Acute Kidney Injury Risk Score Research Articles

Proenkephalin improves risk prediction in acute coronary syndromes: derivation and external validation of the KID-ACS risk score

Abstract Background Early assessment of renal and mortality risk in patients with acute coronary syndromes (ACS) is essential to guide treatment decisions according to international guidelines. Circulating proenkephalin (PENK) is a stable opioid metabolite with fast response to changes in kidney function. Purpose This study examined the predictive value of PENK for acute kidney injury (AKI) and mortality in patients presenting with acute coronary syndromes (ACS). Methods Plasma PENK levels were measured using a validated sandwich immunoassay in a prospective multicentre ACS cohort from Switzerland (n = 4 787) and in validation cohorts from the UK (n = 1 141) and Czechia (n = 920). A biomarker-enhanced risk score for simultaneous prediction of both in-hospital acute kidney injury (AKI) and 30-day death (KID-ACS score) with model coefficients adjusted to the outcome was derived and externally validated. In the development of the in-hospital AKI model, patients from one participating study centre were reserved for external validation (Swiss validation cohort). Results Circulating PENK ranked among the top predictors of renal outcomes and mortality in patients with ACS. Accounting for established risk factors and risk models, circulating PENK levels at presentation were independently associated with in-hospital AKI (per log2 increase: adjusted odds ratio [OR] 1.56, 95% CI 1.14 – 2.12, P = 0.005) and with 30-day mortality (per log2 increase: adjusted OR 2.69, 95% CI 1.89 – 3.84, P < 0.001). Dynamic changes in PENK levels within the first 12-24 hours after presentation were strongly and independently associated with in-hospital AKI (increase vs. decrease: adjusted OR 2.83, 95% CI 1.98 – 4.05, P < 0.001). The simple 6-item KID-ACS risk score for in-hospital AKI and 30-day mortality integrates PENK levels at presentation and showed an AUC of 0.72 (95% CI 0.68 – 0.76) for in-hospital AKI, and of 0.91 (95% CI 0.87 – 0.95) for 30-day mortality in the derivation cohort. Upon external validation, KID-ACS had similarly high performance for in-hospital AKI (Swiss validation cohort: AUC 0.73, 95% CI 0.70 – 0.77; Czech validation cohort: 0.74, 95% CI 0.68 – 0.80) and 30-day mortality (UK validation cohort: AUC of 0.87, 95% CI 0.82 – 0.91; Czech validation cohort: 0.91, 95% CI 0.87 – 0.94). The simple 6-item KID-ACS score was non-inferior to established risk models and significantly outperformed the CA-AKI risk score in predicting in-hospital AKI (Zurich cohort: delta AUC 0.11, 95% CI 0.07 – 0.15, P = 0.001) and the GRACE 2.0 in predicting 30-day mortality (UK cohort: delta AUC 0.05, 95% CI 0.01 – 0.09, P = 0.027). Conclusions Circulating PENK was identified as independent predictor of in-hospital AKI and 30-day mortality in patients with ACS. The simple 6-item KID-ACS risk score integrates circulating PENK levels and provides a novel tool for simultaneous assessment of renal and mortality risk in patients presenting with ACS.Independent Predictive Value PENKPerformance and validation of KID-ACS

A clinical risk score for predicting acute kidney injury in sepsis patients receiving normal saline in Northern Thailand: a retrospective cohort study.

Normal saline is commonly used for resuscitation in sepsis patients but has a high chloride content, potentially increasing the risk of acute kidney injury (AKI). This study evaluated risk factors and developed a predictive risk score for AKI in sepsis patients treated with normal saline. This retrospective cohort study utilized the medical and electronic health records of sepsis patients who received normal saline between January 2018 and May 2020. Predictors of AKI used to construct the predictive risk score were identified through multivariate logistic regression models, with discrimination and calibration assessed using the area under the receiver operating characteristic curve (AUROC) and the expected-to-observed (E/O) ratio. Internal validation was conducted using bootstrapping techniques. AKI was reported in 211 of 735 patients (28.7%). Eight potential risk factors, including norepinephrine, the Acute Physiology and Chronic Health Evaluation II score, serum chloride, respiratory failure with invasive mechanical ventilation, nephrotoxic antimicrobial drug use, history of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers use, history of liver disease, and serum creatinine were used to create the NACl RENAL-Cr score. The model demonstrated good discrimination and calibration (AUROC, 0.79; E/O, 1). The optimal cutoff was 2.5 points, with corresponding sensitivity, specificity, positive predictive value, and negative predictive value scores of 71.6%, 72.5%, 51.2%, and 86.4%, respectively. The NACl RENAL-Cr score, consisting of eight critical variables, was used to predict AKI in sepsis patients who received normal saline. This tool can assist healthcare professionals when deciding on sepsis treatment and AKI monitoring.

Intraoperative fluid management is not predictive of AKI in major pancreatic surgery: a retrospective cohort study

BackgroundPancreatic surgery is associated with a significant risk for acute kidney injury (AKI) and clinically relevant postoperative pancreatic fistula (CR-POPF). This investigation evaluated the impact of intraoperative volume administration, vasopressor therapy, and blood pressure management on the primary outcome of AKI and the secondary outcome of a CR-POPF after pancreatic surgery.MethodsThis retrospective single-center cohort investigated 200 consecutive pancreatic surgeries (January 2018–December 2021). Patients were categorized for the presence/absence of AKI (Kidney Disease Improving Global Outcomes) and CR-POPF. After univariate analysis, multivariable models were constructed to control for the univariate cofactor differences in the primary and secondary outcomes.ResultsAKI was identified in 20 patients (10%) with significant univariate differences in demographics (body mass index and gender), comorbidities, indices of chronic renal insufficiency, and an increased AKI Risk score. Surgical characteristics, intraoperative fluid, vasopressor, and blood pressure management were similar in patients with and without AKI. Patients with AKI had increased blood loss, lower urine output, and packed red blood cell administration. After multivariate analysis, male gender (OR = 7.9, 95% C.I. 1.8–35.1) and the AKI Risk score (OR = 6.3, 95% C.I. 2.4–16.4) were associated with the development of AKI (p < 0.001). Intraoperative and postoperative volume, vasopressor administration, and intraoperative hypotension had no significant impact in the multivariate analysis. CR-POPF occurred in 23 patients (11.9%) with no significant contributing factors in the multivariate analysis. Patients who developed AKI or a CR-POPF had an increase in surgical complications, length of stay, discharge to a skilled nursing facility, and mortality.ConclusionIn this analysis, intraoperative volume administration, vasopressor therapy, and a blood pressure < 55 mmHg for more than 10 min were not associated with an increased risk of AKI. After multivariate analysis, male gender and an elevated AKI Risk score were associated with an increased likelihood of AKI.

Factors that mediate change in creatinine and acute kidney injury after the Norwood operation: insights from high-fidelity haemodynamic monitoring data.

Acute kidney injury is a common postoperative complication of paediatric cardiac surgery associated with increased morbidity and mortality. The purpose of this study is to characterise associations between haemodynamic parameters, clinical parameters, and medical interventions, on acute kidney injury. Nine patients with univentricular physiology undergoing the Norwood procedure from a single-centre tertiary care paediatric cardiac ICU were included (September 2022 to March 2023). Patients were monitored with the T3 software. Data were analysed using a Fisher exact test, Mann-Whitney-U test, LASSO-based machine learning techniques, and receiver operator curve analyses. Over 27,000 datapoints were included. Acute kidney injury occurred in 2 patients (22%) during this period. Net fluid balance and renal oxygen extraction were independently associated with acute kidney injury, while commonly used metrics of pressure (systolic, diastolic, or mean arterial blood pressure) were not. The resulting acute kidney injury risk score was (4.1 × fluid balance) + (1.9 × renal oxygen extraction). The risk score was significantly higher in acute kidney injury with a score of 32.9 compared to 7.9 (p < 0.01). Optimal cut-offs for fluid balance (7 mL/hr) and renal oxygen extraction (29%) were identified. Higher serum creatinine:baseline creatinine ratio was associated with a higher mean airway pressure, higher renal oxygen extraction, higher mean arterial blood pressure, higher vasoactive inotropic score, and fluid balance. Among patients with univentricular physiology undergoing the Norwood procedure, renal oxygen extraction and a higher net fluid balance are independently associated with increased risk of acute kidney injury. Renal perfusion pressure is not significantly associated with acute kidney injury.

Validation of ISN 0by25 Acute Kidney Injury Risk Score for Low- and Low Middle-Income Countries

Validation of ISN 0by25 Acute Kidney Injury Risk Score for Low- and Low Middle-Income Countries

Diagnostic Accuracy Of Hydration Thresholds In Predicting Acute Kidney Injury Risk Score Following Fluid Deprivation

Diagnostic Accuracy Of Hydration Thresholds In Predicting Acute Kidney Injury Risk Score Following Fluid Deprivation

Validation of a Contemporary Acute Kidney Injury Risk Score in Patients With Acute Coronary Syndrome

BackgroundA simple, contemporary risk score for the prediction of contrast-associated acute kidney injury (CA-AKI) after percutaneous coronary intervention (PCI) was recently updated, although its external validation is lacking. ObjectivesThe aim of this study was to validate the updated CA-AKI risk score in a large cohort of acute coronary syndrome patients from the MATRIX (Minimizing Adverse Haemorrhagic Events by Transradial Access Site and Systemic Implementation of angioX) trial. MethodsThe risk score identifies 4 risk categories for CA-AKI. The primary endpoint was to appraise the receiver-operating characteristics of an 8-component and a 12-component CA-AKI model. Independent predictors of Kidney Disease Improving Global Outcomes–based acute kidney injury and the impact of CA-AKI on 1-year mortality and bleeding were also investigated. ResultsThe MATRIX trial included 8,201 patients with complete creatinine values and no end-stage renal disease. CA-AKI occurred in 5.5% of the patients, with a stepwise increase of CA-AKI rates from the lowest to the highest of the 4 risk categories. The receiver-operating characteristic area under the curve was 0.67 (95% CI: 0.64-0.70) with model 1 and 0.71 (95% CI: 0.68-0.74) with model 2. CA-AKI risk was systematically overestimated with both models (Hosmer-Lemeshow goodness-of-fit test: P < 0.05). The 1-year risks of all-cause mortality and bleeding were higher in CA-AKI patients (HR: 7.03 [95% CI: 5.47-9.05] and HR: 3.20 [95% CI: 2.56-3.99]; respectively). There was a gradual risk increase for mortality and bleeding as a function of the CA-AKI risk category for both models. ConclusionsThe updated CA-AKI risk score identifies patients at incremental risks of acute kidney injury, bleeding, and mortality. (Minimizing Adverse Haemorrhagic Events by Transradial Access Site and Systemic Implementation of angioX [MATRIX]; NCT01433627)

Predictive Risk Score for Acute Kidney Injury in Hematopoietic Stem Cell Transplant.

Hematopoietic stem cell transplant (HSCT) is an important treatment option for hematologic malignancies. Acute kidney injury (AKI) is a common complication in HSCTs and is related to worse outcomes. We aimed to create a predictive risk score for AKI in HSCT considering variables available at the time of the transplant. We performed a retrospective cohort study. AKI was defined by the KDIGO classification using creatinine and urinary output criteria. We used survival analysis with competing events. Continuous variables were dichotomized according to the Liu index. A multivariable analysis was performed with a backward stepwise regression. Harrel's C-Statistic was used to evaluate the performance of the model. Points were attributed considering the nearest integer of two times each covariate's hazard ratio. The Liu index was used to establish the optimal cut-off. We included 422 patients undergoing autologous (61.1%) or allogeneic (38.9%) HSCTs for multiple myeloma (33.9%), lymphoma (27.3%), and leukemia (38.8%). AKI cumulative incidence was 59.1%. Variables eligible for the final score were: hematopoietic cell transplant comorbidity index ≥2 (HR: 1.47, 95% CI: 1.08-2.006; p = 0.013), chronic kidney disease (HR: 2.10, 95% CI: 1.31-3.36; p = 0.002), lymphoma or leukemia (HR: 1.69, 95% CI: 1.26-2.25; p < 0.001) and platelet-to-lymphocyte ratio > 171.9 (HR: 1.43, 95% CI: 1.10-1.86; p = 0.008). This is the first predictive risk score for AKI in patients undergoing HSCTs and the first study where the platelet-to-lymphocyte ratio is independently associated with AKI.

Fluid Overload and AKI After the Norwood Operation: The Correlation and Characterization of Routine Clinical Markers.

The purpose of this study was to determine the correlation of different methods of assessing fluid overload and determine which metrics are associated with development of acute kidney injury (AKI) in the period immediately following Norwood palliation. This was a retrospective single-center study of Norwood patients from January 2011 through January 2021. AKI was defined using the Kidney Disease Improving Global Outcomes (KDIGO). Patients were separated into two groups: those with AKI and those without. A logistic regression analysis was conducted with AKI at any point in the study period as the dependent variable and clinical and laboratory data as independent variables. Analysis was conducted as a stepwise regression. The coefficients from the logistic regression were then used to develop a cumulative AKI risk score. Spearman correlations were conducted to analyze the correlation of fluid markers. 116 patients were included, and 49 (42.4%) developed AKI. The duration of open chest, duration of mechanical ventilation, need for dialysis, need for extracorporeal membrane oxygenation, and inpatient mortality were associated with AKI (p ≤ 0.05). Stepwise logistic regression demonstrated the following significant independent associations AKI: age at Norwood in days (p < 0.01), blood urea nitrogen (p < 0.01), central venous pressure (p = 0.04), and renal oxygen extraction ratio (p < 0.01). The area under the receiver operating characteristic curve for the logistic regression was 0.74. The fluid markers had weak R-value. Urea, central venous pressure, and renal oxygen extraction ratio are associated with AKI after the Norwood operation. Common clinical metrics used to assess fluid overload are poorly correlated with each other for postoperative Norwood patients.

Early acute kidney injury after transcatheter aortic valve implantation: predictive value of currently available risk scores.

Acute kidney injury (AKI) after transcatheter aortic valve implantation (TAVI) is a frequent complication associated with adverse outcomes and mortality. Various scores have been developed to predict this complication in the coronary setting. However, none have ever been tested in a large TAVI population. This study aimed to evaluate the power of four different scores in predicting AKI after TAVI. Overall, 1535 consecutive TAVI patients from the observational multicentric "Magna Graecia" TAVI registry were included in the analysis. Of the study population, 235 (15.31%) developed AKI early. The Mehran, William Beaumont Hospital, CR4EATME3AD3, and ACEF scores were calculated retrospectively. The patients who developed TAVI-related AKI had significantly higher absolute values of all risk scores than those who did not. The receiver-operating characteristic analysis also showed a significant correlation between these four scores and AKI, but without a significant difference among all of them (p value=0.176). Nevertheless, based on their area under the curve values (≤0.604 for all), none had adequate diagnostic accuracy in predicting TAVI-related AKI. Importantly, multivariate analysis identified myocardial revascularization close to the TAVI procedure and implantation of self-expanding prostheses, as well as atrial fibrillation, low-osmolar contrast media administration, corrected contrast medium volume, and any transfusion (p value<0.05 for all) as independent risk factors for AKI. Although high values of current AKI risk scores are significantly associated with the development of this complication, these are not sufficiently accurate. Further studies are needed so that a TAVI-dedicated AKI risk score may be created.

Development and validation of a nomogram for the early prediction of acute kidney injury in hospitalized COVID-19 patients.

Acute kidney injury (AKI) is a prevalent complication of coronavirus disease 2019 (COVID-19) and is closely linked with a poorer prognosis. The aim of this study was to develop and validate an easy-to-use and accurate early prediction model for AKI in hospitalized COVID-19 patients. Data from 480 COVID-19-positive patients (336 in the training set and 144 in the validation set) were obtained from the public database of the Cancer Imaging Archive (TCIA). The least absolute shrinkage and selection operator (LASSO) regression method and multivariate logistic regression were used to screen potential predictive factors to construct the prediction nomogram. Receiver operating curves (ROC), calibration curves, as well as decision curve analysis (DCA) were adopted to assess the effectiveness of the nomogram. The prognostic value of the nomogram was also examined. A predictive nomogram for AKI was developed based on arterial oxygen saturation, procalcitonin, C-reactive protein, glomerular filtration rate, and the history of coronary artery disease. In the training set, the nomogram produced an AUC of 0.831 (95% confidence interval [CI]: 0.774-0.889) with a sensitivity of 85.2% and a specificity of 69.9%. In the validation set, the nomogram produced an AUC of 0.810 (95% CI: 0.737-0.871) with a sensitivity of 77.4% and a specificity of 78.8%. The calibration curve shows that the nomogram exhibited excellent calibration and fit in both the training and validation sets. DCA suggested that the nomogram has promising clinical effectiveness. In addition, the median length of stay (m-LS) for patients in the high-risk group for AKI (risk score ≥ 0.122) was 14.0 days (95% CI: 11.3-16.7 days), which was significantly longer than 8.0 days (95% CI: 7.1-8.9 days) for patients in the low-risk group (risk score <0.122) (hazard ratio (HR): 1.98, 95% CI: 1.55-2.53, p < 0.001). Moreover, the mortality rate was also significantly higher in the high-risk group than that in the low-risk group (20.6 vs. 2.9%, odd ratio (OR):8.61, 95%CI: 3.45-21.52). The newly constructed nomogram model could accurately identify potential COVID-19 patients who may experience AKI during hospitalization at the very beginning of their admission and may be useful for informing clinical prognosis.

Lactate level and lactate clearance for acute kidney injury prediction among patients admitted with ST-segment elevation myocardial infarction: A retrospective cohort study.

BackgroundHyperlactatemia is a prognostic marker among patients with ST-segment elevation acute myocardial infarction (STEMI). However, the predictive value of lactate and the dynamic change associated with acute kidney injury (AKI) among patients with STEMI, remain poorly understood. We aimed to compare single lactate values at admission (Lacadm) and 12 h after admission (Lac12h) with lactate clearance (LC) 12 h after admission for AKI prediction in patients with STEMI.MethodsA total of 1,784 patients with STEMI were included. The study endpoint was AKI occurrence during hospitalization. The predictive value of lactate levels measured at admission and 12 h after admission and LC for AKI prediction was determined using multivariate logistic regression analyses and compared with receiver operator characteristic (ROC) curve analysis.ResultsOverall, AKI was observed in 353 (19.8%) patients. In multivariate logistic regression analyses, Lacadm ≥ 4.3 mmol/L (OR: 1.53; 95% CI: 1.01–2.30), Lac12h ≥ 2.1 mmol/L (OR: 1.81; 95% CI: 1.36–2.42), and LC ≥ −7.5% (OR: 0.40; 95% CI: 0.30–0.53) were the independent predictive factors for AKI after adjusting for confounders. ROC curve analysis results revealed that Lac12h (0.639; 95% CI: 0.616–0.661) exhibited a significantly higher area under the curve (AUC) than those of Lacadm (0.551; 95% CI: 0.527–0.574) and LC (0.593; 95% CI: 0.570–0.616) in the prediction of AKI. LC (△AUC = 0.037, p < 0.001) and Lac12h (△AUC = 0.017, p = 0.029) enhanced the discrimination capacity of Mehran Risk Score (MRS) for AKI among patients undergoing emergency coronary angiography.ConclusionLac12h is more effective for AKI prediction among patients with STEMI than Lacadm and LC. Furthermore, Lac12h and LC enhance the prediction capacity of MRS for AKI among patients after emergency coronary angiography.

Mehran 2 Contrast-Associated Acute Kidney Injury Risk Score: Is it Applicable to the Asian Percutaneous Coronary Intervention Population?

Contrast-associated acute kidney injury (CA-AKI) can occur after percutaneous coronary intervention (PCI). The Mehran score is the gold standard for predicting CA-AKI risk, and it has recently been updated. The Mehran 2 CA-AKI risk score, based on existing variables in patients undergoing PCI, can accurately differentiate the risk of CA-AKI. This study aimed to verify whether the new Mehran score is applicable to the Asian PCI population. The study included the clinical data of 2487 patients undergoing PCI from August 2020 to February 2022. The goodness-of-fit test (Hosmer-Lemeshow) was used to evaluate the correction ability of the Mehran 2 score, and the area under the receiver operating characteristic curve (ROC) was used to evaluate the accuracy of the Mehran 2 score in predicting CA-AKI. CA-AKI occurred in 28 of 2487 patients, with an incidence rate of 1.12%. The proportion of high risk factors for AKI in the cohort was lower than that in the Mehran 2 cohort (a large contemporary PCI cohort to develop the Mehran 2 score). The Mehran 2 risk score had excellent goodness-of-fit (χ2 = 5.320, df = 6, P = 0.503) and high predictive accuracy (area under the ROC curve 0.836, P < 0.0001). The Mehran 2 score shows good predictive and correction performance in the Asian population and has good clinical application value. The inclusion of the Mehran 2 risk score in patients hospitalised for coronary angiography appears to be good practice.

Risk factors for acute kidney injury following coronary artery bypass graft surgery in a Chinese population and development of a prediction model.

BACKGROUNDAcute kidney injury (AKI) after coronary artery bypass graft (CABG) surgery is associated with significant morbidity and mortality. This retrospective study aimed to establish a risk score for postoperative AKI in a Chinese population.METHODSA total of 1138 patients undergoing CABG were collected from September 2018 to May 2020 and divided into a derivation and validation cohort. AKI was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. Multivariable logistic regression analysis was used to determine the independent predictors of AKI, and the predictive ability of the model was determined using a receiver operating characteristic (ROC) curve.RESULTSThe incidence of cardiac surgery–associated acute kidney injury (CSA-AKI) was 24.17%, and 0.53% of AKI patients required dialysis (AKI-D). Among the derivation cohort, multivariable logistic regression showed that age ≥ 70 years, body mass index (BMI) ≥ 25 kg/m2, estimated glomerular filtration rate (eGFR) ≤ 60 mL/min per 1.73 m2, ejection fraction (EF) ≤ 45%, use of statins, red blood cell transfusion, use of adrenaline, intra-aortic balloon pump (IABP) implantation, postoperative low cardiac output syndrome (LCOS) and reoperation for bleeding were independent predictors. The predictive model was scored from 0 to 32 points with three risk categories. The AKI frequencies were as follows: 0−8 points (15.9%), 9−17points (36.5%) and ≥ 18 points (90.4%). The area under of the ROC curve was 0.730 (95% CI: 0.691−0.768) in the derivation cohort. The predictive index had good discrimination in the validation cohort, with an area under the curve of 0.735 (95% CI: 0.655−0.815). The model was well calibrated according to the Hosmer-Lemeshow test (P = 0.372). CONCLUSIONThe performance of the prediction model was valid and accurate in predicting KDIGO-AKI after CABG surgery in Chinese patients, and could improve the early prognosis and clinical interventions.

A risk score based on procalcitonin for predicting acute kidney injury in COVID-19 patients.

BackgroundAcute kidney injury (AKI) has been reported developing commonly in coronavirus disease 2019 (COVID‐19) patients and could increase the risk of poor outcomes in these patients. We design this study to explore the value of serum procalcitonin (PCT) on predicting AKI and construct risk score for predicting AKI in COVID‐19 patients.MethodsPatients diagnosed with COVID‐19 and hospitalized in Renmin Hospital of Wuhan University between January 30 and February 24, 2020, were included. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the strongest predictors of AKI. Multivariate logistic regression analysis was conducted to find independent risk factors for AKI and construct risk score using odds ratio (OR) value of those risk factors. Receiver operating characteristics (ROC) curves were plotted, and area under the ROC curve (AUC) value was calculated to evaluate the predictive value of single PCT level and the constructed risk score.ResultsAmong 389 included COVID‐19 patients, 28 (7.2%) patients developed AKI. LASSO regression showed hypertension, saturation of arterial oxygen (SaO2), PCT, and blood urea nitrogen (BUN) were the strongest predictors for AKI. After multivariate logistic regression analysis, only SaO2 (<0.001), PCT (p = 0.004), and BUN (p = 0.005) were independently associated with development of AKI in COVID‐19 patients. The AUC of single PCT and constructed risk score was 0. 881 and 0.928, respectively.ConclusionPCT level is correlated with AKI in COVID‐19 patients. The efficient risk score consisted of SaO2, PCT, and BUN is readily accessible for physicians to evaluate the possibility of AKI in COVID‐19 patients.

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

BackgroundImproving 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.MethodsRoutinely 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.ResultsThe 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.ConclusionWe 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.

High plasma C-terminal FGF-23 levels predict poor outcomes in patients with chronic kidney disease superimposed with 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.

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

BackgroundPredictive 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.MethodsA 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.ResultsThe 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.ConclusionsMAKIPS 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.

Acute Kidney Injury and Septic Shock-Defined by Updated Sepsis-3 Criteria in Critically Ill Patients.

Sepsis is commonly associated with acute kidney injury (AKI), particularly in those requiring dialysis (AKI-D). To date, Sepsis-3 criteria have not been applied to AKI-D patients. We investigated sepsis prevalence defined by Sepsis-3 criteria and evaluated the outcomes of septic-associated AKI-D among critically ill patients. Using the data collected from a prospective multi-center observational study, we applied the Sepsis-3 criteria to critically ill AKI-D patients treated in intensive care units (ICUs) in 30 hospitals between September 2014 and December 2015. We described the prevalence, outcomes, and characteristics of sepsis as defined by the screening Sepsis-3 criteria among AKI-D patients, and compared the outcomes of AKI-D patients with or without sepsis using the Sepsis-3 criteria. A total of 1078 patients (median 70 years; 673 (62.4%) men) with AKI-D were analyzed. The main etiology of AKI was sepsis (71.43%) and the most frequent indication for acute dialysis was oliguria (64.4%). A total of 577 (53.3% of 1078 patients) met the Sepsis-3 criteria, and 206 among the 577 patients (19.1%) had septic shock. Having sepsis and septic shock were independently associated with 90-day mortality among these ICU AKI-D patients (hazard ratio (HR) 1.23 (p = 0.027) and 1.39 (p = 0.004), respectively). Taking mortality as a competing risk factor, AKI-D patients with septic shock had a significantly reduced chance of weaning from dialysis at 90 days than those without sepsis (HR 0.65, p = 0.026). The combination of the Sepsis-3 criteria with the AKI risk score led to better performance in forecasting 90-day mortality. Sepsis affects more than 50% of ICU AKI patients requiring dialysis, and one-fifth of these patients had septic shock. In AKI-D patients, coexistent with or induced by sepsis (as screened by the Sepsis-3 criteria), there is a significantly higher mortality and reduced chance of recovering sufficient renal function, when compared to those without sepsis.

Automated Continuous Acute Kidney Injury Prediction and Surveillance: A Random Forest Model

ObjectiveTo develop and validate a prediction model of acute kidney injury (AKI) of any severity that could be used for AKI surveillance and management to improve clinical outcomes. Patients and MethodsThis retrospective cohort study was conducted in medical, surgical, and mixed intensive care units (ICUs) at Mayo Clinic in Rochester, Minnesota, including adult (≥18 years of age) ICU-unique patients admitted between October 1, 2004, and April 30, 2011. Our primary objective was prediction of AKI using extant clinical data following ICU admission. We used random forest classification to provide continuous AKI risk score. ResultsWe included 4572 and 1958 patients in the training and validation mutually exclusive cohorts, respectively. Acute kidney injury occurred in 1355 patients (30%) in the training cohort and 580 (30%) in the validation cohort. We incorporated known AKI risk factors and routinely measured vital characteristics and laboratory results. The model was run throughout ICU admission every 15 minutes and achieved an area under the receiver operating characteristic curve of 0.88 on validation. It was 92% sensitive and 68% specific and detected 30% of AKI cases at least 6 hours before the criterion standard time (AKI stages 1-3). For discrimination of AKI stages 2 to 3, the model had 91% sensitivity, 71% specificity, and 53% detection of AKI cases at least 6 hours before AKI onset. ConclusionWe developed and validated an AKI prediction model using random forest for continuous monitoring of ICU patients. This model could be used to identify high-risk patients for preventive measures or identifying patients of prospective interventional trials.