Life Engagement Improvement Following Initiation of Brexpiprazole Treatment in Patients with MDD: A Naturalistic, Retrospective Real-World Study.

Purpose: Life engagement encompasses concepts such as life fulfillment, well-being, and participation in meaningful activities, encompassing cognitive, physical, social, and emotional dimensions. Patients with MDD experience impaired functioning across multiple domains of life engagement and have ranked concepts related to life engagement and fulfillment as important predictors of treatment success. Post-hoc analyses of three clinical trials of patients with MDD treated adjunctively with brexpiprazole have reported a significantly greater improvement in life engagement. This study investigated improvements in life engagement among patients with MDD following initiation of brexpiprazole treatment using a real-world dataset. Patients and Methods: Information was extracted from semi-structured clinical notes of the Mental Status Examination (MSE) of patients in a real-world setting to develop an outcome measure for quantifying life engagement of psychiatric patients. Measures of life engagement and its four sub-domains (emotional, physical, social, and cognitive) were calculated at each clinical visit for 624 adult patients with MDD during the 6 months following brexpiprazole initiation. Paired t-tests assessed differences between the index event and time periods within 6 months of the index event. Kaplan-Meier survival analyses were used to quantify the improvement in life engagement scores following brexpiprazole initiation. Results: The study identified 54 clinical features associated with life engagement. Statistically significant improvements were observed from as early as 1 month following brexpiprazole initiation, with 20.6%, 37.9%, and 53.9% of the patients demonstrating improved life engagement scores within 1, 3, and 6 months, respectively. The improvements were particularly apparent for the emotional and social sub-domains. Conclusion: The results of this study provide evidence of improved life engagement following brexpiprazole initiation in a real-world dataset.

Early trajectory of clinical global impression as a transdiagnostic predictor of psychiatric hospitalisation: a retrospective cohort study.

BACKGROUND: Identifying patients most at risk of psychiatric hospitalisation is crucial to improving service provision and patient outcomes. Existing predictors focus on specific clinical scenarios and are not validated with real-world data, limiting their translational potential. This study aimed to determine whether early trajectories of Clinical Global Impression Severity are predictors of 6 month risk of hospitalisation. METHODS: This retrospective cohort study used data from the NeuroBlu database, an electronic health records network from 25 US mental health-care providers. Patients with an ICD-9 or ICD-10 code of major depressive disorder, bipolar disorder, generalised anxiety disorder, post-traumatic stress disorder, schizophrenia or schizoaffective disorder, ADHD, or personality disorder were included. Using this cohort, we assessed whether clinical severity and instability (operationalised using Clinical Global Impression Severity measurements) during a 2-month period were predictors of psychiatric hospitalisation within the next 6 months. FINDINGS: 36 914 patients were included (mean age 29·7 years [SD 17·5]; 21 156 [57·3%] female, 15 748 [42·7%] male; 20 559 [55·7%] White, 4842 [13·1%] Black or African American, 286 [0·8%] Native Hawaiian or other Pacific Islander, 300 [0·8%] Asian, 139 [0·4%] American Indian or Alaska Native, 524 (1·4%) other or mixed race, and 10 264 [27·8%] of unknown race). Clinical severity and instability were independent predictors of risk of hospitalisation (adjusted hazard ratio [HR] 1·09, 95% CI 1·07-1·10 for every SD increase in instability; 1·11, 1·09-1·12 for every SD increase in severity; p<0·0001 for both). These associations were consistent across all diagnoses, age groups, and in both males and females, as well as in several robustness analyses, including when clinical severity and clinical instability were based on the Patient Health Questionnaire-9 rather than Clinical Global Impression Severity measurements. Patients in the top half of the cohort for both clinical severity and instability were at an increased risk of hospitalisation compared with those in the bottom half along both dimensions (HR 1·45, 95% CI 1·39-1·52; p<0·0001). INTERPRETATION: Clinical instability and severity are independent predictors of future risk of hospitalisation, across diagnoses, age groups, and in both males and females. These findings could help clinicians make prognoses and screen patients who are most likely to benefit from intensive interventions, as well as help health-care providers plan service provisions by adding additional detail to risk prediction tools that incorporate other risk factors. FUNDING: National Institute for Health and Care Research, National Institute for Health and Care Research Oxford Health Biomedical Research Centre, Medical Research Council, Academy of Medical Sciences, and Holmusk.

Psychiatric healthcare resource utilization following initiation of aripiprazole once-monthly: a retrospective real-world study.

OBJECTIVES: This observational retrospective real-world study examined changes in healthcare resource utilization (HCRU) pre- and post-initiation of aripiprazole once-monthly (AOM 400) in patients with schizophrenia or bipolar I disorder. METHODS: Electronic health record-derived, de-identified data from the NeuroBlu Database (2013-2020) were used to identify patients ≥18 years with schizophrenia (n = 222) or bipolar I disorder (n = 129) who were prescribed AOM 400, and had visit data within 3, 6, 9, or 12 months pre- and post-initial AOM 400 prescription. Rates of inpatient hospitalization, emergency department visits, inpatient readmissions, and average length of stay were examined and compared over 3, 6, 9, and 12 months pre-/post-AOM 400 using a McNemar test. RESULTS: Statistically significant differences were seen in both schizophrenia and bipolar I disorder patient cohorts pre- and post-AOM 400 in inpatient hospitalization rates (p < .001 all time points, both cohorts) and 30-day readmission per patient rates (p < .001 all time points, both cohorts). Statistically significant improvement in mean length of stay was observed in both cohorts at all time points, except for at six months in patients with schizophrenia. Emergency department visit rates were significantly lower after AOM 400 initiation for both cohorts at all time points (p < .001). CONCLUSIONS: A reduction in the rate of hospitalizations, emergency department visits, 30-day readmissions, and average length-of-stay was observed for patients diagnosed with either schizophrenia or bipolar I disorder, which suggests a positive effect of AOM 400 treatment on HCRU outcomes and is supportive of earlier analyses from different data sources.

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data.

PURPOSE: NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression-Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health. PARTICIPANTS: As of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months). FINDINGS TO DATE: The data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data. FUTURE PLANS: The NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.

CKD subpopulations defined by risk-factors: A longitudinal analysis of electronic health records.

Chronic kidney disease (CKD) is a progressive disease that evades early detection and is associated with various comorbidities. Although clinical comprehension and control of these comorbidities is crucial for CKD management, complex pathophysiological interactions and feedback loops make this a formidable task. We have developed a hybrid semimechanistic modeling methodology to investigate CKD progression. The model is represented as a system of ordinary differential equations with embedded neural networks and takes into account complex disease progression pathways, feedback loops, and effects of 53 medications to generate time trajectories of eight clinical biomarkers that capture CKD progression due to various risk factors. The model was applied to real world data of US patients with CKD to map the available longitudinal information onto a set of time-invariant patient-specific parameters with a clear biological interpretation. These parameters describing individual patients were used to segment the cohort using a clustering approach. Model-based simulations were conducted to investigate cluster-specific treatment strategies. The model was able to reliably reproduce the variability in biomarkers across the cohort. The clustering procedure segmented the cohort into five subpopulations - four with enhanced sensitivity to a specific risk factor (hypertension, hyperlipidemia, hyperglycemia, or impaired kidney) and one that is largely insensitive to any of the risk factors. Simulation studies were used to identify patient-specific strategies to restrain or prevent CKD progression through management of specific risk factors. The semimechanistic model enables identification of disease progression phenotypes using longitudinal data that aid in prioritizing treatment strategies at individual patient level.

Micro-engineered perfusable 3D vasculatures for cardiovascular diseases.

Vessel geometries in microengineered in vitro vascular models are important to recapitulate a pathophysiological microenvironment for the study of flow-induced endothelial dysfunction and inflammation in cardiovascular diseases. Herein, we present a simple and novel extracellular matrix (ECM) hydrogel patterning method to create perfusable vascularized microchannels of different geometries based on the concept of capillary burst valve (CBV). No surface modification is necessary and the method is suitable for different ECM types including collagen, matrigel and fibrin. We first created collagen-patterned, endothelialized microchannels to study barrier permeability and neutrophil transendothelial migration, followed by the development of a biomimetic 3D endothelial-smooth muscle cell (EC-SMC) vascular model. We observed a significant decrease in barrier permeability in the co-culture model during inflammation, which indicates the importance of perivascular cells in ECM remodeling. Finally, we engineered collagen-patterned constricted vascular microchannels to mimic stenosis in atherosclerosis. Whole blood was perfused (1-10 dyne cm-2) into the microdevices and distinct platelet and leukocyte adherence patterns were observed due to increased shear stresses at the constriction, and an additional convective flow through the collagen. Taken together, the developed hydrogel patterning technique enables the formation of unique pathophysiological architectures in organ-on-chip microsystems for real-time study of hemodynamics and cellular interactions in cardiovascular diseases.