Clinical Characteristics of Hospitalized Patients with Drug-Induced Acute Kidney Injury and Associated Risk Factors: A Case-Control Study

Development and validation of a medication-based risk prediction model for acute kidney injury in older outpatients

BACKGROUND

Older adults are at an increased risk of acute kidney injury (AKI), particularly in community settings, often due to medications. Effective prevention hinges on identifying high-risk patients, yet existing models for predicting AKI risk in older outpatients are scarce, particularly those incorporating medication variables. We aimed to develop an AKI risk prediction model that included medication-related variables for older outpatients.

METHODS

We constructed a cohort of 2,272,257 outpatients aged ≥65 years using a national claims database. This cohort was split into a development (70%) and validation (30%) groups. Our primary goal was to identify newly diagnosed AKI within one month of cohort entry in an outpatient context. We screened 170 variables and developed a risk prediction model using logistic regression.

RESULTS

The final model integrated 12 variables: 2 demographic, 4 comorbid, and 6 medication-related. It showed good performance with acceptable calibration. In the validation cohort, the area under the receiver operating characteristic curve value was 0.720 (95% confidence interval, 0.692-0.748). Sensitivity and specificity were 69.9% and 61.9%, respectively. Notably, the model identified high-risk patients as having a 27-fold increased AKI risk compared with low-risk individuals.

CONCLUSION

We have developed a new AKI risk prediction model for older outpatients, incorporating critical medication-related variables with good discrimination. This tool may be useful in identifying and targeting patients who may require interventions to prevent AKI in an outpatient setting.

Vancomycin With Concomitant Piperacillin/Tazobactam vs. Cefepime or Meropenem Associated Acute Kidney Injury in General Ward Patients: A Multicenter Propensity Score-Matched Study

Background: Concurrent administration of vancomycin and piperacillin/tazobactam (VAN+PTZ) may increase the risk of acute kidney injury (AKI) in hospitalized patients. Comprehensive characterization of VAN+PTZ associated AKI and recovery patterns remains lacking in previous reports. Objective: To compare the incidence of AKI associated with VAN+PTZ compared to either cefepime (CEF) or meropenem (MER) with VAN in adult general ward patients. Methods: A multicenter, retrospective, propensity score cohort study was conducted in non-critically ill adult patients. Included patients were concurrently administered VAN+PTZ or VAN+CEF/MER. Patients developing AKI ≤48 hours following combination therapy were excluded. The primary endpoint was to compare the incidence of AKI between study groups. Multivariable Cox regression modeling in predicting AKI was also conducted. Results: A total of 3199 patients met inclusion criteria and were evaluated. The incidence of AKI in VAN+PTZ and VAN+CEF/MER groups were 16.4% and 8.7%, respectively (P < .001). The onset to AKI was 1.8 days earlier with VAN+PTZ compared to VAN+CEF/MER (P < .001). Multivariable prediction model showed concomitant VAN+PTZ was identified as an independent risk factor of developing AKI (HR 2.34, 1.82-3.01, P < .001). The VAN+PTZ group experienced significantly higher rates of severe AKI (stage II or III) compared to the VAN+CEF/MER group (P = .002). No differences in the AKI recovery patterns were found between study groups. Conclusions: Concomitant VAN+PTZ in adult general ward patients was independently associated with an increased risk of AKI overall. More severe AKI was also associated with VAN+PTZ.

Challenges in Pharmacovigilance: Variability in the Criteria for Determining Drug-Associated Acute Kidney Injury in Retrospective, Observational Studies

BACKGROUND

Drug-associated acute kidney injury (D-AKI) accounts for 19-26% of acute kidney injury (AKI) events in hospitalized patients and results in outcomes similar to patients with AKI from other etiologies. Diagnosing D-AKI is complex and various criteria have been used.

SUMMARY

To highlight the variability in D-AKI determination, a review was conducted between January 2017 and December 2022 using PubMed. Search terms included adaptations of "drug associated kidney injury" to identify a sampling of literature discussing definitions and criteria for D-AKI evaluation. The search yielded 291 articles that were uploaded to Rayyan, a software tool used to screen and select studies. Retrospective, observational electronic health record (EHR) studies conducted in hospitalized patients were included. The final sample contained 16 studies for data extraction, representing mostly adult populations (n = 13, 81.3%) in noncritical or unspecified inpatient settings (n = 12, 75%). Nine studies (56.3%) utilized the recommended Kidney Disease: Improving Global Outcome guidelines (KDIGO) criteria to define AKI. Baseline creatinine or laboratory criteria for kidney function were provided in 10 studies (62.5%). Eleven studies (68.8%) established a temporal sequence assessment linking nephrotoxin drug exposure to an AKI event, but these criteria were inconsistent among studies using time frames as soon as 3 months prior to AKI.

CONCLUSION

This review highlights the substantial variability in D-AKI criteria in select studies. Minimum expectations about what should be reported and criteria for the elements reported are needed to assure transparency, consistency, and standardization of pharmacovigilance strategies.

Detection of potential drug-drug interactions for risk of acute kidney injury: a population-based case-control study using interpretable machine-learning models

Background: Acute kidney injury (AKI), with an increase in serum creatinine, is a common adverse drug event. Although various clinical studies have investigated whether a combination of two nephrotoxic drugs has an increased risk of AKI using traditional statistical models such as multivariable logistic regression (MLR), the evaluation metrics have not been evaluated despite the fact that traditional statistical models may over-fit the data. The aim of the present study was to detect drug-drug interactions with an increased risk of AKI by interpreting machine-learning models to avoid overfitting. Methods: We developed six machine-learning models trained using electronic medical records: MLR, logistic least absolute shrinkage and selection operator regression (LLR), random forest, extreme gradient boosting (XGB) tree, and two support vector machine models (kernel = linear function and radial basis function). In order to detect drug-drug interactions, the XGB and LLR models that showed good predictive performance were interpreted by SHapley Additive exPlanations (SHAP) and relative excess risk due to interaction (RERI), respectively. Results: Among approximately 2.5 million patients, 65,667 patients were extracted from the electronic medical records, and assigned to case (N = 5,319) and control (N = 60,348) groups. In the XGB model, a combination of loop diuretic and histamine H₂ blocker [mean (|SHAP|) = 0.011] was identified as a relatively important risk factor for AKI. The combination of loop diuretic and H₂ blocker showed a significant synergistic interaction on an additive scale (RERI 1.289, 95% confidence interval 0.226-5.591) also in the LLR model. Conclusion: The present population-based case-control study using interpretable machine-learning models suggested that although the relative importance of the individual and combined effects of loop diuretics and H₂ blockers is lower than that of well-known risk factors such as older age and sex, concomitant use of a loop diuretic and histamine H₂ blocker is associated with increased risk of AKI.

Analysis of clinical characteristics and automatic monitoring of drug-induced arrhythmias in 167,546 inpatients

OBJECTIVE

The purpose of this study was to analyze the occurrence characteristics, clinical manifestations, medication distribution, and incidence of drug-induced arrhythmias in a real-world inpatient population.

METHODS

According to the inclusion and exclusion criteria as well as the ADR evaluation criteria, we retrospectively evaluated hospitalized patients in 2019 using the arrhythmia module of the Adverse Drug Event Active Surveillance and Assessment System-II (ADE-ASAS-II). A detailed analysis was performed on the demographic data, ADR manifestations, and medication distribution of 2097 patients with drug-induced arrhythmias and QT interval prolongation.

RESULTS

Of the 167,546 hospitalized patients, there were 1809 cases of drug-induced arrhythmias, with an incidence of 1.08%. The ADRs in 45.35% of positive patients occurred within 3 days after medication administration, and 46.73% of the patients were 65 years old or older. The predominant ADRs identified in this study were extrasystole, tachycardia, and QT interval prolongation, of which the incidence was 0.20%. Levofloxacin was the most involved drug, and levofloxacin-associated rates of incidence of arrhythmia and QT interval prolongation were 1.24% and 0.44%, respectively. The risk factors for drug-induced arrhythmias were male sex, advanced age, emaciation, obesity, and underlying illnesses such as cardiovascular diseases, diabetes mellitus, cerebrovascular diseases, and hepatic and renal inadequacy (P < 0.05).

CONCLUSION

The incidence of drug-induced arrhythmias was in the range of common, while QTc interval prolongation was occasional. It is necessary to pay attention to patients with risk factors.

Drug-induced kidney injury in Chinese critically ill pediatric patients

Background: Drug-induced acute kidney injury (DIKI) is a common adverse drug reaction event but is less known in pediatric patients. The study explored the DIKI in Chinese pediatric patients using the Pediatric Intensive Care database (PIC). Method: We screened pediatric patients with acute kidney injury (AKI) using the KDIGO criteria from the PIC and then assessed the relationship between their drugs and DIKI using the Naranjo scale. For the fifteen frequently used DIKI-suspected drugs, we divided patients into drug-exposed and non-exposed groups, using the outcome of whether DIKI was presented or not. Propensity score matching (PSM) was used to control for the effects of four confounders, age, gender, length of hospital stay, and major diagnosis. Unconditional logistic regression was used to identify statistically significant differences between the two groups. Results: A total of 238 drugs were used 1,863 times by the 81 patients with DIKI during their hospital stay. After screening the Naranjo scale to identify the top 15 suspected DIKI drugs with a high frequency of use, we found that furosemide injection (p = 0.001), midazolam injection (p = 0.001), 20% albumin prepared from human plasma injection (p = 0.004), fentanyl citrate injection (p = 0.001), compound glycyrrhizin injection (p = 0.026), vancomycin hydrochloride for intravenous (p = 0.010), and milrinone lactate injection (p = 0.009) were associated with DIKI. Conclusion: In critically ill pediatric patients, DIKI is more likely to occur after using furosemide injection, midazolam injection, 20% albumin prepared from human plasma injection, fentanyl citrate injection, compound glycyrrhizin injection, vancomycin hydrochloride for intravenous, milrinone lactate injection.

Traditional Chinese Medicine in Treating Primary Podocytosis: From Fundamental Science to Clinical Research

Podocytes form a key component of the glomerular filtration barrier. Damage to podocytes is referred to as “podocyte disease.” There are many causes of podocyte injury, including primary injury, secondary injury, and gene mutations. Primary podocytosis mostly manifests as nephrotic syndrome. At present, first-line treatment is based on glucocorticoid administration combined with immunosuppressive therapy, but some patients still progress to end-stage renal disease. In Asia, especially in China, traditional Chinese medicine (TCM) still plays an important role in the treatment of kidney diseases. This study summarizes the potential mechanism of TCM and its active components in protecting podocytes, such as repairing podocyte injury, inhibiting podocyte proliferation, reducing podocyte apoptosis and excretion, maintaining podocyte skeleton structure, and upregulating podocyte-related protein expression. At the same time, the clinical efficacy of TCM in the treatment of primary podocytosis (including idiopathic membranous nephropathy, minimal change disease, and focal segmental glomerulosclerosis) is summarized to support the development of new treatment strategies for primary podocytosis.

Kidney damage from nonsteroidal anti-inflammatory drugs-Myth or truth? Review of selected literature

Nonsteroidal anti-inflammatory drugs (NSAIDs) are widely available drugs with anti-inflammatory and analgesic properties. Their mechanism of action is associated with the enzymes of the arachidonic acid cycle (cyclooxygenases: COX-1 and COX-2). The cyclooxygenase pathway results in the formation of prostanoids (prostaglandins [PGs], prostacyclins, and thromboxanes). It affects various structures of the human body, including the kidneys. Medical literature associates the usage of NSAIDs with acute kidney injury (AKI), tubulointerstitial nephritis (TIN), as well as nephrotic syndrome and chronic kidney disease (CKD). AKI associated with the chronic consumption of NSAIDs is mainly attributed to pharmacological polytherapy and the presence of cardiovascular or hepatic comorbidities. The pathomechanism of AKI and CKD is associated with inhibition of the biosynthesis of prostanoids involved in the maintenance of renal blood flow, especially PGE2 and PGI2. It is suggested that both COX isoforms play opposing roles in renal function, with natriuresis increased by COX-1 inhibition followed by a drop in a blood pressure, whereas COX-2 inhibition increases blood pressure and promotes sodium retention. TIN after NSAID use is potentially associated with glomerular basement membrane damage, reduction in pore size, and podocyte density. Therefore, nephrotic proteinuria and impairment of renal function may occur. The following article analyzes the association of NSAIDs with kidney disease based on available medical literature.

Development and Validation of a Nomogram for Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients: A Case-Control Study Based on Propensity-Score Matching

Background: Drug-induced acute kidney injury (D-AKI) is associated with increased mortality and longer hospital stays. This study aims to establish a nomogram to predict the occurrence of D-AKI in hospitalized patients in a multi-drug environment.

Methods: A single center retrospective study among adult hospitalized patients was conducted from July 2019 to September 2019 based on the Adverse Drug Events Active Surveillance and Assessment System-2 developed by our hospital. According to the propensity score matching algorithm, four controls per case were matched to eliminate the confounding bias caused by individual baseline variables. The predictors for D-AKI were obtained by logistic regression equation and used to establish the nomogram.

Results: Among 51,772 hospitalized patients, 332 were diagnosed with D-AKI. After matching, 288 pairs and 1,440 patients were included in the study, including 1,005 cases in the development group and 435 cases in the validation group. Six variables were independent predictors for D-AKI: alcohol abuse, the concurrent use of nonsteroidal anti-inflammatory drugs or diuretics, chronic kidney disease, lower baseline red blood cell count and neutrophil count ≥7 × 10⁹/L. The area under the curve (AUC) of the prediction model in the development group and validation group were 0.787 (95%CI, 0.752–0.823) and 0.788 (95%CI, 0.736–0.840), respectively. The GiViTI calibration belts showed that the model had a good prediction accuracy for the occurrence of D-AKI (p > 0.05).

Conclusion: This nomogram can help identify patients at high risk of D-AKI, which was useful in preventing the progression of D-AKI and treating it in the early stages.

Incidence, characteristics and risk factors for drug-induced liver injury in hospitalized patients: A matched case-control study

AIMS

The diagnosis of drug-induced liver injury (DILI) is relatively complex and involves a wide variety of drugs. The purpose of this study was to use algorithms to quickly screen DILI patients, determine its incidence and identify risk factors.

METHODS

The Adverse Drug Events Active Surveillance and Assessment System-2 was used to extract the data of patients hospitalized in 2019 according to the set standards and the Roussel Uclaf Causality Assessment Method was used to evaluate patients who met the standards. A retrospective case-control study was conducted according to suspected drugs, length of hospital stay and height- and weight-matched controls, and logistic regression was used to identify risk factors.

RESULTS

Among the 156 570 hospitalized patients, 480 patients (499 cases) with DILI were confirmed and the incidence of DILI was 0.32%. Anti-infective agents, antineoplastic agents and nonsteroidal anti-inflammatory drugs were the major categories of drugs causing DILI, and the highest incidence of DILI was due to voriconazole. The latency period and hospital stay of patients with cholestasis were both relatively long. Patients with hyperlipidaemia (adjusted odds ratio [AOR] 1.884), cardiovascular disease (AOR 1.465), pre-existing liver disease (AOR 1.827) and surgical history (AOR 1.312) were at higher risk for DILI.

CONCLUSIONS

The incidence of DILI in hospitalized patients was uncommon (0.32%) and its pathogenic drugs were widely distributed. The incidence of DILI for many drugs has been seriously underestimated. It is recommended to focus on patients with hyperlipidaemia, cardiovascular disease, pre-existing liver disease and surgical history.