Citations

PURPOSE This randomized, open-label trial compared the efficacy and safety of adjuvant nab-paclitaxel + gemcitabine with those of gemcitabine for resected pancreatic ductal adenocarcinoma (ClinicalTrials.gov identifier: NCT01964430 ). METHODS We assigned 866 treatment-naive patients with pancreatic ductal adenocarcinoma to nab-paclitaxel (125 mg/m 2 ) + gemcitabine (1,000 mg/m 2 ) or gemcitabine alone to one 30-40 infusion on days 1, 8, and 15 of six 28-day cycles. The primary end point was independently assessed disease-free survival (DFS). Additional end points included investigator-assessed DFS, overall survival (OS), and safety. RESULTS Two hundred eighty-seven of 432 patients and 310 of 434 patients completed nab-paclitaxel + gemcitabine and gemcitabine treatment, respectively. At primary data cutoff (December 31, 2018; median follow-up, 38.5 [interquartile range [IQR], 33.8-43 months), the median independently assessed DFS was 19.4 ( nab-paclitaxel + gemcitabine) versus 18.8 months (gemcitabine; hazard ratio [HR], 0.88; 95% CI, 0.729 to 1.063; P = .18). The median investigator-assessed DFS was 16.6 (IQR, 8.4-47.0) and 13.7 (IQR, 8.3-44.1) months, respectively (HR, 0.82; 95% CI, 0.694 to 0.965; P = .02). The median OS (427 events; 68% mature) was 40.5 (IQR, 20.7 to not reached) and 36.2 (IQR, 17.7-53.3) months, respectively (HR, 0.82; 95% CI, 0.680 to 0.996; P = .045). At a 16-month follow-up (cutoff, April 3, 2020; median follow-up, 51.4 months [IQR, 47.0-57.0]), the median OS (511 events; 81% mature) was 41.8 ( nab-paclitaxel + gemcitabine) versus 37.7 months (gemcitabine; HR, 0.82; 95% CI, 0.687 to 0.973; P = .0232). At the 5-year follow-up (cutoff, April 9, 2021; median follow-up, 63.2 months [IQR, 60.1-68.7]), the median OS (555 events; 88% mature) was 41.8 versus 37.7 months, respectively (HR, 0.80; 95% CI, 0.678 to 0.947; P = .0091). Eighty-six percent ( nab-paclitaxel + gemcitabine) and 68% (gemcitabine) of patients experienced grade ≥ 3 treatment-emergent adverse events. Two patients per study arm died of treatment-emergent adverse events. CONCLUSION The primary end point (independently assessed DFS) was not met despite favorable OS seen with nab-paclitaxel + gemcitabine.

Abstract Peritoneal dialysis—(PD) related infections continue to be a major cause of morbidity and mortality in patients on PD. Although great advances have been made in the prevention and treatment of infectious complications over the past two decades, catheter-related infections represent a significant cause of technical failure in PD. Recent studies support the role of exit-site/tunnel infections in causing peritonitis. Peritonitis secondary to tunnel infection led to catheter loss in most cases. Thus, removing the catheter when exit-site/tunnel infection is refractory to medical therapy has been recommended. This approach requires interrupting PD and, after the placement of a central venous catheter, and transferring the patient to haemodialysis. In order to continue PD, simultaneous catheter removal and replacement of the PD catheter has been suggested. Although simultaneous catheter removal and replacement avoids temporary haemodialysis, it implies the removal/reinsertion of the catheter and the immediate initiation of PD with the risk of mechanical complications, such as leakage and malfunction. Hence, several mini-invasive surgical techniques, such as curettage, cuff-shaving, removal of the superficial cuff, and partial reimplantation of the catheter, have been proposed as rescue treatments. These procedures may allow the rescue of the catheter with a success rate of 70–100%. Therefore, in case of refractory exit-site/tunnel infection, a mini-invasive surgical revision should be considered before removing the catheter.

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

AbstractUlcerative colitis (UC) requires surgical management in 20 to 30% of patients. Indications for surgery include medically refractory disease, dysplasia, cancer, and other complications of UC. Appropriate patient selection for timing and staging of surgery is paramount for optimal outcomes. Restorative proctocolectomy is the preferred standard of care and can afford many patients with excellent quality of life. There have been significant shifts in the treatment of UC-associated dysplasia, with less patients requiring surgery and more entering surveillance programs. There is ongoing controversy surrounding the management of UC-associated colorectal cancer and the techniques that should be used. This article reviews the most recent literature on the indications for elective and emergent surgical intervention for UC and the considerations behind the surgical options.

AbstractSeptic pulmonary embolism (SPE) is a rare clinical entity that is distinct from the classic and more common non-septic thrombotic pulmonary embolism. SPE should be suspected in patients with a systemic acute inflammatory reaction or sepsis who develop signs and symptoms of pulmonary involvement. The diagnosis of SPE depends on the specific radiologic finding of multiple, peripheral, nodular, possibly cavitated lesions. SPE should prompt an immediate search for the primary source of infection; typically, right-sided infective endocarditis, cardiac implantable electronic devices, and septic thrombophlebitis as a complication of bone, skin, and soft tissue infection including Lemierre's syndrome, indwelling catheters, or direct inoculation via injection drug use. Invasive treatment of the infection source may be necessary; in thrombophlebitis, the efficacy and safety of anticoagulation remain undefined. Blood cultures may be negative, particularly among patients with recent antibiotic exposure, and broad-spectrum antimicrobial therapy should be considered. The in-hospital mortality of SPE ranges up to 20% in published case series. While trends in the incidence of SPE are unknown, the opioid epidemic, the growing use of cardiac implantable electronic devices worldwide, and the reported increase in cases of septic thrombophlebitis may be leading to an escalation in SPE cases. We provide a contemporary profile of SPE and propose a clinical management algorithm in patients with suspected or confirmed SPE.

Abstract Objective To determine COVID-19 vaccine-related adverse events (AEs) in the seven-day post-vaccination period in patients with SLE vs autoimmune rheumatic diseases (AIRDs), non-rheumatic autoimmune diseases (nrAIDs), and healthy controls (HC). Methods Data were captured through the COVID-19 Vaccination in Autoimmune Diseases (COVAD) questionnaire (March–December 2021). Multivariable regression models accounted for age, gender, ethnicity, vaccine type and background treatment. Results Among 9462 complete respondents, 583 (6.2%) were SLE patients (mean age: 40.1 years; 94.5% females; 40.5% Asian; 42.9% Pfizer-recipients). Minor AEs were reported by 83.0% of SLE patients, major by 2.6%, hospitalization by 0.2%. AE and hospitalization frequencies were similar between patients with active and inactive SLE. Rashes were more frequent in SLE patients vs HC (OR; 95% CI: 1.2; 1.0, 1.5), chills less frequent in SLE vs AIRDs (0.6; 0.4, 0.8) and nrAIDs (0.5; 0.3, 0.8), and fatigue less frequent in SLE vs nrAIDs (0.6; 0.4, 0.9). Pfizer-recipients reported higher overall AE (2.2; 1.1, 4.2) and injection site pain (2.9; 1.6, 5.0) frequencies than recipients of other vaccines, Oxford/AstraZeneca-recipients more body ache, fever, chills (OR: 2.5, 3.0), Moderna-recipients more body ache, fever, chills, rashes (OR: 2.6, 4.3). Hospitalization frequencies were similar across vaccine types. AE frequencies were similar across treatment groups, although chills were less frequent in antimalarial users vs non-users (0.5; 0.3, 0.9). Conclusion While COVID-19 vaccination-related AEs were reported by four-fifths of SLE patients, those were mostly minor and comparable to AEs reported by healthy individuals, providing reassurance regarding COVID-19 vaccination safety in SLE.

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.