Prediction of cardiac surgery associated - acute kidney injury (CSA-AKI) by healthcare professionals and urine cell cycle arrest AKI biomarkers [TIMP-2]*[IGFBP7]: A single center prospective study (the PREDICTAKI trial)

Urinary [TIMP-2]•[IGFBP7], TIMP-2, IGFBP7, NGAL, and L-FABP for the prediction of acute kidney injury following cardiovascular surgery in Japanese patients

BACKGROUND

Acute kidney injury (AKI) following cardiac surgery is common and is associated with poor outcomes. The combination of urinary tissue inhibitor of metalloproteinase 2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) is a strong predictor of AKI after cardiac surgery. However, most studies have focused on non-Asian populations, and comparisons with other AKI biomarkers or the optimal timing for measurement have yet to be explored.

METHODS

We prospectively enrolled adult patients at Kochi Medical School Hospital in Kochi, Japan, to assess the predictive values of [TIMP-2]•[IGFBP7], TIMP-2, IGFBP7, neutrophil gelatinase-associated lipocalin (NGAL), and liver fatty acid-binding protein (L-FABP) measured preoperatively and at 2, 4, 6, and 8 h, as well as on day 1 and day 2 after postoperative intensive care unit (ICU) admission, using receiver operating characteristic curve (ROC) analysis.

RESULTS

Of the 38 patients, 13 (34.2%) developed AKI: seven (18.4%) with stage 1, four (10.5%) with stage 2, and two (5.2%) with stage 3. ROC analysis showed that the area under the curve (AUC) for predicting any stage of AKI peaked at 0-4 h, with the highest value at 2 h after ICU admission. Among the biomarkers, [TIMP-2]•[IGFBP7] showed the best AUC at 2 h after ICU admission, followed by TIMP-2, IGFBP7, L-FABP, and NGAL.

CONCLUSIONS

Our study demonstrated the good predictive performance of urine biomarkers, including [TIMP-2]•[IGFBP7], TIMP-2, IGFBP7, NGAL, and L-FABP, for any stage of cardiac surgery-associated AKI (CSA-AKI). The combination of TIMP-2 and IGFBP7 measured 2 h after postoperative ICU admission effectively predicted CSA-AKI, identifying patients at higher risk.

Can Novel Biomarkers Effectively Predict Acute Kidney Injury in Liver or Kidney Transplant Recipients?

Acute kidney injury (AKI) constitutes a common complication associated with liver or kidney transplantation, which may significantly impact the graft condition and perioperative mortality. Current AKI diagnostic criteria based on serum creatinine (sCr) and urine output alterations are widely utilized in routine clinical practice. However, the diagnostic value of sCr may be limited by various confounding factors, including age, sex, reduced or increased muscle mass, and pre-existing chronic kidney disease (CKD). Furthermore, sCr is rather a late indicator of AKI, as its concentration tends to increase only when the severity of the injury is enough to decrease the estimated glomerular filtration rate (eGFR). Recent expertise highlights the need for novel biomarkers in post-transplantation AKI diagnosis, prediction of event-associated mortality, or evaluation of indications for renal replacement treatment (RRT). Over the last decade, the diagnostic performance of various AKI biomarkers has been assessed, among which some showed the potential to outperform sCr in AKI diagnosis. Identifying susceptible individuals, early diagnosis, and prompt intervention are crucial for successful transplantation, undisturbed graft function in long-term follow-up, and decreased mortality. However, the research on AKI biomarkers in transplantation still needs to be explored. The field lacks consistent results, rigorous study designs, and external validation. Considering the rapidly growing prevalence of CKD and cirrhosis that are associated with the transplantation at their end-stage, as well as the existing knowledge gap, the aim of this article was to provide the most up-to-date review of the studies on novel biomarkers in the diagnosis of post-transplantation AKI.

Effect of Urine Output on the Predictive Precision of NephroCheck in On-Pump Cardiac Surgery With Crystalloid Cardioplegia: Insights from the PrevAKI Study

OBJECTIVES

Previous studies in other settings suggested that urine output (UO) might affect NephroCheck predictive value. We investigated the correlation between NephroCheck and UO in cardiac surgery patients.

DESIGN

Post hoc analysis of a multicenter study.

SETTING

University hospital.

PARTICIPANTS

Patients who underwent cardiac surgery using cardiopulmonary bypass (CPB) and crystalloid cardioplegia.

MEASUREMENTS AND MAIN RESULTS

All patients underwent NephroCheck testing 4 hours after CPB discontinuation. The primary outcome was the correlation between UO, NephroCheck results, and acute kidney injury (AKI, defined according to Kidney Disease: Improving Global Outcomes). Of 354 patients, 337 were included. Median NephroCheck values were 0.06 (ng/mL)2/1,000) for the overall population and 0.15 (ng/mL)2/1,000) for patients with moderate to severe AKI. NephroCheck showed a significant inverse correlation with UO (ρ = -0.17; p = 0.002) at the time of measurement. The area under the receiver characteristic curve (AUROC) for NephroCheck was 0.60 (95% confidence interval [CI], 0.54-0.65), whereas for serum creatinine was 0.82 (95% CI, 0.78-0.86; p < 0.001). When limiting the analysis to the prediction of moderate to severe AKI, NephroCheck had a AUROC of 0.82 (95% CI, 0.77 to 0.86; p<0.0001), while creatinine an AUROC of 0.83 (95% CI, 0.79-0.87; p = 0.001).

CONCLUSIONS

NephroCheck measured 4 hours after the discontinuation from the CPB predicts moderate to severe AKI. However, a lower threshold may be necessary in patients undergoing cardiac surgery with CPB. Creatinine measured at the same time of the test remains a reliable marker of subsequent development of renal failure.

Urine output is an early and strong predictor of acute kidney injury and associated mortality: a systematic literature review of 50 clinical studies

BACKGROUND

Although the present diagnosis of acute kidney injury (AKI) involves measurement of acute increases in serum creatinine (SC) and reduced urine output (UO), measurement of UO is underutilized for diagnosis of AKI in clinical practice. The purpose of this investigation was to conduct a systematic literature review of published studies that evaluate both UO and SC in the detection of AKI to better understand incidence, healthcare resource use, and mortality in relation to these diagnostic measures and how these outcomes may vary by population subtype.

METHODS

The systematic literature review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Data were extracted from comparative studies focused on the diagnostic accuracy of UO and SC, relevant clinical outcomes, and resource usage. Quality and validity were assessed using the National Institute for Health and Care Excellence (NICE) single technology appraisal quality checklist for randomized controlled trials and the Newcastle-Ottawa Quality Assessment Scale for observational studies.

RESULTS

A total of 1729 publications were screened, with 50 studies eligible for inclusion. A majority of studies (76%) used the Kidney Disease: Improving Global Outcomes (KDIGO) criteria to classify AKI and focused on the comparison of UO alone versus SC alone, while few studies analyzed a diagnosis of AKI based on the presence of both UO and SC, or the presence of at least one of UO or SC indicators. Of the included studies, 33% analyzed patients treated for cardiovascular diseases and 30% analyzed patients treated in a general intensive care unit. The use of UO criteria was more often associated with increased incidence of AKI (36%), than was the application of SC criteria (21%), which was consistent across the subgroup analyses performed. Furthermore, the use of UO criteria was associated with an earlier diagnosis of AKI (2.4-46.0 h). Both diagnostic modalities accurately predicted risk of AKI-related mortality.

CONCLUSIONS

Evidence suggests that the inclusion of UO criteria provides substantial diagnostic and prognostic value to the detection of AKI.

Discovery of mmu-lncRNA129814/hsa-lncRNA582795 as a Potential Biomarker and Intervention Target for Ischemia Reperfusion Injury-Induced AKI

Background

Acute kidney injury (AKI) is associated with higher perioperative mortality and morbidity, as well as increased medical expenses. The molecular mechanisms underlying ischemia-reperfusion (I/R)-induced AKI remain unclear.

Methods and Results

We applied an RT-qPCR assay to measure the expression of mmu-lncRNA129814, hsa-lncRNA582795, and miRNA-494-5p, immunoblotting to detect IL-1α and cleaved caspase-3 expression, and TUNEL staining and flow cytometry (FCM) to evaluate apoptosis. The experiments were conducted using BUMPT and HK-2 cells, as well as C57BL/6J mice. Mechanistically, mmu-lncRNA129814 could sponge miRNA-494-5p and upregulate IL-1α expression to promote cell apoptosis. Furthermore, knockdown of mmu-lncRNA129814 ameliorated I/R-induced progression of AKI by targeting the miRNA-494-5p/IL-1α pathways. Interestingly, hsa-lncRNA582795, a homolog of mmu-lncRNA129814, also promoted I/R-stimulated HK-2 cell apoptosis and AKI progression by regulating the miRNA-494-5p/IL-1α axis. Finally, we found that patients with I/R-induced AKI exhibited significantly elevated plasma and urinary levels of hsa-lncRNA582795 compared to those who underwent ischemia-reperfusion without developing AKI. Spearman's test demonstrated a significant correlation between serum creatinine and plasma hsa-lncRNA582795 in I/R patients. Plasma hsa-lncRNA582795 showed high sensitivity but low specificity (86.7%) compared to urinary hsa-lncRNA582795.

Conclusion

The mmu-lncRNA129814/hsa-lncRNA582795/miRNA-494-5p/IL-1α axis was found to modulate the progression of ischemic AKI, and hsa-lncRNA582795 could act as a diagnosis biomarker and potential therapy target of I/R-induced AKI.

Machine learning for dynamic and early prediction of acute kidney injury after cardiac surgery

OBJECTIVE

Acute kidney injury after cardiac surgery increases morbidity and mortality. Diagnosis relies on oliguria or increased serum creatinine, which develop 48 to 72 hours after injury. We hypothesized machine learning incorporating preoperative, operative, and intensive care unit data could dynamically predict acute kidney injury before conventional identification.

METHODS

Cardiac surgery patients at a tertiary hospital (2008-2019) were identified using electronic medical records in the Medical Information Mart for Intensive Care IV database. Preoperative and intraoperative parameters included demographics, Charlson Comorbidity subcategories, and operative details. Intensive care unit data included hemodynamics, medications, fluid intake/output, and laboratory results. Kidney Disease: Improving Global Outcomes creatinine criteria were used for acute kidney injury diagnosis. An ensemble machine learning model was trained for hourly predictions of future acute kidney injury within 48 hours. Performance was evaluated by area under the receiver operating characteristic curve and balanced accuracy.

RESULTS

Within the cohort (n = 4267), there were approximately 7 million data points. Median baseline creatinine was 1.0 g/dL (interquartile range, 0.8-1.2), with 17% (735/4267) of patients having chronic kidney disease. Postoperative stage 1 acute kidney injury occurred in 50% (2129/4267), stage 2 occurred in 8% (324/4267), and stage 3 occurred in 4% (183/4267). For hourly prediction of any acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.82, and balanced accuracy was 75%. For hourly prediction of stage 2 or greater acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.95 and balanced accuracy was 86%. The model predicted acute kidney injury before clinical detection in 89% of cases.

CONCLUSIONS

Ensemble machine learning models using electronic medical records data can dynamically predict acute kidney injury risk after cardiac surgery. Continuous postoperative risk assessment could facilitate interventions to limit or prevent renal injury.

The NephroCheck bedside system for detecting stage 3 acute kidney injury after open thoracoabdominal aortic repair

Acute kidney injury (AKI) is a common complication after complex aortic procedures and it is associated with relevant mortality and morbidity. Biomarkers for early and specific AKI detection are lacking. The aim of this work is to investigate the reliability of the NephroCheck bedside system for diagnosing stage 3 AKI following open aortic surgery. In this prospective, multicenter, observational study,- https://clinicaltrials.gov/ct2/show/NCT04087161 -we included 45 patients undergoing open thoracoabdominal aortic repair. AKI risk (AKIRisk-Index) was calculated from urine samples at 5 timepoints: baseline, immediately postoperatively and at 12, 24, 48, and 72 h post-surgery. AKIs were classified according to the KDIGO criteria. Contributing factors were identified in univariable and multivariable logistic regression. Predictive ability was assessed with the area under the receiver operator curve (ROCAUC). Among 31 patients (68.8%) that developed AKIs, 21 (44.9%) developed stage-3 AKIs, which required dialysis. AKIs were correlated with increased in-hospital mortality (p = .006), respiratory complications (p < .001), sepsis (p < .001), and multi-organ dysfunction syndrome (p < .001). The AKIRisk-Index showed reliable diagnostic accuracy starting at 24 h post-surgery (ROCAUC: .8056, p = .001). In conclusion, starting at 24 h after open aortic repair, the NephroCheck system showed adequate diagnostic accuracy for detecting the patients at risk for stage 3 AKIs.

Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury

Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention and therapeutic approach are the main strategies for reducing the possibility of perioperative AKI. Consequently, several risk-prediction models and risk assessment scores have been developed for the prediction of perioperative AKI. However, a majority of these risk scores are only derived from preoperative data while the intraoperative time-series monitoring data such as heart rate and blood pressure were not included. Moreover, the complexity of the pathophysiology of AKI, as well as its nonlinear and heterogeneous nature, imposes limitations on the use of linear statistical techniques. The development of clinical medicine's digitization, the widespread availability of electronic medical records, and the increase in the use of continuous monitoring have generated vast quantities of data. Machine learning has recently shown promise as a method for automatically integrating large amounts of data in predicting the risk of perioperative outcomes. In this article, we discussed the development, limitations of existing work, and the potential future direction of models using machine learning techniques to predict AKI after a surgery.

Postoperative Acute Kidney Injury

Postoperative AKI is a common complication of major surgery and is associated with significant morbidity and mortality. The Kidney Disease Improving Global Outcomes AKI definition allows consensus classification and identification of postoperative AKI through changes in serum creatinine and/or urine output. However, such conventional diagnostic criteria may be inaccurate in the postoperative period, suggesting a potential to refine diagnosis by application of novel diagnostic biomarkers. Risk factors for the development of postoperative AKI can be thought of in terms of preoperative, intraoperative, and postoperative factors and, as such, represent areas that may be targeted perioperatively to minimize the risk of AKI. The treatment of postoperative AKI remains predominantly supportive, although application of management bundles may translate into improved outcomes.