Comparative Performance of Prediction Models for Contrast-Associated Acute Kidney Injury After Percutaneous Coronary Intervention

Change Management Accompanying Implementation of Decision Support for Prevention of Acute Kidney Injury in Cardiac Catheterization Units: Program Report

Purpose of program

Different models exist to guide successful implementation of electronic health tools into clinical practice. The Contrast Reducing Injury Sustained by Kidneys (Contrast RISK) initiative introduced an electronic decision support tool with physician audit and feedback into all of the cardiac catheterization facilities in Alberta, Canada, with the goal of preventing contrast-associated acute kidney injury (CA-AKI) following coronary angiography and intervention. This report describes the change management approaches used by the initiative and end-user's feedback on these processes.

Sources of information and methods

The Canada Health Infoway Change Management model was used to address 6 activities relevant to project implementation: governance and leadership, stakeholder engagement, communications, workflow analysis and integration, training and education, and monitoring and evaluation. Health care providers and invasive cardiologists from all sites completed preimplementation, usability, and postimplementation surveys to assess integration and change success.

Key findings

Prior to implementation, 67% of health providers were less than satisfied with processes to determine appropriate contrast dye volumes, 47% were less than satisfied with processes for administering adequate intravenous fluids, and 68% were less than satisfied with processes to ensure follow-up of high-risk patients. 48% of invasive cardiologists were less than satisfied with preprocedural identification of patients at risk of acute kidney injury (AKI). Following implementation, there were significant increases among health providers in the odds of satisfaction with processes for identifying those at high risk of AKI (odds ratio [OR] 3.01, 95% confidence interval [CI] 1.36-6.66, P = .007), quantifying the appropriate level of contrast dye for each patient (OR 6.98, 95% CI 3.06-15.91, P < .001), determining the optimal amount of IV fluid for each patient (OR 1.86, 95% CI 0.88-3.91, P = .102), and following up of kidney function of high risk patients (OR 5.49, 95%CI 2.45-12.30, P < .001). There were also significant increases among physicians in the odds of satisfaction with processes for identifying those at high risk of AKI (OR 19.53, 95% CI 3.21-118.76, P = .001), quantifying the appropriate level of contrast dye for each patient (OR 26.35, 95% CI 4.28-162.27, P < .001), and for following-up kidney function of high-risk patients (OR 7.72, 95% CI 1.62-36.84.30, P = .010). Eighty-nine percent of staff perceived the initiative as being successful in changing clinical practices to reduce the risk of CA-AKI. Physicians uniformly agreed that the system was well-integrated into existing workflows, while 42% of health providers also agreed.

Implications

The Canada Health Infoway Change Management model was an effective framework for guiding implementation of an electronic decision support tool and audit and feedback intervention to improve processes for AKI prevention within cardiac catheterization units.

Development of real-time individualized risk prediction models for contrast associated acute kidney injury and 30-day dialysis after contrast enhanced computed tomography

PURPOSE

This study aimed to develop preprocedural real-time artificial intelligence (AI)-based systems for predicting individualized risks of contrast-associated acute kidney injury (CA-AKI) and dialysis requirement within 30 days following contrast-enhanced computed tomography (CECT).

METHOD

This single-center, retrospective study analyzed adult patients from emergency or in-patient departments who underwent CECT; 18,895 patients were included after excluding those who were already on dialysis, had stage V chronic kidney disease, or had missing data regarding serum creatinine levels within 7 days before and after CECT. Clinical parameters, laboratory data, medication exposure, and comorbid diseases were selected as predictive features. The patients were randomly divided into model training and testing groups at a 7:3 ratio. Logistic regression (LR) and random forest (RF) were employed to create prediction models, which were evaluated using receiver operating characteristic curves.

RESULTS

The incidence rates of CA-AKI and dialysis within 30 days post-CECT were 6.69% and 0.98%, respectively. For CA-AKI prediction, LR and RF exhibited similar performance, with areas under curve (AUCs) of 0.769 and 0.757, respectively. For 30-day dialysis prediction, LR (AUC, 0.863) and RF (AUC, 0.872) also exhibited similar performance. Relative to eGFR-alone, the LR and RF models produced significantly higher AUCs for CA-AKI prediction (LR vs. eGFR alone, 0.769 vs. 0.626, p < 0.001) and 30-day dialysis prediction (RF vs. eGFR alone, 0.872 vs. 0.738, p < 0.001).

CONCLUSIONS

The proposed AI prediction models significantly outperformed eGFR-alone for predicting the CA-AKI and 30-day dialysis risks of emergency department and hospitalized patients who underwent CECT.

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.

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.

Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records

IMPORTANCE

Acute kidney injury (AKI) is a heterogeneous syndrome prevalent among hospitalized patients. Personalized risk estimation and risk factor identification may allow effective intervention and improved outcomes.

OBJECTIVE

To develop and validate personalized AKI risk estimation models using electronic health records (EHRs), examine whether personalized models were beneficial in comparison with global and subgroup models, and assess the heterogeneity of risk factors and their outcomes in different subpopulations.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study analyzed EHR data from 1 tertiary care hospital and used machine learning and logistic regression to develop and validate global, subgroup, and personalized risk estimation models. Transfer learning was implemented to enhance the personalized model. Predictor outcomes across subpopulations were analyzed, and metaregression was used to explore predictor interactions. Adults who were hospitalized for 2 or more days from November 1, 2007, to December 31, 2016, were included in the analysis. Patients with moderate or severe kidney dysfunction at admission were excluded. Data were analyzed between August 28, 2019, and May 8, 2022.

EXPOSURES

Clinical and laboratory variables in the EHR.

MAIN OUTCOMES AND MEASURES

The main outcome was AKI of any severity, and AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine criteria. Performance of the models was measured with area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and calibration.

RESULTS

The study cohort comprised 76 957 inpatient encounters. Patients had a mean (SD) age of 55.5 (17.4) years and included 42 159 men (54.8%). The personalized model with transfer learning outperformed the global model for AKI estimation in terms of AUROC among general inpatients (0.78 [95% CI, 0.77-0.79] vs 0.76 [95% CI, 0.75-0.76]; P < .001) and across the high-risk subgroups (0.79 [95% CI, 0.78-0.80] vs 0.75 [95% CI, 0.74-0.77]; P < .001) and low-risk subgroups (0.74 [95% CI, 0.73-0.75] vs 0.71 [95% CI, 0.70-0.72]; P < .001). The AUROC improvement reached 0.13 for the high-risk subgroups, such as those undergoing liver transplant and cardiac surgery. Moreover, the personalized model with transfer learning performed better than or comparably with the best published models in well-studied AKI subgroups. Predictor outcomes varied significantly between patients, and interaction analysis uncovered modifiers of the predictor outcomes.

CONCLUSIONS AND RELEVANCE

Results of this study demonstrated that a personalized modeling with transfer learning is an improved AKI risk estimation approach that can be used across diverse patient subgroups. Risk factor heterogeneity and interactions at the individual level highlighted the need for agile, personalized care.

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.

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.

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

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

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.

Patient Experience After Risk Stratification and Follow-up for Acute Kidney Injury After Cardiac Catheterization: Patient Survey

Background

Acute kidney injury (AKI) after cardiac catheterization procedures is associated with poor health outcomes. We sought to characterize the experiences of patients after receiving standardized information on their risk of AKI accompanied by instructions for follow-up care after cardiac catheterization.

Methods

We implemented an initiative across 3 cardiac catheterization units in Alberta, Canada to provide standardized assessment, followed by guidance for patients at risk of AKI. This was accompanied by communication to primary care providers to improve continuity of care when patients transition from the hospital to the community. A structured survey from a sample of 100 participants at increased risk of AKI determined their perceptions of information provided and experiences with follow-up steps after the initiative was implemented in each cardiac catheterization unit in Alberta.

Results

The mean age of participants was 72.4 (SD 10.4) years, 37% were female, and the mean risk of AKI was 8.8%. Most (63%) participants were able to recall the information provided to them about their risk of kidney injury, 68% recalled the education provided on strategies to reduce risk, and 65% believed their primary care practitioner had received enough information to conduct appropriate follow-up care. Eighty-six percent of patients were satisfied with their transition to the community, and 53% were reassured by the information and follow-up care they received.

Conclusions

These findings suggest that communicating risk information to patients, in combination with education and collaboration for follow-up with primary care providers, is associated with positive patient experiences and satisfaction with care.