Achilles and the Tortoise: Rethinking Evidence Generation in Cardiovascular Surgery and Interventional Cardiology

Abstract

Background: Randomized controlled trials (RCTs) are the foundation of evidence-based medicine. However, the rapid pace of technological innovation in cardiovascular surgery and interventional cardiology challenges the traditional RCT framework. Observational studies may hold renewed value in fields where device evolution outpaces the time required to validate clinical outcomes. Methods: This analysis evaluates 270 randomized and non-randomized studies in transcatheter aortic valve implantation (TAVI), one of the most rapidly evolving areas in cardiovascular medicine. The investigation follows two lines: first, mapping the timeline of major RCTs against the introduction of new prosthetic models; second, comparing the prevalence, duration, and role of randomized (R) versus non-randomized (NR) studies. Results: The timeline reveals a persistent misalignment between innovation and validation. New prosthetic models frequently enter the market while RCTs for prior generations are still ongoing. For example, the Sapien 3 valve was approved, while trials on Sapien XT were still enrolling. Similarly, newer Evolut and Acurate models were introduced during ongoing studies of earlier versions, often prompting new studies before existing ones concluded. This leapfrogging effect fragments the evidence base and delays definitive comparisons. In parallel, randomized trials have increased in number and tend to be shorter in duration, reflecting a maturing field. However, non-randomized studies remain crucial for early testing and post-market surveillance. Conclusions: In a field with rapid technological evolution a sort of Zeno’s paradox occurs: long-term validation cannot keep pace with fast innovation, resetting the evidence base with each new model. To overcome this paradox, a paradigm shift in evidence generation is desirable. Future strategies must augment adaptive trial designs, leverage real-world data and use higher-level, advanced analyses to incorporate subjective variables and phenotypic diversity, to reduce confounding factors and speed up data access. Higher-level, integrative evidence analytics could help Achilles walk alongside the tortoise.

1. Introduction

In the field of cardiovascular surgery and interventional cardiology, the dynamic nature of technological advances poses significant challenges to the traditional clinical framework of RCTs, which have traditionally been considered the cornerstone of evidence-based medicine. It is indeed a rapidly evolving field, supported by solid scientific research and strong economic market interests that merge together. In this context, it can be difficult to extract clinical data from RCTs: they require long follow-ups and, in the meantime, the industrial push leads to the concomitant introduction of new techniques and devices that can make previous results obsolete.
A sort of Zeno’s paradox occurs [1]. Zeno argues that a fast runner like Achilles cannot overtake a slower tortoise by giving it a head start, because the distance between them can be infinitely divided, which implies that Achilles would need an infinite number of steps to catch up with the tortoise, each time a little further away. Similarly, the results of randomized clinical trials (Achilles) cannot keep up with the technological innovations of new prosthetic models (the tortoise), which have a time advantage and often reset the results of previous models. This advantage recurs over time, as a new prosthetic model requires new patients and new procedures, making the data obtained up to that point obsolete.

Zeno’s paradox is cited as a conceptual metaphor for the “gap between innovation and scientific evidence” and is not a literal mathematical statement. Its use captures the essence of the purpose of the Greek paradoxes, which is to be used as part of a method of investigation [2]. The true paradox lies in the difficulty of applying, in daily clinical practice, validated clinical data to a constantly evolving reality, which renders them obsolete as soon as they become definitive.

The most active and debated field of research was chosen to analyze the use and impact of clinical trials, namely transcatheter aortic valve implantation (TAVI), compared or not to surgical valve replacement (SVR). The other two fields of research related to the transition/coexistence of surgical and interventional procedures are coronary artery bypass grafting and the treatment of mitral valve disease. However, the last two topics do not have the same high number of studies that have been performed (and are still being performed) in the field of aortic replacement.

2. Materials and Methods

This manuscript is a registry-based descriptive review and not a meta-analysis, therefore an independent data extraction was beyond the scope of this work; data extraction was single-author with verification of key trial entries against registry pages and primary publications where available.

This review was organized into two lines of analysis, connected to each other.

Analysis 1 concerned the temporal synchronization of the major randomized studies on the TAVI procedure and the market launch (CE mark and FDA approval) of the various models proposed in the years from 2002 (the year of the first TAVI implant in humans [3]) until today. The duration of the studies and the number of patients recruited were also highlighted.

Analysis 2 concerned the comparison between randomized and non-randomized studies on the subject in the same period of time, according to the selection criteria explained below.

Both analyses aim to highlight the multiplicity of studies over time and the “chase”, sometimes ineffective, between the results of the studies and the market launch of new models that “reset” the results themselves. In the field of valve bioprostheses, the results require many years to be able to express a significant value, regardless of the implantation method. Many variables can influence the result and durability of the bioprostheses, from geometry to the tissue used, to the biology and anatomy of the patient, etc. Furthermore, the transcatheter models differ from the surgical ones, therefore the experience and results of surgical replacements (in progress for over 70 years) are not very extendable to this new technique. The changes over time in the use of these studies also shows interesting trends, which cannot otherwise be captured in the analysis of individual studies.

An electronic search was conducted on the ClinicalTrial.gov website (https://clinicaltrials.gov/; accessed on 20 June 2025) by entering the keyword “aortic valve stenosis” in the “Condition/disease” field and the keyword “TAVI” in the “Intervention/treatment” field during a time frame from 2002 (the year in which the first TAVI procedure was performed on a patient) to May 2025.

Within this search, the two large chapters of the “Interventional” and “Observational” studies were selected.

Interventional study (clinical trial) is defined as a type of clinical study in which participants are assigned to groups that receive one or more intervention/treatment (or no intervention) so that researchers can evaluate the effects of the interventions on biomedical or health-related outcomes. The assignments are determined by the study’s protocol. Participants may receive diagnostic, therapeutic, or other types of interventions.

Observational study is defined as a type of clinical study in which participants are identified as belonging to study groups and are assessed for biomedical or health outcomes. Participants may receive diagnostic, therapeutic, or other types of interventions, but the investigator does not assign participants to a specific intervention/treatment. A patient registry is a type of observational study.

This first selection identified 536 studies, both Interventional and Observational, from which only those regarding the devices used in TAVI, namely the prostheses, were selected. Therefore, 259 studies regarding other areas were excluded: pharmacological studies, those on Quality of Life, on devices for cerebral protection during TAVI, on devices for percutaneous closure of vascular access, on diagnostic tests, on virtual reality applied to TAVI, on anesthesiological/pain management and on blood transfusions. These “ancillary” studies follow parallel lines of research and often use the same patients as the main studies dedicated to index valve prostheses.

In this way, 277 studies were selected (163 Interventional and 114 Observational) that exclusively concern the prosthetic models tested and used in TAVI procedures, as they are studies determining the major endpoints of the procedure itself, therefore intended to guide clinical choices, guidelines and finally the market.

From this selection, 7 studies marked as “Withdrawn” in the study “Status” field were excluded because they did not involve any enrolled patients and were not effective as clinical trials. Studies with all other definitions of “Status” (“Not yet recruiting”, “Recruiting”, “Enrolling by invitation”, “Active, not recruiting”, “Suspended”, “Terminated” and “Completed”) were included in the review. The reason for this choice is that all “open” studies, in progress, “use” patients regardless of their completion status.

Finally, 270 studies were selected. Based on the purpose of this review they were divided into two groups: randomized (named “R”, 56 studies) and non-randomized (named “NR”, 214 studies). The non-randomized group includes non-randomized Interventional studies and both prospective and retrospective Observational studies. The two groups included 45,635 and 160,937 patients, respectively. Part of the studies of group R were used for Analysis 1 (14 major trials related to prosthetic models introduced into clinical practice), while both groups were entirely considered in Analysis 2.

The list of 14 randomized trials used in Analysis 1 is reported below. The number of patients enrolled in the study and the main bibliographic reference are reported. This is a subset of randomized trials chosen because they were pivotal, multicenter, or explicitly designed as head-to-head or industry-independent trials; the objective was illustrative chronology rather than exhaustive trial weighting.

PARTNER I AB, 358 patients [4];

STACCATO, 72 patients [5];

NOTION 1, 269 patients [6];

PARTNER II, 2032 patients [7];

SURTAVI, 1746 patients [8];

UK TAVI, 913 patients [9];

PARTNER III, 1000 patients [10];

SOLVE, 447 patients [11];

NOTION 2, 376 patients [12];

DEDICATE, 1414 patients [13];

EARLY, 901 patients [14];

OPERA, 3094 patients [15];

RHEIA, 443 patients [16];

COMPARE, 1031 patients [17].

For all studies, the status, number of patients enrolled, start and completion date of the study, duration in months, acronym when present and the link to the study page on the US government website were considered. The complete list of studies selected for this review is reported in the Supplementary Table S1, maintaining the links to the web pages of the American site. This list is sorted chronologically from oldest (2007) to newest (2025) to show the general trend in their usage.

In Analysis 2, a descriptive analysis of the two groups was performed, considering the number of patients enrolled, the duration and the distribution over time of the two types of study. The statistical comparisons (Student’s t-test and Mann-Whytney) between randomized and non-randomized studies were exploratory and descriptive in nature; they were not adjusted for confounding factors such as valve generation or sponsor, and thus are not intended for causal inference.

3. Results

The results of Analyses 1 and 2 are reported below, highlighting the most significant data.

3.1. Analysis 1

The results of Analysis 1 are visually shown in Figure 1. It is immediately evident from the graph that there is a growing overlap of prosthetic models and randomized studies. It is also highlighted that the approval of the CE mark and that of the FDA always have very different timing. Most prostheses obtained the CE mark first and FDA approval after many years. Only one prosthesis (JenaValve) obtained only FDA approval and three (DirectFlow, Engager and AcurateNeo2) only CE mark. In only two cases did FDA approval precede CE mark (EvolutProPlus and EvolutFX). Two prostheses have currently been recalled from the market (LotusEdge in 2021 and AcurateNEO2 in 2025).

Another consideration evident from the graph is the long period of time that elapsed between the first TAVI implant in humans (2002) and the first randomized study: 5 years. Subsequently, the time between the introduction of the prostheses on the market and the first randomized study on them was on average 1.15 ± 0.5 years, with a minimum of 0.2 and a maximum of 1.83 years. The first study on Sapien was contemporaneous with the CE marking of the prosthesis in 2007, while the first study on CoreValve came approximately two more years after the CE mark, in 2009. The first published results of the first PARTNER I study arrived in 2015, so 13 years after the first implant and 8 years after the CE mark. In the year of publication of this study, two other subsequent models of the Sapien prosthesis had already been approved with the CE mark, the SapienXT (2010) and the Sapien3 (2014), in addition to 5 other prostheses from different Companies.

The NOTION 1 study on CoreValve was concluded in 2014 and the complete 10-year follow-up was published in 2024. This study certainly demonstrated the durability of the prosthesis and clinical outcomes comparable to surgery in low-risk patients but was conducted on a prosthetic model burdened by a high incidence of paravalvular leaks and conduction abnormalities (complete atrioventricular block with definitive pacemaker implantation). It is true that in this study, like others, an intermediate follow-up of 5 years was planned, as well as even earlier echocardiographic checks (3 months, 1 year), but the very nature of the bioprostheses studied requires a very long follow-up to be able to produce consistent data comparable to surgically implanted bioprostheses.

The duration of the enrollment phase of the studies is highlighted by the light blue horizontal bar. There is a tendency over time to extend the enrollment period, without a true proportionality in the number of patients enrolled. For example, the two-year enrollment period in the PARTNER I study has increased to five years in NOTION 2, with approximately the same number of patients enrolled (358 vs. 376). Comparative studies between the various prosthetic models also show differences between them. The SURTAVI, UK TAVI, OPERA and COMPARE studies recruited 1746, 913, 3094 and 1031 patients, respectively, in an average period of approximately 4 years. The SOLVE study enrolled 447 patients in half the time (2 years).

The timeline illustrates a continuous and dynamic evolution of transcatheter aortic valve implantation (TAVI) technology, where clinical studies and the release of new valve models are closely intertwined, often in a discordant sequence. A consistent pattern emerges in which new prosthetic valves are introduced while randomized clinical trials on previous-generation devices are still actively enrolling or have not yet published final outcomes. For example, the PARTNER II study, which investigated the Sapien XT valve, was initiated in 2011, yet by 2014 the Sapien 3 valve had already received CE marking and FDA approval, prompting new trials like PARTNER III and EARLY to assess this newer generation. Similarly, while SURTAVI and UK TAVI were still enrolling patients using CoreValve and Sapien valves, Medtronic introduced Evolut R in 2014, followed shortly by Evolut PRO in 2017 and Evolut PRO+ in 2019. Each of these prompted new comparative studies (e.g., NOTION 2, SOLVE, COMPARE), showing how evolving technology quickly outpaces the evidence base.

This ‘leapfrogging’ behavior is further evident with the Acurate platform. While the DEDICATE study, initiated in 2015, was focused on the original Acurate valve, newer iterations like Acurate NEO2 were introduced by 2020, requiring new studies such as RHEIA and COMPARE before the previous data had fully matured. Similarly, Navitor and Evolut FX both entered the market in 2021, even as studies on Evolut PRO+ and earlier models were still ongoing.

As a general summary, is possible to estimate that in approximately 70–75% of cases, a new transcatheter valve model reached the market before ≥50% of the follow-up data from the prior-generation RCT reached their primary endpoints.

3.2. Analysis 2

A descriptive statistical analysis was performed on the 270 selected studies comparing the two groups R and NR obtained the results reported in

Table 1. Analysis 2: Descriptive and exploratory statistical tests.

Data	Group R	Group NR	t-Test	Mann–Whitney
Duration (Mean) 1	87.7	66.1	0.0006	0.0001
Duration (Median)	81.6	62.1
Enrolled Patients (Mean)	814.9	689.4	0.83	0.0002
Enrolled Patients (Median)	460.0	150.0

¹ Duration in months.

.

The corresponding boxplot is shown in Figure 2. Duration is significantly longer in randomized studies. The number of enrolled patients is on average greater in the R group, but the difference is only visible with a nonparametric test, given the high variability of the number of patients in the NR group studies. In practice, R studies tend to have more patients, with asymmetric and variable distribution, as expected. The variables that influence the number of patients enrolled are multiple and there is no common standardization for all. What is required is that the number must guarantee a statistically significant sample based on the parameter being studied.

The chronological distribution of all studies and the distribution between R and NR is shown in Figure 3. R studies are generally longer and show a broader range of durations (from under 30 months to over 300 months), while NR studies are more tightly clustered. Since 2015–2016, there has been an uptick in RCTs, reflecting a growing interest in and need for high-quality evidence generation for TAVI. Some randomized studies still show long durations even in recent years, likely representing pivotal or extended-follow-up trials. Non-randomized studies appear more frequently, particularly in earlier years.

Regarding the correlation between the number of patients enrolled and the duration of the studies (Figure 4) in the two groups, a greater concentration of studies that last about 100 months and enroll about 600 patients can be seen.

R studies tend to enroll more patients, including several points beyond 2000 and up to 5000–6000 patients. NR studies are heavily clustered below 1000 enrolled patients, with the majority under 500. However, both R and NR studies show a wide range of durations regardless of the number of patients enrolled, indicating that study duration is influenced by multiple other factors (e.g., design complexity, endpoints, regulatory requirements). Many R studies appear in the upper half of the plot (above 100 months), confirming their generally longer timelines compared to NR studies. There are some outliers in both groups: a few NR studies enrolled high patient numbers (>4000) with relatively short durations, and vice versa for R studies, highlighting exceptions driven by study-specific factors.

Figure 5 depicts the number of randomized and non-randomized studies initiated in individual years during the period 2007–2025. There is a clear increase in both randomized (R) and non-randomized (NR) studies over time. NR studies consistently outnumber R studies every year across the entire time frame. A notable spike in NR studies occurred in the period 2014–2018—more than any other year, while the number of R studies started to increase significantly starting from 2015, therefore within the same time period. The total number of NR studies seems to plateau or decline slightly after 2020, possibly influenced by market saturation, more stringent trial standards, or external events (e.g., COVID-19).

4. Discussion

The introduction of transcatheter techniques in the treatment of valvular heart disease, which until now had been exclusively surgical, has opened up a new and very stimulating scientific landscape. In previous years, the topic of coronary artery bypass grafting has undergone the same interaction and integration with coronary angioplasty, but in this field, fundamental studies have been relatively few [18,19,20,21,22,23,24]. The same applies to the treatment of mitral regurgitation with edge-to-edge transcatheter techniques [25,26,27,28,29,30]. The TAVI procedure, on the other hand, has evidently led to one of the largest numbers of prosthetic models and clinical studies in the cardiovascular field, generating one of the most prolific and rapidly evolving evidence bases in cardiovascular medicine, characterized by a constant stream of new prosthetic models and clinical studies.

The history of transcatheter aortic valve implantation is also summarized in some review articles [31,32,33,34,35,36,37,38]. The number of studies, randomized and non-randomized, testifies to a strong industrial as well as scientific push. It was evident how this mixed push has generated over the years a race between new prosthetic models and studies already underway. The result is a constant overlap between innovation and investigation: new studies are initiated not based on the completion of prior ones, but often in response to emerging technology. This leads to a fragmented evidence landscape where head-to-head comparisons are rare, and where clinical decision-making may rely on incomplete or outdated data relative to the current technology in use. This phenomenon underscores the challenge of maintaining rigorous, prospective validation in a field characterized by rapid iterative development and highlights the need for adaptive trial designs or real-world evidence frameworks to close the gap between innovation and clinical evidence. This race between evolving prosthetic technology and clinical validation highlights also the challenges in generating robust comparative data and may contribute to a moving target in defining the standard of care.

4.1. The Structural Drivers of the Evidence-Validation Gap

The core of the problem is a confluence of three distinct, yet interconnected, challenges:

(a) 

The Regulatory and Commercial Timeline: The current regulatory pathways for medical devices, such as the FDA’s 510(k) and the CE marking process, allow new valve iterations to be approved based on substantial equivalence to a predicate device and limited new clinical data. This system permits—and commercial incentives encourage—the market release of new models long before the long-term data for their predecessors has matured. Our timeline (Figure 1) visually captures this phenomenon. Figure 1 is primarily descriptive and historical and serves to illustrate this structural “leapfrogging” problem, not to prove clinical harm which, if present, is already contemplated and reported in the intermediate results of the studies themselves.

(b) 

The Methodological Lag of Traditional RCTs: Conventional RCTs, the gold standard for establishing efficacy, are inherently slow, costly, and ill-suited for a field with rapid technological iteration. By the time a definitive RCT reaches its primary endpoint, the device it studied may no longer be the state-of-the-art.

(c) 

The Fundamental Constraint of Long-Term Assessment: The topic is complex also because the validation of a new prosthesis depends not only on immediate data (mortality, failure, complications) but above all on the long-term durability of the bioprostheses. First, in terms of immediate results, TAVI must compete with the mortality rate of surgical series, which, in centers of excellence, is very close to zero. Then, regarding durability, this figure is obtained with a follow-up of at least 10 years. Therefore, a follow-up period of at least this order of magnitude must be added to the duration of a study’s enrollment phase. Consequently, making a conclusive judgment on a prosthesis’ performance requires an extremely long time compared to the patient’s care needs. In this respect too, TAVI must compete with the durability of surgically implanted bioprostheses, which already have a well-established follow-up. This requirement “seizes” a significant number of patients for a long time from the real world where subsequent models are introduced. Even comparison of these patients with those enrolled in subsequent studies is limited by the poor reliability of matching.

4.2. The Evolving Role of Randomized and Non-Randomized Evidence

Every clinical study, whether randomized or not, requires rigor and commitment from many stakeholders—doctors, nurses, specialists, administrators, ethics committees, analysts, and so on. Every clinical study generates new knowledge, which it would be a shame to “waste” simply because it relates to a device no longer in use. This review has highlighted the need to preserve a large amount of data that has required significant resources. Data on long-term endpoints (duration, quality of life at >10 years) require extensive follow-up and therefore create a discrepancy with frequent device changes, highlighting that these are precisely the endpoints most compromised by the rapid innovation cycle. This "paradox" extends over many years, preventing the collection of comparable data. Added to this is the recent lowering of the recommended age threshold for TAVI (to 70 years): as a consequence, quality of life data, for example, will be much less comparable with those of patients implanted with previous models. What is clear from both analyses is the continued significant use of non-randomized studies throughout all phases of the industrial and clinical development process in this field. The validity of non-randomized studies and the reassessment of their importance are reported in several publications [39,40,41,42,43,44].

Randomized clinical trials (RCTs) and observational studies often share more similarities than traditionally assumed. While RCTs are typically conducted under ideal controlled conditions with highly selected populations, observational studies reflect real-world practice and broader patient diversity. Both designs can suffer from biases—such as loss to follow-up and confounding—and thus neither is inherently superior. The key to sound evidence lies in evaluating each study on its methodological rigor, including design quality, risk of bias, and applicability to real-world settings—not by automatically privileging RCTs over well-designed observational research [39].

Observational studies, when combined with innovations (causal inference techniques, electronic health records, the E-value tool), can offer valuable real-world insights and external validity. Recent methodological advances (adaptive, sequential, and platform trials, as well as the integration of RCTs with electronic health data) may blur the lines between study designs. This opens the way to a more nuanced, context-specific evaluation of evidence, encouraging triangulation across designs [41].

A quite old comparative study systematically analyzes observational studies and randomized controlled trials published between 1985 and 1998. They performed meta-analyses combining effect estimates from OS and RCTs and discovered that in 17 out of 19 cases, the treatment effect sizes from observational studies fell within the 95% confidence intervals of those from RCTs. The age of this study demonstrates that the need to reevaluate observational studies is deeply rooted [42]. The cited literature was meant just to illustrate the long-standing debate on RCTs vs. observational studies, not to claim equivalence in TAVI. RCTs remain the gold standard for causal inference, and observational studies are subject to confounding factors that are not yet clearly measurable, but can sometimes produce similar results, although their study designs are different. This likely happens when a study identifies more confounding factors than others.

A paper on surgical oncology claims that randomized controlled trials face significant challenges, such as slow patient accrual, surgeon learning curves, difficulty with blinding, high costs, and limited generalizability. They review alternative trial designs, including stepped-wedge RCTs, registry-based RCTs, and trials-within-cohorts, that can better address these issues in surgical settings. Oncology surgery is a setting in which the limitations highlighted in this review are taken to the extreme. Keeping pace with a disease and scientific developments that are chasing each other is even more crucial when time plays a crucial role in survival [43].

A state-of-the-art review examines the respective strengths and limitations of randomized clinical trials (RCTs) versus observational studies (OS). It emphasizes that both RCTs and OS have potential design flaws, including confounding, selection bias, loss to follow-up, and measurement errors. It advocates for context-aware interpretation, suggesting that neither RCTs nor OS should automatically be considered superior but should be judged on methodological rigor and relevance to the clinical question. The review concludes by offering practical guidance to help clinicians and policymakers assess when observational evidence can reliably complement or inform RCT data [44].

The use of non-randomized studies is still very widespread, generating a mass of data that cannot be ignored just because it lacks randomization. They may also represent the only mode of investigation when randomization is not ethical or statistically significant.

4.3. Existing and Proposed Frameworks for Adaptive Evidence Generation

Our call for adaptive evidence-generation frameworks is, in fact, already being answered by several pioneering initiatives in the field.

Interesting revisions of real-world data have already been published [45,46,47,48]. Randomized studies do not include data such as device-specific learning curves, center volume, or heterogeneity in operator experience, all of which exacerbate the performance gap between generations. Since it would be useful to introduce these subjective variables, artificial intelligence could play an important role in this analysis, cross-referencing subjective data within a scientific framework and weighting them appropriately [49,50,51].

Registry-based randomized controlled trials (RRCTs) leverage existing clinical registries to streamline patient identification, data collection, and follow-up, dramatically reducing cost and time. A prime example is the UK TAVI Trial [52], which randomized patients between TAVI and surgery using the UK’s national registry infrastructure. Similarly, the Nordic Aortic Valve Intervention (NOTION) trial [53] and its successor have utilized this model. Furthermore, large-scale international registries like STS/ACC TVT Registry in the US and GARY in Germany [54], while observational, provide rich, real-world data on both TAVI and surgical patients.

While the emergence of adaptive trial designs and registry-based studies represents a significant methodological advance, it is crucial to recognize that they do not yet fully resolve the ‘leapfrogging’ paradox.

4.4. A Comprehensive Analysis of the Variables—Evidence Analytics and Precision Medicine

There are many variables not yet included in clinical studies, such as operator experience, treatment plans, technical details, variables that can still impact the results. Furthermore, therapies and treatments are applied to human beings that are the phenotypic expression of an individual genetic heritage, different from all others (except identical twins). Knowledge of this biological variability should be incorporated into clinical trials, as it could better guide the design of randomized and non-randomized studies by identifying variables that could alter the results if ignored.

The case of clopidogrel is a paradigm of how ignoring biological variability leads to preventable harm. The efficacy of this antiplatelet drug depends on activation by the CYP2C19 enzyme, but genetic polymorphisms create a “poor metabolizer” phenotype in a significant portion of the population, rendering the drug ineffective for them and increasing their risk of heart attacks and strokes. This resistance, with observations beginning around 2003–2006 and a definitive genetic link established in 2006, was only formally addressed by the FDA in 2010—a delay of over a decade from the drug’s initial approval in 1997 that harmed countless patients. Crucially, the science of CYP2C19 polymorphism was well-established before 1997; had this existing genetic knowledge been integrated into clopidogrel’s development, it would have identified the at-risk group immediately, preventing adverse events and streamlining trials by focusing on the likely responsive population. This interindividual variability is not noise but a critical determinant of efficacy that must be central to medical development: at the two-year follow-up, 29% of patients with the abnormal phenotype had experienced a non-ST elevation myocardial infarction (NSTEMI), compared to 10.3% of those with the normal phenotype [55].

This is just an example taken by analogy from pharmacology, but not entirely inappropriate: just think of the implications of phenotypic variables in calcium metabolism that can influence structural valve degeneration.

Some evidence analytics methods [56,57,58,59] that include data from different scientific fields could be used more frequently to highlight hidden features that potentially limit the outcome. Certainly, such a higher-level integrative analysis requires a structurally advanced Information Technology system.

5. Limitations

This manuscript is a registry-based descriptive review and not a meta-analysis; therefore, an independent data extraction was beyond the scope of this work. Data extraction was single-author with verification of key trial entries against registry pages and primary publications where available. In particular, the selection of RCTs studies in Analysis 1, was based on the author’s judgment and clinical experience for illustrative purposes, which may introduce selection bias.

Timing markers used were CE mark and FDA public approval dates and registry posting dates as recorded on ClinicalTrials.gov; submission dates and first-in-man dates are frequently unavailable or inconsistently reported across manufacturers and registries, therefore were not used. Analyses relied solely on publicly available data from ClinicalTrials.gov, which may not always be perfectly synchronized with real-world regulatory and clinical timelines.

Statistical t-test/Mann-Whitney were exploratory and descriptive comparisons, not adjusted for clustering by valve generation or sponsor.

Figure 1 does not report an exact overlap quantification, that was not attempted in this current paper; Spearman correlation or Gantt-derived metrics should be a proposed solution for future quantitative work. This figure is primarily descriptive and historical and serves to illustrate the structural problem, not to prove any clinical harm which, if present, is already contemplated and reported in the intermediate results of the studies themselves (on the same website Clinicaltrial.org).

As a narrative review, time-to-evidence maturity (CE to peer-reviewed publication) is an important quantitative metric not calculated here; it could be an explicit next step for future research, using Kaplan–Meier or cumulative incidence. This review also does not address the health economic dimensions, and a quantitative benefit-harm model for all generations of devices was beyond its scope.

Finally, the proposed future directions involving artificial intelligence and the analysis of biological variability are conceptual. The fundamental methodological challenge is represented by unmeasured confounding factors, which artificial intelligence cannot completely overcome but can attempt to integrate into a comprehensive data analysis.

6. Conclusions

This comprehensive chronological analysis of the TAVI evidence base demonstrates a persistent decoupled effect, where rapid device iteration consistently outpaces the maturation of long-term clinical validation. This structural misalignment creates a fragmented evidence landscape, challenging definitive clinical decision-making and obscuring long-term patient-centered outcomes. While randomized trials remain the cornerstone of efficacy assessment, non-randomized studies provide an indispensable, rapid source of real-world insights.

The aforementioned paradox suggests a paradigm shift toward more adaptable and efficient evidence-generation frameworks, including robust registry-based studies and thoughtful integration of real-world data, to ensure that clinical practice evolves in tandem with technological innovation, reduce confounding factors and speed up data access. Precision medicine and evidence analytics can be useful for integrating subjective data that have never been included before but, if ignored, can affect the results. Such data include operator experience, treatment plans, technical details and human phenotypic variability, which has already been shown to limit the reliability and safety of randomized trials.

Higher-level, integrated, multidisciplinary and confluent evidence analytics can help Achilles run alongside the tortoise.