Predicting acute kidney injury after robot-assisted partial nephrectomy: Implications for patient selection and postoperative management

Perioperative and functional outcomes following robot-assisted partial nephrectomy: Descriptive analysis of Indian study group on partial nephrectomy database

Introduction

There is an unmet need for high-quality data for Robot-assisted partial nephrectomy (RAPN) in the Indian population. Indian study group on partial nephrectomy (ISGPN) is a consortium of Indian centers contributing to the partial nephrectomy (PN) database. The current study is a descriptive analysis of perioperative and functional outcomes following RAPN.

Methods

For this study, the retrospective ISGPN database was reviewed, which included patients who underwent RAPN for renal masses at 14 centers across India from September 2010 to September 2022. Demographic, clinical, radiological, perioperative, and functional data were collected and analyzed. Ethics approval was obtained from each of the participating centers.

Results

In this study, 782 patients were included, and 69.7% were male. The median age was 53 years (interquartile range [IQR 44-62]), median operative time was 180 min (IQR 133-240), median estimated blood loss was 100 mL (IQR 50-200), mean warm ischemia time was 22.7 min and positive surgical margin rates were 2.5%. The complication rate was 16.2%, and most of them were of minor grade. Trifecta and pentafecta outcomes were attained in 61.4% and 60% of patients, respectively.

Conclusions

This is the largest Indian multi-centric study using the Indian Robotic PN Collaborative database to evaluate the outcomes of robot-assisted PN, and has proven its safety and efficacy in the management of renal masses.

Biochemical implications of robotic surgery: a new frontier in the operating room

Robotic surgery represents a milestone in surgical procedures, offering advantages such as less invasive methods, elimination of tremors, scaled motion, and 3D visualization. This in-depth analysis explores the complex biochemical effects of robotic methods. The use of pneumoperitoneum and steep Trendelenburg positioning can decrease pulmonary compliance and splanchnic perfusion while increasing hypercarbia. However, robotic surgery reduces surgical stress and inflammation by minimizing tissue trauma. This contributes to faster recovery but may limit immune function. Robotic procedures also limit ischemia-reperfusion injury and oxidative damage compared to open surgery. They also help preserve native antioxidant defenses and coagulation. In a clinical setting, robotic procedures reduce blood loss, pain, complications, and length of stay compared to traditional procedures. However, risks remain, including device failure, the need for conversion to open surgery and increased costs. On the oncology side, there is still debate about margins, recurrence, and long-term survival. The advent of advanced technologies, such as intraoperative biosensors, localized drug delivery systems, and the incorporation of artificial intelligence, may further improve the efficiency of robotic surgery. However, ethical dilemmas regarding patient consent, privacy, access, and regulation of this disruptive innovation need to be addressed. Overall, this review sheds light on the complex biochemical implications of robotic surgery and highlights areas that require additional mechanistic investigation. It presents a comprehensive approach to responsibly maximize the potential of robotic surgery to improve patient outcomes, integrating technical skill with careful consideration of physiological and ethical issues.

The Association of Ischemia Type and Duration with Acute Kidney Injury after Robot-Assisted Partial Nephrectomy

BACKGROUND

Acute kidney injury (AKI) after robot-assisted partial nephrectomy (RAPN) is a robust surrogate for chronic kidney disease. The objective of this study was to evaluate the association of ischemia type and duration during RAPN with postoperative AKI.

MATERIALS AND METHODS

We reviewed all patients who underwent RAPN at our institution since 2011. The ischemia types were warm ischemia (WI), selective artery clamping (SAC), and zero ischemia (ZI). AKI was defined according to the Risk Injury Failure Loss End-Stage (RIFLE) criteria. We calculated ischemia time thresholds for WI and SAC using the Youden and Liu indices. Logistic regression and decision curve analyses were assessed to examine the association with AKI.

RESULTS

Overall, 154 patients met the inclusion criteria. Among all RAPNs, 90 (58.4%), 43 (28.0%), and 21 (13.6%) were performed with WI, SAC, and ZI, respectively. Thirty-three (21.4%) patients experienced postoperative AKI. We extrapolated ischemia time thresholds of 17 min for WI and 29 min for SAC associated with the occurrence of postoperative AKI. Multivariable logistic regression analyses revealed that WIT ≤ 17 min (odds ratio [OR] 0.1, p < 0.001), SAC ≤ 29 min (OR 0.12, p = 0.002), and ZI (OR 0.1, p = 0.035) significantly reduced the risk of postoperative AKI.

CONCLUSIONS

Our results confirm the commonly accepted 20 min threshold for WI time, suggest less than 30 min ischemia time when using SAC, and support a ZI approach if safely performable to reduce the risk of postoperative AKI. Selecting an appropriate ischemia type for patients undergoing RAPN can improve short- and long-term functional kidney outcomes.

Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis

Importance

Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.

Objective

To systematically review published AKI prediction models across all clinical subsettings.

Data Sources

MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.

Study Selection

All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.

Data Extraction and Synthesis

Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.

Main Outcomes and Measures

C statistic was used to measure the discrimination of prediction models.

Results

Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.

Conclusions and Relevance

In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

Evaluation of Renal Function after Partial Nephrectomy and Detection of Clinically Significant Acute Kidney Injury

Background and Objectives: Consequences of partial nephrectomy (PN), intraoperative hypotension (IOH) and postoperative neutrophil to lymphocyte ratio (NLR) may cause postoperative acute kidney injury (AKI) and in long-term-chronic kidney disease (CKD). Our study aimed to identify the AKI incidence after PN, to find clinically significant postoperative AKI and renal dysfunction, and to determine the predictor factors. Materials and Methods: A prospective observational study consisted of 91 patients who received PN with warm ischemia, and estimated preoperative glomerular filtration rate (eGFR) ≥ 60 mL/min and without abnormal albuminuria. Results: 38 (41.8%) patients experienced postoperative AKI. Twenty-one (24.1%) patients had CKD upstage after 1 year follow-up. Sixty-seven percent of CKD upstage patients had AKI 48 h after surgery and 11% after 2 months. All 15 (16.5%) patients with CKD had postoperative AKI. With IOH, OR 1.07, 95% CI 1.03−1.10 and p < 0.001, postoperative NLR after 48 h (OR 1.50, 95% CI 1.19−1.88, p < 0.001) was the major risk factor of AKI. In multivariate logistic regression analysis, the kidney’s resected part volume (OR 1.08, 95% CI 1.03−1.14, p < 0.001) and IOH (OR 1.10, 95% CI 1.04−1.15, p < 0.001) were retained as statistically significant prognostic factors for detecting postoperative renal dysfunction. The independent risk factor for clinically significant postoperative AKI was only IOH (OR, 1.06; p < 0.001). Only AKI with the CKD upstage group has a statistically significant effect (p < 0.0001) on eGFR 6 and 12 months after surgery. Conclusions: The presence of AKI after PN is not rare. IOH and NLR are associated with postoperative AKI. The most important predictive factor of postoperative AKI is an NLR of over 3.5. IOH is an independent risk factor for clinically significant postoperative AKI and together with kidney resected part volume effects postoperative renal dysfunction. Only clinically significant postoperative AKI influences the reduction of postoperative eGFR after 6 and 12 months.

Evaluation of Routine Postoperative Labs Following Robotic Assisted Partial Nephrectomy in Patients With Normal Preoperative Renal Function

OBJECTIVE

To evaluate predictors of abnormal routine postoperative day 1 (POD1) labs in patients with normal pre-operative renal function following robotic assisted partial nephrectomy (RAPN) and the associated clinical outcomes of these lab results.

METHODS

We analyzed 500 consecutive RAPN from a single surgeon series. Patients with chronic kidney disease (CKD) III or greater were excluded from the study. Three hundred ninty-three RAPN were included in the analysis. Routine POD1 lab tests including hemoglobin (Hgb), creatinine, potassium, and sodium were evaluated to determine rates of abnormal values and rates of clinical intervention. Abnormal Hgb at POD1 was defined as <8 g/dL or ≥3 g/dL decrease from the preoperative (baseline) value. Abnormal sodium (Na) preoperatively and postoperatively was defined as <135 mEq/L or >145 mEq/L. Abnormal potassium (K) was defined preoperatively and POD1 as <3.5 mEq/L or >5 mEq/L.

RESULTS

Of 37.4% (147/393) had one or more abnormal labs at POD1. Of the 101 patients with abnormal Hgb, 15 patients required blood transfusion. Twenty-six patients had abnormal sodium for which two were treated with IV fluids. Twenty-seven patients had potassium abnormalities (12/25 were hypokalemia). Acute kidney injury stage I was seen in 27 patients (6.9%) and stage II in 3 (0.8%). Patients with abnormal labs were more likely to have larger renal mass, higher R.E.N.A.L. scores, intraoperative complications, longer operative times, and higher EBL on multivariate analysis.

CONCLUSION

POD1 serum laboratory tests appear to be necessary following RAPN in patients with normal pre-operative renal function.

Predicting the risk of postoperative acute kidney injury: development and assessment of a novel predictive nomogram

OBJECTIVE

This study aimed to establish and internally verify the risk nomogram of postoperative acute kidney injury (AKI) in patients with renal cell carcinoma.

METHODS

We retrospectively collected data from 559 patients with renal cell carcinoma from June 2016 to May 2019 and established a prediction model. Twenty-six clinical variables were examined by least absolute shrinkage and selection operator regression analysis, and variables related to postoperative AKI were determined. The prediction model was established by multiple logistic regression analysis. Decision curve analysis was conducted to evaluate the nomogram.

RESULTS

Independent predictors of postoperative AKI were smoking, hypertension, surgical time, blood glucose, blood uric acid, alanine aminotransferase, estimated glomerular filtration rate, and radical nephrectomy. The C index of the nomogram was 0.825 (0.790-0.860) and 0.814 was still obtained in the internal validation. The nomogram had better clinical benefit when the intervention was decided at the threshold probabilities of >4% and <79% for patients and doctors, respectively.

CONCLUSIONS

This novel postoperative AKI nomogram incorporating smoking, hypertension, the surgical time, blood glucose, blood uric acid, alanine aminotransferase, the estimated glomerular filtration rate, and radical nephrectomy is convenient for facilitating the individual postoperative risk prediction of AKI in patients with renal cell carcinoma.

Unexpected Outcomes of Renal Function after Radical Nephrectomy: Histology Relevance along with Clinical Aspects

Acute kidney injury (AKI) and chronic kidney disease (CKD) are common events after radical nephrectomy (RN). In this study we aimed to predict AKI and CKD after RN relying on specific histological aspects. We collected data from a cohort of 144 patients who underwent radical nephrectomy. A histopathological review of the healthy part of the removed kidney was performed using an established chronicity score (CS). Logistic regression analyses were performed to predict AKI after RN, while linear regression analysis was adopted for estimated glomerular filtration rate (eGFR) variation at 1 year. The outcomes of the study were to determine variables correlated with AKI onset, and with eGFR decay at 1 year. The proportion of AKI was 64%. Logistic analyses showed that baseline eGFR independently predicted AKI (odds ratio 1.04, 95%CI 1.02:1.06). Moreover, AKI (Beta −16, 95%CI −21:−11), baseline eGFR (Beta −0.42, 95%CI −0.52:−0.33), and the presence of arterial narrowing (Beta 10, 95%CI 4:15) were independently associated with eGFR decline. Our findings showed that AKI onset and eGFR decline were more likely to occur with higher baseline eGFR and lower CS, highlighting that RN in normal renal function patients represents a more traumatic event than its CKD counterpart.

Risk factors for progression of chronic kidney disease after robotic partial nephrectomy in elderly patients: results from a multi-institutional collaborative series

BACKGROUND

Robotic partial nephrectomy (RPN) in patients ≥75 years is certainly underused with concerns regarding surgical quality and a negligible impact on renal function. The aim of this study was to identify predictors of progression of chronic kidney disease for purely off-clamp (ocRPN) and on-clamp RPN (onRPN) in elderly patients on a multi-institutional series.

METHODS

A collaborative minimally-invasive renal surgery dataset was queried for "RPN" performed between July 2007 and March 2021 and "age≥75 years". A total of 205 patients matched the inclusion criteria. Descriptive analyses were used. Frequencies and proportions were reported for categorical variables while medians and interquartile ranges (IQR) were reported for continuous variables. Baseline, perioperative and functional data were compared between groups. New-onset of stages 3b,4,5 CKD in onRPN and ocRPN cohorts was computed by Kaplan-Meier analysis. Univariable and multivariable Cox regression analyses were performed to identify predictors of progression to severe CKD (sCKD [stages ≥3b]). For all statistical analyses, a two-sided p < 0.05 was considered significant.

RESULTS

Mean age of the cohort considered was 78 years (IQR 76-80). At a median follow-up of 29 months (IQR 14.5-44.5), new onset CKD-3b and CKD-4,5 stages was observed in 16.6% and 2.4% of patients, respectively. At Kaplan-Meier analysis, onRPN was associated with a significantly higher risk of developing sCKD (p=0.002). On multivariable analysis, hypertension (HR 2.64; 95% CI 1.14-6.11; p=0.023), on-clamp approach (HR 3.41; 95% CI 1.50-7.74; p=0.003) non-achievement of trifecta (HR 0.36; 95% CI 0.17-0.78; p=0.01) were independent predictors of sCKD.

CONCLUSIONS

RPN in patients≥75 years is a safe surgical option. On-clamp approach, hypertension and non-achievement of trifecta were independent predictors of sCKD in the elderly after RPN.

Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database

BACKGROUND

This study aimed to develop and validate a nomogram for predicting acute kidney injury (AKI) during the Intensive Care Unit (ICU) stay of patients with diabetic ketoacidosis (DKA).

METHODS

A total of 760 patients diagnosed with DKA from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included and randomly divided into a training set (70%, n = 532) and a validation set (30%, n = 228). Clinical characteristics of the data set were utilized to establish a nomogram for the prediction of AKI during ICU stay. The least absolute shrinkage and selection operator (LASSO) regression was utilized to identified candidate predictors. Meanwhile, a multivariate logistic regression analysis was performed based on variables derived from LASSO regression, in which variables with P < 0.1 were included in the final model. Then, a nomogram was constructed applying these significant risk predictors based on a multivariate logistic regression model. The discriminatory ability of the model was determined by illustrating a receiver operating curve (ROC) and calculating the area under the curve (AUC). Moreover, the calibration plot and Hosmer-Lemeshow goodness-of-fit test (HL test) were conducted to evaluate the performance of our newly bullied nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit.

RESULTS

A multivariable model that included type 2 diabetes mellitus (T2DM), microangiopathy, history of congestive heart failure (CHF), history of hypertension, diastolic blood pressure (DBP), urine output, Glasgow coma scale (GCS), and respiratory rate (RR) was represented as the nomogram. The predictive model demonstrated satisfied discrimination with an AUC of 0.747 (95% CI, 0.706-0.789) in the training dataset, and 0.712 (95% CI, 0.642-0.782) in the validation set. The nomogram showed well-calibrated according to the calibration plot and HL test (P > 0.05). DCA showed that our model was clinically useful.

CONCLUSION

The nomogram predicted model for predicting AKI in patients with DKA was constructed. This predicted model can help clinical physicians to identify the patients with high risk earlier and prevent the occurrence of AKI and intervene timely to improve prognosis.

Development and Validation of a Nomogram Model to Predict Acute Kidney Disease After Nephrectomy in Patients with Renal Cell Carcinoma

Purpose

To develop and validate a nomogram model to predict the occurrence of acute kidney disease (AKD) after nephrectomy.

Patients and Methods

A retrospective cohort including 378 patients with renal cell carcinoma (RCC) who had undergone radical or partial nephrectomy between March 2013 and December 2017 at the First Affiliated Hospital of Zhengzhou University was analyzed. Of these, patients who had undergone surgery in an earlier period of time formed the training cohort (n=265) for nomogram development, and those who had undergone surgery thereafter formed the validation cohort (n=113) to confirm the model's performance. The incidence rate of AKD was measured. Univariate and multivariate logistics regression analysis was used to estimate the independent risk factors associated with AKD. The independent risk factors were incorporated into the nomogram. The accuracy and utility of the nomogram were evaluated by calibration curve and decision curve analysis, respectively.

Results

Overall, AKD occurred in 27.5% and 28.3% of patients in the training and validation cohorts, separately. The final nomogram included surgery approach, Charlson comorbidity index (CCI), and the decrement of eGFR. This model achieved good concordance indexes of 0.78 (95% CI=0.71-0.84) and 0.76 (95% CI=0.67-0.86) in the training and validation cohorts, respectively. The calibration curves and decision curve analysis (DCA) demonstrated the accuracy and the clinical usefulness of the proposed nomogram, separately.

Conclusion

The nomogram accurately predicts AKD after nephrectomy in patients with RCC. The risk for patients' progress into AKD can be determined, which is useful in guiding clinical decisions.

A visual risk assessment tool for acute kidney injury after intracranial aneurysm clipping surgery

Objective

The aim of the study was to establish a predictive postoperative nomogram for acute kidney injury (AKI) after intracranial aneurysm clipping surgery, in order to early identify patients with high postoperative AKI risk.

Methods

This is a retrospective study, which included patients who underwent intracranial aneurysm clipping surgery. Multivariate logistic regression was employed to select confound factors that associated with AKI, then incorporated into the nomogram. The predictive accuracy of the model was assessed by concordance index (C-Index).

Results

A total of 365 patients after intracranial aneurysm clipping surgery were enrolled in the study eventually, of which 68 (18.63%) suffered postoperative AKI, and the incidence of stage 1, stage 2 and stage 3 were 92.65% (63/68), 5.88% (4/68), and 1.47% (1/68), respectively. Univariate logistic regression revealed that high density lipoprotein (HDL), prothrombin time (PT), estimated glomerular filtration rate (eGFR), size of aneurysm ≥10 mm, and aneurysm ruptured before surgery were associated with AKI after surgery, while multivariate logistic regression showed same results as the size of aneurysm ≥10 mm and aneurysm ruptured were independent AKI risk factors. In addition, the nomogram demonstrated a good accuracy in estimating intracranial aneurysm clipping associated AKI, as a C-Index and a bootstrap-corrected one of 0.772 and 0.737, respectively. Moreover, calibration plots showed consistency with the actual presence of AKI.

Conclusion

The novel nomogram model can serve as a promising predictive tool to improve the identification of AKI among those who underwent intracranial aneurysm clipping surgery.

How to Select the Optimal Candidates for Renal Mass Biopsy

Surgical treatment of small renal masses (RMs) is still characterized by a non-negligible rate of benign histology, ultimately resulting in overtreatment. Since the risk of kidney cancer increases with age and the risk of malignancy usually increases with tumor size, we created a model based on patient age, RM size, and their interaction for predicting malignant histology. As male sex is associated with a higher risk of renal malignancy, we also stratified our analyses by sex. We used data for 2252 patients with cT1N0M0 disease (1551 male [69%], 701 female [31%]). On logistic regression, both age and RM size were predictors of malignant histology. For males, the odds ratio (OR) was 1.82 (95% confidence interval [CI] 1.78-2.80) for age and 2.04 (95% CI 1.69-2.47) for RM size; for females, the OR was 1.82 (95% CI 1.78-2.80) for age and 2.04 (95% CI 1.69-2.47) for RM size (all p ≤ 0.007), with a significant continuous-by-continuous interaction between them (p < 0.001) in both models. On decision curve analysis, the model demonstrated clinical utility for predicting malignancy at a probability of <55% for males and <60% for females. Individuals with lower probability should be considered for renal biopsy and those with higher probability for upfront surgery. The model was also more informative than RM size alone in predicting malignancy, which currently represents the only absolute criterion for active surveillance eligibility. PATIENT SUMMARY: In this study we analyzed the correlation between age and tumor size for predicting tumor malignancy. The aim in management is to balance the utility of performing a biopsy and the appropriateness of upfront surgery against the ultimate goal of decreasing overtreatment.

Toward Individualized Approaches to Partial Nephrectomy: Assessing the Correlation Between Ischemia Time and Patient Health Status (RECORD2 Project)

BACKGROUND

Ischemia time during partial nephrectomy (PN) is among the greatest determinants of acute kidney injury (AKI). Whether this association is affected by the preoperative risk of AKI has never been investigated.

OBJECTIVE

To assess the effect of the interaction between the preoperative risk of AKI and ischemia time on the probability of AKI during PN.

DESIGN, SETTING, AND PARTICIPANTS

Data of 944 patients treated with on-clamp PN for cT1 renal tumors were extracted from the Registry of Conservative and Radical Surgery for Cortical Renal Tumor Disease (RECORD2) database, a prospective multicenter project.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We estimated the preoperative risk of AKI (defined according to the risk/injury/failure/loss/end-stage [RIFLE] criteria) according to age, baseline renal function, clinical stage, preoperative aspects and dimensions used for an anatomical (PADUA) score, and surgical approach. Classification and regression tree (CART) analysis identified patients at "high" and "low" risk of AKI. Finally, we plotted the probability of AKI over ischemia time stratified by the preoperative risk of AKI.

RESULTS AND LIMITATIONS

Overall, 235 (25%) patients experienced AKI after surgery. At multivariable analysis, older patients, those with more complex tumors, those with higher baseline function, and those treated with open surgery had an increased risk of AKI (all p ≤ 0.011). According to the first split at CART analysis, patients were categorized as those with "high" and "low" risk of AKI having a probability of >40% or <40%. For low-risk patients, the probability of AKI in case of <10 versus >20 min of ischemia was 13% versus 28% (absolute risk increase 15%). The risk of AKI for high-risk patients who had <10 versus >20 min of ischemia was 31% versus 77%. This corresponds to an absolute risk increase of 45%. Limitations include retrospective data analyses and lack of surgeons' prior experience.

CONCLUSIONS

Ischemia time during PN has different implications for patients with different health status. Clamp time seems less clinically relevant for patients in good conditions who may endure prolonged ischemia with a mild increase in the risk of AKI, whereas frail patients seem to be more vulnerable to ischemic damage even for short clamp time. For individualized intra- and postoperative management, duration of ischemia needs to be questioned in the context of the individual health status.

PATIENT SUMMARY

Functional sequelae related to ischemia time during partial nephrectomy depend on baseline health status. The correlation between the duration of ischemia and baseline health status should be taken into account toward individualized intra- and postoperative management.

Lower Incidence of Postoperative Acute Kidney Injury in Robot-Assisted Partial Nephrectomy Than in Open Partial Nephrectomy: A Propensity Score-Matched Study

Background: Acute kidney injury (AKI) after partial nephrectomy is attributed to parenchymal reduction and ischemia, but the extent of its effect remains unclear. This study aimed to compare the incidence of postoperative AKI among surgical modalities, robot-assisted partial nephrectomy (RAPN), laparoscopic partial nephrectomy (LPN), and open partial nephrectomy (OPN), and to evaluate the validity of RAPN by comparing it with LPN and OPN in terms of postoperative AKI, perioperative complications, and long-term renal function. Patients and Methods: Patients who underwent RAPN, LPN, and OPN for renal tumors at our institutions between 2004 and 2018 were retrospectively analyzed. RAPN and LPN were performed under warm ischemia and OPN under cold ischemia. En bloc hilar clamping was employed for LPN and OPN and arterial clamping for RAPN. AKI was defined as % decrease in estimated glomerular filtration rate (eGFR) >25% from preoperative eGFR to postoperative nadir eGFR. Multivariate regression analysis was used to test associations of AKI with perioperative factors. Then, we compared the incidence of AKI with two propensity score-matched cohorts: RAPN vs OPN and RAPN vs LPN. Results: This study included 1762 cases (RAPN: 959, LPN: 215, and OPN: 588). After matching, 147 cases each from RAPN and LPN groups and 368 cases each from RAPN and OPN groups were selected. RAPN had shorter warm ischemia time than LPN, lower incidence of AKI, and lower % decrease in eGFR after 6 months. RAPN had a shorter ischemia time and a lower incidence of AKI than OPN, although the % decrease in eGFR after 6 months did not differ significantly. Conclusions: AKI incidence was lower in RAPN than in LPN or OPN, which may be due to the shorter ischemia time or clamping of only arteries in RAPN. Although long-term renal outcomes did not differ between RAPN and OPN, RAPN can help prevent AKI. This supports the validity of RAPN for patients with chronic kidney disease.

Does race impact functional outcomes in patients undergoing robotic partial nephrectomy?

Background

The role of race on functional outcomes after robotic partial nephrectomy (RPN) is still a matter of debate. We aimed to evaluate the clinical and pathologic characteristics of African American (AA) and Caucasian patients who underwent RPN and analyzed the association between race and functional outcomes.

Methods

Data was obtained from a multi-institutional database of patients who underwent RPN in 6 institutions in the USA. We identified 999 patients with complete clinical data. Sixty-three patients (6.3%) were AA, and each patient was matched (1:3) to Caucasian patients by age at surgery, gender, Charlson Comorbidity Index (CCI) and renal score. Bivariate and multivariate logistic regression analyses were used to evaluate predictors of acute kidney injury (AKI). Kaplan-Meier method and multivariable semiparametric Cox regression analyses were performed to assess prevalence and predictors of significant eGFR reduction during follow-up.

Results

Overall, 252 patients were included. AA were more likely to have hypertension (58.7% vs. 35.4%, P=0.001), even after 1:3 match. Overall 42 patients (16.7%) developed AKI after surgery and 35 patients (13.9%) developed significant eGFR reduction between 3 and 15 months after RAPN. On multivariate analysis, AA race did not emerge as a significant factor for predicting AKI (OR 1.10, P=0.8). On Cox multivariable analysis, only AKI was found to be associated with significant eGFR reduction between 3 and 15 months after RAPN (HR 2.49, P=0.019).

Conclusions

Although African American patients were more likely to have hypertension, renal function outcomes of robotic partial nephrectomies were not significantly different when stratified by race. However, future studies with larger cohorts are necessary to validate these findings.

Urine microscopy and neutrophil-lymphocyte ratio are early predictors of acute kidney injury in patients with urinary tract infection

Objective

Urinary tract infection (UTI) is a common cause of morbidity and hospitalisation in the population worldwide. Upper UTI is indolent and causes subclinical acute kidney injury (AKI) resulting in preventable cause of scarring of renal parenchyma. We explored urinary and serum levels of kidney injury molecule-1 (KIM-1), haematological parameters and quantitative urine microscopy parameters to predict kidney injury.

Methods

Neutrophil–lymphocyte ratio (NLR) is obtained by dividing absolute neutrophil count with absolute lymphocyte count. Quantitative urine sediment microscopy was performed and correlated with clinical, biochemical and haematological findings to predict AKI in patients with UTI. Quantitative ELISA was performed for serum and urine levels of KIM-1. Seventy two adult patients with UTI were enrolled, 45 of whom had AKI while 27 were in the non-AKI group.

Results

NLR (p=0.005) and renal tubular epithelial cell-granular cast score in quantitative urine microscopy (p=0.008) are strong predictors of AKI in patients with UTI while rest of quantitative urine microscopy parameters and serum and urinary levels of KIM-1 molecule were not found to be useful in prediction of AKI.

Conclusion

NLR in haemogram is a novel and useful biomarker for predicting AKI in patients with UTI.

Defining Risk Categories for a Significant Decline in Estimated Glomerular Filtration Rate After Robotic Partial Nephrectomy: Implications for Patient Follow-up

Following partial nephrectomy (PN), it is important to prevent any deterioration in estimated glomerular filtration rate (eGFR). At present there are no evidence-based recommendations on when a nephrology consultation should be requested and how to adjust postoperative management when the risk of renal function decline is high. In an effort to address this void, we used our previously published nomogram to define risk groups for a significant decline in eGFR at 3-15 mo after PN. We used the nomogram-derived probability as the independent variable for the classification and regression tree and identified four risk groups: low (0-10%), intermediate (10-21%), high (21-65%), and very high (65-100%). Overall, 336 (34%), 386 (39%), 243 (24%), and 34 (4%) patients fell in the low, intermediate, high, and very high risk groups, respectively. The rates of significant eGFR decline across the low, intermediate, high, and very high risk groups were 4%, 14%, 29%, and 79%. With the low risk category as a reference, the hazard ratio for eGFR decline was 3.21 (95% confidence interval [CI] 1.83-5.64) for the intermediate, 7.80 (95% CI 4.52-13.48) for the high, and 27.24 (95% CI 13.8-53.8) for the very high risk group (all p<0.001). These prognostic risk categories can be used to design postoperative follow-up schedules. A multidisciplinary approach can be considered for patients at high and very high risk of eGFR decline. PATIENT SUMMARY: We propose a new stratification system to identify individuals at high risk of a decline in renal function after robotic partial nephrectomy.