Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC): a cardiometabolic risk prediction algorithm for young people with psychosis

Assessing the generalisability of the psychosis metabolic risk calculator (PsyMetRiC) for young people with first-episode psychosis with validation in a Hong Kong Chinese Han population: a 4-year follow-up study

Background

Metabolic syndrome (MetS) is common following first-episode psychosis (FEP), contributing to substantial morbidity and mortality. The Psychosis Metabolic Risk Calculator (PsyMetRiC), a risk prediction algorithm for MetS following a FEP diagnosis, was developed in the United Kingdom and has been validated in other European populations. However, the predictive accuracy of PsyMetRiC in Chinese populations is unknown.

Methods

FEP patients aged 15-35 y, first presented to the Early Assessment Service for Young People with Early Psychosis (EASY) Programme in Hong Kong (HK) between 2012 and 2021 were included. A binary MetS outcome was determined based on the latest available follow-up clinical information between 1 and 12 years after baseline assessment. The PsyMetRiC Full and Partial algorithms were assessed for discrimination, calibration and clinical utility in the HK sample, and logistic calibration was conducted to account for population differences. Sensitivity analysis was performed in patients aged >35 years and using Chinese MetS criteria.

Findings

The main analysis included 416 FEP patients (mean age = 23.8 y, male sex = 40.4%, 22.4% MetS prevalence at follow-up). PsyMetRiC showed adequate discriminative performance (full-model C = 0.76, 95% C.I. = 0.69-0.81; partial-model: C = 0.73, 95% C.I. = 0.65-0.8). Systematic risk underestimation in both models was corrected using logistic calibration to refine PsyMetRiC for HK Chinese FEP population (PsyMetRiC-HK). PsyMetRiC-HK provided a greater net benefit than competing strategies. Results remained robust with a Chinese MetS definition, but worse for the older age group.

Interpretation

With good predictive performance for incident MetS, PsyMetRiC-HK presents a step forward for personalized preventative strategies of cardiometabolic morbidity and mortality in young Hong Kong Chinese FEP patients.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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.

Implementation of a lifestyle and life-skills intervention to prevent weight-gain and cardiometabolic abnormalities in young people with first-episode psychosis as part of routine care: The Keeping the Body in Mind program

OBJECTIVES

In 2013, a cluster-controlled pilot study found the 12-week Keeping the Body in Mind (KBIM) lifestyle and life skills intervention was able to prevent weight gain in a small sample of youth experiencing first-episode psychosis (FEP) with fewer than 4 weeks of antipsychotic exposure. This study aims to evaluate the effectiveness of KBIM as routine care on anthropometry and metabolic biochemistry in a larger sample of youth with FEP across three community mental health services.

METHOD

This retrospective chart audit was conducted on youth with FEP, prescribed a therapeutic dose of antipsychotic medication, and who engaged with KBIM between 2015 and 2019. Primary outcomes were weight and waist circumference. Secondary outcomes were blood pressure, blood glucose, and blood lipids. Outcomes were collected in at baseline and at 12 weeks. Data on program engagement were obtained from the participant's medical file.

RESULTS

One-hundred and eighty-two people met inclusion criteria, and up to 134 people had baseline and 12-week data on one or more outcome. Mean number of sessions attended was 11.1 (SD = 7.3). Increases in weight and waist circumference were limited to 1.5 kg (SD = 5.3, t(133) = 3.2, p = .002) and 0.7 cm (SD = 5.8, t(109) = 1.2, p = .23) respectively. Eighty-one percent of participants did not experience clinically significant weight gain (>7% of baseline weight). There were no significant changes in blood pressure or metabolic biochemistry.

CONCLUSION

The prevention of substantial gains in weight and waist circumference observed in the initial pilot study was maintained with implementation of KBIM as part of routine clinical care for youth with FEP.

Using digital technology to promote physical health in mental healthcare: A sequential mixed-methods study of clinicians' views

AIM

Recent years have seen innovation in 'mHealth' tools and health apps for the management/promotion of physical health and fitness across the general population. However, there is limited research on how this could be applied to mental healthcare. Therefore, we examined mental healthcare professionals' current uses and perceived roles of digital lifestyle interventions for promoting healthy lifestyles, physical health and fitness in youth mental healthcare.

METHODS

A sequential, mixed-methods design was used, consisting of a quantitative online survey, followed by qualitative in-depth interviews.

RESULTS

A total of 127 mental healthcare professionals participated in the online survey. Participants had limited mHealth experience, and the majority agreed that further training would be beneficial. Thirteen mental healthcare professionals were interviewed. Five themes were generated (i) digital technology's ability to enhance the physical healthcare; (ii) Conditions for the acceptability of apps; (iii) Limitations on staff capability and time; (iv) Motivation as the principal barrier; and (v) Practicalities around receiving lifestyle data. Systematic integration of data produced novel insights around: (i) staff involvement and needs; (ii) ideal focus and content of digital lifestyle interventions; and (iii) barriers towards implementation (including mental healthcare professionals own limited experience using digital lifestyle interventions, which aligned with the appeal of formal training).

CONCLUSIONS

Overall, digital lifestyle interventions were positively received by mental healthcare professionals, particularly for health behaviour-tracking and mHealth support for exercise and nutrition. Practical suggestions for facilitating their uptake/implementation to improve availability of physical health interventions in mental healthcare are presented.

The risk of stroke and post-stroke mortality in people with schizophrenia: A systematic review and meta-analysis study

Sources of heterogeneity in risk of stroke and mortality risk following acute-stroke in schizophrenia are understudied. We systematically searched four electronic-databases until 1-November-2022, and conducted meta-analysis to synthesize estimates of stroke-risk and post-stroke mortality for schizophrenia patients relative to non-schizophrenia counterparts. Subgroup-analyses and meta-regression models stratified by sex, nature of sample (incident/prevalent), geographical region, study-period and time-frame following stroke were conducted when applicable. Fifteen and 5 studies were included for meta-analysis of stroke-risk (n=18,368,253; 129,095 schizophrenia patients) and all-cause post-stroke mortality (n=289,231; 4,477 schizophrenia patients), respectively. Schizophrenia patients exhibited elevated stroke-risk (relative-risk =1.55[95% CI:1.31-1.84]) relative to non-schizophrenia controls. Schizophrenia was associated with increased stroke-risk in both sexes, study-periods of 1990s and 2000s, and irrespective of nature of sample and geographical regions. Meta-regression revealed regional differences in relative-risk for stroke, but limited by small number of studies. After removal of an outlier study, meta-analysis demonstrated that schizophrenia was associated with increased overall (hazard-ratio=1.37[1.30-1.44]), short-term (≤90 days; 1.29[1.14-1.46]) and longer-term (≥1 year; 1.45[1.32-1.60]) post-stroke mortality rates. Raised post-stroke mortality rate for schizophrenia was observed irrespective of nature of sample, geographical regions and study-periods. Taken together, schizophrenia is associated with increased stroke-risk and post-stroke mortality. Multilevel-interventions are required to reduce these physical-health disparities.

30-year Cardiovascular Disease Risk for Young Adults with Serious Mental Illness

Objective

To estimate 30-year CVD risk and modifiable risk factors in young adults with serious mental illness (SMI) versus those without, and assess variations in CVD risk by race, ethnicity, and sex.

Method

In this cross-sectional study, we estimated and compared the Framingham 30-year CVD risk score and individual modifiable CVD risk factors in young adult (20-39 years) primary care patients with and without SMI at two US healthcare systems (January 2016-Septemeber 2018). Interaction terms assessed whether the SMI-risk association differed across demographic groups.

Results

Covariate-adjusted 30-year CVD risk was significantly higher for those with (n=4228) versus those without (n=155,363) SMI (RR 1.28, 95% CI [1.26, 1.30]). Patients with SMI had higher rates of hypertension (OR 2.02 [1.7, 2.39]), diabetes (OR 3.14 [2.59, 3.82]), obesity (OR 1.93 [1.8, 2.07]), and smoking (OR 4.94 [4.6, 5.36]). The increased 30-year CVD risk associated with SMI varied significantly by race and sex: there was an 8% higher risk in Black compared to White patients (RR 1.08, [1.04, 1.12]) and a 9% lower risk in men compared to women (RR 0.91 [0.88, 0.94]).

Conclusions

Young adults with SMI are at increased 30-year risk of CVD, and further disparities exist for Black individuals and women.

Optimizing Clinical Decision Making with Decision Curve Analysis: Insights for Clinical Investigators

A large number of prediction models are published with the objective of allowing personalized decision making for diagnostic or prognostic purposes. Conventional statistical measures of discrimination, calibration, or other measures of model performance are not well-suited for directly and clearly assessing the clinical value of scores or biomarkers. Decision curve analysis is an increasingly popular technique used to assess the clinical utility of a prognostic or diagnostic score/rule, or even of a biomarker. Clinical utility is expressed as the net benefit, which represents the net balance of patients' benefits and harms and considers, implicitly, the consequences of clinical actions taken in response to a certain prediction score, rule, or biomarker. The net benefit is plotted against a range of possible exchange rates, representing the spectrum of possible patients' and clinicians' preferences. Decision curve analysis is a powerful tool for judging whether newly published or existing scores may truly benefit patients, and represents a significant advancement in improving transparent clinical decision making. This paper is meant to be an introduction to decision curve analysis and its interpretation for clinical investigators. Given the extensive advantages, we advocate applying decision curve analysis to all models intended for use in clinical practice.

Predicting states of elevated negative affect in adolescents from smartphone sensors: a novel personalized machine learning approach

BACKGROUND

Adolescence is characterized by profound change, including increases in negative emotions. Approximately 84% of American adolescents own a smartphone, which can continuously and unobtrusively track variables potentially predictive of heightened negative emotions (e.g. activity levels, location, pattern of phone usage). The extent to which built-in smartphone sensors can reliably predict states of elevated negative affect in adolescents is an open question.

METHODS

Adolescent participants (n = 22; ages 13-18) with low to high levels of depressive symptoms were followed for 15 weeks using a combination of ecological momentary assessments (EMAs) and continuously collected passive smartphone sensor data. EMAs probed negative emotional states (i.e. anger, sadness and anxiety) 2-3 times per day every other week throughout the study (total: 1145 EMA measurements). Smartphone accelerometer, location and device state data were collected to derive 14 discrete estimates of behavior, including activity level, percentage of time spent at home, sleep onset and duration, and phone usage.

RESULTS

A personalized ensemble machine learning model derived from smartphone sensor data outperformed other statistical approaches (e.g. linear mixed model) and predicted states of elevated anger and anxiety with acceptable discrimination ability (area under the curve (AUC) = 74% and 71%, respectively), but demonstrated more modest discrimination ability for predicting states of high sadness (AUC = 66%).

CONCLUSIONS

To the extent that smartphone data could provide reasonably accurate real-time predictions of states of high negative affect in teens, brief 'just-in-time' interventions could be immediately deployed via smartphone notifications or mental health apps to alleviate these states.

Metabolic syndrome and related factors in a large sample of antipsychotic naïve patients with first-episode psychosis: 3 years follow-up results from the PAFIP cohort

BACKGROUND

Latest studies in patients with first episode psychosis (FEP) have shown alterations in cardiovascular, immune and endocrinological systems. These findings could indicate a systemic onset alteration in the metabolic disease as opposed to justifying these findings exclusively by antipsychotics' side effects and long-term lifestyle consequences. In any case, this population is considered at higher risk for developing cardiometabolic disorders than their age-matched peers.

METHODS

This is a prospective longitudinal study. Metabolic syndrome (MetS) prevalence between 244 subjects with FEP and 166 controls at 3 years was compared. Additionally, we explored whether baseline differences in any of the MetS components according to Adult Treatment Panel III definition and prescribed antipsychotic could help to predict the MetS development at 3 years.

RESULTS

Patients with FEP present a similar baseline prevalence of MetS (6.6% vs 5.4%, p=0.320), according to ATP-III criteria. but with a higher prevalence of metabolic alterations than controls before the start of antipsychotic treatment. At 3-years follow-up the MetS prevalence had increased from 6.6% to 18.3% in the FEP group, while only from 5.4% to 8.1% in the control group. The multivariate model showed that, before antipsychotic exposure, a baseline altered waist circumference WC (OR=1.1, p=0.011), triglycerides (OR=1.1, p=0.043) and high-density lipoprotein HDL (OR=0.9, p=0.008) significantly predicted the presence of MetS at 3-years. We propose a predictive model of MetS at 3 years in 244 drug-naïve FEP patients.

CONCLUSION

We found that altered WC, HDL and triglycerides at baseline predicted the presence of full MetS after 3-years of initiating antipsychotic treatment. Our findings support the need for interventions to improve factors related to the physical health of FEP individuals.

Assessing violence risk in first-episode psychosis: external validation, updating and net benefit of a prediction tool (OxMIV)

BACKGROUND

Violence perpetration is a key outcome to prevent for an important subgroup of individuals presenting to mental health services, including early intervention in psychosis (EIP) services. Needs and risks are typically assessed without structured methods, which could facilitate consistency and accuracy. Prediction tools, such as OxMIV (Oxford Mental Illness and Violence tool), could provide a structured risk stratification approach, but require external validation in clinical settings.

OBJECTIVES

We aimed to validate and update OxMIV in first-episode psychosis and consider its benefit as a complement to clinical assessment.

METHODS

A retrospective cohort of individuals assessed in two UK EIP services was included. Electronic health records were used to extract predictors and risk judgements made by assessing clinicians. Outcome data involved police and healthcare records for violence perpetration in the 12 months post-assessment.

FINDINGS

Of 1145 individuals presenting to EIP services, 131 (11%) perpetrated violence during the 12 month follow-up. OxMIV showed good discrimination (area under the curve 0.75, 95% CI 0.71 to 0.80). Calibration-in-the-large was also good after updating the model constant. Using a 10% cut-off, sensitivity was 71% (95% CI 63% to 80%), specificity 66% (63% to 69%), positive predictive value 22% (19% to 24%) and negative predictive value 95% (93% to 96%). In contrast, clinical judgement sensitivity was 40% and specificity 89%. Decision curve analysis showed net benefit of OxMIV over comparison approaches.

CONCLUSIONS

OxMIV performed well in this real-world validation, with improved sensitivity compared with unstructured assessments.

CLINICAL IMPLICATIONS

Structured tools to assess violence risk, such as OxMIV, have potential in first-episode psychosis to support a stratified approach to allocating non-harmful interventions to individuals who may benefit from the largest absolute risk reduction.

Predicting treatment resistance from first-episode psychosis using routinely collected clinical information

Around a quarter of people who experience a first episode of psychosis (FEP) will develop treatment-resistant schizophrenia (TRS), but there are currently no established clinically useful methods to predict this from baseline. We aimed to explore the predictive potential for clozapine use as a proxy for TRS of routinely collected, objective biomedical predictors at FEP onset, and to externally validate the model in a separate clinical sample of people with FEP. We developed and externally validated a forced-entry logistic regression risk prediction Model fOr cloZApine tReaTment, or MOZART, to predict up to 8-year risk of clozapine use from FEP using routinely recorded information including age, sex, ethnicity, triglycerides, alkaline phosphatase levels, and lymphocyte counts. We also produced a least-absolute shrinkage and selection operator (LASSO) based model, additionally including neutrophil count, smoking status, body mass index, and random glucose levels. The models were developed using data from two UK psychosis early intervention services (EIS) and externally validated in another UK EIS. Model performance was assessed via discrimination and calibration. We developed the models in 785 patients, and validated externally in 1,110 patients. Both models predicted clozapine use well at internal validation (MOZART: C 0.70; 95%CI 0.63,0.76; LASSO: 0.69; 95%CI 0.63,0.77). At external validation, discrimination performance reduced (MOZART: 0.63; 0.58,0.69; LASSO: 0.64; 0.58,0.69) but recovered after re-estimation of the lymphocyte predictor (C: 0.67; 0.62,0.73). Calibration plots showed good agreement between observed and predicted risk in the forced-entry model. We also present a decision-curve analysis and an online data visualisation tool. The use of routinely collected clinical information including blood-based biomarkers taken at FEP onset can help to predict the individual risk of clozapine use, and should be considered equally alongside other potentially useful information such as symptom scores in large-scale efforts to predict psychiatric outcomes.

Risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for attention-deficit/hyperactivity disorder

BACKGROUND

Available prediction models of cardiovascular diseases (CVDs) may not accurately predict outcomes among individuals initiating pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD).

OBJECTIVE

To improve the predictive accuracy of traditional CVD risk factors for adults initiating pharmacological treatment of ADHD, by considering novel CVD risk factors associated with ADHD (comorbid psychiatric disorders, sociodemographic factors and psychotropic medication).

METHODS

The cohort composed of 24 186 adults residing in Sweden without previous CVDs, born between 1932 and 1990, who started pharmacological treatment of ADHD between 2008 and 2011, and were followed for up to 2 years. CVDs were identified using diagnoses according to the International Classification of Diseases, and dispended medication prescriptions from Swedish national registers. Cox proportional hazards regression was employed to derive the prediction model.

FINDINGS

The developed model included eight traditional and four novel CVD risk factors. The model showed acceptable overall discrimination (C index=0.72, 95% CI 0.70 to 0.74) and calibration (Brier score=0.008). The Integrated Discrimination Improvement index showed a significant improvement after adding novel risk factors (0.003 (95% CI 0.001 to 0.007), p<0.001).

CONCLUSIONS

The inclusion of the novel CVD risk factors may provide a better prediction of CVDs in this population compared with traditional CVD predictors only, when the model is used with a continuous risk score. External validation studies and studies assessing clinical impact of the model are warranted.

CLINICAL IMPLICATIONS

Individuals initiating pharmacological treatment of ADHD at higher risk of developing CVDs should be more closely monitored.

The psychosis metabolic risk calculator (PsyMetRiC) for young people with psychosis: International external validation and site-specific recalibration in two independent European samples

Background

Cardiometabolic dysfunction is common in young people with psychosis. Recently, the Psychosis Metabolic Risk Calculator (PsyMetRiC) was developed and externally validated in the UK, predicting up-to six-year risk of metabolic syndrome (MetS) from routinely collected data. The full-model includes age, sex, ethnicity, body-mass index, smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein, and triglyceride concentrations; the partial-model excludes biochemical predictors.

Methods

To move toward a future internationally-useful tool, we externally validated PsyMetRiC in two independent European samples. We used data from the PsyMetab (Lausanne, Switzerland) and PAFIP (Cantabria, Spain) cohorts, including participants aged 16-35y without MetS at baseline who had 1-6y follow-up. Predictive performance was assessed primarily via discrimination (C-statistic), calibration (calibration plots), and decision curve analysis. Site-specific recalibration was considered.

Findings

We included 1024 participants (PsyMetab n=558, male=62%, outcome prevalence=19%, mean follow-up=2.48y; PAFIP n=466, male=65%, outcome prevalence=14%, mean follow-up=2.59y). Discrimination was better in the full- compared with partial-model (PsyMetab=full-model C=0.73, 95% C.I., 0.68-0.79, partial-model C=0.68, 95% C.I., 0.62-0.74; PAFIP=full-model C=0.72, 95% C.I., 0.66-0.78; partial-model C=0.66, 95% C.I., 0.60-0.71). As expected, calibration plots revealed varying degrees of miscalibration, which recovered following site-specific recalibration. PsyMetRiC showed net benefit in both new cohorts, more so after recalibration.

Interpretation

The study provides evidence of PsyMetRiC's generalizability in Western Europe, although further local and international validation studies are required. In future, PsyMetRiC could help clinicians internationally to identify young people with psychosis who are at higher cardiometabolic risk, so interventions can be directed effectively to reduce long-term morbidity and mortality.

Funding

NIHR Cambridge Biomedical Research Centre (BRC-1215-20014); The Wellcome Trust (201486/Z/16/Z); Swiss National Research Foundation (320030-120686, 324730- 144064, and 320030-173211); The Carlos III Health Institute (CM20/00015, FIS00/3095, PI020499, PI050427, and PI060507); IDIVAL (INT/A21/10 and INT/A20/04); The Andalusian Regional Government (A1-0055-2020 and A1-0005-2021); SENY Fundacion Research (2005-0308007); Fundacion Marques de Valdecilla (A/02/07, API07/011); Ministry of Economy and Competitiveness and the European Fund for Regional Development (SAF2016-76046-R and SAF2013-46292-R).For the Spanish and French translation of the abstract see Supplementary Materials section.

Mortality rates in people with first diagnosis of schizophrenia-spectrum disorders: A 5-year population-based cohort study

OBJECTIVE

Schizophrenia-spectrum disorder (SSD) is associated with increased premature death, with emerging data suggesting early illness course as a high-risk period for excess mortality. This study aimed to examine mortality rate in patients with incident SSD and differential mortality risk between inpatient-diagnosed and outpatient-diagnosed subsamples within 5 years of first diagnosis.

METHOD

This population-based cohort study identified 8826 patients aged 18-39 years receiving first-recorded SSD diagnosis upon service entry, comprising 3877 inpatient-diagnosed and 4949 outpatient-diagnosed patients, between 2006 and 2012 in Hong Kong using a territory-wide medical record database of public health care services. All-cause, natural-cause, and unnatural-cause mortality risks within 5 years after first diagnosis were quantified by standardized mortality ratios (SMRs) relative to the general population. We also directly compared mortality rates between inpatient and outpatient subsamples over 5-year follow-up.

RESULTS

SSD patients exhibited markedly elevated all-cause (SMR: 12.28, 95% confidence interval [CI]: [10.83, 13.88]), natural-cause (SMR: 3.76, 95% CI: [2.77, 4.98]) and unnatural-cause (SMR: 20.64, 95% CI: [17.49, 24.20]) mortality during first 5 years of diagnosis. Increased mortality rate was most pronounced in the first year of treatment, especially for unnatural deaths (SMR 32.2, 95% CI: [24.08, 42.22]). Discharged inpatient-diagnosed patients displayed significantly higher all-cause and unnatural-cause mortality rates than outpatient-diagnosed counterparts within first 3 years of treatment, and differential mortality risks on all-cause (adjusted hazard ratio [aHR]: 7.05, 95% CI: [2.02, 24.64]) and unnatural-cause (aHR: 5.15, 95% CI: [1.38, 19.19]) deaths were the highest in the first month of follow-up.

CONCLUSIONS

Substantial increase in early mortality risk among people with incident SSD, particularly in the first year of diagnosis and the time shortly after discharge, underscores an urgent need of targeted early intervention for effective suicide prevention and physical health improvement to minimize mortality gap.

Mortality, Revascularization, and Cardioprotective Pharmacotherapy After Acute Coronary Syndrome in Patients With Severe Mental Illness: A Systematic Review and Meta-analysis

BACKGROUND AND HYPOTHESIS

People with severe mental illness (SMI) may experience excess mortality and inequitable treatment following acute coronary syndrome (ACS). However, cardioprotective pharmacotherapy and SMI diagnoses other than schizophrenia are rarely examined in previous reviews. We hypothesized that SMI including bipolar disorder (BD) is associated with increased post-ACS mortality, decreased revascularization, and cardioprotective medication receipt relative to those without SMI.

STUDY DESIGN

We performed a meta-analysis to quantitatively synthesize estimates of post-ACS mortality, major adverse cardiac events (MACEs), and receipt of invasive coronary procedures and cardioprotective medications in patients with SMI, comprising schizophrenia, BD, and other nonaffective psychoses, relative to non-SMI counterparts. Subgroup analyses stratified by SMI subtypes (schizophrenia, BD), incident ACS status, and post-ACS time frame for outcome evaluation were conducted.

STUDY RESULTS

Twenty-two studies were included (n = 12 235 501, including 503 686 SMI patients). SMI was associated with increased overall (relative risk [RR] = 1.40 [95% confidence interval = 1.21-1.62]), 1-year (1.68 [1.42-1.98]), and 30-day (1.26 [1.05-1.51]) post-ACS mortality, lower receipt of revascularization (odds ratio = 0.57 [0.49-0.67]), and cardioprotective medications (RR = 0.89 [0.85-0.94]), but comparable rates of any/specific MACEs relative to non-SMI patients. Incident ACS status conferred further increase in post-ACS mortality. Schizophrenia was associated with heightened mortality irrespective of incident ACS status, while BD was linked to significantly elevated mortality only in incident ACS cohort. Both schizophrenia and BD patients had lower revascularization rates. Post-ACS mortality risk remained significantly increased with mild attenuation after adjusting for revascularization.

CONCLUSIONS

SMI is associated with increased post-ACS mortality and undertreatment. Effective multipronged interventions are urgently needed to reduce these physical health disparities.

Cardiovascular disease risk in people with severe mental disorders: an update and call for action

PURPOSE OF REVIEW

Cardiovascular disease (CVD) is a major cause of premature death in people with severe mental disorders (SMDs). This review provides an update on the level of CVD mortality and morbidity, as well as the socioeconomic, psychosocial and genetic factors associated with the comorbidity, and offer directions for improved interventions to reduce CVD in SMDs.

RECENT FINDINGS

The level of CVD mortality and morbidity has sustained high in people with SMDs during the past decades, but the causal mechanism must be further elucidated. Psychosocial and socioeconomic challenges are frequent in SMDs as well as in CVD. Further, recent studies have revealed genetic variants jointly associated with SMDs, CVD risk and social factors. These findings highlight the need for more targeted interventions, prediction tools and psychosocial approaches to comorbid CVD in SMDs.

SUMMARY

The level of CVD comorbidity remains high in SMDs, indicating that most people with SMDs have not benefitted from recent medical advances. A complex interplay between genetic and social vulnerability to CVD, which differs across subgroups of patients, seems to be involved. Further research is required to meet the urgent need for earlier, more efficient intervention approaches and preventive strategies for comorbid CVD in SMD.

Interaction Between Race, Ethnicity, Severe Mental Illness, and Cardiovascular Disease

Severe mental illnesses, such as schizophrenia or bipolar disorder, affect ≈1% of the population who, as a group, experience significant disadvantage in terms of physical health and reduced life expectancy. In this review, we explore the interaction between race, ethnicity, severe mental illness, and cardiovascular disease, with a focus on cardiovascular care pathways. Finally, we discuss strategies to investigate and address disparities in cardiovascular care for patients with severe mental illness.

The psychological journey of weight gain in psychosis

BACKGROUND

Rapid weight gain is common with antipsychotic medication. Lost confidence, low mood and medication non-adherence often follow. Yet, the dynamic interactions between the physical and psychological consequences of weight gain, and implications for intervention, are unknown.

OBJECTIVES

We examined first-person accounts of weight gain to identify preferences for weight change interventions.

DESIGN

A qualitative design was used to explore patients' experiences of weight change in the context of psychosis.

METHOD

Semi-structured interviews, analysed using grounded theory, were conducted with 10 patients with psychosis. Sample validation was conducted with peer researchers with lived experience of psychosis.

RESULTS

Patients described that initially the extent and speed of weight gain was overshadowed by psychotic experiences and their treatment. This led to a shocking realisation of weight gain. The psychological impact of weight gain, most strikingly on the self-concept, was profound. Loss of self-worth and changed appearance amplified a sense of vulnerability. There were further consequences on mood, activity and psychotic experiences, such as voices commenting on appearance, that were additional obstacles in the challenging process of weight loss. Sedative effects of medication also contributed. Unsuccessful weight loss left little hope and few preferences for interventions. Early information about common weight gain trajectories and working with experts-by-experience were valued. Rebuilding self-confidence, efficacy and worth may be a necessary first step.

CONCLUSIONS

The journey of weight gain in patients with psychosis is characterised by loss of self-worth, agency and hope. There are multiple stages in the journey, each with different psychological reactions, that may need different treatment responses.

Pattern of long-term weight and metabolic changes after a first episode of psychosis: Results from a 10-year prospective follow-up of the PAFIP program for early intervention in psychosis cohort

Background

People with psychosis are at higher risk of cardiovascular events, partly explained by a higher predisposition to gain weight. This has been observed in studies on individuals with a first-episode psychosis (FEP) at short and long term (mainly up to 1 year) and transversally at longer term in people with chronic schizophrenia. However, there is scarcity of data regarding longer-term (above 3-year follow-up) weight progression in FEP from longitudinal studies. The aim of this study is to evaluate the longer-term (10 years) progression of weight changes and related metabolic disturbances in people with FEP.

Methods

Two hundred and nine people with FEP and 57 healthy participants (controls) were evaluated at study entry and prospectively at 10-year follow-up. Anthropometric, clinical, and sociodemographic data were collected.

Results

People with FEP presented a significant and rapid increase in mean body weight during the first year of treatment, followed by less pronounced but sustained weight gain over the study period (Δ15.2 kg; SD 12.3 kg). This early increment in weight predicted longer-term changes, which were significantly greater than in healthy controls (Δ2.9 kg; SD 7.3 kg). Weight gain correlated with alterations in lipid and glycemic variables, leading to clinical repercussion such as increments in the rates of obesity and metabolic disturbances. Sex differences were observed, with women presenting higher increments in body mass index than men.

Conclusions

This study confirms that the first year after initiating antipsychotic treatment is the critical one for weight gain in psychosis. Besides, it provides evidence that weight gain keep progressing even in the longer term (10 years), causing relevant metabolic disturbances.

Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England

OBJECTIVES

We aimed to examine agreement between common mental disorders (CMDs) from primary care records and repeated CMD questionnaire data from ALSPAC (the Avon Longitudinal Study of Parents and Children) over adolescence and young adulthood, explore factors affecting CMD identification in primary care records, and construct models predicting ALSPAC-derived CMDs using only primary care data.

DESIGN AND SETTING

Prospective cohort study (ALSPAC) in Southwest England with linkage to electronic primary care records.

PARTICIPANTS

Primary care records were extracted for 11 807 participants (80% of 14 731 eligible). Between 31% (3633; age 15/16) and 11% (1298; age 21/22) of participants had both primary care and ALSPAC CMD data.

OUTCOME MEASURES

ALSPAC outcome measures were diagnoses of suspected depression and/or CMDs. Primary care outcome measure were Read codes for diagnosis, symptoms and treatment of depression/CMDs. For each time point, sensitivities and specificities for primary care CMD diagnoses were calculated for predicting ALSPAC-derived measures of CMDs, and the factors associated with identification of primary care-based CMDs in those with suspected ALSPAC-derived CMDs explored. Lasso (least absolute selection and shrinkage operator) models were used at each time point to predict ALSPAC-derived CMDs using only primary care data, with internal validation by randomly splitting data into 60% training and 40% validation samples.

RESULTS

Sensitivities for primary care diagnoses were low for CMDs (range: 3.5%-19.1%) and depression (range: 1.6%-34.0%), while specificities were high (nearly all >95%). The strongest predictors of identification in the primary care data for those with ALSPAC-derived CMDs were symptom severity indices. The lasso models had relatively low prediction rates, especially in the validation sample (deviance ratio range: -1.3 to 12.6%), but improved with age.

CONCLUSIONS

Primary care data underestimate CMDs compared to population-based studies. Improving general practitioner identification, and using free-text or secondary care data, is needed to improve the accuracy of models using clinical data.