A transdiagnostic prodrome for severe mental disorders: an electronic health record study.

Effective prevention of severe mental disorders (SMD), including non-psychotic unipolar mood disorders (UMD), non-psychotic bipolar mood disorders (BMD), and psychotic disorders (PSY), rely on accurate knowledge of the duration, first presentation, time course and transdiagnosticity of their prodromal stages. Here we present a retrospective, real-world, cohort study using electronic health records, adhering to RECORD guidelines. Natural language processing algorithms were used to extract monthly occurrences of 65 prodromal features (symptoms and substance use), grouped into eight prodromal clusters. The duration, first presentation, and transdiagnosticity of the prodrome were compared between SMD groups with one-way ANOVA, Cohen's f and d. The time course (mean occurrences) of prodromal clusters was compared between SMD groups with linear mixed-effects models. 26,975 individuals diagnosed with ICD-10 SMD were followed up for up to 12 years (UMD = 13,422; BMD = 2506; PSY = 11,047; median[IQR] age 39.8[23.7] years; 55% female; 52% white). The duration of the UMD prodrome (18[36] months) was shorter than BMD (26[35], d = 0.21) and PSY (24[38], d = 0.18). Most individuals presented with multiple first prodromal clusters, with the most common being non-specific ('other'; 88% UMD, 85% BMD, 78% PSY). The only first prodromal cluster that showed a medium-sized difference between the three SMD groups was positive symptoms (f = 0.30). Time course analysis showed an increase in prodromal cluster occurrences approaching SMD onset. Feature occurrence across the prodromal period showed small/negligible differences between SMD groups, suggesting that most features are transdiagnostic, except for positive symptoms (e.g. paranoia, f = 0.40). Taken together, our findings show minimal differences in the duration and first presentation of the SMD prodromes as recorded in secondary mental health care. All the prodromal clusters intensified as individuals approached SMD onset, and all the prodromal features other than positive symptoms are transdiagnostic. These results support proposals to develop transdiagnostic preventive services for affective and psychotic disorders detected in secondary mental healthcare.

Using Electronic Health Records to Facilitate Precision Psychiatry.

The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.

User-Centered Development of STOP (Successful Treatment for Paranoia): Material Development and Usability Testing for a Digital Therapeutic for Paranoia.

BACKGROUND: Paranoia is a highly debilitating mental health condition. One novel intervention for paranoia is cognitive bias modification for paranoia (CBM-pa). CBM-pa comes from a class of interventions that focus on manipulating interpretation bias. Here, we aimed to develop and evaluate new therapy content for CBM-pa for later use in a self-administered digital therapeutic for paranoia called STOP ("Successful Treatment of Paranoia"). OBJECTIVE: This study aimed to (1) take a user-centered approach with input from living experts, clinicians, and academics to create and evaluate paranoia-relevant item content to be used in STOP and (2) engage with living experts and the design team from a digital health care solutions company to cocreate and pilot-test the STOP mobile app prototype. METHODS: We invited 18 people with living or lived experiences of paranoia to create text exemplars of personal, everyday emotionally ambiguous scenarios that could provoke paranoid thoughts. Researchers then adapted 240 suitable exemplars into corresponding intervention items in the format commonly used for CBM training and created 240 control items for the purpose of testing STOP. Each item included newly developed, visually enriching graphics content to increase the engagement and realism of the basic text scenarios. All items were then evaluated for their paranoia severity and readability by living experts (n=8) and clinicians (n=7) and for their item length by the research team. Items were evenly distributed into six 40-item sessions based on these evaluations. Finalized items were presented in the STOP mobile app, which was co-designed with a digital health care solutions company, living or lived experts, and the academic team; user acceptance was evaluated across 2 pilot tests involving living or lived experts. RESULTS: All materials reached predefined acceptable thresholds on all rating criteria: paranoia severity (intervention items: ≥1; control items: ≤1, readability: ≥3, and length of the scenarios), and there was no systematic difference between the intervention and control group materials overall or between individual sessions within each group. For item graphics, we also found no systematic differences in users' ratings of complexity (P=.68), attractiveness (P=.15), and interest (P=.14) between intervention and control group materials. User acceptance testing of the mobile app found that it is easy to use and navigate, interactive, and helpful. CONCLUSIONS: Material development for any new digital therapeutic requires an iterative and rigorous process of testing involving multiple contributing groups. Appropriate user-centered development can create user-friendly mobile health apps, which may improve face validity and have a greater chance of being engaging and acceptable to the target end users.

The Safety of Digital Mental Health Interventions: Systematic Review and Recommendations.

BACKGROUND: Evidence suggests that digital mental health interventions (DMHIs) for common mental health conditions are effective. However, digital interventions, such as face-to-face therapies, pose risks to patients. A safe intervention is considered one in which the measured benefits outweigh the identified and mitigated risks. OBJECTIVE: This study aims to review the literature to assess how DMHIs assess safety, what risks are reported, and how they are mitigated in both the research and postmarket phases and building on existing recommendations for assessing, reporting, and mitigating safety in the DMHI and standardizing practice. METHODS: PsycINFO, Embase, and MEDLINE databases were searched for studies that addressed the safety of DMHIs. The inclusion criteria were any study that addressed the safety of a clinical DMHI, even if not as a main outcome, in an adult population, and in English. As the outcome data were mainly qualitative in nature, a meta-analysis was not possible, and qualitative analysis was used to collate the results. Quantitative results were synthesized in the form of tables and percentages. To illustrate the use of a single common safety metric across studies, we calculated odds ratios and CIs, wherever possible. RESULTS: Overall, 23 studies were included in this review. Although many of the included studies assessed safety by actively collecting adverse event (AE) data, over one-third (8/23, 35%) did not assess or collect any safety data. The methods and frequency of safety data collection varied widely, and very few studies have performed formal statistical analyses. The main treatment-related reported AE was symptom deterioration. The main method used to mitigate risk was exclusion of high-risk groups. A secondary web-based search found that 6 DMHIs were available for users or patients to use (postmarket phase), all of which used indications and contraindications to mitigate risk, although there was no evidence of ongoing safety review. CONCLUSIONS: The findings of this review show the need for a standardized classification of AEs, a standardized method for assessing AEs to statically analyze AE data, and evidence-based practices for mitigating risk in DMHIs, both in the research and postmarket phases. This review produced 7 specific, measurable, and achievable recommendations with the potential to have an immediate impact on the field, which were implemented across ongoing and future research. Improving the quality of DMHI safety data will allow meaningful assessment of the safety of DMHIs and confidence in whether the benefits of a new DMHI outweigh its risks. TRIAL REGISTRATION: PROSPERO CRD42022333181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=333181.

MIRACLE2 Score Compared With Downtime and Current Selection Criterion for Invasive Cardiovascular Therapies After OHCA.

BACKGROUND: The MIRACLE2 score is the only risk score that does not incorporate and can be used for selection of therapies after out-of-hospital cardiac arrest (OHCA). OBJECTIVES: This study sought to compare the discrimination performance of the MIRACLE2 score, downtime, and current randomized controlled trial (RCT) recruitment criteria in predicting poor neurologic outcome after out-of-hospital cardiac arrest (OHCA). METHODS: We used the EUCAR (European Cardiac Arrest Registry), a retrospective cohort from 6 centers (May 2012-September 2022). The primary outcome was poor neurologic outcome on hospital discharge (cerebral performance category 3-5). RESULTS: A total of 1,259 patients (total downtime = 25 minutes; IQR: 15-36 minutes) were included in the study. Poor outcome occurred in 41.8% with downtime <30 minutes and in 79.3% for those with downtime >30 minutes. In a multivariable logistic regression analysis, MIRACLE2 had a stronger association with outcome (OR: 2.23; 95% CI: 1.98-2.51; P < 0.0001) than zero flow (OR: 1.07; 95% CI: 1.01-1.13; P = 0.013), low flow (OR: 1.04; 95% CI: 0.99-1.09; P = 0.054), and total downtime (OR: 0.99; 95% CI: 0.95-1.03; P = 0.52). MIRACLE2 had substantially superior discrimination for the primary endpoint (AUC: 0.877; 95% CI: 0.854-0.897) than zero flow (AUC: 0.610; 95% CI: 0.577-0.642), low flow (AUC: 0.725; 95% CI: 0.695-0.754), and total downtime (AUC: 0.732; 95% CI: 0.701-0.760). For those modeled for exclusion from study recruitment, the positive predictive value of MIRACLE2 ≥5 for poor outcome was significantly higher (0.92) than the CULPRIT-SHOCK (Culprit lesion only PCI Versus Multivessel PCI in Cardiogenic Shock) (0.80), EUROSHOCK (Testing the value of Novel Strategy and Its Cost Efficacy In Order to Improve the Poor Outcomes in Cardiogenic Shock) (0.74) and ECLS-SHOCK (Extra-corporeal life support in Cardiogenic shock) criteria (0.81) (P < 0.001). CONCLUSIONS: The MIRACLE2 score has superior prediction of outcome after OHCA than downtime and higher discrimination of poor outcome than the current RCT recruitment criteria. The potential for the MIRACLE2 score to improve the selection of OHCA patients should be evaluated formally in future RCTs.

Schizophrenia polygenic risk score and type 2 diabetes onset in older adults with no schizophrenia diagnosis.

OBJECTIVES: An association between type 2 diabetes (T2DM) and schizophrenia has long been observed, and recent research revealed presence of shared genetic factors. However, epidemiological evidence was inconsistent, some reported insignificant contribution of genetic factors to T2DM-schizophrenia comorbidity. Prior works studied people with schizophrenia, particularly, antipsychotic-naive patients, or those during the first psychotic experience to limit schizophrenia-related environmental factors. In contrast, we controlled such factors by utilizing a general population sample of individuals undiagnosed with schizophrenia. We hypothesized that if schizophrenia genetics impact T2DM development and such impact is not fully mediated by schizophrenia-related environment, people with high polygenic schizophrenia risk would exhibit elevated T2DM incidence. METHODS: Using a population-representative sample of adults aged ≥50 from English Longitudinal Study of Ageing ( n  = 5968, 493 T2DM cases, average follow-up 8.7 years), we investigated if schizophrenia polygenic risk score (PGS-SZ) is associated with T2DM onset. A proportional hazards model with interval censoring was adjusted for age and sex (Model 1), and age, sex, BMI, hypertension, cardiovascular diseases, exercise, smoking, depressive symptoms and T2DM polygenic risk score (Model 2). According to the power calculations, hazard rates > 1.14 per standard deviation in PGS-SZ could be detected. RESULTS: We did not observe a significant association between PGS-SZ and T2DM incidence (hazard ratio 1.04; 95% CI 0.93-1.15; and 1.01, 95% CI 0.94-1.09). CONCLUSION: Our results suggest low contribution of the intrinsic biological mechanisms driven by the polygenic risk of schizophrenia on future T2DM onset. Further research is needed.

Towards the First Biomarker Test for Bipolar Spectrum Disorder: An Evaluation of 199 Patients in an Outpatient Setting.

Bipolar spectrum disorder seems to be challenging to diagnose, particularly unspecified or subthreshold types. The delay in diagnosis in the UK for bipolar I and II types is a staggering 10-13 years, with only 15% correctly diagnosed without delay. In the USA, the delay is 6-8 years, and there is a 60% incorrect diagnosis rate. The HCL-32 questionnaire is adequate, but not sufficient by itself, and patients may find it difficult to complete, particularly if they are unwell. We have investigated a biomarker test which can be used in day-to-day clinical practice to assist diagnosis. We evaluated 199 patients diagnosed with ICD-10 bipolar I, II, and unspecified disorders, using the HCL-32 questionnaire with a cut-off point of 14 and above, supplemented by history taking and examination using the principles of the CIDI 3, interviews of relatives, and longitudinal mood charts where available. The results were compared to the general population and a sample of patients diagnosed with recurrent depression for assessment of sensitivity and specificity. We evaluated four mutations of SLCO1C1, DiO1, and two DiO2alleles as potential biomarkers for bipolar spectrum disorder, and identified three mutations that exhibited high sensitivity, with rates of up to 87% and specificity of up to 46% in distinguishing bipolar spectrum disorders from recurrent depressive disorder. Additionally, mutations in SLCO1C1 and DiO1 exhibited a sensitivity of up to 86% and a specificity of up to 60% in detecting bipolar spectrum disorder compared to the general population within a clinical setting. These biomarkers have the potential to be used as a diagnostic test that is not open to subjective interpretation and can be administered even if patients are very unwell, requiring, though, the patient's consent. Further studies confirming these results are needed to compare the validity of using individual or a best combination of single nucleotide polymorphisms to identify bipolar spectrum disorders, particularly subthreshold presentations, and to differentiate them from other mood disorders such as major depression and recurrent depressive disorder.

Polygenic Propensity for Longevity, APOE-ε4 Status, Dementia Diagnosis, and Risk for Cause-Specific Mortality: A Large Population-Based Longitudinal Study of Older Adults.

To deepen the understanding of genetic mechanisms influencing mortality risk, we investigated the impact of genetic predisposition to longevity and APOE-ε4, on all-cause mortality and specific causes of mortality. We further investigated the mediating effects of dementia on these relationships. Using data on 7 131 adults aged ≥50 years (mean = 64.7 years, standard deviation [SD] = 9.5) from the English Longitudinal Study of Aging, genetic predisposition to longevity was calculated using the polygenic score approach (PGSlongevity). APOE-ε4 status was defined according to the absence or presence of ε4 alleles. The causes of death were ascertained from the National Health Service central register, which was classified into cardiovascular diseases, cancers, respiratory illness, and all other causes of mortality. Of the entire sample, 1 234 (17.3%) died during an average 10-year follow-up. One-SD increase in PGSlongevity was associated with a reduced risk for all-cause mortality (hazard ratio [HR] = 0.93, 95% confidence interval [CI]: 0.88-0.98, p = .010) and mortalities due to other causes (HR = 0.81, 95% CI: 0.71-0.93, p = .002) in the following 10 years. In gender-stratified analyses, APOE-ε4 status was associated with a reduced risk for all-cause mortality and mortalities related to cancers in women. Mediation analyses estimated that the percent excess risk of APOE-ε4 on other causes of mortality risk explained by the dementia diagnosis was 24%, which increased to 34% when the sample was restricted to adults who were aged ≤75 years old. To reduce the mortality rate in adults who are aged ≥50 years old, it is essential to prevent dementia onset in the general population.

Low rate of secondary surgery and implant removal following superior, precontoured plating of midshaft clavicle fractures.

Background: Surgical fixation of midshaft clavicle fractures with a single 3.5 mm superior clavicular plate has been associated with a high rate of hardware removal due to symptomatic hardware. Because of this, dual-plating techniques with lower-profile implants have been proposed. However, dual-plating systems have disadvantages, including increased cost and increased surgical morbidity. The aim of this study was to define the rate of symptomatic hardware removal for all midshaft clavicle fractures. Methods: We retrospectively reviewed information on all patients from 2014 to 2018 at a single level 1 trauma institution with surgeries performed by two fellowship-trained orthopedic trauma surgeons. Documented removal of hardware and the reason for removal were recorded. We then contacted all patients at their listed telephone number to confirm the hardware was still in place and to administer patient outcome questionnaires. If patients did not answer, attempts were made to contact them multiple times on multiple days. Those who were not reached but had documented hardware removal were included in the total number of patients with hardware removal. Results: The search revealed 158 patients, of whom 89 (61.8%) were included in the study. Average follow up was 4.09 years (range 2.02-6.50 years). Five patients (5.56%) underwent hardware removal. Removal was for symptomatic or irritating hardware in two of these patients (2.22%). Average abbreviated Disability of Arm, Shoulder, and Hand score was 6.27, and average American Society of Shoulder and Elbow Surgeons shoulder score was 93.6. Conclusion: In our series, the rate of symptomatic hardware removal was 2.22%, well below reported removal rates. Hardware removal rates for prominent symptomatic superior clavicular plates may be significantly lower than previously reported, and these fractures may be adequately treated with a single, superior plate.

Formal help for persons with multiple sclerosis-Background factors associated with usage of personal assistance and home help in Sweden.

Multiple sclerosis (MS) is a chronic neurological disease that may cause several different symptoms, some which may entail the need for help in daily life. The aim of this study was to explore the association between sociodemographic background factors and the use of personal assistance and home help services (home help) among persons with MS in Sweden. The study was based on cross-sectional survey data merged with register data and included 3,863 persons with MS aged 20-51. Binary logistic regression analyses were performed to identify factors associated with the use of personal assistance and home help. The central finding of this study was that grade of impairment, as determined by the Expanded Disability Status Scale for Multiple Sclerosis (EDSS), was the most important variable associated with the use of both personal assistance (p < 0.001, OR 18.83) and home help (p < 0.001, OR 6.83). Living alone and receiving sickness benefit were also both associated with the use of personal assistance (p < 0.001, OR 3.32; p 0.001, OR 3.32) and home help (p 0.004, OR 2.56; p 0.011, OR 2.56). Stating a visible symptom of MS as being the most limiting factor of the disease (p 0.001, OR 2.73) and having a disposable income below the limit for poverty risk (p 0.02, OR 2.16) was associated with the use of personal assistance. Receiving informal, meaning unpaid, help (p 0.049, OR 1.89) was associated with the use of home help. Several background factors were controlled for but were not related to differences in the usage of formal help. The results indicated no significant differences in demographic characteristics that could be linked to unequal distribution. However, differences were found between those using personal assistance and home help. The latter were mainly affected by invisible symptoms, suggesting a plausible influencing factor in the chances of obtaining more comprehensive help in the form of personal assistance. Users of home help were also more likely to receive informal help than users of personal assistance, which may suggest that home help is not sufficient.

Influence of cannabis use on incidence of psychosis in people at clinical high risk.

AIMS: Evidence for case-control studies suggests that cannabis use is a risk factor for the development of psychosis. However, there have been limited prospective studies and the direction of this association remains controversial. The primary aim of the present study was to examine the association between cannabis use and the incidence of psychotic disorders in people at clinical high risk of psychosis. Secondary aims were to assess associations between cannabis use and the persistence of psychotic symptoms, and with functional outcome. METHODS: Current and previous cannabis use were assessed in individuals at clinical high risk of psychosis (n = 334) and healthy controls (n = 67), using a modified version of the Cannabis Experience Questionnaire. Participants were assessed at baseline and followed up for 2 years. Transition to psychosis and persistence of psychotic symptoms were assessed using the Comprehensive Assessment of At-Risk Mental States criteria. Level of functioning at follow up was assessed using the Global Assessment of Functioning disability scale. RESULTS: During follow up, 16.2% of the clinical high-risk sample developed psychosis. Of those who did not become psychotic, 51.4% had persistent symptoms and 48.6% were in remission. There was no significant association between any measure of cannabis use at baseline and either transition to psychosis, the persistence of symptoms, or functional outcome. CONCLUSIONS: These findings contrast with epidemiological data that suggest that cannabis use increases the risk of psychotic disorder.

Association of Brain Natriuretic Peptide Levels at Time of Injury with Morbidity and Mortality in Patients with Surgically Treated Hip Fractures.

An elevated brain natriuretic peptide (BNP) level has been shown to be associated with mortality and cardiac events in cardiac surgery, but its utility in the prediction of morbidity and mortality in hip fracture surgery is unknown. The primary aim of this study was to determine if there is a difference in BNP level at the time of injury between patients who do and do not develop complications after hip fracture surgery. The secondary aim was to determine if there is a predictive relationship between complications associated with the initial BNP level and mortality. Methods: A retrospective chart review of 455 hip fractures in patients ≥60 years old that were operatively treated between February 2014 and July 2018 was performed. Patients were included if they had a BNP level within 48 hours after injury (BNPi). Specific perioperative (≤7 days), 30-day, 1-year, and 2-year postoperative complications were recorded. Wilcoxon rank-sum tests were used to determine if higher BNPi values were associated with greater morbidity. The complications associated with higher BNPi values were further analyzed to assess if they were predictive of mortality, using univariate and multivariable analyses. Results: Higher BNPi was significantly associated with greater morbidity at all postoperative time points and with higher mortality at 1 and 2 years postoperatively. Furthermore, several complications including cardiac failure or exacerbation and altered mental status were associated with mortality at all time points in univariate analysis and at many time points in multivariable analysis. Conclusions: Patients with higher BNPi levels were more likely to develop complications up to 1 year postoperatively, and several of these complications were associated with increased mortality. Future studies to determine if delaying surgery until BNP levels are normalized or lowered may help guide management, and may be useful in determining the need for further medical optimization. Future studies aimed at defining a threshold BNP value at the time of injury may also help in better managing patients preoperatively. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Predicting type 2 diabetes prevalence for people with severe mental illness in a multi-ethnic East London population.

BACKGROUND AND AIMS: Prevalence of type two diabetes mellitus (T2DM) in people with severe mental illness (SMI) is 2-3 times higher than in general population. Predictive modelling has advanced greatly in the past decade, and it is important to apply cutting-edge methods to vulnerable groups. However, few T2DM prediction models account for the presence of mental illness, and none seemed to have been developed specifically for people with SMI. Therefore, we aimed to develop and internally validate a T2DM prevalence model for people with SMI. METHODS: We utilised a large cross-sectional sample representative of a multi-ethnic population from London (674,000 adults); 10,159 people with SMI formed our analytical sample (1,513 T2DM cases). We fitted a linear logistic regression and XGBoost as stand-alone models and as a stacked ensemble. Age, sex, body mass index, ethnicity, area-based deprivation, past hypertension, cardiovascular diseases, prescribed antipsychotics, and SMI illness were the predictors. RESULTS: Logistic regression performed well while detecting T2DM presence for people with SMI: area under the receiver operator curve (ROC-AUC) was 0.83 (95 % CI 0.79-0.87). XGBoost and LR-XGBoost ensemble performed equally well, ROC-AUC 0.83 (95 % CI 0.79-0.87), indicating a negligible contribution of non-linear terms to predictive power. Ethnicity was the most important predictor after age. We demonstrated how the derived models can be utilised and estimated a 2.14 % (95 %CI 2.03 %-2.24 %) increase in T2DM prevalence in East London SMI population in 20 years' time, driven by the projected demographic changes. CONCLUSIONS: Primary care data, the setting where prediction models could be most fruitfully used, provide enough information for well-performing T2DM prevalence models for people with SMI. We demonstrated how thorough internal cross-validation of an ensemble of a linear and machine-learning model can quantify the predictive value of non-linearity in the data.

Cognitive bias modification for paranoia (CBM-pa): a randomised controlled feasibility study in patients with distressing paranoid beliefs.

BACKGROUND: Cognitive Bias Modification for paranoia (CBM-pa) is a novel, theory-driven psychological intervention targeting the biased interpretation of emotional ambiguity associated with paranoia. Study objectives were (i) test the intervention's feasibility, (ii) provide effect size estimates, (iii) assess dose-response and (iv) select primary outcomes for future trials. METHODS: In a double-blind randomised controlled trial, sixty-three outpatients with clinically significant paranoia were randomised to either CBM-pa or an active control (text reading) between April 2016 and September 2017. Patients received one 40 min session per week for 6 weeks. Assessments were given at baseline, after each interim session, post-treatment, and at 1- and 3-months post-treatment. RESULTS: A total of 122 patients were screened and 63 were randomised. The recruitment rate was 51.2%, with few dropouts (four out of 63) and follow-up rates were 90.5% (1-month) and 93.7% (3-months). Each session took 30-40 min to complete. There was no statistical evidence of harmful effects of the intervention. Preliminary data were consistent with efficacy of CBM-pa over text-reading control: patients randomised to the intervention, compared to control patients, reported reduced interpretation bias (d = -0.48 to -0.76), improved symptoms of paranoia (d = -0.19 to -0.38), and lower depressed and anxious mood (d = -0.03 to -0.29). The intervention effect was evident after the third session. CONCLUSIONS: CBM-pa is feasible for patients with paranoia. A fully powered randomised control trial is warranted.

A New Treatment Protocol of Combined High-Dose Levothyroxine and Repetitive Transcranial Magnetic Stimulation for the Treatment of Rapid-Cycling Bipolar Spectrum Disorders: A Cohort Evaluation of 55 Patients.

Background: Bipolar spectrum disorders (BSD) are highly disabling, with rapid cycling being treatment resistant. High-dose levothyroxine (HDT) has been reported to be effective. Diagnosis is associated with mutations in thyroid-activating enzymes and cerebral transporter protein carrier. Repetitive transcranial magnetic stimulation (rTMS) has neuroplastic effects. Methods: We report data on 55 severely symptomatic patients with rapid-cycling BSD treated with a combination protocol of HDT and rTMS. Of the patients, 31 patients (56.4%) were female and 40 (72.7%) had at least one additional diagnosis. Results: Patients were evaluated at three monthly intervals after acute treatment. Remission was measured using the Sheehan Disability Scale (SDS). The average number of medications prescribed was 1.8, with 32 patients (58.2%) needing only levothyroxine. The average dose of levothyroxine was 303.7 mcg (50 mcg−1000 mcg). A total of 53 patients were in remission (96.4%), with an average duration of 2.0 years. The SDS scores decreased significantly (Cohen's d = 2.61 (95% C.I. 1.81 to 2.83, p < 0.001). One patient had reversible side effects. A total of 52 (94.3%) patients had Deiodinase 1 and 2 (DiO1/DiO2) or SLCO1C1 protein carrier gene mutations. Conclusion: The data support the safety and acceptability of combined HDT/rTMS. Patients achieved long remissions with substantial improvements in quality of life.

Clinical correlates of early onset antipsychotic treatment resistance.

BACKGROUND: There is evidence of heterogeneity within treatment-resistant schizophrenia (TRS), with some people not responding to antipsychotic treatment from illness onset and others becoming treatment-resistant after an initial response period. These groups may have different aetiologies. AIM: This study investigates sociodemographic and clinical correlates of early onset of TRS. METHOD: Employing a retrospective cohort design, we do a secondary analysis of data from a cohort of people with TRS attending the South London and Maudsley. Regression analyses were conducted to identify the correlates of the length of treatment to TRS. Predictors included the following: gender, age, ethnicity, problems with positive symptoms, problems with activities of daily living, psychiatric comorbidities, involuntary hospitalisation and treatment with long-acting injectable antipsychotics. RESULTS: In a cohort of 164 people with TRS (60% were men), the median length of treatment to TRS was 3 years and 8 months. We observed no cut-off on the length of treatment until TRS presentation differentiating between early and late TRS (i.e. no bimodal distribution). Having mild to very severe problems with hallucinations and delusions at the treatment start was associated with earlier TRS (~19 months earlier). In sensitivity analyses, including only complete cases (subject to selection bias), treatment with a long-acting injectable antipsychotic was additionally associated with later TRS (~15 months later). CONCLUSION: Our findings do not support a clear separation between early and late TRS but rather a continuum of the length of treatment before TRS onset. Having mild to very severe problems with positive symptoms at treatment start predicts earlier onset of TRS.

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.

A predictor model of treatment resistance in schizophrenia using data from electronic health records.

OBJECTIVES: To develop a prognostic tool of treatment resistant schizophrenia (TRS) in a large and diverse clinical cohort, with comprehensive coverage of patients using mental health services in four London boroughs. METHODS: We used the Least Absolute Shrinkage and Selection Operator (LASSO) for time-to-event data, to develop a risk prediction model from the first antipsychotic prescription to the development of TRS, using data from electronic health records. RESULTS: We reviewed the clinical records of 1,515 patients with a schizophrenia spectrum disorder and observed that 253 (17%) developed TRS. The Cox LASSO survival model produced an internally validated Harrel's C index of 0.60. A Kaplan-Meier curve indicated that the hazard of developing TRS remained constant over the observation period. Predictors of TRS were: having more inpatient days in the three months before and after the first antipsychotic, more community face-to-face clinical contact in the three months before the first antipsychotic, minor cognitive problems, and younger age at the time of the first antipsychotic. CONCLUSIONS: Routinely collected information, readily available at the start of treatment, gives some indication of TRS but is unlikely to be adequate alone. These results provide further evidence that earlier onset is a risk factor for TRS.

Virtual Reality Therapy for the Negative Symptoms of Schizophrenia (V-NeST): A pilot randomised feasibility trial.

BACKGROUND: Negative symptoms are typically observed in people with schizophrenia and indicate a loss or reduction of normal function (e.g. reduced motivation and affect display). Despite obstructing people's recovery, intervention development has received limited attention. This study tests the feasibility and acceptability of a novel Virtual Reality Supported Therapy for the Negative Symptoms of Schizophrenia (V-NeST). METHOD: A single (rater) blind randomised study with two conditions; V-NeST plus treatment as-usual (TAU) vs. TAU alone, recruiting people with schizophrenia experiencing debilitating negative symptoms. Assessment was at baseline and 3-month post-randomisation. The pre-specified primary outcome was participants' goal attainment, secondary outcomes were negative symptoms and functioning. The study assessed feasibility and acceptability parameters including recruitment, eligibility, treatment adherence and retention. Acceptability was also evaluated qualitatively using a post-therapy feedback interview. Explorative therapy effect on outcomes was estimated. RESULTS: The study recruited to its pre-specified target of 30 participants (15 randomised to V-Nest). Two participants in each trial arm disengaged and did not complete the study. Therapy engagement for those randomised to V-NeST was appropriate and research procedures were feasible. The experience with therapy and VR was described as positive and useful. Preliminary analysis suggested the therapy may have a large effect on participants goals and a possible effect on negative symptoms. CONCLUSION: V-NeST is a feasible and acceptable intervention. This therapy has the potential to support people with schizophrenia achieving their recovery goals and may reduce negative symptoms. The efficacy results need to be evaluated in an appropriately powered efficacy study.