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Acute kidney injury (AKI) is one of the most frequent complications of critical illness. We aimed to explore the predictors of renal function recovery and the short-term reversibility after AKI by comparing logistic regression with four machine learning models. We reviewed patients who were diagnosed with AKI in the MIMIC-IV database between 2008 and 2019. Recovery from AKI within 72 h of the initiating event was typically recognized as the short-term reversal of AKI. Conventional logistic regression and four different machine algorithms (XGBoost algorithm model, Bayesian networks [BNs], random forest [RF] model, and support vector machine [SVM] model) were used to develop and validate prediction models. The performance measures were compared through the area under the receiver operating characteristic curve (AU-ROC), calibration curves, and 10-fold cross-validation. A total of 12,321 critically ill adult AKI patients were included in our analysis cohort. The renal function recovery rate after AKI was 67.9%. The maximum and minimum serum creatinine (SCr) within 24 h of AKI diagnosis, the minimum SCr within 24 and 12 h, and antibiotics usage duration were independently associated with renal function recovery after AKI. Among the 8364 recovered patients, the maximum SCr within 24 h of AKI diagnosis, the minimum Glasgow Coma Scale (GCS) score, the maximum blood urea nitrogen (BUN) within 24 h, vasopressin and vancomycin usage, and the maximum lactate within 24 h were the top six predictors for short-term reversibility of AKI. The RF model presented the best performance for predicting both renal functional recovery (AU-ROC [0.8295 ± 0.01]) and early recovery (AU-ROC [0.7683 ± 0.03]) compared with the conventional logistic regression model. The maximum SCr within 24 h of AKI diagnosis was a common independent predictor of renal function recovery and the short-term reversibility of AKI. The RF machine learning algorithms showed a superior ability to predict the prognosis of AKI patients in the ICU compared with the traditional regression models. These models may prove to be clinically helpful and can assist clinicians in providing timely interventions, potentially leading to improved prognoses.

Background To explore the influencing factors of perioperative renal function change and their relationship with prognosis on renal cell carcinoma (RCC) patients with tumor thrombus after nephrectomy and thrombectomy. Methods The clinical and pathological data of 135 patients with RCC and tumor thrombus, who underwent nephrectomy and thrombectomy at Peking University Third Hospital from May 2015 to July 2018, was retrospectively analyzed. Absolute change in estimated glomerular filtration rate (eGFR) (ACE) and percent change in eGFR (PCE) were calculated by preoperative and postoperative renal function. Linear regression analysis was used to explore the influencing factors of ACE and PCE, and logistic regression analysis was used to explore the influencing factors of worse postoperative renal function [eGFR≤60 mL/(min × 1.73 m^2)]. Cancer-specific survival (CSS) was estimated by Kaplan-Meier method and multivariate Cox regression, which were used to explore the effect of ACE and PCE on prognosis. Results Of all the 135 patients, 101 patients (74.8%) were male and 34 patients (25.2%) were female. The mean preoperative eGFR was 73.9 ± 21.8 mL/(min × 1.73 m^2) and postoperative eGFR was 69.5 ± 25.2 mL/(min × 1.73 m^2). In multivariate linear regression analysis, preoperative eGFR ( P  < 0.001) and pathological type ( P  = 0.038) were significant predictive factors of ACE. In aspect of PCE, preoperative eGFR (P < 0.001) and pathological type ( P  = 0.002) were significant predictors. In multivariate logistic regression analysis, preoperative eGFR ( P  = 0.016) was the only risk factor of predicting worse postoperative renal function. During follow-up, 22 patients (16.3%) were dead due to RCC. According to ROC analysis, the cut off value of ACE and PCE was 13.9 and 0.16, respectively. ACE> 13.9 and PCE > 0.16 indicated worse CSS ( P  = 0.006 and P  = 0.047, respectively). However, in multivariate Cox regression analysis of several related factors, perinephric tissues invasion ( P  = 0.001), sarcomatoid differentiation (P = 0.001) and ACE> 13.9 ( P  = 0.002) were significant prognostic factors for CSS. PCE > 0.16 seemed to be not ( P  = 0.055). Conclusion We explored several clinicopathological risk factors of predicting renal function change and their relationship with prognosis of RCC patients with tumor thrombus after nephrectomy and thrombectomy. The renal function change, which was associated with preoperative eGFR and pathological type, was prognostic risk factor for CSS and ACE> 13.9 indicated the worse prognosis.

Objective The study’s purpose is to explore the link of serum albumin on renal progression in patients with chronic kidney disease (CKD). Methods This study was a secondary analysis of a prospective cohort study in which a total of 954 participants were non-selectively and consecutively collected from the research of CKD-ROUTE in Japan between November 2010 and December 2011. We evaluated the association between baseline ALB and renal prognosis (initiation of dialysis or 50% decline in eGFR from baseline) and renal function decline (annual eGFR decline) using the Cox proportional-hazards and linear regression models, respectively. We performed a number of sensitivity analyses to ensure the validity of the results. In addition, we performed subgroup analyses. Results The included patients had a mean age of (66.86 ± 13.41) years, and 522 (69.23%) were male. The mean baseline ALB and eGFR were (3.89 ± 0.59) g/dL and (33.43 ± 17.97) ml/min/1.73 m 2 . The annual decline in eGFR was 2.65 mL/min/1.73 m 2 /year. 218 (28.9%) individuals experienced renal prognosis during a median follow-up period of 36.0 months. The baseline ALB was inversely linked with renal prognosis (HR = 0.61, 95%CI: 0.45, 0.81) and renal function decline (β = -1.41, 95%CI: -2.11, -0.72) after controlling for covariates. The renal prognosis and ALB had a non-linear connection, with ALB’s inflection point occurring at 4.3 g/dL. Effect sizes (HR) were 0.42 (0.32, 0.56) and 6.11 (0.98, 38.22) on the left and right sides of the inflection point, respectively. There was also a non-linear relationship between ALB and renal function decline, and the inflection point of ALB was 4.1 g/dL. The effect sizes(β) on the left and right sides of the inflection point were -2.79(-3.62, -1.96) and 0.02 (-1.97, 1.84), respectively. Conclusion This study shows a negative and non-linear association between ALB and renal function decline as well as renal prognosis in Japanese CKD patients. When ALB is lower than 4.1 g/dL, ALB decline was closely related to poor renal prognosis and renal function decline. From a therapeutic point of view, reducing the decline in ALB makes sense for delaying CKD progression.

PurposeTo externally validate the Palacios’ equation estimating the new baseline glomerular filtration rate (NB-GFR) after partial or radical-nephrectomy (PN, RN) for Renal cancer carcinoma (RCC).Materials and methodsOur research group recently published two studies that investigated the association between renal function and cancer-specific survival in RCC. The first one included 3457 patients undergone RN or PN for a cT1-2 RCC coming from five high-volume centers; the second one considered 1767 patients undergone RN or PN for a cT1-4 RCC in a single high-volume center. From such datasets, available complete patients’ data were used to calculate the predicted NB-GFR through the Palacios’ equation: predicted NB-GFR = 35.03 + 0.65 ∙ preoperative GFR – 18.19 ∙ (if radical nephrectomy) – 0.25 ∙ age + 2.83 ∙ (if tumor size > 7 cm) – 2.09 ∙ (if diabetes). The observed NB-GFR was calculated by the CKD-EPI equation on serum creatinine at 3–12 months after surgery. Concordance between observed and predicted NB-GFR was evaluated by Lin’s concordance correlation coefficient and Bland-Altman analysis.Results2419 patients were included (1210, cohort #1; 1219, cohort #2). The median observed NB-GFR value in cohorts #1 and #2 was 73.0 ml/min/1.73 m2 (IQR 56.1–90.1) and 64.2 ml/min/1.73 m2 (IQR 49.6–83); the median predicted NB-GFR was 71.1 ml/min/1.73 m2 (IQR 58–81.5) and 62.6 ml/min/1.73m2 (IQR 47.9–75.9). The concordance line showed a slope of 0.80 and 0.86, and an intercept at 11.02 and 5.41 ml/min/1.73 m2 in the cohort#1 and #2, respectively. The Palacio’s equation moderately over-estimated and under-estimated NB-GFR, for values below and above the cut-off of 50 ml/min/1.73 m2 and 35 ml/min/1.73m2 in cohort#1 and #2. The Lin’s concordance correlation coefficient was 0.79 (95% CI 0.77–0.81) and 0.83 (95% CI 0.82–0.85).ConclusionsWe confirm the predictive performances of Palacios’ equation, supporting its utilization in clinical practice.

Renal dysfunction affects approximately 30 to 50% of heart failure (HF) patients. The unfavourable relationship between heart and kidney dysfunction contributes to worse outcomes through several mechanisms such as inflammation, oxidative stress, impaired hydrosaline homeostasis, and diuretic resistance. Renal dysfunction not only carries important prognostic value both in acute and in chronic HF, but also is a potential precipitating factor after the first diagnosis. Because renal dysfunction encompasses different etiologies, a better understanding of its definition, incidence, and pathophysiology provides additional information. Although old and novel available biomarkers for the detection of renal dysfunction have been recently proposed, there is no general consensus regarding the terminology and definition of renal dysfunction in HF. Due to some specific pathophysiological mechanisms, renal impairment seems to be different on an individual patient level and, recognizing it in acute and chronic settings, could be useful to optimize decongestive treatment. For these reasons, in this review, we aim to describe and evaluate different phenotypes of renal dysfunction in acute and chronic HF and the possible management in these settings.Key messages• Chronic kidney dysfunction and worsening renal function are highly prevalent in acute heart failure and chronic heart failure and associated with poor outcomes.• This association is modified by the context in which it occurs, i.e. worsening renal function in the context of adequate decongestion in acute heart failure, or worsening renal function after initiation of neurohormonal blockers in chronic heart failure.• Future research should be aimed at elucidating the mechanisms involved in these differenct contexts, as well as alternative treatment approaches in the case of true worsening renal function.

The disorder of the “gut-kidney axis” exacerbates renal function decline in chronic kidney disease (CKD), and current CKD therapy is insufficient to address this issue. Hirudin has a palliative effect on the decline of renal function. However, whether hirudin can delay CKD by regulating the “intestinal renal axis” disorder remains unclear. Unilateral ureteral ligation (UUO) induced CKD rat model, and the rats were treated with bifidobacterium and hirudin for 36 days. After 14 and 36 days of modeling, kidney and colon tissues were collected for pathology, western blot (WB) assay, and quantitative real-time PCR (qPCR) detection. Serum samples were collected for renal function testing. Fecal samples were used for 16S rRNA sequencing and research on fecal bacterial transplantation. Lipopolysaccharide combine with adenosine 5’-triphosphate (LPS + ATP)-induced intestinal epithelial cell injury was treated with a nod-like receptor pyrin domain-associated protein 3 (NLRP3) inhibitor and hirudin. Protein expression was detected using WB and qPCR. The kidneys and colons of the CKD rats exhibited varying degrees of lesions. Creatinine (CRE), blood urea nitrogen (BUN), N-acetyl-β-D-glucosidase (NAG) enzyme, and serum uremic toxins were elevated. The expression of claudin-1 and occludin was decreased, NLRP3 inflammatory-related proteins were increased, and the gut microbiota was disrupted. These pathological changes were more pronounced after 36 days of modeling. Meanwhile, high-dose hirudin treatment significantly improved these lesions and restored the intestinal flora to homeostasis in CKD rats. In vitro, hirudin demonstrated comparable effects to NLRP3 inhibitors by upregulating claudin-1 and occludin expression, and downregulating NLRP3 inflammatory-related proteins expression. The dysbiosis of the gut microbiota and impaired intestinal epithelial barrier function in CKD are associated with renal dysfunction in CKD. Hirudin delays the progression of CKD by regulating the disorder of the “gut-kidney axis” and inhibiting the activation of the NLRP3-ASC-caspase-1 pathway.

PurposeTwo to seven percent of the German adult population has a renal impairment (RI) with an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m2. This often remains unrecognized and adjustment of drug therapy is lacking. To determine renal function in clinical routine, the CKD-EPI equation is used to calculate an indexed eGFR (ml/min/1.73m2). For drug dosing, it has to be individualized to a non-indexed eGFR (ml/min) by the patient’s body surface area. Here, we investigated the number of patients admitted to urological wards of a teaching hospital with RI between July and December 2016. Additionally, we correctly used the eGFRnon-indexed for drug and dosage adjustments and to analyse the use of renal risk drugs (RRD) and renal drug-related problems (rDRP).MethodsIn a retrospective observational study, urological patients with pharmacist-led medication reconciliation at hospital admission and eGFRindexed (CKD-EPI) of 15–59 ml/min/1.73m2 were identified. Indexed eGFR (ml/min/1.73m2) was recalculated with body surface area to non-indexed eGFR (ml/min) for correct drug dosing. Medication at admission was reviewed for RRD and based on the eGFRnon-indexed for rDRP, e.g. inappropriate dose or contraindication.ResultsOf 1320 screened patients, 270 (20.5%) presented with an eGFRindexed of 15–59 ml/min/1.73m2. After readjustment, 203 (15.4%) patients had an eGFRnon-indexed of 15–59 ml/min. Of these, 190 (93.6%) used ≥ 1 drugs at admission with 660 of 1209 (54.7%) drugs classified as RRD. At least one rDRP was identified in 115 (60.5%) patients concerning 264 (21.8%) drugs.ConclusionRenal impairment is a common risk factor for medication safety in urologic patients admitted to a hospital. Considerable shifts were seen in eGFR-categories when correctly calculating eGFRnon-indexed for drug dosing purposes. The fact that more than half of the study patients showed rDRP at hospital admission underlines the need to consider this risk factor appropriately.

Background Accurate prediction of post-donor nephrectomy (DN) glomerular filtration rate is potentially useful for evaluating and counselling living kidney donors. Currently, there are limited tools to evaluate post-operative new-baseline glomerular filtration rate (NBGFR) in kidney donors. We aim to validate a conceptually simple formula based on split renal function (SRF) previously developed for radical nephrectomy patients. Methods Eighty-three consecutive patients who underwent DN from 2010 to 2016 were included. Pre-operative CT imaging and functional data including pre-DN baseline Global GFR (108.2 ± 13.2 mL/min/1.73m 2 ) were included. Observed NBGFR was defined as the latest eGFR 3–12 months post-DN. SRF, defined as volume of the contralateral non-resected kidney normalised by total volume of kidneys, was determined from pre-operative cross-sectional imaging (49.2 ± 2.36%). The equation derived from Rathi et al. is as detailed: Predicted NBGFR = 1.24 × (Global GFR Pre-DN) x (SRF). Results The relationship between predicted NBGFR (66.0 ± 8.29 mL/min/1.73m 2 ) and observed NBGFR (74.9 ± 16.4 mL/min/1.73m 2 ) was assessed by evaluating correlation coefficients, bias, precision, accuracy, and concordance. The new SRF-based formula for NBGFR prediction correlated strongly with observed post-operative NBGFR (Pearson’s r  = 0.729) demonstrating minimal bias (median difference = 7.190 mL/min/1.73m 2 ) with good accuracy (96.4% within ± 30%, 62.7% within ± 15%) and precision (IQR of bias =  − 0.094 to 16.227). Conclusion The SRF-based formula was also able to accurately discriminate all but one patient to an NBGFR of > 45 mL/min/1.73m 2 . We utilised the newly developed SRF-based formula for predicting NBGFR in a living kidney donor population. Counselling of donor post-operative renal outcomes may then be optimised pre-operatively.

PurposeTo evaluate a conceptually simple model to predict new-baseline-glomerular-filtration-rate (NBGFR) after radical nephrectomy (RN) based on split-renal-function (SRF) and renal-functional-compensation (RFC), and to compare its predictive accuracy against a validated non-SRF-based model. RN should only be considered when the tumor has increased oncologic potential and/or when there is concern about perioperative morbidity with PN due to increased tumor complexity. In these circumstances, accurate prediction of NBGFR after RN can be important, with a threshold NBGFR > 45 ml/min/1.73m2 correlating with improved overall survival.Methods236 RCC patients who underwent RN (2010–2012) with preoperative imaging (CT/MRI) and relevant functional data were included. NBGFR was defined as GFR 3–12 months post-RN. SRF was determined using semi-automated software that provides differential parenchymal-volume-analysis (PVA) from preoperative imaging. Our SRF-based model was: Predicted NBGFR = 1.24 (× Global GFRPre-RN) (× SRFContralateral), with 1.24 representing the mean RFC estimate from independent analyses. A non-SRF-based model was also assessed: Predicted NBGFR = 17 + preoperative GFR (× 0.65)–age (× 0.25) + 3 (if tumor > 7 cm)–2 (if diabetes). Alignment between predicted/observed NBGFR was assessed by comparing correlation coefficients and area-under-the-curve (AUC) analyses.ResultsThe correlation-coefficients (r) were 0.87/0.72 for SRF-based/non-SRF-based models, respectively (p = 0.005). For prediction of NBGFR > 45 ml/min/1.73m2, the SRF-based/non-SRF-based models provided AUC of 0.94/0.87, respectively (p = 0.044).ConclusionPrevious non-SRF-based models to predict NBGFR post-RN are complex and omit two important parameters: SRF and RFC. Our proposed model prioritizes these parameters and provides a conceptually simple, accurate, and clinically implementable approach to predict NBGFR post-RN. SRF can be easily obtained using PVA software that is affordable, readily available (FUJIFILM-Medical-Systems), and more accurate than nuclear-renal-scans. The SRF-based model demonstrates greater predictive-accuracy than a non-SRF-based model, including the clinically-important predictive-threshold of NBGFR > 45 ml/min/1.73m2.

Impaired renal function (IRF) is associated with an elevated risk of major adverse renal events (MARE). However, the relationship between age-adapted estimated glomerular filtration rate (eGFR) criteria and in-hospital MARE has not been extensively studied in critically ill patients. Furthermore, the impact of eGFR trajectory changes on in-hospital MARE in this patient population remains underexplored. In this study, we analyzed data from 7,423 critically ill patients using version 2.2 of the Medical Information Mart for Intensive Care IV database. Based on the age-adapted eGFR criteria, renal function status was classified as impaired renal function (IRF), subclinical impairment of renal function (SIRF), and normal renal function (NRF). There were 2,438 patients (32.8%) of in-hospital MARE. The incidence of MARE and their individual endpoint components was higher in patients with SIRF and IRF than in patients with NRF. Group-based trajectory modeling revealed that, compared with patients with other renal function status, patients with SIRF demonstrated the most significant decline in eGFR as well as the highest risk of MARE based on the results of the low-level-to-decline trajectory. Additionally, a trend toward an increased risk of MARE was observed in patients with SIRF and IRF, particularly among younger patients, when compared with those with NRF. Critically ill patients with SIRF and IRF had an increased risk of in-hospital MARE. Patients with SIRF experienced the most notable decline in renal function during hospitalization, with the highest risk of MARE noted in this trajectory group. In addition, a trend toward an increased risk of MARE was observed in younger patients. Consequently, active monitoring and timely intervention in younger patients are imperative.