Despite substantial advances in coronary revascularization, a major clinical challenge persists: not all anatomically successful percutaneous coronary interventions (PCI) deliver meaningful physiological improvement or durable symptom relief. A growing body of evidence shows that residual ischemia after PCI is common, often silent, and strongly associated with future adverse events. These insights have shifted attention from angiographic results alone toward a more nuanced physiological characterization of coronary artery disease (CAD), including the pattern—focal versus diffuse—of atherosclerotic involvement [1–3].
The pullback pressure gradient (PPG), derived from hyperaemic pressure pullbacks, represents an important evolution in this field. By capturing the spatial distribution of disease, PPG complements fractional flow reserve (FFR) and provides diagnostic information that angiography alone cannot. PPG captures disease distribution on a continuum from 0 (diffuse disease) to 1 (focal disease), offering a deeper mechanistic understanding of why anatomically successful PCI may fail to restore physiological flow. Integrating PPG into pre-PCI evaluation has the potential to refine patient selection, optimize procedural planning, and anticipate residual ischemia [4]. Focal lesions tend to respond favorably to PCI, while diffuse disease—characterized by gradual pressure loss and often heavier calcification—limits both stent expansion and physiological gain. These concepts have been demonstrated in prior imaging and physiology studies and increasingly influence clinical decision-making [5,6].
In this context, the multicenter analysis by Ikeda et al. [7] offers an important step forward by testing whether a previously validated PPG-based model can predict post-PCI FFR and whether such predicted physiology carries prognostic value. Using data from the PPG Global registry, the authors applied a three-variable model (pre-PCI FFR, PPG, and vessel type) to estimate post-PCI FFR in 855 patients (890 vessels) [7]. The agreement between predicted and measured post-PCI FFR was excellent, with a negligible mean difference and narrow limits of agreement. Predicted ΔFFR strongly correlated with measured ΔFFR (r = 0.92), reinforcing the biological coherence of integrating disease severity and distribution.
More importantly for clinical practice, predicted physiology was strongly associated with 1-year outcomes. Vessels classified as having predicted suboptimal post-PCI FFR experienced significantly higher rates of target vessel failure (TVF), myocardial infarction, and ischemia-driven revascularization at 1 year (adjusted HR 1.97, 95% CI 1.24–3.15) [7]. Suboptimal predicted physiology was also associated with higher rates of spontaneous MI and a five-fold increased likelihood of ischemia-driven target vessel revascularization. These findings persisted after excluding peri-procedural infarctions, underscoring the robustness of the association. This suggests that pre-procedural disease characterization may offer superior prognostic insight compared with post-procedural measurements alone, which can be influenced by hyperemia variability, stent technique, and pressure wire artifacts.
From a mechanistic standpoint, the study adds weight to a principle increasingly recognized in contemporary literature: physiological improvement after PCI depends as much on disease pattern as on stenosis severity. Diffuse disease often coexists with heavier calcification, smaller vessel caliber, longer stented segments, and lower immediate stent expansion—features known to predispose to stent failure and recurrent ischemia. Large OCT studies have shown that calcification burden and underexpansion are among the strongest predictors of late adverse events after stent implantation [8,9]. These factors help explain why, even with technically sound PCI, diffuse disease biology may limit achievable FFR restoration.
Ikeda et al. also observed that measured post-PCI FFR was not associated with outcomes in this cohort, whereas predicted physiology was. Although counterintuitive, this aligns with the growing recognition that post-PCI FFR is susceptible to procedural variability, whereas pre-PCI indices reflect the intrinsic disease substrate. This finding supports a transition toward predictive physiology, where the goal is not only to assess ischemia but to anticipate whether PCI can meaningfully and durably modify coronary flow.
The ability to forecast physiological benefit before stenting may help identify lesions for which PCI is futile—particularly in diffuse disease—thereby reducing unnecessary procedures and procedural risk. In this sense, PPG-guided prediction could complement current revascularization algorithms by stratifying patients according to the expected physiological return of PCI, not merely the presence of ischemia.
If externally validated, PPG-based prediction could influence how future trials are designed. CAD phenotype (focal vs diffuse) may modify the response not only to stents, but also to adjunctive therapies such as intravascular lithotripsy, pharmacological vasodilators, or anti-inflammatory agents. Considering disease pattern in study design may help explain heterogeneity in treatment effect and guide personalized therapy.
Ikeda et al.’s results reinforce the rationale for integrating pressure pullbacks, anatomical imaging, and plaque characterization. Combinations of PPG with OCT- or CT-derived markers could deepen our understanding of which plaques are structurally—and physiologically—unfavorable targets for PCI. This multimodal perspective aligns with the broader movement toward comprehensive lesion assessment in coronary intervention.
Several limitations remain: the prediction model and outcome analysis rely on the same dataset; generalizability beyond experienced physiology centers is unknown; and long-term implications extend beyond the 1-year follow-up presented here. Nevertheless, the study provides compelling evidence that PPG-guided prediction has prognostic value and may refine revascularization strategies in a clinically meaningful way.
By shifting coronary physiology from a diagnostic tool to a predictive and prognostic instrument, this work contributes to redefining how CAD is understood and treated. As we progress toward individualized revascularization, integrating disease pattern, baseline flow, and predicted physiological gain may help ensure that PCI is offered when—and only when—it can deliver durable clinical benefit.
