We read with great interest the recent article by Xin et al. “Predictive Value of Noninvasive Cardiac Function Monitoring Combined with GRACE Score for Short-Term Outcomes in Patients With ST-Segment Elevation Myocardial Infarction” which provides valuable insights into the potential of noninvasive cardiac function monitoring (NCFM) to augment risk stratification in patients with ST-segment elevation myocardial infarction (STEMI). The authors present a novel approach to improving prognostic accuracy for major adverse cardiovascular events (MACE) by integrating hemodynamic parameters with the established GRACE score (Xin et al. 2025). Although the study contributes implicitly to the field, certain aspects warrant further discussion.
First, the study successfully demonstrates that stroke volume (SV), cardiac output (CO), cardiac index (CI), contractility index (CTI), early diastolic filling ratio (EDFR), end-diastolic volume (EDV), and systemic vascular resistance (SVR) are independent predictors of MACE. Moreover, the authors confirm that including SV and CTI into the GRACE score improves predictive performance. While this finding is promising, the study does not assess whether alternative combinations of hemodynamic parameters might offer even greater predictive accuracy. Considering the interaction of different cardiac function parameters, an exploratory analysis using machine-learning techniques such as decision trees or neural networks could help investigate the most effective predictors of short-term outcomes (Patel and Sengupta 2020).
Second, while the study effectively underscores the added predictive value of NCFM in combination with the GRACE score, it does not provide adequate discussion on the probability of integrating NCFM into clinical practice. Extensive implementation of noninvasive cardiac monitoring entails considerations such as availability, cost-effectiveness, and user-friendliness in different healthcare settings (Kim et al. 2019). Addressing these logistical concerns would enhance the study's clinical applicability and guide its possible adoption in routine patient management.
Third, the study does not consider probable confounding variables that may affect the predictive power of NCFM. Variables such as renal function, medication adherence, and previous cardiovascular interventions could affect both hemodynamic parameters and MACE outcomes (Chinwong et al. 2021; Hussain et al. 2023). Adjusting for these factors in a multivariate analysis would support the study's conclusions and provide more precise risk stratification.
Fourth, the study does not investigate the additional benefit of repeated NCFM measurements over time. Although the single-timepoint evaluation at admission provides valuable prognostic information, dynamic changes in cardiac function parameters post-STEMI may offer supplementary predictive value. Future research shou
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