重症大動脈弁狭窄症の診断・治療アルゴリズムの包括的統一レジメン：大動脈弁狭窄症および治療格差特定のためのアルゴリズム検証

Severe aortic stenosis (sAS) leads to high morbidity and mortality when left untreated. We sought to develop and validate an algorithm-based rules engine to identify patients with untreated sAS and to evaluate differences between those who did and did not subsequently receive guideline-concordant treatment with aortic valve replacement (AVR).

We curated discrete and nondiscrete data from our echocardiography system, then created a rules engine to identify and grade aortic stenosis. We assessed sensitivity and specificity of the rules engine to identify sAS using manual adjudication. We additionally conducted a retrospective cohort analysis to identify demographic and socioeconomic factors associated with receipt of guideline-concordant AVR treatment for sAS.

The rules engine demonstrated 100% sensitivity and 95.4% specificity for identifying sAS across n ​= ​2162 echocardiographic studies from unique patients. Univariate analyses revealed patients with untreated sAS were more likely to be older and female, with no appreciated differences by race, ethnicity, insurance status, or neighborhood-level socioeconomic scores. In multivariable analyses, older individuals, women, and those with Medicare/Medicare advantage were less likely to undergo AVR. Among treated patients, those who underwent surgical AVR were more likely to be younger, male, and have lower socioeconomic neighborhood scores.

Untreated sAS is prevalent but can be accurately identified at scale using an echocardiogram report-based rules engine. Disparities in the receipt of AVR persist, particularly among women, older adults, and patients with nonprivate insurance coverage. The systematic use of automated algorithmic protocols may facilitate valvular heart disease identification and reduction of treatment disparities.