Background In the IMPROVE AKI (A Cluster‐Randomized Trial of Team‐Based Coaching Interventions to Improve Acute Kidney Injury) trial, a combination of team‐based coaching and data‐driven surveillance dashboards reduced the odds of AKI following cardiac catheterization by 46%. The objective of this study was to determine if improvements in AKI outcomes would be sustained after completion of the active intervention. Methods and Results A 2×2 factorial cluster‐randomized trial with an 18‐month active intervention phase (October 2019–March 2021) and an 18‐month sustainability phase (April 2021–September 2022) conducted among cardiac catheterization laboratories in 20 Veterans Affairs sites. Interventions included team‐based coaching in a virtual learning collaborative or technical assistance, with and without access to an automated surveillance reporting dashboard. Data were collected on procedures involving adult patients undergoing diagnostic coronary angiography or percutaneous coronary interventions and not receiving chronic dialysis. The main outcome was AKI within 7 days of cardiac catheterization among all participants and those with preexisting chronic kidney disease. In addition, survey and focused interview data were collected to understand barriers and facilitators to sustaining AKI improvements. In this phase, 440 of 4160 patients experienced AKI, including 216 of 1260 patients with chronic kidney disease. Compared with technical assistance alone, we observed a reduction in AKI among virtual learning collaborative + automated surveillance reporting sites (adjusted odds ratio, 0.60 [95% CI, 0.42–0.86]). Sites had implemented standardized orders (11), oral and intravenous hydration standing orders (13), and contrast limiting protocols (10). Conclusions Team‐based coaching coupled with data‐driven surveillance dashboards reduced AKI by 40% during the 18 months after active participation in the trial. Process improvement education, care process standardization, and automated outcome feedback may be effective and durable methods for reducing AKI. Registration URL: https://clinicaltrials.gov/; Unique Identifier: NCT03556293.

Reducing the prevalence of acute kidney injury (AKI) is an important patient safety objective set forth by the National Quality Forum. Despite international guidelines to prevent AKI, there continues to be an inconsistent uptake of these interventions by cardiac teams across practice settings. The IMPROVE-AKI study was designed to test the effectiveness and implementation of AKI preventive strategies delivered through team-based coaching activities. Qualitative methods were used to identify factors that shaped sites' implementation of AKI prevention strategies. Semi-structured interviews were conducted with staff in a range of roles within the cardiac catheterization laboratories, including nurses, laboratory managers, and interventional cardiologists (N = 50) at multiple time points over the course of the study. Interview transcripts were qualitatively coded, and aggregated code reports were reviewed to construct main themes through memoing. In this paper, we report insights from semi-structured interviews regarding workflow, organizational culture, and leadership factors that impacted implementation of AKI prevention strategies.

Background: Hospital Score is a well-known and validated tool for predicting readmission risk among diverse patient populations. Integrating social risk factors using natural language processing with the Hospital Score may improve its ability to predict 30-day readmissions following an acute myocardial infarction. Methods: A retrospective cohort included patients hospitalized at Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary index diagnosis of acute myocardial infarction, who were discharged alive. To supplement ascertainment of 30-day readmissions, data were linked to Center for Medicare & Medicaid Services (CMS) administrative data. Clinical notes from the cohort were extracted, and a natural language processing model was deployed, counting mentions of eight social risk factors. A logistic regression prediction model was run using the Hospital Score composite, its component variables, and the natural language processing-derived social risk factors. ROC comparison analysis was performed. Results: The cohort included 6,165 unique patients, where 4,137 (67.1%) were male, 1,020 (16.5%) were Black or other people of color, the average age was 67 years (SD:13), and the 30-day hospital readmission rate was 15.1% (N=934). The final test-set AUROCs were between 0.635 and 0.669. The model containing the Hospital Score component variables and the natural language processing-derived social risk factors obtained the highest AUROC. Discussion: Social risk factors extracted using natural language processing improved model performance when added to the Hospital Score composite. Clinicians and health systems should consider incorporating social risk factors when using the Hospital Score composite to evaluate risk for readmission among patients hospitalized for acute myocardial infarction.

BACKGROUND:Congenital heart defects (CHD) are the most common birth defects and previous estimates report the disease affects 1% of births annually in the United States. To date, CHD prevalence estimates are inconsistent due to varied definitions, data reliant on birth registries, and are geographically limited. These data sources may not be representative of the total prevalence of the CHD population. It is therefore important to derive high-quality, population-based estimates of the prevalence of CHD to help care for this vulnerable population. METHODS:We performed a descriptive, retrospective 8-year analysis using all-payer claims data from Colorado from 2012 to 2019. Children with CHD were identified by applying International Classification of Diseases-Ninth Revision (ICD-9) and International Classification of Diseases-Tenth Revision (ICD-10) diagnosis codes from the American Heart Association-American College of Cardiology harmonized cardiac codes. We included children with CHD <18 years of age who resided in Colorado, had a documented zip code, and had at least 1 health care claim. CHD type was categorized as simple, moderate, and severe disease. Association with comorbid conditions and genetic diagnoses were analyzed using chi(2) test. We used direct standardization to calculate adjusted prevalence rates, controlling for age, sex, primary insurance provider, and urban-rural residence. RESULTS:We identified 1 566 328 children receiving care in Colorado from 2012 to 2019. Of those, 30 512 children had at least 1 CHD diagnosis, comprising 1.95% (95% CI, 1.93-1.97) of the pediatric population. Over half of the children with CHD also had at least 1 complex chronic condition. After direct standardization, the adjusted prevalence rates show a small increase in simple severity diagnoses across the study period (adjusted rate of 11.5 [2012]-14.4 [2019]; P<0.001). CONCLUSIONS:The current study is the first population-level analysis of pediatric CHD in the United States. Using administrative claims data, our study found a higher CHD prevalence and comorbidity burden compared with previous estimates.

Introduction: Children with congenital heart disease (CHD) typically require care from a multi-disciplinary team of providers and treatment at specialized care centers. Although access to specialty care is known to influence patient outcomes, little is known regarding geographic access to care for children with congenital heart disease. This study calculated travel time to specialized care centers and the relationship with mortality for children residing in Colorado. Methods: We analyzed all payer claims data (APCD) from Colorado (CO) from 2012-2019. Travel times were calculated using a network analysis of the road distance weighted by travel speeds from the geographic centroid of every ZIP code in CO to that of the actual specialized care center. Specialty care centers were uniquely identified by their National Provider ID (NPI) and defined by categorizations from the American Medical Association (AMA). Mortality was defined by discharge status. Results: There were 27,344 children with CHD who received specialized care in the study period, accounting for 437,071 total encounters. Of the children with CHD, there were 355 deaths. Children that died had an average of 98 visits per year, while children that survived had an average of 70 visits (p=<0.001). Among the children who lived, 62.8% of their total specialty care visits were <30 minutes away, compared to 59.7% among those who died. CHD who died had a consistently higher proportion of visits that required greater travel to care. Conclusion: Specialized care centers are often located in urban areas and treat patients from diverse geographic areas. There is significant travel burden for children with CHD.

Introduction: Opioid use has disproportionally impacted pregnant people and their fetuses. Previous studies describing opioid use among pregnant people are limited by geographic location, type of medical coverage, and small sample size. We described characteristics of a large, diverse group of pregnant people who were enrolled in the Environmental Influences on Child Health Outcomes (ECHO) Program, and determined which characteristics were associated with opioid use during pregnancy.Materials and Methods: Cross-sectional data obtained from 21,905 pregnancies of individuals across the United States enrolled in the ECHO between 1990 and 2021 were analyzed. Medical records, laboratory testing, and self-report were used to determine opioid-exposed pregnancies. Multiple imputation methods using fully conditional specification with a discriminant function accounted for missing characteristics data.Results: Opioid use was present in 2.8% (n = 591) of pregnancies. The majority of people who used opioids in pregnancy were non-Hispanic White (67%) and had at least some college education (69%). Those who used opioids reported high rates of alcohol use (32%) and tobacco use (39%) during the pregnancy; although data were incomplete, only 5% reported heroin use and 86% of opioid use originated from a prescription. After adjustment, non-Hispanic White race, pregnancy during the years 2010-2012, higher parity, tobacco use, and use of illegal drugs during pregnancy were each significantly associated with opioid use during pregnancy. In addition, maternal depression was associated with increased odds of opioid use during pregnancy by more than two-fold (adjusted odds ratio 2.42, 95% confidence interval: 1.95-3.01).Conclusions: In this large study of pregnancies from across the United States, we found several factors that were associated with opioid use among pregnant people. Further studies examining screening for depression and polysubstance use may be useful for targeted interventions to prevent detrimental opioid use during pregnancy, while further elucidation of the reasons for use of prescription opioids during pregnancy should be further explored.

Objectives To predict behavioral disruptions in middle childhood, we identified latent classes of prenatal substance use. Study design As part of the Environmental influences on Child Health Outcomes Program, we harmonized prena-tal substance use data and child behavior outcomes from 2195 women and their 6-to 11-year-old children across 10 cohorts in the US and used latent class-adjusted regression models to predict parent-rated child behavior. Results Three latent classes fit the data: low use (90.5%; n = 1986), primarily using no substances; licit use (6.6%; n = 145), mainly using nicotine with a moderate likelihood of using alcohol and marijuana; and illicit use (2.9%; n = 64), predominantly using illicit substances along with a moderate likelihood of using licit substances. Children exposed to primarily licit substances in utero had greater levels of externalizing behavior than children exposed to low or no sub-stances (P = .001, d = .64). Children exposed to illicit substances in utero showed small but significant elevations in internalizing behavior than chil-dren exposed to low or no substances (P < .001, d = .16). Conclusions The differences in prenatal polysubstance use may in-crease risk for specific childhood problem behaviors; however, child out-comes appeared comparably adverse for both licit and illicit polysubstance exposure. We highlight the need for similar multicohort, large-scale studies to examine childhood outcomes based on prenatal substance use profiles.

Background Up to 14% of patients in the United States undergoing cardiac catheterization each year experience AKI. Consistent use of risk minimization preventive strategies may improve outcomes. We hypothesized that team-based coaching in a Virtual Learning Collaborative (Collaborative) would reduce postprocedural AKI compared with Technical Assistance (Assistance), both with and without Automated Surveillance Reporting (Surveillance). Methods The IMPROVE AKI trial was a 2x2 factorial cluster-randomized trial across 20 Veterans Affairs medical centers (VAMCs). Participating VAMCs received Assistance, Assistance with Surveillance, Collaborative, or Collaborative with Surveillance for 18 months to implement AKI prevention strategies. The Assistance and Collaborative approaches promoted hydration and limited NPO and contrast dye dosing. We fit logistic regression models for AKI with site-level random effects accounting for the clustering of patients within medical centers with a prespecified interest in exploring differences across the four intervention arms. Results Among VAMCs' 4517 patients, 510 experienced AKI (235 AKI events among 1314 patients with preexisting CKD). AKI events in each intervention cluster were 110 (13%) in Assistance, 122 (11%) in Assistance with Surveillance, 190 (13%) in Collaborative, and 88 (8%) in Collaborative with Surveillance. Compared with sites receiving Assistance alone, case-mix-adjusted differences in AKI event proportions were -3% (95% confidence interval [CI], -4 to -3) for Assistance with Surveillance, -3% (95% CI, -3 to -2) for Collaborative, and -5% (95% CI, -6 to -5) for Collaborative with Surveillance. The Collaborative with Surveillance intervention cluster had a substantial 46% reduction in AKI compared with Assistance alone (adjusted odds ratio=0.54; 0.40-0.74). Conclusions This implementation trial estimates that the combination of Collaborative with Surveillance reduced the odds of AKI by 46% at VAMCs and is suggestive of a reduction among patients with CKD.

Introduction: Congenital heart disease (CHD) is the most common birth defect and is estimated to affect nearly 37,500 infants born each year in the United States. Children with CHD have complex, nuanced, healthcare needs and frequently require multi-specialty care. The relationship between specialty care utilization and survival for children with CHD is not well known. Methods: We analyzed all payer claims data (APCD) from Colorado from 2012-2019. Children with CHD were identified by applying harmonized ICD-9-CM and ICD-10-CM diagnoses codes. We included children with CHD < 18 years of age who resided in Colorado, had a documented zip code, and had at least one healthcare claim. Specialty care providers were uniquely identified by their National Provider ID. Mortality was defined by discharge status. We evaluated the relationship between specialty care provider visits and mortality using logistic multivariable modeling. Results: There were 24,784 children diagnosed with CHD in Colorado from 2012 - 2019. Of those, 22,413 (90.4%) children had at least one specialty care visit and 465 (2.1%) died during the study period. Children that died had an average of 60 visits per year, while children that survived had an average of 29 visits (p=<0.001). Children primarily commercially insured by are 25% less likely to experience death (OR: 0.75, 95% CI (0.58 - 0.97), p value: <0.001). Children with greater CHD disease severity or presence of a genetic disorder are nearly 2 times more likely to die, and children in the highest community level quartile are less likely to die. Conclusions: In our cohort of children diagnosed with CHD from 2012 - 2019, there was a significant association with the frequency of specialty care visits and likelihood of death. We found that the primary insurer, level of disease severity, presence of any genetic disorder and community level household income were significantly associated with higher likelihood of death.

Introduction: Congenital heart defects (CHD) are the most common birth defects and are estimated to affect almost 1% of births per year in the US. Most CHD prevalence estimates are based on data from population-based birth defects surveillance systems and these estimates are inconsistent due to varied definitions. It is therefore important to derive high-quality, population-based estimates of the prevalence of CHD to help care for this vulnerable population. Methods: We analyzed all payer claims data (APCD) from Colorado from 2012-2019. Children with CHD were identified by applying CHD ICD-9 and ICD-10 diagnoses codes from the Society of Thoracic Surgeons (STS) International Society for Nomenclature of Paediatric and Congenital Heart Disease (ISNPCHD) harmonized cardiac codes. We included children with CHD < 18 years of age who resided in Colorado, had a documented zip code, and had at least one ambulatory healthcare claim. We analyzed the test for linear trends in the proportion of CHD diagnoses from 2012-2019 with the Cochran-Armitage (Z) test. Differences among patient characteristics and CHD diagnosis were tested using the Pearson Chi-square test and Wilcoxon rank sum tests as appropriate. Results: Overall the current study analyzed 1,565,438 children with 36,567 CHD diagnoses (i.e. 23.4 per 1,000 live births), comprising 2.3% of the pediatric population. Between 2012 and 2019 the statewide rate of children diagnosed with CHD significantly increased from 21.9 to 32.3 per 1,000 children per year (Z: 5.38; p<0.001). There were statistically significant differences in the magnitude of the trend in CHD prevalence rate by region (Z: -31.82), urban-rural residence (Z:-24.02), degree of chronic complex conditions (Z: -38.78), disease severity (Z: -44.11), age (Z: -72.89), insurance type (Z: 46.51) and median household income (Z: 12.87; all p<0.001). Conclusion: The current study is the first population-level analysis of pediatric CHD in the US and these findings suggest that the statewide CHD prevalence rate has increased significantly since 2012. Children with CHD are a priority population for quality improvement in pediatrics given their growing prevalence and corresponding risk of adverse outcomes.

Introduction: Hospital readmissions after congenital heart surgery (CHS) are common in pediatric patients and are linked to higher risks of complications, mortality, and increased treatment costs. Accurate prediction of 30-day readmission after CHS could help clinicians identify high risk patients and enable tailored care. Hypothesis: Novel biomarkers alone enhance prediction of readmission risk using XGBoost model and augmenting clinical features to the Society of Thoracic Surgeons (STS)-risk model improves predictions of 30-day readmissions. Methods: The prospective cohort included children of 18 years or younger who underwent congenital heart surgery at Johns Hopkins Hospital. Of the 162 patients, 159 survived discharge and were therefore included in the study. We used pre- and post-operative biomarkers ST2, Galectin-3, NT-proBNP and GFAP as features for the biomarker model, covariates from the STS CHS Database Mortality Risk Model for the STS-risk model and augmenting clinical features to STS-risk variables for clinical model. We implemented the XGBoost model and compared the biomarker model and clinical model performance to the STS-risk model. Results: Readmissions within 30 days of surgery occurred among 9% of pediatric patients. Using XGBoost, the STS-risk model derived an AUROC of 0.978 (95% CI: 0.955-1.0). The biomarker model performed better than the STS-risk model with AUROC of 0.982 (95% CI: 0.959-1.0, p=0.8). The augmented clinical model outperformed the STS-risk model with an AUROC of 0.997 (95% CI: 0.99-1, p=0.1). Conclusions: Novel biomarkers alone perform better compared to the STS- risk model in predicting 30-day readmission risk. XGBoost model improves risk prediction when adding additional clinical features to the existing STS-risk model which outperformed the STS-risk model. Depending on healthcare systems clinical resources and data availability, an optimized XGBoost model can improve the readmission risk prediction outcome.

Introduction: Unplanned readmission is associated with higher risks of complications, death, and increased costs. Accurate statistical models to stratify the risk of 30-day readmission or death after congenital heart surgery could help clinical teams focus care on those patients at highest risk. We hypothesized biomarkers could improve prediction for readmission or mortality. Methods: Levels of pre- and postoperative ST2, Galectin-3, NT-proBNP and GFAP were measured in plasma samples from 162 pediatric congenital heart surgery patients from Johns Hopkins Hospital with external validation in 360 pediatric patients from an international multi-center TRIBE-AKI cohort. A model based on clinical variables from the Society of Thoracic Surgery Congenital database (STS-CHSD) was developed in the Hopkins cohort. We tested and externally validated the clinical models and biomarker panels in the TRIBE-AKI cohort using AUROC statistics and Kaplan-Meier cox hazard regression models. Results: There were 55 patients (10.5%) that experienced unplanned readmission or died within 30 days after congenital heart surgery. The STS-CHSD clinical model resulted in an AUROC of 0.617 (95% CI: 0.47 - 0.76). The derivation cohort with the biomarker augmented STS-CHSD clinical model resulted in a significantly improved AUROC of 0.802 (95%CI: 0.72 - 0.89; p=0.003). External validation of the biomarker augmented STS-CHSD clinical model showed limited improvement (AUROC: 0.60; 95% CI: 0.49-0.72; p value= 0.47). Conclusions: Our findings indicate that these biomarkers can be used for early identification of children at increased risk of readmission or death after pediatric congenital heart surgery. While the addition of biomarkers improve prediction in our derivation cohort, external validation was poor. Our findings suggest there are other potential biomarkers and factors to be explored to improve prediction of readmission or mortality for children following congenital heart surgery.

OBJECTIVE:Several short-term readmission and mortality prediction models have been developed using clinical risk factors or biomarkers among patients undergoing coronary artery bypass graft (CABG) surgery. The use of biomarkers for long-term prediction of readmission and mortality is less well understood. Given the established association of cardiac biomarkers with short-term adverse outcomes, we hypothesized that 5-year prediction of readmission or mortality may be significantly improved using cardiac biomarkers.MATERIALS AND METHODS:Plasma biomarkers from 1149 patients discharged alive after isolated CABG surgery from eight medical centers were measured in a cohort from the Northern New England Cardiovascular Disease Study Group between 2004 and 2007. We assessed the added predictive value of a biomarker panel with a clinical model against the clinical model alone and compared the model discrimination using the area under the receiver operating characteristic (AUROC) curves.RESULTS:In our cohort, 461 (40%) patients were readmitted or died within 5 years. Long-term outcomes were predicted by applying the STS ASCERT clinical model with an AUROC of 0.69. The biomarker panel with the clinical model resulted in a significantly improved AUROC of 0.74 (p value <.0001). Across 5 years, the hazard ratio for patients in the second to fifth quintile predicted probabilities from the biomarker augmented STS ASCERT model ranged from 2.2 to 7.9 (p values <.001).CONCLUSIONS:We report that a panel of biomarkers significantly improved prediction of long-term readmission or mortality risk following CABG surgery. Our findings suggest biomarkers help clinical care teams better assess the long-term risk of readmission or mortality.

Importance:In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models. Objective:To compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model. Design, Setting, and Participants:This was a retrospective cohort study that developed risk prediction models for 30-day readmission among all inpatients discharged from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of AMI who were not transferred from another facility. The model was externally validated at Dartmouth-Hitchcock Medical Center from April 2, 2011, to December 31, 2016. Data analysis occurred between January 4, 2019, and November 15, 2020. Exposures:Acute myocardial infarction that required hospital admission. Main Outcomes and Measures:The main outcome was thirty-day hospital readmission. A total of 141 candidate variables were considered from administrative codes, medication orders, and laboratory tests. Multiple risk prediction models were developed using parametric models (elastic net, least absolute shrinkage and selection operator, and ridge regression) and nonparametric models (random forest and gradient boosting). The models were assessed using holdout data with area under the receiver operating characteristic curve (AUROC), percentage of calibration, and calibration curve belts. Results:The final Vanderbilt University Medical Center cohort included 6163 unique patients, among whom the mean (SD) age was 67 (13) years, 4137 were male (67.1%), 1019 (16.5%) were Black or other race, and 933 (15.1%) were rehospitalized within 30 days. The final Dartmouth-Hitchcock Medical Center cohort included 4024 unique patients, with mean (SD) age of 68 (12) years; 2584 (64.2%) were male, 412 (10.2%) were rehospitalized within 30 days, and most of the cohort were non-Hispanic and White. The final test set AUROC performance was between 0.686 to 0.695 for the parametric models and 0.686 to 0.704 for the nonparametric models. In the validation cohort, AUROC performance was between 0.558 to 0.655 for parametric models and 0.606 to 0.608 for nonparametric models. Conclusions and Relevance:In this study, 5 machine learning models were developed and externally validated to predict 30-day readmission AMI hospitalization. These models can be deployed within an EHR using routinely collected data.

HomeJournal of the American Heart AssociationVol. 10, No. 15Modifying the Risk of Contrast‐Associated Acute Kidney Injury in Percutaneous Coronary Interventions and Transcatheter Aortic Valve Implantations Open AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citations ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toOpen AccessEditorialPDF/EPUBModifying the Risk of Contrast‐Associated Acute Kidney Injury in Percutaneous Coronary Interventions and Transcatheter Aortic Valve Implantations Briggs S. Carhart, MPH, Meagan E. Stabler, and PhD, and Jeremiah R. BrownPhD, MS Briggs S. CarhartBriggs S. Carhart https://orcid.org/0000-0003-0922-8039 Geisel School of Medicine at Dartmouth, , Hanover, , NH , Meagan E. StablerMeagan E. Stabler https://orcid.org/0000-0002-3706-2622 Department of Epidemiology, , Geisel School of Medicine at Dartmouth, , Hanover, , NH , and Jeremiah R. BrownJeremiah R. Brown * Correspondence to: Jeremiah R. Brown, PhD, MS, DHMC 1 Medical Center Drive, Lebanon, NH 03756. E‐mail: E-mail Address: [email protected] https://orcid.org/0000-0003-4512-9716 Department of Epidemiology, , Geisel School of Medicine at Dartmouth, , Hanover, , NH Department of Biomedical Data Science, , Geisel School of Medicine at Dartmouth, , Hanover, , NH Originally published26 Jul 2021https://doi.org/10.1161/JAHA.121.022099Journal of the American Heart Association. 2021;10:e022099This article is a commentary on the followingContrast‐Induced Nephropathy in Patients Undergoing Staged Versus Concomitant Transcatheter Aortic Valve Implantation and Coronary ProceduresContrast‐associated acute kidney injury (CA‐AKI) remains a clinical quandary that increases the rate of morbidity and mortality in patients undergoing various coronary procedures.1, 2 Researchers within the last decade have synthesized and elucidated the various pathophysiological mechanisms of developing this form of nephropathy from iodinated contrast media.2, 3 A recent study estimated the incidence of CA‐AKI in percutaneous coronary intervention (PCI) was about 7.7%.4 Using the Acute Kidney Injury Network criteria, CA‐AKI is considered when there is an absolute increase in serum creatinine of ≥0.3 mg/dL, a relative serum creatinine increase of ≥50% from baseline, or a significant reduction in urine output (ie, <0.5 mL/kg per hour for more than 6 consecutive hours), within 48 hours.5 Catheter administration of contrast, as seen in PCI or transcatheter aortic valve implantation (TAVI), are of particular interest because of the higher volume of contract media being used during the procedure.2 Contrast load in addition to release of arthroemboli from the catheter increases the risk for CA‐AKI to occur. As there is greater understanding about which patients are at higher risk for CA‐AKI, the next step is to determine how to approach a patient's risk profile and understand how to prevent CA‐AKI.In this issue of the Journal of the American Heart Association (JAHA), Shoji and colleagues examined the risk profiles of a retrospective cohort (n=14 702) and compared these profiles to the amount of contrast used in their PCI procedures. An increase in contrast quantity is associated in a dose‐response manner with AKI risk.6 The study calculated NCDR (National Cardiovascular Data Registry) Cath‐PCI Registry AKI‐risk scores for each patient.7 These scores were mapped to contrast quantity and found a uniform quantity across all quartiles. This raises the concern that overall CA‐AKI risk was not considered when determining the amount of contrast that was to be used during the case. Although overall risk did not map to contrast volume, estimated glomerular filtration rate (eGFR) was correlated with a higher quartile of risk receiving less contrast (figure 3 of Ref. [7]). This is a promising result because eGFR, a tool for renal function estimation, is a risk factor in a vast number of preconceived risk scores,4, 8, 9, 10, 11, 12, 13, 14, 15 Approximating a patient's renal function is one of the better predictors of CA‐AKI because eGFR is heavily weighted in the risk calculation for most risk models. For the NCDR risk model, a severe GFR increases risk of CA‐AKI by almost 5% without other risk factors present.4 When a patient presents with accompanying risk factors, such as diabetes mellitus, myocardial infarction, intra‐aortic balloon pump, etc, the interaction of multiple risk factors expontentially increases CA‐AKI risk. For clinicians to gravitate toward eGFR as a tool for determining contrast volume is a good sign that some risk modification is occurring in the field already. A caveat to Shoji et al is the NCDR risk model was derived and validated in 2014 whereas some participants received their intervention as early as 2008. This makes it challenging to attribute the lack of adjustment of the contrast volume to the overall risk quartiles to an evidence‐to‐practice gap. However, the NCDR risk model was validated for the incidence of CA‐AKI with 66.5% (750/1127) of the patients who developed CA‐AKI being from the highest risk quartile.7 Although the majority of the covariates in the risk model are not modifiable, it reinforces its viability to determine the priority of reducing risk in the patient before the coronary angiogram or PCI.Moreover, also in this issue of JAHA, Venturi et al investigated the staging of TAVIs in conjunction with PCIs as an area of potential risk modifications, comparing staged strategy versus concomitant strategy. Studies in this area previously compared the safety of staged procedure. Staged strategy (SS) is defined as having the coronary angiogram/PCI before the TAVI was performed. Concomitant strategy (CS) is defined as having the coronary angiogram/PCI performed at the same time as the TAVI. Studies comparing SS and CS have largely focused on overall morbidity and mortality, not CA‐AKI specifically.16 To compare the absolute difference in risk of CA‐AKI, 339 patient records were retroactively analyzed. When considering individuals developing AKI after the TAVR, the SS and CS group were comparable with 10.6% and 10.1% developing CA‐AKI, respectively (figure 4 of Ref. [17]); however, the SS group had an additional 19.9% of their group develop nephropathy after the initial staging procedure within the 30‐day window before the TAVI.17 Venturi et al characterize the hemodynamics of the patient in the SS to be less stable when going for a SS versus the CS catherization. Hemodynamic stability is one of the factors that drive CA‐AKI risk.3, 9 Interestingly, the patients with eGFR <60 mL/min per 1.73 m2 received hydration therapy/volume expansion as a measure to reduce risk.18 This study was successful at outlining the potential risk reduction methods to protect patients from CA‐AKI during TAVI procedures by changing the staging process.The challenge with CA‐AKI is that the risk reduction methods will not be consistent with each patient. CA‐AKI is an iatrogenic condition from essential diagnostic and intervention tools that aid in the reduction of cardiovascular events in a variety of contexts. Similar intervention methods are used in outpatient care as well as in the acute care setting. Because of time constraints, emergent coronary interventions limit opportunities to consider and reduce the risk of patients developing CA‐AKI. These acute cases need extra consideration. If a high‐risk patient is not considered during PCI/TAVI, correcting one condition might have downstream consequences that question the utility of hospital resources. In the instance that excess caution causes a patient with preexisting chronic kidney disease to be refused necessary treatment (or "renalism"). To avoid these extremes, we offer suggestions.First, when a patient presents with multiple risk factors of CA‐AKI, map the risk factors to a risk score calculation to determine the overall risk. In the acute setting, focusing on eGFR would be the minimum, if that information was able to be ascertained. Formulating a self‐made risk calculator is common for most conditions, not just contrast‐associated nephropathy; however, using a validated tool will aid in considering all preprocedural risk factors because eGFR, although heavily weighted, is one of many risk factors used in modern calculations.4, 8, 9, 10, 11, 12, 13, 14, 15 Shoji et al outlined important covariates that should have indicated a decrease in contrast volume, such as intra‐aortic balloon pump and heart failure for the previous 2 weeks. Instead, those were associated with an increase in contrast volume.7 This result could be from residual confounding and stratification could elucidate if these patients also had other risk factors that would increase their individual probability of contrast‐associated nephropathy. If a patient's risk score surpasses a high threshold, patient engagement and shared decision making will be paramount to minimize risk of CA‐AKI and treat the underlying acute cardiovascular disease. If the patient is decompensating in an acute setting, then PCI is warranted regardless of AKI risk. For elective procedures, delay and follow‐up with the patient are recommended to see if the eGFR would recover or if hemodynamics can stabilize. If this delay is not expected to improve risk, then PCI/TAVI can proceed.Second, all patients will benefit from having shorter nothing by mouth times (2 hours prior to procedure for clear fluids), pre‐, peri‐, and postprocedure hydration and limited contrast volume. Isotonic saline (0.9%) being administered at a rate between 1 and 3 mL/kg per hour before the procedure can aid in preventing CA‐AKI.17, 18, 19 The POSEIDON (Study of Durvalumab + Tremelimumab With Chemotherapy or Durvalumab With Chemotherapy or Chemotherapy Alone for Patients With Lung Cancer) trial studied pressure guided volume expansion based on the patient's left ventricular end‐diastolic pressure.19 This recommendation would be contraindicated for a patient in cardiogenic shock, fluid overloaded, or in congestive heart failure. Furosemide‐induced diuresis can benefit the patient when prescribed at the optimal dose.20 The RenalGuard system from the REMEDIAL II (Renal Insufficiency After Contrast Media Administration II) trial was able to achieve a low dose of furosemide that was also effective at maintaining the balance of fluid overload and volume depletion.20 Volume expansion remains a modifiable risk factor that can be implemented if time permits among patients with normal cardiac output. In Venturi et al, emergency cases were not considered in that portion of the study when 1 mL/kg per hour was not able administered.Lastly, Venturi et al discusses the benefits of transcatheter aortic valve implantations being concomitant with PCI and other interventions. The authors acknowledge the limitation of the number of single‐center, retrospective studies. More investigation into this area is encouraged, but that does not exclude current practices from using the CS to prevent CA‐AKI in high‐risk patients. Other studies suggest the CS, although more protective for CA‐AKI, could be associated with a marginal increase in mortality within the first 30 days.16 There is limited evidence on the optimal time for staging procedures; therefore, we recommend the clinical care teams make the decision on the appropriate timing based on the untreaded lesions, initial contrast load from the first case, and safety of the patient to comply with aggressive medical management until the staged procedure. On average, contrast clears and serum creatinine normalizes levels in 14 days21; though, Venturi et al included patients with a median staging interval of 22 days.17 More investigations should be conducted before this recommendation can be open to low‐risk patients.In summary, CA‐AKI continues to be an area of active research with the goal of decreasing its incidence and associated rates of morbidity and mortality. Cardiac catheterization and PCI have been the focus of CA‐AKI discussions, however, TAVI procedures are becoming more commonplace and CA‐AKI must take its seat at the table when factoring in risk and major adverse events. These studies represent the current issues of encouraging clinical practices to better manage CA‐AKI and investigate realms of kidney injury in TAVI beyond the initial staging. Modern practices need to consider all avenues of risk reduction to protect patients from further iatrogenic effects. There must remain active awareness of the clinical decisions that may bias treatment decisions for patients with preexisting chronic kidney disease and focus on preventive practices to minimize procedural risk of CA‐AKI. New team‐based interventions to implement evidence‐based practices are being evaluated to aid in center‐wide prevention of CA‐AKI.22, 23Sources of FundingDrs Brown and Stabler received grant support from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) R01DK113201 and Dr Brown from R01DK122073.DisclosuresNone.Footnotes* Correspondence to: Jeremiah R. Brown, PhD, MS, DHMC 1 Medical Center Drive, Lebanon, NH 03756. E‐mail: [email protected]eduThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.For Sources of Funding and Disclosures, see page 3.See Articles by Shoji et al. and Venturi et al.References1 Blackman DJ, Pinto R, Ross JR, Seidelin PH, Ing D, Jackevicius C, Mackie K, Chan C, Dzavik V. Impact of renal insufficiency on outcome after contemporary percutaneous coronary intervention. Am Heart J. 2006; 151:146–152. 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Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Crit Care. 2007; 11:R31. DOI: 10.1186/cc5713.CrossrefMedlineGoogle Scholar6 Andò G, de Gregorio C, Morabito G, Trio O, Saporito F, Oreto G. Renal function–adjusted contrast volume redefines the baseline estimation of contrast‐induced acute kidney injury risk in patients undergoing primary percutaneous coronary intervention. Circ Cardiovasc Interv. 2014; 7:465–472. DOI: 10.1161/CIRCINTERVENTIONS.114.001545.LinkGoogle Scholar7 Shoji S, Sawano M, Sandhu AT, Heidenreich PA, Shiraishi Y, Shigetaka N, Suzuki M, Numasawa Y, Fukuda K, Kohsaka S. Evidence‐to‐practice gap for preventing procedure‐related acute kidney injury in patients undergoing percutaneous coronary intervention. J Am Heart Assoc. 2021; 10:e020047. DOI: 10.1161/JAHA.120.020047.LinkGoogle Scholar8 Brown JR, DeVries JT, Piper WD, Robb JF, Hearne MJ, Ver Lee PM, Kellet MA, Watkins MW, Ryan TJ, Silver MT, et al. 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ClinicalTrials.gov Identifier: NCT03556293, 2017.Google Scholar Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesContrast‐Induced Nephropathy in Patients Undergoing Staged Versus Concomitant Transcatheter Aortic Valve Implantation and Coronary ProceduresGabriele Venturi, et al. Journal of the American Heart Association. 2021;10 August 3, 2021Vol 10, Issue 15Article InformationMetrics Download: 787 Copyright © 2021 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley BlackwellThis is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.https://doi.org/10.1161/JAHA.121.022099PMID: 34310175 Originally publishedJuly 26, 2021 Keywordscontrast‐induced nephropathykidneyEditorialsPDF download SubjectsCatheter-Based Coronary and Valvular InterventionsPercutaneous Coronary Intervention

Objective: Over 40,000 infants are born annually with a heart defect; 25% require surgery and of those 20% result in hospital readmissions. We sought to identify risk factors for short- and long-term readmission following pediatric congenital heart surgery (CHS) to reduce avoidable future admissions. Methods: A systematic approach was used to search four electronic databases and retrieve articles published through 05/2020. We included observational and experimental studies that observed factors associated with 30-day or 1-year readmission after CHS. Studies with a composite outcome of readmission and death were excluded. For each independent risk factor, we assessed the pooled effect size and heterogeneity using a random-effects model. Risk of bias was assessed via the Newcastle-Ottawa scale. Results: After removing 970 duplicates, we screened 5,084 studies; 17 were included in the systematic review and 15 (N= 82,794; 9,856 readmitted) in the meta-analysis. Hospital readmission was significantly and positively associated with gestational age, non-white race, Hispanic ethnicity, government insurance, genetic abnormality, renal dysfunction, failure to thrive, mechanical ventilation, intraoperative ventricular dysfunction, RACHS score, STAT mortality score, cross clamp time, gastroesophageal reflux disease, postoperative arrhythmia, valve regurgitation, feeding difficulties, and ICU and hospital length of stay (LOS). Readmission definition (i.e., 1-yr vs 30-day) and LOS dichotomization (i.e., ≥ 10 or ≥ 14) resulted in significant subgroup differences for age at surgery and LOS. Five studies had higher potential for risk of bias. Conclusions: This is the first meta-analysis to identify patient and clinical factors associated with short and long-term readmission after pediatric CHS. Findings may support clinical decisions before undergoing surgery and identify patients that may benefit from receiving more aggressive care transitions prior to discharge to reduce avoidable hospital readmissions.

BACKGROUND:Approximately 10% to 20% of children are readmitted after congenital heart surgery. Very little is known about biomarkers as predictors of risk of unplanned readmission after pediatric congenital heart surgery. Novel cardiac biomarker ST2 may be associated with risk of unplanned readmission. ST2 concentrations are believed to reflect cardiovascular stress and fibrosis. Our objective was to explore the relationship between pre- and postoperative ST2 biomarker levels and risk of readmission within 1 year after congenital heart surgery.METHODS:We prospectively enrolled pediatric patients aged < 18 years who underwent at least 1 congenital heart operation at Johns Hopkins Hospital from 2010 to 2014. Plasma samples were collected immediately before surgery and at the end of bypass. We used Kaplan-Meier survival analysis and multivariable Cox regression models to adjust for variables used in The Society of Thoracic Surgeons Congenital Heart Surgery Database mortality risk model.RESULTS:Of our cohort of 145 patients, we found 39 children with readmissions within 365 days. The median time to unplanned readmission was 54 days (interquartile range, 10-153). Kaplan-Meier analysis demonstrated a significant difference across terciles of pre- and postoperative ST2 biomarker levels. After adjustment, elevated serum levels of ST2 measured preoperatively and postoperatively were associated with increased risk of readmission (hazard ratio, 2.5-3.7; all P < .05).CONCLUSIONS:Elevated levels of ST2 are significantly associated with increased risk of unplanned readmission within 1 year after pediatric congenital heart surgery. Novel serum biomarker ST2 can be used for risk stratification or estimating postsurgical prognosis.

Background. The purpose of this study was to evaluate the association between preoperative biomarker levels and 365-day readmission or mortality after pediatric congenital heart surgery. Methods. Children aged 18 years or younger undergoing congenital heart surgery (n = 145) at Johns Hopkins Hospital from 2010 to 2014 were enrolled in the prospective cohort. Novel biomarkers suppression of tumor-genicity 2, galectin-3, N-terminal prohormone brain natriuretic peptide, and glial fibrillary acidic protein were measured. The composite study endpoint was unplanned readmission within 365 days after discharge or mortality either in hospital during the surgical admission or within 365 days after discharge. A clinical model based on covariates used in The Society of Thoracic Surgeons Congenital Heart Surgery Database mortality risk model and an augmented model using the clinical model in conjunction with a novel biomarker panel were evaluated. Results. Readmission or mortality within 365 days of surgery occurred among 39 pediatric patients (27%). The clinical model alone resulted in a c-statistic of 0.719 (95% confidence interval, 0.63 to 0.81). The clinical model in conjunction with the log-transformed biomarkers improved the c-statistic to 0.805 (95% confidence interval, 0.73 to 0.88). The addition of biomarkers resulted in a significant improvement to the clinical model alone (P value = 0.035). Conclusions. Novel biomarkers may add predictive value when assessing the likelihood of 365-day readmission or mortality after pediatric congenital heart surgery. After adjusting for clinical and novel biomarkers, preoperative and postoperative suppression of tumor-genicity 2 remained associated with 365-day readmission or mortality. Currently, The Society of Thoracic Surgeons clinical congenital mortality risk model can be applied to identify children with increased risk of repeat hospitalizations and postdischarge mortality and may inform preventative care interventions that aim to reduce these adverse events.