A novel tool for reliable and accurate prediction of renal complications in patients undergoing percutaneous coronary intervention

Combined strategy of device-based contrast minimization and urine flow rate-guided hydration to prevent acute kidney injury in high-risk patients undergoing coronary interventional procedures

BACKGROUND AND AIMS

Contrast-associated acute kidney injury (CA-AKI) is a major complication following coronary procedures. We aimed to evaluate the effectiveness of a combination of urine flow rate-(UFR) guided hydration (RenalGuardTM) and device-based contrast media (CM) reduction (DyeVertTM) in CA-AKI prevention.

METHODS

Stable high-risk patients undergoing coronary procedures with the use of DyeVertTM and RenalGuardTM were prospectively included (Combined group) and matched with a similar cohort of patients treated only with RenalGuardTM in whom CM volume was controlled by operator-dependent strategies (Control group). CA-AKI was defined as a serum creatinine increase ≥0.3 mg/dL at 48 h.

RESULTS

Overall, 55 patients were enrolled and matched with comparable controls. Patients in the Combined group were exposed to a lower CM dose (Control: 55 [30-90] mL vs. Combined: 42.1 [24.9-59.4] mL; p = 0.024). A significant interaction was found between treatment allocation and serum creatinine changes (p = 0.048). CA-AKI occurred in five (9.1%) patients in the Combined group and in 14 (25.4%) patients in the Control group (OR 0.29, 95% CI [0.09-0.88]).

CONCLUSIONS

A combined strategy of device-based CM reduction plus UFR-guided hydration is superior to operator-dependent CM sparing strategies plus UFR-guided hydration in preventing CA-AKI in high-risk patient.

Assessment of Predictive Value of SYNTAX-II Score for Adverse Cardiac Events and Clinical Outcomes in Patients With Acute Coronary Syndrome

Prognostic information is important for the management of acute coronary syndrome (ACS). Our aim was to evaluate Synergy between PCI with Taxus and Cardiac Surgery (SYNTAX) score-II (SSII) for predicting contrast induced nephropathy (CIN) and one-year major adverse cardiac events (MACE) in ACS patients. Coronary angiographic recordings of 1304 ACS patients were retrospectively examined. Predictive values of SYNTAX score (SS), SSII-percutaneous coronary intervention (SSII-PCI), SSII-coronary artery bypass graft (SSII-CABG) scores for CIN and MACE were assessed. Combination of CIN and MACE ratios constituted primary composite end-point. Patients with SSII-PCI scores >32.55 were compared with patients with lower scores. All of the three scoring systems predicted the composite primary end-point [SS: Area under the curve (AUC): .718, P < .001 (95% CI: .689-.747), SSII-PCI: AUC: .824, P < .001 (95% CI: .800-.849), SSII-CABG: AUC: .778, P < .001 (95% CI: .751-.805)]. Comparison of AUC of receiver operating characteristic curves showed that SSII-PCI score had better predictive value than that of SS and SSII-CABG scores. In multivariate analysis, the only predictor of the primary composite end-point was SSII-PCI score (odds ratio: 1.126, 95% CI: 1.107-1.146, P < .001). SSII-PCI score was a valuable tool for prediction of shock, CABG, myocardial infarction, stent thrombosis, development of CIN and one-year mortality.

Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models

BACKGROUND

Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive.

OBJECTIVE

This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database.

METHODS

We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT.

RESULTS

Our multimodal model achieved a lead time of at least 12 h ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT.

CONCLUSION

Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Kidney Injury After Minimal Radiographic Contrast Administration in Patients With Acute Coronary Syndromes

BACKGROUND

Acute kidney injury (AKI) is common in patients with acute coronary syndromes (ACS) treated by percutaneous coronary intervention.

OBJECTIVES

Contrast media (CM) volume minimization has been advocated for prevention of AKI. The DyeVert CM diversion system (Osprey Medical, Inc) is designed to reduce CM volume during coronary procedures.

METHODS

In this randomized, single-blind, investigator-driven clinical trial conducted in 4 Italian centers from February 4, 2020 to September 13, 2022, 550 participants with ACS were randomly assigned in a 1:1 ratio to the following: 1) the contrast volume reduction (CVR) group (n = 276), in which CM injection was handled by the CM diversion system; and 2) the control group (n = 274), in which a conventional manual or automatic injection syringe was used. The primary endpoint was the rate of AKI, defined as a serum creatinine (sCr) increase ≥0.3 mg/dL within 48 hours after CM exposure.

RESULTS

There were 412 of 550 (74.5%) participants with ST-segment elevation myocardial infarction (211 of 276 [76.4%] in the CVR group and 201 of 274 [73.3%] in the control group). The CM volume was lower in the CVR group (95 ± 30 mL vs 160 ± 23 mL; P < 0.001). Seven participants (1 in the CVR group and 6 in the control group) did not have postprocedural sCr values. AKI occurred in 44 of 275 (16%) participants in the CVR group and in 65 of 268 (24.3%) participants in the control group (relative risk: 0.66; 95% CI: 0.47-0.93; P = 0.018).

CONCLUSIONS

CM volume reduction obtained using the CM diversion system is effective for prevention of AKI in patients with ACS undergoing invasive procedures. (REnal Insufficiency Following Contrast MEDIA Administration TriaL IV [REMEDIALIV]: NCT04714736).

Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models

Background

Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive.

Objective

This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database.

Methods

We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT.

Results

Our multimodal model achieved a lead time of at least 12 hours ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT.

Conclusion

Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions

BACKGROUND AND AIMS

Predicting personalized risk for adverse events following percutaneous coronary intervention (PCI) remains critical in weighing treatment options, employing risk mitigation strategies, and enhancing shared decision-making. This study aimed to employ machine learning models using pre-procedural variables to accurately predict common post-PCI complications.

METHODS

A group of 66 adults underwent a semiquantitative survey assessing a preferred list of outcomes and model display. The machine learning cohort included 107 793 patients undergoing PCI procedures performed at 48 hospitals in Michigan between 1 April 2018 and 31 December 2021 in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) registry separated into training and validation cohorts. External validation was conducted in the Cardiac Care Outcomes Assessment Program database of 56 583 procedures in 33 hospitals in Washington.

RESULTS

Overall rate of in-hospital mortality was 1.85% (n = 1999), acute kidney injury 2.51% (n = 2519), new-onset dialysis 0.44% (n = 462), stroke 0.41% (n = 447), major bleeding 0.89% (n = 942), and transfusion 2.41% (n = 2592). The model demonstrated robust discrimination and calibration for mortality {area under the receiver-operating characteristic curve [AUC]: 0.930 [95% confidence interval (CI) 0.920-0.940]}, acute kidney injury [AUC: 0.893 (95% CI 0.883-0.903)], dialysis [AUC: 0.951 (95% CI 0.939-0.964)], stroke [AUC: 0.751 (95%CI 0.714-0.787)], transfusion [AUC: 0.917 (95% CI 0.907-0.925)], and major bleeding [AUC: 0.887 (95% CI 0.870-0.905)]. Similar discrimination was noted in the external validation population. Survey subjects preferred a comprehensive list of individually reported post-procedure outcomes.

CONCLUSIONS

Using common pre-procedural risk factors, the BMC2 machine learning models accurately predict post-PCI outcomes. Utilizing patient feedback, the BMC2 models employ a patient-centred tool to clearly display risks to patients and providers (https://shiny.bmc2.org/pci-prediction/). Enhanced risk prediction prior to PCI could help inform treatment selection and shared decision-making discussions.

Clinical and economic outcomes of assigning percutaneous coronary intervention patients to contrast-sparing strategies based on the predicted risk of contrast-induced acute kidney injury

OBJECTIVE

Contrast-sparing strategies have been developed for percutaneous coronary intervention (PCI) patients at increased risk of contrast-induced acute kidney injury (CI-AKI), and numerous CI-AKI risk prediction models have been created. However, the potential clinical and economic consequences of using predicted CI-AKI risk thresholds for assigning patients to contrast-sparing regimens have not been evaluated. We estimated the clinical and economic consequences of alternative CI-AKI risk thresholds for assigning Medicare PCI patients to contrast-sparing strategies.

METHODS

Medicare data were used to identify inpatient PCI from January 2017 to June 2021. A prediction model was developed to assign each patient a predicted probability of CI-AKI. Multivariable modeling was used to assign each patient two marginal predicted values for each of several clinical and economic outcomes based on (1) their underlying clinical and procedural characteristics plus their true CI-AKI status in the data and (2) their characteristics plus their counterfactual CI-AKI status. Specifically, CI-AKI patients above the predicted risk threshold for contrast-sparing were reassigned their no CI-AKI (counterfactual) outcomes. Expected event rates, resource use, and costs were estimated before and after those CI-AKI patients were reassigned their counterfactual outcomes. This entailed bootstrapped sampling of the full cohort.

RESULTS

Of the 542,813 patients in the study cohort, 5,802 (1.1%) had CI-AKI. The area under the receiver operating characteristic curve for the prediction model was 0.81. At a predicted risk threshold for CI-AKI of >2%, approximately 18.0% of PCI patients were assigned to contrast-sparing strategies, resulting in (/100,000 PCI patients) 121 fewer deaths, 58 fewer myocardial infarction readmissions, 4,303 fewer PCI hospital days, $11.3 million PCI cost savings, and $25.8 million total one-year cost savings, versus no contrast-sparing strategies.

LIMITATIONS

Claims data may not fully capture disease burden and are subject to inherent limitations such as coding inaccuracies. Further, the dataset used reflects only individuals with fee-for-service Medicare, and the results may not be generalizable to Medicare Advantage or other patient populations.

CONCLUSIONS

Assignment to contrast-sparing regimens at a predicted risk threshold close to the underlying incidence of CI-AKI is projected to result in significant clinical and economic benefits.

Predicting Contrast-induced Renal Complications

Chronic kidney disease is an independent risk factor for the development of coronary artery disease and overlaps with other risk factors such as hypertension and diabetes. Percutaneous coronary intervention is a cornerstone of therapy for coronary artery disease and requires contrast media, which can lead to renal injury. Identifying patients at risk for contrast-associated acute kidney injury (CA-AKI) is critical for preventing kidney damage, which is associated with both short- and long-term mortality. Determination of the potential risk for CA-AKI and a new need for dialysis using validated risk prediction tools identifies patients at high risk for this complication. Identification of patients at risk for renal injury after contrast exposure is the first critical step in prevention. Contrast media volume, age and sex of the patient, a history of chronic kidney disease and/or diabetes, clinical presentation, and hemodynamic and volume status are factors known to predict incident contrast-induced nephropathy. Recognition of at-risk patient subpopulations allows for targeted, efficient, and cost-effective strategies to reduce the risk of renal complications resulting from contrast media exposure.

Association between elevated CHA2DS2-VASC score and contrast-induced nephropathy among patients undergoing percutaneous coronary intervention: a systematic review and meta-analysis

BACKGROUND

Promising results with the CHA2DS2-VASc risk score (CVRS) have been reported for the prediction of contrast-induced nephropathy (CIN). The aim of this study is to consolidate all the data available and examine the association between elevated CVRS and the incidence of CIN in patients undergoing percutaneous coronary intervention (PCI).

METHODS

We systematically searched PubMed, Embase, and Scopus for abstracts and full-text articles from inception to May 2022. Studies were included if they evaluated the association between a high CVRS and the incidence of CIN in patients undergoing PCI. Data were integrated using the random-effects, generic inverse variance method of DerSimonian and Laird. Prospero registration: CRD42022334065.

RESULTS

Seven studies from 2016 to 2021 with a total of 7,401 patients were included. In patients undergoing PCI, a high CVRS (≥2: Odds ratio [OR]:2.98, 95% confidence interval [95% CI] 2.25-3.94, p < .01, I2 = 1%, ≥3: OR 4.46, 95% CI 2.27-8.78, p < .01, I2=56% and ≥4: OR:2.75, 95% CI 1.76-4.30, p < .01, I2 = 11%) was significantly associated with an increase incidence for CIN. Subgroup analyses were done in patients with acute coronary syndrome, and association with CIN remained statistically significant (≥2: OR 2.93, 95% CI 2.11-4.07, p < .01, I2=22%and ≥4: OR:2.24, 95% CI 1.36-3.69, p < .01, I2 = 0%,).

CONCLUSION

In patients undergoing PCI, an elevated CVRS is associated with an increased risk for CIN. More rigorous studies are needed to clarify this association and to identify strategies to reduce CIN.

Prediction of the development of contrast‑induced nephropathy following percutaneous coronary artery intervention by machine learning

Contrast-induced nephropathy (CIN) is associated with increased mortality and morbidity in patients with coronary artery disease undergoing elective percutaneous coronary intervention(PCI). We developed a machine learning-based risk stratification model to predict contrast-induced nephropathy after PCI. A study retrospectively enrolling 240 patients eligible for PCI from December 2017 to May 2020 was performed. CIN was defined as a rise in serum creatinine levels ≥0.5 mg/dL or ≥25% from baseline within 72 h after surgery. Eight machine learning methods were performed based on clinical variables. Shapley Additive exPlanation values were also used to interpret the best-performing prediction models. Development of CIN was found in 37 patients(16.5%) after PCI. There were 11 significant predictors of CIN, including uric acid, peripheral vascular disease, cystatin C, creatine kinase-MB, haemoglobin, N-terminal pro-brain natriuretic peptide, age, diabetes, systemic immune-inflammatory index, total protein, and low-density lipoprotein. Regarding the efficacy of the machine learning model that accurately predicted CIN, SVM exhibited the most outstanding AUC value of 0.784. The SHAP and radar plots were used to illustrate the positive and negative effects of the 11 features attributed to the SVM. Machine learning models have the potential to identify the risk of CIN for elective PCI patients.

Acute Kidney Injury Management Strategies Peri-Cardiovascular Interventions

In many countries, the aging population and the higher incidence of comorbid conditions have resulted in an ever-growing need for cardiac interventions. Acute kidney injury (AKI) is a common complication of these interventions, associated with higher mortalities, chronic or end-stage kidney disease, readmission rates, and hospital and post-discharge costs. The AKI pathophysiology includes contrast-associated AKI, hemodynamic changes, cardiorenal syndrome, and atheroembolism. Preventive measures include limiting contrast media dose, optimizing hemodynamic conditions, and limiting exposure to other nephrotoxins. This review article outlines the current state-of-art knowledge regarding AKI pathophysiology, risk factors, preventive measures, and management strategies in the peri-interventional period.

A Practical Approach to Preventing Contrast-Associated Renal Complications in the Catheterization Laboratory

Contrast media use is ubiquitous in the catheterization laboratory. Contrast-associated acute kidney injury (CA-AKI) is a key concern among patients undergoing coronary angiography and percutaneous coronary interventions. The risk of CA-AKI can be minimized by careful attention to hydration status and renal function-based contrast dosing in all patients. In patients with Stage IV chronic kidney disease, ultra low contrast procedure (contrast dose ≤ GFR) may be especially beneficial.

Key Considerations regarding the Renal Risks of Iodinated Contrast Media: The Nephrologist's Role

BACKGROUND

The administration of iodinated contrast medium during diagnostic and therapeutic procedures has always been associated with the fear of causing acute kidney injury (AKI) or an exacerbation of chronic kidney disease. This has led, on the one hand, to the deterrence, when possible, of the use of contrast medium (preferring other imaging methods with the risk of loss of diagnostic power), and on the other hand, to the trialling of multiple prophylaxis protocols in an attempt to reduce the risk of kidney injury.

SUMMARY

A literature review on contrast-induced (CI)-AKI risk mitigation strategies was performed, focussing on the recognition of individual risk factors and on the most recent evidence regarding prophylaxis.

KEY MESSAGES

Nephrologists can contribute significantly in the CI-AKI context, from the early stages of the decision-making process to stratifying patients by risk, individualising prophylaxis measures based on the risk profile, and ensuring appropriate evaluation of kidney function and damage post-procedure to improve care.

A Simplified Risk Score to Estimate the Risk of Contrast-Induced Nephropathy after Contrast Exposure

Introduction

Scores are available to predict the probability of contrast-induced nephropathy (CIN) after cardiac interventions, but not many scores are available for non-cardiac interventions and there are none for intravenous exposure to contrast. We designed this study to develop a simplified score to determine the probability of developing CIN in patients exposed to the parenteral contrast medium.

Methods

This was a prospective study of patients who received parenteral contrast. Of 1300 patients, the first 1000 comprised the derivation cohort and the next 300 comprised the validation cohort. The patient variables in the development cohort were studied using univariate analysis. Statistically significant individual variables were used as independent variables, and CIN was used as the dependent variable in the final multivariate logistic regression model. Then, the risk score was obtained and validated.

Results

The incidence of CIN was 3.8%. The risk factors, namely the presence of diabetes mellitus, e-GFR, and route and volume of contrast material were significantly associated with the risk of CIN (P < 0.05). The developed risk score had a sensitivity of 90.4% and specificity of 98.78%. The overall accuracy was 97.8%. The values of AUC of ROC in the development and validation datasets were high. This indicated that the predicted CIN risk score correlated well with the calibration and discriminative characteristics.

Conclusions

The route and volume of contrast administered, low e-GFR, and diabetes mellitus were the significant risk factors. The developed risk score exhibited very good sensitivity and specificity and excellent accuracy in predicting the probability of CIN.

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

BACKGROUND

A 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.

OBJECTIVES

The 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.

METHODS

The 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.

RESULTS

The 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.

CONCLUSIONS

The 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).

Contrast Media Volume Control and Acute Kidney Injury in Acute Coronary Syndrome: Rationale and Design of the REMEDIAL IV Trial

Background

Although the pathogenesis of acute kidney injury (AKI) in patients with acute coronary syndrome (ACS) undergoing invasive treatment is multifactorial, the role of iodinated contrast media (CM) has been well established. The DyeVert system (Osprey Medical) is designed to reduce the CM volume during invasive coronary procedures while maintaining fluoroscopic image quality.

Objective

The aim of the Renal Insufficiency Following Contrast Media Administration Trial IV (REMEDIAL IV) is to test whether the use of the DyeVert system is effective in reducing contrast-associated acute kidney injury (CA-AKI) rate in patients with ACS undergoing urgent invasive procedures.

Trial Design

Patients with ACS treated by urgent invasive approach will be enrolled. Participants will be randomly assigned into one of the following groups: (1) DyeVert group and (2) control group. In participants enrolled in the DyeVert group, CM injection will be handled by the DyeVert system. On the contrary, in the control group, CM injection will be performed by a conventional manual or automatic injection syringe. In all cases, iobitridol (a low-osmolar, nonionic CM) will be administered. Participants will receive intravenous 0.9% sodium chloride as soon as moved to the catheterization laboratory. The primary end points are CM volume administration and CA-AKI rate (ie, an increase in serum creatinine concentration of ≥0.3 mg/dL within 48 hours after CM exposure). A sample size of at least 522 randomized participants (261 in each group) is needed to demonstrate an 8.5% difference in the CA-AKI rate between the groups (that is, from 19% in the control group to 10.5% in the DyeVert group), with a 2-sided 95% confidence interval and 80% power (P < .05).

Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis

Importance

Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.

Objective

To systematically review published AKI prediction models across all clinical subsettings.

Data Sources

MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.

Study Selection

All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.

Data Extraction and Synthesis

Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.

Main Outcomes and Measures

C statistic was used to measure the discrimination of prediction models.

Results

Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.

Conclusions and Relevance

In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge

BACKGROUND

Patient-reported outcomes (PROs) can be obtained outside hospitals and are of great significance for evaluation of patients with chronic heart failure (CHF). The aim of this study was to establish a prediction model using PROs for out-of-hospital patients.

METHODS

CHF-PRO were collected in 941 patients with CHF from a prospective cohort. Primary endpoints were all-cause mortality, HF hospitalization, and major adverse cardiovascular events (MACEs). To establish prognosis models during the two years follow-up, six machine learning methods were used, including logistic regression, random forest classifier, extreme gradient boosting (XGBoost), light gradient boosting machine, naive bayes, and multilayer perceptron. Models were established in four steps, namely, using general information as predictors, using four domains of CHF-PRO, using both of them and adjusting the parameters. The discrimination and calibration were then estimated. Further analyze were performed for the best model. The top prediction variables were further assessed. The Shapley additive explanations (SHAP) method was used to explain black boxes of the models. Moreover, a self-made web-based risk calculator was established to facilitate the clinical application.

RESULTS

CHF-PRO showed strong prediction value and improved the performance of the models. Among the approaches, XGBoost of the parameter adjustment model had the highest prediction performance with an area under the curve of 0.754 (95% CI: 0.737 to 0.761) for death, 0.718 (95% CI: 0.717 to 0.721) for HF rehospitalization and 0.670 (95% CI: 0.595 to 0.710) for MACEs. The four domains of CHF-PRO, especially the physical domain, showed the most significant impact on the prediction of outcomes.

CONCLUSION

CHF-PRO showed strong prediction value in the models. The XGBoost models using variables based on CHF-PRO and the patient's general information provide prognostic assessment for patients with CHF. The self-made web-based risk calculator can be conveniently used to predict the prognosis for patients after discharge.

CLINICAL TRIAL REGISTRATION

URL: http://www.chictr.org.cn/index.aspx ; Unique identifier: ChiCTR2100043337.

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 risk prediction model for contrast-induced nephropathy associated with gadolinium-based contrast agents

OBJECTIVE

This is the first study to explore the risk factors for nephropathy caused by gadolinium-based contrast agents and establish a prediction model to identify high-risk patients.

METHODS

A total of 1404 patients who received gadolinium-based contrast agents in our hospital were included. The participants were randomly assigned in a 7:3 ratio to the modeling and validation groups. The modeling group was divided into a contrast-induced nephropathy group and a non-contrast-induced nephropathy group. The clinical characteristics before the use of contrast agents were compared between the two groups. The risk factors for contrast-induced nephropathy were analyzed by logistic regression. A nomogram that could predict the incidence of contrast-induced nephropathy was plotted. The validation group was used to verify the predictive model.

RESULTS

The incidence of contrast-induced nephropathy caused by gadolinium-based contrast agents was 3.92% (55/1404). The logistic stepwise regression analysis showed that sex, systolic pressure (SBP), absolute neutrophil count, albumin, fasting blood glucose level, and furosemide use were significant predictors of contrast-induced nephropathy caused by gadolinium-based contrast agents. The above predictors were then included in the nomogram construction. The area under the receiver operating characteristic (ROC) curve was 0.82 (p < 0.001). The specificity and sensitivity corresponding to the optimal cutoff point (0.039) based on the area under the ROC curve were 71.9% and 80.5%, respectively.

CONCLUSION

Sex, SBP, absolute neutrophil count, albumin, fasting blood glucose levels, and furosemide use are significant predictors of contrast-induced nephropathy caused by gadolinium-based contrast agents. Therefore, the incidence of contrast-induced nephropathy may be estimated by the prediction model established in this study before the use of contrast agents.

Fibrinogen-to-Albumin Ratio Predicts Postcontrast Acute Kidney Injury in Patients with Non-ST Elevation Acute Coronary Syndrome after Implantation of Drug-Eluting Stents

Background

Postcontrast acute kidney injury (PC-AKI) is an adverse reaction to iodinated contrast agents. In this study, we investigated the use of fibrinogen-to-albumin ratio (FAR) as a novel inflammatory marker to track the development and progression of PC-AKI in patients with non-ST elevation acute coronary syndrome (NSTE-ACS) after the implantation of drug-eluting stents (DESs).

Methods

A total of 872 patients with NSTE-ACS were enrolled in this study. PC-AKI was identified when serum creatinine (SCr) levels increased >26.5 mol/L (0.3 mg/dL) or was 1.5 times the baseline level within 48-72 h of exposure to an iodinated contrast agent. The effects of different variables on PC-AKI were evaluated using univariate regression analysis. Multivariate logistic regression analysis was used to determine the independent predictors of PC-AKI. The predictive value of FAR was assessed by estimating the area under the receiver operating characteristic (ROC) curve.

Results

In total, 114 (13.1%) patients developed PC-AKI. The patients with PC-AKI had lower albumin levels (40.5 ± 3.4 vs. 39.0 ± 3.5, P < 0.001), higher fibrinogen levels (3.7 ± 0.6 vs. 4.1 ± 0.5, P < 0.001), and higher FAR levels (9.2 ± 1.7 vs. 10.5 ± 1.7, P < 0.001) than those with non-PC-AKI. There were no significant differences in the preoperative SCr levels between the two groups. After adjusting for confounding factors, FAR was found to be an independent predictor of PC-AKI (OR = 1.478, 95% CI = 1.298-1.684, P < 0.001). ROC analysis revealed that for PC-AKI prediction, the area under the curve for FAR was 0.702. The optimum cut-off value of FAR was 10.0, with a sensitivity of 64.9% and a specificity of 69.8%. Moreover, FAR had a higher predictive value for PC-AKI than the Mehran score (0.702 vs. 0.645).

Conclusion

Our study showed that elevated preoperative FAR was closely associated with the development of PC-AKI in patients with NSTE-ACS after implantation of DESs. Therefore, it may be worth monitoring FAR as a guide for using preventive measures to avoid the development of PC-AKI.

Management of Patients With Kidney Disease in Need of Cardiovascular Catheterization: A Scientific Workshop Cosponsored by the National Kidney Foundation and the Society for Cardiovascular Angiography and Interventions

Patients with chronic kidney disease (CKD) are at an increased risk of developing cardiovascular disease (CVD), whereas those with established CVD are at risk of incident or progressive CKD. Compared with individuals with normal or near normal kidney function, there are fewer data to guide the management of patients with CVD and CKD. As a joint effort between the National Kidney Foundation and the Society for Cardiovascular Angiography and Interventions, a workshop and subsequent review of the published literature was held. The present document summarizes the best practice recommendations of the working group and highlights areas for further investigation.

Trends and outcomes of percutaneous coronary intervention during the COVID-19 pandemic in Michigan

Background

The COVID-19 pandemic has severely impacted healthcare delivery and patient outcomes globally.

Aims

We aimed to evaluate the influence of the COVID-19 pandemic on the temporal trends and outcomes of patients undergoing percutaneous coronary intervention (PCI) in Michigan.

Methods

We compared all patients undergoing PCI in the BMC2 Registry between March and December 2020 (“pandemic cohort”) with those undergoing PCI between March and December 2019 (“pre-pandemic cohort”). A risk-adjusted analysis of in-hospital outcomes was performed between the pre-pandemic and pandemic cohort. A subgroup analysis was performed comparing COVID-19 positive vs. negative patients during the pandemic.

Results

There was a 15.2% reduction in overall PCI volume from the pre-pandemic (n = 25,737) to the pandemic cohort (n = 21,822), which was more pronounced for stable angina and non-ST-elevation acute coronary syndromes (ACS) presentations, and between February and May 2020. Patients in the two cohorts had similar clinical and procedural characteristics. Monthly mortality rates for primary PCI were generally higher in the pandemic period. There were no significant system delays in care between the cohorts. Risk-adjusted mortality was higher in the pandemic cohort (aOR 1.26, 95% CI 1.07–1.47, p = 0.005), a finding that was only partially explained by worse outcomes in COVID-19 patients and was more pronounced in subjects with ACS. During the pandemic, COVID-19 positive patients suffered higher risk-adjusted mortality (aOR 5.69, 95% CI 2.54–12.74, p<0.001) compared with COVID negative patients.

Conclusions

During the COVID-19 pandemic, we observed a reduction in PCI volumes and higher risk-adjusted mortality. COVID-19 positive patients experienced significantly worse outcomes.

When Prevention is Truly Better than Cure: Contrast-Associated Acute Kidney Injury in Percutaneous Coronary Intervention

Contrast-associated acute kidney injury (CA-AKI) is a fairly frequent complication of cardiovascular angiography and percutaneous coronary intervention (PCI). The risk is significantly higher in patients with advanced chronic kidney disease (CKD). Prevention is the only option for avoiding the significant morbidity and mortality associated with CA-AKI. This review provides a concise and clinically directed appraisal of the latest pre-procedural and peri-procedural strategies to minimize the risk of CA-AKI in all patients undergoing PCI. By broadly implementing these evidence-based care bundles, we can dramatically improve outcomes in this vulnerable patient population.

Optimizing the Outcomes of Percutaneous Coronary Intervention in Patients with Chronic Kidney Disease

Percutaneous coronary intervention (PCI) is one of the most common procedures performed in medicine. However, its net benefit among patients with chronic kidney disease (CKD) is less well established than in the general population. The prevalence of patients suffering from both CAD and CKD is high, and is likely to increase in the coming years. Planning the adequate management of this group of patients is crucial to improve their outcome after PCI. This starts with proper preparation before the procedure, the use of all available means to reduce contrast during the procedure, and the implementation of modern strategies such as radial access and drug-eluting stents. At the end of the procedure, personalized antithrombotic therapy for the patient's specific characteristics is advisable to account for the elevated ischemic and bleeding risk of these patients.

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.

Predictive value of combining the level of fibrinogen and CHA2DS2-VASC Score for contrast-induced acute kidney injury in patients with acute coronary syndromes undergoing percutaneous coronary intervention

OBJECTIVE

The present study aimed to investigate the value of preprocedural fibrinogen (FIB) combined with CHA2DS2-VASC scores in the risk prediction of contrast-induced acute kidney injury (CI-AKI) after percutaneous coronary intervention (PCI) in patients with acute coronary syndromes (ACS).

METHOD

A total of 934 patients (mean age 63.9 ± 11.5 years, and 32.1% female), who were admitted to our hospital for ACS and underwent PCI, were retrospectively enrolled. The patients were divided into two groups: non-CI-AKI group (n = 787) and CI-AKI group (n = 147). Contrast-induced acute kidney injury was defined as an increase of ≥ 0.5 mg/dL or ≥ 25% serum creatinine within 48-72 h after PCI. Spearman correlation analysis was used to determine the relationship between FIB and CHA2DS2-VASC scores.

RESULTS

Patients with high baseline FIB levels and high CHA2DS2-VASC scores had higher CI-AKI incidence. On spearman correlation analysis, FIB and CHA2DS2-VASC scores were positively correlated (R = 0.236, P < 0.001). The ROC statistical analysis showed that the combination had 63.3% sensitivity with 72.6% specificity for the development of CI-AKI (area under the curve: 0.727, 95% CI 0.697-0.755, P < 0.001). A total of 934 ACS patients were divided into low-risk group (404 cases), medium-risk group (383 cases) and high-risk group (147 cases) according to the cut-off values of FIB and CHA2DS2-VASC scores. The incidence of CI-AKI was higher in the high-risk group than in the low-risk and medium-risk groups (Log-rank χ2 = 104.505, 56.647. P < 0.001). Multivariate analysis revealed that albumin (OR = 0.913, 95% CI 0.867-0.962), FIB (OR = 1.451, 95% CI 1.185-1.77), CHA2DS2-VASC score (OR = 1.271, 95% CI 1.504-1.78) were the independent risk factors of CI-AKI (p < 0.05).

CONCLUSION

The preprocedural fibrinogen combined with CHA2DS2-VASC score is independently associated with the risk of CI-AKI in ACS patients treated by PCI.

Development and Validation of a Predictive Model for Chronic Kidney Disease After Percutaneous Coronary Intervention in Chinese

AIM

There is no model for predicting the outcomes for coronary heart disease (CHD) patients with chronic kidney disease (CKD) after percutaneous coronary intervention (PCI). To develop and validate a model to predict major adverse cardiovascular events (MACEs) in patients with comorbid CKD and CHD undergoing PCI.

METHODS

We enrolled 1714 consecutive CKD patients who underwent PCI from January 1, 2008 to December 31, 2017. In the development cohort, we used least absolute shrinkage and selection operator regression for data dimension reduction and feature selection. We used multivariable logistic regression analysis to develop the prediction model. Finally, we used an independent cohort to validate the model. The performance of the prediction model was evaluated with respect to discrimination, calibration, and clinical usefulness.

RESULTS

The predictors included a positive family history of CHD, history of revascularization, ST segment changes, anemia, hyponatremia, transradial intervention, the number of diseased vessels, dose of contrast media >200 ml, and coronary collateral circulation. In the validation cohort, the model showed good discrimination (area under the receiver operating characteristic curve, 0.612; 95% confidence interval: 0.560, 0.664) and good calibration (Hosmer-Lemeshow test, P  =  0.444). Decision curve analysis demonstrated that the model was clinically useful.

CONCLUSIONS

We created a nomogram that predicts MACEs after PCI in CHD patients with CKD and may help improve the screening and treatment outcomes.

Machine learning prediction model of acute kidney injury after percutaneous coronary intervention

Acute kidney injury (AKI) after percutaneous coronary intervention (PCI) is associated with a significant risk of morbidity and mortality. The traditional risk model provided by the National Cardiovascular Data Registry (NCDR) is useful for predicting the preprocedural risk of AKI, although the scoring system requires a number of clinical contents. We sought to examine whether machine learning (ML) techniques could predict AKI with fewer NCDR-AKI risk model variables within a comparable PCI database in Japan. We evaluated 19,222 consecutive patients undergoing PCI between 2008 and 2019 in a Japanese multicenter registry. AKI was defined as an absolute or a relative increase in serum creatinine of 0.3 mg/dL or 50%. The data were split into training (N = 16,644; 2008-2017) and testing datasets (N = 2578; 2017-2019). The area under the curve (AUC) was calculated using the light gradient boosting model (GBM) with selected variables by Lasso and SHapley Additive exPlanations (SHAP) methods among 12 traditional variables, excluding the use of an intra-aortic balloon pump, since its use was considered operator-dependent. The incidence of AKI was 9.4% in the cohort. Lasso and SHAP methods demonstrated that seven variables (age, eGFR, preprocedural hemoglobin, ST-elevation myocardial infarction, non-ST-elevation myocardial infarction/unstable angina, heart failure symptoms, and cardiogenic shock) were pertinent. AUC calculated by the light GBM with seven variables had a performance similar to that of the conventional logistic regression prediction model that included 12 variables (light GBM, AUC [training/testing datasets]: 0.779/0.772; logistic regression, AUC [training/testing datasets]: 0.797/0.755). The AKI risk model after PCI using ML enabled adequate risk quantification with fewer variables. ML techniques may aid in enhancing the international use of validated risk models.

A Novel Prediction Model of Acute Kidney Injury Based on Combined Blood Variables in STEMI

BACKGROUND

Development of acute kidney injury (AKI) is associated with poor prognosis in patients with ST-segment elevation myocardial infarction (STEMI).

OBJECTIVE

This study sought to investigate whether a combination of pre-procedural blood tests could predict the incidence of AKI in patients with STEMI.

METHODS

A total of 908 consecutive Japanese patients with STEMI who underwent primary percutaneous coronary intervention within 48 hours of symptom onset were recruited and divided into derivation (n = 617) and validation (n = 291) cohorts. A risk score model was created based on a combination of parameters assessed on routine blood tests on admission.

RESULTS

In the derivation cohort, multivariate analysis showed that the following 4 variables were significantly associated with AKI: blood sugar ≥200 mg/dL (odds ratio [OR]: 2.07), high-sensitivity troponin I >1.6 ng/mL (upper limit of normal ×50) (OR: 2.43), albumin ≤3.5 mg/dL (OR: 2.85), and estimated glomerular filtration rate <45 mL/min/1.73 m² (OR: 2.64). Zero to 4 points were given according to the number of those factors. Incremental risk scores were significantly associated with a higher incidence of AKI in both cohorts (P < 0.001). Receiver-operating characteristic curve analysis of risk models showed adequate discrimination between patients with and without AKI (derivation cohort, area under the curve: 0.754; 95% confidence interval: 0.733-0.846; validation cohort, area under the curve: 0.754; 95% confidence interval: 0.644-0.839).

CONCLUSIONS

Our novel laboratory-based model might be useful for early prediction of the post-procedural risk of AKI in patients with STEMI.

A contemporary simple risk score for prediction of contrast-associated acute kidney injury after percutaneous coronary intervention: derivation and validation from an observational registry

BACKGROUND

Contrast-associated acute kidney injury can occur after percutaneous coronary intervention (PCI). Prediction of the contrast-associated acute kidney injury risk is important for a tailored prevention and mitigation strategy. We sought to develop a simple risk score to estimate contrast-associated acute kidney injury risk based on a large contemporary PCI cohort.

METHODS

Consecutive patients undergoing PCI at a large tertiary care centre between Jan 1, 2012, and Dec 31, 2020, with available creatinine measurements both before and within 48 h after the procedure, were included; only patients on chronic dialysis were excluded. Patients treated between 2012 and 2017 comprised the derivation cohort and those treated between 2018 and 2020 formed the validation cohort. The primary endpoint was contrast-associated acute kidney injury, defined according to the Acute Kidney Injury Network. Independent predictors of contrast-associated acute kidney injury were derived from multivariate logistic regression analysis. Model 1 included only pre-procedural variables, whereas Model 2 also included procedural variables. A weighted integer score based on the effect estimate of each independent variable was used to calculate the final risk score for each patient. The impact of contrast-associated acute kidney injury on 1-year deaths was also evaluated.

FINDINGS

32 378 PCI procedures were performed and screened for inclusion in the present analysis. After the exclusion of patients without paired creatinine measurements, patients on chronic dialysis, and multiple procedures, 14 616 patients were included in the derivation cohort (mean age 66·2 years, 29·2% female) and 5606 were included in the validation cohort (mean age 67·0 years, 26·4% female). Contrast-associated acute kidney injury occurred in 860 (4·3%) patients. Independent predictors of contrast-associated acute kidney injury included in Model 1 were: clinical presentation, estimated glomerular filtration rate, left ventricular ejection fraction, diabetes, haemoglobin, basal glucose, congestive heart failure, and age. Additional independent predictors in Model 2 were: contrast volume, peri-procedural bleeding, no flow or slow flow post procedure, and complex PCI anatomy. The occurrence of contrast-associated acute kidney injury in the derivation cohort increased gradually from the lowest to the highest of the four risk score groups in both models (2·3% to 34·9% in Model 1, and 2·0% to 38·8% in Model 2). Inclusion of procedural variables in the model only slightly improved the discrimination of the risk score (C-statistic in the derivation cohort: 0·72 for Model 1 and 0·74 for model 2; in the validation cohort: 0·84 for Model 1 and 0·86 for Model 2). The risk of 1-year deaths significantly increased in patients with contrast-associated acute kidney injury (10·2% vs 2·5%; adjusted hazard ratio 1·76, 95% CI 1·31-2·36; p=0·0002), which was mainly due to excess 30-day deaths.

INTERPRETATION

A contemporary simple risk score based on readily available variables from patients undergoing PCI can accurately discriminate the risk of contrast-associated acute kidney injury, the occurrence of which is strongly associated with subsequent death.

FUNDING

None.

A Predictive Model Based on a New CI-AKI Definition to Predict Contrast Induced Nephropathy in Patients With Coronary Artery Disease With Relatively Normal Renal Function

Background: Contrast induced nephropathy (CIN) is a common complication in patients receiving intravascular contrast media. In 2020, the American College of Radiology and the National Kidney Foundation issued a new contrast induced acute kidney injury (CI-AKI) criteria. Therefore, we aimed to explore the potential risk factors for CIN under the new criteria, and develop a predictive model for patients with coronary artery disease (CAD) with relatively normal renal function (NRF).

Methods: Patients undergoing coronary angiography or percutaneous coronary intervention at Zhongshan Hospital, Fudan University between May 2019 and April 2020 were consecutively enrolled. Eligible candidates were selected for statistical analysis. Univariate and multivariate logistic regression analyses were used to identify the predictive factors. A stepwise method and a machine learning (ML) method were used to construct a model based on the Akaike information criterion. The performance of our model was evaluated using the area under the receiver operating characteristic curves (AUC) and calibration curves. The model was further simplified into a risk score.

Results: A total of 2,009 patients with complete information were included in the final statistical analysis. The results showed that the incidence of CIN was 3.2 and 1.2% under the old and new criteria, respectively. Three independent predictors were identified: baseline uric acid level, creatine kinase-MB level, and log (N-terminal pro-brain natriuretic peptide) level. Our stepwise model had an AUC of 0.816, which was higher than that of the ML model (AUC = 0.668, P = 0.09). The model also achieved accurate predictions regarding calibration. A risk score was then developed, and patients were divided into two risk groups: low risk (total score < 10) and high risk (total score ≥ 10).

Conclusions: In this study, we first identified important predictors of CIN in patients with CAD with NRF. We then developed the first CI-AKI model on the basis of the new criteria, which exhibited accurate predictive performance. The simplified risk score may be useful in clinical practice to identify high-risk patients.

Evaluation of Radiographic Contrast-Induced Nephropathy by Functional Diffusion Weighted Imaging

Contrast-induced nephropathy (CIN) resembles an important complication of radiographic contrast medium (XCM) displayed by a rise in creatinine levels 48-72 h after XCM administration. The purpose of the current study was to evaluate microstructural renal changes due to CIN in high-risk patients by diffusion weighted (DWI) and diffusion tensor imaging (DTI). Fifteen patients (five CIN and ten non-CIN) scheduled for cardiological intervention were included in the study. All patients were investigated pre- and post-intervention on a clinical 3T scanner. After anatomical imaging, renal DWI was performed by a paracoronal echo-planar-imaging sequence. Renal clinical routine serum parameters and advanced urinary injury markers were determined to monitor renal function. We observed a drop in cortical and medullar apparent diffusion coefficient (ADC) and fractional anisotropy (FA) before and after XCM administration in the CIN group. In contrast, the non-CIN group differed only in medullary ADC. The decrease of ADC and FA was apparent even before serum parameters of the kidney changed. In conclusion, DWI/DTI may be a useful tool for monitoring high-risk CIN patients as part of multi-modality based clinical protocol. Further studies, including advanced analysis of the diffusion signal, may improve the identification of patients at risk for CIN.

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

BACKGROUND

Reliable preprocedural risk scores for the prediction of Contrast-Induced Acute Kidney Injury (CI-AKI) following Percutaneous Coronary Intervention (pPCI) in patients with ST-elevation myocardial infarction (STEMI) are lacking. Aim of this study was to derive and validate a preprocedural Risk Score in this setting.

METHODS

Two prospectively enrolled patient cohorts were used for derivation and validation (n = 3,736). CI-AKI was defined as creatinine increase ≥0.5 mg/dl <72 h postpPCI. Odds ratios from multivariable logistic regression model were converted to an integer, whose sum represented the Risk Score.

RESULTS

Independent CI-AKI predictors were: diabetes, Killip class II-III (2 points each), age > 75 years, anterior MI (3 points), Killip class IV (4 points), estimated GFR < 60 ml/min/1.73m² (5 points). The Risk Score c-statistic was 0.84 in both cohorts. Compared with patients with Risk Score ≤ 4, the relative risks of CI-AKI among patients scoring 5-9 were 6.2 (derivation cohort) and 7.1 (validation cohort); among patients scoring ≥10, 19.8, and 21.4, respectively.

CONCLUSIONS

Among STEMI patients, a simple preprocedural Risk Score accurately and reproducibly predicted the risk of CI-AKI, identifying ¼ of patients with a seven-fold risk and 1/10 of patients with a 20-fold risk. This knowledge may help tailored strategies, including delaying revascularization of nonculprit vessels in patients at high risk of CI-AKI.

Tailored Versus Standard Hydration to Prevent Acute Kidney Injury After Percutaneous Coronary Intervention: Network Meta-Analysis

Background

Contrast‐induced acute kidney injury (CI‐AKI) is a serious complication after percutaneous coronary intervention. The mainstay of CI‐AKI prevention is represented by intravenous hydration. Tailoring infusion rate to patient volume status has emerged as advantageous over fixed infusion‐rate hydration strategies.

Methods and Results

A systematic review and network meta‐analysis with a frequentist approach were conducted. A total of 8 randomized controlled trials comprising 2312 patients comparing fixed versus tailored hydration strategies to prevent CI‐AKI after percutaneous coronary intervention were included in the final analysis. Tailored hydration strategies included urine flow rate–guided, central venous pressure–guided, left ventricular end‐diastolic pressure–guided, and bioimpedance vector analysis–guided hydration. Primary endpoint was CI‐AKI incidence. Safety endpoint was incidence of pulmonary edema. Urine flow rate–guided and central venous pressure–guided hydration were associated with a lower incidence of CI‐AKI compared with fixed‐rate hydration (odds ratio [OR], 0.32 [95% CI, 0.19–0.54] and OR, 0.45 [95% CI, 0.21–0.97]). No significant difference in pulmonary edema incidence was observed between the different hydration strategies. P score analysis showed that urine flow rate–guided hydration is advantageous in terms of both CI‐AKI prevention and pulmonary edema incidence when compared with other approaches.

Conclusions

Currently available hydration strategies tailored on patients' volume status appear to offer an advantage over guideline‐supported fixed‐rate hydration for CI‐AKI prevention after percutaneous coronary intervention. Current evidence suggests that urine flow rate–guided hydration as the most convenient strategy in terms of effectiveness and safety.

Urinary Dickkopf-3 and Contrast-Associated Kidney Damage

BACKGROUND

Administration of iodinated contrast medium (CM) during invasive cardiovascular procedures may be associated with impairment of kidney function.

OBJECTIVES

Urinary dickkopf-3 (DKK3), a stress-induced renal tubular epithelium-derived glycoprotein, has been identified as a biomarker predicting both acute kidney injury (AKI) and persistent kidney dysfunction.

METHODS

Urinary DKK3/creatinine ratio (uDKK3/uCr), urine and serum neutrophil gelatinase-associated lipocalin (uNGAL, sNGAL) and serum cystatin C (sCyC) were assessed in 458 patients with chronic kidney disease scheduled for invasive cardiovascular procedures requiring CM administration with universal adoption of nephroprotective interventions. Contrast-associated AKI (CA-AKI) was defined as serum creatinine increase ≥0.3 mg/dl at 48 h after CM administration. Persistent kidney dysfunction was defined as persistent estimated glomerular filtration rate reduction ≥25% at 1 month compared with baseline.

RESULTS

CA-AKI occurred in 64 or the 458 patients (14%), and baseline uDKK3/uCr ≥491 pg/mg was the best threshold for its prediction. Net reclassification improvement (NRI) was significantly increased by adding baseline uDKK3/uCr to the Mehran, Gurm, and National Cardiovascular Data Registry (NCDR) scores (all p < 0.05), and the same applied to integrated discrimination improvement (IDI) when adding uDKK3/uCr to the Gurm and NCDR scores (p < 0.001). Persistent kidney dysfunction occurred in 57 of the 458 patients (12%) and baseline uDKK3/uCr ≥322 pg/mg appeared as the best threshold for its prediction. Adding baseline uDKK3/uCr to the Mehran, Gurm, and NCDR scores significantly increased IDI and NRI (all p < 0.001).

CONCLUSIONS

Baseline uDKK3/uCr seems to be a reliable marker for improving the identification of patients with chronic kidney disease undergoing invasive coronary and peripheral procedures at risk for AKI and persistent kidney dysfunction.

SCAI expert consensus update on best practices in the cardiac catheterization laboratory: This statement was endorsed by the American College of Cardiology (ACC), the American Heart Association (AHA), and the Heart Rhythm Society (HRS) in April 2021

The current document commissioned by the Society for Cardiovascular Angiography and Interventions (SCAI) and endorsed by the American College of Cardiology, the American Heart Association, and Heart Rhythm Society represents a comprehensive update to the 2012 and 2016 consensus documents on patient-centered best practices in the cardiac catheterization laboratory. Comprising updates to staffing and credentialing, as well as evidence-based updates to the pre-, intra-, and post-procedural logistics, clinical standards and patient flow, the document also includes an expanded section on CCL governance, administration, and approach to quality metrics. This update also acknowledges the collaboration with various specialties, including discussion of the heart team approach to management, and working with electrophysiology colleagues in particular. It is hoped that this document will be utilized by hospitals, health systems, as well as regulatory bodies involved in assuring and maintaining quality, safety, efficiency, and cost-effectiveness of patient throughput in this high volume area.

[Contrast-induced acute kidney injury]

The nephrotoxicity of iodinated contrast agent/media is defined by acute renal failure occurring within 48 to 72 hours after injection of iodized contrast product, in the absence of other etiology. The risk factors for contrast agent renal injury must systematically be sought before the exam. The presence of risk factors, including the existence of a renal failure defined by a creatinine clearance (eGFR) of less than 60 mL/min/1.73 m², requires to take prevention measures including hydration. If eGFR is less than 30 mL/min/1.73 m², the advice of a nephrologist is necessary.

The DISCO study-Does Interventionalists' Sex impact Coronary Outcomes?

OBJECTIVES

To examine the association of operator sex with appropriateness and outcomes of percutaneous coronary intervention (PCI).

BACKGROUND

Recent studies suggest that physician sex may impact outcomes for specific patient cohorts. There are no data evaluating the impact of operator sex on PCI outcomes.

METHODS

We studied the impact of operator sex on PCI outcome and appropriateness among all patients undergoing PCI between January 2010 and December 2017 at 48 non-federal hospitals in Michigan. We used logistic regression models to adjust for baseline risk among patients treated by male versus female operators in the primary analysis.

RESULTS

During this time, 18 female interventionalists and 385 male interventionalists had performed at least one PCI. Female interventionalists performed 6362 (2.7%) of 239,420 cases. There were no differences in the odds of mortality (1.48% vs. 1.56%, adjusted OR [aOR] 1.138, 95% CI: 0.891-1.452), acute kidney injury (3.42% vs. 3.28%, aOR 1.027, 95% CI: 0.819-1.288), transfusion (2.59% vs. 2.85%, aOR 1.168, 95% CI: 0.980-1.390) or major bleeding (0.95% vs. 1.07%, aOR 1.083, 95% CI: 0.825-1.420) between patients treated by female versus male interventionalist. While the absolute differences were small, PCIs performed by female interventional cardiologists were more frequently rated as appropriate (86.64% vs. 84.45%, p-value <0.0001). Female interventional cardiologists more frequently prescribed guideline-directed medical therapy.

CONCLUSIONS

We found no significant differences in risk-adjusted in-hospital outcomes between PCIs performed by female versus male interventional cardiologists in Michigan. Female interventional cardiologists more frequently performed PCI rated as appropriate and had a higher likelihood of prescribing guideline-directed medical therapy.

Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics

Objectives

To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma.

Methods

From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis.

Results

Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set.

Conclusions

The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.

Left Ventricular End-Diastolic Pressure Versus Urine Flow Rate-Guided Hydration in Preventing Contrast-Associated Acute Kidney Injury

OBJECTIVES

This study compared left ventricular end-diastolic pressure (LVEDP)-guided and urine flow rate (UFR)-guided hydration.

BACKGROUND

Tailored hydration regimens improve the prevention of contrast-associated acute kidney injury (CA-AKI).

METHODS

Between July 15, 2015, and June 6, 2019, patients at high risk for CA-AKI scheduled for coronary and peripheral procedures were randomized to 2 groups: 1) normal saline infusion rate adjusted according to the LVEDP (LVEDP-guided group); and 2) hydration controlled by the RenalGuard System in order to reach UFR ≥300 ml/h (UFR-guided group). The primary endpoint was the composite of CA-AKI (i.e., serum creatinine increase ≥25% or ≥0.5 mg/dl at 48 h) and acute pulmonary edema (PE). Major adverse events (all-cause death, renal failure requiring dialysis, PE, and sustained kidney injury) at 1 month were assessed.

RESULTS

The primary endpoint occurred in 20 of 351 (5.7%) patients in the UFR-guided group and in 36 of 351 (10.3%) patients in the LVEDP-guided group (relative risk [RR]: 0.560; 95% confidence interval [CI]: 0.390 to 0.790; p = 0.036). CA-AKI and PE rates in the UFR-guided group and LVEDP-guided group were 5.7% and 10.0% (RR: 0.570; 95% CI: 0.300 to 0.960; p = 0.048), and, respectively, 0.3% and 2.0% (RR: 0.070; 95% CI: 0.020 to 1.160; p = 0.069). Three patients in the UFR-guided group experienced complications related to the Foley catheter. Hypokalemia rate was 6.2% in the UFR-guided group and 2.3% in the LVEDP-guided group (p = 0.013). The 1-month major adverse events rate was 7.1% in the UFR-guided group and 12.0% in the LVEDP-guided group (p = 0.030).

CONCLUSIONS

The study demonstrates that UFR-guided hydration is superior to LVEDP-guided hydration to prevent the composite of CA-AKI and PE.

Measuring the Variation in the Prevention and Treatment of CI-AKI Among Interventional Cardiologists

Contrast-induced acute kidney injury (CI-AKI) occurs in up to 10% of cardiac catheterizations and coronary interventions, resulting in increased morbidity, mortality, and cost. One main reason for these complications and costs is under-recognition of CI-AKI risk and under-treatment of patients with impaired renal status. 157 interventional cardiologists each cared for three simulated patients with common conditions requiring intravascular contrast media in three typical settings: pre-procedurally, during the procedure, and post-procedure. We evaluated their ability to assess the risk of developing CI-AKI, make the diagnosis, and treat CI-AKI, including proper volume expansion and withholding nephrotoxic medications. Overall, the quality-of-care scores averaged 46.0% ± 10.5, varying between 18% to 78%. The diagnostic scores for accurately assessing risk of CI-AKI were low at 57.1% ± 21.2% and the accuracy of diagnosis pre-existing chronic kidney disease was 50.2%. Poor diagnostic accuracy led to poor treatment: proper volume expansion done in only 30.7% of cases, in-hospital repeat creatinine evaluation performed in 32.1%, and avoiding nephrotoxic medications occurred in 14.2%. While volume expansion was relatively similar across the three settings (P = 0.287), the cardiologists were less likely to discontinue nephrotoxic medications in pre-procedurally (9.7%) compared to the other settings (27.0%), and to order in-hospital creatinine testing in peri-procedurally (18.8%) compared to post-procedure (57.8%) (P < 0.05 for both). The overall care of patients at risk for contrast-induced acute kidney injury varied widely and showed room for improvement. Improving care for this condition will require greater awareness by cardiologists and better diagnostic tools to guide them.

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.