ABSTRACT
BACKGROUND: Pelvic fractures (PF) with concomitant injuries are on the rise due to an increase of high-energy trauma. Increase of the elderly population with age related comorbidities further complicates the management. Abdominal organ injuries are kindred with PF due to the proximity to pelvic bones. Presence of contrast blush (CB) on computed tomography in patients with PF is considered a sign of active bleeding, however, its clinical significance and association with outcomes is debatable. AIM: To analyze polytrauma patients with PF with a focus on the geriatric population, co-injuries and the value of contrast blush. METHODS: This retrospective cohort study included 558 patients with PF admitted to level 1 trauma center (01/2017-01/2023). Analyzed variables included: Age, sex, mechanism of injury (MOI), injury severity score (ISS), Glasgow coma scale (GCS), abbreviated injury scale (AIS), co-injuries, transfusion requirements, pelvic angiography, embolization, laparotomy, orthopedic pelvic surgery, intensive care unit and hospital lengths of stay, discharge disposition and mortality. The study compared geriatric and non-geriatric patients, patients with and without CB and abdominal co-injuries. Propensity score matching was implemented in comparison groups. RESULTS: PF comprised 4% of all trauma admissions. 89 patients had CB. 286 (52%) patients had concomitant injuries including 93 (17%) patients with abdominal co-injuries. Geriatric patients compared to non-geriatric had more falls as MOI, lower ISS and AIS pelvis, higher GCS, less abdominal co-injuries, similar CB and angio-embolization rates, less orthopedic pelvic surgeries, shorter lengths of stay and higher mortality. After propensity matching, orthopedic pelvic surgery rates remained lower (8% vs 19%, P < 0.001), hospital length of stay shorter, and mortality higher (13% vs 4%, P < 0.001) in geriatric patients. Out of 89 patients with CB, 45 (51%) were embolized. After propensity matching, patients with CB compared to without CB had more pelvic angiography (71% vs 12%, P < 0.001), higher embolization rates (64% vs 22%, P = 0.02) and comparable mortality. CONCLUSION: Half of the patients with PF had concomitant co-injuries, including abdominal co-injuries in 17%. Similarly injured geriatric patients had higher mortality. Half of the patients with CB required an embolization.
ABSTRACT
Background: Artificial intelligence (AI) algorithms can be applied in breast cancer risk prediction and prevention by using patient history, scans, imaging information, and analysis of specific genes for cancer classification to reduce overdiagnosis and overtreatment. This scoping review aimed to identify the barriers encountered in applying innovative AI techniques and models in developing breast cancer risk prediction scores and promoting screening behaviors among adult females. Findings may inform and guide future global recommendations for AI application in breast cancer prevention and care for female populations. Methods: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O'Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. Results: In the field of breast cancer risk detection and prevention, the following AI techniques and models have been applied: Machine and Deep Learning Model (ML-DL model) (n = 1), Academic Algorithms (n = 2), Breast Cancer Surveillance Consortium (BCSC), Clinical 5-Year Risk Prediction Model (n = 2), deep-learning computer vision AI algorithms (n = 2), AI-based thermal imaging solution (Thermalytix) (n = 1), RealRisks (n = 2), Breast Cancer Risk NAVIgation (n = 1), MammoRisk (ML-Based Tool) (n = 1), Various MLModels (n = 1), and various machine/deep learning, decision aids, and commercial algorithms (n = 7). In the 11 included studies, a total of 39 barriers to AI applications in breast cancer risk prediction and screening efforts were identified. The most common barriers in the application of innovative AI tools for breast cancer prediction and improved screening rates included lack of external validity and limited generalizability (n = 6), as AI was used in studies with either a small sample size or datasets with missing data. Many studies (n = 5) also encountered selection bias due to exclusion of certain populations based on characteristics such as race/ethnicity, family history, or past medical history. Several recommendations for future research should be considered. AI models need to include a broader spectrum and more complete predictive variables for risk assessment. Investigating long-term outcomes with improved follow-up periods is critical to assess the impacts of AI on clinical decisions beyond just the immediate outcomes. Utilizing AI to improve communication strategies at both a local and organizational level can assist in informed decision-making and compliance, especially in populations with limited literacy levels. Conclusions: The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy.
