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Background. There is no consensus guidance on when to reinitiate Pneumocystis jirovecii pneumonia (PJP) prophylaxis in solid organ transplant (SOT) recipients at increased risk. The 2019 American Society of Transplantation Infectious Diseases Community of Practice (AST IDCOP) guidelines suggested to continue or reinstitute PJP prophylaxis in those receiving intensified immunosuppression for graft rejection, cytomegalovirus (CMV) infection, higher dose of corticosteroids, or prolonged neutropenia. Methods. A literature search was conducted evaluating all literature from existence through April 22, 2020, using MEDLINE and EMBASE. (The International Prospective Register of Systematic Reviews registration number: CRD42019134204). Results. A total of 30 studies with 413 276 SOT recipients were included. The following factors were associated with PJP development: acute rejection (pooled odds ratio [pOR], 2.35; 95% confidence interval [CI], 1.69-3.26); study heterogeneity index [I2] = 23.4%), CMV-related illnesses (pOR, 3.14; 95% CI, 2.30-4.29; I2 = 48%), absolute lymphocyte count <500 cells/mm3 (pOR, 6.29; 95% CI, 3.56-11.13; I2 = 0%), BK polyomavirus-related diseases (pOR, 2.59; 95% CI, 1.22-5.49; I2 = 0%), HLA mismatch ≥3 (pOR, 1.83; 95% CI, 1.06-3.17; I2 = 0%), rituximab use (pOR, 3.03; 95% CI, 1.82-5.04; I2 = 0%), and polyclonal antibodies use for rejection (pOR, 3.92; 95% CI, 1.87-8.19; I2 = 0%). On the other hand, sex, CMV mismatch, interleukin-2 inhibitors, corticosteroids for rejection, and plasmapheresis were not associated with developing PJP. Conclusions. PJP prophylaxis should be considered in SOT recipients with lymphopenia, BK polyomavirus-related infections, and rituximab exposure in addition to the previously mentioned risk factors in the American Society of Transplantation Infectious Diseases Community of Practice guidelines.

Background: Recent studies suggested that fragmented (fQRS) is associated with poor clinical outcomes in heart failure with reduced ejection fraction (HFrEF) patients. However, no systematic review or meta-analysis has been done. We conducted a systematic review and meta-analysis to assess the association between baseline fQRS and all-cause mortality in HFrEF. Methods:We comprehensively reviewed the databases of MEDLINE and EMBASE from inception to February 2018. Published studies of HFrEF that reported fQRS and outcome of all-cause mortality and major arrhythmic event (sudden cardiac death, sudden cardiac arrest, ventricular fibrillation, or sustained ventricular tachycardia) were included. Data were integrated using the random-effects, generic inverse-variance method of DerSimonian and Laird.Results: Ten studies from 2010 to 2017 were included. Baseline fQRS was associated with increased all-cause mortality (risk ratio [RR] 1.63, 95% confidence interval [CI] 1.22-2.19, p < 0.0001, I 2 = 73%) as well as major arrhythmic events (RR = 1.74, 95% CI 1.09-2.80, I 2 = 89%). Baseline fQRS increased all-cause mortality in both Asian and Caucasian cohorts (RR = 2.17 with 95% CI 1.33-3.55 and RR = 1.45 with 95% CI 1.05-1.99, respectively) as well as increased major arrhythmic events in Asian cohort (RR = 1.50, 95% CI 1.05-2.13). Baseline fQRS also increased all-cause mortality in patients who had not received implantable cardioverter-defibrillator, significantly more than in patients who had received implantable cardioverter-defibrillator

SCN5A mutations in BrS increase the risk of MAE in Asian populations, symptomatic BrS patients, and individuals with spontaneous type-1 Brugada pattern. Our study suggests that SCN5A mutation status should be an important tool for risk assessment in BrS patients.

Very-low-carbohydrate diets or ketogenic diets are frequently used for weight loss in adults and as a therapy for epilepsy in children. The incidence and characteristics of kidney stones in patients on ketogenic diets are not well studied. Methods: A systematic literature search was performed, using MEDLINE, EMBASE, and Cochrane Database of Systematic Reviews from the databases’ inception through April 2020. Observational studies or clinical trials that provide data on the incidence and/or types of kidney stones in patients on ketogenic diets were included. We applied a random-effects model to estimate the incidence of kidney stones. Results: A total of 36 studies with 2795 patients on ketogenic diets were enrolled. The estimated pooled incidence of kidney stones was 5.9% (95% CI, 4.6–7.6%, I2 = 47%) in patients on ketogenic diets at a mean follow-up time of 3.7 +/− 2.9 years. Subgroup analyses demonstrated the estimated pooled incidence of kidney stones of 5.8% (95% CI, 4.4–7.5%, I2 = 49%) in children and 7.9% (95% CI, 2.8–20.1%, I2 = 29%) in adults, respectively. Within reported studies, 48.7% (95% CI, 33.2–64.6%) of kidney stones were uric stones, 36.5% (95% CI, 10.6–73.6%) were calcium-based (CaOx/CaP) stones, and 27.8% (95% CI, 12.1–51.9%) were mixed uric acid and calcium-based stones, respectively. Conclusions: The estimated incidence of kidney stones in patients on ketogenic diets is 5.9%. Its incidence is approximately 5.8% in children and 7.9% in adults. Uric acid stones are the most prevalent kidney stones in patients on ketogenic diets followed by calcium-based stones. These findings may impact the prevention and clinical management of kidney stones in patients on ketogenic diets.

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Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

Currently, the world is facing a global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19. So far, there are no clear recommendations regarding hospital discharge and aftercare for COVID-19. Here, we briefly discuss the current understanding of recommendations for discharge criteria, discharge treatment regimens, and follow-up.