Patient navigation across the cancer care continuum: An overview of systematic reviews and emerging literature

Patient navigation is a strategy for overcoming barriers to reduce disparities and to improve access and outcomes. The aim of this umbrella review was to identify, critically appraise, synthesize, and present the best available evidence to inform policy and planning regarding patient navigation across the cancer continuum. Systematic reviews examining navigation in cancer care were identified in the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Embase, Cumulative Index of Nursing and Allied Health (CINAHL), Epistemonikos, and Prospective Register of Systematic Reviews (PROSPERO) databases and in the gray literature from January 1, 2012, to April 19, 2022. Data were screened, extracted, and appraised independently by two authors. The JBI Critical Appraisal Checklist for Systematic Review and Research Syntheses was used for quality appraisal. Emerging literature up to May 25, 2022, was also explored to capture primary research published beyond the coverage of included systematic reviews. Of the 2062 unique records identified, 61 systematic reviews were included. Fifty-four reviews were quantitative or mixed-methods reviews, reporting on the effectiveness of cancer patient navigation, including 12 reviews reporting costs or cost-effectiveness outcomes. Seven qualitative reviews explored navigation needs, barriers, and experiences. In addition, 53 primary studies published since 2021 were included. Patient navigation is effective in improving participation in cancer screening and reducing the time from screening to diagnosis and from diagnosis to treatment initiation. Emerging evidence suggests that patient navigation improves quality of life and patient satisfaction with care in the survivorship phase and reduces hospital readmission in the active treatment and survivorship care phases. Palliative care data were extremely limited. Economic evaluations from the United States suggest the potential cost-effectiveness of navigation in screening programs.     

Orientation and mobility outcome measures

Despite orientation and mobility (O&M) being a significant factor determining quality of life of people with low vision or blindness, there are no gold standard measures or agreement on how to measure O&M performance. In the first part of this systematic review, an inventory of O&M outcome measures used by recent studies to assess the performance of orientation and/or mobility of adults with vision impairment (low vision and blindness) is presented. A wide variety of O&M outcome measures have been implemented in different fields of study, such as epidemiologic research and interventional studies evaluating training, assistive technology, vision rehabilitation and vision restoration. The most frequent aspect of outcome measures is efficiency such as time, distance, speed and percentage of preferred walking speed, followed by obstacle contacts and avoidance, and dis/orientation and veering. Other less commonly used aspects are target identification, safety and social interaction and self-reported outcome measures. Some studies employ sophisticated equipment to capture and analyse O&M performance in a laboratory setting, while others carry out their assessment in real-world indoor or outdoor environments. In the second part of this review, the appropriateness of implementing the identified outcome measures to assess O&M performance in clinical and functional O&M practice is evaluated. Nearly a half of these outcome measures meet all four criteria of face validity (either clinical or functional), responsiveness, reliability and feasibility and have the potential to be implemented in clinical or functional O&M practice. The findings of this review confirm the complicated and dynamic nature of O&M. Multiple measures are required in any evaluation of O&M performance to facilitate holistic assessment of O&M abilities and limitations of each individual.     

Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk

Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.