Medicine-Induced Acute Kidney Injury Findings from Spontaneous Reporting Systems, Sequence Symmetry Analysis and a Case-Control Study with a Focus on Medicines Used in Primary Care

Spironolactone for Preventing Contrast-Induced Nephropathy After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction and Chronic Kidney Disease

Patients with acute myocardial infarction (AMI) and chronic kidney disease (CKD) are at high risk of contrast-induced nephropathy (CIN), which can subsequently worsen the overall prognosis. To evaluate the efficacy of spironolactone for CIN prevention, 410 patients with AMI and CKD receiving percutaneous coronary intervention (PCI) were retrospectively analyzed. Among them, 240 and 170 patients were enrolled in the standard treatment and spironolactone groups (spironolactone was administered 2 days before and 3 days after PCI), respectively. The primary endpoint of CIN was defined as a 0.5 mg/dL or >25% increase from the baseline serum creatinine level within 48-72 h post-PCI. CIN incidence was significantly lower in the spironolactone group than in the standard treatment group (11.2 vs 26.7%, P < .001). Further, cardiac re-hospitalization (hazard ratio [HR]: 0.515; 95% CI: 0.382-0.694; P < .001) and cardiac death (HR: 0.612; 95% CI: 0.429-0.872; P = .007) risks were significantly lower in patients who received long-term spironolactone with a median treatment duration of 42 months after discharge. Spironolactone might lower the risk of CIN, and long-term use of spironolactone reduces the risk of cardiac re-hospitalization and cardiac death in patients with AMI and CKD undergoing PCI.

Prevalence and clinical significance of potential drug-drug interactions among lung transplant patients

Background: Drug-drug interactions (DDIs) are a major but preventable cause of adverse drug reactions. There is insufficient information regarding DDIs in lung transplant recipients. Objective: This study aimed to determine the prevalence of potential DDIs (pDDIs) in intensive care unit (ICU) lung transplant recipients, identify the real DDIs and the most frequently implicated medications in this vulnerable population, and determine the risk factors associated with pDDIs. Methods: This retrospective cross-sectional study included lung transplant recipients from January 2018 to December 2021. Pertinent information was retrieved from medical records. All prescribed medications were screened for pDDIs using the Lexicomp® drug interaction software. According to this interaction software, pDDIs were classified as C, D, or X (C = monitor therapy, D = consider therapy modification, X = avoid combination). The Drug Interaction Probability Scale was used to determine the causation of DDIs. All statistical analysis was performed in SPSS version 26.0. Results: 114 patients were qualified for pDDI analysis, and total pDDIs were 4051. The most common type of pDDIs was category C (3323; 82.0%), followed by D (653; 16.1%) and X (75; 1.9%). Voriconazole and posaconazole were the antifungal medicine with the most genuine DDIs. Mean tacrolimus concentration/dose (Tac C/D) before or after co-therapy was considerably lower than the Tac C/D during voriconazole or posaconazole co-therapy (p < 0.001, p = 0.027). Real DDIs caused adverse drug events (ADEs) in 20 patients. Multivariable logistic regression analyses found the number of drugs per patient (OR, 1.095; 95% CI, 1.048-1.145; p < 0.001) and the Acute Physiology and Chronic Health Evaluation II (APACHE Ⅱ) score (OR, 1.097; 95% CI, 1.021-1.179; p = 0.012) as independent risk factors predicting category X pDDIs. Conclusion: This study revealed a high incidence of both potential and real DDIs in ICU lung transplant recipients. Immunosuppressive drugs administered with azole had a high risk of causing clinically significant interactions. The number of co-administered drugs and APACHE Ⅱ score were associated with an increased risk of category × drug interactions. Close monitoring of clinical and laboratory parameters is essential for ensuring successful lung transplantation and preventing adverse drug events associated with DDIs.

A Case Report: Lithium-Induced Neurotoxicity, a Differential to Always Consider

Lithium, a mood stabilizer commonly prescribed for bipolar disorder, has a narrow therapeutic index that increases the risk of toxicity for patients who are prescribed this medication. Patients presenting with lithium toxicity could have a wide array of symptoms triggered by several factors that mimic other neurological conditions. In this paper, we discuss the case of an 81-year-old male who presented to the emergency department with worsening tremors and visual hallucinations, ataxia, and cognitive decline. He was initially thought to have Parkinson's disease with dementia in the outpatient setting and was later found to have lithium toxicity. Swift identification and management, involving fluid diuresis, led to the complete resolution of the patient's neurological symptoms by the fourth day of hospitalization. This case calls attention to the challenges of diagnosing lithium toxicity due to the variability in presentation as well as precipitating factors that clinicians must be cognizant of when working up patients who are prescribed lithium.

Signal detection statistics of adverse drug events in hierarchical structure for matched case-control data

The tree-based scan statistic is a data mining method used to identify signals of adverse drug reactions in a database of spontaneous reporting systems. It is particularly beneficial when dealing with hierarchical data structures. One may use a retrospective case-control study design from spontaneous reporting systems (SRS) to investigate whether a specific adverse event of interest is associated with certain drugs. However, the existing Bernoulli model of the tree-based scan statistic may not be suitable as it fails to adequately account for dependencies within matched pairs. In this article, we propose signal detection statistics for matched case-control data based on McNemar's test, Wald test for conditional logistic regression, and the likelihood ratio test for a multinomial distribution. Through simulation studies, we demonstrate that our proposed methods outperform the existing approach in terms of the type I error rate, power, sensitivity, and false detection rate. To illustrate our proposed approach, we applied the three methods and the existing method to detect drug signals for dizziness-related adverse events related to antihypertensive drugs using the database of the Korea Adverse Event Reporting System.

Hospitalized patients received furosemide undergoing acute kidney injury: the risk and prediction tool

PURPOSE

Furosemide, a frequently prescribed diuretic for managing congestive heart failure and edema, remains a topic of debate regarding its potential risk of inducing acute kidney injury (AKI) in patients. Consequently, this study aims to examine the occurrence of hospital-acquired AKI (HA-AKI) in hospitalized patients who are administered furosemide and to investigate potential risk factors associated with this outcome.

METHODS

This study encompassed a cohort of 22374 hospitalized patients who either received furosemide treatment or not from June 1, 2012, to December 31, 2017. Propensity score matching was employed to establish comparability between the two groups regarding covariates. Subsequently, a nomogram was constructed to predict the probability of AKI occurrence among patients who underwent furosemide treatment.

RESULTS

The regression analysis identified the single-day total dose of furosemide as the most significant factor for AKI, followed by ICU administration, estimated glomerular filtration rate, antibiotic, statin, NSAIDs, β-blockers, proton pump inhibitor, chronic kidney disease, and 7 other indicators. Subgroup analysis revealed a synergistic effect of furosemide with surgical operation, previous treatment with β-blockers, ACEI/ARB and antibiotics, leading to an increased risk of AKI when used in combination. Subsequently, a visually represented prognostic nomogram was developed to predict AKI occurrence in furosemide users. The predictive accuracy of the nomogram was assessed through calibration analyses, demonstrating an excellent agreement between the nomogram predictions and the actual likelihood of AKI, with a probability of 77.40%.

CONCLUSIONS

Careful consideration of factors such as dosage, concurrent medication use, and renal function of the patient is necessary for clinical practice when using furosemide. Our practical prognostic model for HA-AKI associated with furosemide use can be utilized to assist clinicians in making informed decisions about patient care and treatment.