Olmesartan medoxomil-induced acute renal failure in a premature newborn following maternal exposure during pregnancy: a case report and review of the literature

Machine Learning for Identifying Medication-Associated Acute Kidney Injury

One of the prominent problems in clinical medicine is medication-induced acute kidney injury (AKI). Avoiding this problem can prevent patient harm and reduce healthcare expenditures. Several researches have been conducted to identify AKI-associated medications using statistical, data mining, and machine learning techniques. However, these studies are limited to assessing the impact of known nephrotoxic medications and do not comprehensively explore the relationship between medication combinations and AKI. In this paper, we present a population-based retrospective cohort study that employs automated data analysis techniques to identify medications and medication combinations that are associated with a higher risk of AKI. By integrating multivariable logistic regression, frequent itemset mining, and stratified analysis, this study is designed to explore the complex relationships between medications and AKI in such a way that has never been attempted before. Through an analysis of prescription records of one million older patients stored in the healthcare administrative dataset at ICES (an independent, non-profit, world-leading research organization that uses population-based health and social data to produce knowledge on a broad range of healthcare issues), we identified 55 AKI-associated medications among 595 distinct medications and 78 AKI-associated medication combinations among 7748 frequent medication combinations. In addition, through a stratified analysis, we identified 37 cases where a particular medication was associated with increasing the risk of AKI when used with another medication. We have shown that our results are consistent with previous studies through consultation with a nephrologist and an electronic literature search. This research demonstrates how automated analysis techniques can be used to accomplish data-driven tasks using massive clinical datasets.

Is there an embryopathy associated with first-trimester exposure to angiotensin-converting enzyme inhibitors and angiotensin receptor antagonists? A critical review of the evidence

Drugs that interfere with the renin-angiotensin system, such as angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), are widely used to manage hypertension and heart failure. Adequate functioning of the RAS is essential for normal fetal kidney development. The potential for ACEIs and ARBs to impair fetal and neonatal renal function if taken after the first trimester of pregnancy has been well documented. Although these drugs were not found to be teratogenic in animals, until recently little was known about the teratogenic effects of ACEIs and ARBs in humans when exposure was limited to the first trimester of pregnancy. New evidence from epidemiologic studies indicates that there may be an elevated teratogenic risk when these drugs are taken during the first trimester of pregnancy. However, this elevated risk does not appear to be specific to ACEIs and ARBs, but is instead related to maternal factors and diseases that typically coexist with hypertension in pregnancy, such as diabetes, advanced maternal age, and obesity. Women who become pregnant while being treated with an ACEI or ARB should be advised to avoid exposure to these drugs during the second and third trimesters of pregnancy by switching to a different class of antihypertensive drugs between weeks 8 and 10 after conception.