Negative Symptoms in Early-Onset Psychosis and Their Association With Antipsychotic Treatment Failure

A review of information sources and analysis methods for data driven decision aids in child and adolescent mental health services

OBJECTIVE

Clinical data analysis relies on effective methods and appropriate data. Recognizing distinctive clinical services and service functions may lead to improved decision-making. Our first objective is to categorize analytical methods, data sources, and algorithms used in current research on information analysis and decision support in child and adolescent mental health services (CAMHS). Our secondary objective is to identify the potential for data analysis in different clinical services and functions in which data-driven decision aids can be useful.

MATERIALS AND METHODS

We searched related studies in Science Direct and PubMed from 2018 to 2023(Jun), and also in ACM (Association for Computing Machinery) Digital Library, DBLP (Database systems and Logic Programming), and Google Scholar from 2018 to 2021. We have reviewed 39 studies and extracted types of analytical methods, information content, and information sources for decision-making.

RESULTS

In order to compare studies, we developed a framework for characterizing health services, functions, and data features. Most data sets in reviewed studies were small, with a median of 1,550 patients and 46,503 record entries. Structured data was used for all studies except two that used textual clinical notes. Most studies used supervised classification and regression. Service and situation-specific data analysis dominated among the studies, only two studies used temporal, or process features from the patient data. This paper presents and summarizes the utility, but not quality, of the studies according to the care situations and care providers to identify service functions where data-driven decision aids may be relevant.

CONCLUSIONS

Frameworks identifying services, functions, and care processes are necessary for characterizing and comparing electronic health record (EHR) data analysis studies. The majority of studies use features related to diagnosis and assessment and correspondingly have utility for intervention planning and follow-up. Profiling the disease severity of referred patients is also an important application area.

Identification and treatment of individuals with childhood-onset and early-onset schizophrenia

Approximately 8 % of patients with schizophrenia are diagnosed before age 18, and 18 % experience their first symptoms before age 18. This narrative review explores the management of patients with early-onset schizophrenia (EOS) and childhood-onset schizophrenia (COS) from diagnosis to their transition to adult care settings. Early diagnosis of schizophrenia in children and adolescents is essential for improving outcomes, but delays are common due to overlapping of symptoms with developmental phenomena and other psychiatric conditions, including substance use, and lack of clinicians' awareness. Once diagnosed, antipsychotic treatment is key, with specific second-generation agents generally being preferred due to better tolerability and their broader efficacy evidence-base in youth. Dosing should be carefully individualized, considering age-related differences in drug metabolism and side effect liability. Clinicians must be vigilant in detecting early non-response and consider switching or dose escalation when appropriate. Since early age of illness onset is a consistent risk factor for treatment-resistant schizophrenia (TRS), clinicians need to be competent in diagnosing TRS and using clozapine. Since COS and EOS are associated with cognitive deficits and impaired functioning, psychosocial interventions should be considered to improve overall functioning and quality of life. Good long-term outcomes depend on continuous treatment engagement, and successful transitioning from pediatric to adult care requires careful planning, early preparation, and collaboration between pediatric and adult clinicians. Targeting functional outcomes and quality of life in addition to symptom remission can improve overall patient well-being. Comprehensive evaluations, age-specific assessments, and targeted interventions are needed to address the unique challenges of EOS and COS.

Schizophrenia in Autistic People with Intellectual Disabilities. Treatment and Interventions

Autistic people with intellectual disabilities appear to be at increased risk of schizophrenia. While current recommendations emphasize adapting interventions used for people with schizophrenia in general, few studies to date have investigated treatment of co-occurring schizophrenia in this specific population. To explore what interventions are provided to autistic people with intellectual disabilities and co-occurring schizophrenia in specialized mental health services, and to investigate whether changes in mental health symptoms and challenging behavior occurred during treatment. Using data from a longitudinal, national multicenter study, interventions provided to 26 autistic individuals with intellectual disabilities and co-occurring schizophrenia were explored. Symptoms were measured using the Psychopathology in Autism Checklist (PAC) and the Aberrant Behavior Checklist ABC) at referral (T1), at the end of treatment (T2), and at follow-up 12 months after T2 (T3). A broad range of interventions were provided to the participants, including inpatient admission, psychopharmacological treatment, various psychosocial interventions, and supportive interventions. Scores on the PAC and ABC were significantly lower at T2 than T1 for most scales, and no significant change was found from T2 to T3.Treatment of co-occurring schizophrenia appears feasible and effective in autistic people with intellectual disabilities.

Challenges and opportunities in the diagnosis and treatment of early-onset psychosis: a case series from the youth affective disorders clinic in Stockholm, Sweden

Early-onset psychosis is linked to adverse long-term outcomes, recurrent disease course, and prolonged periods of untreated illness; thus highlighting the urgency of improving early identification and intervention. This paper discusses three cases where initial emphasis on psychosocial treatments led to diagnostic and therapeutic delays: (1) a 15-year-old misdiagnosed with emotionally unstable personality disorder and autism, who improved on bipolar medication and antipsychotics; (2) another 15-year-old misdiagnosed with autism, who stabilized on lithium and antipsychotics, subsequently allowing for gender dysphoria evaluation; (3) a 9-year-old autistic boy incorrectly treated for ADHD, who recovered with appropriate antipsychotic treatment. These cases illuminate the vital importance of adhering to a diagnostic hierarchy, prioritizing diagnostic utility, and conducting longitudinal evaluations to facilitate early targeted treatment of psychotic symptoms in early-onset psychosis. Adherence to such strategies can minimize delays in managing early-onset psychosis and improve long-term prognoses.

Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review

OBJECTIVE

Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care.

METHODS

We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs.

RESULTS

Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP.

CONCLUSIONS

This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.

Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses

Converging evidence suggests that schizophrenia (SZ) with primary, enduring negative symptoms (i.e., Deficit SZ (DSZ)) represents a distinct entity within the SZ spectrum while the neurobiological underpinnings remain undetermined. In the largest dataset of DSZ and Non-Deficit (NDSZ), we conducted a meta-analysis of data from 1560 individuals (168 DSZ, 373 NDSZ, 1019 Healthy Controls (HC)) and a mega-analysis of a subsampled data from 944 individuals (115 DSZ, 254 NDSZ, 575 HC) collected across 9 worldwide research centers of the ENIGMA SZ Working Group (8 in the mega-analysis), to clarify whether they differ in terms of cortical morphology. In the meta-analysis, sites computed effect sizes for differences in cortical thickness and surface area between SZ and control groups using a harmonized pipeline. In the mega-analysis, cortical values of individuals with schizophrenia and control participants were analyzed across sites using mixed-model ANCOVAs. The meta-analysis of cortical thickness showed a converging pattern of widespread thinner cortex in fronto-parietal regions of the left hemisphere in both DSZ and NDSZ, when compared to HC. However, DSZ have more pronounced thickness abnormalities than NDSZ, mostly involving the right fronto-parietal cortices. As for surface area, NDSZ showed differences in fronto-parietal-temporo-occipital cortices as compared to HC, and in temporo-occipital cortices as compared to DSZ. Although DSZ and NDSZ show widespread overlapping regions of thinner cortex as compared to HC, cortical thinning seems to better typify DSZ, being more extensive and bilateral, while surface area alterations are more evident in NDSZ. Our findings demonstrate for the first time that DSZ and NDSZ are characterized by different neuroimaging phenotypes, supporting a nosological distinction between DSZ and NDSZ and point toward the separate disease hypothesis.

Negative symptoms in drug-naive patients with a first-episode psychosis (FEP)

INTRODUCTION

Negative symptoms are nuclear features of schizophrenia that may be present from the onset of the disease. In recent years, it has been described 2 subdomains of negative symptoms: experiential and expressive deficits. The aim of the study is to examine the relationship between negative symptoms and demographic and clinical variables in patients with first-episode psychosis. Also, to explore whether there are differences in the association among these variables and negative symptoms when divided into both subdomains.

MATERIAL AND METHODS

A cross-sectional study was performed in 160 patients (52 females and 108 males) with a diagnosis of a first episode psychosis. A questionnaire was administered to collect demographic and clinical variables.

RESULTS

A backward stepwise linear regressions analysis was performed in order to observe potential associations between demographic and clinical variables and the presence of negative symptoms. All three models are predicted by worse PSP score, a higher CDSS, a higher disorganized factor score and a lower excited factor score. A longer duration of untreated psychosis (DUP) is associated to a higher score in the experiential deficit subdomain only.

CONCLUSIONS

Our work highlights some clinical and phenomenological differences between experiential and expressive deficits. We think that taking into account both subdomains in future studies may lead to more accurate clinical assessment and interventions.

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.

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.

Replication of the abnormal niacin response in first episode psychosis measured using laser Doppler flowmeter

INTRODUCTION

Impaired sensitivity of the skin flush response to niacin is found in approximately 30% of patients with schizophrenia. Although the niacin response abnormality (NRA) may serve as a useful endophenotype for schizophrenia, few studies have directly replicated NRA in patients with first-episode psychosis (FEP).

METHODS

In total, 204 patients with FEP, 16 with psychotic mood disorder (PMD), and 68 healthy controls (HC) were included. The log₁₀ (EC₅₀ ) values represent the concentration of methyl nicotinate required to elicit a half-maximal blood flow (MBF) response, and the MBF value was calculated. The NRA was defined as having log₁₀ (EC₅₀ ) molar value above the 90% and an MBF value below the 60% of those in the HC group.

RESULTS

In total, 13.7% of the FEP, 12.5% of the PMD, and 7.4% of the HC group met the definition of NRA. Significant differences were found in the log₁₀ (EC₅₀ ) values between the FEP and HC groups (p = .014) and in the MBF between the FEP and PMD groups (p = .011). Patients with FEP and NRA had more severe negative symptoms than those with a normal niacin response.

DISCUSSION

These data represent the NRA in patients with FEP, defining a small subgroup of patients with early-phase psychosis possessing a clinically significant phospholipid-signaling defect.

Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

INTRODUCTION

Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.

METHODS

We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.

RESULTS

Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).

IMPLICATIONS

Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.

Identifying clinical and psychological correlates of persistent negative symptoms in early-onset psychotic disorders

Persistent negative symptoms (PNS) contribute to impairment in psychosis. The characteristics of PNS seen in youth remained under-investigated. We aimed to demonstrate clinical, treatment-related, and psychosocial characteristics of PNS in early-onset schizophrenia-spectrum disorders (EOSD). 132 patients with EOSD were assessed with Positive and Negative Symptom Scale, Brief Negative Symptom Scale, Calgary Depression Scale for Schizophrenia, and Simpson-Angus Scale. Parenting skills and resilience were evaluated using Parental Attitude Research Instrument and Child and Youth Resilience Measure-12. Longer duration of untreated psychosis (DUP) and prodromal phase were found in primary and secondary PNS groups, compared to the non-PNS group. The primary PNS group was characterized by earlier age-onset, lower smoking rates, and more common clozapine use. Resilience and egalitarian/democratic parenting were negatively correlated with symptoms related to motivation/pleasure and blunted expression. More blunted expression-related symptoms and longer DUP in the first episode significantly predicted primary/secondary PNS at follow-up. Using the data from total negative symptom scores and DUP, Receiver Operating Characteristic analyses significantly differentiated primary/secondary PNS groups from the non-PNS counterparts. PNS associated with blunted expression and low motivation/pleasure in the first episode could persist into clinical follow-up. Effective pharmacological treatment and psychosocial interventions are needed in youth.

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.

Annual Research Review: Translational machine learning for child and adolescent psychiatry

Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.

Comparison of clinical characteristics and treatment efficacy in childhood-onset schizophrenia and adolescent-onset schizophrenia in mainland China: A retrospective study

AIM

The comparative study of childhood-onset schizophrenia (COS) and adolescent-onset schizophrenia (AOS) is scarce. This study aimed to examine the differences in clinical presentations and treatment efficacy between COS and AOS and further analyse the factors affecting the efficacy of early-onset schizophrenia (EOS).

METHODS

A total of 582 electronic medical records of inpatients with EOS (216 COS and 366 AOS inpatients) between 2012 and 2019 were retrospectively analysed. The positive and negative syndrome scale (PANSS) was used to assess psychotic symptoms. Logistic regression analysis was performed to analyse the predictors of efficacy.

RESULTS

The mean age of onset of EOS was 12.87 ± 2.19 years. The importance of better diagnosing COS appeared in a longer illness course, more frequently insidious onset, less frequent delusions, more severe negative symptoms and bizarre behaviours than AOS. Besides, COS had more frequent visual hallucinations and impulsive behaviours than AOS. After hospitalization, the improvement rate of psychotic symptoms in COS and AOS were 38.3% and 47.8%, respectively. The difference of efficacy between the two groups was statistically significant. Days of hospitalization, age of onset, presence of flat affect, PANSS total and negative score at admission were predictors of treatment efficacy in EOS individuals.

CONCLUSIONS

COS inpatients suffer more obvious negative symptoms, bizarre behaviours, visual hallucinations and impulsive behaviours and worse efficacy than AOS inpatients. The severity of negative symptoms and age of onset seem the most noteworthy predictors of efficacy. These findings highlight the importance of early detection and early intervention.

Individuals with schizophrenia who act violently towards others profit unequally from inpatient treatment-Identifying subgroups by latent class analysis

BACKGROUND

People with schizophrenia show a higher risk of committing violent offenses. Previous studies indicate that there are at least three subtypes of offenders with schizophrenia.

OBJECTIVES

Employing latent class analysis, the goals of this study were to investigate the presence of homogeneous subgroups of offender patients in terms of remission in psychopathology during inpatient treatment and whether or not these are related to subtypes found in previous studies. Results should help identify patient subgroups benefitting insufficiently from forensic inpatient treatment and allow hypotheses on possibly more suitable therapy option for these patients.

METHODS

A series of latent class analyses was used to explore extensive and detailed psychopathological reports of 370 offender patients with schizophrenia before and after inpatient treatment.

RESULTS

A framework developed by Hodgins to identify subgroups of offenders suffering from schizophrenia is useful in predicting remission of psychopathology over psychiatric inpatient treatment. While "early starters" were most likely to experience remission of psychopathology over treatment, "late late starters" and a subgroup including patients from all three of Hodgins' subgroups in equal proportions benefited least. Negative symptoms generally seemed least likely to remit.

CONCLUSION

Psychiatric treatment may have to be more tailored to offender patient subgroups to allow them to benefit more equally.

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

OBJECTIVE

To determine whether gender differences in symptom presentation at first episode psychosis (FEP) remain even when controlling for substance use, age and ethnicity, using natural language processing applied to electronic health records (EHRs).

DESIGN, SETTING AND PARTICIPANTS

Data were extracted from EHRs of 3350 people (62% male patients) who had presented to the South London and Maudsley NHS Trust with a FEP between 1 April 2007 and 31 March 2017. Logistic regression was used to examine gender differences in the presentation of positive, negative, depressive, mania and disorganisation symptoms.

EXPOSURES FOR OBSERVATIONAL STUDIES

Gender (male vs female).

MAIN OUTCOMES AND MEASURES

Presence of positive, negative, depressive, mania and disorganisation symptoms at initial clinical presentation.

RESULTS

Eight symptoms were significantly more prevalent in men (poverty of thought, negative symptoms, social withdrawal, poverty of speech, aggression, grandiosity, paranoia and agitation). Conversely, tearfulness, low energy, reduced appetite, low mood, pressured speech, mood instability, flight of ideas, guilt, mutism, insomnia, poor concentration, tangentiality and elation were more prevalent in women than men. Negative symptoms were more common among men (OR 1.85, 95% CI 1.33 to 2.62) and depressive and manic symptoms more common among women (OR 0.30, 95% CI 0.26 to 0.35). After adjustment for illicit substance use, the strength of associations between gender and negative, manic and depression symptoms increased, whereas gender differences in aggression, agitation, paranoia and grandiosity became insignificant.

CONCLUSIONS

There are clear gender differences in the clinical presentation of FEP. Our findings suggest that gender can have a substantial influence on the nature of clinical presentation in people with psychosis, and that this is only partly explained by exposure to illicit substance use.

Impact of the COVID-19 pandemic on remote mental healthcare and prescribing in psychiatry: an electronic health record study

OBJECTIVES

The recent COVID-19 pandemic has disrupted mental healthcare delivery, with many services shifting from in-person to remote patient contact. We investigated the impact of the pandemic on the use of remote consultation and on the prescribing of psychiatric medications.

DESIGN AND SETTING

The Clinical Record Interactive Search tool was used to examine deidentified electronic health records of people receiving mental healthcare from the South London and Maudsley (SLaM) NHS Foundation Trust. Data from the period before and after the onset of the pandemic were analysed using linear regression, and visualised using locally estimated scatterplot smoothing.

PARTICIPANTS

All patients receiving care from SLaM between 7 January 2019 and 20 September 2020 (around 37 500 patients per week).

OUTCOME MEASURES

(i) The number of clinical contacts (in-person, remote or non-attended) with mental healthcare professionals per week.(ii) Prescribing of antipsychotic and mood stabiliser medications per week.

RESULTS

Following the onset of the pandemic, the frequency of in-person contacts was significantly reduced compared with that in the previous year (β coefficient: -5829.6 contacts, 95% CI -6919.5 to -4739.6, p<0.001), while the frequency of remote contacts significantly increased (β coefficient: 3338.5 contacts, 95% CI 3074.4 to 3602.7, p<0.001). Rates of remote consultation were lower in older adults than in working age adults, children and adolescents. Despite this change in the type of patient contact, antipsychotic and mood stabiliser prescribing remained at similar levels.

CONCLUSIONS

The COVID-19 pandemic has been associated with a marked increase in remote consultation, particularly among younger patients. However, there was no evidence that this has led to changes in psychiatric prescribing. Nevertheless, further work is needed to ensure that older patients are able to access mental healthcare remotely.

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk

BACKGROUND

Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis.

METHOD

The study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell's C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors: tearfulness, poor appetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia.

RESULTS

A total of 92 151 patients with a first index diagnosis of nonorganic and nonpsychotic mental disorder within the SLaM Trust were included in the derivation (n = 28 297) or external validation (n = 63 854) data sets. Mean age was 33.6 years, 50.7% were women, and 67.0% were of white race/ethnicity. Mean follow-up was 1590 days. The overall 6-year risk of psychosis in secondary mental health care was 3.4 (95% CI, 3.3-3.6). External validation indicated strong performance on unseen data (Harrell's C 0.85, 95% CI 0.84-0.86), an increase of 0.06 from the original model.

CONCLUSIONS

Using NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.

The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature

Background: The analysis of clinical free text from patient records for research has potential to contribute to the medical evidence base but access to clinical free text is frequently denied by data custodians who perceive that the privacy risks of data-sharing are too high. Engagement activities with patients and regulators, where views on the sharing of clinical free text data for research have been discussed, have identified that stakeholders would like to understand the potential clinical benefits that could be achieved if access to free text for clinical research were improved. We aimed to systematically review all UK research studies which used clinical free text and report direct or potential benefits to patients, synthesizing possible benefits into an easy to communicate taxonomy for public engagement and policy discussions. Methods: We conducted a systematic search for articles which reported primary research using clinical free text, drawn from UK health record databases, which reported a benefit or potential benefit for patients, actionable in a clinical environment or health service, and not solely methods development or data quality improvement. We screened eligible papers and thematically analyzed information about clinical benefits reported in the paper to create a taxonomy of benefits. Results: We identified 43 papers and derived five themes of benefits: health-care quality or services improvement, observational risk factor-outcome research, drug prescribing safety, case-finding for clinical trials, and development of clinical decision support. Five papers compared study quality with and without free text and found an improvement of accuracy when free text was included in analytical models. Conclusions: Findings will help stakeholders weigh the potential benefits of free text research against perceived risks to patient privacy. The taxonomy can be used to aid public and policy discussions, and identified studies could form a public-facing repository which will help the health-care text analysis research community better communicate the impact of their work.

Negative Symptom Domains Are Associated With Verbal Learning in Adolescents With Early Onset Psychosis

Background: Early-onset psychosis (EOP) is among the leading causes of disease burden in adolescents. Negative symptoms and cognitive deficits predicts poorer functional outcome. A better understanding of the association between negative symptoms and cognitive impairment may inform theories on underlying mechanisms and elucidate targets for development of new treatments. Two domains of negative symptoms have been described in adult patients with schizophrenia: apathy and diminished expression, however, the factorial structure of negative symptoms has not been investigated in EOP. We aimed to explore the factorial structure of negative symptoms and investigate associations between cognitive performance and negative symptom domains in adolescents with EOP. We hypothesized that (1) two negative symptom factors would be identifiable, and that (2) diminished expression would be more strongly associated with cognitive performance, similar to adult psychosis patients. Methods: Adolescent patients with non-affective EOP (n = 169) were included from three cohorts: Youth-TOP, Norway (n = 45), Early-Onset Study, Norway (n = 27) and Adolescent Schizophrenia Study, Mexico (n = 97). An exploratory factor analysis was performed to investigate the underlying structure of negative symptoms (measured with the Positive and Negative Syndrome Scale (PANSS)). Factor-models were further assessed using confirmatory factor analyses. Associations between negative symptom domains and six cognitive domains were assessed using multiple linear regression models controlling for age, sex and cohort. The neurocognitive domains from the MATRICS Consensus Cognitive Battery included: speed of processing, attention, working memory, verbal learning, visual learning, and reasoning and problem solving. Results: The exploratory factor analysis of PANSS negative symptoms suggested retaining only a single factor, but a forced two factor solution corroborated previously described factors of apathy and diminished expression in adult-onset schizophrenia. Results from confirmatory factor analysis indicated a better fit for the two-factor model than for the one-factor model. For both negative symptom domains, negative symptom scores were inversely associated with verbal learning scores. Conclusion: The results support the presence of two domains of negative symptoms in EOP; apathy and diminished expression. Future studies on negative symptoms in EOP should examine putative differential effects of these symptom domains. For both domains, negative symptom scores were significantly inversely associated with verbal learning.

Cariprazine in the management of negative symptoms of schizophrenia: state of the art and future perspectives

In schizophrenia, dopaminergic hyperactivity in the mesolimbic regions, or possibly even selectively so in the dorsal striatum, seems to cause the emergence of psychotic symptoms, whereas dopaminergic hypoactivity in cortical regions underlies the negative symptoms and cognitive deficits. Managing the negative symptoms is a major current challenge in the treatment of schizophrenia with a dearth of novel modalities to address this clinical issue. Cariprazine is a novel second-generation antipsychotic that specifically targets the D3 receptor mainly associated to negative symptoms. The review summarizes the main issues regarding negative symptom management and the role of cariprazine treatment.

Subtypes of Clinical High Risk for Psychosis that Predict Antipsychotic Effectiveness in Long-Term Remission

INTRODUCTION

In a previous report, we used canonical correlation analysis to classify individuals with clinical high risk (CHR) of psychosis into the 3 subtypes: subtype-1, characterized by extensive negative symptoms and cognitive deficits, appeared to have the highest risk for conversion to psychosis; subtype-2, characterized by thought and behavioral disorganization, with moderate cognitive impairment; subtype-3, characterized by the mildest symptoms and cognitive deficits. The present study attempted to identify these subtypes' response to antipsychotic (AP) treatment.

METHODS

A total of 289 individuals with CHR were identified and followed up for 2 years. Individuals with CHR were classified by subtype. Use of APs was examined at 2-month, 1-year, and 2-year follow-up interviews that inquired after the subjects' medication history since the first visit. The main outcome was remission, determined according to global assessment of function (GAF) score (i. e., functional outcome) and SIPS positive symptom score (symptomatic outcome) at the follow-up points.

RESULTS

Among the 289 individuals with CHR included in the current analysis, 223 (77.2%) were treated using APs for at least 2 weeks during the follow-up period. Individuals with CHR tended to show significant improvement in both symptoms and function after 2 years, but subtypes exhibited significantly different trajectories. Subtype status can predict AP treatment outcome in terms of remission. The likelihood of remission differed significantly among the subtype groups. The remission rates for individuals with subtypes 1-3 treated using AP were 13.5%, 36.1%, and 67.0%, respectively.

DISCUSSION

These subtypes may be of clinical value in AP treatment decision-making in the CHR population.

Efficacy of transcranial direct current stimulation in ameliorating negative symptoms and cognitive impairments in schizophrenia: A systematic review and meta-analysis

AIMS

Negative symptoms and cognitive impairments in schizophrenia patients are associated with the patients' functional outcomes and quality of life. However, pharmacotherapy has little effect on such symptoms. This study aimed to systematically evaluate the efficacy of transcranial direct current stimulation (tDCS) in ameliorating negative symptoms and cognitive impairments in schizophrenia patients.

METHODS

A literature search was performed in the PubMed, Embase, PsycINFO and Cochrane Library databases through March 23, 2020. Studies were included if they met all the following criteria: (1) subjects were exclusively patients with schizophrenia, schizoaffective disorder or psychosis, (2) active tDCS and shame stimulation were conducted in two parallel groups, (3) sufficient data were present, and (4) the study design was based on a randomized controlled trial. Two authors conducted the search strategy, publication assessment and data extraction independently, and a third person was consulted when any disagreement emerged.

RESULTS

A total of 14 studies were included (12 studies included negative symptoms and 7 studies included cognitive impairments). The overall meta-analysis showed no significant difference between active and sham tDCS in ameliorating negative symptoms in schizophrenia patients (SMD: -0.14, 95% CI: -0.33- 0.05). Subgroup analysis including studies with a high stimulation frequency, twice daily, revealed a significant difference in therapeutic effects between active tDCS and sham stimulation (SMD: -0.31, 95% CI: -0.58 to -0.05). With respect to cognitive impairments, there was a trend indicating that active tDCS might improve cognitive impairment (SMD: -0.21, 95% CI: -0.46- 0.04), but the overall meta-analysis failed to obtain statistically significant results.

CONCLUSION

Our meta-analysis indicates that tDCS is a potential strategy for improving negative symptoms, but the therapeutic benefit for negative symptoms requires a high stimulation frequency (twice a day).

Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper

BACKGROUND

Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product.

OBJECTIVE

This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit.

METHODS

We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text.

RESULTS

We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders.

CONCLUSIONS

By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

Admixture analysis of age at onset in older patients with schizophrenia spectrum disorders

The nature of schizophrenia spectrum disorders with an onset in middle or late adulthood remains controversial. The aim of our study was to determine in patients aged 60 and older if clinically relevant subtypes based on age at onset can be distinguished, using admixture analysis, a data-driven technique. We conducted a cross-sectional study in 94 patients aged 60 and older with a diagnosis of schizophrenia or schizoaffective disorder. Admixture analysis was used to determine if the distribution of age at onset in this cohort was consistent with one or more populations of origin and to determine cut-offs for age at onset groups, if more than one population could be identified. Results showed that admixture analysis based on age at onset demonstrated only one normally distributed population. Our results suggest that in older schizophrenia patients, early- and late-onset ages form a continuum.

Persistent negative symptoms in recent-onset psychosis: Relationship to treatment response and psychosocial functioning

Negative symptoms are associated with poor clinical and psychosocial outcome in schizophrenia. Their prevalence and identification in first-episode patients remains controversial. In a large cohort of patients in the early stage of schizophrenia, schizophreniform or schizoaffective disorder, we investigated, over the different phases of the OPTiMiSE trial (baseline, 4, 10 and 22 weeks of treatment), the prevalence of negative symptoms of moderate severity, unconfounded by depression and extrapyramidal symptoms at baseline. Moreover, we assessed symptomatic remission, attrition rate and psychosocial functioning in subjects with short-term (4 weeks) persistent unconfounded negative symptoms (PNS) and in those with negative symptoms that did not persist at follow-up and/or were confounded at baseline (N-PNS). Negative symptoms of moderate severity were observed in 59% of subjects at baseline. They were associated with worse psychosocial functioning and longer duration of psychosis at intake in the study. Eleven percent of subjects had PNS unconfounded at baseline and 7.9% had PNS unconfounded at both baseline and end of 4-week treatment. Psychosocial functioning was comparable in PNS and N-PNS subjects at baseline but it was significantly worse in the former group after 4-weeks. PNS subjects showed lower remission and higher attrition rates at the end of all treatment phases. Fifty-six percent of subjects completing phase 3 (clozapine treatment) had PNS, and 60% of them were non-remitters at the end of this phase. The presence of short-term PNS during the first phases of psychosis was associated with poor clinical outcome and resistance to antipsychotic treatment, including clozapine.

Predictors of mortality in people with late-life depression: A retrospective cohort study

OBJECTIVES

Late-life depression (LLD) is associated with an increased mortality risk in the general older population. It remains however unclear which signs or symptoms are predictive of mortality in those suffering from LLD.

SETTING AND PARTICIPANTS

Patients aged 65 years or older with depressive disorder diagnosed in Southeast London between January 2008 and December 2017.

METHODS

We assembled patients diagnosed with late-life depression from the Maudsley Biomedical Research Centre Case Register, which is linked to national mortality data. Using depression diagnosis as index date, we followed patients until death or censoring point. Sociodemographic data, scores of Health of the Nation Outcome Scales (HoNOS65+), which include a physical illness scale, profiles of depressive symptoms, and psychotropic medications were extracted and modeled in multivariable survival analyses to determine predictors of mortality.

RESULTS

Of 4,243 patients with LLD (mean age 77.0 years; 61.2% female), 2,327 (54.8%) died over a median follow-up time of 3.5 years. In multivariable Cox regression models, an increased risk of all-cause mortality was associated with older age, cognitive problems, physical illness/disability, impaired activities of daily living, apathy, lack of appetite and mirtazapine prescription; conversely, female gender, non-white ethnicity, guilt feelings, tearfulness, impaired concentration, disturbed sleep and delusions were associated with lower mortality risk.

CONCLUSIONS

Besides demographic factors, physical health, functioning and cognition, different depressive symptoms were significantly associated with the prognosis of LLD. Elderly patients presenting with depressive symptoms predicting higher mortality risk should be examined and followed more closely.

Investigating Bullying as a Predictor of Suicidality in a Clinical Sample of Adolescents with Autism Spectrum Disorder

For typically developing adolescents, being bullied is associated with increased risk of suicidality. Although adolescents with autism spectrum disorder (ASD) are at increased risk of both bullying and suicidality, there is very little research that examines the extent to which an experience of being bullied may increase suicidality within this specific population. To address this, we conducted a retrospective cohort study to investigate the longitudinal association between experiencing bullying and suicidality in a clinical population of 680 adolescents with ASD. Electronic health records of adolescents (13–17 years), using mental health services in South London, with a diagnosis of ASD were analyzed. Natural language processing was employed to identify mentions of bullying and suicidality in the free text fields of adolescents' clinical records. Cox regression analysis was employed to investigate the longitudinal relationship between bullying and suicidality outcomes. Reported experience of bullying in the first month of clinical contact was associated with an increased risk suicidality over the follow‐up period (hazard ratio = 1.82; 95% confidence interval = 1.28–2.59). In addition, female gender, psychosis, affective disorder diagnoses, and higher intellectual ability were all associated with suicidality at follow‐up. This study is the first to demonstrate the strength of longitudinal associations between bullying and suicidality in a clinical population of adolescents with ASD, using automated approaches to detect key life events within clinical records. Our findings provide support for identifying and dealing with bullying in schools, and for antibullying strategy's incorporation into wider suicide prevention programs for young people with ASD. Autism Res 2020, 13: 988‐997. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.

Pharmacological treatment of negative symptoms in schizophrenia: update and proposal of a clinical algorithm

The clinical presentation of schizophrenia encompasses symptoms divided into three dimensions: positive, negative, and cognitive. Negative symptoms (NS), in particular, have a major impact on the quality of life of the affected subject, and, differing from positive symptoms, are often associated with a limited response to pharmacotherapy. To date, studies specifically investigating NS in schizophrenia are scant; therefore, proper selection of therapy for NS remains a major unmet medical need. Given the heterogeneity of the clinical presentation of schizophrenia, the treatment of NS, as well as therapy for other associated symptoms, should be largely individualized according to a patient's specific characteristics. In this paper, we review current knowledge on NS and construct a clinical algorithm for the treatment of schizophrenic conditions with pronounced NS. Overall, data from the literature suggest that second-generation antipsychotics, such as cariprazine and amisulpride, should be preferred over first-generation antipsychotics (FGAs), as they are associated with better functional outcomes and lower cognitive impairment. The combination of antipsychotics and antidepressants may also improve NS while addressing some affective disorders associated with schizophrenia; however, no clear information is available on the effects of this combination on primary NS or on the mechanism of action of the combination. In the proposed clinical algorithm, we suggest that cariprazine should be used as first-line treatment for patients with predominant NS, and that amisulpride should be considered as an alternative in cases of cariprazine failure. Further treatment lines may include the use of olanzapine and quetiapine, and add-on therapy with antidepressants.

An approach to linking education, social care and electronic health records for children and young people in South London: a linkage study of child and adolescent mental health service data

OBJECTIVES

Creation of linked mental health, social and education records for research to support evidence-based practice for regional mental health services.

SETTING

The Clinical Record Interactive Search (CRIS) system was used to extract personal identifiers who accessed psychiatric services between September 2007 and August 2013.

PARTICIPANTS

A clinical cohort of 35 509 children and young people (aged 4-17 years).

DESIGN

Multiple government and ethical committees approved the link of clinical mental health service data to Department for Education (DfE) data on education and social care services. Under robust governance protocols, fuzzy and deterministic approaches were used by the DfE to match personal identifiers (names, date of birth and postcode) from National Pupil Database (NPD) and CRIS data sources.

OUTCOME MEASURES

Risk factors for non-matching to NPD were identified, and the potential impact of non-match biases on International Statistical Classification of Diseases, 10th Revision (ICD-10) classifications of mental disorder, and persistent school absence (<80% attendance) were examined. Probability weighting and adjustment methods were explored as methods to mitigate the impact of non-match biases.

RESULTS

Governance challenges included developing a research protocol for data linkage, which met the legislative requirements for both National Health Service and DfE. From CRIS, 29 278 (82.5%) were matched to NPD school attendance records. Presenting to services in late adolescence (adjusted OR (aOR) 0.67, 95% CI 0.59 to 0.75) or outside of school census timeframes (aOR 0.15, 95% CI 0.14 to 0.17) reduced likelihood of matching. After adjustments for linkage error, ICD-10 mental disorder remained significantly associated with persistent school absence (aOR 1.13, 95% CI 1.07 to 1.22).

CONCLUSIONS

The work described sets a precedent for education data being used for medical benefit in England. Linkage between health and education records offers a powerful tool for evaluating the impact of mental health on school function, but biases due to linkage error may produce misleading results. Collaborative research with data providers is needed to develop linkage methods that minimise potential biases in analyses of linked data.

Predictive Factors of Treatment Resistance in First Episode of Psychosis: A Systematic Review

Background: Clinical and functional outcome improvement in psychotic disorders is a challenge for the investigators. Recent advances offered opportunities for ameliorating the course of the illness during its early stages and for identifying treatment-resistant patients. Patients who had not response to two different antipsychotics, administered at correct doses for a sufficient period, can be operationally considered treatment-resistant. Available evidence suggested that the response's trajectory to the antipsychotic treatment revealed that a small proportion of subjects are poor responders (8.2%), the majority of patients have a moderate response (76.4%), and only 15.4% can be considered rapid responders with the greatest magnitude of response. Patients with first episode of psychosis generally obtain a more favorable response profile. Nevertheless, in around 25% of these patients symptoms of psychosis persist with a worse long-term course of illness. Objectives: The aim of this review is to report current evidences on the main predictors of treatment non-response in patients at early stage of psychosis. Methods: We used a specific string that guaranteed a high sensitive search in pubmed. We included the following types of publications: randomized-controlled trials, observational studies, longitudinal studies, retrospective studies, case-control studies, open-label investigations, cohort studies, and reviews. Publications must concern predictors of treatment resistance in early psychosis. Results: Forty-seven records were included: 5 reviews, 3 meta-analyses, 22 longitudinal studies, 2 retrospective studies, 1 naturalistic study, 6 randomized controlled trials, 2 open-label studies, 2 case-control studies, 4 cohort studies, 2 retrospective studies. Several factors were identified as predictors of treatment resistance: lower premorbid functioning; lower level of education; negative symptoms from first psychotic episode; comorbid substance use; younger age at onset; lack of early response; non-adherence to treatment; and longer duration of untreated psychosis. The role of gender and marital status is still controversial. More evidences are needed about neurobiological, genetic, and neuroimaging factors. Conclusions: The identification of specific predictive factors of treatment resistance in patients with first episode of psychosis ameliorates the quality of clinical management of these patients in the critical early phase of schizophrenia.

Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior

Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.

The Group of Treatment Resistant Schizophrenias. Heterogeneity in Treatment Resistant Schizophrenia (TRS)

Schizophrenia is composed of a heterogeneous group of patient segments. Our current notion of the heterogeneity in schizophrenia is based on patients presenting with diverse disease symptom phenotypes, risk factors, structural and functional neuropathology, and a mixed range of expressed response to treatment. It is important for clinicians to recognize the various clinical presentations of resistance to treatment in schizophrenia and to understand how heterogeneity across treatment resistant patient segments may potentially inform new strategies for the development of effective treatments for Treatment Resistant Schizophrenia (TRS). The heterogeneity of schizophrenia may be reduced by parsing patient segments based on whether patients demonstrate an adequate or inadequate response to treatment. In our current concept of TRS, TRS is defined as non-response to at least two adequate trials of antipsychotic medication and is estimated to affect about 30% of all patients with schizophrenia. In this narrative review, the author discusses that the demonstration of inadequate response to antipsychotic drugs (APDs) may infer that some TRS patients may be suffering from a non-dopamine pathophysiology since D2 receptor antagonist-based treatment is ineffective. Preliminary neurobiological findings may further support the pathophysiologic distinction of TRS from that of general schizophrenia. Investigation of the basis for heterogeneity in TRS through the systematic investigation of relevant "clusters" of similarly at risk individuals may hopefully bring us closer to realize a precision medicine approach for developing effective therapies for TRS patient segments.