose this advantage within the first few years of living with SSD. There are benefits to be gained across different areas in the care currently offered to women with psychosis. Aim   To describe point of improvement in the care for women with SSD. Method   Review or relevant literature. Results   An important point for improvement is the early detection of female-specific signs of a first episode of psychosis, to shorten the duration of untreated psychosis, with prompt access to early intervention services. Special attention should be paid to sexual health, and to any history of childhood trauma. Antipsychotics clearly require dosing and prescription tailored to the female body, considering hormonal life phases such as menopause. Switching to prolactin-sparing medications can benefit both mental and somatic health. Finally, hormone replacement therapy should be considered for postmenopausal women. Conclusion   By providing female-specific care, women with SSD can live up to their full potential.

[Tapering of psychotropic drugs: current practice and needs of patients and their relatives]. / Deprescriptie van psychofarmaca; praktijk en behoeften van patiënten en naasten.

BACKGROUND: The prevalence of mental illness has remained stable in recent decades, yet the use of psychotropic drugs has increased. This trend suggests that psychotropic drugs are being prescribed with an unnecessary frequency. Internationally, there is growing attention for deprescribing. AIM: To investigate what experiences and needs patients and their loved ones/relatives have with regard to deprescribing of psychotropics. METHOD: An online questionnaire was distributed among members of the MIND mental health care panel, which consists of (former) patients with a psychiatric disorder and their loved ones. RESULTS: A total of 564 respondents took part in this survey. Most patients have phased out/stopped their psychotropic drugs (83.8%). This was usually done at the initiative of the patient (66.7%), in consultation with the practitioner (72.9%). The practitioner only took the initiative to deprescribe in 15.1% of the cases. In 68.6% tapering was not discussed at the start of psychotropic drug use. Patients did not experience willingness from practitioners in deprescribing, and would like to discuss deprescribing more often (79.5%). CONCLUSION: There is an undeniable demand among patients and near ones for more emphasis on deprescribing of psychotropic drugs. We advise to include this topic in the shared decision making process.

A data-driven linguistic characterization of hallucinated voices in clinical and non-clinical voice-hearers.

BACKGROUND: Auditory verbal hallucinations (AVHs) are heterogeneous regarding phenomenology and etiology. This has led to the proposal of AVHs subtypes. Distinguishing AVHs subtypes can inform AVHs neurocognitive models and also have implications for clinical practice. A scarcely studied source of heterogeneity relates to the AVHs linguistic characteristics. Therefore, in this study we investigate whether linguistic features distinguish AVHs subtypes, and whether linguistic AVH-subtypes are associated with phenomenology and voice-hearers' clinical status. METHODS: Twenty-one clinical and nineteen non-clinical voice-hearers participated in this study. Participants were instructed to repeat verbatim their AVHs just after experiencing them. AVH-repetitions were audio-recorded and transcribed. AVHs phenomenology was assessed using the Auditory Hallucinations Rating Scale of the Psychotic Symptom Rating Scales. Hierarchical clustering analyses without a priori group dichotomization were performed using quantitative measures of sixteen linguistic features to distinguish sets of AVHs. RESULTS: A two-AVHs-cluster solution best partitioned the data. AVHs-clusters significantly differed in linguistic features (p < .001); AVHs phenomenology (p < .001); and distribution of clinical voice-hearers (p < .001). The "expanded-AVHs" cluster was characterized by more determiners, more prepositions, longer utterances (all p < .01), and mainly contained non-clinical voice-hearers. The "compact-AVHs" cluster had fewer determiners and prepositions, shorter utterances (all p < .01), more negative content, higher degree of negativity (both p < .05), and predominantly came from clinical voice-hearers. DISCUSSION: Two voice-speech clusters were recognized, differing in syntactic-grammatical complexity and negative phenomenology. Our results suggest clinical voice-hearers often hear negative, "compact-voices", understandable under Broca's right hemisphere homologue and memory-based mechanisms. Conversely, non-clinical voice-hearers experience "expanded-voices", better accounted by inner speech AVHs models.

Negative content in auditory verbal hallucinations: a natural language processing approach.

INTRODUCTION: Negative content of auditory verbal hallucinations (AVH) is a strong predictor of distress and impairment. This paper quantifies emotional voice-content in order to explore both subjective (i.e. perceived) and objectively (i.e. linguistic sentiment) measured negativity and investigates associations with distress. METHODS: Clinical and non-clinical participants with frequent AVH (n = 40) repeated and recorded their AVH verbatim directly upon hearing. The AVH were analyzed for emotional valence using Pattern, a rule-based sentiment analyzer for Dutch. The AVH of the clinical individuals were compared to those of non-clinical voice-hearers on emotional valence and associated with experienced distress. RESULTS: The mean objective valence of AVH in patients was significantly more negative than those of non-clinical voice-hearers. In the clinical individuals a larger proportion of the voice-utterances was negative (34.7% versus 18.4%) in objective valence. The linguistic valence of the AVH showed a significant, strong association with the perceived negativity, amount of distress and disruption of life, but not with the intensity of distress. CONCLUSIONS: Our results indicate that AVH of patients have a more negative linguistic content than those of non-clinical voice-hearers, which is associated with the experienced distress. Thus, patients not only perceive their voices as more negative, objective analyses confirm this.

Implementation of automatic speech analysis for early detection of psychiatric symptoms: What do patients want?

Psychiatry is in dire need of a method to aid early detection of symptoms. Recent developments in automatic speech analysis prove promising in this regard, and open avenues for implementation of speech-based applications to detect psychiatric symptoms. The current survey was conducted to assess positions with regard to speech recordings among a group (n = 675) of individuals who experience psychiatric symptoms. Overall, respondents are open to the idea of speech recordings in light of their mental welfare. Importantly, concerns with regard to privacy were raised. Given that speech recordings are privacy sensitive, this requires special attention upon implementation of automatic speech analysis techniques. Furthermore, respondents indicated a preference for speech recordings in the presence of a clinician, as opposed to a recording made at home without the clinician present. In developing a speech marker for psychiatry, close collaboration with the intended users is essential to arrive at a truly valid and implementable method.

Quantified language connectedness in schizophrenia-spectrum disorders.

Language abnormalities are a core symptom of schizophrenia-spectrum disorders and could serve as a potential diagnostic marker. Natural language processing enables quantification of language connectedness, which may be lower in schizophrenia-spectrum disorders. Here, we investigated connectedness of spontaneous speech in schizophrenia-spectrum patients and controls and determine its accuracy in classification. Using a semi-structured interview, speech of 50 patients with a schizophrenia-spectrum disorder and 50 controls was recorded. Language connectedness in a semantic word2vec model was calculated using consecutive word similarity in moving windows of increasing sizes (2-20 words). Mean, minimal and variance of similarity were calculated per window size and used in a random forest classifier to distinguish patients and healthy controls. Classification based on connectedness reached 85% cross-validated accuracy, with 84% specificity and 86% sensitivity. Features that best discriminated patients from controls were variance of similarity at window sizes between 5 and 10. We show impaired connectedness in spontaneous speech of patients with schizophrenia-spectrum disorders even in patients with low ratings of positive symptoms. Effects were most prominent at the level of sentence connectedness. The high sensitivity, specificity and tolerability of this method show that language analysis is an accurate and feasible digital assistant in diagnosing schizophrenia-spectrum disorders.

Language disturbances in schizophrenia: the relation with antipsychotic medication.

Language disturbances are key aberrations in schizophrenia. Little is known about the influence of antipsychotic medication on these symptoms. Using computational language methods, this study evaluated the impact of high versus low dopamine D2 receptor (D2R) occupancy antipsychotics on language disturbances in 41 patients with schizophrenia, relative to 40 healthy controls. Patients with high versus low D2R occupancy antipsychotics differed by total number of words and type-token ratio, suggesting medication effects. Both patient groups differed from the healthy controls on percentage of time speaking and clauses per utterance, suggesting illness effects. Overall, more severe negative language disturbances (i.e. slower articulation rate, increased pausing, and shorter utterances) were seen in the patients that used high D2R occupancy antipsychotics, while less prominent disturbances were seen in low D2R occupancy patients. Language analyses successfully predicted drug type (sensitivity = 80.0%, specificity = 76.5%). Several language disturbances were more related to drug type and dose, than to other psychotic symptoms, suggesting that language disturbances may be aggravated by high D2R antipsychotics. This negative impact of high D2R occupancy drugs may have clinical implications, as impaired language production predicts functional outcome and degrades the quality of life.

Language in schizophrenia: relation with diagnosis, symptomatology and white matter tracts.

Language deviations are a core symptom of schizophrenia. With the advances in computational linguistics, language can be easily assessed in exact and reproducible measures. This study investigated how language characteristics relate to schizophrenia diagnosis, symptom, severity and integrity of the white matter language tracts in patients with schizophrenia and healthy controls. Spontaneous speech was recorded and diffusion tensor imaging was performed in 26 schizophrenia patients and 22 controls. We were able to classify both groups with a sensitivity of 89% and a specificity of 82%, based on mean length of utterance and clauses per utterance. Language disturbances were associated with negative symptom severity. Computational language measures predicted language tract integrity in patients (adjusted R2 = 0.467) and controls (adjusted R2 = 0.483). Quantitative language analyses have both clinical and biological validity, offer a simple, helpful marker of both severity and underlying pathology, and provide a promising tool for schizophrenia research and clinical practice.

Efficacy and tolerability of riluzole in psychiatric disorders: A systematic review and preliminary meta-analysis.

There is a pressing need for better pharmacological treatment strategies for psychiatric disorders as current treatment often results in partial symptom remission and unwanted side effects. A point of entry may be the glutamatergic system since glutamatergic dysregulation contributes to multiple psychiatric disorders. We evaluated the evidence from randomized controlled trials (RCTs) regarding the use of the glutamatergic drug riluzole in mental illnesses; and conducted preliminary meta-analyses of its effectiveness in treating obsessive-compulsive disorder (OCD) and depression. A systematic search was performed using PubMed (Medline), Embase, Cochrane Database of Systematic Reviews and PsycINFO. Meta-analyses were performed using Comprehensive Meta-Analysis software. Twenty-three RCTs were included for qualitative analysis and showed positive effects of adjunctive/monotherapy riluzole in patients with OCD, depression, autism, substance abuse and schizophrenia. Seven studies were also used for quantitative analysis, which revealed positive but non-significant effects on OCD and depression. Riluzole was generally well tolerated with few serious adverse events. The studies included in this systematic review were highly heterogeneous and the number of studies was limited per diagnostic condition. Moreover, few studies have examined riluzole as a single treatment. We suggest carrying out further work to provide definitive evidence for the benefit of riluzole in psychiatric illness.