Refinement of a Meaning-Centered Counseling Program for Chinese Patients with Advanced Cancer: Integrating Cultural Adaptation and Implementation Science Approaches.

Background: This mixed methods study identified needed refinements to a telehealth-delivered cultural and linguistic adaptation of Meaning-Centered Psychotherapy for Chinese patients with advanced cancer (MCP-Ch) to enhance acceptability, comprehensibility, and implementation of the intervention in usual care settings, guided by the Ecological Validity Model (EVM) and the Practical, Robust Implementation and Sustainability Model (PRISM). Methods: 15 purposively sampled mental health professionals who work with Chinese cancer patients completed surveys providing Likert-scale ratings on acceptability and comprehensibility of MCP-Ch content (guided by the EVM) and pre-implementation factors (guided by PRISM), followed by semi-structured interviews. Survey data were descriptively summarized and linked to qualitative interview data. Three analysts independently coded the transcripts according to EVM and PRISM domains; discrepancies were resolved through discussion and consensus. Results: Quantitative findings showed high appropriateness and relevance of MCP-Ch across five EVM domains of Language, Metaphors/Stories, Goals, Content, and Concepts. Qualitative analysis yielded 23 inductive codes under the seven EVM domains: (1) Language (3 subcodes), (2) Persons (2 subcodes), (3) Metaphors/Stories (2 subcodes), (4) Methods (8 subcodes), (5) Content (2 subcodes), (6) Goals (4 subcodes), and (7) Concepts (2 subcodes). Themes based on PRISM included (1) Intervention characteristics (organizational perspective, 7 subcodes; and patient perspective, 6 subcodes) (2) External environment (2 subcodes), (3) Implementation and sustainability infrastructure (4 subcodes), and (4) Recipients (organizational characteristics, 5 subcodes; and patient characteristics, 4 subcodes). Conclusion: Recommendations for next steps include increasing the MCP-Ch protocol's flexibility and adaptability to allow interventionists to flexibly tailor MCP-Ch material to meet patients' individual needs, simplifying content to improve comprehension and acceptability, providing additional training to Chinese-serving providers to increase adoption and sustainability, and considering interpreter-assisted delivery to increase access. Findings yielded important information to maximize cultural relevance as well as the implementation and sustainability potential of MCP-Ch in real-world settings.

Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy.

Background: Immunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T-score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy. Methods: A baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1,168 patients were used as training and testing sets to develop the pathology T-score prediction (T pre) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T-score (base model plus T bio), and clinical variables and T pre (base model plus T pre) were developed separately in 1,168 patients with regular follow-up to evaluate whether T pre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using T pre. Results: The features selected by AUCRF for the T pre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the T pre was 0.82 (95% CI: 0.80-0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75-0.97). When the T bio was added to the base model, there was an increase in AUC [from 0.86 (95% CI: 0.75-0.97) to 0.92 (95% CI: 0.85-0.98); P = 0.03]. There was no difference in AUC between the base model plus T pre and the base model plus T bio [0.90 (95% CI: 0.82-0.99) vs. 0.92 (95% CI: 0.85-0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using T pre was 0.93 (95% CI: 0.87-0.99) in the external validation set. Conclusion: A pathology T-score prediction (T pre) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores.