Dataset Information

The Incidence, Causes, and Risk Factors of Acute Kidney Injury in Patients Receiving Immune Checkpoint Inhibitors.

ABSTRACT: Background and objectives:Immune checkpoint inhibitor use in oncology is increasing rapidly. We sought to determine the frequency, severity, cause, and predictors of AKI in a real-world population receiving checkpoint inhibitors.

Design, setting, participants, & measurements:We included all patients who received checkpoint inhibitor therapy from May 2011 to December 2016 at Massachusetts General Hospital. Baseline serum creatinine, averaged 6 months before checkpoint inhibitor start date, was compared with all subsequent creatinine values within 12 months of starting therapy. AKI was defined by Kidney Disease: Improving Global Outcomes criteria for fold changes in creatinine from baseline. Sustained AKI events lasted at least 3 days and was our primary outcome. The cause of sustained AKI was determined by chart review. Cumulative incidence and subdistribution hazard models were used to assess the relationship between baseline demographics, comorbidities, and medications, and sustained AKI and potential checkpoint inhibitor-related AKI.

Results:We included 1016 patients in the analysis. Average age was 63 (SD 13) years, 61% were men, and 91% were white. Mean baseline creatinine was 0.9 mg/dl (SD 0.4 mg/dl), and 169 (17%) had CKD (eGFR<60 ml/min per 1.73 m²) at baseline. A total of 169 patients (17%) experienced AKI, defined by an increase in creatinine at least 1.5 times the baseline within 12 months; 82 patients (8%) experienced sustained AKI and 30 patients (3%) had potential checkpoint inhibitor-related AKI. The first episode of sustained AKI occurred, on average, 106 days (SD 85) after checkpoint inhibitor initiation. Sixteen (2%) patients experienced stage 3 sustained AKI and four patients required dialysis. Proton pump inhibitor use at baseline was associated with sustained AKI.

Conclusions:AKI is common in patients receiving checkpoint inhibitor therapy. The causes of sustained AKI in this population are heterogenous and merit thorough evaluation. The role of PPI and other nephritis-inducing drugs in the development of sustained AKI needs to be better defined.

SUBMITTER: Seethapathy H

PROVIDER: S-EPMC6895474 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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The Incidence, Causes, and Risk Factors of Acute Kidney Injury in Patients Receiving Immune Checkpoint Inhibitors.

Seethapathy Harish H Zhao Sophia S Chute Donald F DF Zubiri Leyre L Oppong Yaa Y Strohbehn Ian I Cortazar Frank B FB Leaf David E DE Leaf David E DE Mooradian Meghan J MJ Villani Alexandra-Chloé AC Sullivan Ryan J RJ Reynolds Kerry K Sise Meghan E ME

Clinical journal of the American Society of Nephrology : CJASN 20191031 12

<h4>Background and objectives</h4>Immune checkpoint inhibitor use in oncology is increasing rapidly. We sought to determine the frequency, severity, cause, and predictors of AKI in a real-world population receiving checkpoint inhibitors.<h4>Design, setting, participants, & measurements</h4>We included all patients who received checkpoint inhibitor therapy from May 2011 to December 2016 at Massachusetts General Hospital. Baseline serum creatinine, averaged 6 months before checkpoint inhibitor sta ...[more]

PMID: 31672794

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Project description:Background: The benefits of immune checkpoint inhibitors (ICPis) in the treatment of patients with malignancies emerged recently, but immune-related adverse events (IRAEs), including acute kidney injury (AKI), cannot be ignored. The present study established and validated an ICPi-AKI prediction model based on machine learning algorithms to achieve early prediction of AKI events and timely intervention adjustment. Methods: We performed a retrospective study based on data from the First Medical Center of the PLA General Hospital. Patients with malignancy who received at least one dose of ICPi between January 2014 and December 2019 were included in the study. The characteristics of available variables were included after case review, and the baseline characteristics and clinical data of ICPi AKI and non-AKI patients were compared. After variable preprocessing, eight machine learning algorithms were used to construct a full variable availability model. Variable simplification models were constructed after screening important variables using the random forest recursive feature elimination method, and the performance of different machine learning methods and two types of modeling strategies were evaluated using multiple indicators. Results: Among the 1616 patients receiving checkpoint inhibitors, the overall incidence of AKI was 6.9% during the total follow-up time. Sixty-eight patients were associated with ICPi treatment after chart review, primarily in AKI stage 1 (70.5%), with a median time from first ICPi administration to AKI of 12.7 (IQR 2 to 56) weeks. The demographic characteristics, comorbidities, and proportions of malignancy types were similar between the ICPi-AKI and non-AKI groups, but there were significant differences in multiple characteristics, such as concomitant medications and laboratory test indicators. For model performance evaluation and comparison, the AUC values of all 38 variable availability models ranged from 0.7204-0.8241, and the AUC values of the simplicity model constructed using 16 significant variables ranged from 0.7528-0.8315. The neural networks model (NNs) and support vector machine (SVM) model had the best performance in the two types of modeling strategies, respectively; however, there was no significant difference in model performance comparison (p > 0.05). In addition, compared with the full variable availability model, the performance of the variable simplicity model was slightly improved. We also found that concomitant medications contributed more to the model prediction performance by screening the optimal feature combination. Conclusion: We successfully developed a machine learning-based ICPi-AKI prediction model and validated the best prediction performance of each machine model. It is reasonable to believe that clinical decision models driven by artificial intelligence can improve AKI prediction in patients with malignancies treated with ICPi. These models can be used to assist clinicians in the early identification of patients at high risk of AKI, support effective prevention and intervention, and ultimately improve the overall benefit of antitumor therapy in the target population.

Project description:Background The incidence and risk factors of acute kidney injury (AKI) in patients with malignancies receiving immune checkpoint inhibitors (ICIs) are being extensively reported with their widespread application. Objective This study aimed to quantify the incidence and identify risk factors of AKI in cancer patients treated with ICIs. Methods We searched the electronic databases of PubMed/Medline, Web of Science, Cochrane and Embase before 1 February 2023 on the incidence and risk factors of AKI in patients receiving ICIs and registered the protocol in PROSPERO (CRD42023391939). A random-effect meta-analysis was performed to quantify the pooled incidence estimate of AKI, identify risk factors with pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) and investigate the median latency period of ICI-AKI in patients treated with ICIs. Assessment of study quality, meta-regression, and sensitivity and publication bias analyses were conducted. Results In total, 27 studies consisting of 24048 participants were included in this systematic review and meta-analysis. The overall pooled incidence of AKI secondary to ICIs was 5.7% (95% CI: 3.7%-8.2%). Significant risk factors were older age (OR: 1.01, 95% CI: 1.00–1.03), preexisting chronic kidney disease (CKD) (OR: 2.90, 95% CI: 1.65–5.11), ipilimumab (OR: 2.66, 95% CI: 1.42–4.98), combination of ICIs (OR: 2.45, 95% CI: 1.40–4.31), extrarenal immune-related adverse events (irAEs) (OR: 2.34, 95% CI: 1.53-3.59), and proton pump inhibitor (PPI) (OR: 2.23, 95% CI: 1.88–2.64), nonsteroidal anti-inflammatory drug (NSAID) (OR: 2.61, 95% CI: 1.90–3.57), fluindione (OR: 6.48, 95% CI: 2.72–15.46), diuretic (OR: 1.78, 95% CI: 1.32–2.40) and angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin-receptor blockers (ARBs) (pooled OR: 1.76, 95% CI: 1.15–2.68) use. Median time from ICIs initiation to AKI was 108.07 days. Sensitivity and publication bias analyses indicated robust results for this study. Conclusion The occurrence of AKI following ICIs was not uncommon, with an incidence of 5.7% and a median time interval of 108.07 days after ICIs initiation. Older age, preexisting chronic kidney disease (CKD), ipilimumab, combined use of ICIs, extrarenal irAEs, and PPI, NSAID, fluindione, diuretics and ACEI/ARB use are risk factors for AKI in patients receiving ICIs. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023391939.

Project description:IntroductionImmune checkpoint inhibitors (ICIs) induce impressive antitumor responses but may lead to acute kidney injury (AKI) associated with ICI therapy (AKI-ICI). Biomarkers distinguishing AKI-ICI from AKI because of other causes (AKI-other) are currently lacking. Because ICIs block immunoregulatory pathways, we hypothesized that biomarkers related to immune cell dysregulation, including tumor necrosis factor alpha (TNF-α) and other markers of B and T cell activation in the systemic circulation and kidney tissue, may aid with the diagnosis of AKI-ICI.MethodsThis is a prospective study consisting of 24 participants who presented with AKI during ICI therapy, adjudicated to either have AKI-ICI (n = 14) or AKI-other (n = 10). We compared markers of kidney inflammation and injury (neutrophil gelatinase-associated lipocalin, kidney injury molecule-1) as well as plasma and urine levels of T cell-associated cytokines (TNF-α, interferon-γ, interleukin (IL)-2, IL-4, IL-6, IL-8, IL-9, and IL-10) between groups. We also compared T-cell responses in the systemic circulation and in kidney tissue across groups, using mass cytometry systems.ResultsWe observed increase in several specific immune cells, including CD4 memory, T helper cells, and dendritic cells in the kidney tissue, as well as in the urine cytokines IL-2, IL-10, and TNF-α, in patients who developed AKI-ICI compared to patients with AKI-other (P < 0.05 for all). The discriminatory ability of TNF-α on AKI cause was strong (area under the curve = 0.814, 95% confidence interval: 0.623-1.00. The CD4+ T cells with memory phenotype formed the dominant subset.ConclusionThese results suggest that specific T-cell responses and their respective cytokines may be indicative of AKI associated with ICI therapy and may help to differentiate AKI-ICI from AKI-other. Urine TNF-α is a promising biomarker for AKI-ICI, which is most often caused by acute interstitial nephritis (AIN), and TNF-α pathway may serve as a potential target for therapeutic intervention.

Project description:With the development and introduction of immune checkpoint inhibitors (ICIs) in cancer patients, immune-related side effects have increasingly attracted attention. However, the risks of immune-related renal toxicity are poorly characterized. In this study, we performed a network meta-analysis (NMA) of ICI-related randomized clinical trials (RCTs) to elucidate the comparative risk of acute kidney injury (AKI) in cancer patients receiving different ICIs. We also sought to identify other factors potentially affecting the risk of AKI. PubMed and EMBASE were searched for peer-reviewed trial reports published between January 2000 and May 2021. Eligible studies were RCTs studying ICIs in cancer patients and reporting AKI data. We performed a frequentist NMA to evaluate the risk ratios for grade 1-5 and grade 3-5 AKI between the treatment groups. We also assessed the absolute incidence of AKI in the ICI-containing arm using traditional direct meta-analysis. Once significant heterogeneity was detected in a traditional direct meta-analysis, multivariable meta-regression analysis was applied to identify factors that significantly affected the absolute incidence of AKI. A total of 85 RCTs were included in this study. In the NMA for the risk of grade 1-5 and 3-5 AKI, ipilimumab showed a significantly higher risk than avelumab and durvalumab, whereas 1 mg/kg nivolumab plus 3 mg/kg ipilimumab (N1I3) showed a significantly higher risk than other groups. In terms of treatment ranking, durvalumab ± low-dose tremelimumab and avelumab were consistently among the top three safest treatments for grade 1-5 or 3-5 AKI, whereas N1I3, ipilimumab and tremelimumab were consistently among the top three treatments with the highest risk for grade 1-5 or 3-5 AKI. Compared with other cancers, renal cell carcinoma and urothelial carcinoma showed a significantly higher risk of AKI. The incidence of AKI was significantly higher with ICI+chemotherapy than with ICI monotherapy. In this NMA involving large-scale up-to-date ICI trials, we demonstrated the comparative safety of existing ICI drugs for grade 1-5 and grade 3-5 AKI. Based on data from the ICI arms of these trials, we also revealed several potential risk factors for immune-related AKI, including tumor type and treatment paradigm.

Dataset Information

The Incidence, Causes, and Risk Factors of Acute Kidney Injury in Patients Receiving Immune Checkpoint Inhibitors.

Publications

The Incidence, Causes, and Risk Factors of Acute Kidney Injury in Patients Receiving Immune Checkpoint Inhibitors.

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