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

BackgroundUsing novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis. MethodThe study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell’s C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors: tearfulness, poor appetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia. ResultsA total of 92 151 patients with a first index diagnosis of nonorganic and nonpsychotic mental disorder within the SLaM Trust were included in the derivation (n = 28 297) or external validation (n = 63 854) data sets. Mean age was 33.6 years, 50.7% were women, and 67.0% were of white race/ethnicity. Mean follow-up was 1590 days. The overall 6-year risk of psychosis in secondary mental health care was 3.4 (95% CI, 3.3–3.6). External validation indicated strong performance on unseen data (Harrell’s C 0.85, 95% CI 0.84–0.86), an increase of 0.06 from the original model. ConclusionsUsing NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.     

Progressive brain changes in schizophrenia related to antipsychotic treatment? A meta-analysis of longitudinal MRI studies

Context

Antipsychotic treatment is the first-line treatment option for schizophrenia. Individual studies suggested they can significantly affect brain structure and account for progressive brain changes observed during the illness.

Objectives

To quantitatively examine the effect of antipsychotics as compared to illness related factors on progressive brain changes in schizophrenia.

Data sources

Electronic databases were searched until April 2012. All magnetic resonance imaging studies reporting progressive brain changes in schizophrenia subjects and antipsychotic exposure were retrieved.

Study selection

30 longitudinal MRI studies with antipsychotic administration in schizophrenia patients met the inclusion criteria.

Data extraction

Brain volumes before and after antipsychotic exposure, duration of illness, severity of psychotic symptoms as well as demographic, clinical, and methodological variables were extracted from each publication, or obtained directly from its authors.

Data synthesis

The overall sample was of 1046 schizophrenia patients and 780 controls for a median duration of follow-up of 72.4 weeks. At baseline, patients showed significant whole brain volume reductions and enlarged lateral ventricle (LV) volumes compared to controls. No baseline volumetric abnormalities were detected in the gray matter volumes (GMV), white matter volumes, cerebrospinal fluid and caudate nucleus. Longitudinally, there were progressive GMV decreases and LV enlargements in patients but not in controls. The GMV decreases were inversely correlated with cumulative exposure to antipsychotic treatments, while no effects were observed for duration of illness or illness severity.

Conclusions

Schizophrenia is characterized by progressive gray matter volume decreases and lateral ventricular volume increases. Some of these neuroanatomical alterations may be associated with antipsychotic treatment.     

Tracking cerebral white matter changes across the lifespan: insights from diffusion tensor imaging studies

Delineating the normal development of brain white matter (WM) over the human lifespan is crucial to improved understanding of underlying WM pathology in neuropsychiatric and neurological conditions. We review the extant literature concerning diffusion tensor imaging studies of brain WM development in healthy individuals available until October 2012, summarise trends of normal development of human brain WM and suggest possible future research directions. Temporally, brain WM maturation follows a curvilinear pattern with an increase in fractional anisotropy (FA) from newborn to adolescence, decelerating in adulthood till a plateau around mid-adulthood, and a more rapid decrease of FA from old age onwards. Spatially, brain WM tracts develop from central to peripheral regions, with evidence of anterior-to-posterior maturation in commissural and projection fibres. The corpus callosum and fornix develop first and decline earlier, whilst fronto-temporal WM tracts like cingulum and uncinate fasciculus have protracted maturation and decline later. Prefrontal WM is most vulnerable with greater age-related FA reduction compared with posterior WM. Future large scale studies adopting longitudinal design will better clarify human brain WM changes over time.     

Moving beyond transition outcomes: Meta-analysis of remission rates in individuals at high clinical risk for psychosis

Recent evidence suggests that transition risks from initial clinical high risk (CHR) status to psychosis are decreasing. The role played by remission in this context is mostly unknown. The present study addresses this issue by means of a meta-analysis including eight relevant studies published up to January 2012 that reported remission rates from an initial CHR status. The primary effect size measure was the longitudinal proportion of remissions compared to non-remission in subjects with a baseline CHR state. Random effect models were employed to address the high heterogeneity across studies included. To assess the robustness of the results, we performed sensitivity analyses by sequentially removing each study and rerunning the analysis. Of 773 subjects who met initial CHR criteria, 73% did not convert to psychosis along a 2-year follow. Of these, about 46% fully remitted from the baseline attenuated psychotic symptoms, as evaluated on the psychometric measures usually employed by prodromal services. The corresponding clinical remission was estimated as high as 35% of the baseline CHR sample. The CHR state is associated with a significant proportion of remitting subjects that can be accounted by the effective treatments received, a lead time bias, a dilution effect, a comorbid effect of other psychiatric diagnoses.     

Development of the Korea-Polyenvironmental Risk Score for Psychosis

Objective Comprehensive understanding of polyenvironmental risk factors for the development of psychosis is important. Based on a review of related evidence, we developed the Korea Polyenvironmental Risk Score (K-PERS) for psychosis. We investigated whether the K-PERS can differentiate patients with schizophrenia spectrum disorders (SSDs) from healthy controls (HCs). Methods We reviewed existing tools for measuring polyenvironmental risk factors for psychosis, including the Maudsley Environmental Risk Score (ERS), polyenviromic risk score (PERS), and Psychosis Polyrisk Score (PPS). Using odds ratios and relative risks for Western studies and the “population proportion” (PP) of risk factors for Korean data, we developed the K-PERS, and compared the scores thereon between patients with SSDs and HCs. In addition, correlation was performed between the K-PERS and Positive and Negative Syndrome Scale (PANSS). Results We first constructed the “K-PERS-I,” comprising five factors based on the PPS, and then the “K-PERS-II” comprising six factors based on the ERS. The instruments accurately predicted participants’ status (case vs. control). In addition, the K-PERS-I and -II scores exhibited significant negative correlations with the negative symptom factor score of the PANSS. Conclusion The K-PERS is the first comprehensive tool developed based on PP data obtained from Korean studies that measures polyenvironmental risk factors for psychosis. Using pilot data, the K-PERS predicted patient status (SSD vs. HC). Further research is warranted to examine the relationship of K-PERS scores with clinical outcomes of psychosis and schizophrenia.     

Striatal Presynaptic Dopamine in Schizophrenia,Part II: Meta-Analysis of [18F/11C]-DOPA PET Studies

Background: Alterations in striatal dopamine neurotransmission are central to the emergence of psychotic symptoms and to the mechanism of action of antipsychotics. Although the functional integrity of the presynaptic system can be assessed by measuring striatal dopamine synthesis capacity (DSC), no quantitative meta-analysis is available. Methods: Eleven striatal (caudate and putamen) [¹¹C/¹⁸F]-DOPA positron emission tomography studies comparing 113 patients with schizophrenia and 131 healthy controls were included in a quantitative meta-analysis of DSC. Demographic, clinical, and methodological variables were extracted from each study or obtained from the authors and tested as covariates. Hedges’ g was used as a measure of effect size in Comprehensive Meta-Analysis. Publication bias was assessed with funnel plots and Egger’s intercept. Heterogeneity was addressed with the Q statistic and I ² index. Results: Patients and controls were well matched in sociodemographic variables (P > .05). Quantitative evaluation of publication bias was nonsignificant (P = .276). Heterogeneity across study was modest in magnitude and statistically nonsignificant (Q = 19.19; P = .078; I ² = 39.17). Patients with schizophrenia showed increased striatal DSC as compared with controls (Hedges’ g = 0.867, CI 95% from 0.594 to 1.140, Z = 6.222, P < .001). The DSC schizophrenia/control ratio showed a relatively homogenous elevation of around 14% in schizophrenic patients as compared with controls. DSC elevation was regionally confirmed in both caudate and putamen. Controlling for potential confounders such as age, illness duration, gender, psychotic symptoms, and exposure to antipsychotics had no impact on the results. Sensitivity analysis confirmed robustness of meta-analytic findings. Conclusions: The present meta-analysis showed consistently increased striatal DSC in schizophrenia, with a 14% elevation in patients as compared with healthy controls.