A majority (>80%) of maternal deaths in the United States are preventable. Using machine learning (ML) models that are generated from electronic medical records (EMRs) may be a promising approach to predict the risk of adverse maternal outcomes and enable proactive intervention to prevent maternal mortality. Current evidence syntheses of such ML approaches either focus only on specific maternal outcomes, aspects other than risk prediction, or do not consider the full pipeline of studies from the development to implementation in clinical practice. The goal of this scoping review is to document evidence for the use of ML models for predicting the risk of maternal morbidity and mortality outcomes (research objective [RO1]), the translation of such models into applications for clinical use by providers (RO2), and factors associated with the implementation of clinical applications in practice (RO3). The review was limited to studies in health care settings, using data from EMRs. A detailed search string was developed in collaboration with a health sciences librarian and implemented on February 20, 2023, on PubMed, CINAHL Plus, Scopus, Embase, and IEEE Xplore. Two reviewers independently reviewed titles and abstracts for inclusion, and a third reviewer resolved conflicts. Only full-length journal articles published in English were included. Studies using non-EMR data exclusively were excluded. Two reviewers independently reviewed full texts for inclusion, and a third reviewer resolved conflicts. A structured template was used for data extraction, and findings were summarized descriptively. From 480 deduplicated studies identified from the search, 142 studies were included for full-text review, and 39 studies were included in the review. More than half of the included studies were conducted in 2022, and 34 studies were from just 3 countries (United States, China, and Israel). More studies focused on identifying the risk of pregnancy and delivery outcomes compared with postpartum outcomes. The top 3 most common outcomes for risk prediction were cardiovascular risks and hypertensive disorders of pregnancy (9 studies), gestational diabetes (7 studies), and postpartum hemorrhage (6 studies). Data were labeled with computable phenotypes in 30 studies, and the most often used method in ML models was boosting methods (18 studies). The most common metric used to assess model performance was area under the precision-recall curve (AUPRC; 33 studies). No studies described clinical applications of ML models for providers (RO2) or associated implementation factors (RO3). Key recommendations for future research and practice include expanding efforts to study maternal morbidity and mortality outcomes in the postpartum period, increasing transparency and reproducibility of studies through use of reporting checklists, and expanding efforts to implement ML models in clinical practice.

Guided by the World Health Organization quality of life (WHOQOL) framework, this systematic review aimed to examine evidence about the prevalence and severity of QOL-related health problems and their influencing factors in Sub-Saharan Africa (SSA). We identified eligible publications in English language from PubMed, Cumulative Index of Nursing and Allied Health Literature Plus with Full Text, Embase, APA PsycInfo, Scopus, and African Index Medicus databases. We included quantitative descriptive studies that measured overall and subdomains of QOL as the outcome in adult patients/survivors with cancer in SSA. Twenty-six descriptive cross-sectional studies (27 papers) that were conducted since 1988 in different SSA countries among patients with various types of cancer met our inclusion criteria. We found inconsistencies in how the prevalence and severity of QOL-related health problems have been researched and reported across studies, which complicated comparing findings and drawing conclusions. The most common factors that influenced the overall and subdomains of QOL included coping; internal and external locus of control; symptoms and symptom management; and religious beliefs and religious care. Demographics (e.g., age and marital status), cancer-related factors (cancer stage and type of treatment), and social determinants of health (e.g., education, access to information and resources, financial distress, and urban vs rural residency) also impacted QOL and its subdomains. Our findings indicate the significant need for recognizing and managing QOL-related problems for cancer patients and caregivers in SSA. Research needs to use culturally adapted, standardized assessment tools and analysis approaches to better understand the QOL challenges this population faces. Comprehensive supportive care is needed to address the complex QOL issues in resource-limited SSA.

Key Points: Although kidney transplantation can increase risk for post-traumatic stress disorder, many recipients experience psychologic growth in response to the same stressors. Underlying characteristics such as resilience affect how recipients respond to kidney failure and transplantation. Investigators are just starting to understand the role of psychologic stress during kidney failure and transplantation. Background: Kidney transplantation (KT) is a life-saving therapy for kidney failure. However, KT recipients can suffer from debilitating depression, post-traumatic stress disorder (PTSD), and suicide. In contrast to PTSD, post-traumatic growth (PTG) is a positive psychologic change in response to a challenging situation. PTG has been studied in other chronic diseases, but less is known about its role in the setting of KT. We sought to elucidate the prevalence, predictors, and the effect of PTSD and PTG on post-KT outcomes. We also considered the roles of benefit finding and resilience. Methods: In a literature review, we identified publications that examined PTSD, PTG, benefit finding, and/or resilience in KT recipients. We excluded case reports and first-person narratives. Publications meeting the specified criteria after full text review underwent data abstraction and descriptive analysis. Results: Of the 1013 unique citations identified, 39 publications met our criteria. PTSD was the most common construct evaluated (16 publications). Resilience was studied in 11 publications, PTG in nine, and benefit finding in five. Up to 21% of adult and 42% of pediatric KT recipients may experience PTSD, which is associated with lower quality of life (QOL), impaired sleep, and other psychiatric comorbidity. PTG was associated with improved QOL, kidney function, and reduced risk of organ rejection. Although benefit finding tended to increase post KT, resilience remained stable post KT. Like PTG, resilience was associated with lower psychologic distress and increased treatment adherence and confidence in the health care team. Conclusions: PTG, resilience, and benefit finding appear to reduce the risk of PTSD, promote well-being, and reduce risk of graft failure in KT recipients. Future research to understand these relationships better will allow clinicians and researchers to develop interventions to promote PTG, resilience, and benefit finding, and potentially improve post-transplant outcomes such as adherence and reducing risk of organ rejection.