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A Biomarker-Enhanced Model for Prediction of Acute Kidney Injury and Cardiovascular Risk Following Angiographic Procedures: CASABLANCA AKI Prediction Substudy.

ABSTRACT: Background The 2020 Acute Disease Quality Initiative Consensus provided recommendations on novel acute kidney injury biomarkers. In this study, we sought to assess the added value of novel kidney biomarkers to a clinical score in the CASABLANCA (Catheter Sampled Blood Archive in Cardiovascular Diseases) study. Methods and Results We evaluated individuals undergoing coronary and/or peripheral angiography and added 4 candidate biomarkers for acute kidney injury (kidney injury molecule-1, interleukin-18, osteopontin, and cystatin C) to a previously described contrast-associated acute kidney injury (CA-AKI) risk score. Participants were categorized into integer score groups based on the risk assigned by the biomarker-enhanced CA-AKI model. Risk for incident cardiorenal outcomes during a median 3.7 years of follow-up was assessed. Of 1114 participants studied, 55 (4.94%) developed CA-AKI. In adjusted models, neither kidney injury molecule-1 nor interleukin-18 improved discrimination for CA-AKI; addition of osteopontin and cystatin C to the CA-AKI clinical model significantly increased the c-statistic from 0.69 to 0.73 (P for change <0.001) and resulted in a Net Reclassification Index of 59.4. Considering those with the lowest CA-AKI integer score as a reference, the intermediate, high-risk, and very-high-risk groups were associated with adverse cardiorenal outcomes. The corresponding hazard ratios of the very-high-risk group were 3.39 (95% CI, 2.14-5.38) for nonprocedural acute kidney injury, 5.58 (95% CI, 3.23-9.63) for incident chronic kidney disease, 6.21 (95% CI, 3.67-10.47) for myocardial infarction, and 8.94 (95% CI, 4.83-16.53) for all-cause mortality. Conclusions A biomarker-enhanced risk model significantly improves the prediction of CA-AKI beyond clinical variables alone and may stratify the risk of future cardiorenal outcomes. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT00842868.

SUBMITTER: Mohebi R

PROVIDER: S-EPMC9238548 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Acute kidney injury (AKI) is a common post-cardiac surgery complication and influences patient morbidity and prognosis. This study was designed to identify preoperative candidate urine biomarkers in patients undergoing cardiac surgery. METHODS:A prospective cohort study of adults undergoing cardiac surgery at increased risk for AKI at a single hospital between July 2010 and September 2012 was performed. The primary outcome was the development of AKI, defined as an absolute serum creatinine (SCr) level increase ≥ 0.5 mg/dL or a ≥ 50% relative increase within 72 h of surgery. A secondary outcome was development of AKI defined by Kidney Disease Improving Global Outcomes (KDIGO). Urine collected by voiding within 4 h prior to surgery was used for proteomic analysis and confirmatory enzyme linked immunosorbent assays (ELISAs) studies. Biomarkers were tested for AKI-prediction using Cox and Snell R2, area under the receiver operating curve (AUROC), and percent of corrected classifications. To evaluate the added effect of each candidate biomarker on AKI discrimination, receiver operator characteristic (ROC) curves, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were calculated. RESULTS:Forty-seven of 755 patients met screening criteria including 15 with AKI. Proteomic analysis identified 29 proteins with a significant ≥2-fold change. Confirmatory ELISA measurements of five candidate markers showed urinary complement factor B (CFB) and histidine rich glycoprotein (HRG) concentrations were significantly increased in patients with AKI. By multivariate analysis, NRI, and IDI the addition of CFB and HRG to the standard clinical assessment significantly improved risk prediction for the primary outcome. Only HRG was a significant predictor in the 21 patients with AKI defined by KDIGO criteria. CONCLUSIONS:Pre-operative urine measurement of CFB or HRG significantly enhanced the current post-surgery AKI risk stratification for more restrictive definition of AKI. HRG, but not CFB or clinical risk stratification, predicted AKI defined by KDIGO. The ability of these biomarkers to predict risk for dialysis-requiring AKI or death could not be reliably assessed in our study due to a small number of patients with either outcome.

Project description:Rationale & objectiveThe PRESERVE trial used a 2 × 2 factorial design to compare intravenous saline solution with intravenous sodium bicarbonate solution and oral N-acetylcysteine with placebo for the prevention of 90-day major adverse kidney events and death (MAKE-D) and contrast-associated acute kidney injury (CA-AKI) among patients with chronic kidney disease undergoing angiography. In this ancillary study, we evaluated the predictive capacities of preangiography injury and repair proteins in urine and plasma for MAKE-D, CA-AKI, and their impact on trial design.Study designLongitudinal analysis.Setting & participantsA subset of participants from the PRESERVE trial.ExposuresInjury (KIM-1, NGAL, and IL-18) and repair (MCP-1, UMOD, and YKL-40) proteins in urine and plasma 1 to 2 hours preangiography.OutcomesMAKE-D and CA-AKI.Analytical approachWe analyzed the associations of preangiography biomarkers with MAKE-D and with CA-AKI. We evaluated whether the biomarker levels could enrich the MAKE-D event rate and improve future clinical trial efficiency through an online biomarker prognostic enrichment tool available at prognosticenrichment.com.ResultsWe measured plasma biomarkers in 916 participants and urine biomarkers in 797 participants. After adjusting for urinary albumin-creatinine ratio and baseline estimated glomerular filtration rate, preangiography levels of 4 plasma (KIM-1, NGAL, UMOD, and YKL-40) and 3 urine (NGAL, IL-18, and YKL-40) biomarkers were associated with MAKE-D. Only plasma KIM-1 level was significantly associated with CA-AKI after adjustment. Biomarker levels provided modest discriminatory capacity for MAKE-D. Screening patients using the 50th percentile of preangiography plasma KIM-1 or YKL-40 levels would have reduced the required sample size by 30% (∼2,000 participants).LimitationsEvaluation of prognostic enrichment does not account for changing trial costs, time needed to screen patients, or loss to follow-up. Most participants were male, limiting the generalizability of our findings.ConclusionsPreangiography levels of injury and repair biomarkers modestly predict the development of MAKE-D and can be used to improve the efficiency of future CA-AKI trials.

Project description:BackgroundAcute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines.MethodsEHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predictors, such as creatinine rate of change, to automatically assess real-time AKI risk. The primary outcome is prediction of moderate to severe AKI (Stage 2/3), and secondary outcomes are prediction of any AKI (Stage 1/2/3) and requirement of renal replacement therapy (RRT). Predictions generate alerts allowing fast assessment and reduction of AKI risk, such as: "patient has 90% risk of developing AKI in the next 48 h" along with contextual information and suggested response such as "patient on aminoglycosides, suggest check level and review dose and indication".ResultsThe model was successful in predicting Stage 2/3 AKI prior to detection by conventional criteria with a median lead-time of 30 h at AUROC of 0.89. The model predicted 70% of subsequent RRT episodes, 58% of Stage 2/3 episodes, and 41% of any AKI episodes. The ratio of false to true alerts of any AKI episodes was approximately one-to-one (PPV 47%). Among patients predicted, 79% received potentially nephrotoxic medication after being identified by the model but before development of AKI.ConclusionsAs the first multi-center validated AKI prediction model for all pediatric critical care patients, the machine learning model described in this study accurately predicts moderate to severe AKI up to 48 h in advance of AKI onset. The model may improve outcome of pediatric AKI by providing early alerting and actionable feedback, potentially preventing or reducing AKI by implementing early measures such as medication adjustment.

Project description:ObjectiveCritically appraise prediction models for hospital-acquired acute kidney injury (HA-AKI) in general populations.DesignSystematic review.Data sourcesMedline, Embase and Web of Science until November 2016.EligibilityStudies describing development of a multivariable model for predicting HA-AKI in non-specialised adult hospital populations. Published guidance followed for data extraction reporting and appraisal.Results14 046 references were screened. Of 53 HA-AKI prediction models, 11 met inclusion criteria (general medicine and/or surgery populations, 474 478 patient episodes) and five externally validated. The most common predictors were age (n=9 models), diabetes (5), admission serum creatinine (SCr) (5), chronic kidney disease (CKD) (4), drugs (diuretics (4) and/or ACE inhibitors/angiotensin-receptor blockers (3)), bicarbonate and heart failure (4 models each). Heterogeneity was identified for outcome definition. Deficiencies in reporting included handling of predictors, missing data and sample size. Admission SCr was frequently taken to represent baseline renal function. Most models were considered at high risk of bias. Area under the receiver operating characteristic curves to predict HA-AKI ranged 0.71-0.80 in derivation (reported in 8/11 studies), 0.66-0.80 for internal validation studies (n=7) and 0.65-0.71 in five external validations. For calibration, the Hosmer-Lemeshow test or a calibration plot was provided in 4/11 derivations, 3/11 internal and 3/5 external validations. A minority of the models allow easy bedside calculation and potential electronic automation. No impact analysis studies were found.ConclusionsAKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. Similar predictors reflect an elderly demographic with chronic comorbidities. Reporting deficiencies mirrors prediction research more broadly, with handling of SCr (baseline function and use as a predictor) a concern. Future research should focus on validation, exploration of electronic linkage and impact analysis. The latter could combine a prediction model with AKI alerting to address prevention and early recognition of evolving AKI.

Project description:BackgroundSoluble tumor necrosis factor receptor (sTNFR)1, sTNFR2, and plasma kidney injury molecule-1 (KIM-1) are associated with kidney events in patients with and without diabetes. However, their associations with clinical outcomes when obtained pre-operatively have not been explored.MethodsThe TRIBE-AKI cohort study is a prospective, multicenter, cohort study of high-risk adults undergoing cardiac surgery. We assessed the associations between pre-operative concentrations of plasma sTNFR1, sTNFR2, and KIM-1 and post-operative long-term outcomes including mortality, cardiovascular events, and chronic kidney disease (CKD) incidence or progression after discharge.ResultsAmong 1378 participants included in the analysis with a median follow-up period of 6.7 (IQR 4.0-7.9) years, 434 (31%) patients died, 256 (19%) experienced cardiovascular events and out of 837 with available long-term kidney function data, 30% developed CKD. After adjustment for clinical covariates, each log increase in biomarker concentration was independently associated with mortality with 95% CI adjusted hazard ratios (aHRs) of 3.0 (2.3-4.0), 2.3 (1.8-2.9), and 2.0 (1.6-2.4) for sTNFR1, sTNFR2, and KIM-1, respectively. For cardiovascular events, the 95% CI aHRs were 2.1 (1.5-3.1), 1.9 (1.4-2.6) and 1.6 (1.2-2.1) for sTNFR1, sTNFR2 and KIM-1, respectively. For CKD events, the aHRs were 2.2 (1.5-3.1) for sTNFR1, 1.9 (1.3-2.7) for sTNFR2, and 1.7 (1.3-2.3) for KIM-1. Despite the associations, each of the biomarkers alone or in combination failed to result in robust discrimination on an absolute basis or compared to a clinical model.ConclusionsTNFR1, sTNFR2, and KIM-1 were independently associated with longitudinal outcomes after discharge from a cardiac surgery hospitalization including death, cardiovascular, and CKD events when obtained pre-operatively in high-risk individuals. Pre-operative plasma biomarkers could serve to assist during the evaluation of patients in whom cardiac surgery is planned.

Project description:BackgroundAKI requiring dialysis (AKI-D) is associated with prolonged hospitalization, mortality, and progressive CKD among survivors. Previous studies have examined only select urine or serum biomarkers for predicting kidney recovery from AKI.MethodsSerum samples collected on day 8 of randomized RRT from 72 patients enrolled in the Veteran's Affairs/National Institutes of Health Acute Renal Failure Trial Network study were analyzed by the SOMAscan proteomic platform to profile 1305 proteins in each sample. Of these patients, 38 recovered kidney function and dialysis was discontinued, whereas another 34 patients remained on dialysis by day 28.ResultsDifferential serum levels of 119 proteins, with 53 higher and 66 lower, were detected in samples from patients who discontinued dialysis, compared with patients who remained on dialysis by day 28. Patients were classified into tertiles on the basis of SOMAscan protein measurements for the 25 proteins most differentially expressed. The association of serum levels of each protein with kidney recovery was further evaluated using logistic regression analysis. Higher serum levels of CXCL11, CXCL2/CXCL3, CD86, Wnt-7a, BTK, c-Myc, TIMP-3, CCL5, ghrelin, PDGF-C, survivin, CA2, IL-9, EGF, and neuregulin-1, and lower levels of soluble CXCL16, IL1RL1, stanniocalcin-1, IL-6, and FGF23 when classified in tertiles were significantly associated with better kidney recovery. This significant association persisted for each of these proteins after adjusting for potential confounding risk factors including age, sex, cardiovascular SOFA score, congestive heart failure, diabetes, modality of intensive dialysis treatment, cause of AKI, baseline serum creatinine, day 8 urine volume, and estimated 60-day mortality risk.ConclusionsThese results suggest concerted changes between survival-related proteins and immune-regulatory chemokines in regulating angiogenesis, endothelial and epithelial remodeling, and kidney cell regeneration, illustrating potential mechanisms of kidney recovery. Thus, this study identifies potential novel predictive biomarkers of kidney recovery in patients with AKI-D.

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A Biomarker-Enhanced Model for Prediction of Acute Kidney Injury and Cardiovascular Risk Following Angiographic Procedures: CASABLANCA AKI Prediction Substudy.

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