From Assessment to Action: A Clinical Decision Support Application Translating SIPAT into Multidomain Psychosocial Risk Profiles in Lung Transplant Candidates

Background: Psychosocial assessment is a critical component of transplant candidate evaluation, yet its clinical utility is often limited by the descriptive nature of existing tools such as the Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT). Translating multidimensional assessment data into actionable clinical insights remains a challenge in routine practice. Methods: We developed a clinical decision support application that integrates SIPAT item-level data with probabilistic risk estimation, visualization, and cohort-referenced interpretation. The application was based on a retrospective dataset of 496 lung transplant candidates evaluated at a single tertiary transplant center. Random forest–based models were used to transform SIPAT item-level data into probabilistic risk representations to estimate domain-specific risks, including depression, anxiety, nicotine-related risk, alcohol-related risk, illicit drug use, social support deficits, and non-adherence. Risk estimates were expressed as calibrated probabilities and categorized into clinically interpretable levels. Additional components included domain-level burden scoring and unsupervised clustering of multidomain risk profiles. Results: Estimated risks were predominantly low across the cohort, with high-risk subgroups identified for depression (6.5%), anxiety (2.2%), nicotine-related risk (11.3%), alcohol-related risk (4.4%), illicit drug use (2.2%), social support deficits (8.1%), and non-adherence (1.4%). Clustering analysis revealed three distinct profiles: a low-risk majority group, a subgroup characterized by elevated nicotine-related risk, and a small high-burden group with substantially elevated psychological distress, reduced social support, and increased non-adherence risk. Risk estimates showed strong and domain-consistent correlations with SIPAT scores (Spearman rho up to 0.80, p < 0.001). Feature importance analyses confirmed that risk estimation was primarily driven by clinically relevant SIPAT items. The application generated structured outputs integrating risk estimates, visualization, and intervention prioritization. Conclusions: The proposed application translates SIPAT-based psychosocial assessment into structured, multidomain risk profiles that enhance clinical interpretability and support targeted psychosocial prehabilitation. This approach provides a practical framework for translating psychosocial assessment into individualized intervention planning in lung transplant settings.