Acute kidney injury in patients with cancer receiving anti-PD-1/PD-L1 antibodies: incidence, risk factors, and prognosis.

Anti-PD-1/PD-L1 antibodies are widely used in anti-cancer therapy. While they have improved cancer prognoses, immune-related adverse events, which can cause acute kidney injury (AKI), cannot be ignored. The purpose of this retrospective cohort study was to assess the incidence, risk factors, and prognosis of AKI associated with anti-PD-1/PD-L1 antibodies. Patients who received anti-PD-1/PD-L1 antibody treatment at our hospital between January 2018 and December 2022 were enrolled. Clinical information, combined medications, concomitant diseases, tumor types, and laboratory indicators were collected from patient records, and the incidence of AKI was determined. The risk factors for AKI were assessed using univariate and multivariate logistic regression analyses. Overall, 1418 patients were enrolled. The median follow-up time was 112 days and 92 (6.5%) developed AKI. The median time from the initial anti-PD-1/PD-L1 antibody treatment to AKI was 99.85 days. Head and neck cancer and combined use of diuretics, non-steroidal anti-inflammatory drugs (NSAIDs), lower hemoglobin level, and other types of chemotherapeutic drugs were independent risk factors for AKI. The complete recovery, partial recovery, non-recovery, and unknown AKI rates were 7.6%, 28.3%, 52.2%, and 11.9%, respectively. Kidney biopsies were performed on two patients with AKI and pathology confirmed diagnosis of acute tubulointerstitial nephritis. In this cohort, AKI was not uncommon in patients treated with anti-PD-1/PD-L1 antibodies; therefore, it is necessary to monitor renal function and identify AKI early, especially in patients with head and neck tumors. Improving anemia and minimizing the use of diuretics, NSAIDs, and chemotherapeutics may reduce AKI.

A risk prediction model for contrast-induced nephropathy associated with gadolinium-based contrast agents.

OBJECTIVE: This is the first study to explore the risk factors for nephropathy caused by gadolinium-based contrast agents and establish a prediction model to identify high-risk patients. METHODS: A total of 1404 patients who received gadolinium-based contrast agents in our hospital were included. The participants were randomly assigned in a 7:3 ratio to the modeling and validation groups. The modeling group was divided into a contrast-induced nephropathy group and a non-contrast-induced nephropathy group. The clinical characteristics before the use of contrast agents were compared between the two groups. The risk factors for contrast-induced nephropathy were analyzed by logistic regression. A nomogram that could predict the incidence of contrast-induced nephropathy was plotted. The validation group was used to verify the predictive model. RESULTS: The incidence of contrast-induced nephropathy caused by gadolinium-based contrast agents was 3.92% (55/1404). The logistic stepwise regression analysis showed that sex, systolic pressure (SBP), absolute neutrophil count, albumin, fasting blood glucose level, and furosemide use were significant predictors of contrast-induced nephropathy caused by gadolinium-based contrast agents. The above predictors were then included in the nomogram construction. The area under the receiver operating characteristic (ROC) curve was 0.82 (p < 0.001). The specificity and sensitivity corresponding to the optimal cutoff point (0.039) based on the area under the ROC curve were 71.9% and 80.5%, respectively. CONCLUSION: Sex, SBP, absolute neutrophil count, albumin, fasting blood glucose levels, and furosemide use are significant predictors of contrast-induced nephropathy caused by gadolinium-based contrast agents. Therefore, the incidence of contrast-induced nephropathy may be estimated by the prediction model established in this study before the use of contrast agents.

To Ligate or Not to Ligate: A Meta-analysis of Cardiac Effects and Allograft Function following Arteriovenous Fistula Closure in Renal Transplant Recipients.

BACKGROUND: There are no available guidelines regarding preserving or ligating an arteriovenous fistula following successful kidney transplantation. Therefore, we performed a meta-analysis to formulate a clear opinion regarding the status of arteriovenous fistula in renal allograft recipients. METHODS: We performed an electronic literature search of PubMed, EMBASE, and the Cochrane Library for eligible studies up to January 2019. We aimed to systematically evaluate cardiac systolic and diastolic function and allograft function after arteriovenous fistula closure. The primary outcome was the differences in left ventricular mass index, left ventricular end-diastolic diameter, ejection fraction, and serum creatinine levels between patients with occluded arteriovenous fistula versus patent arteriovenous fistula. RESULTS: Renal transplant recipients with occluded arteriovenous fistula had lower left ventricular mass index values and left ventricular end-diastolic diameter compared with those with patent arteriovenous fistula. The pooled mean differences were 21.67 (95% confidence interval [CI] 19.94 to 23.39, P < 0.001) and 2.75 (95% CI 1.47 to 4.04, P < 0.001), respectively. Patients with occluded arteriovenous fistula also had lower serum creatinine levels versus those with patent arteriovenous fistula. The pooled mean difference was 0.10 (95% CI 0.04 to 0.17, P = 0.003). We saw no difference regarding ejection fraction between the groups. The pooled mean difference was 0.84 (95% CI -0.85 to 2.53, P = 0.33). CONCLUSIONS: Our meta-analysis demonstrated that arteriovenous fistula closure improves cardiac morphology and has better kidney graft function. Arteriovenous fistula closure may be considered in patients with a well-functioning allograft.

An optimized machine learning model for predicting hospitalization for COVID-19 infection in the maintenance dialysis population.

COVID-19 has a high rate of infection in dialysis patients and poses a serious risk to human health. Currently, there are no dialysis centers in China that have analyzed the prevalence of COVID-19 infection in dialysis patients and the mortality rate. Although machine learning-based disease prediction methods have proven to be effective, redundant attributes in the data and the interpretability of the predictive models are still worth investigating. Therefore, this paper proposed a wrapper feature selection classification model to achieve the prediction of the risk of COVID-19 infection in dialysis patients. The method was used to optimize the feature set of the sample through an enhanced JAYA optimization algorithm based on the dispersed foraging strategy and the greedy levy mutation strategy. Then, the proposed method combines fuzzy K-nearest neighbor for classification prediction. IEEE CEC2014 benchmark function experiments as well as prediction experiments on the uremia dataset are used to validate the proposed model. The experimental results showed that the proposed method has a high prediction accuracy of 95.61% for the prevalence risk of COVID-19 infection in dialysis patients. Furthermore, it was shown that proalbumin, CRP, direct bilirubin, hemoglobin, albumin, and phosphorus are of great value for clinical diagnosis. Therefore, the proposed method can be considered as a promising method.