Citations

The COVID-19 pandemic has significantly impacted the production, distribution, and demand of essential laboratory supplies worldwide. In 2021, severe shortages in required laboratory supplies such as blood collection tubes, butterfly needles, and blood gas syringes became a critical issue across Canada. Many hospitals or institutions had to instruct physicians and patients to limit laboratory testing where possible and, in some cases, required emergency shipments of tubes from alternative vendors or nearby hospitals. Laboratory testing is ubiquitous in managing patients. It is used for screening, diagnosis, and monitoring purposes. With limited blood collection tubes, consideration for restricting non-urgent testing is needed to conserve supply and protect acute care departments that manage critically ill patients. In addition, laboratories across Canada have experienced significant staffing shortages, resulting in an even greater need for appropriate laboratory utilization. Consequently, the Canadian Society of Clinical Chemists (CSCC) and the Canadian Association of Medical Biochemists (CAMB) curated utilization recommendations for both hospital and primary care settings in collaboration with Choosing Wisely Canada (CWC). These recommendations were specifically selected from those previously published by Choosing Wisely Canada (CWC), with impact statements and rationales added to conserve at-risk laboratory resources. Supply chain disruptions of clinical laboratory resources are expected to continue throughout 2022, indicating that now, more than ever, a focus on appropriate laboratory utilization is essential.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

Background: Hybrid debranching repair of pararenal and thoracoabdominal aortic aneurysms was initially designed as a better alternative to standard open repair, addressing the limitations of endovascular repair involving the visceral aorta. We reviewed the collective outcomes of hybrid debranching repairs using extra-anatomic, open surgical debranching of the renal-mesenteric arteries, followed by endovascular aortic stenting. Methods: Data from patients who underwent hybrid repair in 14 North American institutions during 10 years were retrospectively reviewed. Society of Vascular Surgery scores were used to assess comorbidity risk. Early and late outcomes, including mortality, morbidity, reintervention, and patency were analyzed. Results: A total of 208 patients (118 male; mean age, 71±8 years old) were treated by hybrid repair with extraanatomic reconstruction of 657 renal and mesenteric arteries (mean 3.2 vessels/patient). Mean aneurysm diameter was 6.6±1.3 cm. Thoracoabdominal aortic aneurysms were identified in 163 (78%) patients and pararenal aneurysms in 45 (22%). A single-stage repair was performed in 92 (44%) patients. The iliac arteries were the most common source of inflow (n=132; 63%), and most (n=150; 72%) had 3 or more bypasses. There were 30 (14%) early deaths, ranging widely across sites (0%–21%). A Society of Vascular Surgery comorbidity score >15 was the primary predictor of early mortality ( P <0.01), whereas mortality was 3% in a score ≤9. Early complications occurred in 140 (73%) patients and included respiratory complications in 45 patients (22%) and spinal cord ischemia in 22 (11%), of whom 10 (45%) fully recovered. At 5 years, survival was 61±5%, primary graft patency was 90±2%, and secondary patency was 93±2%. The most significant predictor of late mortality was renal insufficiency ( P <0.0001). Conclusions: Mortality after hybrid repair and visceral debranching is highly variable by center, but strongly affected by preoperative comorbidities and the centers’ experience with the technique. With excellent graft patency at 5 years, the outcomes of hybrid repair done at centers of excellence and in carefully selected patients may be comparable (or better) than traditional open or even totally endovascular approaches. However, in patients already considered as high-risk for surgery, it may not offer better outcomes.

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

e14591 Background: Combined immune checkpoint inhibitors (ICPI) have revolutionized treatment of solid tumors. Their use can lead to treatment-related adverse events (TRAEs) resulting in hospital admissions, treatment discontinuation and increased morbidity. We aim to evaluate gastrointestinal (GI) TRAEs of different combination ICPI. Methods: A comprehensive search of multiple databases from inception to October 2021 was performed. Phase 2 and 3 trials assessing safety and efficacy of combined ICPI in solid tumors were included. Trials reporting concurrent use of chemotherapy, immunosuppressants, targeted therapy, or sequential ICPI therapy were excluded. Primary outcome was pooled luminal, hepatic and pancreatic TRAEs. A meta-analysis of proportions was done for outcomes using a random-effects model. I2 % was used to assess heterogeneity. Results: Out of 3696 citations screened, 53 trials reporting TRAEs in 6186 patients were included. Data from 41 study arms of 35 trials in nivolumab plus ipilimumab (N+I), 15 study arms of 15 trials in durvalumab plus tremelimumab (D+T), 4 study arms of 3 trials in pembrolizumab plus ipilimumab (P+I). The most common indications for combined ICPI were melanoma (26.4%) and lung cancer (24.5%). Pooled GI TRAE rates from the D+T, P+I and two different dosing combinations of N+I are shown in the table. Conclusions: Our study provides pooled data of TRAEs from different combination ICPI use in solid tumors and demonstrates high incidence of all grades and ≥ 3 GI TRAEs. Combined ICPI related TRAEs should be recognized early, as it may require treatment discontinuation. Further studies are required to assess overall impact of TRAEs on treatment course and outcomes.[Table: see text]

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann–Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.