Stratified medicine in schizophrenia: how accurate would a test of drug response need to be to achieve cost-effective improvements in quality of life?

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.

Psychotic disorders as a framework for precision psychiatry

People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.

Anterior cingulate glutamate metabolites as a predictor of antipsychotic response in first episode psychosis: data from the STRATA collaboration

Elevated brain glutamate has been implicated in non-response to antipsychotic medication in schizophrenia. Biomarkers that can accurately predict antipsychotic non-response from the first episode of psychosis (FEP) could allow stratification of patients; for example, patients predicted not to respond to standard antipsychotics could be fast-tracked to clozapine. Using proton magnetic resonance spectroscopy (1H-MRS), we examined the ability of glutamate and Glx (glutamate plus glutamine) in the anterior cingulate cortex (ACC) and caudate to predict response to antipsychotic treatment. A total of 89 minimally medicated patients with FEP not meeting symptomatic criteria for remission were recruited across two study sites. 1H-MRS and clinical data were acquired at baseline, 2 and 6 weeks. Response was defined as >20% reduction in Positive and Negative Syndrome Scale (PANSS) Total score from baseline to 6 weeks. In the ACC, baseline glutamate and Glx were higher in Non-Responders and significantly predicted response (P < 0.02; n = 42). Overall accuracy was greatest for ACC Glx (69%) and increased to 75% when symptom severity at baseline was included in the model. Glutamate metabolites in the caudate were not associated with response, and there was no significant change in glutamate metabolites over time in either region. These results add to the evidence linking elevations in ACC glutamate metabolites to a poor antipsychotic response. They indicate that glutamate may have utility in predicting response during early treatment of first episode psychosis. Improvements in accuracy may be made by combining glutamate measures with other response biomarkers.

Decision-analytic modeling for early health technology assessment of medical devices - a scoping review

Objective

The goal of this review was to identify decision-analytic modeling studies in early health technology assessments (HTA) of high-risk medical devices, published over the last three years, and to provide a systematic overview of model purposes and characteristics. Additionally, the aim was to describe recent developments in modeling techniques.

Methods

For this scoping review, we performed a systematic literature search in PubMed and Embase including studies published in English or German. The search code consisted of terms describing early health technology assessment and terms for decision-analytic models. In abstract and full-text screening, studies were excluded that were not modeling studies for a high-risk medical device or an in-vitro diagnostic test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to report on the search and exclusion of studies. For all included studies, study purpose, framework and model characteristics were extracted and reported in systematic evidence tables and a narrative summary.

Results

Out of 206 identified studies, 19 studies were included in the review. Studies were either conducted for hypothetical devices or for existing devices after they were already available on the market. No study extrapolated technical data from early development stages to estimate potential value of devices in development. All studies except one included cost as an outcome. Two studies were budget impact analyses. Most studies aimed at adoption and reimbursement decisions. The majority of studies were on in-vitro diagnostic tests for personalized and targeted medicine. A timed automata model, to our knowledge a model type new to HTA, was tested by one study. It describes the agents in a clinical pathway in separate models and, by allowing for interaction between the models, can reflect complex individual clinical pathways and dynamic system interactions. Not all sources of uncertainty for in-vitro tests were explicitly modeled. Elicitation of expert knowledge and judgement was used for substitution of missing empirical data. Analysis of uncertainty was the most valuable strength of decision-analytic models in early HTA, but no model applied sensitivity analysis to optimize the test positivity cutoff with regard to the benefit-harm balance or cost-effectiveness. Value-of-information analysis was rarely performed. No information was found on the use of causal inference methods for estimation of effect parameters from observational data.

Conclusion

Our review provides an overview of the purposes and model characteristics of nineteen recent early evaluation studies on medical devices. The review shows the growing importance of personalized interventions and confirms previously published recommendations for careful modeling of uncertainties surrounding diagnostic devices and for increased use of value-of-information analysis. Timed automata may be a model type worth exploring further in HTA. In addition, we recommend to extend the application of sensitivity analysis to optimize positivity criteria for in-vitro tests with regard to benefit-harm or cost-effectiveness. We emphasize the importance of causal inference methods when estimating effect parameters from observational data.

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.

Pharmacogenomic-guided clozapine administration based on HLA-DQB1, HLA-B and SLCO1B3-SLCO1B7 variants: an effectiveness and cost-effectiveness analysis

The identification of pharmacogenetic factors that increase the susceptibility to clozapine-induced agranulocytosis or granulocytopenia (CIAG) has received increasing interest. The SLCO1B3-SCLO1B7 variant (rs149104283) and single amino acid changes in human leukocyte antigen (HLA) HLA-DQB1 (126Q) and HLA-B (158T) were associated with an increased risk of CIAG. In this study, we evaluated the effectiveness and cost-effectiveness of adding the SLCO1B3-SCLO1B7 to HLA variants as a new pharmacogenomic (PGx) approach and explored the evolution of a cohort of schizophrenic patients taking long-term clozapine as a third-line antipsychotic medication. The decision model included probabilistic and deterministic sensitivity analyses to assess the expected costs and quality-adjusted life-years (QALYs). The current monitoring scheme was compared with the PGx-guided strategy, where all patients underwent pre-emptively a genetic test before taking clozapine, over 10 years. By adding the SLCO1B3-SCLO1B7 variant into HLA variants, CIAG sensitivity increased from 36.0% to 43.0%, the specificity decreased from 89.0% to 86.9%, and the probability of cost-effectiveness improved from 74.1% to 87.8%. The incremental cost-effectiveness ratio was £16,215 per QALY and remained below the conventional decision threshold (£30,000 or US$50,000 per QALY). Therefore, the SLCO1B3-SCLO1B7 variant, as an additional risk allele to HLA variants, increases preemptive test sensitivity and improves the effectiveness and cost-effectiveness of PGx-guided clozapine administration.

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.

Acknowledging Patient Heterogeneity in Economic Evaluations in Schizophrenia: A Systematic Review

OBJECTIVES

Schizophrenia is a severe mental illness with heterogeneous etiology, range of symptoms, and course of illness. Cost-effectiveness analysis often applies averages from populations, which disregards patient heterogeneity even though there are a range of methods available to acknowledge patient heterogeneity. This review evaluates existing economic evaluations of interventions in schizophrenia to understand how patient heterogeneity is currently reflected in economic evaluation.

METHODS

Electronic searches of MEDLINE, Embase, and PsycINFO via Ovid and the Health Technology Assessment database were run to identify full economic evaluations of interventions aiming to reduce the symptoms associated with schizophrenia. Two levels of screening were used, and explicit inclusion criteria were applied. Prespecified data extraction and critical appraisal were performed.

RESULTS

Seventy-six relevant studies were identified. More than half (41 of 76) of the articles acknowledged patient heterogeneity in some way through discussion or methods. There was a range of patient characteristics considered, including demographics and socioeconomic factors (eg, age, educational level, ethnicity), clinical characteristics (eg, symptom severity, comorbidities), and preferences (eg, preferences related to outcomes or symptoms). Subgroup analyses were rarely reported (8 of 76).

CONCLUSIONS

Patient heterogeneity was frequently mentioned in studies but was rarely thoroughly investigated in the identified economic evaluations. When investigated, included patient characteristics and methods were found to be heterogeneous. Understanding and acknowledging patient heterogeneity may alter the conclusions of cost-effectiveness evaluations; subsequently, we would encourage further research in this area.

Cost effectiveness of pharmacogenetic-guided clozapine administration based on risk of HLA variants in Japan and the UK

Pharmacogenetics/pharmacogenomics have enabled the detection of risk of human leukocyte antigen (HLA) variants for clozapine-induced agranulocytosis/granulocytopenia (CIAG). To apply this evidence to the clinical setting, we compared the cost-effectiveness of the proposed "HLA-guided treatment schedule" and the "current schedule" being used in Japan and the United Kingdom (UK) (absolute neutrophil count (ANC) cutoff at 1500/mm³); in the "HLA-guided treatment schedules," we considered a situation wherein the HLA test performed before clozapine initiation could provide "a priori information" by detecting patients harboring risk of HLA variants (HLA-B*59:01 and "HLA-B 158T/HLA-DQB1 126Q" for Japanese and Caucasian populations, respectively), a part of whom can then avoid CIAG onset (assumed 30% "prevention rate"). For the primary analysis, we estimated the incremental cost-effectiveness ratio (ICER) of "HLA-guided treatment schedule" and "current schedule" used in Japan and the UK, using a Markov model to calculate the cost and quality-adjusted life years (QALYs) over a 10-year time period. Furthermore, as an explorative analysis, we simulated several situations with various ANC cutoffs (1000/mm³ and 500/mm³) and plotted the cost/QALYs for each option to identify the best, or estimate the next best candidate option applicable in actual clinical settings. The primary probabilistic analysis showed that the "HLA-guided treatment schedule" was more cost effective than the "current schedule"; the ICER was £20,995 and £21,373 for the Japanese and the UK populations, respectively. Additional simulation revealed that the treatment option of ANC cutoff at 500/mm³ without HLA screening was the most cost-effective option; however, several options may be candidates to break away from the "current schedule" of ANC cutoff at 1500/mm³. Owing to its cost-effectiveness, we propose such pharmacogenetic-guided/pharmacogenomic-guided clozapine treatment for use in the real-world setting, which provides key information for optimization of clinical guidelines for high-risk patients for gradual change of clozapine treatment schedule under the safety consideration.

A potential biomarker for treatment stratification in psychosis: evaluation of an [18F] FDOPA PET imaging approach

[¹⁸F]FDOPA PET imaging has shown dopaminergic function indexed as K_i^cer differs between antipsychotic treatment responders and non-responders. However, the theragnostic potential of this biomarker to identify non-responders has yet to be evaluated. In view of this, we aimed to evaluate this as a theragnostic test using linear and non-linear machine-learning (i.e., Bernoulli, support vector, random forest and Gaussian processes) analyses and to develop and evaluate a simplified approach, standardised uptake value ratio (SUVRc). Both [¹⁸F]FDOPA PET approaches had good test-rest reproducibility across striatal regions (K_i^cer ICC: 0.68-0.94, SUVRc ICC: 0.76-0.91). Both our linear and non-linear classification models showed good predictive power to distinguish responders from non-responders (receiver operating curve area under the curve for region-of-interest approach: K_i^cer = 0.80, SUVRc = 0.79; for voxel-wise approach using a linear support vector machine: 0.88) and similar sensitivity for identifying treatment non-responders with 100% specificity (K_i^cer: ~50%, SUVRc: 40-60%). Although the findings were replicated in two independent datasets, given the total sample size (n = 84) and single setting, they warrant testing in other samples and settings. Preliminary economic analysis of [¹⁸F]FDOPA PET to fast-track treatment-resistant patients with schizophrenia to clozapine indicated a potential healthcare cost saving of ~£3400 (equivalent to $4232 USD) per patient. These findings indicate [¹⁸F]FDOPA PET dopamine imaging has potential as biomarker to guide treatment choice.

Investigating Patient Acceptability of Stratified Medicine for Schizophrenia: A Mixed Methods Study

BACKGROUND

Health services have advocated a stratified medicine approach in mental health, but little is known about whether service users would accept this approach.

AIMS

To explore service users' views of the acceptability of stratified medicine for treatment-resistant schizophrenia compared to the traditional "trial-and-error" approach.

METHODS

A mixed methods observational study that explored questionnaire responses on acceptability and whether these responses were affected by demographic or clinical variables. We also investigated whether treatment responsiveness or experience of invasive tests (brain scans and blood tests) affected participants' responses. Questionnaire generated qualitative data were analyzed thematically. Participants (N108) were aged 18-65, had a diagnosis of schizophrenia, and were adherent to antipsychotic medication.

RESULTS

Acceptability of a stratified approach was high, even after participants had experienced invasive tests. Most rated it as safer (62% vs 43%; P < .01 [CI: -1.69 to 2.08]), less risky (77% vs 44%; P < .01 [CI: -1.75 to 1.10]), and less painful (90% vs 73%; P < 0.01 [CI: -0.84 to 0.5]) and this was not affected by treatment responsiveness or test experience. Although not statistically significant, treatment nonresponders were more willing to undergo invasive tests. Qualitatively, all participants raised concerns about the risks, discomfort, and potential side effects associated with the invasive tests.

CONCLUSIONS

Service users were positive about a stratified approach for choosing treatments but were wary of devolving clinical decisions to purely data-driven algorithms. These results reinforce the value of service user perspectives in the development and evaluation of novel treatment approaches.

A Systematic Review of Economic Models Across the Entire Schizophrenia Pathway

BACKGROUND

Schizophrenia is associated with a high economic burden. Economic models can help to inform resource allocation decisions to maximise benefits to patients.

OBJECTIVES

This systematic review aims to assess the availability, quality and consistency of conclusions of health economic models evaluating the cost effectiveness of interventions for schizophrenia.

METHODS

An electronic search was performed on multiple databases (MEDLINE, EMBASE, PsycINFO, Cochrane database of systematic reviews, NHS Economic Evaluation Database and Health Technology Assessment database) to identify economic models of interventions for schizophrenia published between 2005 and 2020. Two independent reviewers selected studies for inclusion. Study quality was assessed using the National Institute for Health and Care Excellence (NICE) checklist and the Cooper hierarchy. Model characteristics and conclusions were descriptively summarised.

RESULTS

Seventy-three models met inclusion criteria. Seventy-eight percent of existing models assessed antipsychotics; however, due to inconsistent conclusions reported by different studies, no antipsychotic can be considered clearly cost effective compared with the others. A very limited number of models suggest that the following non-pharmacological interventions might be cost effective: psychosocial interventions, stratified tests, employment intervention and intensive intervention to improve liaison between primary and secondary care. The quality of included models is generally low due to use of a short time horizon, omission of adverse events of interventions, poor data quality and potential conflicts of interest.

CONCLUSIONS

This review highlights a lack of models for non-pharmacological interventions, and limitations of the existing models, including low quality and inconsistency in conclusions. Recommendations on future modelling approaches for schizophrenia are provided.