Nevin Manimala

JCO Glob Oncol. 2024 Oct;10:e2400131. doi: 10.1200/GO.24.00131. Epub 2024 Oct 24.

ABSTRACT

PURPOSE: Cancer continues to be a significant public health concern. Sub-Saharan Africa (SSA) struggles with a lack of proper infrastructure and adequate cancer care workforce. This has led to some countries relying on referrals of cancer care to countries with higher income levels. In some instances, patients refer themselves. Some countries have made it their goal to attract patients from other countries, a term that has been referred to as medical tourism. In this article, we explore the current status of oncology-related medical tourism in SSA.

METHODS: This was a cross-sectional study. The study participants included oncologists, surgeons, and any other physicians who take care of patients with cancer. A predesigned questionnaire was distributed through African Organization for Research and Training in Cancer member mailing list and through study team personal contacts and social media.

RESULTS: A total of 52 participants from 17 African countries with a 1.6:2 male to female ratio responded to the survey. Most (55.8%) of the respondents were from Eastern African countries. The majority (92%) of study participants reported that they knew patients who referred themselves abroad, whereas 75% referred patients abroad, and the most common (94%) referral destination was India. The most common (93%) reason for referral was perception of a higher quality of care in foreign health institutions.

CONCLUSION: The findings suggest the need to improve local health care systems including building trust of the system among general population. The study highlights potential financial toxicity, and it adds to the current emphasis on return of investment on homegrown workforce and cancer treatment infrastructure.

PMID:39447099 | DOI:10.1200/GO.24.00131

JCO Precis Oncol. 2024 Oct;8:e2400145. doi: 10.1200/PO.24.00145. Epub 2024 Oct 24.

ABSTRACT

PURPOSE: Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups.

MATERIALS AND METHODS: The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI’s reclassification and prognostic performance were compared with the three-tier NCCN risk groups.

RESULTS: The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively.

CONCLUSION: The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

PMID:39447096 | DOI:10.1200/PO.24.00145

Shock. 2024 Oct 21. doi: 10.1097/SHK.0000000000002474. Online ahead of print.

ABSTRACT

INTRODUCTION: The prehospital environment is fraught with operational constraints, making it difficult to assess the need for resources such as lifesaving interventions (LSI) for adults with traumatic injuries. While invasive methods such as lactate have been found to be highly predictive for estimating injury severity and resource requirements, noninvasive methods, to include continuous vital signs (VS), have the potential to provide prognostic information that can be quickly acquired, interpreted, and incorporated into decision making. In this work, we hypothesized that an analysis of continuous VS would have predictive capacity comparable to lactate and other laboratory tests for the prediction of injury severity, need for LSIs and intensive care unit (ICU) admission.

METHODS: In this pre-planned secondary analysis of 300 prospectively enrolled patients, venous blood samples were collected in the prehospital environment aboard a helicopter and analyzed with a portable lab device. Patients were transported to the primary adult resource center for trauma in the state of Maryland. Continuous VS were simultaneously collected. Descriptive statistics were used to describe the cohort and predictive models were constructed using a regularized gradient boosting framework with 10-fold cross-validation with additional analyses using Shapley additive explanations (SHAP).

RESULTS: Complete VS and laboratory data from 166 patients were available for analysis. The continuous VS models had better performance for prediction of receiving LSIs and ICU length of stay compared to single (initial) VS measurements. The continuous VS models had comparable performance to models using only laboratory tests in predicting discharge within 24 hours (continuous VS model: AUROC 0.71; 95% CI, 0.68-0.75 vs. lactate model: AUROC 0.65; 95% CI, 0.68; 95% CI, 0.66-0.71). The model using all laboratory data yielded the highest sensitivity and sensitivity (AUROC 0.77; 95% CI, 0.74-0.81).

DISCUSSION: The results from this study suggest that continuous VS obtained from autonomous monitors in an aeromedical environment may be helpful for predicting LSIs and the critical care requirements for traumatically injured adults. The collection and use of noninvasively obtained physiological data during the early stages of prehospital care may be useful for in developing user-friendly early warning systems for identifying potentially unstable trauma patients.

PMID:39447081 | DOI:10.1097/SHK.0000000000002474

J Natl Cancer Inst. 2024 Oct 24:djae265. doi: 10.1093/jnci/djae265. Online ahead of print.

ABSTRACT

BACKGROUND: Previous epidemiologic studies have reported an association of serum immunoglobulin E (IgE) levels with reduced glioma risk, but the association between IgE and glioma prognosis has not been characterized. This study aimed to examine how sex, tumor subtype, and IgE class modulate the association of serum IgE levels with glioma risk and survival.

METHODS: We conducted a case-control study using participants from the University of California, San Francisco Adult Glioma Study (1997-2010). Serum IgE levels for total, respiratory and food allergy were measured in adults diagnosed with glioma (n = 1319) and cancer-free controls (n = 1139) matched based on age, sex, and race and ethnicity. Logistic regression was adjusted for patient demographics to assess the association between IgE levels and glioma risk. Multivariable Cox regression adjusted for patient-specific and tumor-specific factors compared survival between the elevated and normal IgE groups. All statistical tests were 2-sided.

RESULTS: Elevated total IgE was associated with reduced risk of IDH-wildtype (RR = 0.78, 95% CI: 0.71-0.86) and IDH-mutant glioma (RR = 0.73, 95% CI: 0.63-0.85). In multivariable Cox regression, positive respiratory IgE was associated with improved survival for IDH-wildtype glioma (RR = 0.79, 95% CI: 0.67-0.93). The reduction in mortality risk was significant in females only (RR = 0.75, 95% CI: 0.57-0.98) with an improvement in median survival of 6.9 months (P<.001).

CONCLUSION: Elevated serum IgE was associated with improved prognosis for IDH-wildtype glioma, with a more pronounced protective effect in females than males, which has implications for the future study of IgE-based immunotherapies for glioma.

PMID:39447063 | DOI:10.1093/jnci/djae265

Br J Radiol. 2024 Oct 24:tqae211. doi: 10.1093/bjr/tqae211. Online ahead of print.

ABSTRACT

OBJECTIVE: We examine pathways of airway alteration due to wall thinning, narrowing, and obliteration at different COPD severity stages using CT-derived airway metrics.

METHODS: Ex-smokers (N = 649; age mean±std: 69 ± 6years; 52% male) from the COPDGene Iowa cohort (September 2013-July 2017) were studied. Total airway count (TAC), peripheral TAC beyond 7th generation (TACp), and airway wall thickness (WT) were computed from chest CT scans using previously validated automated methods. Causal relationships among demographic, smoking, spirometry, COPD severity, airway counts, WT, and scanner variables were analyzed using causal inference techniques including direct acyclic graphs (DAGs) to quantitatively assess multi-pathway alterations of airways in COPD.

RESULTS: TAC, TACp, and WT were significantly lower (p < 0.0001) in mild, moderate, and severe COPD compared to the preserved lung function group. TAC (TACp) losses attributed to narrowing and obliteration of small airways were 4.59, 13.29, and 32.58% (4.64, 17.82, and 45.51%) in mild, moderate, and severe COPD, while the losses attributed to wall thinning were 8.24, 17.01, and 22.95% (12.79, 25.66, and 33.95%) in respective groups.

CONCLUSIONS: Different pathways of airway alteration in COPD are observed using CT-derived automated airway metrics. Wall thinning is a dominant contributor to both TAC and TACp loss in mild and moderate COPD while narrowing and obliteration of small airways is dominant in severe COPD.

ADVANCES IN KNOWLEDGE: This automated CT-based study shows that wall thinning dominates airway alteration in mild and moderate COPD while narrowing and obliteration of small airways leads the alteration process in severe COPD.

PMID:39447037 | DOI:10.1093/bjr/tqae211

Med Phys. 2024 Oct 24. doi: 10.1002/mp.17473. Online ahead of print.

ABSTRACT

BACKGROUND: The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report.

PURPOSE: The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain-relevant assessments via the analysis of relevant image statistics.

METHODS: As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 $\times$ 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM-generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts.

RESULTS: Fifty-eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top-ranked submission employed a conditional latent diffusion model, whereas the joint runners-up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID-based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts.

CONCLUSIONS: This Grand Challenge highlighted the need for domain-specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

PMID:39447007 | DOI:10.1002/mp.17473

JBJS Rev. 2024 Oct 24;12(10). doi: 10.2106/JBJS.RVW.24.00149. eCollection 2024 Oct 1.

ABSTRACT

BACKGROUND: Seasonal trends in hospitalization for childhood bone and joint infection (BJI) are reported inconsistently. True seasonal variation would suggest an element of disease risk from environmental factors. This review evaluates all reported seasonal variations in childhood BJI, with additional analysis of seasonal trends for diseases secondary to Kingella kingae.

METHODS: A systematic review of the literature was undertaken from January 1, 1980, to August 1, 2024. Data were extracted on the hospitalization rate by season and/or month. Pathogen-specific studies for BJI secondary to K. kingae were examined separately.

RESULTS: Twenty studies met inclusion criteria encompassing 35,279 cases of childhood BJI. Most studies reported seasonal variation (n = 15, 75%). Eight studies specifically considered disease secondary to K. kingae, and all reported more frequent hospitalization in autumn and/or winter. This is in keeping with the role of respiratory pathogens and seasonal viruses in disease etiology for K. kingae BJI. Findings from other studies on the seasonality of childhood BJI were inconsistent. There were reported seasonal peaks in autumn/winter (4 studies), summer/spring (5 studies), or no variation (5 studies). Where microbiologic data were available, Staphylococcus aureus was the primary pathogen. The quality assessment demonstrated confounding and heterogeneous inclusion criteria affecting the seasonal analysis.

CONCLUSION: For childhood BJI caused by K. kingae, there appears to be a higher risk of hospitalization in autumn and/or winter months. This may relate to the seasonal circulation of respiratory viruses. There is currently insufficient evidence to support other forms of seasonal variation. Reported findings are likely affected by regional disease and pathogen characteristics.

LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

PMID:39446985 | DOI:10.2106/JBJS.RVW.24.00149

Clin Exp Dermatol. 2024 Oct 24:llae460. doi: 10.1093/ced/llae460. Online ahead of print.

ABSTRACT

Basal cell carcinomas (BCCs) are slow growing keratinocyte tumours with limited literature reporting the natural history of untreated BCCs. This study evaluated the natural progression and patient outcomes of BCCs whilst awaiting surgical intervention. Only patients with histologically proven BCCs were included in the data collection. Retrospective data analysis was performed on 55 patients (total of 70 lesions) and showed a statistically significant correlation between average growth of BCCs and time waiting for a procedure. Twenty percent of the cases had a larger procedure than originally planned at the time of booking. The top three symptoms reported include itching (39.4%), crusting (36.4%) and bleeding (30.3%). In conclusion, we reported a positive relationship between BCC growth and length of time from initial presentation to surgical treatment where patients often ended up with more symptoms, larger and complex surgical procedure than originally planned especially on the head and neck.

PMID:39446979 | DOI:10.1093/ced/llae460

PLoS One. 2024 Oct 24;19(10):e0304932. doi: 10.1371/journal.pone.0304932. eCollection 2024.

ABSTRACT

This study investigates the technology acceptance of a proposed multimodal wearable sensing framework, named mSense, within the context of non-invasive real-time neurofeedback for student stress and anxiety management. The COVID-19 pandemic has intensified mental health challenges, particularly for students. Non-invasive techniques, such as wearable biofeedback and neurofeedback devices, are suggested as potential solutions. To explore the acceptance and intention to use such innovative devices, this research applies the Technology Acceptance Model (TAM), based on the co-creation approach. An online survey was conducted with 106 participants, including higher education students, health researchers, medical professionals, and software developers. The TAM key constructs (usage attitude, perceived usefulness, perceived ease of use, and intention to use) were validated through statistical analysis, including Partial Least Square-Structural Equation Modeling. Additionally, qualitative analysis of open-ended survey responses was performed. Results confirm the acceptance of the mSense framework for neurofeedback-based stress and anxiety management. The study contributes valuable insights into factors influencing user intention to use multimodal wearable devices in educational settings. The findings have theoretical implications for technology acceptance and practical implications for extending the usage of innovative sensors in clinical and educational environments, thereby supporting both physical and mental health.

PMID:39446926 | DOI:10.1371/journal.pone.0304932

PLoS One. 2024 Oct 24;19(10):e0312498. doi: 10.1371/journal.pone.0312498. eCollection 2024.

ABSTRACT

BACKGROUND/OBJECTIVES: Frailty is a common condition among older adults and is associated with an increased risk of adverse health outcomes, including mortality, disability, dysmobility, falls, and hospitalization. In patients with atrial fibrillation (AF), these risks are further exacerbated. However, evidence linking AF and frailty, particularly in the South American context, is limited. This study aimed to assess frailty and other geriatric conditions in older outpatients with atrial fibrillation in a resource-limited setting in Lima, Peru.

METHODS: In this cross-sectional study, we included adults aged 60 years and older diagnosed with atrial fibrillation who were attending outpatient check-ups. Patients who were hospitalized, receiving chemotherapy induction, or presenting with acute infections or exacerbations were excluded. Standardized questionnaires were used to assess frailty, cognitive impairment, and functional dependence. Statistical analysis was performed using R Studio version 4.3.1, with a significance level set at p < 0.05.

RESULTS: Among the 200 patients who agreed to participate (mean age 74.76 ± 8.42 years, 41% females), 28.5% exhibited frailty, and 46.5% were classified as prefrail. Frailty and prefrailty were significantly associated with older age (p<0.01), female gender (p = 0.01), illiteracy (p<0.01), heart failure (p<0.01), falls (p<0.05), cognitive impairment (p<0.01), and functional dependence (p<0.01). Multivariate analysis revealed significant associations between frailty and cognitive impairment (p<0.05), frailty and functional dependence (p<0.05), and cognitive impairment and functional dependence (p<0.05).

CONCLUSIONS: One-third of older outpatients with atrial fibrillation were identified as frail, while half were classified as prefrail. In this population, frailty frequently coexists with cognitive impairment and functional dependence, highlighting the need for timely screening and the implementation of evidence-based interventions for individuals with atrial fibrillation in resource-limited settings.

PMID:39446924 | DOI:10.1371/journal.pone.0312498