Tag: pubmed

JMIR Med Inform. 2024 Oct 24;12:e57035. doi: 10.2196/57035.

ABSTRACT

Before deploying a clinical prediction model (CPM) in clinical practice, its performance needs to be demonstrated in the population of intended use. This is also called “targeted validation.” Many CPMs developed in tertiary settings may be most useful in secondary care, where the patient case mix is broad and practitioners need to triage patients efficiently. However, since structured or rich datasets of sufficient quality from secondary to assess the performance of a CPM are scarce, a validation gap exists that hampers the implementation of CPMs in secondary care settings. In this viewpoint, we highlight the importance of targeted validation and the use of CPMs in secondary care settings and discuss the potential and challenges of using electronic health record (EHR) data to overcome the existing validation gap. The introduction of software applications for text mining of EHRs allows the generation of structured “big” datasets, but the imperfection of EHRs as a research database requires careful validation of data quality. When using EHR data for the development and validation of CPMs, in addition to widely accepted checklists, we propose considering three additional practical steps: (1) involve a local EHR expert (clinician or nurse) in the data extraction process, (2) perform validity checks on the generated datasets, and (3) provide metadata on how variables were constructed from EHRs. These steps help to generate EHR datasets that are statistically powerful, of sufficient quality and replicable, and enable targeted development and validation of CPMs in secondary care settings. This approach can fill a major gap in prediction modeling research and appropriately advance CPMs into clinical practice.

PMID:39447145 | DOI:10.2196/57035

Neurology. 2024 Nov 26;103(10):e210004. doi: 10.1212/WNL.0000000000210004. Epub 2024 Oct 24.

ABSTRACT

BACKGROUND AND OBJECTIVES: Recent technological advances have enabled visualizing in vivo a subset of chronic active brain lesions in persons with multiple sclerosis (pwMS), referred to as “paramagnetic rim lesions” (PRLs), with iron-sensitive MRI. PRLs predict future clinical disease progression, making them a promising clinical and translational imaging marker. However, it is unknown how disease progression is modified by PRL evolution (PRL disappearance, new PRL appearance). This is key to understanding MS pathophysiology and may help inform selection of sensitive endpoints for clinical trials targeting chronic active inflammation. To this end, we assessed the longitudinal associations between PRL disappearance and new PRL appearance and clinical disability progression and brain atrophy.

METHODS: PwMS and healthy controls (HCs) were included from a larger prospective, longitudinal cohort study at the University at Buffalo if they had available 3T MRI and clinical visits at baseline and follow-up timepoints. PwMS with sufficient clinical data for confirmed disability progression (CDP) analysis were included in a Disability Progression Cohort, and pwMS and HCs with brain volumetry data at baseline and follow-up were included in MS and HC Brain Atrophy cohorts. PRLs were assessed at baseline and follow-up and assigned as disappearing, newly appearing, or persisting at follow-up. Linear models were fit to compare annualized PRL disappearance rates or new PRL appearance (yes/no) with annualized rates of CDP and progression independent of relapse activity (PIRA) or with annualized rates of brain atrophy, adjusting for covariates including baseline PRL number and follow-up time. Statistical analyses were corrected for false discovery rate (FDR; i.e., q-value).

RESULTS: In total, 160 pwMS (73.8% female; mean baseline age 46.6 ± 11.4 years; mean baseline disease duration 13.8 ± 10.6 years; median follow-up time 5.6 [interquartile range 5.2-7.8] years; 26.9% progressive MS) and 27 HCs (74.1% female; mean baseline age 43.9 ± 13.6 years; median follow-up time 5.4 [5.2-5.6] years) were enrolled. Greater PRL disappearance rates were associated with reduced rates of CDP (β mean = -0.262, 95% CI -0.475 to -0.049, q = 0.028) and PIRA (β mean = -0.283, 95% CI -0.492 to -0.073, q = 0.036), and new PRL appearance was associated with increased rates of PIRA (β mean = 0.223, 95% CI 0.049-0.398, q = 0.036). By contrast, no associations between new PRL appearance or PRL disappearance and brain volume changes survived FDR correction (q > 0.05).

DISCUSSION: Our results show that resolution of existing PRLs and lack of new PRLs are associated with improved clinical outcomes. These findings further motivate the need for novel therapies targeting microglia-mediated brain inflammation and adoption of clinical strategies to prevent appearance of new PRL.

PMID:39447104 | DOI:10.1212/WNL.0000000000210004

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