Gender differences in clinical presentation and illicit substance use during first episode psychosis: a natural language processing, electronic case register study

Sex differences in patient-reported outcome measure of psychotic symptoms in schizophrenia

PURPOSE

to study sex differences in self-reported symptoms measured with the Scale of Patient-Reported Impact of Symptoms in Schizophrenia (PRISS), to investigated sex differences in the degree of agreements between self-reported symptoms and clinical symptoms assessed by professionals, and to identify which clinical and sociodemographic variables predicted a greater presence of self-reported symptoms split by sex.

METHODS

161 patients (37 females; 124 males), aged between 18 and 65 years, with a diagnosis of schizophrenia assisted in non-acute mental health services at four mental health catchment areas in Andalucia and Catalonia were included. The PRISS scale was administered to asses self-reported symptoms.

RESULTS

males reported higher presence of excitement, grandiosity, motor retardation and poor attention) than women. There was less agreement in the presence of psychotic symptoms in men than in women when comparing self-reported symptoms and clinical symptoms assessed by professionals. Finally, in men the predictors variables for the greater presence of self-perceived symptoms were greater psychotic symptomatology and more disability, while in women were greater presence of alogia and higher doses of chlorpromazine.

CONCLUSIONS

Assessing and being aware of the self-perceived symptoms of patients with schizophrenia should be considered in the clinic, especially in men, as there appears to be a lack of agreement on certain items. This would allow treatments to be more focused on patients' need by sex, and would make them feel part of the therapeutic process, improving their therapeutic adherence, evolution and quality of life.

The association of childhood trauma with depressive and negative symptoms in recent onset psychosis: a sex-specific analysis

BACKGROUND

Childhood trauma may impact the course of schizophrenia spectrum disorders (SSD), specifically in relation to the increased severity of depressive or negative symptoms. The type and impact of trauma may differ between sexes. In a large sample of recent-onset patients, we investigated the associations of depressive and negative symptoms with childhood trauma and whether these are sex-specific.

METHODS

A total of 187 first-episode psychosis patients in remission (Handling Antipsychotic Medication: Long-term Evaluation of Targeted Treatment study) and 115 recent-onset SSD patients (Simvastatin study) were included in this cross-sectional study (men: n = 218; women: n = 84). Total trauma score and trauma subtypes were assessed using the Childhood Trauma Questionnaire Short Form; depressive and negative symptoms were rated using the Positive And Negative Symptoms Scale. Sex-specific regression analyses were performed.

RESULTS

Women reported higher rates of sexual abuse than men (23.5% v. 7.8%). Depressive symptoms were associated with total trauma scores and emotional abuse ratings in men (β: 0.219-0.295; p ≤ 0.001). In women, depressive symptoms were associated with sexual abuse ratings (β: 0.271; p = 0.011). Negative symptoms were associated with total trauma score and emotional neglect ratings in men (β: 0.166-0.232; p ≤ 0.001). Negative symptoms in women were not linked to childhood trauma, potentially due to lack of statistical power.

CONCLUSIONS

Depressive symptom severity was associated with different types of trauma in men and women with recent-onset SSD. Specifically, in women, depressive symptom severity was associated with childhood sexual abuse, which was reported three times as often as in men. Our results emphasize the importance of sex-specific analyses in SSD research.

Gender differences in outcomes of early intervention services for first episode psychosis

AIMS

There is growing interest in early intervention in psychotic disorders. However, gender differences in the outcomes of such treatment have not been studied in a randomized clinical trial.

METHODS

Patients diagnosed with schizophrenia spectrum disorders with less than 6 months antipsychotic exposure entered a cluster randomized trial of early intervention services compared to usual care in the Recovery After an Initial Schizophrenia Episode Early Treatment Program (RAISE-ETP) study. Masked evaluators assessed the Quality of Life Scale (QLS) and the Positive and Negative Syndrome Scale (PANSS) every 6 months. Our secondary analyses examined gender differences in baseline characteristics, 2-year gender outcomes, and intervention responses.

RESULTS

Altogether 404 individuals aged 15-40 entered the study: 111 (27.4%) women and 293 (72.5%) men. At baseline, women were significantly more likely to have been married (p = .007) and to be living independently (p = .012) than men. Women were also more likely to be diagnosed with schizoaffective disorder, bipolar type (p = .006) and scored higher on the depression subscale of the PANSS (p = .0004) but not the CDSS. Women were less likely to use or abuse cannabis (p = .0004), though no less likely to abuse alcohol. Controlling for these differences, there were no significant gender differences in the QLS or PANSS outcomes.

CONCLUSION

Baseline gender differences in comorbid substance use and prevalence of mood symptoms in women with first episode psychosis are consistent with previous studies. The absence of significant gender differences in outcomes with early intervention has not been previously reported in a multi-site randomized US clinical trial.

Psychosocial stress, interpersonal sensitivity, and social withdrawal in clinical high risk for psychosis: a systematic review

Stress has repeatedly been implicated in the onset and exacerbation of positive symptoms of psychosis. Increasing interest is growing for the role of psychosocial stress in the development of psychosis symptoms in individuals at Clinical High Risk (CHR) for psychosis. A systematic review was therefore conducted to summarize the existing evidence base regarding psychosocial stress, interpersonal sensitivity, and social withdrawal in individuals at CHR for psychosis. An electronic search of Ovid (PsychINFO, EMBASE, MEDLINE, and GLOBAL HEALTH) was conducted until February 2022. Studies that examined psychosocial stress in CHR were included. Twenty-nine studies were eligible for inclusion. Psychosocial stress, interpersonal sensitivity, and social withdrawal were higher in CHR individuals compared to healthy controls and there was some evidence of their association with positive symptoms of psychosis. Two types of psychosocial stressors were found to occur more frequently with CHR status, namely daily stressors, and early and recent trauma, while significant life events did not appear to be significant. Greater exposure to psychosocial stress, emotional abuse, and perceived discrimination significantly increased risk of transition to psychosis in CHR. No studies examined the role of interpersonal sensitivity on transition to psychosis in CHR. This systematic review provides evidence for the association of trauma, daily stressors, social withdrawal, and interpersonal sensitivity with CHR status. Further studies investigating the impact of psychosocial stress on psychosis symptom expression in individuals at CHR and its effects on transition to psychosis are therefore warranted.

Sex differences in the clinical presentation of early psychosis in a primary care setting

Primary care is an important part of the help-seeking pathway for young people experiencing early psychosis, but sex differences in clinical presentation in these settings are unexplored. We aimed to identify sex differences in clinical presentation to primary care services in the 1-year period prior to a first diagnosis of psychotic disorder. We identified first-onset cases of non-affective psychotic disorder over a 10-year period (2005-2015) using health administrative data linked with electronic medical records (EMRs) from primary care (n = 465). Detailed information on encounters in the year prior to first diagnosis was abstracted, including psychiatric symptoms, other relevant behaviours, and diagnoses recorded by the family physician (FP). We used modified Poisson regression models to examine sex differences in the signs, symptoms, and diagnoses recorded by the FP, adjusting for various clinical and sociodemographic factors. Positive symptoms (PR = 0.76, 95%CI: 0.58, 0.98) and substance use (PR = 0.54, 95%CI: 0.40, 0.72) were less prevalent in the medical records of women. Visits by women were more likely to be assigned a diagnosis of depression or anxiety (PR = 1.18, 95%CI: 1.00, 1.38), personality disorder (PR = 5.49, 95%CI: 1.22, 24.62), psychological distress (PR = 11.29, 95%CI: 1.23, 103.91), and other mental or behavioral disorders (PR = 3.49, 95%CI: 1.14, 10.66) and less likely to be assigned a diagnosis of addiction (PR = 0.33, 95%CI: 0.13, 0.87). We identified evidence of sex differences in the clinical presentation of early psychosis and recorded diagnoses in the primary care EMR. Further research is needed to better understand sex differences in clinical presentation in the primary care context, which can facilitate better understanding, detection, and intervention for first-episode psychotic disorders.

Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review

An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP-derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP-assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health.

Identifying Military Service Status in Electronic Healthcare Records from Psychiatric Secondary Healthcare Services: A Validation Exercise Using the Military Service Identification Tool

Electronic healthcare records (EHRs) are a rich source of information with a range of uses in secondary research. In the United Kingdom, there is no pan-national or nationally accepted marker indicating veteran status across all healthcare services. This presents significant obstacles to determining the healthcare needs of veterans using EHRs. To address this issue, we developed the Military Service Identification Tool (MSIT), using an iterative two-staged approach. In the first stage, a Structured Query Language approach was developed to identify veterans using a keyword rule-based approach. This informed the second stage, which was the development of the MSIT using machine learning, which, when tested, obtained an accuracy of 0.97, a positive predictive value of 0.90, a sensitivity of 0.91, and a negative predictive value of 0.98. To further validate the performance of the MSIT, the present study sought to verify the accuracy of the EHRs that trained the MSIT models. To achieve this, we surveyed 902 patients of a local specialist mental healthcare service, with 146 (16.2%) being asked if they had or had not served in the Armed Forces. In total 112 (76.7%) reported that they had not served, and 34 (23.3%) reported that they had served in the Armed Forces (accuracy: 0.84, sensitivity: 0.82, specificity: 0.91). The MSIT has the potential to be used for identifying veterans in the UK from free-text clinical documents and future use should be explored.

Substance use Specificities in Women with Psychosis: A Critical Review

BACKGROUND

Women with schizophrenia or other psychotic disorders differ from male patients in many respects, including psychopathology, prognosis, disease course, and substance use comorbidities. Most studies performed to date to investigate the association between drug use and psychosis have not evaluated gender differences, although this has started to change in recent years.

METHODS

We briefly summarize the available evidence on gender differences in drug use and substance use disorders (SUD) in psychotic patients during the early phases of the psychotic illness and during the course of schizophrenia.

RESULTS

Substance use and SUD are both less prevalent in women, both in the general population and at all phases of the psychotic spectrum. Some studies suggest that SUD may be under diagnosed in female patients, in part due to their more vulnerable profile. Substance use, especially cannabis, may more negatively impact females, especially on the disease course and prognosis. The available data suggest that it may be more difficult to treat SUD in female patients with schizophrenia, which could negatively impact prognosis.

CONCLUSION

Women with concomitant psychotic illness and SUD comprise a highly vulnerable subgroup. This should be considered when selecting the treatment approach, especially in the early phases of the illness, to ensure better outcomes.

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Sex and gender differences in symptoms of early psychosis: a systematic review and meta-analysis

First-episode psychosis (FEP) can be quite variable in clinical presentation, and both sex and gender may account for some of this variability. Prior literature on sex or gender differences in symptoms of psychosis have been inconclusive, and a comprehensive summary of evidence on the early course of illness is lacking. The objective of this study was to conduct a systematic review and meta-analysis of the literature to summarize prior evidence on the sex and gender differences in the symptoms of early psychosis. We conducted an electronic database search (MEDLINE, Scopus, PsycINFO, and CINAHL) from 1990 to present to identify quantitative studies focused on sex or gender differences in the symptoms of early psychosis. We used random effects models to compute pooled standardized mean differences (SMD) and risk ratios (RR), with 95% confidence intervals (CI), for a range of symptoms. Thirty-five studies met the inclusion criteria for the systematic review, and 30 studies were included in the meta-analysis. All studies examined sex differences. Men experienced more severe negative symptoms (SMD =  - 0.15, 95%CI =  - 0.21, - 0.09), whereas women experienced more severe depressive symptoms (SMD = 0.21, 95%CI = 0.14, 0.27) and had higher functioning (SMD = 0.16, 95%CI = 0.10, 0.23). Women also had a lower prevalence of substance use issues (RR = 0.65, 95%CI = 0.61, 0.69). Symptoms of early psychosis varied between men and women; however, we were limited in our ability to differentiate between biological sex and gender factors. These findings may help to inform early detection and intervention efforts to better account for sex and gender differences in early psychosis presentation.

Best Practices on Big Data Analytics to Address Sex-Specific Biases in Our Understanding of the Etiology, Diagnosis, and Prognosis of Diseases

A bias in health research to favor understanding diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature on machine learning or natural language processing (NLP) techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (a) "women," "men," or "sex"; (b) "big data," "artificial intelligence," or "NLP"; and (c) "disparities" or "differences." From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in these studies is disproportionately less than women. Even though artificial intelligence and NLP techniques are widely applied in health research, few studies use them to take advantage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process toward correction is slow. We reflect on best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

PURPOSE

NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression-Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health.

PARTICIPANTS

As of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months).

FINDINGS TO DATE

The data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data.

FUTURE PLANS

The NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.

A scoping review of ethics considerations in clinical natural language processing

Objectives

To review through an ethics lens the state of research in clinical natural language processing (NLP) for the study of bias and fairness, and to identify gaps in research.

Methods

We queried PubMed and Google Scholar for articles published between 2015 and 2021 concerning clinical NLP, bias, and fairness. We analyzed articles using a framework that combines the machine learning (ML) development process (ie, design, data, algorithm, and critique) and bioethical concepts of beneficence, nonmaleficence, autonomy, justice, as well as explicability. Our approach further differentiated between biases of clinical text (eg, systemic or personal biases in clinical documentation towards patients) and biases in NLP applications.

Results

Out of 1162 articles screened, 22 met criteria for full text review. We categorized articles based on the design (N = 2), data (N = 12), algorithm (N = 14), and critique (N = 17) phases of the ML development process.

Discussion

Clinical NLP can be used to study bias in applications reliant on clinical text data as well as explore biases in the healthcare setting. We identify 3 areas of active research that require unique ethical considerations about the potential for clinical NLP to address and/or perpetuate bias: (1) selecting metrics that interrogate bias in models; (2) opportunities and risks of identifying sensitive patient attributes; and (3) best practices in reconciling individual autonomy, leveraging patient data, and inferring and manipulating sensitive information of subgroups. Finally, we address the limitations of current ethical frameworks to fully address concerns of justice. Clinical NLP is a rapidly advancing field, and assessing current approaches against ethical considerations can help the discipline use clinical NLP to explore both healthcare biases and equitable NLP applications.

Gender differences in screening self-reported psychotic symptoms in a first help-seeking population

Gender differences in the frequency and severity of psychotic symptoms have been widely reported. However, in the screening process for the detection of early psychosis, gender differences were largely overlooked in China. This study investigated gender differences in self-reported psychotic symptoms in a clinical population who initially visited a mental health service. In total, 1931 consecutive new patients were included in the current analysis, with a mean age of 25.3 years, including 852 (44.1%) men and 1079 (55.9%) women, of whom 388 (20.1%) had psychotic disorders and 1543 (79.9%) had non-psychotic disorders. Psychotic symptoms were assessed using the PRIME Screen-Revised (PS-R) questionnaire. The cohort was grouped according to gender, age (adolescents ≤ 21 years, adults > 21 years), and clinical diagnosis. Within the full sample, gender differences in psychotic symptoms were not significant, except that females appeared to have more severe symptoms of disorganized communication than males. However, gender differences began to appear at subgroup levels, after stratification by age and diagnosis. Female adolescents reported more severe psychotic symptoms than male adolescents, especially in the symptom of perceptual abnormalities, which refer to hallucinations. Different patterns and predictors were found to significantly discriminate between psychotic and non-psychotic disorders among age and gender groups. Our study highlights gender differences in the severity, frequency, and pattern of self-reported psychotic symptoms when screening in a first help-seeking population. Therefore, gender differences should be considered during psychotic symptoms screening.

Towards better care for women with schizophrenia-spectrum disorders

Women with a schizophrenia-spectrum disorder (SSD) have a better clinical profile than do men at the start of their illness but progress to the same state 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. 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 require dosing and prescription tailored to the female physiology that consider 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. By providing female-specific care, women with schizophrenia-spectrum disorders will have optimal chances to fare well.

Social preference in Roman rats: age and sex variations relevance for modeling negative schizophrenia-like features

Social withdrawal is one of the most relevant negative symptoms of schizophrenia. Animal models that mimic schizophrenia's symptoms, in general, and negative symptoms, in particular, are difficult to develop because of the high complexity of symptoms and neurochemical disturbances that schizophrenia patients display throughout their lives. In recent years we have shown that Roman High- Avoidance (RHA) rats exhibit some phenotypes that are thought to represent positive symptoms, cognitive/attentional symptoms, as well as some negative symptoms of the disease. In the present study, we aimed at elucidating whether the social interaction (SI) deficits exhibited by adult male RHA rats, compared to their Roman Low-Avoidance (RLA) counterparts, are also present during adolescence, as well as whether there are between-strain differences in adolescent and adult female rats. The results of the present study show that adult male RHA rats exhibited a deficit in social preference compared to their RLA counterparts. Such a deficit was not observed in adolescent RHA rats or female rats of any age. The results also show that the adult male rats of both strains had significant decreases in social preference compared to the adolescent male rats. Additionally, we also show that female adult RHA rats have greater social preference than their male counterparts. These results seem to be in line with previous rodent and human studies and add face validity to the RHA rats as a model of schizophrenia.

Psychosis in Women: Time for Personalized Treatment

Early detection and prompt treatment of psychosis is of the utmost importance. The great variability in clinical onset, illness course, and response to pharmacological and psychosocial treatment is in great part gender-related. Our aim has been to review narratively the literature focusing on gender related differences in the psychoses, i.e., schizophrenia spectrum disorders. We searched the PubMed/Medline, Scopus, Embase, and ScienceDirect databases on 31 July 2021, focusing on recent research regarding sex differences in early psychosis. Although women, compared to men, tend to have better overall functioning at psychotic symptom onset, they often present with more mood symptoms, may undergo misdiagnosis and delay in treatment and are at a higher risk for antipsychotic drug-induced metabolic and endocrine-induced side effects. Furthermore, women with schizophrenia spectrum disorders have more than double the odds of having physical comorbidities than men. Tailored treatment plans delivered by healthcare services should consider gender differences in patients with a diagnosis of psychosis, with a particular attention to early phases of disease in the context of the staging model of psychosis onset.