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Cardiology Research

Review

Volume 10, Number 6, December 2019, pages 323-330

Risk Stratification for Transcatheter Aortic Valve Replacement

Approximately 12% of the US population (2.7 million Americans) beyond 75 years age has aortic stenosis [1]. Over 67,000 surgical aortic valve replacements (SAVRs) procedures are performed in the USA annually [2]. An additional 290,000 elderly patients at high or prohibitive surgical risk could potentially be treated with transcatheter aortic valve replacement (TAVR) in Europe and North America. This number is expected to increase due to the ageing US population and the recent Food and Drug Administration (FDA) approval for TAVR in low- and intermediate-surgical-risk patients. Accurate risk assessment for cardiac procedures forms an essential component of patient centric care. Although risk models have been in use for many years, their importance has increased only as recently as 1986 after the Health Care Financing Administration (HFCA), now known as the Center for Medicare and Medicaid Services (CMS) published the hospital outcomes data [3]. Such administrative databases were used to compare outcomes amongst care providers and hospitals. However, physicians rightly argued that such data did not take factors such as disease severity or patient heterogeneity into account. This led to the development of a number of high quality clinical databases and risk models related to cardiac procedures. Coronary artery bypass graft (CABG) surgery in particular became a focus of attention reflecting clinician’s desire to not only accurately predict peri-operative mortality, perform better patient counseling, and operative decision making but also improve patient outcomes [4-6].

It is well recognized that no risk assessment model is better than the data from which it is devised [3]. Missing or incorrect data may underestimate surgical risk leading to devastating consequences in certain instances. Incorrect data may also lead patients to make misinformed decisions regarding providers while reviewing institutional quality of care outcomes. Additionally, a reliable model to predict mortality may be extremely helpful regarding both the informed consent of the patient and to give advice to the heart team [7].

Primarily built for billing purposes, administrative databases contain clinical data, thus becoming one of the commonest sources for observational studies. They have been frequently used to formulate cardiac surgery risk models. The CMS database (MEDPAR) is one such example, which provides readily available and relatively inexpensive information on millions of patients [4, 8]. International Classification of Diseases Codes is used to collect patient-specific data and diagnoses. Based on the combination of codes, patients are further stratified into diagnosis-related groups for reimbursement purposes. Unfortunately, since these administrative databases are primarily built for billing purposes rather than for clinical studies, critical information such as the severity of comorbidities or ejection fractions is often missing. It is also often difficult to discern complication from comorbidity using these databases. They often have insufficient flexibility to properly classify certain comorbidities and sometimes exclude important variables that are not billable diagnoses [4, 9].

Two important administrative databases in the USA are the Nationwide Inpatient Sample (NIS) and the University Health System Consortium (UHC) [10-12]. Founded in 1988 as part of the Healthcare Cost and Utilization Project (HCUP), NIS contains data on roughly 8 million hospital stays per year [11]. Similarly, UHC is an alliance of 302 affiliated hospitals representative of academic medical centers.

Despite all their flaws, administrative databases have shown similar discrimination capabilities as traditional clinical databases [13]. In a study of 152,523 isolated CABG surgeries in the UK between 1996 and 2004, risk prediction models derived from administrative databases were able to forecast operative mortality with similar discrimination compared to that of a risk model built on clinical datasets (European system for cardiac operative risk evaluation (EuroSCORE); receiver operative characteristic curve score 0.77 versus 0.78) [13].

Clinical databases provide detailed accounts of hospital admissions and operative procedures, and are frequently used as a source of data for risk modeling in cardiology and cardiac surgery [14]. Although various cardiac surgery databases such as the Northern New England Cardiovascular Disease Study Group [15] and the Veterans Affairs Administration previously existed, the release of HFCA outcome data triggered the Society of Thoracic Surgeons (STS) to formulate a newer, more inclusive database known as the STS National Cardiac Database. The database was formally founded in 1989. During the subsequent three decades, it has evolved to become one of the biggest and most robust single specialty databases. It contains data covering over 3 million procedures performed at over 1,000 cardiac surgery centers throughout the USA in the last decade alone [16]. Today, it is considered the gold standard for specialty outcome databases [14, 17, 18]. The number of variables collected by STS database has steadily increased to over 600 individual data points in its latest edition (version 2.81) [19]. Although, an attractive proposition, the increased number of data points is not only time consuming, but may also lead to increased chances of coding errors, affecting the quality and accuracy of the data entered [20]. Indeed, such databases require substantial financial investment on part of the participating institutions. Ideally, such databases entail employing professional data abstractors, who are adequately trained and intermittently recertified to remain well versed with updated data definitions [21].

Documentation of variables such as preoperative risk factors and patient demographics is fairly accurate in clinical databases [14, 22]. On the other hand, data abstraction of operative details and disease etiology becomes increasingly difficult especially in the absence of uniform definitions. Even a simple endpoint like mortality may be open to different interpretations such as: 1) Thirty days all-cause mortality; 2) In-hospital mortality, irrespective of when it occurs; 3) Operative mortality, defined as a combination of 1) or 2). The importance of clear and unambiguous definitions is re-emphasized by a recent study which showed only 89% median agreement between four abstractors gathering data on etiology of valve disease, procedural details and surgical acuity [23]. Interestingly, all the data abstractors were cardiothoracic surgery residents and fellows. The STS regularly updates variable definitions between successive database versions with the aim to improve diagnostic accuracy and clarity. However, frequent changes in definitions may often become problematic when it comes to data abstraction [20].

Risk assessment model development entails the use of statisticians with considerable experience in this area, since different multivariable equations could be used by various statisticians for the same data [24]. Similarly, the type of modeling strategy and validation techniques may differ depending on whether the model is aimed at probabilistic prediction to aid in patient selection and counseling or merely meant to provide a comparison between providers and treatments [25, 26].

Three principal techniques have been used for developing cardiac risk models. Bayesian models were initially used for the STS database due to significant amount of missing data. By 1995, as data omissions resolved, logistic regression models became more favorable and are currently the most widely used statistical technique for cardiac risk modeling [27, 28]. Numerous risk models including the STS risk score, Veterans Affairs risk score [29] and the New York State risk score [30] currently utilize logistic regression. Although much more robust, the application of Bayesian and logistic regression models is limited at bedside since these models require complex calculations. This served as an impetus to form simpler models such as the EuroSCORE, which utilizes additive scores with weights derived from the logistic regression model. Some comparative studies have shown better overall performance of models based on logistic regression [31].

During risk model development, the study population is often randomly divided into a development or training sample and a validation or test sample. For instance, the STS isolated valve risk model was divided into 60% derivation/developmental sample and 40% validation sample. Data from the development sample are used to identify predictive variables and risk model coefficients. The validation sample is used to assess overall clinical performance and model fit, as defined by calibration (reliability) and discrimination (resolution). Calibration describes how closely the model estimated death rates align with observed mortality, quantified through the Hosmer-Lemeshow statistical test [32]. Discrimination is the model’s ability to predict whether a specific patient will survive or not, measured using the area under the receiver operator curve (AUROC), also known as the c-index [3]. The c-index ranges between 0.5 and 1.0. Higher values of c-index imply better discrimination. On the other hand, values approaching 0.5 indicate the model’s ability to discriminate is not better than random chance or “flip of a coin” [33]. For instance, risk models with low discrimination may accurately predict five out of 100 post-operative deaths for a particular set of risk factors, but fail to identify those five patients. A summary of usefulness of risk prediction models particularly in TAVR patients is shown in Table 1.

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Table 1. Advantages of Risk Prediction Models

Important limitations (Table 2) need to be taken into account when employing risk adjustment models for assessing operative outcomes. Firstly, risk algorithms are most accurate in the population and time frame from which they were derived. For instance, the EuroSCORE that is widely popular in Europe does not predict outcomes accurately when applied to surgical practice in North America [34-36]. Similarly, model calibration can be lost over time leading to mis-estimation of operative outcomes, necessitating the need for periodic recalibrations. Loss of model calibration was one of the reasons why EuroSCORE had to be updated with the EuroSCORE II [37, 38]. The STS risk score regularly gets recalibrated as well, with the last update taking place as recently as 2008 [39].

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Table 2. Limitations of Risk Prediction Models

Risk algorithms require homogeneity of treatment between the study groups and the population from which the algorithm is modeled. This has far reaching implications for patients with aortic stenosis. Although both SAVR and TAVR serve as viable treatment options for patients with aortic stenosis, risk algorithms based on SAVR outcomes may not be as accurate when applied to TAVR patients.

It is also important to underscore that risk adjustment loses accuracy at extremes of populations. The tail of the bell shaped curve often has too few patients on which to build a statistically valid model. This factor gains importance in the setting of TAVR patients where severe aortic stenosis patients lie at the end of the spectrum, often resulting in overestimation of mortality witnessed with many models [40-42].

Lastly, risk assessment models depend on the accurate data entry of a core set of variables. They often cannot account for certain variables that have not been collected. Such omission of data primarily has two causes: 1) The occurrence of the condition is so infrequent that its impact is not measurable; 2) The causal factor cannot be measured accurately. For instance, frailty parameters have often been excluded from risk scores due to lack of a gold standard [43].

The most frequently modeled outcome in cardiac procedures is mortality. Two most commonly used risk assessment models are the logistic EuroSCORE in Europe and the STS risk score in North America.

The EuroSCORE was developed in 1995 as a user-friendly system, in the hopes of encouraging as many units as possible to embark on programs of risk-adjusted quality monitoring. It was derived from data involving over 19,000 patients from 128 centers in eight European countries [44]. Originally, a simple additive score was published, enabling clinicians to calculate peri-operative mortality at bedside. Unfortunately, the model generally overestimated mortality at lower scores (EuroSCORE ≤ 6) and underestimated mortality at higher scores (EuroSCORE > 13) [45].

As access to information technology increased and interest regarding quality control grew throughout Europe, the score was updated to a logistic regression model in 1999. In order to further improve the accuracy of the original EuroSCORE and counter calibration drift, the model was updated to EuroSCORE II, which was derived from more than 22,000 patients operated upon in 2010 in over 43 countries [46]. This model uses 18 covariates for predicting mortality in surgical aortic valve replacement. Whether the updated version fares better than the original version remains debatable. A recent multicenter validation study found that the EuroSCORE II has optimal calibration until 30% predicted mortality and consistently over predicts at higher risk [47].

The STS risk score that is primarily used in the USA was developed in a later era (2002 - 2006). It provides highly predictive estimation of mortality in both the short and the long term [39]. Not only does it predict mortality in coronary artery bypass grafting, valve replacement procedures and combined cases, but also offers risk prediction for numerous other outcomes including stroke, deep sternal wound infection, renal dysfunction, reoperation, prolonged ventilation, and the length of hospital stay. The STS risk score is calculated through the STS website and continually adjusted to improve accuracy. The latest version 2.81 contains various risk factors not previously collected such as frailty, previous radiation exposure and liver disease.

The STS provides a quarterly report to participating institutions, whereby predicted risk is recalibrated to account for calibration drift due to improvements in surgical outcomes over time. Unlike the quarterly STS institutional reports, the STS online risk calculator which is used by most clinicians at bedside is still based on the 2008 STS isolated valve risk model and lacks the above mentioned recalibration factor [49]. Health care providers making clinical decisions using the online Calculator should bear these limitations in mind.

The advent of TAVR, as a viable alternative to SAVR in patients with severe aortic stenosis who are deemed suboptimal for surgery has revolutionized valvular repairs [50, 51]. Naturally, it has sparked interest in developing risk models for management of patients with aortic stenosis. Below we will discuss the use of traditional and novel risk assessment models in patients undergoing TAVR.

The most commonly used model for this population is the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM), which was neither developed nor intended for use in this population. Supporters of TAVR cite the lower observed mortality with TAVR compared to predict mortality based on STS score, to promote TAVR. However, there are a number of reasons why these risk models do not fit well in TAVR patients. Firstly, these scores were primarily derived and validated in a totally different treatment group i.e. SAVR and are therefore highly likely to be invalid. Secondly, TAVR patients are mostly at higher risk, lying on the extreme right of the bell curve, and the current models have very few patients at “high risk” for the model to offer robust discrimination [40-42]. Thirdly, some of the variables, such as sternal wound infection do not pertain to TAVR patients.

It is generally agreed that the widely utilized EuroSCORE overestimates mortality in most patients [49-51]. Additionally, EuroSCORE II, although more accurate than the original logistic score is largely based on a dataset consisting of coronary procedures. It may therefore be less well adapted to aortic procedures [52-52].

Realizing the dearth of data related to TAVR procedures, the American College of Cardiology (ACC) in close partnership with STS and a number of other stakeholders including the FDA and CMS developed the Transcatheter Valve Therapy (TVT) Registry in 2011. The registry provides a rich source of information regarding what impact transcatheter heart valve procedures have on patient outcomes and how they are being deployed throughout the USA. The TVT Registry currently has more than 20,000 patient records with detailed demographic and post TAVR outcomes including procedural mortality. The national coverage determination from the CMS [56] specifies that reimbursement is contingent on participation in a national registry with a set number of specifications all of which are met by the TVT Registry. This increases the external validity of the TVT Registry sample to nearly a real-world TAVR experience [57]. As the number of patients enrolled in the TVT Registry increase, valuable information regarding the evolution of outcomes and clinical profiles of patients undergoing TAVR will emerge. It is expected the predicted risk will change over time and provide future analysis using the registry may provide useful insights regarding the clinical subsets of patients who might benefits from TAVR most. Conversely, it will also reveal subsets not suitable for TAVR. TVT Registry participant sites will be able to compare their individual risk adjusted outcomes data to national benchmarks as this information gets disseminated to them several times each year.

Using the TVT Registry data Edwards et al [57] developed a statistical model predicting in-hospital mortality after TAVR in 2016. The model is based on 13,718 consecutive US patients who underwent TAVR in 265 participating centers throughout the USA from 2011 to 2014. The model is unique in that it was developed to assess patient-level outcome predictions rather that center-level risk adjusted outcomes. The final model covariates used to predict to mortality included age, glomerular filtration rate, dialysis dependence, New York Heart Association (NYHA) class IV, severe chronic lung disease, non-femoral access site and patient acuity category. The model displayed a high degree of discrimination as measured by a C-statistic of 0.66. In comparison, The C-statistic was recently reported to be 0.60 for the standard STS model and 0.53 for logistic EuroSCORE when applied to a population of TAVR patients [58].

The Edwards et al model is a small step in the long journey of risk prediction for TAVR patients. Based on initial data, the model appears to be much more accurate than the previous surgical models. The model does however have a number of limitations, including missing some key risk factors such as frailty and quality of life measures. This model only accounts for in-hospital mortality in contrast to a 30-day mortality model. This is important because even though a majority of TAVR patients survive their hospitalization, a large number eventually succumb within 30 days. Additionally, although the TVT Registry undergoes rigorous internal quality checks, the external audit process is still in its early stages, compromising the quality of the data forming the model [56]. Table 3 summarizes some of the commonly used risk prediction models for aortic valve intervention.

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Table 3. Summary of Commonly Used Risk Prediction Models for Aortic Valve Intervention (Advantages and Disadvantages of Risk Assessment Models)

The linkage of clinical databases that capture short-term outcomes with administrative databases that capture long-term outcomes will allow risk models to be developed that capture long-term data. Efforts are underway to include data in the TVT Registry for both 30-day follow-up as well as 1-year survival information after TAVR. Currently this information is sparse in the database, although future models may have adequate information regarding these two important variables. In addition models to predict the probability of neurological deficit are also currently under development. Overall, approximately one-third of individuals aged > 80 years are considered to be frail indicating the high prevalence of this risk factor [59]. Variables such as patient frailty measured using standardized indices such as the preprocedural 5-m walk test, Katz index or Instrumental Activities of Daily Living Score are also likely to be included as a covariate in future TVT Registry risk model [60-62]. The TVT Registry has the advantage of including quality of life metrics measured using the Kansas City Cardiomyopathy Questionnaire. Linking the information gained from the questionnaire with the clinical data in the TVT Registry will enable models to be developed that accurately estimate procedural benefit. Recently Thourani et al developed a risk model for in-hospital stroke after TAVR using STS TVT Registry using a cohort of 97,600 patients from 521 sites of TVT Registry. Their model developed prediction model to allow assessment of risk-relevant patient risk factors for in-hospital stroke risk. This data will be updated and transmitted to the TVT Registry [63].

Indeed, a risk model that can predict not only mortality but also stroke risk, and post-operative quality of life is on the horizon and will serve as a powerful adjunct in the process of appropriate patient selection.

Since existing risk stratification models consistently overestimate the mortality observed in the German population, a new score was needed to estimate preoperative risk in the context of inter-hospital benchmarking and quality control [64]. First published in 2012, the GAVS I was based on a comprehensive national data pool from 2008 known as the GARY Registry [65, 66]. GARY is unique in that it includes all interventional and surgical treatment options for aortic valve disease that are currently available in Germany. Since this score was developed based on data from the early period of transcatheter techniques, it was limited in its applicability due to the small proportion of TAVR procedures performed at the time (634 cases, 5.2% of the total patients enrolled in the database). Additionally, the score failed to account for hemodynamic results, long-term survival and quality of daily life. In order to avoid diminishing the number of transcatheter aortic valve implantation (TAVI) cases for calculation further, data were not divided into a learning and validation part, limiting the external validity of the data set. By 2012, the proportion of patients with aortic stenosis treated percutaneously with TAVR had grown almost 10-fold to 44.9%. Wolfgang Schiller et al re-calibrated GAVS I to account for the higher number of patients having TAVR forming the new GAVS II score [67]. The GAVS fits best to the German public health service, where the prevalence of TAVR is high compared to other countries. Unfortunately, the score can only be used to predict the short-term risk mortality and fails to benchmark SAVR and TAVR patients separately.

A rapidly increasing number of TAVR procedures have been observed over the last 5 years, particularly in countries with no restrictions on reimbursement, which requires further assessment and validation of these risk scores. While there is no specific scoring system tested in the Asian population, STS scoring is still widely used to select patients for TAVR in Asian countries. Several studies have shown good predictability of STS score in Asian population. Moreover, a recent objective clinical frailty score used in a Japanese cohort showed good discriminability to exclude frail patients who would not benefit from invasive therapy [68].

Multiple risk assessment models are available to identify patients with severe aortic stenosis who are candidates for TAVR due to high predicted mortality with SAVR. Each model suffers from limitations e.g., EuroSCORE can overestimate surgical risk and misclassify patients, and focuses on in-hospital mortality and short-term outcomes. The STS score may offer better risk stratification due to addition of more relevant risk factors and ability to predict both short and long-term outcomes; however it is still not perfect due to being developed in the SAVR population. Novel risk assessment models such as Edwards et al and GAVS may improve selection of TAVR patients and predict procedural risk, but need long-term data and refinement.

Acknowledgments

None to declare.

Financial Disclosure

This research did not receive any financial or grant support.

Conflict of Interest

The authors report no financial relationship or conflict of interest regarding the content herein.

Author Contributions

All authors are involved in manuscript writing, preparation, review and final approval.

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