Effectiveness and Safety of Laser-Assisted Removal of Inferior Vena Cava (IVC) Filters in a Single Tertiary Care Center.

INTRODUCTION: Laser sheath-assisted removal of inferior vena cava (IVC) filters with long dwelling time is a technique that utilizes laser-tipped sheaths. The laser light only penetrates vascular tissue by one hundred microns, causing the target tissues to disintegrate into particles less than 5 microns in size. This approach reduces the energy used during difficult retrieval procedures, allowing permanent filters to be removed in less fluoroscopic and procedural time overall. MATERIALS AND METHODS: The radiology information system and electronic health records were used in this retrospective cohort study to retrieve the data. A total of nine consecutive patients who underwent laser-assisted filter removal utilizing GlideLightTM were included in the study between January 2016 and January 2017. The study took place at King Abdulaziz Medical City in Riyadh. In this study, five patients were male and four were female with ages ranging from 19 to 57 years with a median age of 31. RESULTS: During the period of the study, a total of nine patients had their IVC filters removed using a laser. The success rate was 100%. The indications were trauma (n=4) followed by deep vein thrombosis (DVT) (n=3) and one patient indication was prolonged immobilization. The dwelling time ranged from seven to 70 months, with a dwelling median of 19 months. CONCLUSION: A laser sheath might be necessary for closed-cell filters in order to improve the likelihood of a successful and secure retrieval. Technical efficiency, filter type, the necessity of applying a laser sheath based on an open versus closed filter design, dwell times, and unfavorable results. As a result, after typical procedures failed to successfully retrieve IVC filters with long dwell durations, laser-assisted filter removal is thought to be practical and safe.

Comparative Risk of Acute Kidney Injury Following Concurrent Administration of Vancomycin with Piperacillin/Tazobactam or Meropenem: A Systematic Review and Meta-Analysis of Observational Studies.

The study aims to comparatively assess the nephrotoxicity of vancomycin when combined with piperacillin-tazobactam (V + PT) or meropenem (V + M) in adult patients hospitalized in general wards or intensive care units. We searched MEDLINE, Google Scholar, and Web of Science for observational studies evaluating incidences of AKI in adult patients receiving V + PT or V + M for at least 48 h in general wards or intensive care units. The primary outcome was AKI events, while the secondary outcomes were hospital length of stay, need for renal replacement therapy (RRT), and mortality events. The odds ratio (OR), or mean difference for the hospital length of stay, with a corresponding 95% confidence interval (CI) from the inverse variance weighting random-effects model were estimated for the risk of AKI, RRT, and mortality. Of the 112 studies identified, twelve observational studies were included in this meta-analysis with a total of 14,511 patients. The odds of having AKI were significantly higher in patients receiving V + PT compared with V + M (OR = 2.31; 95%CI 1.69-3.15). There were no differences between V + PT and V + M in the hospital length of stay, RRT, or mortality outcomes. Thus, clinicians should be vigilant while using V + PT, especially in patients who are at high risk of AKI.

Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face. Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (TD) children. This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz., a deep learning-based web application for detecting autism based on experimentally tested facial features using a convolutional neural network with transfer learning and a flask framework. MobileNet, Xception, and InceptionV3 were the pretrained models used for classification. The facial images were taken from a publicly available dataset on Kaggle, which consists of 3,014 facial images of a heterogeneous group of children, i.e., 1,507 autistic children and 1,507 nonautistic children. Given the accuracy of the classification results for the validation data, MobileNet reached 95% accuracy, Xception achieved 94%, and InceptionV3 attained 0.89%.

Automated Diagnosis of Chest X-Ray for Early Detection of COVID-19 Disease.

In March 2020, the World Health Organization announced the COVID-19 pandemic, its dangers, and its rapid spread throughout the world. In March 2021, the second wave of the pandemic began with a new strain of COVID-19, which was more dangerous for some countries, including India, recording 400,000 new cases daily and more than 4,000 deaths per day. This pandemic has overloaded the medical sector, especially radiology. Deep-learning techniques have been used to reduce the burden on hospitals and assist physicians for accurate diagnoses. In our study, two models of deep learning, ResNet-50 and AlexNet, were introduced to diagnose X-ray datasets collected from many sources. Each network diagnosed a multiclass (four classes) and a two-class dataset. The images were processed to remove noise, and a data augmentation technique was applied to the minority classes to create a balance between the classes. The features extracted by convolutional neural network (CNN) models were combined with traditional Gray-level Cooccurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms in a 1-D vector of each image, which produced more representative features for each disease. Network parameters were tuned for optimum performance. The ResNet-50 network reached accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 95%, 94.5%, 98%, and 97.10%, respectively, with the multiclasses (COVID-19, viral pneumonia, lung opacity, and normal), while it reached accuracy, sensitivity, specificity, and AUC of 99%, 98%, 98%, and 97.51%, respectively, with the binary classes (COVID-19 and normal).

The use of direct oral anticoagulants for thromboprophylaxis or treatment of cancer-associated venous thromboembolism: a meta-analysis and review of the guidelines.

BACKGROUND: Venous thromboembolism (VTE) is a common complication among patients with cancer and is one of the most common causes of increased morbidity and mortality. The use of direct oral anticoagulants (DOACs) for thromboprophylaxis and treatment of cancer-associated venous thromboembolism (CA-VTE) has been evaluated in several randomized clinical trials (RCTs). The aim of this meta-analysis was to assess efficacy and safety of using DOACs for thromboprophylaxis and treatment of CA-VTE and provide a summary for available guidelines' recommendations. METHODS: MEDLINE was searched to identify studies evaluating the use of DOACs for thromboprophylaxis or treatment in patients with cancer. Search was limited to peer-reviewed studies published in English. Studies were excluded if they were not RCTs or subgroup analyses of data derived from RCTs, if they did not report efficacy and safety data on patients with active cancer, or if they were published as an abstract. New VTE or VTE recurrence, and major or clinically relevant non-major bleeding (CRNMB) were used to assess the efficacy and safety, respectively. The Mantel-Haenszel random-effects model risk ratios (RRs) and the corresponding 95% confidence intervals (CIs) were calculated to estimate the pooled treatment effects of DOACs. RESULTS: Four studies evaluating DOACs use for thromboprophylaxis and four - for treatment of CA-VTE were included. Thromboprophylaxis with DOACs was associated with a significant reduction in the risk of symptomatic VTE (RR = 0.58; 95%CI 0.37,0.91) but with an incremental risk of major bleeding or CRNMB (RR = 1.57; 95%CI 1.10,2.26). CA-VTE treatment with DOACs was linked with a significant reduction in VTE recurrence (RR = 0.62; 95%CI 0.44,0.87) but with an incremental risk of CRNMB (RR = 1.58; 95%CI 1.11,2.24). CONCLUSIONS: The DOACs are associated with a lower risk of symptomatic VTE and VTE recurrence, but the risk of bleeding remains a considerable concern. Clinical decisions should be made by assessing individual patient's risk of VTE and bleeding.

Real-World Effectiveness and Safety of Apixaban versus Warfarin in Patients with Acute Venous Thromboembolism: Experience of a Large Tertiary Hospital in Saudi Arabia.

PURPOSE: The main objective of this study was to evaluate the effectiveness and safety of apixaban versus warfarin in patients with venous thromboembolism (VTE) in a "real-world" setting. PATIENTS AND METHODS: A retrospective cohort study was conducted using data from a large tertiary hospital in Saudi Arabia. Patients were included if they were adults (≥18 years), diagnosed with VTE, and treated with either apixaban or warfarin between January 2016 and September 2018. Patients who had received anticoagulation therapy within three months of the date of the index event were excluded. The effectiveness outcomes were incidence of VTE recurrence (ie, deep vein thrombosis DVT or pulmonary embolism [PE]), while the safety outcome was incidence of any major bleeding (MB) event within 90 days of follow-up. RESULTS: Among the 492 patients included for study, 212 (43.1%) received apixaban and 280 (56.1%) received warfarin. The mean age of patients was 53.6±19.1 years and 62% of the cohort was female. Comparable rates of VTE recurrence were observed for apixaban and warfarin treatment groups during follow-up (adjusted odds ratio (AOR) =0.95; 95% CI 0.53-1.68), including DVT (AOR=1.06; 95% CI 0.52-2.17) and PE (AOR=0.78; 95% CI 0.31-1.96). However, apixaban was associated with significantly fewer MB events than warfarin (AOR=0.18; 95% CI 0.04-0.83). CONCLUSION: The use of apixaban for the treatment of Saudi patients with acute VTE is associated with a VTE recurrence rate comparable to that of warfarin, with significantly fewer MB events.

Correction: The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities.

[This corrects the article DOI: 10.2196/21929.].

Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination Techniques.

Chronic kidney disease (CKD) is among the top 20 causes of death worldwide and affects approximately 10% of the world adult population. CKD is a disorder that disrupts normal kidney function. Due to the increasing number of people with CKD, effective prediction measures for the early diagnosis of CKD are required. The novelty of this study lies in developing the diagnosis system to detect chronic kidney diseases. This study assists experts in exploring preventive measures for CKD through early diagnosis using machine learning techniques. This study focused on evaluating a dataset collected from 400 patients containing 24 features. The mean and mode statistical analysis methods were used to replace the missing numerical and the nominal values. To choose the most important features, Recursive Feature Elimination (RFE) was applied. Four classification algorithms applied in this study were support vector machine (SVM), k-nearest neighbors (KNN), decision tree, and random forest. All the classification algorithms achieved promising performance. The random forest algorithm outperformed all other applied algorithms, reaching an accuracy, precision, recall, and F1-score of 100% for all measures. CKD is a serious life-threatening disease, with high rates of morbidity and mortality. Therefore, artificial intelligence techniques are of great importance in the early detection of CKD. These techniques are supportive of experts and doctors in early diagnosis to avoid developing kidney failure.

The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities.

BACKGROUND: Information technology has shifted paper-based documentation in the health care sector into a digital form, in which patient information is transferred electronically from one place to another. However, there remain challenges and issues to resolve in this domain owing to the lack of proper standards, the growth of new technologies (mobile devices, tablets, ubiquitous computing), and health care providers who are reluctant to share patient information. Therefore, a solid systematic literature review was performed to understand the use of this new technology in the health care sector. To the best of our knowledge, there is a lack of comprehensive systematic literature reviews that focus on Fast Health Interoperability Resources (FHIR)-based electronic health records (EHRs). In addition, FHIR is the latest standard, which is in an infancy stage of development. Therefore, this is a hot research topic with great potential for further research in this domain. OBJECTIVE: The main aim of this study was to explore and perform a systematic review of the literature related to FHIR, including the challenges, implementation, opportunities, and future FHIR applications. METHODS: In January 2020, we searched articles published from January 2012 to December 2019 via all major digital databases in the field of computer science and health care, including ACM, IEEE Explorer, Springer, Google Scholar, PubMed, and ScienceDirect. We identified 8181 scientific articles published in this field, 80 of which met our inclusion criteria for further consideration. RESULTS: The selected 80 scientific articles were reviewed systematically, and we identified open questions, challenges, implementation models, used resources, beneficiary applications, data migration approaches, and goals of FHIR. CONCLUSIONS: The literature analysis performed in this systematic review highlights the important role of FHIR in the health care domain in the near future.

Safety and efficacy of dual versus triple antithrombotic therapy (DAT vs TAT) in patients with atrial fibrillation following a PCI: a systematic review and network meta-analysis.

OBJECTIVE: Creating an appropriate antithrombotic therapy for patients with atrial fibrillation (AF) who have undergone percutaneous coronary intervention (PCI) remains a dilemma. Several clinical trials compared the use of a dual antithrombotic therapy (DAT) regimen with a direct oral anticoagulants including (apixaban, dabigatran, edoxaban or rivaroxaban) and a P2Y12 inhibitor versus a triple antithrombotic therapy (TAT) that includes a vitamin K antagonist plus aspirin and a P2Y12 inhibitor in patients with AF who have undergone PCI. However, there are no head-to-head trials comparing the DAT regimens to each other. We aimed to compare the efficacy and safety of DAT regimens using a network meta-analysis (NMA) approach. DESIGN: A systematic review and NMA of randomised clinical trials. METHODS: We conducted a systematic literature review to identify relevant randomised clinical trials and performed a Bayesian NMA for International Society on Thrombosis and Haemostasis (ISTH) major or clinically relevant non-major (CRNM) bleeding, all-cause mortality, stroke, myocardial infarction (MI) and stent thrombosis outcomes. We used NetMetaXL V.1.6.1 and WinBUGS V.1.4.3 for the NMA and estimated the probability of ranking the treatments based on the surface under the cumulative ranking curve. RESULTS: The comparison between DAT regimens showed no significant difference in the safety or efficacy outcomes. Apixaban regimen was ranked first as the preferred therapy in terms of ISTH major or CRNM bleeding and stroke, with a probability of 52% and 54%, respectively. Rivaroxaban regimen was the preferred therapy in terms of MI and stent thrombosis, with a probability of 34% and 27%, respectively. Dabigatran regimen was ranked first in terms of all-cause mortality, with a probability of 28%. CONCLUSION: The DAT regimens are as safe and effective as TAT regimens. However, ranking probabilities for the best option in the selected outcomes can be used to guide the selection among these agents based on different patients' conditions.

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms.

Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people's lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.

The use of direct oral anticoagulants for extended duration thromboprophylaxis in medically ill patients: a systematic review and meta-analysis.

The extended use of thromboprophylaxis with direct oral anticoagulants (DOACs) for more than 30 days has been evaluated as an alternative for the standard duration thromboprophylaxis (7-10 days) with low molecular weight heparin in medically ill patients to reduce the risk of venous thromboembolism (VTE) after hospital discharge. EMBASE and MEDLINE were searched for studies evaluating extended duration thromboprophylaxis with DOACs versus standard thromboprophylaxis with enoxaparin in medically ill patients through October 2018. Search was limited to randomized-controlled trials. Symptomatic VTE, VTE-related death, and death from any cause, and major and clinically relevant non-major bleeding were used to assess the efficacy and safety, respectively. The Mantel-Haenszel random-effects model risk ratio (RR) and corresponding 95% CIs were calculated using the metan routine in Stata (version 14.2) to estimate the pooled treatment effects. Heterogeneity was assessed by the I2 statistics. Four studies met the inclusion criteria. DOACs were superior to enoxaparin in preventing symptomatic VTE (RR = 0.59, 95% CI 0.44-0.79). There were no significant differences in thromboprophylactic efficacy between extended and standard thromboprophylaxis as to VTE-related death (RR = 0.81, 95% CI 0.60-1.10) and death from any cause (RR = 0.98, 95% CI 0.87-1.09). Compared to the standard duration, extended thromboprophylaxis was associated with approximately two-fold greater risk of major (RR = 1.95, 95% CI 1.25-3.04), and clinically relevant non-major (RR = 1.81, 95% CI 1.29-2.53) bleeding. The superior efficacy was diminished by the unfortunate safety profile. Therefore, we continue to support both the American Society of Hematology (ASH) and the American College of Chest Physicians (ACCP) guidelines recommendation against the extended use of thromboprophylaxis beyond the hospital stay.

Direct oral anticoagulants for the treatment of venous thromboembolism in patients with active malignancy: a systematic review and meta-analysis.

Low molecular weight heparins (LMWHs) are considered the standard of care for the treatment of venous thromboembolism (VTE) associated with cancer. We conducted a meta-analysis to evaluate the safety and efficacy of direct oral anticoagulants (DOACs) in patients with cancer. We systematically searched Medline for potential randomized-control clinical trials (RCTs) and post-hoc analyses. For each study, data on recurrent VTE, major or clinically relevant non-major bleeding (CRNMB), and major bleeding (MB) were extracted. Initially, a total of 1395 citations were identified. Eight studies met our eligibility criteria. The utilization of DOACs in patients with cancer showed a statistically significant reduction in the risk of VTE recurrence compared to LMWH or warfarin (RR = 0.64; 95% CI 0.46-0.88). Similar rates of major or CRNMB were observed between DOACs and LMWH or warfarin (RR = 1.00; 95% CI 0.75-1.33). There was no significant difference in the rate of MB between DOACs and LMWH or warfarin (RR = 1.31; 95% CI 0.71-2.44). Our results suggest that DOACs might reduce the incidence of VTE recurrence in patients with cancer without putting them at high risk for MB/CRNMB or MB. Our findings were mainly driven by the results of the Hokusai VTE Cancer trial. Given the level of investigated evidence, our findings should be interpreted with caution since the majority of the data were originated from sub-group analyses of large (RCTs). Future studies that are adequately powered are warranted to assess efficacy and safety data of DOACs for the treatment of VTE in patients with different types of cancer.