Cardiac Remodeling in Preeclampsia: A Large-Language-Model-Assisted Meta-Analysis and Meta-Regression,

Preeclampsia is a hypertensive disorder of pregnancy associated with substantial maternal morbidity and long-term cardiovascular risk, but the consistency of echocardiographic remodeling remains unclear. We conducted a mega-meta-analysis of left ventricular function and geometry, enabled by a large language model based suite of tools. A PROSPERO-registered review (CRD420251109103) searched PubMed, Scopus, and Embase without date limits. Synthesa AI screened more than 18,000 abstracts, extracted data, assessed risk of bias, and generated Bayesian analytic code, with all outputs validated by human reviewers. Seventy-five studies including met eligibility criteria. Preeclampsia was associated with a small but statistically significant reduction in ejection fraction (mean difference –0.87%, 95% CrI –1.58 to –0.16) and a clinically meaningful impairment in global longitudinal strain (–3.08%, 95% CrI –4.13 to –2.06). Left ventricular mass index was substantially higher in the preeclampsia group (+13.10 g/m 2 , 95% CrI 10.06 to 16.21), as was relative wall thickness (+0.062, 95% CrI 0.042 to 0.081), whereas fractional shortening showed no significant difference (–0.60%, 95% CrI –2.15 to +0.86). Moderator analyses revealed that BMI and parity significantly influenced strain, while gestational age at diagnosis accounted for nearly all variance in ventricular mass. This mega-meta-analysis defines a remodeling phenotype of preserved ejection fraction, impaired strain, and hypertrophic adaptation consistent with subclinical systolic dysfunction. Equally, it demonstrates the transformative role of LLM-based tools, showing that evidence syntheses of this magnitude can be automated, scaled, and standardized in ways previously unattainable.