A randomized control trial of stress management for caregivers of stem cell transplant patients: Effect on patient quality of life and caregiver distress.

BACKGROUND: Psychological interventions reduce caregiver distress (CG-distress). Less distress in caregivers may contribute to improved patient quality of life (QoL), but empirical evidence is lacking. Will a caregiver stress management intervention improve patient QoL? METHODS: In this replication study, we randomized 155 allogeneic hematopoietic stem cell transplant (Allo-HSCT) patients and caregivers to PsychoEducation, Paced Respiration, and Relaxation (PEPRR) or enhanced treatment as usual (eTAU). We provided PEPRR over 3 months following transplant. Functional Assessment of Cancer Therapy-Bone Marrow Transplant (FACT-BMT) evaluated patient QoL, and CG-distress was based on depressive, anxious, and stress symptoms. Hierarchical linear models tested intervention, time, and interactions as fixed effects with participant as random effects. RESULTS: Patients whose caregivers received PEPRR did not differ on FACT-BMT between baseline and 6 months (mean = +3.74; 95% CI, -3.54 to 11.02) compared with patients of caregivers in eTAU (mean = +3.16; 95% CI, -2.88 to 9.20) even though CG-distress was decreased by PEPRR (mean = -0.23; 95% CI, -0.448 to -0.010) compared with those receiving eTAU (mean = +0.27; 95% CI, 0.033-0.504) at 6 months. CONCLUSIONS: PEPRR reduced CG-distress without affecting their patient's FACT-BMT score. The FACT-BMT may not have distinguished unique psychological changes associated with their caregiver receiving PEPRR.

Machine Learning-Based Exploratory Clinical Decision Support for Newly Diagnosed Patients With Acute Myeloid Leukemia Treated With 7 + 3 Type Chemotherapy or Venetoclax/Azacitidine.

PURPOSE: There are currently limited objective criteria to help assist physicians in determining whether an individual patient with acute myeloid leukemia (AML) is likely to do better with induction with either standard 7 + 3 chemotherapy or targeted therapy with venetoclax plus azacitidine. The study goal was to address this need by developing exploratory clinical decision support methods. PATIENTS AND METHODS: Univariable and multivariable analysis as well as comparison of a range of machine learning (ML) predictors were performed using cohorts of 120 newly diagnosed 7 + 3-treated AML patients compared with 101 venetoclax plus azacitidine-treated patients. RESULTS: A variety of features in the two patient cohorts were identified that may potentially correlate with short- and long-term outcomes, toxicities, and other considerations. A subset of these diagnostic features was then used to develop ML-based predictors with relatively high areas under the curve of short- and long-term outcomes, hospital stays, transfusion requirements, and toxicities for individual patients treated with either venetoclax/azacitidine or 7 + 3. CONCLUSION: Potential ML-based approaches to clinical decision support to help guide individual patients with newly diagnosed AML to either 7 + 3 or venetoclax plus azacitidine induction therapy were identified. Larger cohorts with separate test and validation studies are necessary to confirm these initial findings.