Study protocol for the development and internal validation of Schizophrenia Prediction of Resistance to Treatment (SPIRIT): a clinical tool for predicting risk of treatment resistance to antipsychotics in first-episode schizophrenia

Default Mode Network, Disorganization, and Treatment-Resistant Schizophrenia

BACKGROUND AND HYPOTHESIS

Disorganized thinking is a prominent feature of schizophrenia that becomes persistent in the presence of treatment resistance. Disruption of the default mode network (DMN), which regulates self-referential thinking, is now a well-established feature of schizophrenia. However, we do not know if DMN disruption affects disorganization and contributes to treatment-resistant schizophrenia (TRS).

STUDY DESIGN

This study investigated the DMN in 48 TRS, 76 non-TRS, and 64 healthy controls (HC) using a spatiotemporal approach with resting-state functional magnetic resonance imaging. We recovered DMN as an integrated network using multivariate group independent component analysis and estimated its loading coefficient (reflecting spatial prominence) and Shannon Entropy (reflecting temporal variability). Additionally, voxel-level analyses were conducted to examine network homogeneity and entropy within the DMN. We explored the relationship between DMN measures and disorganization using regression analysis.

RESULTS

TRS had higher spatial loading on population-level DMN pattern, but lower entropy compared to HC. Non-TRS patients showed intermediate DMN alterations, not significantly differing from either TRS or HC. No voxel-level differences were noted between TRS and non-TRS, emphasizing the continuum between the two groups. DMN's loading coefficient was higher in patients with more severe disorganization.

CONCLUSIONS

TRS may represent the most severe end of a spectrum of spatiotemporal DMN dysfunction in schizophrenia. While excessive spatial contribution of the DMN (high loading coefficient) is specifically associated with disorganization, both excessive spatial contribution and exaggerated temporal stability of DMN are features of schizophrenia that become more pronounced with refractoriness to first-line treatments.

Predicting treatment resistance in positive and negative symptom domains from first episode psychosis: Development of a clinical prediction model

BACKGROUND

Treatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP).

METHODS

Longitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, n = 51 (4.97 %) participants were treatment resistant in the positive domain and n = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures.

RESULTS

The prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56).

CONCLUSIONS

Treatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.

Development and initial evaluation of a clinical prediction model for risk of treatment resistance in first-episode psychosis: Schizophrenia Prediction of Resistance to Treatment (SPIRIT)

BACKGROUND

A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication.

AIMS

To develop and evaluate a model that could predict the risk of TRS in routine clinical practice.

METHOD

We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model.

RESULTS

We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723-0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism.

CONCLUSIONS

We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services.

Prediction model protocols indicate better adherence to recommended guidelines for study conduct and reporting

BACKGROUND AND OBJECTIVE

Protocols are invaluable documents for any research study, especially for prediction model studies. However, the mere existence of a protocol is insufficient if key details are omitted. We reviewed the reporting content and details of the proposed design and methods reported in published protocols for prediction model research.

METHODS

We searched MEDLINE, Embase, and the Web of Science Core Collection for protocols for studies developing or validating a diagnostic or prognostic model using any modeling approach in any clinical area. We screened protocols published between Jan 1, 2022 and June 30, 2022. We used the abstract, introduction, methods, and discussion sections of The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement to inform data extraction.

RESULTS

We identified 30 protocols, of which 28 were describing plans for model development and six for model validation. All protocols were open access, including a preprint. 15 protocols reported prospectively collecting data. 21 protocols planned to use clustered data, of which one-third planned methods to account for it. A planned sample size was reported for 93% development and 67% validation analyses. 16 protocols reported details of study registration, but all protocols reported a statement on ethics approval. Plans for data sharing were reported in 13 protocols.

CONCLUSION

Protocols for prediction model studies are uncommon, and few are made publicly available. Those that are available were reasonably well-reported and often described their methods following current prediction model research recommendations, likely leading to better reporting and methods in the actual study.

Discharge destinations for young people with a first episode of psychosis after attending an early intervention for psychosis service

OBJECTIVE

Early intervention for psychosis services result in superior outcomes in the domains of symptomatic and functional recovery, hospitalisation and employment compared to standard services; however, the optimal duration of care with these services is unknown. Knowledge on the discharge destinations, specifically the proportion discharged to high- and low-intensity services, could provide insights into the proportion of who may require a longer tenure of care. This study aimed to determine (1) the discharge destinations from early intervention for psychosis services and (2) baseline and intra-episode factors associated with discharge to the secondary care/adult mental health service.

METHODOLOGY

This study was conducted at the Early Psychosis Prevention and Intervention Centre in Melbourne and included all young people treated by the service with a first episode of psychosis over a 6-year period. Discharge destinations were categorised according to high-intensity services, namely, secondary mental health care, or lower intensity services, such as private practitioners or primary care.

RESULTS

A total of 1101 young people with a first episode of psychosis were included in the study, of whom 58.8% were male and the median age was 20.0 years (interquartile range: 17-22). After a median of 95.4 weeks (interquartile range: 66.7-105.7), 36.6% were discharged to the adult mental health services, which was associated with being not in employment, education or training at presentation (odds ratio = 1.71, 95% confidence interval [1.23, 2.37]); experiencing a relapse (odds ratio = 1.76, 95% confidence interval [1.24, 2.49]); and being admitted to a mental health unit (odds ratio = 3.98, 95% confidence interval [2.61, 6.09]). Young people who lived with their parents were less likely to be discharged to secondary care services (odds ratio = 0.52, 95% confidence interval [0.37, 0.73]), as were those who were achieving symptomatic remission within 12 weeks (odds ratio = 0.60, 95% confidence interval [0.43, 0.83]). Migrant status and the duration of untreated psychosis were not associated with discharge destination.

CONCLUSION

These findings indicate that there is a sizable, identifiable minority who may benefit from a longer episode of care with early intervention for psychosis services.