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Aims: Monitoring drug safety in real‐world settings is the primary aim of pharmacovigilance. Frequent adverse drug reactions (ADRs) are usually identified during drug development. Rare ones are mostly characterized through post‐marketing scrutiny, increasingly with the use of data mining and disproportionality approaches, which lead to new drug safety signals. Nonetheless, waves of excessive numbers of reports, often stirred up by social media, may overwhelm and distort this process, as observed recently with levothyroxine or COVID‐19 vaccines. As human resources become rarer in the field of pharmacovigilance, we aimed to evaluate the performance of an unsupervised co‐clustering method to help the monitoring of drug safety. Methods: A dynamic latent block model (dLBM), based on a time‐dependent co‐clustering generative method, was used to summarize all regional ADR reports (n = 45 269) issued between 1 January 2012 and 28 February 2022. After analysis of their intra and extra interrelationships, all reports were grouped into different cluster types (time, drug, ADR). Results: Our model clustered all reports in 10 time, 10 ADR and 9 drug collections. Based on such clustering, three prominent societal problems were detected, subsequent to public health concerns about drug safety, including a prominent media hype about the perceived safety of COVID‐19 vaccines. The dLBM also highlighted some specific drug–ADR relationships, such as the association between antiplatelets, anticoagulants and bleeding. Conclusions: Co‐clustering and dLBM appear as promising tools to explore large pharmacovigilance databases. They allow, 'unsupervisedly', the detection, exploration and strengthening of safety signals, facilitating the analysis of massive upsurges of reports. [ABSTRACT FROM AUTHOR]

Background Immune checkpoint inhibitors (ICIs) foster anti-cancer immune responses. Their efficacy comes at the cost of immune-related adverse events (IRAEs). The latter affects various organs, including kidneys, mostly as acute tubulointerstitial nephritis, the pathophysiology of which remains unclear. We conducted a multicentre case–control study to compare the characteristics of patients with renal IRAEs (ICI-AKI) with those of patients diagnosed with other IRAEs. Methods We queried the French pharmacovigilance database for all adverse events involving ICIs. Reports were classified as ICI-AKI or extrarenal IRAE. For each ICI-AKI report, four reports of extrarenal IRAEs were randomly included (control group, 4:1 ratio). Variables showing an association with a P < 0.05 were included as covariates in a multivariate analysis. Results Therefore, 167 ICI-AKI reports were compared with 668 extrarenal IRAEs. At least one concomitant extrarenal IRAE was mentioned in 44.3% of ICI-AKI reports. Patients with ICI-AKI were significantly older than patients with extrarenal IRAEs (69.1 versus 64.6 years; P = 0.0135), and chronic kidney disease was significantly more prevalent (12.0% versus 3.3%; P = 0.0125). Patients with ICI-AKI were significantly more likely to be treated with fluindione [adjusted odds ratio (OR) 6.53, 95% confidence interval (95% CI) 2.21–19.31; P = 0.0007], a non-steroidal anti-inflammatory drug (NSAID, OR 3.18, 95% CI 1.07–9.4; P = 0.0368) or a proton-pump inhibitor (PPI, OR 2.18, 95% CI 1.42–3.34; P = 0.0004). Conclusion This study is limited by a lack of data, preventing confirmation of numerous reports therefore not included in the analysis. We are unable to draw definite pathophysiological conclusions from our data. Nonetheless, we suggest that ICIs may be a 'second-hit' that precipitates acute kidney injury caused by another concomitant drug (fluindione, NSAID or PPI). [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICIs), aiming to foster cancer-targeted immune response, proved to be effective in several advanced malignancies at the price of immune-related adverse events affecting various organs, notably the kidneys. Herein, a retrospective descriptive analysis was performed on all biopsy-confirmed cases of ICI-induced nephropathy notified to the French Pharmacovigilance database to date. Data were gathered about patients' characteristics, acute kidney injuries and histopathological features. A total of 63 biopsy-proven cases were included for analysis. Immune-related nephropathy occurred after a mean of 105.5 ± 98.6 (standard deviation) days after the introduction of the ICI. Kidney Disease: Improving Global Outcomes acute kidney injury stage 3 occurred in 36.5% of patients, and the mean peak serum creatinine was 288 µmol/L. Histopathology suggested acute tubule-interstitial nephritis in 52 patients (83%), while signs of acute tubular necrosis were found in 18 (29%) and glomerular involvement in 5 of them (8%). Another immune-related adverse event was documented in 25 patients (39.7%). Patients were treated with corticosteroids in 88.9% of cases. All in all, 27.0% fully recovered, 54.0% partially recovered, 12.7% did not recover. Rechallenge was attempted in 19 patients and one patient relapsed. Three-quarters of patients received a medication known to cause acute tubule-interstitial nephritis. The major limits of this study are those inherent to pharmacovigilance studies, such as its retrospective nature and incomplete data. Although it cannot pretend drawing any pathophysiological conclusion, this study depicts the clinical and histopathological pictures of ICI-induced nephropathies in a large cohort of biopsied patients with all grades of severity. [ABSTRACT FROM AUTHOR]

The article offers information on the role of Empagliflozin Outcome Trial in Patients with Chronic Heart Failure and a Reduced Ejection Fraction. It mentions that empaglif1ozin was associated with a lower risk than placebo of a composite outcome of cardiovascular death or hospitalization for heart failure among patients who had heart failure with reduced ejection fraction.

Introduction: Rituximab is a first-line treatment for membranous nephropathy. Nephrotic syndrome limits rituximab exposure due to urinary drug loss. Rituximab underdosing (serum level <2 μg/ml at month-3) is a risk factor for treatment failure. We developed a machine learning algorithm to predict the risk of underdosing based on patients' characteristics at rituximab infusion. We investigated the relationship between the predicted risk of underdosing and the cumulative dose of rituximab required to achieve remission., Methods: Rituximab concentrations were measured at month-3 in 92 sera from adult patients with primary membranous nephropathy, split into a training (75%) and a testing set (25%). A forward-backward machine-learning procedure determined the best combination of variables to predict rituximab underdosing in the training data set, which was tested in the test set. The performances were evaluated for accuracy, sensitivity, and specificity in 10-fold cross-validation training and test sets., Results: The best variables combination to predict rituximab underdosing included age, gender, body surface area (BSA), anti-phospholipase A2 receptor type 1 (anti-PLA2R1) antibody titer on day-0, serum albumin on day-0 and day-15, and serum creatinine on day-0 and day-15. The accuracy, sensitivity, and specificity were respectively 79.4%, 78.7%, and 81.0% (training data set), and 79.2%, 84.6% and 72.7% (testing data set). In both sets, the algorithm performed significantly better than chance ( P  < 0.05). Patients with an initial high probability of underdosing experienced a longer time to remission with higher rituximab cumulative doses required to achieved remission., Conclusion: This algorithm could allow for early intensification of rituximab regimen in patients at high estimated risk of underdosing to increase the likelihood of remission., (© 2023 Published by Elsevier, Inc., on behalf of the International Society of Nephrology.)