RELATIONSHIP BETWEEN WARM ISCHEMIC TIME AND RESISTIVE INDEX ON CREATININ REDUCTION RATIO DAY TWO AFTER LIVING KIDNEY DONOR TRANSPLANTATION

Kidney transplantation (KT) may improve kidney function, via filtration, excretion, and hormonal function better than other kidney replacement therapies. Many factors may cause graft rejection or delayed graft function which may decrease the prognosis for graft survival. This study aims to determine associated factors of serum creatinine reduction ratio day 2 (CRR2) after living kidney donor transplants. This research used a retrospective cohort study design, with total sampling based on complete documents was done. A total 44 respondents (from 2012 to January 2020) and 22 respondents (based on the complete Resistive Index (RI) were recorded since 2018). Early Graft Function was defined using CRR2. Immediate Graft Function (IGF) was defined if CRR2 > 30% and Delayed Graft Function (DGF) if CRR2 ≤ 30%. The results of Multiple logistic regression analysis from 44 samples showed that Warm Ischemic Time (WIT) ≤ 40 minutes was significantly associated with IGF (OR 10.78; 95%CI: 1.66 to 70.16; p=0.01). A result with 22 samples showed that, only RI ≤ 0.7was significantly associated with IGF (OR 0.11; 95%CI: 0.03-0.41; p=0.002). In conclusion, WIT and RI influence on EGF with parameters CRR2 of living-donors. KT Patients with WIT ≤40 minutes and RI ≤0.7 had a higher risk of IGF.

Association of Postoperative Urinary Output in the First 24 Hours with Delayed Graft Function After Living and Deceased Donor Kidney Transplant: A Systematic Review

Context: Delayed graft function (DGF) is an important clinical outcome following renal transplantation; therefore, it is important to be correctly diagnosed. The DGF is thought to correlate with the first 24-hour urine output (UOP1), and this clinical sign is expected to predict DGF. Objectives: This study aimed to discover whether the UOP1 correlates significantly to the DGF incidence and can be a DGF predicting factor. Data Sources: This study compared the incidence of DGF with the UOP1 reported by studies obtained from the electronic databases, namely MEDLINE, Cochrane, and EBSCO. Studies that performed multivariate or bivariate analysis and/or reported sensitivity and specificity were included in this review. Results: A total of 1719 studies were obtained from the database search, and 2 studies were enrolled from other sources. Out of 1721 studies, 9 studies were recruited in this review, 5 of which reported sensitivity and specificity. Overall, nine of these studies had a low to moderate risk of bias. Almost all studies reported a significant relationship between the UOP1 and DGF. All studies agreed that the UOP1 is a sensitive predictive factor in predicting DGF. The specificity reported by the studies examined in this review varied greatly. The use of optimum cut-off in each study is considered to be the cause of this variability. Conclusions: The UOP1 is significantly related to the incidence of DGF and is a proper parameter for the prediction of DGF events.

Predictors of Delayed Graft Function in Renal Transplantation

<b><i>Purpose:</i></b> This study aimed to analyze our data on delayed graft function (DGF) and to identify associated factors. <b><i>Methods:</i></b> This is a retrospective case-control study of all patients transplanted in our center over a period of 11 years (January 1, 2003, to December 31, 2014) comparing patients with immediate graft function (<i>n</i> = 332) to those with DGF (<i>n</i> = 165). DGF was defined as the need for hemodialysis within the first 7 days after transplantation. Donor and recipient characteristics as well as procedural factors were compared by univariate and multivariate logistic regression analyses. <b><i>Results:</i></b> Overall, 33% of patients had DGF. The rate of DGF declined from 2003 to 2011. In cases with DGF, donors and recipients were significantly older (<i>p</i> = 0.004 and <i>p</i> = 0.005, respectively), had longer cold ischemia times (<i>p</i> = 0.039), more revision surgeries (<i>p</i> &#x3c; 0.001), and more HLA mismatches (<i>p</i> = 0.001), especially in the DR locus (<i>p</i> = 0.002). Neither donor nor recipient gender, waiting time, nor CMV status had any influence. In multivariable analysis, significant risk factors were ischemia time and mismatches at the HLA-DR loci. <b><i>Conclusions:</i></b> DGF is a common complication in renal transplantation which occurred in 33% of our cases. Important factors identified were donor and recipient age, ischemia time, HLA mismatching, and revision surgery.

Clinical factors associated with severe hypophosphataemia after kidney transplant

Background The mechanism by which hypophosphataemia develops following kidney transplantation remains debated, and limited research is available regarding risk factors. This study aimed to assess the association between recipient and donor variables, and the severity of post-transplantation hypophosphataemia. Methods We performed a single-centre retrospective observational study. We assessed the association between demographic, clinical and biochemical variables and the development of hypophosphataemia. We used linear regression analysis to assess association between these variables and phosphate nadir. Results 87.6% of patients developed hypophosphataemia. Patients developing hypophosphataemia were younger, had a shorter time on renal replacement therapy, were less likely to have had a parathyroidectomy or to experience delayed graft function, were more likely to have received a living donor transplant, from a younger donor. They had higher pre-transplantation calcium levels, and lower alkaline phosphatase levels. Receipt of a living donor transplant, lower donor age, not having had a parathyroidectomy, receiving a transplant during the era of tacrolimus-based immunosuppression, not having delayed graft function, higher pre-transplantation calcium, and higher pre-transplantation phosphate were associated with lower phosphate nadir by multiple linear regression. Conclusions This analysis demonstrates an association between variables relating to better graft function and hypophosphataemia. The links with biochemical measures of mineral-bone disease remain less clear.

Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant

Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor’s eGFR, recipient’s BMI, donor’s BMI, and recipient–donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor’s age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieved an accuracy of 93.75% and an AUC of 0.92. Using machine learning, we were able to assess the risk of DGF in recipients after kidney transplant from a deceased donor. Our solution is scalable and can be improved during subsequent transplants. Based on the new data, the models can achieve better outcomes.

Trajectories of Systolic Blood Pressure Decline in Kidney Donors After Circulatory Death and Delayed Graft Function

Background: Kidneys donated after circulatory death (DCD) suffer a period of functional warm ischemia before death. This study aimed to assess the risk of delayed graft function (DGF) using patterns of trajectories of systolic blood pressure (BP) decline in DCD kidneys. Methods: We studied all Australian DCD kidneys transplanted between 2014 - 2019, divided in a derivation (n=462, April 2014-January 2018) and validation (n=324, January 2018-December 2019) cohort, using latent class models and two-stage linear mixed-effect models. Results: Eight different trajectories, with distinct patterns of systolic BP decline, were identified. Compared to recipients of donors with the fastest decline in systolic BP after withdrawal of cardio-respiratory support, the adjusted odds ratios (OR) (95%CI) for DGF in recipients who had received donors with the slowest systolic BP decline were 0.36 (0.16 – 0.80, random forest model) and 0.38 (0.17 – 0.86, least absolute shrinkage and selection operator models, LASSO), respectively. For every 1 mmHg per minute reduction in the rate of decline of systolic BP, the adjusted OR (95%CI) for DGF were 0.95 (0.91-0.99). Similar comparison was conducted in the validation cohort. Recipients who received donors with the slowest systolic BP decline from withdrawal of cardio-respiratory support till death did not experience an increased risk of DGF (Adjusted OR (95%): random forest: 1.01 (0.42-4.2) and LASSO: 1.17 (0.5-2.74)). Conclusions: In DCD kidney donors, a slow decline in systolic BP during the agonal phase was not associated with adverse short-term outcomes after transplantation.