Derivation and validation of a risk score for contrast-induced nephropathy after cardiac catheterization in Chinese patients

A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study

BACKGROUND

In patients undergoing percutaneous coronary intervention (PCI), contrast-induced acute kidney injury (CIAKI) is a frequent complication, especially in diabetics, and is connected with severe mortality and morbidity in the short and long term. Therefore, we aimed to develop a CIAKI predictive model for diabetic patients.

METHODS

3514 patients with diabetes from four hospitals were separated into three cohorts: training, internal validation, and external validation. We developed six machine learning (ML) algorithms models: random forest (RF), gradient-boosted decision trees (GBDT), logistic regression (LR), least absolute shrinkage and selection operator with LR, extreme gradient boosting trees (XGBT), and support vector machine (SVM). The area under the receiver operating characteristic curve (AUC) of ML models was compared to the prior score model, and developed a brief CIAKI prediction model for diabetes (BCPMD). We also validated BCPMD model on the prospective cohort of 172 patients from one of the hospitals. To explain the prediction model, the shapley additive explanations (SHAP) approach was used.

RESULTS

In the six ML models, XGBT performed best in the cohort of internal (AUC: 0.816 (95% CI 0.777-0.853)) and external validation (AUC: 0.816 (95% CI 0.770-0.861)), and we determined the top 15 important predictors in XGBT model as BCPMD model variables. The features of BCPMD included acute coronary syndromes (ACS), urine protein level, diuretics, left ventricular ejection fraction (LVEF) (%), hemoglobin (g/L), congestive heart failure (CHF), stable Angina, uric acid (umol/L), preoperative diastolic blood pressure (DBP) (mmHg), contrast volumes (mL), albumin (g/L), baseline creatinine (umol/L), vessels of coronary artery disease, glucose (mmol/L) and diabetes history (yrs). Then, we validated BCPMD in the cohort of internal validation (AUC: 0.819 (95% CI 0.783-0.855)), the cohort of external validation (AUC: 0.805 (95% CI 0.755-0.850)) and the cohort of prospective validation (AUC: 0.801 (95% CI 0.688-0.887)). SHAP was constructed to provide personalized interpretation for each patient. Our model also has been developed into an online web risk calculator. MissForest was used to handle the missing values of the calculator.

CONCLUSION

We developed a novel risk calculator for CIAKI in diabetes based on the ML model, which can help clinicians achieve real-time prediction and explainable clinical decisions.

A novel pre-operative risk assessment tool to identify patients at risk of contrast associated acute kidney injury after endovascular abdominal aortic aneurysm repair

OBJECTIVES

Contrast-associated acute kidney injury (CA-AKI) after endovascular abdominal aortic aneurysm repair (EVAR) is associated with mortality and morbidity. Risk stratification remains a vital component of preoperative evaluation. We sought to generate and validate a pre-procedure CA-AKI risk stratification tool for elective EVAR patients.

METHODS

We queried the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) database for elective EVAR patients and excluded those on dialysis, with a history of renal transplant, death during procedure, and without creatinine measures. Association with CA-AKI (rise in creatinine > 0.5 mg/dL) was tested using mixed effects logistic regression. Variables associated with CA-AKI were used to generate a predictive model via a single classification tree. The variables selected by the classification tree were then validated by fitting a mixed effects logistic regression model into the Vascular Quality Initiative (VQI) dataset.

RESULTS

Our derivation cohort included 7,043 patients, 3.5% of whom developed CA-AKI. After multivariate analysis, age (OR 1.021, 95% CI 1.004-1.040), female sex (OR 1.393, CI 1.012-1.916), GFR < 30 ml/min (OR 5.068, CI 3.255-7.891), current smoking (OR 1.942, CI 1.067-3.535), COPD (OR 1.402, CI 1.066-1.843), maximum AAA diameter (OR 1.018, CI 1.006-1.029), and presence of iliac artery aneurysm (OR 1.352, CI 1.007-1.816) were associated with increased odds of CA-AKI. Our risk prediction calculator demonstrated that patients with a GFR <30 ml/min, females, and patients with a maximum AAA diameter of > 6.9 cm are at higher risk of CA-AKI after EVAR. Using the VQI dataset (N = 62,986), we found that GFR <30 ml/min (OR 4.668, CI 4.007-5.85), female sex (OR 1.352, CI 1.213-1.507), and maximum AAA diameter > 6.9 cm (OR 1.824, CI 1.212-1.506) were associated with increased risk of CA-AKI after EVAR.

CONCLUSIONS

Herein, we present a simple and novel risk assessment tool that can be used pre-operatively to identify patients at risk of CA-AKI after EVAR. Patients with a GFR < 30 ml/min, maximum AAA diameter > 6.9 cm, and females who are undergoing EVAR may be at risk for CA-AKI after EVAR. Prospective studies are needed to determine the efficacy of our model.

Contrast-induced acute kidney injury and its contemporary prevention

The complexity and application range of interventional and diagnostic procedures using contrast media (CM) have recently increased. This allows more patients to undergo procedures that involve CM administration. However, the intrinsic CM toxicity leads to the risk of contrast-induced acute kidney injury (CI-AKI). At present, effective therapy of CI-AKI is rather limited. Effective prevention of CI-AKI therefore becomes crucially important. This review presents an in-depth discussion of CI-AKI incidence, pathogenesis, risk prediction, current preventive strategies, and novel treatment possibilities. The review also discusses the difference between CI-AKI incidence following intraarterial and intravenous CM administration. Factors contributing to the development of CI-AKI are considered in conjunction with the mechanism of acute kidney damage. The need for ultimate risk estimation and the prediction of CI-AKI is stressed. Possibilities of CI-AKI prevention is evaluated within the spectrum of existing preventive measures aimed at reducing kidney injury. In particular, the review discusses intravenous hydration regimes and pre-treatment with statins and N-acetylcysteine. The review further focuses on emerging alternative imaging technologies, alternative intravascular diagnostic and interventional procedures, and new methods for intravenous hydration guidance; it discusses the applicability of those techniques in complex procedures and their feasibility in current practise. We put emphasis on contemporary interventional cardiology imaging methods, with a brief discussion of CI-AKI in non-vascular and non-cardiologic imaging and interventional studies.

A pattern-discovery-based outcome predictive tool integrated with clinical data repository: design and a case study on contrast related acute kidney injury

Background

Clinical data repositories (CDR) including electronic health record (EHR) data have great potential for outcome prediction and risk modeling. We built a prediction tool integrated with CDR based on pattern discovery and demonstrated a case study on contrast related acute kidney injury (AKI).

Methods

Patients undergoing cardiac catheterization from January 2015 to April 2017 were included. AKI was identified based on Acute Kidney Injury Network definition. Predictive model including 16 variables covered in existing AKI models was built. A visual analytics tool based on pattern discovery was trained on 70% data up to August 2016 with three interactive knowledge incorporation modes to develop 3 models: (1) pure data-driven, (2) domain knowledge, and (3) clinician-interactive, which were tested and compared on 30% consecutive cases dated afterwards.

Results

Among 2560 patients in the final dataset, 189 (7.3%) had AKI. We measured 4 existing models, whose areas under curves (AUCs) of receiver operating characteristics curve for the test dataset were 0.70 (Mehran's), 0.72 (Chen's), 0.67 (Gao's) and 0.62 (AGEF), respectively. A pure data-driven machine learning method achieves AUC of 0.72 (Easy Ensemble). The AUCs of our 3 models are 0.77, 0.80, 0.82, respectively, with the last being top where physician knowledge is incorporated.

Conclusions

We developed a novel pattern-discovery-based outcome prediction tool integrated with CDR and purely using EHR data. On the case of predicting contrast related AKI, the tool showed user-friendliness by physicians, and demonstrated a competitive performance in comparison with the state-of-the-art models.

Contrast-Associated Acute Kidney Injury

Contrast-associated acute kidney injury (CA-AKI) is an impairment of renal function, which occurs within days of intravascular administration of iodinated contrast media. Taking into account that minimally invasive cardiac interventions are becoming increasingly popular, compared to traditional surgery, given their impact on prognosis and costs, CA-AKI remains a subject of increasing interest for patients and physicians. This review summarizes the epidemiology and risk stratification, diagnostic criteria, pathophysiology and clinical implications of CA-AKI, providing evidence for the most studied preventive strategies.

Endorsement of the TRIPOD statement and the reporting of studies developing contrast-induced nephropathy prediction models for the coronary angiography/percutaneous coronary intervention population: a cross-sectional study

OBJECTIVE

Clear and specific reporting of a research paper is essential for its validity and applicability. Some studies have revealed that the reporting of studies based on the clinical prediction models was generally insufficient based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. However, the reporting of studies on contrast-induced nephropathy (CIN) prediction models in the coronary angiography (CAG)/percutaneous coronary intervention (PCI) population has not been thoroughly assessed. Thus, the aim is to evaluate the reporting of the studies on CIN prediction models for the CAG/PCI population using the TRIPOD checklist.

DESIGN

A cross-sectional study.

METHODS

PubMed and Embase were systematically searched from inception to 30 September 2021. Only the studies on the development of CIN prediction models for the CAG/PCI population were included. The data were extracted into a standardised spreadsheet designed in accordance with the 'TRIPOD Adherence Assessment Form'. The overall completeness of reporting of each model and each TRIPOD item were evaluated, and the reporting before and after the publication of the TRIPOD statement was compared. The linear relationship between model performance and TRIPOD adherence was also assessed.

RESULTS

We identified 36 studies that developed CIN prediction models for the CAG/PCI population. Median TRIPOD checklist adherence was 60% (34%-77%), and no significant improvement was found since the publication of the TRIPOD checklist (p=0.770). There was a significant difference in adherence to individual TRIPOD items, ranging from 0% to 100%. Moreover, most studies did not specify critical information within the Methods section. Only 5 studies (14%) explained how they arrived at the study size, and only 13 studies (36%) described how to handle missing data. In the Statistical analysis section, how the continuous predictors were modelled, the cut-points of categorical or categorised predictors, and the methods to choose the cut-points were only reported in 7 (19%), 6 (17%) and 1 (3%) of the studies, respectively. Nevertheless, no relationship was found between model performance and TRIPOD adherence in both the development and validation datasets (r=-0.260 and r=-0.069, respectively).

CONCLUSIONS

The reporting of CIN prediction models for the CAG/PCI population still needs to be improved based on the TRIPOD checklist. In order to promote further external validation and clinical application of the prediction models, more information should be provided in future studies.

Hypertension as a Risk Factor for Contrast-Associated Acute Kidney Injury: A Meta-Analysis Including 2,830,338 Patients

OBJECTIVE

Previous studies have shown that the relationship between hypertension (HT) and contrast-associated acute kidney injury (CA-AKI) is not clear. We apply a systematic review and meta-analysis to assess the association between HT and CA-AKI.

METHODS

We searched for articles on the study of risk factors for CA-AKI in the Embase, Medline, and Cochrane Database of Systematic Reviews (by March 25, 2021). Two authors independently performed quality assessment and extracted data such as the studies' clinical setting, the definition of CA-AKI, and the number of patients. The CA-AKI was defined as a serum creatinine (SCr) increase ≥25% or ≥0.5 mg/dL from baseline within 72 h. We used fixed or random models to pool adjusted OR (aOR) by STATA.

RESULTS

A total of 45 studies (2,830,338 patients) were identified, and the average incidence of CA-AKI was 6.48%. There was an increased risk of CA-AKI associated with HT (aOR: 1.378, 95% CI: 1.211-1.567, I2 = 67.9%). In CA-AKI with a SCr increase ≥50% or ≥0.3 mg/dL from baseline within 72 h, an increased risk of CA-AKI was associated with HT (aOR: 1.414, 95% CI: 1.152-1.736, I2 = 0%). In CA-AKI with a Scr increase ≥50% or ≥0.3 mg/dL from baseline within 7 days, HT increases the risk of CA-AKI (aOR: 1.317, 95% CI: 1.049-1.654, I2 = 51.5%).

CONCLUSION

Our meta-analysis confirmed that HT is an independent risk factor for CA-AKI and can be used to identify risk stratification. Physicians should pay more attention toward prevention and treatment of patients with HT in clinical practice.

Acute Kidney Injury Following Percutaneous Coronary Intervention for Acute Coronary Syndrome: Incidence, Aetiology, Risk Factors and Outcomes

We investigated the predictors, aetiology and long-term outcomes of acute kidney injury (AKI) following urgent percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS). Acute kidney injury occurred in 198 (7.2%) of 2917 patients: 14.1% of AKI cases were attributed to cardiogenic shock and 5.1% were classified as atheroembolic renal disease (AERD). Significant risk factors for AKI included age (odds ratio [OR] 1.05, 95% confidence limits [CI] 1.03-1.06), diabetes (OR 1.73, 95% CI 1.20-2.47), hypertension (OR 1.43, 95% CI 1.03-2.00), heart failure (OR 3.01, 95% CI 1.58-5.57), femoral access (OR 1.50, 95% CI 1.03-2.15), cardiogenic shock (OR 2.03, 95% CI 1.19-3.37) and ST-elevation myocardial infarction (STEMI) (OR 3.89, 95% CI 2.80-5.47). One-year mortality after AERD was 44.4% and renal replacement therapy (RRT) requirement 22.2% (compared with mortality 33.3% and RRT requirement 7.4%, respectively, in all other AKI patients). Mortality at 1 year was associated with AKI (OR 4.33, 95% CI 2.89-6.43), age (OR 1.08, 95% CI 1.06-1.09), heart failure (OR 1.92, 95% CI 1.05-3.44), femoral access (OR 2.05, 95% CI 1.41-2.95) and cardiogenic shock (OR 3.63, 95% CI 2.26-5.77). Acute kidney injury after urgent PCI is strongly associated with worse outcomes. Atheroembolic renal disease has a poor outcome and a high likelihood of long-term RRT requirement.

A Simple Nomogram to Predict Contrast-Induced Acute Kidney Injury in Patients with Congestive Heart Failure Undergoing Coronary Angiography

Background

Patients with congestive heart failure (CHF) are vulnerable to contrast-induced kidney injury (CI-AKI), but few prediction models are currently available. Therefore, we aimed to establish a simple nomogram for CI-AKI risk assessment for patients with CHF undergoing coronary angiography.

Methods

A total of 1876 consecutive patients with CHF (defined as New York Heart Association functional class II-IV or Killip class II-IV) were enrolled and randomly (2:1) assigned to a development cohort and a validation cohort. The endpoint was CI-AKI defined as serum creatinine elevation of ≥0.3 mg/dL or 50% from baseline within the first 48–72 hours following the procedure. Predictors for the simple nomogram were selected by multivariable logistic regression with a stepwise approach. The discriminative power was assessed using the area under the receiver operating characteristic (ROC) curve and was compared with the classic Mehran score in the validation cohort. Calibration was assessed using the Hosmer–Lemeshow test and 1000 bootstrap samples.

Results

The incidence of CI-AKI was 9.06% (170) in the total sample, 8.64% (n = 109) in the development cohort, and 9.92% (n = 61) in the validation cohort (P=0.367). The simple nomogram including four predictors (age, intra-aortic balloon pump, acute myocardial infarction, and chronic kidney disease) demonstrated a similar predictive power as the Mehran score (area under the curve: 0.80 vs. 0.75, P=0.061), as well as a well-fitted calibration curve.

Conclusions

The present simple nomogram including four predictors is a simple and reliable tool to identify CHF patients at risk of CI-AKI, whereas further external validations are needed.

Contrast-associated acute kidney injury: An update of risk factors, risk factor scores, and preventive measures

As lifespans lengthen, age-related diseases such as cardiovascular disease and diabetes are becoming more prevalent. Correspondingly, the use of contrast agents for medical imaging is also becoming more common, and there is increasing awareness of contrast-associated acute kidney injury (CA-AKI). There is no specific treatment for CA-AKI, and clinicians currently focus on prevention, interventions that alter its pathogenesis, and identification of risk factors. Although the incidence of CA-AKI is low in the general population, the risk of CA-AKI can reach 20% to 30% in patients with multiple risk factors. Many models have been applied in the clinic to assess the risk factors for CA-AKI, enable identification of high-risk groups, and improve clinical management. Hypotonic or isotonic contrast media are recommended to prevent CA-AKI in high-risk patients. Patients with risk factors should avoid using contrast media multiple times within a short period of time. All nephrotoxic drugs should be stopped at least 24 h before the administration of contrast media in high-risk populations, and adequate hydration is recommended for all patients. This review summarizes the pathophysiology of CA-AKI and the progress in diagnosis and differential diagnosis; updates the risk factors and risk factor scoring systems; reviews the latest advances related to prevention and treatment; discusses current problems in epidemiological studies; and highlights the importance of identifying high-risk subjects to control modifiable risk factors and use of a rating scale to estimate the risk and implement appropriate prevention strategies.

Advances in the pathogenesis and prevention of contrast-induced nephropathy

With the increasing application of medical imaging contrast materials, contrast-induced nephropathy has become one of the leading causes of iatrogenic renal insufficiency. The underlying mechanism is associated with renal medullary hypoxia, direct toxicity of contrast agents, oxidative stress, apoptosis, immune/inflammation and epigenetic regulation in contrast-induced nephropathy. Up to date, there is no effective therapy for contrast-induced nephropathy, and thus risk predication and effective preventive strategies are keys to reduce the occurrence of contrast-induced nephropathy. It was found that the proper use of contrast medium, personalized hydration, and high-dose statins may reduce the occurrence of contrast-induced nephropathy, while antioxidants have not shown significant therapeutic benefits. Additionally, the role of remote ischemia preconditioning and vasodilators in the prevention of contrast-induced nephropathy needs further study. This review aims to discuss the incidence, pathogenesis, risk prediction, and preventive strategies for contrast-induced nephropathy.

A prediction model of contrast-associated acute kidney injury in patients with hypoalbuminemia undergoing coronary angiography

BACKGROUND

Risk stratification is recommended as the key step to prevent contrast-associated acute kidney injury (CA-AKI) among at-risk patients following coronary angiography (CAG) and/or percutaneous coronary intervention (PCI). Patients with hypoalbuminemia are prone to CA-AKI and do not have their own risk stratification tool. Therefore, this study developed and validated a new model for predicting CA-AKI among hypoalbuminemia patients CAG/PCI.

METHODS

1272 patients with hypoalbuminemia receiving CAG/PCI were enrolled and randomly allocated (2:1 ratio) into the development cohort (n = 848) and the validation cohort (n = 424). CA-AKI was defined as an increase of ≥0.3 mg/dL or 50% in serum creatinine (SCr) compared to baseline in the 48 to 72 h after CAG/PCI. A prediction model was established with independent predictors according to stepwise logistic regression, showing as a nomogram. The discrimination of the new model was evaluated by the area under the curve (AUC) and was compared to the classic Mehran CA-AKI model. The Hosmer-Lemeshow test was conducted to assess the calibration of our model.

RESULTS

Overall, 8.4% (71/848) patients of the development group and 11.2% (48/424) patients of the validation group experienced CA-AKI. A new nomogram included estimated glomerular filtration rate (eGFR), serum albumin (ALB), age and the use of intra-aortic balloon pump (IABP); showed better predictive ability than the Mehran score (C-index 0.756 vs. 0.693, p = 0.02); and had good calibration (Hosmer-Lemeshow test p = 0.187).

CONCLUSIONS

We developed a simple model for predicting CA-AKI among patients with hypoalbuminemia undergoing CAG/PCI, but our findings need validating externally.

TRIAL REGISTRATION

http://www.ClinicalTrials.gov NCT01400295 , retrospectively registered 21 July 2011.

Performance of a pre-procedural Mehran score to predict acute kidney injury after percutaneous coronary intervention

AIM

Acute kidney injury (AKI) is a known complication of patients undergoing cardiac catheterization or percutaneous coronary interventions (PCI).The Mehran score was developed to identify patients at risk for AKI after cardiac catheterization or PCI, but its use of contrast volume as part of the score calculation limits its application prior to the procedure. In this study, we evaluated the utility of a modified Mehran score that utilizes only pre-procedural data by excluding contrast volume.

METHODS

This was done in a retrospective fashion using data from patients who received PCI at our institution between July 2015 and December 2017 by evaluating the discriminative ability of the scoring systems for predicting outcomes through a receiver-operator characteristic curve analysis.

RESULTS

One thousand five hundred and seven patients were included in the study. A total of 70 (4.6%) patients developed AKI. The removal of contrast volume from the Mehran score resulted in a small loss of discrimination with AUROC 0.73 vs 0.74, P = .01 for the pre-procedural Mehran and the original Mehran, respectively. When compared to the original score, the pre-procedural Mehran score had a four-category net discrimination index (NRI) of -0.10 and an integrated discrimination index (IDI) for of -0.12.

CONCLUSION

Despite a small loss in discrimination, there was no difference in the four-category net discrimination index between the two scores. The pre-procedural modified Mehran score is a useful clinical predictor of the risk of AKI in patients undergoing PCI.

Acute kidney injury prediction models: current concepts and future strategies

PURPOSE OF REVIEW

Acute kidney injury (AKI) is a critical condition associated with poor patient outcomes. We aimed to review the current concepts and future strategies regarding AKI risk prediction models.

RECENT FINDINGS

Recent studies have shown that AKI occurs frequently in patients with common risk factors and certain medical conditions. Prediction models for AKI risk have been reported in medical fields such as critical care medicine, surgery, nephrotoxic agent exposure, and others. However, practical, generalizable, externally validated, and robust AKI prediction models remain relatively rare. Further efforts to develop AKI prediction models based on comprehensive clinical data, artificial intelligence, improved delivery of care, and novel biomarkers may help improve patient outcomes through precise AKI risk prediction.

SUMMARY

This brief review provides insights for current concepts for AKI prediction model development. In addition, by overviewing the recent AKI prediction models in various medical fields, future strategies to construct advanced AKI prediction models are suggested.

Application of 17 Contrast-Induced Acute Kidney Injury Risk Prediction Models

INTRODUCTION

Contrast-induced acute kidney injury (CI-AKI) is a frequent complication of percutaneous coronary interventions (PCI). Various groups have developed and validated risk scores for CI-AKI. Although the majority of these risk scores achieve an adequate accuracy, their usability in clinical practice is limited and greatly debated.

OBJECTIVE

With the present study, we aimed to prospectively assess the diagnostic performance of recently published CI-AKI risk scores (up to 2018) in a cohort of patients undergoing PCI.

METHODS

We enrolled 1,247 consecutive patients (80% men, mean age 62 ± 10 years) treated with elective or urgent PCI. For each patient, we calculated the individual CI-AKI risk score based on 17 different risk models. CI-AKI was defined as an increase of ≥25% (liberal) or ≥0.5 mg/dL (strict) in pre-PCI serum creatinine 48 h after PCI.

RESULTS

CI-AKI definition and, therefore, CI-AKI incidence have a significant impact on risk model performance (median negative predictive value increased from 85 to 99%; median c-statistic increased from 0.516 to 0.603 using more strict definition criteria). All of the 17 published models were characterized by a weak-to-moderate discriminating ability mainly based on the identification of "true-negative" cases (median positive predictive value 19% with liberal criterion and 3% with strict criterion). In none of the models, c-statistic was >0.800 with either CI-AKI definition. Novel, different combinations of the >35 independent variables used in the published models either by down- or by up-scaling did not result in significant improvement in predictive performance.

CONCLUSIONS

The predictive ability of all models was similar and only modest, derived mainly by identifying true-negative cases. A new approach is probably needed by adding novel markers or periprocedural characteristics.

Validation of pre-operative risk scores of contrast-induced acute kidney injury in a Chinese cohort

Background

Pre-operative risk scores are more valuable than post-procedure risk scores because of lacking effective treatment for contrast-induced acute kidney injury (CI-AKI). A number of pre-operative risk scores have been developed, but due to lack of effective external validation, most of them are also difficult to apply accurately in clinical practice. It is necessary to review and validate the published pre-operative risk scores for CI-AKI.

Materials and methods

We systematically searched PubMed and EMBASE databases for studies of CI-AKI pre-operative risk scores and assessed their calibration and discriminatory in a cohort of 2669 patients undergoing coronary angiography or percutaneous coronary intervention (PCI) from September 2007 to July 2017. The definitions of CI-AKI may affect the validation results, so three definition were included in this study, CI-AKI broad1 was defined as an increase in serum creatinine (Scr) of 44.2 μmol/L or 25%; CI-AKI broad2, an increase in Scr of 44.2 μmol/L or 50%; and CI-AKI-narrow, an increase in Scr of 44.2 μmol/L. The calibration of the model was assessed with the Hosmer-Lemeshow test and the discriminatory capacity was identified by C-statistic.

Results

Of the 8 pre-operative risk scores for CI-AKI identified, 7 were single-center study and only 1 was based on multi-center study. In addition, 7 of the scores were just validated internally and only Chen score was externally validated. In the validation cohort of 2669 patients, the incidence of CI-AKI ranged from 3.0%(Liu) to 16.4%(Chen) for these scores. Furthermore, the incidence of CI-AKI was 6.59% (178) for CI-AKI broad1, 1.44% (39) for CI-AKI broad2, and 0.67% (18) for CI-AKI-narrow. For CI-AKI broads, C-statistics varied from 0.44 to 0.57. For CI-AKI-narrow, the Maioli score had the best discrimination and calibration, what’s more, the C-statistics of Maioli, Chen, Liu and Ghani was ≥0.7.

Conclusion

Most pre-operative risk scores were established based on single-center studies and most of them lacked external validation. For CI-AKI broads, the prediction accuracy of all risk scores was low. The Maioli score had the best discrimination and calibration, when using the CI-AKI-narrow definition.

Three-dimensional speckle-tracking echocardiography for evaluating myocardial motion in patients with cardiorenal syndrome

Because of better awareness and understanding of its pathophysiology, the cardiorenal syndrome (CRS) is more often diagnosed and better managed. The echocardiographic evaluation of CRS now benefits from three-dimensional speckle tracking echocardiography (3D-STE), which allows multidimensional and real-time evaluation of regional myocardial and overall cardiac function, and helps assessing the degree of myocardial damage. This article describes the application of 3D-STE in evaluating the myocardial motion in patients with CRS.

Derivation and validation of a prediction score for acute kidney injury secondary to acute myocardial infarction in Chinese patients

Background

Acute kidney injury (AKI) is a major complication of acute myocardial infarction(AMI), which can significantly increase mortality. This study is to analyze the related risk factors and establish a prediction score of acute kidney injury in order to take early measurement for prevention.

Methods

The medical records of 6014 hospitalized patients with AMI in Beijing Anzhen Hospital from January 2010 to December 2016 were retrospectively analyzed. These patients were randomly assigned into two cohorts: one was for the derivation of prediction score (n = 4252) and another for validation (n = 1762). The criterion for AKI was defined as an increase in serum creatinine of ≥ 0.3 mg/dL or ≥ 50% from baseline within 48 h. On the basis of odds ratio obtained from multivariate logistic regression analysis, a prediction score of acute kidney injury after AMI was built up.

Results

In this prediction score, risk score 1 point included hypertension history, heart rate > 100 bpm on admission, peak serum troponin I ≥ 100 μg/L, and time from admission to coronary reperfusion > 120 min; risks score 2 points included Killip classification ≥ class 3 on admission; and maximum dosage of intravenous furosemide ≥ 60 mg/d; risks score 3 points only included shock during hospitalization. In addition, when baseline estimated glomerular filtration rate (eGFR) was less than 90 ml/min·1.73 m², every 10 ml/min·1.73 m² reduction of eGFR increased risk score 1 point. Youden index showed that the best cut-off value for prediction of AKI was 3 points with a sensitivity of 71.1% and specificity 74.2%. The datasets of derivation and validation both displayed adequate discrimination (an area under the ROC curve, 0.79 and 0.81, respectively) and satisfactory calibration (Hosmer–Lemeshow statistic test, P = 0.63 and P = 0.60, respectively).

Conclusions

In conclusion, a prediction score for AKI secondary to AMI in Chinese patients was established, which may help to prevent AKI early.

A simple risk score model for predicting contrast-induced nephropathy after coronary angiography in patients with diabetes

BACKGROUND

Contrast-induced nephropathy (CIN) is a common complication in patients undergoing coronary angiography (CAG) or percutaneous coronary intervention (PCI) and associated with poor outcome. Some previous studies have already set up models to predict CIN, but there is no model for patients with diabetes mellitus (DM) especially. Therefore, we aim to develop and validate a simple risk score for predicting the risk of CIN in patients with DM undergoing CAG/PCI.

METHODS

A total of 1157 consecutive patients with DM undergoing CAG/PCI were randomly assigned to a development cohort (n = 771) and a validation cohort (n = 386). The primary endpoint was CIN, which was defined as an absolute increase in serum creatinine (SCr) by 0.5 mg/dL from the baseline within 48-72 h after contrast exposure. The independent predictors for CIN were identified by multivariate logistic regression, and the discrimination and calibration of the risk score were assessed by ROC curve and Hosmer-Lemeshow test, respectively.

RESULTS

The overall incidence of CIN was 45 (3.9%). The new simple risk score (Chen score), which included four independent variables (age > 75 years, acute myocardial infarction, SCr > 1.5 mg/dL, the use of intra-aortic balloon pump), exhibited a similar discrimination and predictive ability on CIN (AUC 0.813, 0.843, 0.796, P > 0.05, respectively), mortality (AUC 0.735, 0.771, 0.826, respectively) and MACEs when being compared with the classical Mehran or ACEF risk score.

CONCLUSION

Our data suggest that the new simple risk score might be a good tool for predicting CIN in patients with DM undergoing CAG/PCI.

Analysis of the risk factors for contrast-induced nephropathy in over-aged patients receiving coronary intervention

Contrast-induced nephropathy has been the common cause of hospital-acquired acute kidney injury in the elderly patients. This study aimed to analyze the risk factors for contrast-induced nephropathy in over-aged patients undergoing coronary angiography or percutaneous coronary intervention. A total of 470 over-aged patients (≥80 years old) were judged as the contrast-induced nephropathy group ( n = 46) and non-contrast-induced nephropathy group ( n = 424) according to the postoperative 48-h serum creatinine levels. The patients' clinical information such as hypertension grade, number and degree of coronary artery stenosis, and death rate was compared. The risk factors for contrast-induced nephropathy were also analyzed. The hypertension grade in the contrast-induced nephropathy group was significantly higher than that in the non-contrast-induced nephropathy group ( P = 0.004). The degree of coronary artery stenosis was significantly more in the contrast-induced nephropathy group compared with the non-contrast-induced nephropathy group ( P = 0.003). The death rate of the contrast-induced nephropathy group (15.8%) was significantly higher than that of the non-contrast-induced nephropathy group (0.6%; P = 0.000). The percentage of patients with abnormal urine microalbumin was significantly bigger in the contrast-induced nephropathy group (62.5%) when comparing to the non-contrast-induced nephropathy group (23.6%; P = 0.00). Besides, there was also significant difference in the emergency/selective operation between the contrast-induced nephropathy group and non-contrast-induced nephropathy group ( P = 0.001). Further, hypertension grade ( P = 0.019), emergency/selective operation ( P = 0.025), degree of coronary artery stenosis ( P = 0.038), eGFR ( P = 0.034), and urine microalbumin ( P = 0.005) were the risk factors for contrast-induced nephropathy. Hypertension grade, emergency/selective operation, degree of coronary artery stenosis, eGFR, and urine microalbumin were the risk factors for contrast-induced nephropathy in over-aged patients receiving coronary angiography and percutaneous coronary intervention, providing guidance for the clinical prevention of contrast-induced nephropathy. Impact statement In this work, we evaluated the risk factors for contrast-induced nephropathy (CIN) in over-aged patients receiving coronary angiography (CAG) and percutaneous coronary intervention (PCI). We found that hypertension grade, emergency/selective operation, degree of coronary artery stenosis, eGFR, and urine microalbumin were the risk factors for CIN in over-aged patients receiving CAG and PCI. This study provides guidance for the clinical prevention of CIN in over-aged patients undergoing coronary intervention, highlighting that a perioperative comprehensive management strategy is needed to improve the prognosis.

A New and Simple Risk Predictor of Contrast-Induced Nephropathy in Patients Undergoing Primary Percutaneous Coronary Intervention: TIMI Risk Index

Background

The thrombolysis in myocardial infarction risk index (TRI) was developed to estimate prognosis at the initial contact of the healthcare provider in coronary artery disease patients without laboratory parameters. In this study, we aimed to investigate the relationship of the baseline TRI and contrast-induced nephropathy (CIN) in patients with ST-elevation myocardial infarction (STEMI).

Methods

A total of 963 consecutive STEMI diagnosed patients who underwent primary percutaneous intervention were included in the study. TRI was calculated using the formula “heart rate × (age/10) 2/SBP” on admission. CIN was defined as an increase in serum creatinine concentration ≥25%, 48 hours later over the baseline.

Results

Of the total of 963 patients, CIN was observed in 13% (n=128). TRI was significantly higher in the CIN (+) group compared with the CIN (−) group (32.9 ± 18.8 vs 19.9 ± 9.9, P < 0.001). There was a stronger correlation between CIN and age, diastolic blood pressure, heart rate, Killip class, left ventricular ejection fraction, amount of contrast media, and diabetes mellitus. The amount of contrast media (OR 1.010, 95% CI 1.007–1.012, P < 0.001) and TRI (OR 1.047, 95% CI 1.020–1.075, P=001) were independent predictors of CIN. The best threshold TRI for predicting CIN was ≥25.8, with a 67.1% sensitivity and 80.4% specificity (area under the curve (AUC): 0.740, 95% CI: 0.711–0.768, P < 0.001).

Conclusion

TRI is an independent predictor of CIN, and it may be used as a simple and reliable risk assessment of CIN in STEMI patients without the need for laboratory parameters.

Development of a preprocedure nomogram for predicting contrast-induced acute kidney injury after coronary angiography or percutaneous coronary intervention

Most of the risk models for predicting contrast-induced acute kidney injury (CI-AKI) are available for postcontrast exposure prediction, thus have limited values in practice. We aimed to develop a novel nomogram based on preprocedural features for early prediction of CI-AKI in patients after coronary angiography (CAG) or percutaneous coronary intervention (PCI). A total of 245 patients were retrospectively reviewed from January 2015 to January 2017. Least absolute shrinkage and selection operator (Lasso) regression model was applied to select most strong predictors for CI-AKI. The CI-AKI risk score was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients. The discrimination of nomogram was assessed by C-statistic. The occurrence of CI-AKI was 13.9% (34 out of 245). We identified ten predictors including sex, diabetes mellitus, lactate dehydrogenase level, C-reactive protein, years since drinking, chronic kidney disease (CKD), stage of CKD, stroke, acute myocardial infarction, and systolic blood pressure. The CI-AKI prediction nomogram obtained good discrimination (C-statistic, 0.718, 95%CI: 0.637-0.800, p = 7.23 × 10^-5). The cutoff value of CI-AKI risk score was -1.953. Accordingly, the novel nomogram we developed is a simple and accurate tool for preprocedural prediction of CI-AKI in patients undergoing CAG or PCI.

The 6R's of drug induced nephrotoxicity

Drug induced kidney injury is a frequent adverse event which contributes to morbidity and increased healthcare utilization. Our current knowledge of drug induced kidney disease is limited due to varying definitions of kidney injury, incomplete assessment of concurrent risk factors and lack of long term outcome reporting. Electronic surveillance presents a powerful tool to identify susceptible populations, improve recognition of events and provide decision support on preventative strategies or early intervention in the case of injury. Research in the area of biomarkers for detecting kidney injury and genetic predisposition for this adverse event will enhance detection of injury, identify those susceptible to injury and likely mitigate risk. In this review we will present a 6R framework to identify and mange drug induced kidney injury – risk, recognition, response, renal support, rehabilitation and research.

Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients

Background

Several models have been developed for prediction of contrast‐induced nephropathy (CIN); however, they only contain patients receiving intra‐arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure‐related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media.

Methods and Results

A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5‐fold cross‐validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver‐operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level.

Conclusions

The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN.

Comparison of Different Risk Scores for Predicting Contrast Induced Nephropathy and Outcomes After Primary Percutaneous Coronary Intervention in Patients With ST Elevation Myocardial Infarction

Accurate risk stratification for contrast-induced nephropathy (CIN) is important for patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). We aimed to compare the prognostic value of validated risk scores for CIN. We prospectively enrolled 422 consecutive patients with STEMI undergoing PPCI. Mehran; Gao; Chen; age, serum creatinine (SCr), or glomerular filtration rate, and ejection fraction (ACEF or AGEF); and Global Registry for Acute Coronary Events risk scores were calculated for each patient. The prognostic accuracy of the 6 scores for CIN, and in-hospital and 3-year all-cause mortality and major adverse clinical events (MACEs), was assessed using the c-statistic for discrimination and the Hosmer-Lemeshow test for calibration. CIN was defined as either CIN-narrow (increase in SCr ≥0.5 mg/dl) or CIN broad (≥0.5 mg/dl and/or a ≥25% increase in baseline SCr). All risk scores had relatively high predictive values for CIN-narrow (c-statistic: 0.746 to 0.873) and performed well for prediction of in-hospital death (0.784 to 0.936), MACEs (0.685 to 0.763), and 3-year all-cause mortality (0.655 to 0.871). The ACEF and AGEF risk scores had better discrimination and calibration for CIN-narrow and in-hospital outcomes. However, all risk score exhibited low predictive accuracy for CIN-broad (0.555 to 0.643) and 3-year MACEs (0.541 to 0.619). In conclusion, risk scores for predicting CIN perform well in stratifying the risk of CIN-narrow, in-hospital death or MACEs, and 3-year all-cause mortality in patients with STEMI undergoing PPCI. The ACEF and AGEF risk scores appear to have greater prognostic value.

Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference

The data contained within the electronic health record (EHR) is "big" from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the "Big Data" era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display.

Risk prediction models for contrast induced nephropathy: systematic review

OBJECTIVES

To look at the available literature on validated prediction models for contrast induced nephropathy and describe their characteristics.

DESIGN

Systematic review.

DATA SOURCES

Medline, Embase, and CINAHL (cumulative index to nursing and allied health literature) databases.

REVIEW METHODS

Databases searched from inception to 2015, and the retrieved reference lists hand searched. Dual reviews were conducted to identify studies published in the English language of prediction models tested with patients that included derivation and validation cohorts. Data were extracted on baseline patient characteristics, procedural characteristics, modelling methods, metrics of model performance, risk of bias, and clinical usefulness. Eligible studies evaluated characteristics of predictive models that identified patients at risk of contrast induced nephropathy among adults undergoing a diagnostic or interventional procedure using conventional radiocontrast media (media used for computed tomography or angiography, and not gadolinium based contrast).

RESULTS

16 studies were identified, describing 12 prediction models. Substantial interstudy heterogeneity was identified, as a result of different clinical settings, cointerventions, and the timing of creatinine measurement to define contrast induced nephropathy. Ten models were validated internally and six were validated externally. Discrimination varied in studies that were validated internally (C statistic 0.61-0.95) and externally (0.57-0.86). Only one study presented reclassification indices. The majority of higher performing models included measures of pre-existing chronic kidney disease, age, diabetes, heart failure or impaired ejection fraction, and hypotension or shock. No prediction model evaluated its effect on clinical decision making or patient outcomes.

CONCLUSIONS

Most predictive models for contrast induced nephropathy in clinical use have modest ability, and are only relevant to patients receiving contrast for coronary angiography. Further research is needed to develop models that can better inform patient centred decision making, as well as improve the use of prevention strategies for contrast induced nephropathy.

Mehran contrast nephropathy risk score: Is it still useful 10 years later?

BACKGROUND

Nowadays, contrast-induced nephropathy (CIN) is the third cause of acquired acute renal impairment in hospital. CIN is related to increased in-hospital morbidity, mortality, costs of medical care, and long admissions. Because of this, we hypothesized it would be useful to determine the risk of CIN with scores such as the Mehran score. The aim of this study was to validate the Mehran score in a contemporary cohort of Spanish patients with acute coronary syndrome (ACS).

METHODS

We assessed the calibration and discriminatory capacity of Mehran score to predict CIN in a cohort of 1520 patients with a definitive diagnosis of ACS and who underwent coronary angiography between March 2008 and June 2012. We excluded patients on chronic dialysis and those without data of contrast volume. The calibration of the model was assessed with the Hosmer-Lemeshow goodness-of-fit test and discriminatory capacity was assessed by C-statistic, which is equivalent to the area under the receiver-operating characteristic curve.

RESULTS

From the total group, 118 patients (7.8%) developed CIN. They were older, with higher rates of diabetes (DM) and hypertension and worse renal function and anemia (p<0.001). The odds ratios for different score components in Mehran's population versus our study were similar except for DM, hypotension, and intra-aortic balloon pump (1.6%, 2.68%, 2.55% vs 0.9%, 1.89%, and 2.86%, respectively). Calibration and discriminatory capacity of Mehran score were excellent with a Hosmer-Lemeshow p=0.7, C-statistic value >0.8.

CONCLUSIONS

Mehran risk score has been validated in our study as a good score for predicting CIN in patients with ACS who underwent coronary angiography. According to this, we support its use in patients hospitalized for ACS in order to identify the ones at risk, and to optimize CIN prophylactic therapy prior to and after catheterization.

Clinical prediction scores for type 1 cardiorenal syndrome derived and validated in chinese cohorts

Type 1 cardiorenal syndrome is one of the major diseases threatening human life in China. The incidence of acute kidney injury (AKI) associated with acute heart failure (AHF), acute myocardial infarction (AMI), cardiac surgery, and coronary angiography has been reported to be 32.2, 14.7, 40.2, and 4.5%, respectively. In the past 2 years, we derived and validated 4 risk scores for the prediction of AKI associated with the above acute heart diseases as well as for examination and treatment in Chinese cohorts. A univariable comparison and a subsequent multivariate logistic regression analysis of the potential predictive variables of AKI in the derivation set were conducted and used to establish the prediction scores, which were then verified in the validation set. The area under the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow goodness-of-fit statistic test were performed to assess the discrimination and calibration of the prediction scores, respectively. These 4 prediction scores all showed adequate discrimination (area under the ROC curve, ≥0.70) and good calibration (p > 0.05). Both Forman's risk score (for AKI associated with AHF) and Mehran's risk score (for AKI associated with coronary angiography) are widely applied around the world. The external validation of these 2 risk scores was performed in our patients, but their discriminative power was quite low (area under the ROC curve, 0.65 and 0.57, respectively). Therefore, these prediction scores derived from Chinese cohorts might be more accurate than those derived from different races when they are applied in Chinese patients.