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Rule-based Clinical Decision Support System Matches Expert Assessments in Heart Failure With Preserved Ejection FractionAI Helps Doctors Spot Heart Failure Faster

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Key Takeaway
Consider CDSS utility for diastolic function assessment in heart failure with preserved ejection fraction using cohort data.

This prospective cohort study involved 134 patients with exertional dyspnea and preserved left ventricular ejection fraction defined as LVEF >50%. The investigation utilized semi-supine bicycle stress echocardiography to evaluate diagnostic performance. Data collection occurred during the stress testing session. The study design allowed for direct comparison of automated versus manual interpretation methods. The primary objective focused on the automated assessment of left ventricular diastolic function within this specific clinical population.

Researchers compared a rule-based Clinical Decision Support System against expert assessments as the comparator. The system achieved 93% of cases matching expert assessments for the primary outcome. Additionally, the intervention demonstrated 85% correct identification of stress-induced diastolic dysfunction. Diagnostic agreement between the automated system and experts showed an ICC > 0.94. Discrimination performance was quantified with an AUC = 0.92. These metrics reflect the system's analytical capabilities.

Safety data regarding adverse events, serious adverse events, discontinuations, and tolerability were not reported in this study. No specific limitations were documented in the provided evidence structure. The practice relevance indicates support for improved diagnostic consistency and augmented physician decision-making in cardiovascular care. However, the prospective cohort design does not establish causality, and follow-up duration was not reported. Clinicians should interpret these findings as preliminary evidence supporting diagnostic tool utility rather than definitive outcome improvement. Further research is needed to confirm long-term clinical impact. Additional validation in diverse populations is required before widespread implementation.

Imagine running on a treadmill until you are out of breath. You feel fine at rest, but the moment you push harder, your lungs feel heavy and your heart races. This is exertional dyspnea, a common sign of heart failure with preserved ejection fraction (HFpEF).

Doctors often struggle to diagnose this specific type of heart failure. The problem lies in how they look at the heart under stress. They must measure many moving parts while the patient is exercising. It is a high-pressure situation that leaves little room for error.

HFpEF affects millions of people, mostly older adults. Unlike other heart failures where the heart muscle pumps too weakly, the heart in HFpEF pumps normally. The problem is that the heart becomes stiff and cannot fill up with blood properly when you need it most.

Current tests are frustrating. Doctors must manually measure blood flow and pressure changes. They have to do this quickly while the patient is still moving. This process is prone to human error. One missed measurement can change the diagnosis entirely. Patients often wait too long for a clear answer.

The surprising shift

For years, doctors relied entirely on their own eyes and experience. They looked at complex graphs and made a split-second judgment. But here is the twist: a new tool is changing the game.

A computer system now helps doctors read these graphs. It does not replace the doctor. Instead, it acts like a very careful second pair of eyes. This system follows strict medical rules to check the heart's function. It ensures that no measurement is ignored.

Think of the heart like a busy highway. Blood is the traffic, and the heart valves are the toll booths. In a healthy heart, traffic flows smoothly. In HFpEF, the toll booths get jammed, and cars back up.

The new AI system watches this traffic jam in real time. It uses simple rules, like a checklist, to spot the jams. If the blood flow slows down too much during exercise, the system flags it. It looks at specific numbers, like how fast the heart relaxes.

What scientists didn't expect

The team tested this system on 134 patients. These patients had shortness of breath but normal pumping strength. The computer analyzed their heart scans in just three minutes.

It matched the expert doctors' opinions in 93% of cases. It correctly spotted the heart problems in 85% of the cases. The agreement between the computer and the doctors was very high. This means the tool is reliable and consistent.

This doesn't mean this treatment is available yet.

This new tool helps doctors make faster, more accurate diagnoses. It reduces the stress on the medical team. It also gives patients a clearer picture of their condition sooner.

However, this technology is still in the research phase. It is not ready for every clinic today. You should talk to your doctor if you have heart symptoms. They can tell you if this type of advanced testing is available near you.

The limitations

Every new tool has limits. This study only included 134 patients. It did not include everyone with heart problems. The system was tested on specific types of heart scans. It might need more testing to work for everyone.

Researchers will continue to test this system in larger groups. They want to see if it works for different types of heart scans. Eventually, this tool could become standard care. It will help millions of people get the right diagnosis faster. Until then, it remains a powerful aid for skilled doctors.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
Heart failure with preserved ejection fraction (HFpEF) remains challenging to diagnose due to the complexity of diastolic function assessment during stress echocardiography, where multiple hemodynamic parameters must be evaluated under time pressure. Explainable artificial intelligence, specifically rule-based Clinical Decision Support Systems (CDSS), offers promising improvements in reproducibility and interpretability. A rule-based CDSS was developed and clinically validated to automate left ventricular diastolic function assessment during semi-supine bicycle stress echocardiography. A prospective cohort of 134 patients (mean age 61.3 ± 8.7 years) with exertional dyspnea and preserved left ventricular ejection fraction (LVEF >50%) was enrolled, excluding individuals with significant valvular pathologies, arrhythmias, or unstable ischemia. Echocardiographic and Doppler data were collected using Toshiba Aplio500 and Esaote MyLabSIGMA systems. The algorithm incorporated manual input of measurements, computed derived indices (e.g., diastolic reserve index, myocardial stiffness, vascular resistance), and applied rule-based logic in accordance with ASE/EACVI (2016/2022) guidelines and the ESC HFpEF consensus. The CDSS generated diagnostic conclusions within 3 min per case, matching expert assessments in 93% of cases and correctly identifying stress-induced diastolic dysfunction in 85%. It demonstrated high diagnostic agreement (ICC > 0.94) and discrimination (AUC = 0.92). Rule-based outputs, such as “Impaired diastolic reserve” or “Right ventricular dysfunction under load,” were based on combinations of parameters (e.g., E/e′ > 15, Δe′ ≤ 0, TAPSE  12 mmHg). The explainable, guideline-compliant CDSS enables real-time, transparent analysis of diastolic function, supporting improved diagnostic consistency and augmented physician decision-making in cardiovascular care.
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