Development of 2 Registry-Based Risk Models Suitable for Characterizing Hospital Performance on 30-Day All-Cause Mortality Rates Among Patients Undergoing Percutaneous Coronary Intervention

Preoperative Variables of 30-Day Mortality in Adults Undergoing Percutaneous Coronary Intervention: A Systematic Review

BACKGROUND AND AIM

Risk adjustment following percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and clinical decision-making. There remains significant variation in the accuracy and nature of risk adjustment models utilised in international PCI registries/databases. Therefore, the current systematic review aims to summarise preoperative variables associated with 30-day mortality among patients undergoing PCI, and the other methodologies used in risk adjustments.

METHOD

The MEDLINE, EMBASE, CINAHL, and Web of Science databases until October 2022 without any language restriction were systematically searched to identify preoperative independent variables related to 30-day mortality following PCI. Information was systematically summarised in a descriptive manner following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. The quality and risk of bias of all included articles were assessed using the Prediction Model Risk Of Bias Assessment Tool. Two independent investigators took part in screening and quality assessment.

RESULTS

The search yielded 2,941 studies, of which 42 articles were included in the final assessment. Logistic regression, Cox-proportional hazard model, and machine learning were utilised by 27 (64.3%), 14 (33.3%), and one (2.4%) article, respectively. A total of 74 independent preoperative variables were identified that were significantly associated with 30-day mortality following PCI. Variables that repeatedly used in various models were, but not limited to, age (n=36, 85.7%), renal disease (n=29, 69.0%), diabetes mellitus (n=17, 40.5%), cardiogenic shock (n=14, 33.3%), gender (n=14, 33.3%), ejection fraction (n=13, 30.9%), acute coronary syndrome (n=12, 28.6%), and heart failure (n=10, 23.8%). Nine (9; 21.4%) studies used missing values imputation, and 15 (35.7%) articles reported the model's performance (discrimination) with values ranging from 0.501 (95% confidence interval [CI] 0.472-0.530) to 0.928 (95% CI 0.900-0.956), and four studies (9.5%) validated the model on external/out-of-sample data.

CONCLUSIONS

Risk adjustment models need further improvement in their quality through the inclusion of a parsimonious set of clinically relevant variables, appropriately handling missing values and model validation, and utilising machine learning methods.

Predicting Major Adverse Events in Patients Undergoing Transcatheter Left Atrial Appendage Occlusion

BACKGROUND

The National Cardiovascular Data Registry Left Atrial Appendage Occlusion Registry (LAAO) includes the vast majority of transcatheter LAAO procedures performed in the United States. The objective of this study was to develop a model predicting adverse events among patients undergoing LAAO with Watchman FLX.

METHODS

Data from 41 001 LAAO procedures with Watchman FLX from July 2020 to September 2021 were used to develop and validate a model predicting in-hospital major adverse events. Randomly selected development (70%, n=28 530) and validation (30%, n=12 471) cohorts were analyzed with 1000 bootstrapped samples, using forward stepwise logistic regression to create the final model. A simplified bedside risk score was also developed using this model.

RESULTS

Increased age, female sex, low preprocedure hemoglobin, no prior attempt at atrial fibrillation termination, and increased fall risk most strongly predicted in-hospital major adverse events and were included in the final model along with other clinically relevant variables. The median in-hospital risk-standardized adverse event rate was 1.50% (range, 1.03%-2.84%; interquartile range, 1.42%-1.64%). The model demonstrated moderate discrimination (development C-index, 0.67 [95% CI, 0.65-0.70] and validation C-index, 0.66 [95% CI, 0.62-0.70]) with good calibration. The simplified risk score was well calibrated with risk of in-hospital major adverse events ranging from 0.26% to 3.90% for a score of 0 to 8, respectively.

CONCLUSIONS

A transcatheter LAAO risk model using National Cardiovascular Data Registry and LAAO Registry data can predict in-hospital major adverse events, demonstrated consistency across hospitals and can be used for quality improvement efforts. A simple bedside risk score was similarly predictive and may inform shared decision-making.

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.

Myocardial infarction with non-obstructive coronary arteries as compared with myocardial infarction and obstructive coronary disease: outcomes in a Medicare population

AIMS

The prognosis of patients with MINOCA (myocardial infarction with non-obstructive coronary arteries) is poorly understood. We examined major adverse cardiac events (MACE) defined as all-cause mortality, re-hospitalization for acute myocardial infarction (AMI), heart failure (HF), or stroke 12-months post-AMI in patients with MINOCA versus AMI patients with obstructive coronary artery disease (MICAD).

METHODS AND RESULTS

Multicentre, observational cohort study of patients with AMI (≥65 years) from the National Cardiovascular Data Registry CathPCI Registry (July 2009-December 2013) who underwent coronary angiography with linkage to the Centers for Medicare and Medicaid (CMS) claims data. Patients were classified as MICAD or MINOCA by the presence or absence of an epicardial vessel with ≥50% stenosis. The primary endpoint was MACE at 12 months, and secondary endpoints included the components of MACE over 12 months. Among 286 780 AMI admissions (276 522 unique patients), 16 849 (5.9%) had MINOCA. The 12-month rates of MACE (18.7% vs. 27.6%), mortality (12.3% vs. 16.7%), and re-hospitalization for AMI (1.3% vs. 6.1%) and HF (5.9% vs. 9.3%) were significantly lower for MINOCA vs. MICAD patients (P < 0.001), but was similar between MINOCA and MICAD patients for re-hospitalization for stroke (1.6% vs. 1.4%, P = 0.128). Following risk-adjustment, MINOCA patients had a 43% lower risk of MACE over 12 months (hazard ratio = 0.57, 95% confidence interval 0.55-0.59), in comparison to MICAD patients. This pattern was similar for adjusted risks of the MACE components.

CONCLUSION

This study confirms an unfavourable prognosis in elderly patients with MINOCA undergoing coronary angiography, with one in five patients with MINOCA suffering a major adverse event over 12 months.

Hospital Performance on Percutaneous Coronary Intervention Process and Outcomes Measures

BACKGROUND

The Physician Consortium for Performance Improvement recently proposed percutaneous coronary intervention (PCI)-specific process measures. However, information about hospital performance on these measures and the association of PCI process and outcomes measures are not available.

METHODS AND RESULTS

We linked the National Cardiovascular Data Registry (NCDR) CathPCI Registry with Medicare claims data to assess hospital performance on established PCI process measures (aspirin, thienopyridines, and statins on discharge; door-to-balloon time; and referral to cardiac rehabilitation), newly proposed PCI process measures (documentation of contrast dose, glomerular filtration rate, and PCI indication; appropriate indication for elective PCI; and use of embolic protection device), and a composite of all process measures. We calculated weighted pair-wise correlations between each set of process metrics and performed weighted correlation analyses to assess the association between composite measure performance with corresponding 30-day risk-standardized mortality and readmission rates. We reported the variance in risk-standardized 30-day outcome rates explained by process measures. We analyzed 1 268 860 PCIs from 1331 hospitals. For many process measures, median hospital performance exceeded 90%. We found strong correlations between medication-specific process measures (P<0.01) and weak correlations between hospital performance on the newly proposed and established process measures. The composite process measure explained only 1.3% and 2.0% of the observed variation in mortality and readmission rates, respectively.

CONCLUSIONS

Hospital performance on many PCI-specific process measures demonstrated little opportunity for improvement and explained only a small percentage of hospital variation in 30-day outcomes. Efforts to measure and improve hospital quality for PCI patients should focus on both process and outcome measures.

Developing a risk model for in-hospital adverse events following implantable cardioverter-defibrillator implantation: a report from the NCDR (National Cardiovascular Data Registry)

OBJECTIVES

To better inform patients and physicians of the expected risk of adverse events and to assist hospitals' efforts to improve the outcomes of patients undergoing implantable cardioverter-defibrillator (ICD) implantation, we developed and validated a risk model using data from the NCDR (National Cardiovascular Data Registry) ICD Registry.

BACKGROUND

ICD prolong life in selected patients, but ICD implantation carries the risk of periprocedural complications.

METHODS

We analyzed data from 240,632 ICD implantation procedures between April 1, 2010, and December 31, 2011 in the registry. The study group was divided into a derivation (70%) and a validation (30%) cohort. Multivariable logistic regression was used to identify factors associated with in-hospital adverse events (complications or mortality). A parsimonious risk score was developed on the basis of beta estimates derived from the logistic model. Hierarchical models were then used to calculate risk-standardized complication rates to account for differences in case mix and procedural volume.

RESULTS

Overall, 4,388 patients (1.8%) experienced at least 1 in-hospital complication or death. Thirteen factors were independently associated with an increased risk of adverse outcomes. Model performance was similar in the derivation and validation cohorts (C-statistics = 0.724 and 0.719, respectively). The risk score characterized patients into low- and-high risk subgroups for adverse events (≤10 points, 0.3%; ≥30 points, 4.2%). The risk-standardized complication rates varied significantly across hospitals (median: 1.77, interquartile range 1.54, 2.14, 5th/95th percentiles: 1.16/3.15).

CONCLUSIONS

We developed a simple model that predicts risk for in-hospital adverse events among patients undergoing ICD placement. This can be used for shared decision making and to benchmark hospital performance.