Review Article
Hip Fracture and Anemia in the Elderly: A Systematic Review of Transfusion Strategies and Patient Blood Management
Juan Rodrigo Tuesta-Nole*
Issue:
Volume 12, Issue 2, June 2026
Pages:
14-20
Received:
4 February 2026
Accepted:
20 February 2026
Published:
22 July 2026
Abstract: Background: Perioperative anemia is a common and consequential comorbidity in elderly patients with hip fracture, yet the optimal management strategy between transfusion and alternative therapies remains a critical clinical dilemma. Objective: This systematic review synthesizes contemporary evidence (2016-2026) on transfusion thresholds and perioperative anemia management in geriatric hip fracture patients, focusing on clinical outcomes and guideline recommendations. Methods: A systematic literature search was conducted in PubMed, Embase, and the Cochrane Library. Randomized controlled trials, meta-analyses, cohort studies, and clinical guidelines were included. Data were extracted and synthesized narratively. Results: Perioperative anemia affects 40-46% of patients and is an independent predictor of increased mortality, delirium, prolonged hospitalization, and functional decline. High-level evidence supports a restrictive transfusion strategy (hemoglobin threshold <8 g/dL for asymptomatic patients), which reduces transfusion exposure without increasing mortality or impairing recovery compared to liberal strategies. Liberal transfusion is associated with higher risks of infection, acute kidney injury, and cardiovascular events. Adjunctive strategies, particularly intravenous iron and tranexamic acid, significantly reduce transfusion requirements safely. Conclusion: Management should prioritize a restrictive, symptom-guided transfusion policy integrated within a multimodal Patient Blood Management protocol. Preoperative optimization, blood conservation techniques, and targeted pharmacotherapy are essential to improve outcomes and minimize avoidable transfusions in this frail population.
Abstract: Background: Perioperative anemia is a common and consequential comorbidity in elderly patients with hip fracture, yet the optimal management strategy between transfusion and alternative therapies remains a critical clinical dilemma. Objective: This systematic review synthesizes contemporary evidence (2016-2026) on transfusion thresholds and periope...
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Research Article
Pectoral Muscle Detection and Removal in Mammograms Using Information Gain Based FCM Algorithm
Navneet Kaur*
Issue:
Volume 12, Issue 2, June 2026
Pages:
21-32
Received:
15 May 2026
Accepted:
30 May 2026
Published:
10 August 2026
DOI:
10.11648/j.ijbecs.20261202.12
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Abstract: The widest used modality for detection of breast cancer is Mammography. Automated cancer detection is a tedious work because there are many types of high intensity artifacts found in MLO views of mammograms. So, preprocessing here becomes an essential step for successful classification. To get the region of interest, we carried out all the steps including removal of pectoral muscle as it has high intensity level similar to the intensity level of abnormalities already present in the mammogram so if we will take the input image as it is including the pectoral muscle and other artifacts it will give inaccurate results. This paper proposes a methodology for segmenting and removing the pectoral muscles using a technique named information gain based fcm. The proposed algorithm combines fuzzy C mean and neutroscopic L-means methods for correctly extracting the pectoral muscle. To validate the work, the proposed methodology is tested on different mammographic images of MIAS database and the results obtained nearly follows that marked by an expert radiologist. The performance of the proposed approach is evaluated using several quantitative metrics, including Hausdorff Distance (HD), Mean Error (ME1), Relative Foreground Area Error (RFAE), Misclassification Rate (ME2), Extraction Error Rate (EER), and Region Non-Uniformity (RNU).
Abstract: The widest used modality for detection of breast cancer is Mammography. Automated cancer detection is a tedious work because there are many types of high intensity artifacts found in MLO views of mammograms. So, preprocessing here becomes an essential step for successful classification. To get the region of interest, we carried out all the steps in...
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