Urinary cystatin C as an early biomarker of acute kidney injury after open and endovascular abdominal aortic aneurysm repair.

BACKGROUND: Acute kidney injury (AKI) after open repair (OR) and endovascular repair (EVAR) of abdominal aortic aneurysm (AAA) is associated with increased mortality and hospital costs. Early detection of AKI is critical to prevent its progression. Recent findings demonstrate that elevated levels of urinary cystatin C (uCysC) may reflect tubular dysfunction. We prospectively evaluated whether uCysC can detect renal dysfunction earlier than serum creatinine (sCr). METHODS: In a prospective study, 126 consecutive patients (mean age ± SD, 69.1 ± 8.66 years) with AAA (EVAR = 87, OR = 39) were enrolled. sCr and uCysC were measured preoperatively (baseline) and at 6, 24, and 48 hr postoperatively. A final measurement was made on day 5. AKI was defined according to Acute Kidney Injury Network criteria. RESULTS: The incidence of AKI was significantly higher (χ(2) test, P < 0.05) in the OR group (n = 13, 33%) than in the EVAR group (n = 15, 17%). The baseline median (interquartile range) value of uCysC was significantly higher (t-test, P < 0.05) in patients of both groups (OR-EVAR) who developed AKI from those who did not (OR/AKI group: 0.06 [0.02-0.12] mg/L, EVAR/AKI group: 0.08 [0.05-0.11] mg/L versus no-AKI subjects: 0.04 [0.02-0.07] mg/L). Subsequent analysis showed that at 6 hr postoperatively, the patients who developed AKI increased their uCysC levels significantly from baseline (OR/AKI group: 0.58 [0.42-0.70] mg/L, EVAR/AKI group: 0.59 [0.30-1.07] mg/L). The median value of uCysC in AKI patients increased at 24 hr (OR/AKI group: 1.37 [0.78-3.40] mg/L, EVAR/AKI group: 2.11 [0.70-2.42] mg/L) and peaked at 48 hr (OR/AKI group: 6.16 [1.74-10.73] mg/L, EVAR/AKI group: 2.57 [1.21-7.40] mg/L), while no increase was observed among those who did not develop AKI at the same time points (0.06 [0.04-0.14] vs. 0.08 [0.04-0.19] mg/L). The diagnostic accuracy of uCysC at 6 hr post-surgery was excellent (area under the curve - receiver-operating characteristic [AUC-ROC] = 0.968), significantly higher than sCr (AUC-ROC = 0.844) and a cutoff value set at 0.30 mg/L can diagnose AKI with a sensitivity of 85.71% and a specificity of 98.97%. CONCLUSIONS: uCysC is superior to sCr in the early diagnosis of AKI following open and endovascular AAA repair.

Evaluation of apoptosis in varicose vein disease complicated by superficial vein thrombosis.

BACKGROUND: The factors contributing to superficial vein thrombosis (SVT) in patients with varicose vein disease are unclear. Differences in vein wall apoptotic activity could be associated with the pathogenesis of SVT. The aim of the study is to address the role of the programmed cell death in the vein wall by comparing varicose veins with history of SVT to uncomplicated varicose veins. PATIENTS AND METHODS: Vein segments from the proximal part of the great saphenous vein (GSV), the distal part of the vein and from a varicose tributary, from 16 patients with varicose vein disease and one episode of SVT, were evaluated for the immunohistochemical expression of pro-apoptotic (Bax, p53, Caspase 3, BCL-6, BCL-xs), anti-apoptotic (BCL-xl and BCL-2) and proliferation (Ki-67) markers. The results of this study were compared to the results from the evaluation of 19 patients suffering from uncomplicated varicose vein disease and 10 healthy GSVs as controls. RESULTS: Overall, there was increased apoptosis in the distal part of GSV compared to the proximal part documented by increased expression of Bax (p < 0.01), Caspase 3 (p = 0.01), BCL-xs (p < 0.01). The comparisons of the markers' expression between patients with varicose veins and patients with a history of SVT showed significant differences among the three different anatomic locations. In the proximal GSV, only BCL-xs was higher in patients with SVT (p = 0.029). In the tributaries, Bax, BCL-xl and Ki-67 were higher in patients with SVT (p < 0.01). In the distal GSV, increased Bax, BCL-xs, BCL-xl and Ki-67 staining was observed in the thrombosis group compared to uncomplicated veins (p < 0.01). CONCLUSIONS: The vein wall in SVT shows increased pro-apoptotic activity compared to uncomplicated disease and normal veins. Whether increased vein wall cell apoptosis is a causative factor for SVT in varicose veins disease or a repairing mechanism of the thrombosis itself needs further research.

From Affect Regulation to BMI: Unveiling Childhood Obesity Patterns Through Advanced Clustering Techniques.

This study explores the relationship between psychological factors and children's BMI, using clustering methods like Gaussian Mixture Models and Spectral Clustering. Affinity Propagation was particularly effective, suggesting that tailored interventions based on psychological assessments could improve obesity management in children.

A Deep Learning Model for Classifying Histological Types of Colorectal Polyps.

In this study a deep learning architecture based on a convolutional neural network has been evaluated for the classification of white light images of colorectal polyps acquired during the process of a colonoscopy, to estimate the accuracy of the optical recognition of histologic types of polyps. Convolutional neural networks (CNNs), a subclass of artificial neural networks that have gained dominance in several computer vision tasks, are gaining popularity in many medical fields, including endoscopy. The TensorFlow framework was used for implementing EfficientNetB7, which was trained with 924 images, drawn from 86 patients. 55% of the polyps were adenomas, 22% were hyperplastic, and 17% were lesions with sessile serrations. The validation loss, accuracy, and AUC ROC were 0.4845, 0.7778, and 0.8881 respectively.

Convolutional Neural Networks for Optical Discrimination Between Histological Types of Colorectal Polyps Based on White Light Endoscopic Images.

The objective of this study was to compare different convolutional neural networks (CNNs), as employed in a Python-produced deep learning process, used on white light images of colorectal polyps acquired during the process of a colonoscopy, in order to estimate the accuracy of the optical recognition of particular histologic types of polyps. The TensorFlow framework was used for Inception V3, ResNet50, DenseNet121, and NasNetLarge, which were trained with 924 images, drawn from 86 patients.

Flow-diverting stents for the treatment of arterial aneurysms.

BACKGROUND: Anatomic factors may limit the application of stent grafts for the treatment of arterial aneurysms. Flow- diverting stents (FDSs) are specially designed to reduce flow velocity in the aneurysm sac and promote thrombosis while maintaining flow in the main artery and branch vessels. FDSs include the Pipeline Embolization Device (ev3, Plymouth, Minn), the SILK Arterial Reconstruction Device (Balt Extrusion, Montmorency, France), and the Cardiatis Multilayer Stent (Cardiatis, Isnes, Belgium). The first two have been mainly used for the treatment of intracranial aneurysms. The aim of this study was to review the current role of FDSs in the treatment of extracranial arterial aneurysms. METHODS: A systematic electronic health database search was conducted using PubMed, Ovid, Medline, and the Cochrane Database on all accessible published articles through March 2012. An additional search for abstracts presented in international congresses for vascular surgery was also performed. Full-text articles and abstracts were analyzed separately due to the heterogeneity of the data. RESULTS: Results of the use of FDSs in arterial aneurysms were reported in 12 full-text articles including 35 patients (26 men, age 65.4) with 38 aneurysms. The aneurysms were located in the hepatic (n = 12), splenic (n = 6), renal (n = 5), celiac (n = 4), superior mesenteric (n = 3), subclavian (n = 2), gastroduodenal (n = 1), and popliteal arteries (n = 1) and in the descending thoracic (n = 1), suprarenal (n = 1) and infrarenal aorta (n = 2). The 30-day mortality was 5.7% (2 of 35 patients). Three stent thromboses occurred (8.3%), none of them with clinical consequences. Thirty patients with 33 aneurysms and patent FDSs were monitored for an average of 9.2 months. Thrombosis occurred in 90.6%, and volume reduction was observed in 81% of the aneurysms. No branch vessel occlusion occurred. Twelve abstracts were identified, including 133 patients (mean age, 64.7 years). They included 62 peripheral, 28 visceral, and 43 abdominal and thoracoabdominal aneurysms. The Cardiatis Multilayer Stent was used in all cases. Thrombosis was achieved in all but two peripheral and visceral aneurysms. Volume reduction was observed in 82.7%, and no branch vessel occlusion occurred. In aortic aneurysms, better results regarding aneurysm thrombosis, reduction of the volume, and patency of collateral branches were reported at 12 months rather than at 6 months postoperatively. No aneurysm rupture has yet been described. CONCLUSIONS: Initial clinical experience with the use of FDSs in the treatment of visceral and peripheral aneurysms yielded satisfactory results in technical success, aneurysm thrombosis and shrinkage, and in patency of branch vessels. The results in aortic aneurysms are still under investigation. No aneurysm rupture has yet been described. There is a significant incidence of FDS thrombosis. Volume reduction of the aneurysm is a clearer evidence of the clinical success after treatment with FDSs than aneurysm thrombosis.

New considerations for colorectal cancer screening based on the demographic profile of colorectal cancer in a Greek population.

Colorectal cancer screening has long been recommended for middle age and older individuals. Recent evidence indicates increasing incidence and mortality among young adults. Therefore, the present study re-examined the current recommendations using an asymptomatic average-risk population screened by colonoscopy. A total of 716 participants of a wide age range were prospectively enrolled in an open-access endoscopic screening program based on self-referral. Comparisons between different age, gender and location groups, and receiver operating characteristic curves (ROC) curves for best age selection for detection of lesions were employed. Increased incidence of advanced lesions was observed in adults <50 years old. Although the polyp size was <1 cm in 85% of the cohort, a significant number of participants harbored advanced lesions. A disturbing incidence of lesions in women 30-49 years was located mainly in the left colon. One-third of the important pathology resides exclusively in the right colon. ROC curves demonstrated that with the current starting age of screening at 50 years, 92% of polyps and 95% of adenomas could be detected by colonoscopy, but a number of potential precancerous lesions will appear at an earlier age and therefore will be missed. The present study supported the notion that it is critical to reduce screening initiation below the currently accepted age of 50 years. Colonoscopy is a suitable method for addressing the increased prevalence of proximal lesions and the meticulous resection of smaller polyps.

Discovering Association Rules in Antimicrobial Resistance in Intensive Care Unit.

Multidrug resistant infections in intensive care units represent a worldwide problem with adverse health effects and negative economic implications. As artificial intelligence techniques are increasingly applied in diagnosing, treating, and preventing antimicrobial resistance, in this study, we explore the use of association rule mining in the antibiotic resistance profile of critically ill patients suffering from multidrug resistant infections.

Predicting Hospital Admission for Emergency Department Patients: A Machine Learning Approach.

The objective of this study was to establish a machine learning model and to evaluate its predictive capability of admission to the hospital. This observational retrospective study included 3204 emergency department visits to a public tertiary care hospital in Greece from 14 March to 4 May 2019. We investigated biochemical markers and coagulation tests that are routinely checked in patients visiting the Emergency Department (ED) in relation to the ED outcome (admission or discharge). Among the most popular classification techniques of the scikit-learn library through a 10-fold cross-validation approach, a GaussianNB model outperformed other models with respect to the area under the receiver operating characteristic curve.

Admission and Discharge Following Ambulance Transport to the Emergency Department.

Emergency ambulance use is deemed necessary for the transport of acutely ill patients to hospital emergency departments (ED). However, some patients are discharged as they present low acuity or chronic problems and should receive primary healthcare services, while the most severely ill are admitted. In the present study, we examined the descriptive epidemiology of ambulance transports for emergencies in the ED by utilizing the data of the information systems of a public tertiary general hospital in Greece. More than half of the patients transferred to the ED by an ambulance were finally admitted to the hospital (52.25%), whereas only one-third (33.74%) of those transferred by other means. A statistically significant association was detected between ambulance use and hospital admission. Age was also statistically significantly higher in the ambulance group. Higher mean values of creatinine, CRP, LDH, urea, white-blood-cell count, and neutrophils were detected in the ambulance group, in contrast to hemoglobin and lymphocyte count which were higher in the non-ambulance group.

Prediction of Hospitalization Using Machine Learning for Emergency Department Patients.

The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area under the receiver operating characteristic curve (AUC ROC).

Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department.

Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare. Aim of the study: Our objective was to find an algorithm using ML techniques to assist clinical decision-making in the emergency setting. Material and methods: We assessed the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, DDi-mer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed. Results: The proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model. Conclusions: Developing robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.

Using Machine Learning for Predicting the Hospitalization of Emergency Department Patients.

Artificial intelligence processes are increasingly being used in emergency medicine, notably for supporting clinical decisions and potentially improving healthcare services. This study investigated demographics, coagulation tests, and biochemical markers routinely used for patients seen in the Emergency Department (ED) concerning hospitalization. This retrospective observational study included 13,991 emergency department visits of patients who had undergone biomarker testing to a tertiary public hospital in Greece during 2020. After applying five well-known classifiers of the caret package for machine learning of the R programming language in the whole data set and to each ED unit separately, the best performance regarding AUC ROC was observed in the Pulmonology ED unit. Furthermore, among the five classification techniques evaluated, a random forest classifier outperformed other models.

Exploratory Clustering for Emergency Department Patients.

Emergency department (ED) overcrowding is an increasing global problem raising safety concerns for the patients. Elaborating an effective triage system that properly separates patients requiring hospital admission remains difficult. The objective of this study was to compare a clustering-related technique assignment of emergency department patients with the admission output using the k-means algorithm. Incorporating such a model into triage practice could theoretically shorten waiting times and reduce ED overcrowding.

Endograft accommodation on the aortic bifurcation: an overview of anatomical fixation and implications for long-term stent-graft stability.

In light of the results of randomized trials, it seems that despite the favorable short and midterm outcomes of standard endografts, concern over endograft migration has escalated, as this event will be responsible for almost all late complications in endovascular aneurysm repair (EVAR). Migration forces, both caudal and sideways, depend heavily on blood pressure, inlet diameter, and angulation of the stent-graft, while the bifurcation generates more force than any other segment of the stent-graft. It thus seems that the position of the endograft's flow divider influences force distribution and migration risk. Additionally, due to concomitant ongoing aortic degeneration, postoperative dilatation of the infrarenal aortic neck poses a threat to EVAR patients as soon as the diameter of the infrarenal neck reaches the dimensions of the proximal graft. This review evaluates the significance of endograft accommodation on the aortic bifurcation and cumulative experience of the only endografts utilizing this feature: the Zenith Composite and the Powerlink.

Aortoenteric fistulae: present-day management.

An aortoenteric fistula (AEF) is a communication between the aorta and an adjacent loop of the bowel. The three most useful diagnostic modalities for detecting AEF are abdominal computed tomography scan with intravenous contrast, esophagogastroduodenoscopy, and arteriography. The treatment of AEFs has improved in recent years, but despite the multiple surgical techniques reported, many of the patients do not survive or are left debilitated after treatment. Endovascular repair is an efficient and safe method to stabilize patients with life-threatening AEFs. The aim of this study is to provide a comprehensive and synthetic review of the latest advantages on the diagnosis and management of primary and secondary AEF.

Analyzing Acute Care Surgery Patient Flow in the Emergency Department During COVID-19 Pandemic.

During the COVID-19 pandemic, the number of visits in emergency departments (ED) worldwide decreased significantly based on several studies. This study aims to compare the patient flow in the emergency surgery department during the COVID-19 pandemic and a control period in the emergency department of a public tertiary care hospital in Greece. The overall patient flow reduction regarding the ED visits between the two examined periods was 49.07%. The emergency surgery department's corresponding visits were 235 and 552, respectively, which indicated an overall patient flow decrease of 57.43%. Chi-square analysis showed that age groups and ambulance use had statistically significant associations with the periods examined. An independent samples t-test was applied and deduced that the average patient's age was statistically significantly higher in the COVID-19 pandemic than in the non-pandemic period. By analyzing hospital information system data, useful conclusions can be drawn to prepare a surgical emergency unit better and optimize resource allocation in a healthcare facility in similar critical situations.

Using Machine Learning to Predict Antimicrobial Resistance of Acinetobacter Baumannii, Klebsiella Pneumoniae and Pseudomonas Aeruginosa Strains.

Hospital-acquired infections, particularly in ICU, are becoming more frequent in recent years, with the most serious of them being Gram-negative bacterial infections. Among them, Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa are considered the most resistant bacteria encountered in ICU and other wards. Given the fact that about 24 hours are usually required to perform common antibiotic resistance tests after the bacteria identification, the use of machine learning techniques could be an additional decision support tool in selecting empirical antibiotic treatment based on the sample type, bacteria, and patient's basic characteristics. In this article, five machine learning (ML) models were evaluated to predict antimicrobial resistance of Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. We suggest implementing ML techniques to forecast antibiotic resistance using data from the clinical microbiology laboratory, available in the Laboratory Information System (LIS).

Conversion to open repair after endografting for abdominal aortic aneurysm: a review of causes, incidence, results, and surgical techniques of reconstruction.

PURPOSE: To review the incidence, causes, and mortality rates of early and late conversion to open surgery after endovascular aneurysm repair (EVAR) of abdominal aortic aneurysm (AAA). METHODS: A systematic search of the English-language literature from 2002 to 2009 was performed by interrogation of the PubMed, MEDLINE, and EMBASE databases. Studies were included if they: (1) had >100 patients treated with EVAR and (2) provided adequate data to calculate incidence and associated mortality rates. The search yielded 13 articles with sufficient data to analyze early conversion (12,236 patients, 178 conversions) and 15 articles with available data for late conversion (14,298 patients, 279 conversions). RESULTS: The rate of early conversion among the 13 articles reviewed ranged from 0.8% to 5.9%; more recent studies carried lower rates of early conversion. Mortality rates of early conversion varied between 0% and 28.5%. Overall, there were 178 (1.5%) early conversions among the 12,236 AAAs treated with EVAR, with an average mortality of 12.4%. The rates of late conversion ranged from 0.4% to 22%. Of the 14,289 AAA patients undergoing endovascular repair, 279 (1.9%) required late conversion; the mortality rate was 10%. CONCLUSION: Though the incidence is gradually declining, secondary interventions persist as the Achilles' heel of EVAR. A lifelong follow-up strategy for AAA patients treated with EVAR is essential for early detection and treatment of complications of the procedure. Vascular surgeons should be familiar with the complex open conversion procedures.