Risk Stratification Of Acute Kidney Injury Research Articles

Evaluation of risk stratification for acute kidney injury: a comparative analysis of EKFC, 2009 and 2021 CKD-EPI glomerular filtration estimating equations.

The adoption of the 2021 CKD-EPIcr equation for glomerular filtration rate (GFR) estimation provided a race-free eGFR calculation. However, the discriminative performance for AKI risk has been rarely validated. We aimed to evaluate the differences in acute kidney injury (AKI) prediction or reclassification power according to the three eGFR equations. We performed a retrospective observational study within a tertiary hospital from 2011 to 2021. Acute kidney injury was defined according to KDIGO serum creatinine criteria. Glomerular filtration rate estimates were calculated by three GFR estimating equations: 2009 and 2021 CKD-EPIcr, and EKFC. In three equations, AKI prediction performance was evaluated with area under receiver operator curves (AUROC) and reclassification power was evaluated with net reclassification improvement analysis. A total of 187,139 individuals, including 27,447 (14.7%) AKI and 159,692 (85.3%) controls, were enrolled. In the multivariable regression prediction model, the 2009 CKD-EPIcr model (continuous eGFR model 2, 0.7583 [0.755-0.7617]) showed superior performance in AKI prediction to the 2021 CKD-EPIcr (0.7564 [0.7531-0.7597], < 0.001) or EKFC model in AUROC (0.7577 [0.7543-0.761], < 0.001). Moreover, in reclassification of AKI, the 2021 CKD-EPIcr and EKFC models showed a worse classification performance than the 2009 CKD-EPIcr model. (-7.24 [-8.21--6.21], -2.38 [-2.72--1.97]). Regarding AKI risk stratification, the 2009 CKD-EPIcr equation showed better discriminative performance compared to the 2021 CKD-EPIcr equationin the study population.

Incidence and predictors of acute kidney injury after elective surgery for lumbar degenerative disease: A 13-year analysis of the US Nationwide Inpatient Sample.

Acute kidney injury (AKI) is a severe postoperative complication associated with poor clinical outcomes, including the development of chronic kidney disease (CKD) and death. This study aimed to investigate the incidence and determinants of AKI following elective surgeries for degenerative lumbar spine disease. All patient data were extracted from the US Nationwide Inpatient Sample database. After surgery, AKI's incidence and risk factors were identified for lumbar degenerative disease. ICD-9 and ICD-10 codes defined lumbar spine degenerative disease, fusion, decompression, and AKI. The study cohort was categorized by type of surgery, that is, decompression alone or spinal fusion. Regression analysis was used to identify associations between AKI and risk factors organized by surgery type. The incidence of AKI after decompression or fusion was 1.1% and 1.8%, respectively. However, the incidence of AKI in the United States is rising. The strongest predictor of AKI was underlying CKD, which was associated with an 9.0- to 12.9-fold more significant risk of AKI than in subjects without comorbid CKD. In this setting, older age, congestive heart failure, anemia, obesity, coagulopathy and hospital-acquired infections were also strong predictors of AKI. In contrast, long-term aspirin/anticoagulant usage was associated with lowered AKI risk. Findings of this study inform risk stratification for AKI and may help to optimize treatment decisions and care planning after elective surgery for lumbar degenerative disease.

Prognostic Value of Leukocyte-Based Risk Model for Acute Kidney Injury Prediction in Critically Ill Acute Exacerbation of Chronic Obstructive Pulmonary Disease Patients.

Acute kidney injury (AKI) is a common complication of acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and inflammation is the potential link between AKI and AECOPD. However, little is known about the incidence and risk stratification of AKI in critically ill AECOPD patients. In this study, we aimed to establish risk model based on white blood cell (WBC)-related indicators to predict AKI in critically ill AECOPD patients. For the training cohort, data were taken from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database, and for the validation cohort, data were taken from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The study employed logistic regression analysis to identify the major predictors of WBC-related biomarkers on AKI prediction. Subsequently, a risk model was developed by multivariate logistic regression, utilizing the identified significant indicators. Finally, 3551 patients were enrolled in training cohort, 926 patients were enrolled in validation cohort. AKI occurred in 1206 (33.4%) patients in training cohort and 521 (56.3%) patients in validation cohort. According to the multivariate logistic regression analysis, four WBC-related indicators were finally included in the novel risk model, and the risk model had a relatively good accuracy for AKI in the training set (C-index, 0.764, 95% CI 0.749-0.780) as well as in the validation set (C-index, 0.738, 95% CI: 0.706-0.770). Even after accounting for other models, the critically ill AECOPD patients in the high-risk group (risk score > 3.44) still showed an increased risk of AKI (odds ratio: 4.74, 95% CI: 4.07-5.54) compared to those in low-risk group (risk score ≤ 3.44). Moreover, the risk model showed outstanding calibration capability as well as therapeutic usefulness in both groups for AKI and ICU mortality and in-hospital mortality of critical ill AECOPD patients. The novel risk model showed good AKI prediction performance. This risk model has certain reference value for the risk stratification of AECOPD complicated with AKI in clinically.

A proposed framework for advancing acute kidney injury risk stratification and diagnosis in children: a report from the 26th Acute Disease Quality Initiative (ADQI) conference

Acute kidney injury (AKI) in children is associated with increased morbidity, reduced health-related quality of life, greater resource utilization, and higher mortality. Improvements in the timeliness and precision of AKI diagnosis in children are needed. In this report, we highlight existing, novel, and on-the-horizon diagnostic and risk-stratification tools for pediatric AKI, and outline opportunities for integration into clinical practice. We also summarize pediatric-specific high-risk diagnoses and exposures for AKI, as well as the potential role of real-time risk stratification and clinical decision support to improve outcomes. Lastly, the key characteristics of important pediatric AKI phenotypes will be outlined. Throughout, we identify key knowledge gaps, which represent prioritized areas of focus for future research that will facilitate a comprehensive, timely and personalized approach to pediatric AKI diagnosis and management.

Ambulatory urine biomarkers associations with acute kidney injury and hospitalization in people with HIV.

People with HIV (PWH) generally have worse ambulatory levels of kidney injury biomarkers and excess risk of acute kidney injury (AKI) compared to persons without HIV. We evaluated whether ambulatory measures of subclinical kidney injury among PWH are associated with subsequent AKI. In the Predictors of Acute Renal Injury Study (PARIS), which enrolled 468 PWH from April 2016 to August 2019, we measured 10 urine biomarkers of kidney health (albumin, a1m, b2M, NGAL, IL18, KIM-1, EGF, UMOD, MCP-1, YKL40) at baseline and annually during follow-up. Using multivariable Cox regression models, we evaluated baseline and time-updated biomarker associations with the primary outcome of AKI (≥0.3 mg/dl or ≥1.5-times increase in serum creatinine from baseline) and secondary outcome of all-cause hospitalization. At baseline, the mean age was 53 years old, and 45% self-identified as female. In time-updated models adjusting for sociodemographic factors, comorbidities, albuminuria, estimated glomerular filtration rate, and HIV-associated factors, higher KIM-1 [hazard ratio (HR) = 1.30 per twofold higher; 95% confidence interval (CI) 1.03-1.63] and NGAL concentrations (HR = 1.24, 95% CI 1.06-1.44) were associated with higher risk of hospitalized AKI. Additionally, in multivariable, time-updated models, higher levels of KIM-1 (HR = 1.19, 95% CI 1.00, 1.41), NGAL (HR = 1.13, 95% CI 1.01-1.26), and MCP-1 (HR = 1.20, 95% CI 1.00, 1.45) were associated with higher risk of hospitalization. Urine biomarkers of kidney tubular injury, such as KIM-1 and NGAL, are strongly associated with AKI among PWH, and may hold potential for risk stratification of future AKI.

Machine learning in the prediction of cardiac surgery associated acutekidney injury with early postoperative biomarkers.

To establish novel prediction models for predicting acute kidney injury (AKI) after cardiac surgery based on early postoperative biomarkers. This study enrolled patients who underwent cardiac surgery in a Chinese tertiary cardiac center and consisted of a discovery cohort (n = 452, from November 2018 to June 2019) and a validation cohort (n = 326, from December 2019 to May 2020). 43 biomarkers were screened using the least absolute shrinkage and selection operator and logistic regression to construct a nomogram model. Three tree-based machine learning models were also established: eXtreme Gradient Boosting (XGBoost), random forest (RF) and deep forest (DF). Model performance was accessed using area under the receiver operating characteristic curve (AUC). AKI was defined according to the Kidney Disease Improving Global Outcomes criteria. Five biomarkers were identified as independent predictors of AKI and were included in the nomogram: soluble ST2 (sST2), N terminal pro-brain natriuretic peptide (NT-proBNP), heart-type fatty acid binding protein (H-FABP), lactic dehydrogenase (LDH), and uric acid (UA). In the validation cohort, the nomogram achieved good discrimination, with AUC of 0.834. The machine learning models also exhibited adequate discrimination, with AUC of 0.856, 0.850, and 0.836 for DF, RF, and XGBoost, respectively. Both nomogram and machine learning models had well calibrated. The AUC of sST2, NT-proBNP, H-FABP, LDH, and UA to discriminate AKI were 0.670, 0.713, 0.725, 0.704, and 0.749, respectively. In addition, all of these biomarkers were significantly correlated with AKI after adjusting clinical confounders (odds ratio and 95% confidence interval of the third vs. the first tertile: sST2, 3.55 [2.34-5.49], NT-proBNP, 5.50 [3.54-8.71], H-FABP, 6.64 [4.11-11.06], LDH, 7.47 [4.54-12.64], and UA, 8.93 [5.46-15.06]). Our study provides a series of novel predictive models and five biomarkers for enhancing the risk stratification of AKI after cardiac surgery.

Validation of the STARZ neonatal acute kidney injury risk stratification score in an independent prospective cohort.

A rapid AKI risk assessment score would allow for improving management and outcomes. STARZ (Sethi, Tibrewal, Agrawal, Raina, waZir) score was developed for acute kidney injury (AKI) risk stratification of critically ill neonates. This is the first independent validation for the novel score outside the original enrolled centres. 750 neonates were included in the study. The STARZ score was calculated after 12 hours of admission. Neonates admitted in NICU and receiving IV fluids for at least 48 hours were included. A total of 8.8% neonates had AKI in the first 7 days post admission. The duration of hospital stay was significantly higher among neonates with AKI [10.5 (7-19) vs. 7 (5-10) days; p < 0.001]. Mortality risk was 6.4 times higher among those with AKI [8 (12.1%) vs. 13 (1.9%); p < 0.001; RR (95% CI): 6.38 (2.74-14.83)]. In this study, the STARZ neonatal scoring model showed a sensitivity of 89.4% in detecting AKI with a 90.9% specificity and a high negative predictive value of 98.9%. The area under ROC was 0.958 (0.934-0981) - a high discriminative power. The STARZ score allows for AKI risk stratification, providing opportunity for therapeutic interventions which may improve outcomes in critically ill neonates.

Risk Score for Prediction of Acute Kidney Injury in Patients with Acute ST-Segment Elevation Myocardial Infarction.

Acute kidney injury (AKI) is an important comorbidity of ST-Segment Elevation Myocardial Infarction (STEMI) and worsens the prognosis. The purpose of this study was to investigate the relationship between clinical data, test results, surgical findings, and AKI in STEMI patients and to develop a simple, practical model for predicting the risk of AKI. Prognostic prediction research with clinical risk score development was conducted. The data used for model development was derived from the database of the Henan Province Cardiovascular Disease Clinical Data and Sample Resource Bank Engineering Research Center. The data used for external validation was derived from the China Chest Pain Center database. The study endpoint was defined as the occurrence of AKI. Logistic regression analysis was used to identify independent predictors of AKI. Logistic coefficients of each predictor were used for score weighting and transformation. The predictive performance of the newly derived risk scores was validated, respectively, by receiver operating characteristic (ROC) regression in the development population and the external validation population. A total of 364 patients, 57 (15.6%) with AKI and 307 (84.4%) without AKI, were included for score derivation. The validation crowd includes 88 STEMI patients in different institutions. A total of 11 potential predictors were explored under the multivariable logistic regression model. The new risk score was based on five final predictors which were age > 72 years, ejection fraction of no more than 40%, baseline serum creatinine > 102.7 mmol/L, red blood cell distribution width > 13.15, and culprit lesion located in the middistal segment. With only five predictor variables, the score predicted the risk of AKI with the effective discriminative ability (area under the receiver operating characteristic curve (AuROC): 0.721, 95% confidence interval (CI): 0.652-0.790). In the external validation, the newly developed score confirmed a similar discrimination as the crowd used for derivation (AuROC: 0.731, 95% CI: 0.624-0.838). The newly developed score was proved to have good predictive performance and could be practically applied for risk stratification of AKI in STEMI patients.

Pneumoperitoneum and Acute Kidney Injury-An Integrative Clinical Concept Review.

An increased intraabdominal pressure, particularly when occurring during periods of hemodynamic instability or fluid overload, is regarded as a major contributor to acute kidney injury (AKI) in intensive care units. During abdominal laparoscopic procedures, intraoperative insufflation pressures up to 15 mmHg are applied, to enable visualization and surgical manipulation but with the potential to compromise net renal perfusion. Despite the widely acknowledged renal arterial autoregulation, net arterial perfusion pressure is known to be narrow, and the effective renal medullary perfusion is disproportionately impacted by venous and lymphatic congestion. At present, the potential risk factors, mitigators and risk-stratification of AKI during surgical pneumoperitoneum formation received relatively limited attention among nephrologists and represent an opportunity to look beyond mere blood pressure and intake-output balances. Careful charting and reporting duration and extent of surgical pneumoperitoneum represents an opportunity for anesthesia teams to better communicate intraoperative factors affecting renal outcomes for the postoperative clinical teams. In this current article, the authors are integrating preclinical data and clinical experience to provide a better understanding to optimize renal perfusion during surgeries. Future studies should carefully consider intrabdominal insufflation pressure as a key variable when assessing outcomes and blood pressure goals in these settings.

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients.

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of >0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes.

Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases

BackgroundPredictive models based on machine learning have been widely used in clinical practice. Patients with acute myocardial infarction (AMI) are prone to the risk of acute kidney injury (AKI), which results in a poor prognosis for the patient. The aim of this study was to develop a machine learning predictive model for the identification of AKI in AMI patients.MethodsPatients with AMI who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV database were enrolled. The primary outcome was the occurrence of AKI during hospitalization. We developed Random Forests (RF) model, Naive Bayes (NB) model, Support Vector Machine (SVM) model, eXtreme Gradient Boosting (xGBoost) model, Decision Trees (DT) model, and Logistic Regression (LR) models with AMI patients in MIMIC-IV database. The importance ranking of all variables was obtained by the SHapley Additive exPlanations (SHAP) method. AMI patients in MIMIC-III databases were used for model evaluation. The area under the receiver operating characteristic curve (AUC) was used to compare the performance of each model.ResultsA total of 3,882 subjects with AMI were enrolled through screening of the MIMIC database, of which 1,098 patients (28.2%) developed AKI. We randomly assigned 70% of the patients in the MIMIC-IV data to the training cohort, which is used to develop models in the training cohort. The remaining 30% is allocated to the testing cohort. Meanwhile, MIMIC-III patient data performs the external validation function of the model. 3,882 patients and 37 predictors were included in the analysis for model construction. The top 5 predictors were serum creatinine, activated partial prothrombin time, blood glucose concentration, platelets, and atrial fibrillation, (SHAP values are 0.670, 0.444, 0.398, 0.389, and 0.381, respectively). In the testing cohort, using top 20 important features, the models of RF, NB, SVM, xGBoost, DT model, and LR obtained AUC of 0.733, 0.739, 0.687, 0.689, 0.663, and 0.677, respectively. Placing RF models of number of different variables on the external validation cohort yielded their AUC of 0.711, 0.754, 0.778, 0.781, and 0.777, respectively.ConclusionMachine learning algorithms, particularly the random forest algorithm, have improved the accuracy of risk stratification for AKI in AMI patients and are applied to accurately identify the risk of AKI in AMI patients.

STARZ Neonatal AKI Risk Stratification Cut-off Scores for Severe AKI and Need for Dialysis in Neonates

Neonatal acute kidney injury (AKI) is a significant pathology associated with higher mortality rates, longer neonatal intensive care stay, and worse clinical outcomes.1,2 In order to mitigate the avoidable outcomes, it is important to identify AKI early and start early therapeutic measures.2,3 There have been previous attempts to derive illness severity scores, such as the Clinical Risk Index for Babies, the Simplified age-weight-sex score, Pediatric Risk of Mortality and Pediatric Index of Mortality among neonates and children.

Validation of the STARZ neonatal acute kidney injury risk stratification score.

Neonatal acute kidney injury (AKI) is common in neonatal intensive care units (NICU) and leads to worse outcomes.Stratifying neonates into an "at risk" category allows health care providers to objectively recognize opportunities for improvements in quality of care. The "Neonatal AKI Risk Prediction Scoring" was devised as the "STARZ [Sethi, Tibrewal, Agrawal, Raina, waZir]" Score. The STARZ score was derived from our prior multicentre study analysing risk factors for AKI in neonates admitted to the NICU. This tool includes 10 variables with a total score ranging from 0 to 100 and a cut-off score of 31.5. In the present study, the scoring model has been validated in our multicentre cohort of 744 neonates. In the validation cohort, this scoring model had sensitivity of 82.1%, specificity 91.7%, positive predictive value 81.2%, negative predictive value 92.2% and accuracy 88.8%. Based on the STARZ cut-off score of ≥ 31.5, an area under the receiver operating characteristic (ROC) curve was observed to be 0.932 (95% CI, 0.910-0.954; p < 0.001) signifying that the discriminative power was high. In the validation cohort, the probability of AKI was less than 20% for scores up to 32, 20-40% for scores between 33 and 36, 40-60% for scores between 37 and 43, 60-80% for scores between 44 and 49, and ≥ 80% for scores ≥ 50. To promote the survival of susceptible neonates, early detection and prompt interventional measures based on highly evidenced research is vital. The risk of AKI in admitted neonates can be quantitatively determined by the rapid STARZ scoring system. A higher resolution version of the Graphical abstract is available as Supplementary information.

Survival and recovery modeling of acute kidney injury in critically ill adults.

Objectives:Acute kidney injury is common among the critically ill. However, the incidence, medication use, and outcomes of acute kidney injury have been variably described. We conducted a single-center, retrospective cohort study to examine the risk factors and correlates associated with acute kidney injury in critically ill adults with a particular focus on medication class usage.Methods:We reviewed the electronic medical records of all adult patients admitted to an intensive care unit between 1 February and 30 August 2020. Acute kidney injury was defined by the 2012 Kidney Disease: Improving Global Outcomes guidelines. Data included were demographics, comorbidities, symptoms, laboratory parameters, interventions, and outcomes. The primary outcome was acute kidney injury incidence. A Least Absolute Shrinkage and Selection Operator regression model was used to determine risk factors associated with acute kidney injury. Secondary outcomes including acute kidney injury recovery and intensive care unit mortality were analyzed using a Cox regression model.Results:Among 226 admitted patients, 108 (47.8%) experienced acute kidney injury. 37 (34.3%), 39 (36.1%), and 32 patients (29.6%) were classified as acute kidney injury stages I–III, respectively. Among the recovery and mortality cohorts, analgesics/sedatives, anti-infectives, and intravenous fluids were significant (p-value < 0.05). The medication classes IV-fluid electrolytes nutrition (96.7%), gastrointestinal (90.2%), and anti-infectives (81.5%) were associated with an increased odds of developing acute kidney injury, odd ratios: 1.27, 1.71, and 1.70, respectively. Cox regression analyses revealed a significantly increased time-varying mortality risk for acute kidney injury-stage III, hazard ratio: 4.72 (95% confidence interval: 1–22.33). In the recovery cohort, time to acute kidney injury recovery was significantly faster in stage I, hazard ratio: 9.14 (95% confidence interval: 2.14–39.06) cohort when compared to the stage III cohort.Conclusion:Evaluation of vital signs, laboratory, and medication use data may be useful to determine acute kidney injury risk stratification. The influence of particular medication classes further impacts the risk of developing acute kidney injury, necessitating the importance of examining pharmacotherapeutic regimens for early recognition of renal impairment and prevention.

Prevention of cardiac surgery-associated acute kidney injury by risk stratification using (TIMP-2)*(IGFBP7).

Aim: The purpose of this study was to assess urinary (TIMP-2)*(IGFBP7) for prevention of acute kidney injury (AKI) in patients undergoing elective cardiac surgery. Materials & methods: Two retrospective cohorts were analyzed before and after the implementation of urinary (TIMP-2)*(IGFBP7). The control cohort had a standard supportive care. For the (TIMP-2)*(IGFBP7) cohort, patients with the (TIMP-2)*(IGFBP7) >0.3 received renal supportive measures. Results: A total of382 patients were included, 197 in the control cohort and 185 in intervention cohort. The incidence of AKI was significantly reduced in the (TIMP-2)*(IGFBP7) cohort (20.5vs 29.9%, p<0.05). In multivariateanalysis, patients of the (TIMP-2)*(IGFBP7) cohort had a lower risk of developing AKI (p=0.029). Conclusion: In conclusion, renal supporting care based on AKI risk stratification using urinary (TIMP-2)*(IGFBP7) may reduce AKI incidence.

Utility of suPAR and NGAL for AKI Risk Stratification and Early Optimization of Renal Risk Medications among Older Patients in the Emergency Department.

Diagnosis of acute kidney injury (AKI) based on plasma creatinine often lags behind actual changes in renal function. Here, we investigated early detection of AKI using the plasma soluble urokinase plasminogen activator receptor (suPAR) and neutrophil gelatinase-sssociated lipocalin (NGAL) and observed the impact of early detection on prescribing recommendations for renally-eliminated medications. This study is a secondary analysis of data from the DISABLMENT cohort on acutely admitted older (≥65 years) medical patients (n = 339). Presence of AKI according to kidney disease: improving global outcomes (KDIGO) criteria was identified from inclusion to 48 h after inclusion. Discriminatory power of suPAR and NGAL was determined by receiver-operating characteristic (ROC). Selected medications that are contraindicated in AKI were identified in Renbase®. A total of 33 (9.7%) patients developed AKI. Discriminatory power for suPAR and NGAL was 0.69 and 0.78, respectively, at a cutoff of 4.26 ng/mL and 139.5 ng/mL, respectively. The interaction of suPAR and NGAL yielded a discriminatory power of 0.80, which was significantly higher than for suPAR alone (p = 0.0059). Among patients with AKI, 22 (60.6%) used at least one medication that should be avoided in AKI. Overall, suPAR and NGAL levels were independently associated with incident AKI and their combination yielded excellent discriminatory power for risk determination of AKI.

Derivation and External Validation of a Risk Index for Predicting Acute Kidney Injury Requiring Kidney Replacement Therapy After Noncardiac Surgery

Severe acute kidney injury (AKI) is a serious postoperative complication. A tool for predicting the risk of AKI requiring kidney replacement therapy (KRT) after major noncardiac surgery might assist with patient counseling and targeted use of measures to reduce this risk. To derive and validate a predictive model for AKI requiring KRT after major noncardiac surgery. In this prognostic study, 5 risk prediction models were derived and internally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Alberta, Canada, between January 1, 2004, and December 31, 2013. The best performing model and corresponding risk index were externally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Ontario, Canada, between January 1, 2007, and December 31, 2017. Data analysis was conducted from September 1, 2019, to May 31, 2021. Demographic characteristics, surgery type, laboratory measures, and comorbidities before surgery. Acute kidney injury requiring KRT within 14 days after surgery. Discrimination was assessed using the C statistic; calibration was assessed using calibration intercept and slope. Logistic recalibration was used to optimize model calibration in the external validation cohort. The derivation cohort included 92 114 patients (52.2% female; mean [SD] age, 62.3 [18.0] years), and the external validation cohort included 709 086 patients (50.8% female; mean [SD] age, 61.0 [16.0] years). A total of 529 patients (0.6%) developed postoperative AKI requiring KRT in the derivation cohort, and 2956 (0.4%) developed postoperative AKI requiring KRT in the external validation cohort. The following factors were consistently associated with the risk of AKI requiring KRT: younger age (40-69 years: odds ratio [OR], 2.07 [95% CI, 1.69-2.53]; <40 years: OR, 3.73 [95% CI, 2.61-5.33]), male sex (OR, 1.55; 95% CI, 1.28-1.87), surgery type (colorectal: OR, 4.86 [95% CI, 3.28-7.18]; liver or pancreatic: OR, 6.46 [95% CI, 3.85-10.83]; other abdominal: OR, 2.19 [95% CI, 1.66-2.89]; abdominal aortic aneurysm repair: OR, 19.34 [95% CI, 14.31-26.14]; other vascular: OR, 7.30 [95% CI, 5.48-9.73]; thoracic: OR, 3.41 [95% CI, 2.07-5.59]), lower estimated glomerular filtration rate (OR, 0.97; 95% CI, 0.97-0.97 per 1 mL/min/1.73 m2 increase), lower hemoglobin concentration (OR, 0.99; 95% CI, 0.98-0.99 per 0.1 g/dL increase), albuminuria (mild: OR, 1.88 [95% CI, 1.52-2.33]; heavy: OR, 3.74 [95% CI, 2.98-4.69]), history of myocardial infarction (OR, 1.63; 95% CI, 1.32-2.03), and liver disease (mild: OR, 2.32 [95% CI, 1.66-3.24]; moderate or severe: OR, 4.96 [95% CI, 3.58-6.85]). In external validation, a final model including these variables showed excellent discrimination (C statistic, 0.95; 95% CI, 0.95-0.96), with sensitivity of 21.2%, specificity of 99.9%, positive predictive value of 38.1%, and negative predictive value of 99.7% at a predicted risk threshold of 10% or greater. The findings suggest that this risk model can predict AKI requiring KRT after noncardiac surgery using routine preoperative data. The model may be feasible for implementation in clinical perioperative risk stratification for severe AKI.

Neonatal acute kidney injury risk stratification score: STARZ study.

Neonates admitted in the neonatal intensive care unit are vulnerable to acute kidney injury leading to worse outcomes. It is important to identify "at-risk" neonates for early preventive measures. The study was a multicenter, national, prospective cohort study done in 11 centers in India. A multivariable logistic regression technique with step-wise backward elimination method was used, and a "Risk Prediction Scoring" was devised [the STARZ score]. The neonates with admission in the NICU within <25.5 h of birth, requirement of positive pressure ventilation in the delivery room, <28 weeks gestational age, sepsis, significant cardiac disease, urine output <1.32 ml/kg/h or serum creatinine ≥0.98 mg/dl during the first 12 h post admission, use of nephrotoxic drugs, use of furosemide, or use of inotrope had a significantly higher risk of AKI at 7 days post admission in the multivariate logistic regression model. This scoring model had a sensitivity of 92.8%, specificity of 87.4% positive predictive value of 80.5%, negative predictive value of 95.6%, and accuracy of 89.4%. The STARZ neonatal score serves to rapidly and quantitatively determine the risk of AKI in neonates admitted to the neonatal intensive care unit. The STARZ neonatal score serves to rapidly and quantitatively determine the risk of AKI in neonates admitted to the neonatal intensive care unit. These neonates with a higher risk stratification score need intense monitoring and daily kidney function assessment. With this intensification of research in the field of AKI risk stratification prediction, there is hope that we will be able to decrease morbidity and mortality associated with AKI in this population.

Assessment of a modified renal angina index for AKI prediction in critically ill adults.

The renal angina index (RAI) is a useful tool for risk stratification of acute kidney injury (AKI) in critically ill children. We evaluated the performance of a modified adult RAI (mRAI) for the risk stratification of AKI in critically ill adults. We used two independent intensive care unit (ICU) cohorts: 13 965 adult patients from the University of Kentucky (UKY) and 4789 from University of Texas Southwestern (UTSW). The mRAI included: diabetes, presence of sepsis, mechanical ventilation, pressor/inotrope use, percentage change in serum creatinine (SCr) in reference to admission SCr (ΔSCr) and fluid overload percentage within the first day of ICU admission. The primary outcome was AKI Stage ≥2 at Days 2-7. Performance and reclassification metrics were determined for the mRAI score compared with ΔSCr alone. The mRAI score outperformed ΔSCr and readjusted probabilities to predict AKI Stage ≥2 at Days 2-7: C-statistic: UKY 0.781 versus 0.708 [integrated discrimination improvement (IDI) 2.2%] and UTSW 0.766 versus 0.696 (IDI 1.8%) (P < 0.001 for both). In the UKY cohort, only 3.3% of patients with mRAI score <10 had the AKI event, while 16.4% of patients with mRAI score of ≥10 had the AKI event (negative predictive value 96.8%). Similar findings were observed in the UTSW cohort as part of external validation. In critically ill adults, the adult mRAI score determined within the first day of ICU admission outperformed changes in SCr for the prediction of AKI Stage ≥2 at Days 2-7 of ICU stay. The mRAI is a feasible tool for AKI risk stratification in adult patients in the ICU.

Neutrophil gelatinase-associated lipocalin (NGAL) for the prediction of acute kidney injury in chronic kidney disease patients treated with primary percutaneous coronary intervention

Elevated plasma levels of neutrophil gelatinase-associated lipocalin (NGAL) is a marker of tubular damage and aid in the early identification of acute kidney injury (AKI). We evaluated NGAL levels for identification of AKI superimposed on chronic kidney disease (CKD) vs. "de novo" AKI among ST elevation myocardial infarction (STEMI) patients undergoing primary coronary intervention (PCI). 217 STEMI patients treated with PCI were prospectively included, 34 (16%) had baseline CKD. Plasma NGAL levels were drawn 24h following PCI. Receiver-operator characteristic (ROC) methods were used to identify optimal sensitivity and specificity for the observed NGAL range in AKI patients with and without CKD. Overall AKI incidence was 13%. NGAL levels were significantly higher for patients with AKI compared to no-AKI, irrespective of CKD. Different optimal cutoff value for NGAL to predict AKI were found for patients with CKD (133ng/ml, sensitivity of 73% and specificity of 75%; AUC: 0.837, p<0.001) and for non-CKD (104ng/ml with sensitivity of 79% and specificity of 82%; AUC: 0.844, p<0.001). In a multivariate logistic regression model, NGAL levels were independently associated with AKI in patients with and without CKD (HR 1.04, 95% CI: 1.01-1.08; p=0.024; and HR 1.03, 95% CI: 1.01-1.04; p=0.001), respectively. Elevated plasma NGAL levels identify patients who are at high-risk to develop AKI following primary PCI. Determining different cutoff values of plasma NGAL for de novo AKI and AKI superimposed on CKD may be necessary for accurate AKI diagnosis and risk stratification.