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The association between acute kidney injury and outcomes in cancer patients receiving immune checkpoint inhibitor therapy: a systematic review and meta-analysis.

ABSTRACT:

Background

Immune checkpoint inhibitors (ICPIs) are a novel therapeutic approach to cancer treatment that have changed the landscape of cancer therapy but also have some considerable drawbacks. Acute kidney injury (AKI) is one of these potential complications that may have effects on patient outcomes. In this review, we assessed the effect of AKI on mortality outcomes in cancer patients receiving this immunotherapy.

Methods

We performed a systematic review and meta-analysis of prospective, retrospective, randomized and non-randomized studies, which examined the effects of AKI in cancer patients receiving immune checkpoint inhibitors. We searched through PubMed, Medline, Web of Science, Scopus and Cochrane Library databases.

Results

Seven studies were included in the final analysis, with a total number of patients of 761. Overall, the risk of death was higher in patients that developed AKI during ICPI treatment [hazard ratio (HR) 1.42, 95% confidence interval (CI) 1.05-1.92, P = 0.02; heterogeneity χ² = 11.68, I² = 66%, P = 0.02] compared with patients that did not develop AKI. In addition, there was a trend to a better survival in those with less severe AKI patients compared with those with more severe AKI (HR 1.35, 95% CI 0.99-1.83, P = 0.05). Lastly, it was seen that patients with persistent kidney dysfunction (non-recovery) had an increased risk for all-cause mortality (HR 2.93, 95% CI 1.41-6.08, P = 0.004; heterogeneity χ² = 0.53, I² = 0%, P = 0.47).

Conclusions

Development of AKI in patients with cancer receiving immune checkpoint inhibitors is associated with increased risk of mortality.

SUBMITTER: Kanbay M

PROVIDER: S-EPMC10157786 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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The association between acute kidney injury and outcomes in cancer patients receiving immune checkpoint inhibitor therapy: a systematic review and meta-analysis.

Kanbay Mehmet M Copur Sidar S Siriopol Dimitrie D Yildiz Abdullah Burak AB Berkkan Metehan M Popa Raluca R Hasbal Nuri Baris NB Ortiz Alberto A Perazella Mark A MA

Clinical kidney journal 20220831 5

<h4>Background</h4>Immune checkpoint inhibitors (ICPIs) are a novel therapeutic approach to cancer treatment that have changed the landscape of cancer therapy but also have some considerable drawbacks. Acute kidney injury (AKI) is one of these potential complications that may have effects on patient outcomes. In this review, we assessed the effect of AKI on mortality outcomes in cancer patients receiving this immunotherapy.<h4>Methods</h4>We performed a systematic review and meta-analysis of pro ...[more]

PMID: 37151409

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Project description:ObjectivesImmune checkpoint inhibitors (ICPi) have significantly improved survival for patients with advanced cancers. However, the occurrence of ICPi-associated acute kidney injury (AKI) and its clinical impact remains unclear. This study evaluates the effects of ICPi-associated AKI (ICPi-AKI) on mortality, kidney and cardiovascular outcomes in patients undergoing ICPi treatments.DesignThis multicentre retrospective cohort study with propensity score matching to balance baseline characteristics. The International Classification of Diseases, 10th Revision codes were used to identify individuals with cancer and treated with ICPi concurrently. Kaplan-Meier analyses coupled with log-rank tests were conducted to estimate the survival probabilities.SettingData were sourced from the TriNetX database spanning records from 25 March 2011 to 5 April 2024.ParticipantsPatients with cancer aged ≥18 years treated with ICPi with or without AKI occurrence.Primary and secondary outcome measuresThe primary outcome was all-cause mortality, and secondary outcomes included major adverse kidney events (MAKE), major adverse cardiovascular events (MACE), the composite of MAKE or MACE with death, and end-stage renal disease.ResultsThe study identified 926 patients with cancer who developed ICPi-AKI (mean age, 67.1±11.8 years; 57.4% men). The control group consisted of 48 147 patients treated with ICPi but did not develop AKI (mean age, 65.3±13.1 years; 53.7% men). After matching, the ICPi-AKI group exhibited a higher risk of all-cause mortality (HR=1.27; 95% CI 1.02 to 1.61), MAKE (HR=3.83; 95% CI 1.72 to 8.40), MACE (HR=1.35; 95% CI 1.03 to 1.75)) compared with the non-ICPi-AKI group. Subgroup analyses confirmed these findings across various patient's characteristics.ConclusionIndividuals with ICPi-AKI are associated with an increased risk of all-cause mortality, MAKE and MACE. Enhancing awareness and timely intervention for ICPi-AKI are crucial for improving prognosis and reducing complications among patients with cancer.

Project description:BackgroundAcute kidney injury (AKI) is a critical complication of immune checkpoint inhibitor therapy. Since the etiology of AKI in patients undergoing cancer therapy varies, clarifying underlying causes in individual cases is critical for optimal cancer treatment. Although it is essential to individually analyze immune checkpoint inhibitor-treated patients for underlying pathologies for each AKI episode, these analyses have not been realized. Herein, we aimed to individually clarify the underlying causes of AKI in immune checkpoint inhibitor-treated patients using a new clustering approach with Shapley Additive exPlanations (SHAP).MethodsWe developed a gradient-boosting decision tree-based machine learning model continuously predicting AKI within 7 days, using the medical records of 616 immune checkpoint inhibitor-treated patients. The temporal changes in individual predictive reasoning in AKI prediction models represented the key features contributing to each AKI prediction and clustered AKI patients based on the features with high predictive contribution quantified in time series by SHAP. We searched for common clinical backgrounds of AKI patients in each cluster, compared with annotation by three nephrologists.ResultsOne hundred and twelve patients (18.2%) had at least one AKI episode. They were clustered per the key feature, and their SHAP value patterns, and the nephrologists assessed the clusters' clinical relevance. Receiver operating characteristic analysis revealed that the area under the curve was 0.880. Patients with AKI were categorized into four clusters with significant prognostic differences (p = 0.010). The leading causes of AKI for each cluster, such as hypovolemia, drug-related, and cancer cachexia, were all clinically interpretable, which conventional approaches cannot obtain.ConclusionOur results suggest that the clustering method of individual predictive reasoning in machine learning models can be applied to infer clinically critical factors for developing each episode of AKI among patients with multiple AKI risk factors, such as immune checkpoint inhibitor-treated patients.

Project description:BackgroundImmune checkpoint inhibitors (ICIs) have been associated with acute kidney injury (AKI). However, the occurrence rate of ICI-related AKI has not been systematically examined. Additionally, exposure to proton pump inhibitors (PPIs) and non-steroidal anti-inflammatory drugs (NSAIDs) were considered as risk factors for AKI, but with inconclusive results in ICI-related AKI. Our aim was to analyse the occurrence rate of all-cause AKI and ICI-related AKI and the occurrence rates of severe AKI and dialysis-requiring AKI, and to determine whether exposure to PPIs and NSAIDs poses a risk for all-cause and ICI-related AKI.MethodsThis study population was adult ICI recipients. A systematic review was conducted by searching MEDLINE, Embase and PubMed through October 2023. We included prospective trials and observational studies that reported any of the following outcomes: the occurrence rate of all-cause or ICI-related AKI, the relationship between PPI or NSAID exposure and AKI development or the mortality rate in the AKI or non-AKI group. Proportional meta-analysis and pairwise meta-analysis were performed. The evidence certainty was assessed using the Grading of Recommendations Assessment, Development and Evaluation framework.ResultsA total of 120 studies comprising 46 417 patients were included. The occurrence rates of all-cause AKI were 7.4% (14.6% from retrospective studies and 1.2% from prospective clinical trials). The occurrence rate of ICI-related AKI was 3.2%. The use of PPIs was associated with an odds ratio (OR) of 1.77 [95% confidence interval (CI) 1.43-2.18] for all-cause AKI and an OR of 2.42 (95% CI 1.96-2.97) for ICI-related AKI. The use of NSAIDs was associated with an OR of 1.77 (95% CI 1.10-2.83) for all-cause AKI and an OR of 2.57 (95% CI 1.68-3.93) for ICI-related AKI.ConclusionsOur analysis revealed that approximately 1 in 13 adult ICI recipients may experience all-cause AKI, while 1 in 33 adult ICI recipients may experience ICI-related AKI. Exposure to PPIs and NSAIDs was associated with an increased OR risk for AKI in the current meta-analysis.

Project description: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.

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:ObjectiveReports of tuberculosis (TB) during anticancer treatment with immune checkpoint inhibitors (ICIs) are increasing. However, it is not clear whether the use of ICIs is a significant risk factor for TB, including reactivation or latent TB infection (LTBI).MethodsTo determine the risk of TB reactivation in patients with lung cancer who use ICIs or tyrosine kinase inhibitors (TKIs), we conducted a retrospective study using a hospital-based cancer registry. In addition, we monitored patients with cancer using ICI or TKI in a multicenter prospective study to check the incidence of LTBI.ResultsIn the retrospective study, several demographic factors were imbalanced between the ICI and TKI groups: the ICI group was younger, had more males, exhibited more squamous cell carcinoma in histology rather than adenocarcinoma, had fewer EGFR mutations, and received more chemotherapy. Propensity score matching was used to control for confounding factors, and we found that the incidence of TB was higher among patients with lung cancer who received ICIs than among those who received TKIs (2298 vs 412 per 100 000 person-years, P = .0165). Through multivariable analysis, group (ICI vs TKI) was the independent risk factor for TB development (adjusted hazard ratio (aHR): 6.29, 95% CI, 1.23-32.09, P = .0269). In the prospective cohort, which included 72 patients receiving ICIs and 50 receiving TKIs, we found that the incidence of positive seroconversion of LTBI by interferon gamma release assay (IGRA) was significantly higher in patients receiving ICIs (18% vs 0%, aHR: 9.88, P = 0.035) under multivariable Cox regression.ConclusionThe use of ICIs may be linked to a higher likelihood of TB reactivation and LTBI than individuals solely receiving TKIs as anticancer therapy. Consequently, the implementation of a screening program for TB reactivation and LTBI among patients undergoing ICI treatment could prove advantageous by enabling early detection and prompt treatment of the infection.

Project description:ImportanceSystemic steroids are commonly used to manage immune-related adverse events (irAEs), but it remains unclear whether they may undermine immune checkpoint inhibitor (ICI) therapy outcomes. Few studies have assessed the impact of steroid timing and its association with continuation or cessation of ICI therapy.ObjectiveTo characterize how systemic steroids and steroid timing for irAEs are associated with survival in patients receiving ICI therapy.Design, setting, and participantsThis multicenter retrospective cohort study encompassed veterans receiving ICI for cancer between January 1, 2010, and December 31, 2021. Data analysis was conducted September 8, 2023.ExposuresIdentifiable primary diagnosis of cancer. Patients were categorized into 3 cohorts: those receiving no steroids, systemic steroids for irAEs, and steroids for non-irAE-associated reasons. All eligible patients received 1 or more doses of an ICI (atezolizumab, avelumab, cemiplimab, durvalumab, ipilimumab, nivolumab, or pembrolizumab). Eligible patients in the steroid group received at least 1 dose (intravenous, intramuscular, or oral) of dexamethasone, hydrocortisone, methylprednisolone, prednisone, or prednisolone. Steroid use at baseline for palliation or infusion prophylaxis or delivered as a single dose was deemed to be non-irAE associated. All other patterns of steroid use were assumed to be for irAEs.Main outcomes and measuresThe primary outcome was overall survival, with a 5-year follow-up after ICI initiation. Kaplan-Meier survival analyses were performed with pairwise log-rank tests to determine significance. Risk was modeled with Cox proportional hazard regression.ResultsThe cohort consisted of 20 163 veterans receiving ICI therapy including 12 221 patients (mean [SD] age, 69.5 [8.0] years; 11 830 male patients [96.8%]; 9394 White patients [76.9%]) who received systemic steroids during ICI treatment and 7942 patients (mean [SD] age, 70.3 [8.5] years; 7747 male patients [97.5%]; 6085 White patients [76.6%]) who did not. Patients with an irAE diagnosis had significantly improved overall survival (OS) compared with those without (median [IQR] OS, 17.4 [6.6 to 48.5] months vs 10.5 [3.5 to 36.8] months; adjusted hazard ratio, 0.84; 95% CI, 0.81-0.84; P < .001). For patients with irAEs, systemic steroids for irAEs were associated with significantly improved survival compared with those who received steroids for non-irAE-related reasons or no steroid treatment (median [IQR] OS, 21.3 [9.3 to 58.2] months vs 13.6 [5.5 to 33.7] months vs 15.8 [4.9 to not reached] months; P <.001). However, among those who received steroids for irAEs, early steroid use (<2 months after ICI initiation) was associated with reduced relative survival benefit vs later steroid use, regardless of ICI continuation or cessation following steroid initiation (median [IQR] OS after ICI cessation 4.4 [1.9 to 19.5] months vs 16.0 [8.0 to 42.2] months; median [IQR] OS after ICI continuation, 16.0 [7.1 to not reached] months vs 29.2 [16.5 to 53.5] months; P <.001).Conclusions and relevanceThis study suggests that steroids for irAE management may not abrogate irAE-associated survival benefits. However, early steroid administration within 2 months of ICI initiation is associated with shorter survival despite continuation of ICI therapy.

Dataset Information

The association between acute kidney injury and outcomes in cancer patients receiving immune checkpoint inhibitor therapy: a systematic review and meta-analysis.

Background

Methods

Results

Conclusions

Publications

The association between acute kidney injury and outcomes in cancer patients receiving immune checkpoint inhibitor therapy: a systematic review and meta-analysis.

Similar Datasets

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