Exercise training is one of the central components of cardiac rehabilitation for people with coronary artery disease, including patients recovering from a heart attack, angioplasty, stent placement, or bypass surgery. Regular, supervised exercise can improve aerobic capacity, vascular function, quality of life, and long-term cardiovascular prognosis. Yet the response to training varies substantially between individuals. While many patients become fitter, a considerable proportion—often estimated at one in five or more—experience minimal change in peak oxygen uptake, commonly written as V̇O₂peak. This measurement reflects the maximum amount of oxygen the body can use during intense exercise and is considered one of the most important indicators of cardiorespiratory fitness. Low or unchanged V̇O₂peak is associated with poorer functional capacity and a higher risk of future cardiovascular complications.

The study included 353 patients with coronary artery disease who completed three to four weeks of inpatient cardiac rehabilitation. The participants had experienced a heart attack or undergone coronary procedures such as angioplasty or bypass surgery. At the beginning of rehabilitation, the research team collected data from cardiopulmonary exercise testing and pulse wave analysis, together with standard demographic and clinical information. Cardiopulmonary exercise testing measures how the heart, lungs, blood vessels, and muscles respond while a person exercises, typically on a bicycle or treadmill. Pulse wave analysis provides non-invasive information about the movement of pressure waves through the arteries, including pulse wave velocity, a widely used indicator of arterial stiffness. The researchers then used baseline information to predict whether each patient would achieve a clinically meaningful improvement in V̇O₂peak by the end of rehabilitation.

Ten machine-learning algorithms were evaluated, including approaches designed to identify complex and non-linear relationships among multiple clinical variables. The strongest results came from a Random Forest model, an ensemble method that combines the predictions of many decision trees. Each tree evaluates the data through a series of branching decisions, while the final model aggregates their outputs to produce a more stable prediction. This approach can be particularly useful in medical datasets where several biological factors interact and where a single variable rarely determines the outcome on its own. In this study, the model correctly classified responders and non-responders 77% of the time. Although that level of accuracy is not sufficient to replace clinical judgment, it suggests that routinely collected physiological data may contain signals that are invisible when patients are assessed using conventional risk factors alone.

The most surprising finding was that responders and non-responders appeared broadly similar at the start of rehabilitation when judged by standard clinical characteristics. Age, sex, body mass index, baseline fitness, and aspects of medical history did not reliably separate the two groups. Explainable artificial-intelligence analysis, using a technique known as SHAP, helped reveal which variables contributed most strongly to the model’s predictions. SHAP, or Shapley Additive Explanations, estimates how much each feature pushes an individual prediction toward one outcome or another. Rather than treating the algorithm as a black box, this method allows researchers to examine the relative influence of physiological measurements and understand why a particular patient may be predicted to respond poorly.

The most influential predictors were linked to breathing efficiency during exercise and the condition of the arteries. Patients who required more ventilation to consume a given amount of oxygen were less likely to achieve a substantial improvement in aerobic capacity. This relationship can be expressed through the ventilatory equivalent for oxygen, which describes how much air a person must move through the lungs for each unit of oxygen taken up by the body. A higher value may indicate that breathing is less efficient during exercise or that the circulation and respiratory systems are working under greater physiological strain. Reduced breathing reserve—the limited capacity remaining between exercise ventilation and the maximum ventilatory ability of the lungs—also contributed to predictions of a weaker training response.

Arterial stiffness provided another important signal. Patients with higher pulse wave velocity were less likely to improve their V̇O₂peak after standard rehabilitation. Healthy arteries expand and recoil as blood is pumped from the heart, helping regulate pressure and maintain efficient blood flow. Stiffer arteries transmit pressure waves more rapidly and can increase the workload placed on the heart while impairing the delivery of blood to working muscles. These vascular limitations may help explain why two patients with similar age, medical history, and baseline exercise capacity can respond very differently to the same training program. The model also identified the use of angiotensin II receptor blockers and calcium channel blockers as factors that influenced predictions, although the study does not establish that these medications directly caused a reduced response.

The findings suggest that the biology of exercise adaptation may be more individualized than traditional rehabilitation models assume. A standard aerobic program can produce strong benefits for many patients, but those with impaired vascular elasticity or inefficient ventilatory responses may require a different dose, intensity, duration, or progression of exercise. Instead of waiting until the end of rehabilitation to measure whether a patient has improved, clinicians could eventually use baseline pulse wave and exercise-test data to identify people who need closer monitoring or an adjusted program. Such interventions might include more carefully controlled aerobic intervals, longer training periods, additional resistance exercise, or treatment of underlying vascular and respiratory limitations. The researchers emphasize that the model is intended to support—not replace—medical decision-making.

Professor Boris Schmitz and Professor Frank Mooren of the University of Witten/Herdecke led the study in collaboration with researchers from DRV Clinic Königsfeld in Germany and FORTH in Greece. The team’s next step is a randomized controlled trial examining whether patients predicted to be non-responders can benefit from individually adjusted aerobic interval training. That experiment will be critical because prediction alone does not demonstrate that changing treatment will improve outcomes. A model may identify a group at higher risk of limited improvement, but only prospective testing can show whether acting on that information leads to greater gains in fitness, better symptoms, or improved cardiovascular health.

The researchers also caution that the current results should not yet be generalized to every cardiac rehabilitation population. The model was developed using patients treated in a specific clinical setting and may perform differently in older adults, people with multiple chronic conditions, or those completing outpatient programs with different exercise schedules. It will need external validation in larger and more diverse groups before it can be integrated into routine care. Even so, the study offers a compelling glimpse of how artificial intelligence could transform rehabilitation: not by replacing exercise, but by helping clinicians determine which kind of exercise is most likely to work for each patient. If future trials confirm the approach, a simple combination of cardiopulmonary exercise testing and pulse wave analysis could help prevent patients from completing rehabilitation without achieving meaningful improvements in cardiovascular fitness.

Subject of Research: People with coronary artery disease undergoing exercise-based cardiac rehabilitation

Article Title: A machine learning approach predicts improvement of physical exercise capacity based on pulse wave analysis in coronary artery disease patients

News Publication Date: 5 May 2026

Web References: https://doi.org/10.1016/j.jshs.2026.101144

References: Journal of Sport and Health Science; DOI: 10.1016/j.jshs.2026.101144

Image Credits: Hendrik Schäfer, University of Witten/Herdecke, Germany

Keywords: cardiac rehabilitation, coronary artery disease, machine learning, Random Forest, exercise response, non-responders, cardiopulmonary exercise testing, pulse wave analysis, arterial stiffness, V̇O₂peak, personalized medicine, cardiovascular health

The development of predictive models for clinical outcomes in pediatric patients has always been hampered by heterogeneous patient presentations, diverse anomaly types, and the intricate interplay of comorbidities. This study strategically focuses on children with congenital gastrointestinal defects—a group that experiences some of the highest rates of perioperative mortality. The capacity to generate reliable mortality predictions within a short timeframe post-diagnosis could revolutionize treatment strategies, enabling personalized clinical pathways that allocate resources efficiently while minimizing invasive interventions.

Random forest classifiers operate by creating an ensemble of decision trees, each trained on distinct subsets of the data, and synthesizing their outputs to improve classification accuracy. This method excels particularly in handling complex, nonlinear relationships amongst variables, providing resilience against overfitting and accommodating noisy or incomplete data sets—a typical challenge in clinical environments. By integrating this technique into pediatric surgical prognostics, researchers have achieved a significant step towards precision medicine, leveraging computational power to complement clinical judgment.

In constructing the predictive model, the researchers meticulously curated a comprehensive dataset encompassing demographic information, detailed clinical parameters, laboratory findings, and perioperative variables from a diverse cohort of pediatric patients. The inclusion of a wide spectrum of features ensured that the classifier could capture subtle patterns and interactions indicative of mortality risk. Crucially, this data-driven methodology bypassed reliance on preconceived clinical heuristics, which often fail to capture the complexity inherent in congenital gastrointestinal anomalies.

The performance evaluation of the random forest classifier revealed impressive predictive capabilities. Statistical metrics such as sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve illustrated the model’s ability to discern high-risk patients effectively. This level of accuracy surpasses conventional scoring systems used in neonatal and pediatric intensive care units, highlighting the transformative potential of machine learning applications in acute clinical decision-making spaces.

Beyond its predictive prowess, the study addresses the interpretability of the model’s outputs—a critical component in clinical adoption. Techniques like feature importance ranking elucidated which variables most significantly influenced mortality risk, thus aligning the computational insights with clinical relevance. Such transparency fosters trust among healthcare providers and facilitates the integration of AI predictions into multidisciplinary care discussions.

In addition, the temporal dynamics of prediction were explored, enabling clinicians to understand how risk estimates evolve during the critical early phases of treatment. This dynamic modeling supports ongoing patient monitoring and may prompt timely adjustments in therapeutic approaches. The capacity to update risk predictions based on real-time data mirrors the fluid nature of pediatric critical care, where rapid physiological changes necessitate agile responses.

The potential implications of this research span beyond immediate clinical applications. By demonstrating the successful utilization of random forest classifiers in a sensitive and complex patient population, the study paves the way for broader incorporation of AI-driven tools in pediatric surgery and intensive care. This paradigm shift promises not only enhanced patient outcomes but also a redefinition of clinical workflows, where predictive analytics guide strategic planning and resource allocation.

Ethical considerations accompanying the deployment of AI in pediatric care are acknowledged and thoughtfully addressed. Ensuring data privacy, mitigating biases inherent in training datasets, and preserving the clinician’s role as the ultimate decision-maker remain central tenets. The research emphasizes that the model functions as a decision support tool rather than a replacement for human expertise, promoting a symbiotic relationship between technology and practitioners.

Furthermore, the scalability and adaptability of the model to diverse healthcare settings were evaluated. The randomized structure of the classifier supports its application in various institutional contexts, irrespective of specific patient demographics or treatment protocols. This flexibility is vital for translating research findings into widespread clinical practice across different geographic and socioeconomic landscapes.

Collaboration between data scientists, pediatric surgeons, and critical care specialists was instrumental in shaping the study’s design and implementation. This interdisciplinary approach ensured that the model’s development was grounded in clinical realities while leveraging the latest computational methodologies. Such synergy exemplifies the future of medical innovation, where teamwork propels technology from theoretical promise to practical utility.

Importantly, the study not only contributes to mortality prediction but also offers insights into the pathophysiological factors driving poor outcomes in gastrointestinal congenital anomalies. By identifying key predictive features, clinicians gain deeper understanding of disease mechanisms and potential intervention points, informing both surgical strategy and postoperative care.

Looking ahead, the research sets a precedent for integrating longitudinal data streams, including genetic profiles and imaging modalities, which hold the promise of refining prognostication further. The incorporation of multimodal data sources stands to elevate the precision of predictive analytics, facilitating bespoke therapeutic regimens tailored to individual patient profiles.

In sum, this pioneering work on short-term mortality prediction using random forest classifiers represents a confluence of technological innovation and clinical urgency. By unlocking refined risk assessment capabilities for vulnerable pediatric populations, it heralds a new era in which artificial intelligence empowers healthcare providers to save lives through informed, data-driven decisions.

Subject of Research: Short-term mortality prediction in children with gastrointestinal congenital anomalies using machine learning approaches.

Article Title: Short-term mortality prediction in children with gastrointestinal congenital anomalies using a random forest classifier.

Article References:
Serban, A.M. Short-term mortality prediction in children with gastrointestinal congenital anomalies using a random forest classifier. Pediatr Res (2025). https://doi.org/10.1038/s41390-025-04378-2

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41390-025-04378-2