External Multicenter Validation of the Mehran Risk Score for Contrast Induced Acute Kidney Injury

Background: Elevated plasma C-terminal fibroblast growth factor-23 (cFGF-23) levels are associated with higher mortality in patients with chronic kidney disease (CKD) and acute kidney injury (AKI). Our study explored the outcome forecasting accuracy of cFGF-23 in critically ill patients with CKD superimposed with AKI (ACKD). Methods: Urine and plasma biomarkers from 149 CKD patients superimposed with AKI before dialysis were checked in this multicenter prospective observational cohort study. Endpoints were 90-day mortality and 90 days free from dialysis after hospital discharge. Associations with study endpoints were assessed using hierarchical clustering analysis, the generalized additive model, the Cox proportional hazard model, competing risk analysis, and discrimination evaluation. Results: Over a median follow up of 40 days, 67 (45.0%) patients died before the 90th day after hospital discharge and 39 (26.2%) progressed to kidney failure with replacement therapy (KFRT). Hierarchical clustering analysis demonstrated that cFGF-23 levels had better predictive ability for 90-day mortality than did other biomarkers. Higher serum cFGF-23 levels were independently associated with greater risk for 90-day mortality [hazard ratio (HR): 2.5; 95% confidence interval (CI) 1.5–4.1; p < 0.001]. Moreover, adding plasma cFGF-23 to the Demirjian AKI risk score model substantially improved risk prediction for 90-day mortality than the Demirjian model alone (integrated discrimination improvement: 0.06; p < 0.05; 95% CI 0.02–0.10). The low plasma cFGF-23 group was predicted having more weaning from dialysis in surviving patients (HR = 0.53, 95% CI, 0.29–0.95, p = 0.05). Conclusions: In patients with ACKD, plasma cFGF-23 levels are an independent risk factor to forecast 90-day mortality and 90-day progression to KFRT. In combination with the clinical risk score, plasma cFGF-23 levels could substantially improve mortality risk prediction.

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Stratifying risk of acute kidney injury in pre and post cardiac surgery patients using a novel biomarker-based algorithm and clinical risk score

Scientific Reports ◽

10.1038/s41598-019-53349-1 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

William T. McBride ◽

Mary Jo Kurth ◽

Gavin McLean ◽

Anna Domanska ◽

John V. Lamont ◽

...

Keyword(s):

At Risk ◽

Acute Kidney Injury ◽

Cardiac Surgery ◽

Risk Score ◽

Kidney Injury ◽

Predictive Ability ◽

Clinical Risk Score ◽

Clinical Risk ◽

Patients At Risk ◽

Post Cardiac Surgery

AbstractAcute kidney injury (AKI) following cardiac surgery significantly increases morbidity and mortality risks. Improving existing clinical methods of identifying patients at risk of perioperative AKI may advance management and treatment options. This study investigated whether a combination of biomarkers and clinical factors pre and post cardiac surgery could stratify patients at risk of developing AKI. Patients (n = 401) consecutively scheduled for elective cardiac surgery were prospectively studied. Clinical data was recorded and blood samples were tested for 31 biomarkers. Areas under receiver operating characteristic (AUROCs) were generated for biomarkers pre and postoperatively to stratify patients at risk of AKI. Preoperatively sTNFR1 had the highest predictive ability to identify risk of developing AKI postoperatively (AUROC 0.748). Postoperatively a combination of H-FABP, midkine and sTNFR2 had the highest predictive ability to identify AKI risk (AUROC 0.836). Preoperative clinical risk factors included patient age, body mass index and diabetes. Perioperative factors included cardio pulmonary bypass, cross-clamp and operation times, intra-aortic balloon pump, blood products and resternotomy. Combining biomarker risk score (BRS) with clinical risk score (CRS) enabled pre and postoperative assignment of patients to AKI risk categories. Combining BRS with CRS will allow better management of cardiac patients at risk of developing AKI.

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A preprocedural risk score predicts acute kidney injury following primary percutaneous coronary intervention

Catheterization and Cardiovascular Interventions ◽

10.1002/ccd.29176 ◽

2020 ◽

Author(s):

Stefano Buratti ◽

Gabriele Crimi ◽

Alberto Somaschini ◽

Stefano Cornara ◽

Rita Camporotondo ◽

...

Keyword(s):

Acute Kidney Injury ◽

Percutaneous Coronary Intervention ◽

Risk Score ◽

Primary Percutaneous Coronary Intervention ◽

Kidney Injury ◽

Coronary Intervention ◽

Percutaneous Coronary

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Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury

JAMA Network Open ◽

10.1001/jamanetworkopen.2020.12892 ◽

2020 ◽

Vol 3 (8) ◽

pp. e2012892 ◽

Cited By ~ 2

Author(s):

Matthew M. Churpek ◽

Kyle A. Carey ◽

Dana P. Edelson ◽

Tripti Singh ◽

Brad C. Astor ◽

...

Keyword(s):

Machine Learning ◽

Acute Kidney Injury ◽

Risk Score ◽

External Validation ◽

Kidney Injury

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Development and external validation of an acute kidney injury risk score for use in the general population

Clinical Kidney Journal ◽

10.1093/ckj/sfaa072 ◽

2020 ◽

Vol 13 (3) ◽

pp. 402-412

Author(s):

Samira Bell ◽

Matthew T James ◽

Chris K T Farmer ◽

Zhi Tan ◽

Nicosha de Souza ◽

...

Keyword(s):

Acute Kidney Injury ◽

Logistic Regression ◽

Risk Score ◽

Validation Cohort ◽

External Validation ◽

Kidney Injury ◽

Routinely Collected Data ◽

C Statistic ◽

Increased Risk ◽

The Impact

Abstract Background Improving recognition of patients at increased risk of acute kidney injury (AKI) in the community may facilitate earlier detection and implementation of proactive prevention measures that mitigate the impact of AKI. The aim of this study was to develop and externally validate a practical risk score to predict the risk of AKI in either hospital or community settings using routinely collected data. Methods Routinely collected linked datasets from Tayside, Scotland, were used to develop the risk score and datasets from Kent in the UK and Alberta in Canada were used to externally validate it. AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine–based criteria. Multivariable logistic regression analysis was performed with occurrence of AKI within 1 year as the dependent variable. Model performance was determined by assessing discrimination (C-statistic) and calibration. Results The risk score was developed in 273 450 patients from the Tayside region of Scotland and externally validated into two populations: 218 091 individuals from Kent, UK and 1 173 607 individuals from Alberta, Canada. Four variables were independent predictors for AKI by logistic regression: older age, lower baseline estimated glomerular filtration rate, diabetes and heart failure. A risk score including these four variables had good predictive performance, with a C-statistic of 0.80 [95% confidence interval (CI) 0.80–0.81] in the development cohort and 0.71 (95% CI 0.70–0.72) in the Kent, UK external validation cohort and 0.76 (95% CI 0.75–0.76) in the Canadian validation cohort. Conclusion We have devised and externally validated a simple risk score from routinely collected data that can aid both primary and secondary care physicians in identifying patients at high risk of AKI.

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Development and internal validation of a prediction model for hospital-acquired acute kidney injury

Clinical Kidney Journal ◽

10.1093/ckj/sfz139 ◽

2019 ◽

Author(s):

Catalina Martin-Cleary ◽

Luis Miguel Molinero-Casares ◽

Alberto Ortiz ◽

Jose Miguel Arce-Obieta

Keyword(s):

Acute Kidney Injury ◽

Risk Score ◽

Bayesian Model Averaging ◽

Characteristic Curve ◽

Tertiary Care ◽

Kidney Injury ◽

Risk Scores ◽

Internal Validation ◽

Clinical Records ◽

Hospital Acquired

Abstract Background Predictive models and clinical risk scores for hospital-acquired acute kidney injury (AKI) are mainly focused on critical and surgical patients. We have used the electronic clinical records from a tertiary care general hospital to develop a risk score for new-onset AKI in general inpatients that can be estimated automatically from clinical records. Methods A total of 47 466 patients met inclusion criteria within a 2-year period. Of these, 2385 (5.0%) developed hospital-acquired AKI. Step-wise regression modelling and Bayesian model averaging were used to develop the Madrid Acute Kidney Injury Prediction Score (MAKIPS), which contains 23 variables, all obtainable automatically from electronic clinical records at admission. Bootstrap resampling was employed for internal validation. To optimize calibration, a penalized logistic regression model was estimated by the least absolute shrinkage and selection operator (lasso) method of coefficient shrinkage after estimation. Results The area under the curve of the receiver operating characteristic curve of the MAKIPS score to predict hospital-acquired AKI at admission was 0.811. Among individual variables, the highest odds ratios, all >2.5, for hospital-acquired AKI were conferred by abdominal, cardiovascular or urological surgery followed by congestive heart failure. An online tool (http://www.bioestadistica.net/MAKIPS.aspx) will facilitate validation in other hospital environments. Conclusions MAKIPS is a new risk score to predict the risk of hospital-acquired AKI, based on variables present at admission in the electronic clinical records. This may help to identify patients who require specific monitoring because of a high risk of AKI.

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