Clinical Research Directory

Cross-sectional Functional Stratification Based on Psychometric Profiling and Machine Learning in Patients With Substance Use Disorders (SUD)

Substance use disorders (SUDs) show considerable clinical heterogeneity that limits the usefulness of traditional categorical diagnoses. This observational, cross-sectional study aims to apply an unsupervised deep learning method - an autoencoder - to learn continuous latent representations from standardised psychometric data and to explore whether those representations can help stratify clinical subpopulations. The investigators will recruit 155 adults undergoing residential treatment for SUD. Participants will complete six validated instruments assessing impulsivity (BIS-11), anger regulation (STAXI-2), behavioural activation/avoidance (BADS), borderline symptomatology (BSL-23), generalised anxiety (GAD-7), and environmental reward (EROS). Demographic and clinical variables (age, sex, primary substance, years of use, prior treatments) will also be recorded. After data cleaning and standardisation (z-scores), a symmetric autoencoder with a 12-dimensional bottleneck (architecture 21-32-24-12-24-32-21) will be trained using mean squared error loss. Regularisation includes L2 weight decay and dropout. The model will be trained 30 times with different random seeds to assess stability; the five best models (by validation pseudo-R²) will be combined into a weighted ensemble. Five-fold cross-validation will evaluate generalisation. For comparison, principal component analysis (PCA) will be applied to the same data. Gaussian mixture models (GMM) will be fitted on the latent space to explore potential clinical subgroups. The primary outcome is the stability of the latent representation (coefficient of variation of validation MSE across runs). Secondary outcomes include reconstruction performance (pseudo-R²) of the ensemble, comparison with PCA, and the interpretability of latent dimensions via correlations with original variables. GMM results will be described using BIC, silhouette width, bootstrap stability, and clinical characterisation of clusters. This study does not involve any intervention. Results will be hypothesis-generating and require external validation. No automated clinical decisions will be made.

OSA PAP Treatment for Veterans With SUD and PTSD on Residential Treatment Unit

Substance use disorder (SUD) and posttraumatic stress disorder (PTSD) frequently co-occur and having both disorders is associated with greater psychological and functional impairment than having either disorder alone. This is especially true in residential settings where both disorders are more severe than outpatient settings. Obstructive sleep apnea (OSA) is highly comorbid with both disorders and untreated OSA is associated with worse functional impairment across multiple domains, worse quality of life, worse PTSD, higher suicidal ideation, and higher substance use and relapse rates. Treating OSA with evidence-based positive airway pressure (PAP) in Veterans with SUD/PTSD on a residential unit is a logical way to maximize treatment adherence and treatment outcomes. This study compares OSA treatment while on a SUD/PTSD residential unit to a waitlist control group. The investigators hypothesize that treating OSA on the residential unit, compared to the waitlist control, will have better functional, SUD, and PTSD outcomes.