46 research outputs found
A Real-World Exploration into Clinical Outcomes of Direct Oral Anticoagulant Dosing Regimens in Morbidly Obese Patients Using Data-Driven Approaches
Full text linkIntroduction: The clinical outcomes of direct oral anticoagulant (DOAC) dosage regimens in morbid obesity are uncertain due to limited clinical evidence. This study seeks to bridge this evidence gap by identifying the factors associated with clinical outcomes following the dosing of DOACs in morbidly obese patients. Method: A data-driven observational study was carried out using supervised machine learning (ML) models with a dataset extracted from electronic health records and preprocessed. Following 70%:30% partitioning of the overall dataset via stratified sampling, the selected ML classifiers (e.g., random forest, decision trees, bootstrap aggregation) were applied to the training dataset (70%). The outcomes of the models were evaluated against the test dataset (30%). Multivariate regression analysis explored the association between DOAC regimens and clinical outcomes. Results: A sample of 4,275 morbidly obese patients was extracted and analysed. The decision trees, random forest, and bootstrap aggregation classifiers achieved acceptable (excellent) values of precision, recall, and F1 scores in terms of their contribution to clinical outcomes. The length of stay, treatment days, and age were ranked highest for relevance to mortality and stroke. Among DOAC regimens, apixaban 2.5 mg twice daily ranked highest for its association with mortality, increasing the mortality risk by 43% (odds ratio [OR] 1.430, 95% confidence interval [CI] 1.181–1.732, p = 0.001). On the other hand, apixaban 5 mg twice daily reduced the odds of mortality by 25% (OR 0.751, 95% CI 0.632–0.905, p = 0.003) but increased the odds of stroke events. No clinically relevant non-major bleeding events occurred in this group. Conclusion: Data-driven approaches can identify key factors associated with clinical outcomes following the dosing of DOACs in morbidly obese patients. This will help design further studies to explore well tolerated and effective DOAC doses for morbidly obese patients.</p
A real-world exploration into clinical outcomes of direct oral anticoagulant therapy in people with chronic kidney disease: a large hospital-based study
No full textBackground: there is limited evidence to support definite clinical outcomes of direct oral anticoagulant (DOAC) therapy in chronic kidney disease (CKD). By identifying the important variables associated with clinical outcomes following DOAC administration in patients in different stages of CKD, this study aims to assess this evidence gap. Methods: an anonymised dataset comprising 97,413 patients receiving DOAC therapy in a tertiary health setting was systematically extracted from the multidimensional electronic health records and prepared for analysis. Machine learning classifiers were applied to the prepared dataset to select the important features which informed covariate selection in multivariate logistic regression analysis. Results: for both CKD and non-CKD DOAC users, features such as length of stay, treatment days, and age were ranked highest for relevance to adverse outcomes like death and stroke. Patients with Stage 3a CKD had significantly higher odds of ischaemic stroke (OR 2.45, 95% Cl: 2.10–2.86; p = 0.001) and lower odds of all-cause mortality (OR 0.87, 95% Cl: 0.79–0.95; p = 0.001) on apixaban therapy. In patients with CKD (Stage 5) receiving apixaban, the odds of death were significantly lowered (OR 0.28, 95% Cl: 0.14–0.58; p = 0.001), while the effect on ischaemic stroke was insignificant. Conclusions: a positive effect of DOAC therapy was observed in advanced CKD. Key factors influencing clinical outcomes following DOAC administration in patients in different stages of CKD were identified. These are crucial for designing more advanced studies to explore safer and more effective DOAC therapy for the population. Graphical Abstract: (Figure presented.)</p
Challenges and Possible Solutions to Direct-Acting Oral Anticoagulants (DOACs) Dosing in Patients with Extreme Bodyweight and Renal Impairment
Full text linkThis article aims to highlight the dosing issues of direct oral anticoagulants (DOACs) in patients with renal impairment and/or obesity in an attempt to develop solutions employing advanced data-driven techniques. DOACs have become widely accepted by clinicians worldwide because of their superior clinical profiles, more predictable pharmacokinetics, and hence more convenient dosing relative to other anticoagulants. However, the optimal dosing of DOACs in extreme bodyweight patients and patients with renal impairment is difficult to achieve using the conventional dosing approach. The standard dosing approach (fixed-dose) is based on limited data from clinical studies. The existing formulae (models) for determining the appropriate doses for these patient groups leads to suboptimal dosing. This problem of mis-dosing is worsened by the lack of standardized laboratory parameters for monitoring the exposure to DOACs in renal failure and extreme bodyweight patients. Model-informed precision dosing (MIPD) encompasses a range of techniques like machine learning and pharmacometrics modelling, which could uncover key variables and relationships as well as shed more light on the pharmacokinetics and pharmacodynamics of DOACs in patients with extreme bodyweight or renal impairment. Ultimately, this individualized approach—if implemented in clinical practice—could optimise dosing for the DOACs for better safety and efficacy.</p
Direct oral anticoagulants and the risk of adverse clinical outcomes among patients with different body weight categories:a large hospital-based study
No full textObjective: Through predictable pharmacokinetics—including a convenient fixed-dose regimen, direct oral anticoagulants (DOACs) are preferred over previous treatments in anticoagulation for various indications. However, the association between higher body weight and the risk of adverse consequences is not well studied among DOAC users. We aim to explore the association of body weight and adverse clinical outcomes in DOAC users.Methods: A total of 97,413 anonymised DOAC users in a tertiary care setting were identified following structured queries on the electronic health records (EHRs) to extract the feature-rich anonymised dataset. The prepared dataset was analysed, and the features identified with machine learning (ML) informed the adjustments of covariates in the multivariate regression analysis to examine the association. Kaplan–Meier analysis was performed to evaluate the mortality benefits of DOACs. Results: Among DOAC users, the odds of adverse clinical outcomes, such as clinically relevant non-major bleeding (CRNMB), ischaemic stroke, all-cause mortality, and prolonged hospital stay, were lower in patients with overweight, obesity, or morbid obesity than in patients with normal body weight. The odds of ischaemic stroke (OR 0.42, 95% CI: 0.36–0.88, p = 0.001) and all-cause mortality (OR 0.87, 95% CI: 0.81–0.95, p = 0.001) were lower in patients with morbid obesity than in patients with normal body weight. In the Kaplan–Meier analysis, apixaban was associated with a significantly lower rate of mortality overall and in obesity and overweight subgroups than other DOACs (p < 0.001). However, rivaroxaban performed better than apixaban in the morbid obesity subgroup (p < 0.001). Conclusion: This study shows the positive effects of DOAC therapy on clinical outcomes, particularly in patients with high body weight. However, this still needs validation by further studies particularly among patients with morbid obesity.</p
The identification of micro-defect anomalies in laser powder bed fusion via deep machine learning models
Full text linkA Layer-Wise Surface Deformation Defect Detection by Convolutional Neural Networks in Laser Powder-Bed Fusion Images
No full textSurface deformation is a multi-factor, laser powder-bed fusion (LPBF) defect that cannot be avoided entirely using current monitoring systems. Distortion and warping, if left unchecked, can compromise the mechanical and physical properties resulting in a build with an undesired geometry. Increasing dwell time, pre-heating the substrate, and selecting appropriate values for the printing parameters are common ways to combat surface deformation. However, the absence of real-time detection and correction of surface deformation is a crucial LPBF problem. In this work, we propose a novel approach to identifying surface deformation problems from powder-bed images in real time by employing a convolutional neural network-based solution. Identifying surface deformation from powder-bed images is a significant step toward real-time monitoring of LPBF. Thirteen bars, with overhangs, were printed to simulate surface deformation defects naturally. The carefully chosen geometric design overcomes problems relating to unlabelled data by providing both normal and defective examples for the model to train. To improve the quality and robustness of the model, we employed several deep learning techniques such as data augmentation and various model evaluation criteria. Our model is 99% accurate in identifying the surface distortion from powder-bed images
فیلڈ مارشل محمدایوب خاں کی خود نوشت ’’جس رزق سے آتی ہو پرواز میں کوتاہی‘‘ :ایک مطالعہ
No full textThis article is a review of autobiography of Field Marshal Muhammad Ayub Khan, Ex. President of Pakistan. The book entitled "Friends not masters" which was translated into Urdu by a famous writer Ghulam Abbas. Ayub Khan was born in a village "Rehana" near Rawalpindi. After compilation of educatin he got commissin in Royal Army and proceeded to England for professional training. When Pakistan came into existence, he joined Pakistand Army and gradually became Field Marshal of Pakistan Army. According to the author, the book may be called a verbal creation regarding his autobiography which is anthology of conversations among some close friends of the author. Basically book is based on the ideology that the citizens of developing countries are desirous for the friendship and help of the advanced / developed countries especially America, but it would be equal level like friends not masters. The book also discussed the political ups and downs in Pakistan because the author remained the President of Pakistan for more than 10 years
Keyhole Porosity Identification and Localization via X-Ray Imaging With YOLO
No full textThe advent of Additive manufacturing (AM) of 3D printed objects is revolutionising the manufacturing industry. Despite its promise, the extensive post-processing requirements of 3D objects remains a significant barrier to AM’s wider adoption. Identifying defects in real-time from powder bed images taken before and after the laser melting of the current layer presents a promising strategy to reduce post-processing efforts. Traditional methods focusing on the top-layer images fall short in identifying multi-layer defects such as keyhole porosity, balling, and lack of fusion, which are critical to the integrity of 3D printed objects. Addressing this challenge, our study introduces an innovative multi-layer technique for the detection of keyhole porosity using high-quality X-ray Computed Tomography (XCT) images, leveraging the capabilities of the cutting-edge YOLO (You Only Look Once) object detection algorithm. Our findings reveal that this approach achieves a remarkable mean average precision (mAP) score of 92.585%, underscoring the efficacy of deep learning models in accurately identifying keyhole porosity across XCT images. This research not only demonstrates the potential for improving the quality and reliability of AM processes but also paves the way for reducing the dependency on labour-intensive post-processing steps
