Acute Kidney Injury Risk Prediction Research Articles

Implications of a Race Term in GFR Estimates Used to Predict AKI After Coronary Intervention

BackgroundThe prediction of mortality, bleeding, and acute kidney injury (AKI) after percutaneous coronary intervention (PCI) traditionally relied on race-based estimates of the glomerular filtration rate (GFR). Recently, race agnostic equations were developed to advance equity. ObjectivesThe authors aimed to compare the accuracy and implications of various GFR equations when used to predict AKI after PCI. MethodsUsing the National Cardiovascular Data Registry (NCDR) CathPCI data set, we identified patients undergoing PCI in 2020 and calculated their AKI risk using the 2014 NCDR AKI risk model. We created 4 AKI models per patient for each estimate of baseline renal function: the traditional GFR equation with a race term, 2 GFR equations without a race term, and serum creatinine alone. We then compared each model’s performance predicting AKI. ResultsAmong 455,806 PCI encounters, the median age was 67 years, 32.2% were women, and 8.5% were Black. In Black patients, risk models without a race term were better calibrated than models incorporating an equation with a race term (intercept: −0.01 vs 0.15). Race-agnostic models reclassified 6% of Black patients into higher-risk categories, potentially prompting appropriate mitigation efforts. However, even with a race-agnostic model, AKI occurred in Black patients 18% more often than expected, which was not explained by captured patient or procedural characteristics. ConclusionsIncorporating a GFR estimate without a Black race term into the NCDR AKI risk prediction model yielded more accurate prediction of AKI risk for Black patients, which has important implications for reducing disparities and benchmarking.

Metabolomics in Acute Kidney Injury: The Clinical Perspective.

Acute kidney injury (AKI) affects increasing numbers of hospitalized patients worldwide. The diagnosis of AKI is made too late in most individuals since it is still based on dynamic changes in serum creatinine. In recent years, new AKI biomarkers have been identified; however, none of these can reliably replace serum creatinine yet. Metabolomic profiling (metabolomics) allows the concomitant detection and quantification of large numbers of metabolites from biological specimens. The current article aims to summarize clinical studies on metabolomics in AKI diagnosis and risk prediction. The following databases were searched for references: PubMed, Web of Science, Cochrane Library, and Scopus, and the period lasted from 1940 until 2022. The following terms were utilized: 'AKI' OR 'Acute Kidney Injury' OR 'Acute Renal Failure' AND 'metabolomics' OR 'metabolic profiling' OR 'omics' AND 'risk' OR 'death' OR 'survival' OR 'dialysis' OR 'KRT' OR 'kidney replacement therapy' OR 'RRT' OR 'renal replacement therapy' OR 'recovery of kidney function' OR 'renal recovery' OR 'kidney recovery' OR 'outcome'. Studies on AKI risk prediction were only selected if metabolomic profiling allowed differentiation between subjects that fulfilled a risk category (death or KRT or recovery of kidney function) and those who did not. Experimental (animal-based) studies were not included. In total, eight studies were identified. Six studies were related to the diagnosis of AKI; two studies were performed on metabolic analysis in AKI risk (death) prediction. Metabolomics studies in AKI already helped to identify new biomarkers for AKI diagnosis. The data on metabolomics for AKI risk prediction (death, KRT, recovery of kidney function), however, are very limited. Both the heterogenous etiology and the high degree of pathogenetic complexity of AKI most likely require integrated approaches such as metabolomics and/or additional types of '-omics' studies to improve clinical outcomes in AKI.

Identification of AKI signatures and classification patterns in ccRCC based on machine learning.

Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive. Four public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers. Seven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability. Our study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.

Development of an acute kidney injury risk prediction model for patients undergoing extracorporeal membrane oxygenation

BackgroundSome studies have reported to use some predictors before extracorporeal membrane oxygenation (ECMO) initiation to predict the acute kidney injury (AKI) risk. However, injury during the ECMO operation and the response of patients to ECMO may significantly influence the prognosis, and they are unpredictable before ECMO initiation. This study aims to develop a potential model based clinical characteristics at the 2-hour time point during ECMO for the early prediction of AKI in patients receiving ECMO. Methods139 patients who underwent ECMO were enrolled in this study. The clinical characteristics and the laboratory examinations at 2-hour time point during ECMO were recorded. The least absolute shrinkage and selection operator (LASSO) regression method was performed to select predictors, and logistic regression and a nomogram were used to establish the prediction model. The area under curve (AUC) of the receiver operating characteristic and calibration curve were used to analyze the discrimination and calibration of the model. K-fold cross-validation method was performed to validate the accuracy of this model. ResultsAmong the 139 patients receiving ECMO, 106 participants (76.26%) developed AKI. Four predictive variables including ECMO model, serum creatinine (Scr-2h), uric acid(UA-2h), and serum lactate (Lac-2h) at the 2-hour time point during ECMO were filtered from 39 clinical parameters by LASSO regression. These four predictors were incorporated to develop a model for predicting AKI risk using logistic regression. The AUC of the model was 0.905 (0.845–0.965), corresponding to 81.1% sensitivity, 90.9% specificity and 83.5% accuracy. Moreover, this model showed good consistency between observed and predicted probability based on the calibration curve (P > 0.05). The validation performed by K-fold cross-validation method showed that the accuracy was 0.874 ± 0.006 in training sets, 0.827 ± 0.053 in test sets, indicating a good capability for AKI risk prediction. Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice. ConclusionThe nomogram incorporating Scr-2h,Lac-2h, UA-2h, and ECMO model may facilitate the individualized prediction of the AKI risk among patients undergoing ECMO.

A Risk Prediction Score for Renal Replacement Therapy in Critically Ill Septic – Acute Kidney Injury Patients

Introduction: Acute kidney injury (AKI) is a major complication in critically ill septic patients and is associated with increased morbidity and mortality. A recent study suggested a risk score based on patient’s chronic comorbidities and acute events at intensive care unit (ICU) admission as a reliable tool for predicting AKI in critically ill adult population. The aim of this study was to adapt this score to septic-AKI patients and evaluate its prognostic value as predictor of the need for renal replacement therapy (RRT) at ICU admission. Methods: This is a retrospective analysis of 399 septic-AKI patients admitted to the Division of Intensive Medicine of the Centro Hospitalar Universitário Lisboa Norte between January 2008 and December 2014. The Kidney Disease Improving Global Outcomes (KDIGO) classification was used to define AKI. The Renal Replacement Therapy Risk Score was adapted from the AKI risk prediction score proposed by Malhotra et al (Nephrol Dial Transplant. 2017;32:814-22). Results: Fifty two percent of patients were KDIGO stage 3, 25.8% KDIGO stage 2 and 22.3% KDIGO stage 1. Twenty seven percent of patients required RRT. Patients requiring RRT had higher risk score than those who did not (6.6±2.5 vs 5.1 ±2.6, p<0.001). An optimal cut-off value of ≥ 6 in this score predicted the need for RRT with sensitivity 0.630 and specificity 0.391. Conclusion: The RRT risk score at ICU admission was independently associated with the requirement of RRT septic-AKI patients. The assessment of this ratio is simple and can prove useful in identifying patients at risk for need of RRT.

Artificial Intelligence in Acute Kidney Injury Prediction.

The use of artificial intelligence (AI) in nephrology and its associated clinical research is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques. We discuss the relative improvement in AKI detection as well as the relative dearth of data around the clinical implementation and patient outcomes using these models. The use of AI for AKI detection and clinical care is in its infancy, and this review describes how we arrived at our current position and hints at the promise of the future.

Interventions, Barriers, and Proposed Solutions Associated With the Implementation of a Protocol That Uses Clinical Decision Support and a Stress Biomarker Test to Identify ICU Patients at High-Risk for Drug Associated Acute Kidney Injury.

Damage biomarkers are helpful in early identification of patients who are at risk of developing acute kidney injury (AKI). Investigations are ongoing to identify the optimal role of stress/damage biomarkers in clinical practice regarding AKI risk prediction, surveillance, diagnosis, and prognosis. To determine the impact of utilizing a clinical decision support system (CDSS) to guide stress biomarker testing in intensive care unit (ICU) patients at risk for drug-induced acute kidney injury (D-AKI). A protocol was designed utilizing a clinical decision support system (CDSS) alert to identify patients that were ordered 3 or more potentially nephrotoxic medications, suggesting risk for progressing to AKI from nephrotoxic burden. Once alerted to these high-risk patients, the pharmacist determined if action was needed by ordering a stress biomarker test, tissue inhibitor of metalloproteinase-2-insulin-like growth factor-binding protein 7 (TIMP-2•IGFBP7). If the biomarker test result was elevated, the pharmacist provided nephrotoxin stewardship recommendations to the team. Pharmacists recorded the response to the clinical decision support alert, ordering, and interpreting the TIMP-2•IGFBP7, and information regarding clinical interventions. An alert in conjunction with TIMP-2•IGFBP7 as a strategy for AKI risk prediction and stimulant for patient care management was assessed. In addition, barriers and solutions to protocol implementation were evaluated. There were 394 total activities recorded by pharmacists for 345 unique patients. Ninety-three (93/394; 23.6%) actionable alerts resulted in a TIMP-2•IGFBP7 test being ordered. Thirty-one TIMP-2•IGFBP7 results were >0.3 (31/81; 38.3%), suggesting a high-risk of progression to AKI, which prompted 191 pharmacist/team interventions. On average, there were 1.64 interventions per patient in the low-risk patients, 3.43 in high-risk patients, and 3.75 in the highest-risk patients. Stress biomarkers can be used in conjunction with CDSS alerts to affect therapeutic decisions in ICU patients at high-risk for D-AKI.

Predicting Risk or Adjusting for Risk?

HomeCirculation: Cardiovascular Quality and OutcomesVol. 15, No. 8Predicting Risk or Adjusting for Risk? No AccessEditorialRequest AccessFull TextAboutView Full TextView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toNo AccessEditorialRequest AccessFull TextPredicting Risk or Adjusting for Risk? Sharon-Lise T. Normand, PhD Sharon-Lise T. NormandSharon-Lise T. Normand Correspondence to: Sharon-Lise T. Normand, PhD, Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02115. Email E-mail Address: [email protected] https://orcid.org/0000-0001-7027-4769 Department of Health Care Policy, Harvard Medical School and the Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA. Search for more papers by this author Originally published12 Aug 2022https://doi.org/10.1161/CIRCOUTCOMES.122.009082Circulation: Cardiovascular Quality and Outcomes. 2022;15This article is a commentary on the followingMaintaining a National Acute Kidney Injury Risk Prediction Model to Support Local Quality Benchmarking"Predicting Risk or Adjusting for Risk?." Circulation: Cardiovascular Quality and Outcomes, 15(8), pp. e009082–FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.For Disclosures, see page 634.Correspondence to: Sharon-Lise T. Normand, PhD, Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02115. Email [email protected]med.harvard.edu

Maintaining a National Acute Kidney Injury Risk Prediction Model to Support Local Quality Benchmarking.

The utility of quality dashboards to inform decision-making and improve clinical outcomes is tightly linked to the accuracy of the information they provide and, in turn, accuracy of underlying prediction models. Despite recognition of the need to update prediction models to maintain accuracy over time, there is limited guidance on updating strategies. We compare predefined and surveillance-based updating strategies applied to a model supporting quality evaluations among US veterans. We evaluated the performance of a US Department of Veterans Affairs-specific model for postcardiac catheterization acute kidney injury using routinely collected observational data over the 6 years following model development (n=90 295 procedures in 2013-2019). Predicted probabilities were generated from the original model, an annually retrained model, and a surveillance-based approach that monitored performance to inform the timing and method of updates. We evaluated how updating the national model impacted regional quality profiles. We compared observed-to-expected outcome ratios, where values above and below 1 indicated more and fewer adverse outcomes than expected, respectively. The original model overpredicted risk at the national level (observed-to-expected outcome ratio, 0.75 [0.74-0.77]). Annual retraining updated the model 5×; surveillance-based updating retrained once and recalibrated twice. While both strategies improved performance, the surveillance-based approach provided superior calibration (observed-to-expected outcome ratio, 1.01 [0.99-1.03] versus 0.94 [0.92-0.96]). Overprediction by the original model led to optimistic quality assessments, incorrectly indicating most of the US Department of Veterans Affairs' 18 regions observed fewer acute kidney injury events than predicted. Both updating strategies revealed 16 regions performed as expected and 2 regions increasingly underperformed, having more acute kidney injury events than predicted. Miscalibrated clinical prediction models provide inaccurate pictures of performance across clinical units, and degrading calibration further complicates our understanding of quality. Updating strategies tailored to health system needs and capacity should be incorporated into model implementation plans to promote the utility and longevity of quality reporting tools.

Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease

Purpose Acute kidney injury (AKI) is a common complication and associated with a poor clinical outcome. In this study, we developed and validated a model for predicting the risk of AKI through machine learning methods in critical care patients with acute cerebrovascular disease. Methods This study was a retrospective study based on two different cohorts. Five machine learning methods were used to develop AKI risk prediction models. We used six popular metrics (AUROC, F2-Score, accuracy, sensitivity, specificity and precision) to evaluate the performance of these models. Results We identified 2935 patients in the MIMIC-III database and 499 patients in our local database to develop and validate the AKI risk prediction model. The incidence of AKI in these two different cohorts was 18.3% and 61.7%, respectively. Analysis showed that several laboratory parameters (serum creatinine, hemoglobin, white blood cell count, bicarbonate, blood urea nitrogen, sodium, albumin, and platelet count), age, and length of hospital stay, were the top ten important factors associated with AKI. The analysis demonstrated that the XGBoost had higher AUROC (0.880, 95%CI: 0.831–0.929), indicating that the XGBoost model was better at predicting AKI risk in patients with acute cerebrovascular disease than other models. Conclusions This study developed machine learning methods to identify critically ill patients with acute cerebrovascular disease who are at a high risk of developing AKI. This result suggested that machine learning techniques had the potential to improve the prediction of AKI risk models in critical care.

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.

Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model

Rationale & ObjectiveRisk factors for acute kidney injury (AKI) in the hospital have been well studied. Yet, risk factors for identifying high-risk patients for AKI occurring and managed in the outpatient setting are unknown and may differ.Study DesignPredictive model development and external validation using observational electronic health record data.Setting & ParticipantsPatients aged 18-90 years with recurrent primary care encounters, known baseline serum creatinine, and creatinine measured during an 18-month outcome period without established advanced kidney disease.New Predictors & Established PredictorsEstablished predictors for inpatient AKI were considered. Potential new predictors were hospitalization history, smoking, serum potassium levels, and prior outpatient AKI.OutcomesA ≥50% increase in the creatinine level above a moving baseline of the recent measurement(s) without a hospital admission within 7 days defined outpatient AKI.Analytical ApproachLogistic regression with bootstrap sampling for backward stepwise covariate elimination was used. The model was then transformed into 2 binary tests: one identifying high-risk patients for research and another identifying patients for additional clinical monitoring or intervention.ResultsOutpatient AKI was observed in 4,611 (3.0%) and 115,744 (2.4%) patients in the development and validation cohorts, respectively. The model, with 18 variables and 3 interaction terms, produced C statistics of 0.717 (95% CI, 0.710-0.725) and 0.722 (95% CI, 0.720-0.723) in the development and validation cohorts, respectively. The research test, identifying the 5.2% most at-risk patients in the validation cohort, had a sensitivity of 0.210 (95% CI, 0.208-0.213) and specificity of 0.952 (95% CI, 0.951-0.952). The clinical test, identifying the 20% most at-risk patients, had a sensitivity of 0.494 (95% CI, 0.491-0.497) and specificity of 0.806 (95% CI, 0.806-0.807).LimitationsOnly surviving patients with measured creatinine levels during a baseline period and outcome period were included.ConclusionsThe outpatient AKI risk prediction model performed well in both the development and validation cohorts in both continuous and binary forms.

Predicting the risk of postoperative acute kidney injury: development and assessment of a novel predictive nomogram.

ObjectiveThis study aimed to establish and internally verify the risk nomogram of postoperative acute kidney injury (AKI) in patients with renal cell carcinoma.MethodsWe retrospectively collected data from 559 patients with renal cell carcinoma from June 2016 to May 2019 and established a prediction model. Twenty-six clinical variables were examined by least absolute shrinkage and selection operator regression analysis, and variables related to postoperative AKI were determined. The prediction model was established by multiple logistic regression analysis. Decision curve analysis was conducted to evaluate the nomogram.ResultsIndependent predictors of postoperative AKI were smoking, hypertension, surgical time, blood glucose, blood uric acid, alanine aminotransferase, estimated glomerular filtration rate, and radical nephrectomy. The C index of the nomogram was 0.825 (0.790–0.860) and 0.814 was still obtained in the internal validation. The nomogram had better clinical benefit when the intervention was decided at the threshold probabilities of >4% and <79% for patients and doctors, respectively.ConclusionsThis novel postoperative AKI nomogram incorporating smoking, hypertension, the surgical time, blood glucose, blood uric acid, alanine aminotransferase, the estimated glomerular filtration rate, and radical nephrectomy is convenient for facilitating the individual postoperative risk prediction of AKI in patients with renal cell carcinoma.

Risk prediction models for acute kidney injury in adults: An overview of systematic reviews.

BackgroundThe incidence of Acute Kidney Injury (AKI) and its human and economic cost is increasing steadily. One way to reduce the burden associated with AKI is to prevent the event altogether. An important step in prevention lies in AKI risk prediction. Due to the increasing number of available risk prediction models (RPMs) clinicians need to be able to rely on systematic reviews (SRs) to provide an objective assessment on which RPM can be used in a specific setting. Our aim was to assess the quality of SRs of RPMs in AKI.MethodsThe protocol for this overview was registered in PROSPERO. MEDLINE and Embase were searched for SRs of RPMs of AKI in any setting from 2003 till August 2020. We used the ROBIS tool to assess the methodological quality of the retrieved SRs.ResultsEight SRs were retrieved. All studies were assessed as being at high risk for bias using the ROBIS tool. Eight reviews had a high risk of bias in study eligibility criteria (domain 1), five for study identification and selection (domain 2), seven for data collection and appraisal (domain 3) and seven for synthesis and findings (domain 4). Five reviews were scored at high risk of bias across all four domains. Risk of bias assessment with a formal risk of bias tool was only performed in five reviews. Primary studies were heterogeneous and used a wide range of AKI definitions. Only 19 unique RPM were externally validated, of which 11 had only 1 external validation report.ConclusionThe methodological quality of SRs of RPMs of AKI is inconsistent. Most SRs lack a formal risk of bias assessment. SRs ought to adhere to certain standard quality criteria so that clinicians can rely on them to select a RPM for use in an individual patient.Trial registrationPROSPERO registration number is CRD 42020204236, available at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=204236.

External Validation of the Acute Kidney Injury Risk Prediction Score for Critically Ill Surgical Patients Who Underwent Major Non-Cardiothoracic Surgery.

Background: Acute kidney injury (AKI) is a common complication encountered in an intensive care unit (ICU). In 2020, the AKI prediction score was developed specifically for critically ill surgical patients who underwent major non-cardiothoracic surgeries. This study aimed to externally validate the AKI prediction score in terms of performance and clinical utility. Methods: External validation was carried out in a prospective cohort of patients admitted to the ICU of the Faculty of Medicine Vajira Hospital between September 2014 and September 2015. The endpoint was AKI within seven days following ICU admission. Discriminative ability was based on the area under the receiver operating characteristic curves (AuROC). Calibration and clinical usefulness were evaluated. Results: A total of 201 patients were included in the analysis. AKI occurred in 37 (18.4%) patients. The discriminative ability dropped from good in the derivation cohort, to acceptable in the validation cohort (0.839 (95%CI 0.825–0.852) vs. 0.745 (95%CI 0.652–0.838)). No evidence of lack-of-fit was identified (p = 0.754). The score had potential clinical usefulness across the range of threshold probability from 10 to 50%. Conclusions: The AKI prediction score showed an acceptable discriminative performance and calibration with potential clinical usefulness for predicting AKI risk in surgical patients who underwent major non-cardiothoracic surgery.

The use of diagnostic tools for pediatric AKI: applying the current evidence to the bedside.

Given the known deleterious consequences of acute kidney injury (AKI), exciting recent research efforts have focused on developing strategies for the earlier recognition of AKI in the pediatric population. Recognizing the limitations of serum creatinine, investigators have focused on the study of novel biomarkers and practical bedside tools for identifying patients at risk for AKI prior to a rise in serum creatinine. In PubMed, there are presently over 30 original research papers exploring the use of pediatric AKI risk prediction tools in just the last 2years. The following review highlights the most recent advances in the literature regarding opportunities to refine our ability to detect AKI early. Importantly, this reviewdiscusses how prediction tools including novel urine and serum biomarkers, practical risk stratification tests, renal functional reserve, and electronic medical record alerts may ultimately be applied to routine clinical practice.

Artificial Intelligence in Acute Kidney Injury: From Static to Dynamic Models.

Artificial intelligence (AI) is the development of computer systems that normally require human intelligence. In the field of acute kidney injury (AKI) AI has led to an evolution of risk prediction models. In the past, static prediction models were developed using baseline (eg, preoperative) data to evaluate AKI risk. Newer models which incorporated baseline as well as evolving data collected during a hospital admission have shown improved predicative abilities. In this review, we will summarize the advances made in AKI risk prediction over the last several years, including a shift toward more dynamic, real-time, electronic medical record-based models. In addition, we will be discussing the role of electronic AKI alerts and decision support tools. Recent studies have demonstrated improved patient outcomes through the use of these tools which monitor for nephrotoxin medication exposures as well as provide kidney focused care bundles for patients at high risk for severe AKI. Finally, we will briefly discuss the pitfalls and implications of implementing these scores, alerts, and support tools.

Performance of Prediction Models for Contrast-Induced Acute Kidney Injury after Transcutaneous Aortic Valve Replacement

Background: Acute kidney injury (AKI) has shown to adversely affect outcomes in patients undergoing transcutaneous aortic valve replacement (TAVR), and its correct risk estimation may interfere in procedural planning and strategies. The aim of the study was to test and compare 6 scores in predicting AKI after TAVR. Methods: We tested 6 scores (the contrast material limit score, volume-to-creatinine clearance ratio, ACEF, CR4EATME3AD3, Mehran model A, and Mehran model B) in a total of 559 consecutive patients included in the Brazilian TAVR registry. Results: All scores had a poor accuracy and calibration to predict the occurrence of AKI grade 1 or 2. All scores improved the accuracy of AKI risk prediction when stratified for AKI grade 2/3 and AKI grade 3 for all scores. The CR4EATME3AD3 was the best predictor of AKI stage 2/3 (AUC: 0.62; OR: 1.12; 95% CI 1.01–1.26; p = 0.04) and AKI stage 3 (AUC: 0.64; OR: 1.16; 95% CI 1.02–1.32; p = 0.02). Mehran models A and B were both good models for AKI stage 3 (AUC: 0.63; OR: 1.10; 95% CI 1.01–1.22; p = 0.05; and AUC: 0.62; OR: 1.10; 95% CI 1.00–1.21; p = 0.05, respectively). Conclusions: None of the current models demonstrated validity in detecting AKI when its lower grades were evaluated. CR4EATME3AD3 was the best score in predicting moderate to severe AKI after TAVR. These findings suggest that contrast-induced AKI may not be the only factor related to kidney injury after TAVR.

Acute kidney injury risk prediction score for critically-ill surgical patients

BackgroundThere has been a global increase in the incidence of acute kidney injury (AKI), including among critically-ill surgical patients. AKI prediction score provides an opportunity for early detection of patients who are at risk of AKI; however, most of the AKI prediction scores were derived from cardiothoracic surgery. Therefore, we aimed to develop an AKI prediction score for major non-cardiothoracic surgery patients who were admitted to the intensive care unit (ICU).MethodsThe data of critically-ill patients from non-cardiothoracic operations in the Thai Surgical Intensive Care Unit (THAI-SICU) study were used to develop an AKI prediction score. Independent prognostic factors from regression analysis were included as predictors in the model. The outcome of interest was AKI within 7 days after the ICU admission. The AKI diagnosis was made according to the Kidney Disease Improving Global Outcomes (KDIGO)-2012 serum creatinine criteria. Diagnostic function of the model was determined by area under the Receiver Operating Curve (AuROC). Risk scores were categorized into four risk probability levels: low (0–2.5), moderate (3.0–8.5), high (9.0–11.5), and very high (12.0–16.5) risk. Risk of AKI was presented as likelihood ratios of positive (LH+).ResultsA total of 3474 critically-ill surgical patients were included in the model; 333 (9.6%) developed AKI. Using multivariable logistic regression analysis, older age, high Sequential Organ Failure Assessment (SOFA) non-renal score, emergency surgery, large volume of perioperative blood loss, less urine output, and sepsis were identified as independent predictors for AKI. Then AKI prediction score was created from these predictors. The summation of the score was 16.5 and had a discriminative ability for predicting AKI at AuROC = 0.839 (95% CI 0.825–0.852). LH+ for AKI were: low risk = 0.117 (0.063–0.200); moderate risk = 0.927 (0.745–1.148); high risk = 5.190 (3.881–6.910); and very high risk = 9.892 (6.230–15.695), respectively.ConclusionsThe function of AKI prediction score to predict AKI among critically ill patients who underwent non-cardiothoracic surgery was good. It can aid in early recognition of critically-ill surgical patients who are at risk from ICU admission. The scores could guide decision making for aggressive strategies to prevent AKI during the perioperative period or at ICU admission.Trial registrationTCTR20190408004, registered on April 4, 2019.

Artificial Intelligence in Acute Kidney Injury Risk Prediction.

Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes. It is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI in order to improve patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed for the development of artificial intelligence predictive models of risk estimation in AKI. In this review, we discussed the progress of AKI risk prediction from risk scores to electronic alerts to machine learning methods.