A Prospective Study of Incidence, Risk Factors, and Outcomes of Acute Kidney Injury in Coronavirus Disease 2019.

Background: Acute kidney injury (AKI) is common after coronavirus 2 infection (COVID-19), leading to higher morbidity and mortality. There is little prospective data from India regarding the incidence, risk factors, and outcome of AKI in COVID-19. Materials and Methods: This study was conducted prospectively in adult patients between September and December 2020 in a tertiary care hospital in the national capital region of Delhi. A total of 856 patients with COVID-19 infection were enrolled in the study. Survivors were followed for 3 months after discharge. Results: Out of 856 patients, 207 (24%) developed AKI. AKI was significantly higher in those with severe disease as compared to mild-moderate disease (88% vs. 12%, P = 0.04). Out of all AKI, 3.4% had stage 1, 9.2% had stage 2, and the rest 87.4% had stage 3 AKI. 183/207 (88%) patients were on mechanical ventilators, 133 (64%) required inotropic support, and 137/207 (83.6%) patients required kidney replacement therapy. Out of 207 AKI patients, 74% (153) died as compared to 4% (27) in non-AKI group (P = 0.0001). After 3 months, chronic kidney disease (CKD) developed in 10/54 (18.5%) patients. On multivariable analysis, the presence of diabetes mellitus, severe COVID-19 disease, high levels of C reactive protein, lactate dehydrogenase, D-Dimer, and use of intravenous steroids, tocilizumab and remdesivir, were found to be significant predictors of AKI. Conclusion: AKI is common after COVID-19 infection and it is a significant risk factor for mortality in COVID-19. Patients with diabetes and high levels of inflammatory markers have higher mortality. CKD may develop in many patients after discharge.

Using artificial intelligence to predict mortality in AKI patients: a systematic review/meta-analysis.

Background: Acute kidney injury (AKI) is associated with increased morbidity/mortality. With artificial intelligence (AI), more dynamic models for mortality prediction in AKI patients have been developed using machine learning (ML) algorithms. The performance of various ML models was reviewed in terms of their ability to predict in-hospital mortality for AKI patients. Methods: A literature search was conducted through PubMed, Embase and Web of Science databases. Included studies contained variables regarding the efficacy of the AI model [the AUC, accuracy, sensitivity, specificity, negative predictive value and positive predictive value]. Only original studies that consisted of cross-sectional studies, prospective and retrospective studies were included, while reviews and self-reported outcomes were excluded. There was no restriction on time and geographic location. Results: Eight studies with 37 032 AKI patients were included, with a mean age of 65.3 years. The in-hospital mortality was 18.0% in the derivation and 15.8% in the validation cohorts. The pooled [95% confidence interval (CI)] AUC was observed to be highest for the broad learning system (BLS) model [0.852 (0.820-0.883)] and elastic net final (ENF) model [0.852 (0.813-0.891)], and lowest for proposed clinical model (PCM) [0.765 (0.716-0.814)]. The pooled (95% CI) AUC of BLS and ENF did not differ significantly from other models except PCM [Delong's test P = .022]. PCM exhibited the highest negative predictive value, which supports this model's use as a possible rule-out tool. Conclusion: Our results show that BLS and ENF models are equally effective as other ML models in predicting in-hospital mortality, with variability across all models. Additional studies are needed.

Prevention of dialysis disequilibrium syndrome in children with advanced uremia with a structured hemodialysis protocol: A quality improvement initiative study.

BACKGROUND: Dialysis disequilibrium syndrome (DDS) is a rare but significant concern in adult and pediatric patients undergoing dialysis initiation with advanced uremia or if done after an interval. It is imperative to gain insights into the epidemiological patterns, pathophysiological mechanisms, and preventive strategies aimed at averting the onset of this ailment. DESIGN: Prospective observational quality improvement initiative cohort study. SETTING AND PARTICIPANTS: A prospective single-center study involving 50 pediatric patients under 18 years recently diagnosed with chronic kidney disease stage V with blood urea ≥200 mg/dL, admitted to our tertiary care center for dialysis initiation from January 2017 to October 2023. QUALITY IMPROVEMENT PLAN: A standardized protocol was developed and followed for hemodialysis in pediatric patients with advanced uremia. This protocol included measures such as lower urea reduction ratios (targeted at 20%-30%) with shorter dialysis sessions and linear dialysate sodium profiling. Prophylactic administration of mannitol and 25% dextrose was also done to prevent the incidence of dialysis disequilibrium syndrome. MEASURES: Incidence of dialysis disequilibrium syndrome and severe dialysis disequilibrium syndrome, mortality, urea reduction ratios (URRs), neurological outcome at discharge, and development of complications such as infection and hypotension. Long-term outcomes were assessed at the 1-year follow-up including adherence to dialysis, renal transplantation, death, and loss to follow-up. RESULTS: The median serum creatinine and urea levels at presentation were 7.93 and 224 mg/dL, respectively. A total of 20% of patients had neurological symptoms attributable to advanced uremia at the time of presentation. The incidence of dialysis disequilibrium syndrome was 4% (n = 2) with severe dialysis disequilibrium syndrome only 2% (n = 1). Overall mortality was 8% (n = 4) but none of the deaths were attributed to dialysis disequilibrium syndrome. The mean urea reduction ratios for the first, second, and third dialysis sessions were 23.45%, 34.56%, and 33.50%, respectively. The patients with dialysis disequilibrium syndrome were discharged with normal neurological status. Long-term outcomes showed 88% adherence to dialysis and 38% renal transplantation. LIMITATIONS: This study is characterized by a single-center design, nonrandomized approach, and limited sample size. CONCLUSIONS: Our structured protocol served as a framework for standardizing procedures contributing to low incidence rates of dialysis disequilibrium syndrome.

Concurrent use of continuous kidney replacement therapy during extracorporeal membrane oxygenation: what pediatric nephrologists need to know-PCRRT-ICONIC practice points.

Extracorporeal membrane oxygenation (ECMO) provides temporary cardiorespiratory support for neonatal, pediatric, and adult patients when traditional management has failed. This lifesaving therapy has intrinsic risks, including the development of a robust inflammatory response, acute kidney injury (AKI), fluid overload (FO), and blood loss via consumption and coagulopathy. Continuous kidney replacement therapy (CKRT) has been proposed to reduce these side effects by mitigating the host inflammatory response and controlling FO, improving outcomes in patients requiring ECMO. The Pediatric Continuous Renal Replacement Therapy (PCRRT) Workgroup and the International Collaboration of Nephrologists and Intensivists for Critical Care Children (ICONIC) met to highlight current practice standards for ECMO use within the pediatric population. This review discusses ECMO modalities, the pathophysiology of inflammation during an ECMO run, its adverse effects, various anticoagulation strategies, and the technical aspects and outcomes of implementing CKRT during ECMO in neonatal and pediatric populations. Consensus practice points and guidelines are summarized. ECMO should be utilized in patients with severe acute respiratory failure despite the use of conventional treatment modalities. The Extracorporeal Life Support Organization (ELSO) offers guidelines for ECMO initiation and management while maintaining a clinical registry of over 195,000 patients to assess outcomes and complications. Monitoring and preventing fluid overload during ECMO and CKRT are imperative to reduce mortality risk. Clinical evidence, resources, and experience of the nephrologist and healthcare team should guide the selection of ECMO circuit.

Use of furosemide in preterm neonates with acute kidney injury is associated with increased mortality: results from the TINKER registry.

BACKGROUND: Diuretics are commonly used in neonatal AKI with the rationale to decrease positive fluid balance in critically sick neonates. The patterns of furosemide use vary among hospitals, which necessitates the need for a well-designed study. METHODS: The TINKER (The Indian Iconic Neonatal Kidney Educational Registry) study provides a database, spanning 14 centres across India since August 2018. Admitted neonates (≤ 28 days) receiving intravenous fluids for at least 48 h were included. Neonatal KDIGO criteria were used for the AKI diagnosis. Detailed clinical and laboratory parameters were collected, including the indications of furosemide use, detailed dosing, and the duration of furosemide use (in days). RESULTS: A total of 600 neonates with AKI were included. Furosemide was used in 8.8% of the neonates (53/600). Common indications of furosemide use were significant cardiac disease, fluid overload, oliguria, BPD, RDS, hypertension, and hyperkalemia. The odds of mortality was higher in neonates < 37 weeks gestational age with AKI who received furosemide compared to those who did not receive furosemide 3.78 [(1.60-8.94); p = 0.003; univariate analysis] and [3.30 (1.11-9.82); p = 0.03]; multivariate logistic regression]. CONCLUSIONS: In preterm neonates with AKI, mortality was independently associated with furosemide treatment. The furosemide usage rates were higher in neonates with associated co-morbidities, i.e. significant cardiac diseases or surgical interventions. Sicker babies needed more resuscitation at birth, and died early, and hence needed shorter furosemide courses. Thus, survival probability was higher in neonates treated with long furosemide courses vs. short courses.

Renal Transplantation in Patients With Tuberculosis: A Single-center Experience From an Endemic Region.

Background: Despite being a common infection in end-stage kidney disease patients, there are no evidence-based guidelines to suggest the ideal time of transplantation in patients on antitubercular therapy (ATT). This study aimed to examine the outcome of transplantation in patients while on ATT compared with those without tuberculosis (TB). Methods: This was a retrospective study. Renal transplant recipients transplanted while on ATT were compared with a 1:1 matched group (for age, sex, diabetic status, and type of induction agent) of patients without TB at the time of transplant. Patient outcomes included relapse of TB and graft and patient survival. Results: There were 71 patients in each group. The mean duration for which ATT was given pretransplant was 3.8 ± 2.47 mo. The average total duration of ATT received was 12.27 ± 1.25 mo. Mortality in both the groups was similar (8.4% in the TB group versus 4.5% in the non-TB group; P = 0.49). None of the surviving patients had recurrence of TB during the follow-up. Death-censored graft survival (98.5% in the TB group versus 97% in the non-TB group; P = 1) and biopsy-proven acute rejection rates (9.86% in the TB group versus 8.45% in the non-TB group; P = 1) were also similar in both the groups. Conclusions: Successful transplantation in patients with end-stage kidney disease on ATT is possible without any deleterious effect on patient and graft survival and no risk of disease recurrence. Multicentric prospective studies are needed.

Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions.

Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagnosis and treatment intervention of established AKI. Recently, artificial intelligence (AI) has provided more advent predictive models for early detection/prediction of AKI utilizing machine learning (ML). By providing strong detail and evidence from risk scores and electronic alerts, this review outlines a comprehensive and holistic insight into the current state of AI in AKI in pediatric/neonatal patients. In the pediatric population, AI models including XGBoost, logistic regression, support vector machines, decision trees, naïve Bayes, and risk stratification scores (Renal Angina Index (RAI), Nephrotoxic Injury Negated by Just-in-time Action (NINJA)) have shown success in predicting AKI using variables like serum creatinine, urine output, and electronic health record (EHR) alerts. Similarly, in the neonatal population, using the "Baby NINJA" model showed a decrease in nephrotoxic medication exposure by 42%, the rate of AKI by 78%, and the number of days with AKI by 68%. Furthermore, the "STARZ" risk stratification AI model showed a predictive ability of AKI within 7 days of NICU admission of AUC 0.93 and AUC of 0.96 in the validation and derivation cohorts, respectively. Many studies have reported the superiority of using biomarkers to predict AKI in pediatric patients and neonates as well. Future directions include the application of AI along with biomarkers (NGAL, CysC, OPN, IL-18, B2M, etc.) in a Labelbox configuration to create a more robust and accurate model for predicting and detecting pediatric/neonatal AKI.