Machine learning-derived left atrial strain index (LASi) detected elevated LVEDP with 79% accuracy, compared to 75% for peak LAS and 76% for the ASE/EACVI guideline algorithm.
Observational (n=294)
Does a machine learning-derived left atrial strain index (LASi) improve the accuracy of detecting elevated LVEDP compared to peak LAS and ASE/EACVI guidelines in patients undergoing left heart catheterization?
A novel machine learning-derived left atrial strain index (LASi) accurately detects elevated LVEDP and resolves indeterminate classifications from current diastolic function guidelines.
Absolute Event Rate: 79% vs 76%
AIMS: While transthoracic echocardiography (TTE) assessment of left ventricular end-diastolic pressure (LVEDP) is critically important, the current paradigm is subject to error and indeterminate classification. Recently, peak left atrial strain (LAS) was found to be associated with LVEDP. We aimed to test the hypothesis that integration of the entire LAS time curve into a single parameter could improve the accuracy of peak LAS in the noninvasive assessment of LVEDP with TTE. METHODS AND RESULTS: We retrospectively identified 294 patients who underwent left heart catheterization and TTE within 24 h. LAS curves were trained using machine learning (100 patients) to detect LVEDP ≥ 15 mmHg, yielding the novel parameter LAS index (LASi). The accuracy of LASi was subsequently validated (194 patients), side by side with peak LAS and ASE/EACVI guidelines, against invasive filling pressures. Within the validation cohort, invasive LVEDP was elevated in 116 (59.8%) patients. The overall accuracy of LASi, peak LAS, and American Society of Echocardiography/European Association for Cardiovascular Imaging (ASE/EACVI) algorithm was 79, 75, and 76%, respectively (excluding 37 patients with indeterminate diastolic function by ASE/EACVI guidelines). When the number of LASi indeterminates (defined by near-zero LASi values) was matched to the ASE/EACVI guidelines (n = 37), the accuracy of LASi improved to 87%. Importantly, among the 37 patients with ASE/EACVI-indeterminate diastolic function, LASi had an accuracy of 81%, compared with 76% for peak LAS. CONCLUSION: LASi allows the detection of elevated LVEDP using invasive measurements as a reference, at least as accurately as peak LAS and current diastolic function guideline algorithm, with the advantage of no indeterminate classifications in patients with measurable LAS.
Gruca et al. (Thu,) conducted a observational in Elevated left ventricular end-diastolic pressure (n=294). Machine learning-derived left atrial strain index (LASi) vs. Peak left atrial strain (LAS) and ASE/EACVI guidelines was evaluated on Overall accuracy to detect LVEDP ≥ 15 mmHg. Machine learning-derived left atrial strain index (LASi) detected elevated LVEDP with 79% accuracy, compared to 75% for peak LAS and 76% for the ASE/EACVI guideline algorithm.