Diagnosis-Driven, Cross-Disciplinary QA System for Coronary Artery Disease—Study Protocol

Abstract

Background

Percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) are invasive treatment options for coronary artery disease (CAD), aiming to improve quality of life and reduce cardiovascular morbidity and mortality. Guidelines-based revascularization decisions should consider anatomical complexity, comorbidities, and patient preferences, with procedural risk assessed through validated scoring systems. However, the current legal quality assurance (QA) programs in Germany remain procedure specific and therefore lack a patient-centered, diagnosis-oriented approach. This study proposes a paradigm shift toward diagnosis-based QA to optimize individualized treatment selection, improve outcome attribution, and ensure transparent quality assessment. By integrating guideline recommendations with enhanced data linkage, this framework aims to standardize and improve CAD care quality while addressing limitations of existing QA schemes.

Methods

This mixed-methods study aims to develop a cross-disciplinary QA framework for CAD patients undergoing elective PCI or CABG. Qualitative methods will be employed to formulate preliminary evidence-based quality indicators (QI), while secondary data analyses will provide empirical support for QI prioritization, modeling, and future evaluation. Findings from both approaches will undergo a structured consensus process to establish validated QI as basis of a redesigned QA scheme.

Results

The resulting framework seeks to standardize and improve QA procedures across CAD care pathways, integrating clinical expertise with real-world data to enhance patient outcome.

Conclusion

The study proposes a patient-centered, diagnosis-based quality assurance framework for coronary artery disease care, aiming to improve treatment decisions and outcomes. By integrating guideline, expert input, and real-world data, it seeks to enhance transparency and standardization in quality assessment across CAD treatment pathways.

 

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Keywords

CABG - CAD - coronary artery disease - coronary bypass - coronary stents - heart failure - heart team - PCI - quality assurance - quality indicators

Introduction

Cardiovascular diseases are major global issues and have relatively high mortality rates as compared with other diseases. Researchers, health care providers, and health care organizations are continuously struggling to improve coronary artery disease (CAD) prevention and care and provide better and on-time treatment to patients to reduce its disease burden. In Germany,[1] the rate of CAD continuously declined during the last few years, from 8.8% in 2017 to a still high rate of 8.1% of the population in 2024.[2] A further decline is predicted in Germany for CAD, except for the 70 to 79 age group.[3] [4] [5]

For the invasive treatment of CAD, two therapeutic options are available, percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG).[6] Generally, PCI is primarily recommended for patients with coronary one- or two-vessel disease, while CABG can be preferred in patients with coronary three-vessel (multi-vessel) disease and in case of left main stenosis.[7] Both the treatments are effective especially combined with a positive change in lifestyle. According to the results of studies focusing on quality of life and clinical results, there is no need for an invasive treatment in asymptomatic patients. The latest ESC 2024 and ACC/AHA 2025 guidelines provide updated recommendations for coronary revascularization, with general agreement on PCI as first-line in one- or two-vessel disease (synergy between PCI with Taxus and cardiac surgery [SYNTAX] of ≤22) and CABG preferred for left main/three-vessel disease, particularly in patients with diabetes or reduced left ventricular ejection fraction (LVEF). Key differences emerge in the approach to complex cases: ESC 2024 maintains a more conservative stance, favoring culprit-only PCI in STEMI and restricting left main PCI to low SYNTAX scores, while ACC/AHA 2025 permits single-session multivessel PCI and is more permissive of left main stenting. Both the guidelines emphasize radial access, complete revascularization, and recommend patient informed consent based on heart team evaluations, but ACC/AHA offers greater flexibility in PCI for complex anatomy when surgical risk is high.[8] [9] The ESC 2023 guideline for acute coronary syndromes updates provided foundational evidence for these current recommendations, particularly in establishing CABG superiority for multivessel disease with diabetes.[10]

Since re-hospitalization is common for the patients having acute myocardial infarction (MI), and almost half of the patients die within 5 years with a recurrence,[11] it is crucial to ensure high-quality care for CAD patients. The indication for invasive treatment decision-making must adhere to clinical guidelines, considering anatomical complexity, comorbidities, and patient preferences.[12] Individual risk of patients should be evaluated using validated scoring systems to support informed decision-making.

However, current legal QA programs in Germany remain narrowly focused on procedures rather than adopting a comprehensive, patient-centered, and diagnosis-driven approach. Therefore, this study proposes a transformative shift toward a diagnosis-based QA framework designed to optimize individualized patient treatment selection, accurately assess mid- and long-term outcomes, and establish transparent quality benchmarks. By integrating established guideline recommendations with real-world evidence from extended patients' pathway analyses, this approach aims to optimize indications, standardize patient care, bridge gaps in existing QA models, and conclusively improve patient outcome.

 

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Objective

The study aims to contribute to the redesign of quality assurance (QA) for patients with CAD undergoing CABG and/or PCI, isolated or consecutive. It is intended to merge the previously introduced separate QA schemes in Germany for CABG or PCI procedures. The integration of previously separate QA schemes into a single unified framework enables a more comprehensive and comparative assessment of quality indicators and outcomes in CAD patients undergoing PCI and/or CABG. For specific patient groups, improved outcomes can be achieved by selecting the most appropriate procedure (PCI or CABG).

 

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Hypothesis

The redesign of previously separate QA schemes into one combined QA scheme facilitates to assess the quality of indications and outcomes in CAD patients undergoing PCI and/or CABG in an elaborate and comparative way. For specific patient groups, outcome benefits can be achieved by choosing the appropriate invasive procedure (PCI/CABG).

 

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Methods

The study is based on a mixed-methods approach to develop a redesigned cross-disciplinary and cross-procedural QA scheme for patients with CAD undergoing elective isolated or consecutive PCI/CABG. As a basis for the redesign, preliminary quality indicators will be formulated using qualitative methods and secondary data analyses will be performed to provide the empirical basis for prioritizing and modeling the quality indicators and a future evaluation concept. The insights gathered from these two methods will be used in a modified Delphi process involving inter- and cross-disciplinary expertise to reach a consensus on the quality indicators that will form the basis of the redesigned QA system.

 

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Study Design

The study's mixed-methods approach combines (step 1) qualitative analyses of treatment pathways, guidelines, and expert interviews based on (step 2) secondary data analyses of two different data sources within the framework of a retrospective cohort study: First, data from the German mandatory external (comparative) quality assurance, collected by the Institute for Quality Assurance and Transparency in Health Care (IQTIG) and in which each approved health care provider performing PCI and/or CABG has to transmit standardized data from patients with statutory health insurance. Second, routine data from the largest German statutory health insurance, the AOK-die Gesundheitskasse, which insures around one-third of the German population.

Data Sources for Step 1

Guidelines for CAD treatment and CAD-related quality indicators will be retrieved by a systematic literature and quality indicator data basis analysis and expert surveys.[6] [13]

 

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Searching for and Consenting on Quality Indicators

The development of quality indicators (QI) for treatment of CAD follows a structural, multi-step process involving both clinical experts and patient representatives. It starts with a process analysis workshop to describe common diagnostic and therapeutic pathways and patient journeys for CAD. Next, a systematic literature and guideline review identifies existing QIs, which are compiled into a preliminary list after removing duplicates. This is supplemented by an analysis of current QA procedures to compare indicators, data sources, and evaluation methods. Using these findings, a draft QI set will be created, prioritizing indication quality, outcomes, and patient experience. Then the indicators will be prioritized through an expert Delphi consensus process, requiring 75% agreement for inclusion. Finally, suitable data sources are identified for implementation, and potential limitations, such as validity or statistical concerns, and patient preferences are addressed to ensure meaningful interpretation of the QI results. The process emphasizes evidence-based, interdisciplinary collaboration to produce actionable, patient-centered quality measures.[14] [15] [16]

The results of step 1 and step 2 will be used for a modified Delphi process involving inter- and cross-disciplinary expertise to reach a consensus on the final quality indicator set that will form the basis of the redesigned QA system.

 

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Data Sources for Step 2

AOK: The AOK Research Institute (WIdO) provides access to its database on all reimbursement-related data of the AOK insurees. The database generally contains demographic variables (age, sex, region of residence, insurance status), “International Statistical Classification of Diseases and Related Health Problems” (ICD) and “operation and procedure codes” (OPS) of inpatient (hospitalization/rehospitalization) and outpatient visits, and medications (ATC codes).

IQTIG: The Institute for Quality Assurance and Transparency in Health Care (IQTIG) operates the external QA in Germany. In relation to CAD the IQTIG runs two different QA schemes: one for coronary angiography and PCI and another for isolated CABG. Health care providers are obliged to submit pseudonymized clinical and in-hospital outcome data for each patient with statutory health insurance (for CABG Germany-wide to IQTIG, for PCI at first to Federal State–related QA institutions and then to IQTIG). The IQTIG analyses, among others, measured values of predefined quality indicators and other basic information. The results are published annually. Further analyses that are not included in the publicly available reports can be performed upon request as part of so-called secondary data use (according to § 137a, paragraph 10, sentence 4, Social Code Book V).

 

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Patient Selection

On the one hand, our study includes all AOK-insured patients with CAD who underwent an invasive coronary procedure (isolated/consecutive PCI/CABG) in the years 2019 to 2023. On the other hand, those cases are included for which the IQTIG had proper QA documentation from the PCI or CABG service areas for the years 2021 to 2023.

 

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Inclusion and Exclusion Criteria

AOK data: Key elements of inclusion criteria are: age ≥18 years with a diagnosis of CAD (ICD-10-GM Version 2019–23: I20-I25, I42-I43, I46 and I50), who underwent an invasive coronary procedure (isolated/consecutive PCI/CABG) during the years 2019 to 2023 (procedure codes: OPS 5–360*, 5–361*, 5–362*, 5–363*, 5–364*, 8–837*; EBM 34291, 34292), and were insured at AOK at least 1 year before and after treatment or until death.

IQTIG data: All cases that were registered within the IQTIG service area “KCHK-KC” or “PCI” for the years 2021 to 2023.

Patients with missing key data such as diagnosis code or treatment record are excluded.

 

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Study Variables

Several distinct variables associated with CAD are considered for our study. This will capitalize on a comprehensive set of variables, extracted from AOK database and IQTIG quality reports on demographic, clinical, treatment, and outcome measures. Age (≥18), sex/gender, and region (urban versus rural based on postal code/AOK exclusive) are generally considered as demographics.

Clinical data include cardiac and non-cardiac comorbidities such as hypertension (ICD-10 code I10-I15), diabetes mellitus (E10-E14), acute or chronic kidney disease (N17, N18), heart failure (I50), prior myocardial infarction (I21-I22), and other heart, vascular, or neurological diseases. Additionally, the disease categorization and associated information related to CAD from IQTIG/OPS codes, e.g., multi-vessel disease, left ventricular ejection fraction, and ACS type (STEMI (I21.0-I21.3) versus NSTEMI (I21.4) versus unstable angina (I20.0) are recorded.

Treatment variables encompass revascularization types, PCI (OPS 8–837*; EBM 34291 + 34292) or CABG (OPS 5–36*), and pharmacotherapy adherence (Proportion of Days Covered, PDC) ≥80% for antiplatelets (ATC code B01AC), statins (ATC code C10AA), β-blockers (ATC code C07AB), and ACE inhibitors (ATC code C09AA)/ARBs (ATC code C09CA), date of hospitalization, date of treatment, and transfer between hospitals.[17] [18]

In our study, survival, 30-day and 1-year all-cause mortality of CAD patients will be considered as primary outcomes.

Major adverse cardiovascular and cerebrovascular events (MACCE,[19] e.g., AMI, stroke, and cardiovascular death), cardiovascular related rehospitalization within 12 months, and a deterioration in long-term care dependence will be considered as secondary outcomes.

 

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Data Cleaning

Incomplete, irrelevant, and duplicate data will be removed. Considering our large dataset, we will not use imputation, as it can be misleading or can be considered as data manipulation. Besides that, normalization will also be performed to organize the attributes of database and to overcome data redundancy.

 

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Secondary Data Analysis

Our data analysis is based on two purposes in connection with IQTIG and AOK patient's secondary data.

Regarding IQTIG secondary data, PCI and CABG cases from the PCI and KCHK-KC QA schemes for the years 2021 to 2023 will be compared regarding the indication and procedure-related data and—if available—the 1-year follow-up outcomes. For this purpose, the most comparable elective cases will be selected using propensity score matching (variables taken into account: isolated/consecutive cases, age, gender, and other recorded risk factors), and the achieved treatment/indicator results will be compared using inferential statistics.[20] These analyses demonstrate the extent to which comparable cases have different short-term and 1-year outcomes depending on the invasive procedure type and how often the indication criteria were met, e.g., depending on gender.

Based on secondary AOK data of CAD patients who underwent an invasive coronary therapy (isolated/consecutive PCI/CABG) between 2019 and 2023, their treatment courses and short- and long-term outcomes (between 2014 and 2023) will be analyzed retrospectively. The aim of the analyses is to identify typical treatment courses (patient journeys, e.g., various, possibly consecutive, multiple procedures) and to evaluate the procedure related as well as the potential determinants of these outcomes.

The isolation of typical treatment procedures and their related outcomes can reveal the most relevant criteria for determining the indication for PCI or CABG and also identify strata of patients for whom a meaningful comparison of outcome indicators is possible. This enables fair comparison of service provision related to diagnosis-based rather than procedure-based QA, which can lead to long-term improvements in the quality of care.

After an initial review of the various treatment courses, propensity score matching will be used to create comparable groups (age and gender, pre-existing conditions, and degree of severity/indications, for example, acute myocardial infarction and/or three-vessel disease). The associated outcomes (survival/recurrence) will be compared using inferential statistics (survival analysis; logistic regression) depending on the last selected intervention (PCI/CABG). Based on the findings on gender-specific differences in the conservative and invasive treatments of CAD, the analyses are also performed on a gender-specific basis.[21] [22]

 

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Statistical Methods

By evaluating the outcomes of comparable patients who have undergone either PCI or CABG,[23] the aim is to determine whether the choice of a particularly suitable invasive procedure can achieve outcome benefits for selected patient groups.

Once data are properly cleaned, normality test can be performed to check either our data are normally distributed or skewed. If all the assumptions of normal distribution are fulfilled, parametric tests will be used for statistical analysis, otherwise non-parametric tests are recommended. Not normal or skewed data can be transformed using log transformation and then we can apply the parametric test to the transformed data and interpret the results after anti-transformation. We will check the power using actual sample size, under the assumptions of two-sided tail and 0.05 level of significance.[24]

Descriptive analysis will summarize baseline characteristics. Generally, continuous variables will be summarized using means and standard deviations and will be compared between groups using the unpaired student's t-test. Categorical variables measured on nominal scale will be analyzed using the Fisher's exact test, while ordinal categorical variables will be assessed using non-parametric methods, including the Wilcoxon rank sum test or Mann-Whitney U test, as appropriate. Data visualization will be used for temporal trends and distributions.

Discrete outcomes will be reported as counts and percentages. Comparative group analyses will involve calculating relative risks along with 95% confidence intervals.

Event-free survival will be estimated using the Kaplan-Meier method, differences between groups will be assessed using the log rank test, and cox regression analyses will determine the influence of different variables on survival time. Multivariable logistic regression models will be developed to identify the independent predictors of the primary outcomes—event-free survival/all-cause mortality at 1 year. The models will include baseline clinical and angiographic characteristics, as well as procedural variables such as the revascularization strategy (CABG versus PCI).[24] [25] [26]

 

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Strengths and Limitations

Overall, we have a relatively big and comprehensive dataset based on nationwide IQTIG procedural data and CAD patients registered with AOK.

Due to robust up to 5-year follow-up (2019–2023), long-term insights will enable to capture the critical endpoints, which is somehow not possible in short-term quality metrics (QM).

In some cases, patients switch their insurance from AOK to another statutory health or private health insurance company. In these cases, follow-up will be impossible, and it may cause attrition bias, especially if registration cancellation relates to unmeasured health status. If we try to cover this attrition bias as a censoring event by using competing risk regression, still there can be traces of residual bias for long-term outcomes.

As we use secondary data, some treatment indications may be challenging to verify, if the necessary clinical data are missing, since it was not collected as part of our study. In addition, the interpretation of results may be challenging due to the possibility of coding errors or incomplete information when using partially unverified data.

In terms of generalizability, although the AOK covers almost 33% of the German population, this health insurance only covers those with statutory health insurance, so our conclusions and decisions regarding this data obviously cannot be applied one-to-one to non-statutory health insured patients (approximately 11% of the German population).

Finally, observational studies[27] are always confronted with the problem that unmeasured confounding factors such as patient preferences or surgical skills influence the observed results and causal interpretations are not possible.

Expected Outcomes and Impact

Step 1 of the study will provide a set of quality indicators as basis of a new combined QA scheme for CAD patients who have undergone PCI or CABG.

In step 2 of the study, we will assess the association between treatment courses and clinical outcomes of PCI and CABG patients by analyzing real-world data, thereby providing evidence-based insights into how the procedures influence patients' health. Based on these findings, the study will generate targeted policy recommendations to redesign QA for CAD patients undergoing isolated or consecutive CABG and/or PCI.

Ultimately, it would be possible to evaluate the indication quality and outcome quality of CAD patients undergoing PCI and/or CABG in a more detailed and comparative manner.

 

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Software

PostgreSQL (Postgres) or other more efficient databases, e.g., DuckDB, will be used for data management. Regarding data analyses, preferably we will use Python for data analyses and Draw.io to draw flow diagram/charts and other relationship model as per requirement. Multiple python libraries such as Pandas, Numpy, Matplotlib, Lifeline, and Plotly will be used as per settings and specifications.[28] Other statistical software like SPSS, R, and Stata can also be helpful during our data analyses process.

 

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Ethical and Legal Aspects

Person-identifying data are removed by the 11 regional AOKs through encryption before the data are delivered to the WIdO. The WIdO carries out a second pseudonymization, so that the subsequent routine data analyses are only performed with double-pseudonymized and thus anonymized data. This ensures that no social security data pursuant to Section 67 of the German Social Code Book X (SGB X) are used in this research project. Only aggregated data will be passed on to external consortium partners in compliance with the rules. The processing of research datasets within the WIdO is safe from a data protection perspective.

Ethical and scientific standards will be adhered. In particular, the general principles of scientific work are considered. The data registration, acquisition, and evaluation are according to the guidelines and recommendations for good epidemiological and clinical practice and Helsinki declaration for human subject and ethical principles in medical research are particularly relevant to the project's research questions. Secondary data will be evaluated using appropriate methods. Standardized and established procedures to ensure data protection will be used in the planning and implementation of secondary data analyses. The data will be analyzed on-site at the WIdO by staff from the IGVE, Marburg.

Regarding the expert survey data and consensus conferences, the purpose of the data collection is transparently explained to the participants in the consent processes, and their consent to participate is obtained in advance.

Further, this study has been approved by the Ethics Committee of the Faculty of Medicine at Marburg University, Germany (no. 25–193-BO).

 

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Conflict Of Interest

None declared.

^* These authors contributed equally as first authors.

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Publication History

Received: 23 July 2025

Accepted: 11 August 2025

Accepted Manuscript online:
12 August 2025

Article published online:
22 August 2025