Association of overweight with postoperative acute kidney injury among patients receiving orthotopic liver transplantation: an observational cohort study

A new prediction model for acute kidney injury following liver transplantation using grafts from donors after cardiac death

Acute kidney injury (AKI) is a major complication following liver transplantation (LT), which utilizes grafts from donors after cardiac death (DCD). We developed a machine-learning-based model to predict AKI, using data from 894 LT recipients (January 2015-March 2021), split into training and testing sets. Five machine learning algorithms were employed to construct the prediction models using 17 clinical variables. The performance of the models was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. The best-performing model was further validated in an independent cohort of 195 LT recipients who received DCD grafts between April 2021 and December 2021. The Shapley additive explanations method was utilized to elucidate the predictions and identify the most crucial features. The gradient boosting machine (GBM) model demonstrated the highest AUC (0.76, 95% CI: 0.70-0.82), F1-score (0.73, 95% CI: 0.66-0.79) and sensitivity (0.74, 95% CI: 0.66-0.80) in the testing set and a comparable AUC (0.75, 95% CI: 0.67-0.81) in the validation set. The GBM model identified high preoperative indirect bilirubin, low intraoperative urine output, prolonged anesthesia duration, low preoperative platelet count and graft steatosis graded NASH Clinical Research Network 1 and above as the top five important features for predicting AKI following LT using DCD grafts. The GBM model is a reliable and interpretable tool for predicting AKI in recipients of LT using DCD grafts. This model can assist clinicians in identifying patients at high risk and providing timely interventions to prevent or mitigate AKI.

Development of a nomogram for the prediction of acute kidney injury after liver transplantation: a model based on clinical parameters and postoperative cystatin C level

BACKGROUND

Acute kidney injury (AKI) is common after liver transplantation (LT). We developed a nomogram model to predict post-LT AKI.

METHODS

A total of 120 patients were eligible for inclusion in the study. Clinical information was extracted from the institutional electronic medical record system. Blood samples were collected prior to surgery and immediately after surgery. Univariable and multivariate logistic regression were used to identify independent risk factors. Finally, a nomogram was developed based on the final multivariable logistic regression model.

RESULTS

In total, 58 (48.3%) patients developed AKI. Multivariable logistic regression revealed four independent risk factors for post-LT AKI: operation duration [odds ratio (OR) = 1.728, 95% confidence interval (CI) = 1.121-2.663, p = 0.013], intraoperative hypotension (OR = 3.235, 95% CI = 1.316-7.952, p = 0.011), postoperative cystatin C level (OR = 1.002, 95% CI = 1.001-1.004, p = 0.005) and shock (OR = 4.002, 95% CI = 0.893-17.945, p = 0.070). Receiver operating characteristic curve analysis was used to evaluate model discrimination. The area under the curve value was 0.815 (95% CI = 0.737-0.894).

CONCLUSION

The model based on combinations of clinical parameters and postoperative cystatin C levels had a higher predictive performance for post-LT AKI than the model based on clinical parameters or postoperative cystatin C level alone. Additionally, we developed an easy-to-use nomogram based on the final model, which could aid in the early detection of AKI and improve the prognosis of patients after LT.

Association of Body Mass Index and Acute Kidney Injury Incidence and Outcome: A Systematic Review and Meta-Analysis

This study aims to provide pooled estimates for the incidence of acute kidney injury (AKI) in overweight, obese, and normal body mass index (BMI) patients, and to assess impact of BMI on mortality and chronic kidney disease (CKD) rates. We conducted literature search using online databases to analyze outcomes of BMI. This meta-analysis included 22 studies. Compared to normal BMI, underweight, overweight, or obese patients had higher risk of having AKI. Underweight individuals had 17% lower CKD risk (relative risk [RR]: 0.83, 95% confidence interval [CI]: 0.75, 0.90) while patients that were overweight (RR: 1.15, 95% CI: 1.08, 1.22) and obese (RR: 1.21, 95% CI: 1.10, 1.33) had higher risk of having CKD. Lower than normal BMI was associated with higher mortality risk (RR: 1.58, 95% CI: 1.35, 1.85), while being overweight or obese correlated with the decreased risk of mortality. An increased risk of AKI combined with an increased risk of mortality calls for renal protective strategies in subjects who are underweight at the time of hospital admission.

Risk factors for acute kidney injury after Stanford type A aortic dissection repair surgery: a systematic review and meta-analysis

Background: Risk factors for acute kidney injury (AKI) after Stanford type A aortic dissection (TAAD) repair are inconsistent in different studies. This meta-analysis systematically analyzed the risk factors so as to early identify the therapeutic targets for preventing AKI.

Methods: Studies exploring risk factors for AKI after TAAD repair were searched from four databases from inception to June 2022. The synthesized incidence and risk factors of AKI and its impact on mortality were calculated.

Results: Twenty studies comprising 8223 patients were included. The synthesized incidence of postoperative AKI was 50.7%. Risk factors for AKI included cardiopulmonary bypass (CPB) time >180 min [odds ratio (OR), 4.89, 95% confidence interval (CI), 2.06–11.61, I² = 0%], prolonged operative time (>7 h) (OR, 2.73, 95% CI, 1.95–3.82, I² = 0), advanced age (per 10 years) (OR, 1.34, 95% CI, 1.21–1.49, I² = 0], increased packed red blood cells (pRBCs) transfusion perioperatively (OR, 1.09, 95% CI, 1.07–1.11, I² = 42%), elevated body mass index (per 5 kg/m²) (OR, 1.23, 95% CI, 1.18–1.28, I² = 42%) and preoperative kidney injury (OR, 3.61, 95% CI, 2.48–5.28, I² = 45%). All results were meta-analyzed using fixed-effects model finally (p < 0.01). The in-hospital or 30-day mortality was higher in patients with postoperative AKI than in that without AKI [risk ratio (RR), 3.12, 95% CI, 2.54–3.85, p < 0.01].

Conclusions: AKI after TAAD repair increased the in-hospital or 30-day mortality. Reducing CPB time and pRBCs transfusion, especially in elderly or heavier weight patients, or patients with preoperative kidney injury were important to prevent AKI after TAAD repair surgery.

Divergent trajectories of lean vs obese non-alcoholic steatohepatitis patients from listing to post-transplant: A retrospective cohort study

BACKGROUND

Non-alcoholic steatohepatitis (NASH) cirrhosis is the second most common indication for liver transplantation (LT). The role of body mass index (BMI) on outcomes of NASH cirrhosis has been conflicting.

AIM

To compare the longitudinal trajectories of patients with lean vs obese NASH cirrhosis, from listing up to post-transplant, having adjusted their BMI for ascites.

METHODS

We retrospectively reviewed all adult NASH patients listed for LT in our program from 2012 to 2019. Fine-Gray Competing Risk analyses and Cox Proportional-Hazard Models were performed to examine the cumulative incidence of transplant and survival outcomes respectively.

RESULTS

Out of 265 NASH cirrhosis listed patients, 176 were included. Median age was 61.0 years; 46% were females. 111 patients underwent LT. Obese robust patients had better waitlist survival [hazard ratio (HR): 0.12; 95%CI: 0.05–0.29, P < 0.0001] with higher instantaneous rate of transplant (HR: 5.71; 95%CI: 1.26–25.9, P = 0.02). Lean NASH patients had a substantially higher risk of graft loss within 90 d post-LT (1.2% vs 13.8%, P = 0.032) and death post-LT (2.4% vs 17.2%, P = 0.029). 1- 3- and 5-year graft survival was poor for lean NASH (78.6%, 77.3% and 41.7% vs 98.6%, 96% and 85% respectively). Overall patient survival post-LT was significantly worse in lean NASH (HR: 0.17; 95%CI: 0.03–0.86, P = 0.0142) with 83% lower instantaneous rate of death in obese group.

CONCLUSION

Although lean NASH is considered to be more benign than obese NASH, our study suggests a paradoxical correlation of lean NASH with waitlist outcomes, and graft and patient survival post-LT.

Unsupervised clustering reveals phenotypes of AKI in ICU COVID-19 patients

Background

Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors.

Methods

We adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe COVID-19 patients hospitalized in the ICU of the Geneva University Hospitals.

Results

Among the 248 patients analyzed, we found 7 factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, baseline eGFR, use of dexamethasone and AKI severity. The three clusters expressed distinct characteristics in terms of AKI severity and recovery, metabolic patterns and hospital mortality.

Conclusion

We propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of COVID-19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflect a distinct pathophysiology.

Development and validation of a nomogram for predicting acute kidney injury after orthotopic liver transplantation

BACKGROUND

We aim to develop and validate a nomogram model for predicting severe acute kidney injury (AKI) after orthotopic liver transplantation (OLT).

METHODS

A total of 576 patients who received OLT in our center were enrolled. They were assigned to the development and validation cohort according to the time of inclusion. Univariable and multivariable logistic regression using the forward variable selection routine were applied to find risk factors for post-OLT severe AKI. Based on the results of multivariable analysis, a nomogram was developed and validated. Patients were followed up to assess the long-term mortality and development of chronic kidney disease (CKD).

RESULTS

Overall, 35.9% of patients were diagnosed with severe AKI. Multivariable logistic regression analysis revealed that recipients' BMI (OR 1.10, 95% CI 1.04-1.17, p = 0.012), hypertension (OR 2.32, 95% CI 1.22-4.45, p = 0.010), preoperative serum creatine (sCr) (OR 0.96, 95% CI 0.95-0.97, p < 0.001), and intraoperative fresh frozen plasm (FFP) transfusion (OR for each 1000 ml increase 1.34, 95% CI 1.03-1.75, p = 0.031) were independent risk factors for post-OLT severe AKI. They were all incorporated into the nomogram. The area under the ROC curve (AUC) was 0.73 (p < 0.05) and 0.81 (p < 0.05) in the development and validation cohort. The calibration curve demonstrated the predicted probabilities of severe AKI agreed with the observed probabilities (p > 0.05). Kaplan-Meier survival analysis showed that patients in the high-risk group stratified by the nomogram suffered significantly poorer long-term survival than the low-risk group (HR 1.92, p < 0.01). The cumulative risk of CKD was higher in the severe AKI group than no severe AKI group after competitive risk analysis (HR 1.48, p < 0.05).

CONCLUSIONS

With excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for severe AKI and poor long-term prognosis after OLT.

Risk of Postoperative Renal Failure in Radical Nephrectomy and Nephroureterectomy: A Validated Risk Prediction Model

INTRODUCTION

The study aimed to construct and validate a risk prediction model for incidence of postoperative renal failure (PORF) following radical nephrectomy and nephroureterectomy.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database years 2005-2014 were used for the derivation cohort. A stepwise multivariate logistic regression analysis was conducted, and the final model was validated with an independent cohort from the ACS-NSQIP database years 2015-2017.

RESULTS

In cohort of 14,519 patients, 296 (2.0%) developed PORF. The final 9-factor model included age, gender, diabetes, hypertension, BMI, preoperative creatinine, hematocrit, platelet count, and surgical approach. Model receiver-operator curve analysis provided a C-statistic of 0.79 (0.77, 0.82; p < 0.001), and overall calibration testing R2 was 0.99. Model performance in the validation cohort provided a C-statistic of 0.79 (0.76, 0.81; p < 0.001).

CONCLUSION

PORF is a known risk factor for chronic kidney disease and cardiovascular morbidity, and is a common occurrence after unilateral kidney removal. The authors propose a robust and validated risk prediction model to aid in identification of high-risk patients and optimization of perioperative care.