Impact of Stress Hyperglycemia Ratio on Adverse Outcomes in Patients With Chronic Thromboembolic Pulmonary Hypertension

ABSTRACT Prognostic assessment of chronic thromboembolic pulmonary hypertension (CTEPH) remains challenging. Stress hyperglycemia, a condition where glucose metabolism is regulated during stress, has been regarded as an indicator of acute hyperglycemia. The stress hyperglycemia ratio (SHR) serves as a more precise marker for this state. This research intended to investigate the prognostic function of SHR in CTEPH, as its link to poor outcomes is uncertain. Clinical deterioration events were the primary endpoint. LASSO regression was utilized for variable selection and dimensionality reduction to identify key features and mitigate overfitting. Kaplan–Meier analysis, multivariable Cox regression, and restricted cubic splines were primarily used to investigate the link between SHR and adverse clinical outcomes. Internal validation was performed using bootstrap resampling. Sensitivity analysis was performed to assess the robustness of the findings. The study included a sum of 451 patients with CTEPH; over a median follow‐up of 21 months, 89 (19.7%) patients encountered adverse outcomes. Kaplan–Meier analysis indicated that patients having elevated SHR levels exhibited a markedly higher overall incidence of adverse events. Restricted cubic spline analysis showed a linear relationship between SHR and adverse events. Multivariable Cox regression analysis indicated that higher SHR independently predicts adverse outcomes, whether treated as continuous (hazard ratio, 1.541; 95% confidence interval, 1.252–1.896, p < 0.001) or categorical (hazard ratio, 2.419; 95% confidence interval, 1.268–4.616, p = 0.007). Internal validation demonstrated that the original C‐index was 0.693, with a bias‐corrected C‐index of 0.675 after bootstrap validation. In patients with CTEPH, higher SHR is independently associated with clinical worsening. The prognostic significance of SHR remained robust across internal validation and multiple sensitivity analyses.