Proteinuria frequency and subsequent renal dysfunction in bevacizumab-treated patients: a single center, retrospective, observational study.

BACKGROUND: Proteinuria is a common adverse event observed during treatment with antivascular endothelial growth factor (VEGF) antibodies. Proteinuria is a risk factor for renal dysfunction and cardiovascular complications in patients with chronic kidney disease. However, the association between anti-VEGF antibody-induced proteinuria and renal dysfunction or cardiovascular complications remains unclear. METHODS: This retrospective, observational study included patients with cancer that were treated with bevacizumab (BV) at Kyoto University Hospital (Kyoto, Japan) between January 2006 and March 2018. Adverse event rates were compared between patients who developed qualitative ≥ 2 + proteinuria and those who developed < 1 + proteinuria. Adverse events were defined as renal dysfunction (i.e., ≥ 57% decrease in the eGFR, compared to the rate at the initial treatment) and hospitalization due to BV-associated cardiovascular complications and other adverse events. RESULTS: In total, 734 patients were included in this analysis. Renal dysfunction was more common in patients with ≥ 2 + proteinuria than in those with < 1 + proteinuria (13/199, 6.5% vs. 12/535, 2.3%). Seven of these 13 patients with ≥ 2 + proteinuria had transient reversible renal dysfunction. Only four (2.0%) patients had BV-associated renal dysfunction. Of the 734 patients, six patients, 16 patients, and 13 patients were hospitalized because of the adverse events of cardiovascular complications, thromboembolisms, and cerebrovascular complications, respectively. No relationship was observed between these adverse events and proteinuria. CONCLUSION: BV treatment-induced proteinuria was not associated with renal dysfunction or other adverse events. Continuing BV with caution is a possible treatment option, even after proteinuria develops, in patients with cancer and a limited prognosis.

Low serum concentrations of bevacizumab and nivolumab owing to excessive urinary loss in patients with proteinuria: a case series.

PURPOSE: Proteinuria can cause interindividual variability in the pharmacokinetics of therapeutic antibodies and may affect therapeutic efficacy. Here, we measured the serum and urinary concentrations of bevacizumab (BV) and nivolumab (NIVO) in patients with proteinuria and reported a case series of these patients. METHODS: Thirty-two cancer patients who received BV every 3 weeks or NIVO every 2 weeks between November 2020 and September 2021 at Kyoto University Hospital were enrolled in this study. The serum and urinary concentrations of BV and NIVO were measured using liquid chromatography-tandem mass spectrometry. RESULTS: We divided the BV-treated patients and the NIVO-treated patients into two groups based on the urine protein-creatinine ratio (UPCR): UPCR 1 g/g or higher (BV, n = 9; NIVO, n = 3) and UPCR less than 1 g/g (BV, n = 14; NIVO, n = 6). Serum concentrations of the therapeutic antibodies adjusted by their doses were significantly lower in both BV- and NIVO-treated patients with UPCR 1 g/g or higher compared to those with less than 1 g/g. In patients with UPCR 1 g/g or higher, urinary concentrations of the therapeutic antibodies adjusted by their serum concentrations and urinary creatinine concentrations tended to increase. CONCLUSION: This case-series study suggests a possibility of reduction in serum concentrations of BV and NIVO in patients with proteinuria by urinary excretion of these drugs.

Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy.

BACKGROUND: Acute 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). METHODS: We 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. RESULTS: One 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. CONCLUSION: Our 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.

Association between Proton Pump Inhibitors, Immune Checkpoint Inhibitors, and Acute Kidney Injury: A Nested Case-Control Study.

BACKGROUND: An association between proton pump inhibitor (PPI) use and an increased risk of acute kidney injury (AKI) has been confirmed. This study aimed to evaluate the effects of PPI use on the risk of AKI in patients with cancer who were administered immune checkpoint inhibitors (ICIs), a class of drugs used in cancer treatment, and in those who were not. METHODS: We used a database provided by the Health, Clinic, and Education Information Evaluation Institute, which included demographic data, diagnoses, prescriptions, and laboratory results. We conducted a nested case-control study of 38,930 patients with cancer who were new PPI or ICI users and had no history of AKI before cohort entry. The odds ratio (OR) for AKI was estimated using conditional logistic regression models. RESULTS: During a mean follow-up of 8.3 months, 5,870 cases of AKI were identified (incidence rate, 21.9/100 person-years). Compared to never or past PPI use without ICI use, the adjusted ORs of AKI for current PPI use without ICI use, past or never PPI use with prior ICI use, current PPI use with prior ICI use were 1.82 (95% CI, 1.67 to 2.00), 1.47 (95% CI, 1.17 to 1.86), or 2.13 (95% CI, 1.42 to 3.20), respectively. The risk of AKI in patients treated with both PPIs and ICIs was not higher than the additional or multiplication of the risks in those who were treated with PPIs or ICIs alone. CONCLUSIONS: This study reinforces the association between PPIs and ICIs use and the increased risk of AKI. Although the interaction between the two drug classes was not detected, these findings highlight the need for careful monitoring and evaluation of kidney function in patients treated with PPIs and ICIs.