Enhanced Mortality Risk Prediction With a Focus on High-Risk Percutaneous Coronary Intervention

Clair JR, Knowlton KU, Khan ND, Bhalla N, Anderson JL. Contemporary Predictors of Major Adverse Cardiovascular Events Following Percutaneous Coronary Intervention: A Nationally Representative US Sample

Background: Patient outcomes after percutaneous coronary intervention (PCI) have improved over the last 30 years due to better techniques, therapies, and care processes. This study evaluated contemporary predictors of post-PCI major adverse cardiovascular events (MACE) and summarized risk in a parsimonious risk prediction model. Methods: The Cardiovascular Patient-Level Analytical Platform (CLiPPeR) is an observational dataset of baseline variables and longitudinal outcomes from the American College of Cardiology's CathPCI Registry® and national claims data. Cox regression was used to evaluate 2-6 years of patient follow-up (mean: 2.56 years), ending in December 2017, after index PCI between 2012 and 2015 (N = 1,450,787), to examine clinical and procedural predictors of MACE (first myocardial infarction, stroke, repeat PCI, coronary artery bypass grafting, and mortality). Cox analyses of post-PCI MACE were landmarked 28 days after index PCI. Results: Overall, 12.4% (n = 179,849) experienced MACE. All variables predicted MACE, with cardiogenic shock, cardiac arrest, four diseased coronary vessels, and chronic kidney disease having hazard ratios (HRs) ≥ 1.50. Other major predictors of MACE were in-hospital stroke, three-vessel disease, anemia, heart failure, and STEMI presentation. The index revascularization and discharge prescription of aspirin, P2Y12 inhibitor, and lipid-lowering medication had HR ≤ 0.67. The primary Cox model had c-statistic c = 0.761 for MACE versus c = 0.701 for the parsimonious model and c = 0.752 for the parsimonious model plus treatment variables. Conclusions: In a nationally representative US sample of post-PCI patients, predictors of longitudinal MACE risk were identified, and a parsimonious model efficiently encapsulated them. These findings may aid in assessing care processes to further improve care post-PCI outcomes.

Contemporary Risk Models for In-Hospital and 30-Day Mortality After Percutaneous Coronary Intervention

PURPOSE OF REVIEW

Risk models for mortality after percutaneous coronary intervention (PCI) are underutilized in clinical practice though they may be useful during informed consent, risk mitigation planning, and risk adjustment of hospital and operator outcomes. This review analyzed contemporary risk models for in-hospital and 30-day mortality after PCI.

RECENT FINDINGS

We reviewed eight contemporary risk models. Age, sex, hemodynamic status, acute coronary syndrome type, heart failure, and kidney disease were consistently found to be independent risk factors for mortality. These models provided good discrimination (C-statistic 0.85-0.95) for both pre-catheterization and comprehensive risk models that included anatomic variables. There are several excellent models for PCI mortality risk prediction. Choice of the model will depend on the use case and population, though the CathPCI model should be the default for in-hospital mortality risk prediction in the United States. Future interventions should focus on the integration of risk prediction into clinical care.

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.

Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models

Aims

Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting.

Methods and results

Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69-0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk.

Conclusion

Machine learning-derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted.

Registration

Clinicaltrial.gov identifier is NCT02188355.

Development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJN

PURPOSE

Percutaneous coronary intervention (PCI) is a common treatment modality for coronary artery disease. Accurate prediction of patients at risk for complications and hospital readmission after PCI could improve the overall clinical management. We aimed to develop and validate predictive models to predict any cardiac event within a year post PCI procedure.

METHODS

This is a retrospective cohort study utilizing data from the National Cardiovascular Disease (NCVD)-PCI registry. The data collected (N = 28,007) were split into training set (n = 24,409) and testing set (n = 3598). Four predictive models (logistic regression [LR], random forest method, support vector machine [SVM], and artificial neural network) were developed and validated. The outcome on risk prediction were compared.

RESULTS

The demographic and clinical features of patients in the training and testing cohorts were similar. Patients had mean age ± standard deviation of 58.15 ± 10.13 years at admission with a male majority (82.66%). In over half of the procedures (50.61%), patients had chronic stable angina. Within 1 year of follow up mortality, target vessel revascularization (TVR), and composite event of mortality and TVR were 3.92%, 9.48%, and 12.98% respectively. LR was the best model in predicting mortality event within 1-year post-PCI (AUC: 0.820). SVM had the highest discrimination power for both TVR event (AUC: 0.720) and composite event of mortality and TVR (AUC: 0.720).

CONCLUSIONS

This study successfully identified optimal prediction models with the good discriminatory ability for mortality outcome and good discrimination ability for TVR and composite event of mortality and TVR with a simple machine learning framework.

New York Risk Model and Simplified Risk Score for In-Hospital/30-Day Mortality for Percutaneous Coronary Intervention

Risk models and risk scores derived from those models require periodic updating to account for changes in procedural performance, patient mix, and new risk factors added to existing systems. No risk model or risk score exists for predicting in-hospital/30-day mortality for percutaneous coronary interventions (PCIs) using contemporary data. This study develops an updated risk model and simplified risk score for in-hospital/30-day mortality following PCI. To accomplish this, New York's Percutaneous Coronary Intervention Reporting System was used to develop a logistic regression model and a simplified risk score model for predicting in-hospital/30-day mortality and to validate both models based on New York data from the previous year. A total of 54,770 PCI patients from 2019 were used to develop the models. Twelve different risk factors and 27 risk factor categories were used in the models. Both models displayed excellent discrimination for the development and validation samples (range from 0.894 to 0.896) and acceptable calibration, but the full logistic model had superior calibration, particularly among higher-risk patients. In conclusion, both the PCI risk model and its simplified risk score model provide excellent discrimination and although the full risk model requires the use of a hand-held device for estimating individual patient risk, it provides somewhat better calibration, especially among higher-risk patients.

Dyspnea-Related Ticagrelor Discontinuation After Percutaneous Coronary Intervention

BACKGROUND

Nearly 20% of patients on ticagrelor experience dyspnea, which may lead to treatment discontinuation in up to one-third of cases.

OBJECTIVES

The authors sought to evaluate the incidence, predictors, and outcomes of dyspnea-related ticagrelor discontinuation after percutaneous coronary intervention (PCI).

METHODS

In the TWILIGHT (Ticagrelor With Aspirin or Alone in High-Risk Patients After Coronary Intervention) trial, after 3 months of ticagrelor plus aspirin, patients were maintained on ticagrelor and randomized to aspirin or placebo for 1 year. The occurrence of dyspnea associated with ticagrelor discontinuation was evaluated among all patients enrolled in the trial. A landmark analysis was performed at 3 months after PCI, that is, the time of randomization. Predictors of dyspnea-related ticagrelor discontinuation were obtained from multivariable Cox regression with stepwise selection of candidate variables.

RESULTS

The incidence of dyspnea-related ticagrelor discontinuation was 6.4% and 9.1% at 3 and 15 months after PCI, respectively. Independent predictors included Asian race (lower risk), smoking, prior PCI, hypercholesterolemia, prior coronary artery bypass, peripheral artery disease, obesity, and older age. Among 179 patients who discontinued ticagrelor because of dyspnea after randomization, ticagrelor monotherapy was not associated with a higher risk of subsequent ischemic events (composite of all-cause death, myocardial infarction, or stroke) compared with ticagrelor plus aspirin (5.0% vs 7.1%; P = 0.566).

CONCLUSIONS

In the TWILIGHT trial, dyspnea-related ticagrelor discontinuation occurred in almost 1 in 10 patients and tended to occur earlier rather than late after PCI. Several demographic and clinical conditions predicted its occurrence, and their assessment may help identify subjects at risk for therapy nonadherence.

Risk-adjusted management in catheterization procedures for non-ST-segment elevation myocardial infarction: A standard operating procedure pilot study

BACKGROUND

The effects of standardized risk-adjusted periprocedural management of cardiac catheterization procedures in Non-ST segment elevation myocardial infarction (NSTEMI) remain unknown. We implemented a standard operating procedure (SOP) specifying risk assessment (RA, using National Cardiovascular Data Registry (NCDR) risk models) and risk-adjusted management (RM, e.g. intensified monitoring) in 2018 and aimed to investigate staff SOP adherence and associations with patient outcomes.

METHODS AND RESULTS

All 430 invasively managed NSTEMI patients (mean age 72y; 70.9% male) in 2018 were analyzed for staff SOP adherence and in-hospital clinical outcomes. 207 patients (48.1%; RM+) received both RA and RM; 92 patients (21.4%; RM-) received RA but no RM; 131 patients (30.5%; RA-) received neither RA nor RM. Lower staff adherence to RA was associated with emergency settings (51.9% (RA-) vs. 22.1% (RA+); p<0.01), presentation in cardiogenic shock (17.6% (RA-) vs. 6.4% (RA+); p<0.01) and invasive mechanical ventilation (12.2% (RA-) vs. 3.3% (RA+); p<0.01). Early sheath removal (87.9% (RM+) vs. 56.5% (RM-); p<0.01) and intensified monitoring (p<0.01) were more frequent in the RM+ group. All-cause mortality was not different (1.4% (RM+) vs. 4.3% (RM-); p=0.13), but there were fewer major bleeding events with associated with RM (2.4% (RM+) vs. 12% (RM-); p<0.01), which remained independently associated with RM in a multivariate logistic regression model correcting for confounders (p<0.01).

CONCLUSION

In an all-comer patient cohort with NSTEMI, staff adherence to risk-adjusted periprocedural management was independently associated with fewer major bleeding events. Staff adherence to SOP-specified risk assessment was frequently neglected in more critical clinical situations.

Risk-standardized mortality metric to monitor hospital performance for chest pain presentations

AIMS

Risk-standardized mortality rates (RSMR) have been used to monitor hospital performance in procedural and disease-based registries, but limitations include the potential to promote risk-averse clinician decisions and a lack of assessment of the whole patient journey. We aimed to determine whether it is feasible to use RSMR at the symptom-level to monitor hospital performance using routinely collected, linked, clinical and administrative data of chest pain presentations.

METHODS AND RESULTS

We included 192 978 consecutive adult patients (mean age 62 years; 51% female) with acute chest pain without ST-elevation brought via emergency medical services (EMS) to 53 emergency departments in Victoria, Australia (1/1/2015-30/6/2019). From 32 candidate variables, a risk-adjusted logistic regression model for 30-day mortality (C-statistic 0.899) was developed, with excellent calibration in the full cohort and with optimism-adjusted bootstrap internal validation. Annual 30-day RSMR was calculated by dividing each hospital's observed mortality by the expected mortality rate and multiplying it by the annual mean 30-day mortality rate. Hospital performance according to annual 30-day RSMR was lower for outer regional or remote locations and at hospitals without revascularisation capabilities. Hospital rates of angiography or transfer for patients diagnosed with non-ST elevation myocardial infarction (NSTEMI) correlated with annual 30-day RSMR, but no correlations were observed with other existing key performance indicators.

CONCLUSION

Annual hospital 30-day RSMR can be feasibly calculated at the symptom-level using routinely collected, linked clinical, and administrative data. This outcome-based metric appears to provide additional information for monitoring hospital performance in comparison with existing process of care key performance measures.

Clinical Use of CathPCI Registry Risk Score and Its Validation to Predict Long-Term Mortality

Risk models to estimate percutaneous coronary intervention (PCI) mortality have limited value in complex high-risk patients. However, it was improved by a recently developed bedside model to predict in-hospital mortality using data from the American College of Cardiology CathPCI Registry that included 706,263 patients. The median risk-standardized in-hospital mortality rate was 1.9%. In an attempt to validate this model in patients admitted because of acute coronary ischemia to predict in-hospital, 30-day, and 1-year mortality, we applied the proposed risk score to the study population of the Acute Coronary Syndrome Israeli Survey (ACSIS). This study was conducted for 2 months in 2018 and included all patients admitted to 25 coronary care units and cardiology departments in Israel. The ACSIS included 1,155 patients admitted because of acute myocardial infarction and who underwent PCI. In-hospital, 30-day, and 1-year mortality were 2.3%, 3.1%, and 6.2%, respectively. The CathPCI risk score yielded an area under the receiver operating characteristic curve of 0.96 (95% confidence interval [CI] 0.94 to 0.99) for in-hospital mortality; 0.96 (95% CI 0.94 to 0.98) for the 30-day mortality, and 0.88 (95% CI 0.83 to 0.93) for the 1-year mortality. The current model also included frail patients, and those with aortic stenosis, refractory shock, and after cardiac arrest. In conclusion, the CathPCI Registry risk score was validated using data from the ACSIS. Because the ACSIS population comprised patients with acute ischemia including those with high-risk features this model demonstrates a wider scope of application compared with previous ones. In addition, the model seems to be suitable to predict also the 30-day and 1-year mortality.

Complete Versus Incomplete Percutaneous Coronary Intervention-Mediated Revascularization in Patients With Chronic Coronary Syndromes

Multivessel coronary artery disease (CAD) is associated with worse outcomes across the spectrum of clinical presentations. The prognostic implications of completeness of revascularization in CAD patients, especially those with chronic coronary syndromes (CCS), remain highly debated. This is largely due to the use of non-standardized definitions for complete revascularization (CR) and incomplete revascularization (ICR) within previously published studies, lack of randomized clinical data, varying revascularization methods and heterogenous study populations. In particular, the utility and effectiveness of PCI-mediated CR for CCS remains unknown. In this review, we discuss the various definitions used for CR vs. ICR, highlight the rationale for pursuing CR and summarise the current literature regarding the effects of PCI-mediated CR on clinical outcomes in patients with CCS.

Comparative analysis of percutaneous revascularization practice in the United States and the United Kingdom: Insights from the BMC2 and BCIS databases

BACKGROUND

International registry comparisons provide insight into regional differences in clinical practice patterns, procedural outcomes, and general trends in population health and resource utilization in percutaneous coronary intervention (PCI). We sought to compare data from a state-wide PCI registry in the United States with a national registry from the United Kingdom (UK).

METHODS

We analyzed all PCI cases from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium and the British Cardiovascular Intervention Society registries from 2010 to 2017. Procedural characteristics and in-hospital outcomes were stratified by PCI indication.

RESULTS

A total of 248,283 cases were performed in Michigan and 773,083 in the United Kingdom during the study period. The proportion of patients with a prior diagnosis of diabetes in Michigan was nearly double that in the United Kingdom (38.9% vs. 21.0%). PCI for ST-elevation myocardial infarction was more frequent in the UK (25% UK vs. 14.3% Michigan). Radial access increased in both registries, reaching 86.8% in the United Kingdom versus 45.1% in Michigan during the final study year. Mechanical support utilization was divergent, falling to 0.9% of cases in the United Kingdom and rising to 3.95% of cases in Michigan in 2017. Unadjusted crude mortality rates were similar in the two cohorts, with higher rates of post-PCI transfusion and other complications in the Michigan population.

CONCLUSIONS

In a real-world comparison using PCI registries from the US and UK, notable findings include marked differences in the prevalence of diabetes and other comorbidities, a greater proportion of primary PCI with more robust adoption of transradial PCI in the United Kingdom, and divergent trends in mechanical support with increasing use in Michigan.

In-Hospital Mortality of Index Percutaneous Coronary Intervention in Patients With and Without Prior Percutaneous Revascularization: A Single-Institution Analysis

Percutaneous coronary intervention (PCI) is increasingly performed for symptom relief and survival benefit, particularly in patients presenting with acute coronary syndromes. It remains controversial whether prior PCI, and specifically when index PCI is performed on previously treated lesion(s), affects peri-procedural and in-hospital mortality. We queried an institutional PCI registry for all unique patients undergoing PCI during a 4-year period and classified them as having had or not prior PCI. If prior PCI had occurred, we further defined index PCI as a target lesion (TLR) PCI or non-TLR PCI, according to lesion(s) treated during the prior PCI. Multivariable analysis was performed to identify predictors of in-hospital mortality. Prior PCI was an independent predictor of in-hospital survival or lower mortality (HR 0.41 [0.22-0.76], P = 0.004), together with lower age (per 5 years, HR 0.73 [0.66-0.82], P < 0.001) and elective PCI (HR 0.63 [0.58-0.70], P < 0.0001). Among prior PCI patients, TLR PCI was associated with higher mortality (HR 3.03 [1.05-8.33]. P = 0.045), while elective PCI status was associated with lower mortality (HR 0.10 [0.01-0.80], P = 0.03). This excess mortality was only present in non-elective PCI cases (P_INT = 0.02). We conclude that PCI mortality risk is decreased in patients with prior PCI, particularly when index PCI is performed electively on a lesion not previously treated.

Risk prediction models in patients undergoing percutaneous coronary intervention: A collaborative analysis from a Japanese administrative dataset and nationwide academic procedure registry

BACKGROUND

Contemporary guidelines emphasize the importance of risk stratification in improving the quality of care for patients undergoing percutaneous coronary intervention (PCI). We aimed to investigate whether adding information from a procedure-based academic registry to administrative claims data would improve the performance of risk prediction model.

METHODS

We combined two nationally representative administrative and clinical databases. The study cohort comprised 43,095 patients; 18,719 and 23, 525 with acute [ACS] and chronic [CCS] coronary syndrome, respectively. Each population was randomly divided into the logistic regression model (derivation cohort, 80%) and model validation (validation cohort, 20%) groups. The performances of the following models were compared using C-statistics: (1) variables restricted to baseline claims data (model #1), (2) clinical registry data (model #2), and (3) expanded to both claims and clinical registry data (model #3). The primary outcomes were in-hospital mortality and bleeding.

RESULTS

The primary outcomes occurred in 3.7% (in-hospital mortality)/5.0% (bleeding) of patients with ACS and 0.21%/0.95% of CCS patients. For each event, the model performance was 0.65 (95% confidence interval [CI], 0.60-0.69) /0.67 (0.63-0.71) in ACS and 0.52 (0.35-0.76) /0.62 (0.54-0.70) for CCS patients in model #1, 0.83 (0.80-0.87) /0.77 (0.74-0.81) in ACS and 0.76 (0.60-0.92) /0.67 (0.59-0.75) in CCS for model #2, and 0.83 (0.79-0.86) /0.78 (0.75-0.81) in ACS and 0.76 (0.61-0.92) /0.67 (0.58-0.74) in CCS for model #3.

CONCLUSIONS

Combining clinical information from the academic registry with claims databases improved its performance in predicting adverse events.

Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries

Catheter-induced dissections (CID) of coronary arteries and/or the aorta are among the most dangerous complications of percutaneous coronary procedures, yet the data on their risk factors are anecdotal. Logistic regression and five more advanced machine learning techniques were applied to determine the most significant predictors of dissection. Model performance comparison and feature importance ranking were evaluated. We identified 124 cases of CID in electronic databases containing 84,223 records of diagnostic and interventional coronary procedures from the years 2000-2022. Based on the f1-score, Extreme Gradient Boosting (XGBoost) was found to have the optimal balance between positive predictive value (precision) and sensitivity (recall). As by the XGBoost, the strongest predictors were the use of a guiding catheter (angioplasty), small/stenotic ostium, radial access, hypertension, acute myocardial infarction, prior angioplasty, female gender, chronic renal failure, atypical coronary origin, and chronic obstructive pulmonary disease. Risk prediction can be bolstered with machine learning algorithms and provide valuable clinical decision support. Based on the proposed model, a profile of 'a perfect dissection candidate' can be defined. In patients with 'a clustering' of dissection predictors, a less aggressive catheter and/or modification of the access site should be considered.

Use of machine learning models to predict in-hospital mortality in patients with acute coronary syndrome

BACKGROUND

Cardiovascular diseases are a significant health burden with the prevalence increasing worldwide. Thus, a highly accurate assessment and prediction of death risk are crucial to meet the clinical demand. This study sought to develop and validate a model to predict in-hospital mortality among patients with the acute coronary syndrome (ACS) using nonlinear algorithms.

METHODS

A total of 2414 ACS patients were enrolled in this study. All samples were divided into five groups for cross-validation. The logistic regression (LR) model and XGboost model were applied to predict in-hospital mortality. The results of two models were compared between the variable set by the global registry of acute coronary events (GRACE) score and the selected variable set.

RESULTS

The in-hospital mortality rate was 3.5% in the dataset. Model performance on the selected variable set was better than that on GRACE variables: a 3% increase in area under the receiver operating characteristic (ROC) curve (AUC) for LR and 1.3% for XGBoost. The AUC of XGBoost is 0.913 (95% confidence interval [CI]: 0.910-0.916), demonstrating a better discrimination ability than LR (AUC = 0.904, 95% CI: 0.902-0.905) on the selected variable set. Almost perfect calibration was found in XGBoost (slope of predicted to observed events, 1.08; intercept, -0.103; p < .001).

CONCLUSIONS

XGboost modeling, an advanced machine learning algorithm, identifies new variables and provides high accuracy for the prediction of in-hospital mortality in ACS patients.

Patient characteristics and survival outcomes of cardiac arrest in the cardiac catheterization laboratory: Insights from get with the Guidelines®-Resuscitation registry

BACKGROUND

Characteristics and outcomes of patients with in-hospital cardiac arrest (IHCA) in the cardiac catheterization laboratory (CCL) have not been well-described. Thus, we compared the outcomes of patients with an IHCA in the CCL versus those in the intensive care unit (ICU) and operating rooms (OR).

METHODS

Within the American Heart Association's Get With the Guidelines®-Resuscitation registry, we identified patients ≥ 18 years old with IHCA in the CCL, ICU, or OR between 2000-2019. Using hierarchical multivariable logistic regression, we compared rates of survival to discharge for patients with IHCA in the CCL versus ICU and OR.

RESULTS

Across 428 hospitals, 193,950 patients had IHCA, of whom 6865, 181,905 and 5180 were in the CCL, ICU and OR, respectively. Overall, 2614 (38.1%) patients with IHCA in the CCL survived to discharge, whereas 30,830 (16.9%) and 2096 (40.5%) survived to discharge from the ICU and OR, respectively. After adjustment, patients with IHCA in CCL were more likely to survive to discharge as compared to those with IHCA in the ICU (odds ratio, 1.37 [95%CI: 1.29-1.46], p < 0.001). In contrast, those who had IHCA in the CCL were less likely to survive to discharge as compared to patients with IHCA in the OR (odds ratio, 0.81 [95%CI: 0.69-0.94], p = 0.006).

CONCLUSION

IHCA in the CCL is not uncommon and has a lower survival rate when compared with IHCA in the OR. The reasons for this difference deserve further study given that cardiac arrest in both settings is witnessed and response time should be similar.

Clinical expert consensus document on the use of percutaneous left ventricular assist devices during complex high-risk PCI in India using a standardised algorithm

Over the past decade, percutaneous left ventricular assist devices (pLVAD), such as the Impella microaxial flow pump (Abiomed), have been increasingly used to provide haemodynamic support during complex and high-risk revascularisation procedures to reduce the risk of intraprocedural haemodynamic compromise and to facilitate complete and optimal revascularisation. A global consensus on patient selection for the use of pLVADs, however, is currently lacking. Access to these devices is different across the world, thus, individual health care environments need to create and refine patient selection paradigms to optimise the use of these devices. The Impella pLVAD has recently been introduced in India and is being used in several centres in the management of high-risk percutaneous coronary intervention (PCI) and cardiogenic shock. With this increasing utilisation, there is a need for a standardised evaluation protocol to guide Impella use that factors in the unique economic and infrastructural characteristics of India's health care system to ensure that the needs of patients are optimally managed. In this consensus document, we present an algorithm to guide Impella use in Indian patients: to establish a standardised patient selection and usage paradigm that will allow both optimal patient outcomes and ongoing data collection.

In-hospital outcomes and readmission in older adults treated with percutaneous coronary intervention for stable ischemic heart disease

Background

Percutaneous coronary intervention (PCI) for stable ischemic heart disease (SIHD) in older adults requires a meticulous assessment of procedural risks and benefits, but contemporary data on outcomes in this population is lacking. Therefore, we examined the risk of near-term readmission, bleeding, and mortality in high-risk cohort of older adults undergoing inpatient PCI for SIHD.

METHODS

We analyzed the National Readmissions Database from 2017 to 2018 to identify index hospitalizations in which PCI was performed for SIHD. Patients were stratified into those ≥ 75 years old (older adults) and those < 75 years old. The primary outcome was 90-day readmission. Secondary outcomes included in-hospital mortality, hospital length of stay (LOS), and total hospital charge.

RESULTS

A total of 74,516 patients underwent inpatient PCI for SIHD, of whom 24,075 were older adults. Older adult patients had higher odds of in-hospital mortality (OR = 2.00, 95% CI: 1.68-2.38), intracranial hemorrhage (OR = 2.03, 95% CI: 1.24-3.34), and gastrointestinal hemorrhage (OR = 1.72, 95% CI: 1.43-2.07) during index hospitalization, with longer LOS and in-hospital charge. Older adults also experienced a higher hazard of 90-day readmission for any cause (HR = 1.61, 95% CI: 1.57-1.66) and cardiovascular causes (HR = 1.84, 95% CI: 1.77-1.91).

CONCLUSION

Older adults undergoing inpatient PCI for SIHD were at increased risk for in-hospital mortality, periprocedural morbidities, higher cost, and readmissions compared with younger adults. Understanding these differences may improve shared decision-making for patients with SIHD being considered for PCI.

Government Regulation and Percutaneous Coronary Intervention Volume, Access and Outcomes: Insights From the Washington State Cardiac Care Outcomes Assessment Program

Background

It is unclear how to geographically distribute percutaneous coronary intervention (PCI) programs to optimize patient outcomes. The Washington State Certificate of Need program seeks to balance hospital volume and patient access through regulation of elective PCI.

Methods and Results

We performed a retrospective cohort study of all non‐Veterans Affairs hospitals with PCI programs in Washington State from 2009 to 2018. Hospitals were classified as having (1) full PCI services and surgical backup (legacy hospitals, n=17); (2) full services without surgical backup (new certificate of need [CON] hospitals, n=9); or (3) only nonelective PCI without surgical backup (myocardial infarction [MI] access hospitals, n=9). Annual median hospital‐level volumes were highest at legacy hospitals (605, interquartile range, 466–780), followed by new CON, (243, interquartile range, 146–287) and MI access, (61, interquartile range, 23–145). Compared with MI access hospitals, risk‐adjusted mortality for nonelective patients was lower for legacy (odds ratio [OR], 0.59 [95% CI, 0.48–0.72]) and new‐CON hospitals (OR, 0.55 [95% CI, 0.45–0.65]). Legacy hospitals provided access within 60 minutes for 90% of the population; addition of new CON and MI access hospitals resulted in only an additional 1.5% of the population having access within 60 minutes.

Conclusions

Many PCI programs in Washington State do not meet minimum volume standards despite regulation designed to consolidate elective PCI procedures. This CON strategy has resulted in a tiered system that includes low‐volume centers treating high‐risk patients with poor outcomes, without significant increase in geographic access. CON policies should re‐evaluate the number and distribution of PCI programs.

A nomogram predicting 30-day mortality in patients undergoing percutaneous coronary intervention

Background and aims

Early detection of mortality after percutaneous coronary intervention (PCI) is crucial, whereas most risk prediction models are based on outdated cohorts before the year 2000. This study aimed to establish a nomogram predicting 30-day mortality after PCI.

Materials and methods

In total, 10,444 patients undergoing PCI in National Center for Cardiovascular Diseases in China were enrolled to establish a nomogram to predict 30-day mortality after PCI. The nomogram was generated by incorporating parameters selected by logistic regression with the stepwise backward method.

Results

Five features were selected to build the nomogram, including age, male sex, cardiac dysfunction, STEMI, and TIMI 0–2 after PCI. The performance of the nomogram was evaluated, and the area under the curves (AUC) was 0.881 (95% CI: 0.8–0.961). Our nomogram exhibited better performance than a previous risk model (AUC = 0.7, 95% CI: 0.586–0.813) established by Brener et al. The survival curve successfully stratified the patients above and below the median score of 4.

Conclusion

A novel nomogram for predicting 30-day mortality was established in unselected patients undergoing PCI, which may help risk stratification in clinical practice.

The predictive value of hemoglobin to creatinine ratio for contrast-induced nephropathy in percutaneous coronary interventions

OBJECTIVES

Hemoglobin and creatinine levels are important factors for contrast induced nephropathy (CIN) development. Our aim in this study is to investigate the predictive value of hemoglobin to creatinine ratio for CIN development in patients with percutaneous coronary intervention (PCI).

METHODS

A total of 500 patients who underwent PCI in our clinic were evaluated prospectively in terms of CIN. Hemoglobin to creatinine ratio is calculated as baseline hemoglobin/baseline serum creatinine value. glomerular filtration rate (GFR) was calculated with Cockcroft-Gault formula. The definition of CIN includes absolute (≥0.5 mg/dL) or relative increase (≥25%) in serum creatinine at 48-72 h after exposure to a contrast agent compared to baseline serum creatinine values.

RESULTS

CIN was detected in 13.8% (69 patients) of 500 patients. In multivariate lineer regression analysis, hemoglobin to creatinine ratio (beta: -0.227, p=0.03) and ejection fraction (EF) (beta: -0.161, p<0.001), contrast amount used (beta: 0.231, p<0.001) were found to be significant predictors for the development of CIN. In receiver operating characteristics (ROC) analysis; AUC=0.730 (0.66-0.79) for hemoglobin to creatinine ratio, p<0.001, AUC=0.694 (0.62-0.76) for EF, p<0.001 and AUC=0.731 (0.67-0.78) for contrast amount used p<0.001.

CONCLUSIONS

Hemoglobin to creatinine ratio, EF and contrast amount used were independent predictors for CIN development in patients with PCI (NCT04703049).

Improved outcomes in patients with severely depressed LVEF undergoing percutaneous coronary intervention with contemporary practices

BACKGROUND

Contemporary practices for hemodynamically supported high-risk percutaneous coronary intervention have evolved over the last decade. This study sought to compare outcomes of the prospective, multicenter, PROTECT III study to historic patients treated with Impella in the PROTECT II randomized controlled trial.

METHODS

Of 1,134 patients enrolled in PROTECT III from March 2017 to March 2020, 504 were "PROTECT II-like" (met eligibility for PROTECT II randomized controlled trial) and are referred to as PROTECT III for comparative analysis. Major adverse cardiac and cerebrovascular events (MACCE), comprising all-cause mortality, stroke/transient ischemic attack, myocardial infarction, and repeat revascularization, were compared at hospital discharge and 90 days.

RESULTS

Compared with PROTECT II (N = 216), PROTECT III patients were less often Caucasian (77.1% vs 83.8%, P = .045), with less prior CABG (13.7% vs 39.4%; P < .001) and prior myocardial infarction (40.7% vs 69.3%; P < .001). More PROTECT III patients underwent rotational atherectomy (37.1% vs 14.8%, P < .001) and duration of support was longer (median 1.6 vs 1.3 hours; p<0.001), with greater improvement achieved in myocardial ischemia jeopardy scores (7.0±2.4 vs 4.4±2.9; P < .001) and SYNTAX scores (21.4±10.8 vs 15.7±9.5; P < .001). In-hospital bleeding requiring transfusion was significantly lower in PROTECT III (1.8% vs 9.3%; P < .001), as was procedural hypotension (2.2% vs 10.1%; P < .001) and cardiopulmonary resuscitation or ventricular arrhythmia (1.6% vs 6.9%; P < .001). At 90 days, MACCE was 15.1% and 21.9% in PROTECT III and PROTECT II, respectively (p=0.037). Following propensity score matching, Kaplan-Meier analysis showed improved 90-day MACCE rates in PROTECT III (10.4% vs 16.9%, P = .048).

CONCLUSIONS

The PROTECT III study demonstrates improved completeness of revascularization, less bleeding, and improved 90-day clinical outcomes compared to PROTECT II for Impella-supported high-risk percutaneous coronary intervention among patients with severely depressed LVEF.

Outcomes of Percutaneous Coronary Intervention in Patients With Acquired Immunosuppression

There are limited data on the clinical outcomes of percutaneous coronary intervention (PCI) in patients with acquired immunosuppression who are frequently underrepresented in clinical trials. All PCI procedures between October 2015 and December 2018 in the Nationwide Inpatient Sample were retrospectively analyzed, stratified by immunosuppression status. Multivariable logistic regression models were performed to examine (1) the association between immunosuppression status and in-hospital outcomes, expressed as adjusted odds ratio (aOR) with 95% confidence intervals (CIs) and (2) predictors of mortality among patients with severe acquired immunosuppression. In this contemporary analysis of nearly 1.5 million PCI procedures, approximately 4% of patients who underwent PCI had acquired immunosuppression. Of these, chronic steroid use accounted for approximately half of the cohort who underwent PCI who had acquired immunosuppression, with the remainder divided between hematologic cancer, solid organ active malignancy, and metastatic cancer, with the latter group having the highest rates of composite of in-hospital mortality or stroke (9.3%) (mortality 7.5% and acute ischemic stroke 2.4%). In conclusion, immunosuppression was independently associated with increased adjusted odds of adverse clinical outcomes, specifically mortality or stroke (aOR 1.11, 95% CI 1.06 to 1.15, p <0.001) and in-hospital mortality (aOR 1.21, 95% CI 1.13 to 1.29, p <0.001), with outcomes dependent on the cause of immunosuppression.

Machine learning models for prediction of adverse events after percutaneous coronary intervention

An accurate prediction of major adverse events after percutaneous coronary intervention (PCI) improves clinical decisions and specific interventions. To determine whether machine learning (ML) techniques predict peri-PCI adverse events [acute kidney injury (AKI), bleeding, and in-hospital mortality] with better discrimination or calibration than the National Cardiovascular Data Registry (NCDR-CathPCI) risk scores, we developed logistic regression and gradient descent boosting (XGBoost) models for each outcome using data from a prospective, all-comer, multicenter registry that enrolled consecutive coronary artery disease patients undergoing PCI in Japan between 2008 and 2020. The NCDR-CathPCI risk scores demonstrated good discrimination for each outcome (C-statistics of 0.82, 0.76, and 0.95 for AKI, bleeding, and in-hospital mortality) with considerable calibration. Compared with the NCDR-CathPCI risk scores, the XGBoost models modestly improved discrimination for AKI and bleeding (C-statistics of 0.84 in AKI, and 0.79 in bleeding) but not for in-hospital mortality (C-statistics of 0.96). The calibration plot demonstrated that the XGBoost model overestimated the risk for in-hospital mortality in low-risk patients. All of the original NCDR-CathPCI risk scores for adverse periprocedural events showed adequate discrimination and calibration within our cohort. When using the ML-based technique, however, the improvement in the overall risk prediction was minimal.

Management of Acute Coronary Syndrome in Cancer Patients: It's High Time We Dealt with It

Cancer patients have an increased risk of cardiovascular disease and, notably, a significant prevalence of acute coronary syndrome (ACS). It has been shown that an elevated presence of cardiovascular risk factors in this setting leads to an interaction between these two conditions, influencing their therapeutic strategies and contributing to higher mortality. Nonetheless, cancer patients have generally not been evaluated in ACS trials, so that the treatment in these cases is still not fully known. We reviewed the current literature and discussed the best management for these very high-risk patients. The treatment strategy must be tailored based on the cancer type and stage, balancing thrombotic and bleeding risks. When the prognosis is longer than six months, especially if a clinical instability coexists, patients with ACS and cancer should be referred for percutaneous coronary intervention (PCI) as soon as possible. Moreover, an invasive strategy should be preferred in STEMI patients as well as in NSTEMI patients who are considered as high risk. On the contrary, in clinically stable NSTEMI patients, a conservative non-invasive strategy could be adopted, especially in cases of a poor life expectancy and/or of high risk of bleeding. Drug-Eluting-Stents (DES) should be the first choice if an invasive strategy is adopted. Conservative therapy could instead be considered in cancer patients with more stable CAD at an increased risk of major bleeding complications. However, the duration of dual antiplatelet therapy (DAPT) with aspirin and clopidogrel is recommended, but it should be as short as possible, whereas triple antithrombotic therapy is non-advised because it significantly increases the risk of bleeding. ACS management among cancer patients should be based on an accurate evaluation of the risk of thrombosis and bleeding. Future studies focused on choosing optimal strategies in tumor patients with ACS should be performed to treat this subset of patients better.

Efficacy of AutoPulse for Mechanical Chest Compression in Patients with Shock-Resistant Ventricular Fibrillation

INTRODUCTION

Sudden cardiac arrest is one of the most common causes of death. In cases of shock-resistant ventricular fibrillation, immediate transport of patients to the hospital is essential and made possible with use of devices for mechanical chest compression.

OBJECTIVES

The efficacy of AutoPulse in patients with shock-resistant ventricular fibrillation was studied.

METHODS

This is a multicentre observational study on a population of 480,000, with 192 reported cases of out-of-hospital cardiac arrest. The study included patients with shock-resistant ventricular fibrillation defined as cardiac arrest secondary to ventricular fibrillation requiring ≥3 consecutive shocks. Eventually, 18 patients met the study criteria.

RESULTS

The mean duration of resuscitation was 48.4±43 min, 55% of patients were handed over to the laboratory while still in cardiac arrest, 83.3% of them underwent angiography and, in 93.3% of them, infarction was confirmed. Coronary intervention was continued during mechanical resuscitation in 50.0% of patients, 60% of patients survived the procedure, and 27.8% of the patients survived.

CONCLUSIONS

Resistant ventricular fibrillation suggests high likelihood of a coronary component to the cardiac arrest. AutoPulse is helpful in conducting resuscitation, allowing the time to arrival at hospital to be reduced.

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.

The bleeding risk treatment paradox at the physician and hospital level: Implications for reducing bleeding in patients undergoing percutaneous coronary intervention

BACKGROUND

Bleeding is a common and costly complication of percutaneous coronary intervention (PCI). Bleeding avoidance strategies (BAS) are used paradoxically less in patients at high-risk of bleeding: "bleeding risk-treatment paradox" (RTP). We determined whether hospitals and physicians, who do not align BAS to PCI patients' bleeding risk (ie, exhibit a RTP) have higher bleeding rates.

METHODS

We examined 28,005 PCIs from the National Cardiovascular Data Registry CathPCI Registry for 7 hospitals comprising BJC HealthCare. BAS included transradial intervention, bivalirudin, and vascular closure devices. Patients' predicted bleeding risk was based on National Cardiovascular Data Registry CathPCI bleeding model and categorized as low (<2.0%), moderate (2.0%-6.4%), or high (≥6.5%) risk tertiles. BAS use was considered risk-concordant if: at least 1 BAS was used for moderate risk; 2 BAS were used for high risk and bivalirudin or vascular closure devices were not used for low risk. Absence of risk-concordant BAS use was defined as RTP. We analyzed inter-hospital and inter-physician variation in RTP, and the association of RTP with post-PCI bleeding.

RESULTS

Amongst 28,005 patients undergoing PCI by 103 physicians at 7 hospitals, RTP was observed in 12,035 (43%) patients. RTP was independently associated with a higher likelihood of bleeding even after adjusting for predicted bleeding risk, mortality risk and potential sources of variation (OR 1.66, 95% CI 1.44-1.92, P < .001). A higher prevalence of RTP strongly and independently correlated with worse bleeding rates, both at the physician-level (Wilk's Lambda 0.9502, F-value 17.21, P < .0001) and the hospital-level (Wilk's Lambda 0.9899, F-value 35.68, P < .0001). All the results were similar in a subset of PCIs conducted since 2015 - a period more reflective of the contemporary practice.

CONCLUSIONS

Bleeding RTP is a strong, independent predictor of bleeding. It exists at the level of physicians and hospitals: those with a higher rate of RTP had worse bleeding rates. These findings not only underscore the importance of recognizing bleeding risk upfront and using BAS in a risk-aligned manner, but also inform and motivate national efforts to reduce PCI-related bleeding.

Contemporary Clinical and Coronary Anatomic Risk Model for 30-Day Mortality After Percutaneous Coronary Intervention

BACKGROUND

Percutaneous coronary intervention (PCI) procedures are increasing in clinical and anatomic complexity, likely increasing the calculated risk of mortality. There is need for a real-time risk prediction tool that includes clinical and coronary anatomic information that is integrated into the electronic medical record system.

METHODS

We assessed 70 503 PCIs performed in 73 Veterans Affairs hospitals from 2008 to 2019. We used regression and machine-learning strategies to develop a prediction model for 30-day mortality following PCI. We assessed model performance with and without inclusion of the Veterans Affairs SYNTAX score (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery), an assessment of anatomic complexity. Finally, the discriminatory ability of the Veterans Affairs model was compared with the CathPCI mortality model.

RESULTS

The overall 30-day morality rate was 1.7%. The final model included 14 variables. Presentation status (salvage, emergent, urgent), ST-segment-elevation myocardial infarction, cardiogenic shock, age, congestive heart failure, prior valve disease, chronic kidney disease, chronic lung disease, atrial fibrillation, elevated international normalized ratio, and the Veterans Affairs SYNTAX score were all associated with increased risk of death, while increasing body mass index, hemoglobin level, and prior coronary artery bypass graft surgery were associated with lower risk of death. C-index for the development cohort was 0.93 (95% CI, 0.92-0.94) and for the 2019 validation cohort and the site validation cohort was 0.87 (95% CI, 0.83-0.92) and 0.86 (95% CI, 0.83-0.89), respectively. The positive likelihood ratio of predicting a mortality event in the top decile was 2.87% more accurate than the CathPCI mortality model. Inclusion of anatomic information in the model resulted in significant improvement in model performance (likelihood ratio test P<0.01).

CONCLUSIONS

This contemporary risk model accurately predicts 30-day post-PCI mortality using a combination of clinical and anatomic variables. This can be immediately implemented into clinical practice to promote personalized informed consent discussions and appropriate preparation for high-risk PCI cases.

Comparison of Patients Undergoing Percutaneous Coronary Intervention in Contemporary U.S. Practice With ISCHEMIA Trial Population

OBJECTIVES

The study sought to assess the proportion of patients in modern U.S. interventional practice that fulfilled criteria for enrollment in the ISCHEMIA (International Study of Comparative Health Effectiveness With Medical and Invasive Approaches) trial.

BACKGROUND

The ISCHEMIA trial, which enrolled patients with stable ischemic heart disease (SIHD), showed that revascularization improved angina symptoms with little effect on death or myocardial infarction.

METHODS

A cross-sectional analysis of the National Cardiovascular Data Registry CathPCI Registry (v5.0), including 1,662 hospitals, was performed. Patients undergoing percutaneous coronary intervention (PCI) for SIHD in routine clinical practice meeting ISCHEMIA trial inclusion criteria and those that did not were evaluated.

RESULTS

During the study period, 388,212 patients underwent PCI for SIHD, comprising 41.88% of all patients undergoing PCI during the study period. Of these, 125,302 (32.28%; 13.52% of all patients undergoing PCI) met criteria for enrollment in the ISCHEMIA trial. Among SIHD patients that did not meet criteria, 71,852 (18.51%) had SIHD with high-risk features (35.2% left main disease, 43.7% left ventricular systolic dysfunction, 16.8% end-stage renal disease), 67,159 (17.3%) had SIHD with negative or low-risk functional testing, and 123,899 (31.92%) either had no stress testing or did not have ischemic burden reported. At the median hospital, 32.1% (interquartile range: 23.5%-40.6%) of SIHD patients met criteria for enrollment in the ISCHEMIA trial, with these patients experiencing lower unadjusted in-hospital mortality rate than comparator groups who met exclusion criteria for the trial (0.11%) (P < 0.01 for all comparisons).

CONCLUSIONS

Among contemporary U.S. patients undergoing PCI for SIHD, 32.28% clearly met enrollment criteria for the ISCHEMIA trial. There was significant variation among individual centers in the proportion of SIHD patients meeting criteria for the ISCHEMIA trial.

Differential impact of type 1 and type 2 diabetes mellitus on outcomes among 1.4 million US patients undergoing percutaneous coronary intervention

BACKGROUND

The aim was to determine the impact of diabetes mellitus (DM) on outcomes after percutaneous coronary intervention (PCI). There is limited data on the impact of DM and its subtypes among patients who underwent PCI during hospitalization.

METHODS

All PCI hospitalizations from the National Inpatient Sample (October 2015-December 2018) were stratified by the presence and subtype of DM. Multivariable logistic regression was performed to determine the adjusted odds ratios (aOR) of in-hospital adverse outcomes in type 1 DM (T1DM) and type 2 DM (T2DM) compared to no-DM.

RESULTS

Out of 1,363,800 individuals undergoing PCI, 12,640 (0.9%) had T1DM and 539,690 (39.6%) had T2DM. T1DM patients had increased aOR of major adverse cardiovascular and cerebrovascular events (MACCE) (1.26, 95%CI 1.17-1.35), mortality (1.56, 95%CI 1.41-1.72), major bleeding (1.63, 95%CI 1.45-1.84), and stroke (1.75, 95%CI 1.51-2.02), while T2DM patients had only increased aOR of MACCE (1.02, 95%CI 1.01-1.04), mortality (1.10, 95%CI 1.08-1.13) and stroke (1.22, 95%CI 1.18-1.27), compared to no-DM patients. However, both T1DM and T2DM had lower aOR of cardiac complications (0.87, 95%CI 0.77-0.97 and 0.87, 95%CI 0.85-0.89, respectively), in comparison to no-DM patients. When accounting for the indication, both DM subgroups had higher aOR of MACCE, mortality, and stroke compared to no-DM patients in the acute coronary syndrome setting (p < 0.001, for all), while only increased aOR of stroke (1.59, 95%CI 1.17-2.15 for T1DM and 1.12, 95%CI 1.05-1.20 for T2DM) persisted in the elective setting.

CONCLUSIONS

Patients with DM who have undergone PCI during hospitalization are more likely to experience adverse in-hospital outcomes, and T1DM patients are a particularly high-risk cohort.

Prognostic implications and outcomes of cardiac arrest among contemporary patients with STEMI treated with PCI

Background

Cardiac arrest (CA) complicating ST-elevation myocardial infarction (STEMI) is associated with a disproportionately higher risk of mortality. We described the contemporary presentation, management, and outcomes of CA patients in the era of primary percutaneous coronary intervention (PCI).

Methods

We reviewed 1,272 consecutive STEMI patients who underwent PCI between 1/1/2011-12/31/2016 and compared characteristics and outcomes between non-CA (N = 1,124) and CA patients (N = 148), defined per NCDR definitions as pulseless arrest requiring cardiopulmonary resuscitation and/or defibrillation within 24-hr of PCI.

Results

Male gender, cerebrovascular disease, chronic kidney disease, in-hospital STEMI, left main or left anterior descending culprit vessel, and initial TIMI 0 or 1 flow were independent predictors for CA. CA patients had longer door-to-balloon-time (106 [83,139] vs. 97 [74,121] minutes, p = 0.003) and greater incidence of cardiogenic shock (48.0% vs. 5.9%, p < 0.001), major bleeding (25.0% vs. 9.4%, p < 0.001), and 30-day mortality (16.2% vs. 4.1%, p < 0.001). Risk score for 30-day mortality based on presenting characteristics provided excellent prognostic accuracy (area under the curve = 0.902). However, over long-term follow-up of 4.5 ± 2.4 years among hospital survivors, CA did not portend any additional mortality risk (HR: 1.01, 95% CI: 0.56–1.82, p = 0.97).

Conclusions

In a contemporary cohort of STEMI patients undergoing primary PCI, CA occurs in >10% of patients and is an important mechanism of mortality in patients with in-hospital STEMI. While CA is associated with adverse outcomes, it carries no additional risk of long-term mortality among survivors highlighting the need for strategies to improve the in-hospital care of STEMI patients with CA.

Outcomes of Acute Myocardial Infarction in Patients with Familial Hypercholesteremia

BACKGROUND

There is a paucity of contemporary data regarding the outcomes of acute myocardial infarction among patients with familial hypercholesteremia.

METHODS

We queried the Nationwide Readmissions Database (2016-2018) for hospitalizations with acute myocardial infarction. Multivariable regression analysis was used to compare in-hospital outcomes and 30-day readmissions among patients with and without familial hypercholesteremia.

RESULTS

The analysis included 1,363,488 hospitalizations with acute myocardial infarction. The prevalence of familial hypercholesteremia was 0.07% among acute myocardial infarction admissions. Compared with those without familial hypercholesteremia, admissions with familial hypercholesteremia were younger and had less comorbidities but were more likely to have had prior infarct and revascularization. Admissions with familial hypercholesteremia were more likely to present with ST-elevation myocardial infarction and undergo revascularization. After multivariable adjustment, there was no difference in in-hospital case fatality among patients with hypercholesteremia compared with those without it (adjusted odds ratio [aOR] = 0.76; 95% confidence interval [CI] 0.41-1.39). Admissions with acute myocardial infarction and familial hypercholesteremia had higher adjusted rates of cardiac arrest and utilization of mechanical support. There were no group differences in overall 30-day readmission (aOR 0.75; 95% CI 0.51-1.10) or 30-day readmission for acute myocardial infarction. However, a nonsignificant trend toward higher readmission for percutaneous coronary intervention was observed among patients with familial hypercholesteremia (aOR 1.89; 95% CI 0.98-3.64).

CONCLUSION

In this contemporary nationwide observational analysis, patients with familial hypercholesteremia represent a small proportion of the overall population with acute myocardial infarction and have a distinctive clinical profile but do not appear to have worse in-hospital case fatality compared with those without familial hypercholesteremia.

Transradial Access for High-Risk Percutaneous Coronary Intervention: Implications of the Risk-Treatment Paradox

[Figure: see text].

ST-Elevation Myocardial Infarction Complicated by Out-of-Hospital Cardiac Arrest

5-10% of ST-elevated myocardial infarctions (STEMI) present with out-of-hospital cardiac arrest (OHCA). Although this subgroup of patients carries the highest in-hospital mortality among the STEMI population, it is the least likely to undergo coronary angiography and revascularization. Due to the concomitant neurologic injury, patients with OHCA STEMI require prolonged hospitalization and adjustments to standard MI management. This review systematically assesses the course of patients with OHCA STEMI from development of the arrest to hospital discharge, assesses the limiting factors for their treatment access, and presents the evidence-based optimal intervention strategy for this high-risk MI population.

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.

Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention

BACKGROUND

Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients.

OBJECTIVES

This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables.

METHODS

Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance.

RESULTS

In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%).

CONCLUSIONS

The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts.

Mechanical Circulatory Support in High-Risk Percutaneous Coronary Intervention

The use of mechanical circulatory devices to support high-risk elective percutaneous coronary intervention (PCI) has become more common as the group of patients considered inoperable or high risk for surgical revascularization has grown. Most of the data examining outcomes in high-risk PCI are observational and retrospective. Limited prospective randomized studies have been unable to show improved clinical outcomes with routine mechanical circulatory support (MCS) in patients with a high burden of coronary artery disease and reduced ejection fraction. The role for MCS in high-risk PCI continues to evolve as understanding of the appropriate groups for this therapy evolves.