Development and Validation of a Risk Prediction Model for Diastolic Dysfunction in Patients with Sepsis

Background This study aimed to identify risk factors for diastolic dysfunction in septic patients, establish a predictive model, enable early intervention, reduce the incidence of septic cardiomyopathy (SCM), and improve patient prognosis. Methods Ninety-eight sepsis patients admitted to the Affiliated Hospital of Chengde Medical College were divided into two groups: the diastolic dysfunction group and the normal cardiac function group. Baseline clinical data, echocardiographic parameters related to diastolic function, and serum biomarkers of myocardial injury were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression combined with binary logistic regression was used for variable selection, followed by model construction, evaluation, visualization, and internal validation. Results Key predictive variables included Sequential Organ Failure Assessment (SOFA) score, 28-day mortality, Cardiac Troponin I (cTnI), A (Atrial systole peak), E/A (Early diastolic peak/Atrial systole peak), e' (Early diastolic myocardial velocity peak), E/e' (Early diastolic peak/Early diastolic myocardial velocity peak), and Relative Wall Thickness (RWT). Nomogram analysis confirmed these as risk factors for SCM. The model showed high predictive value (Receiver Operating Characteristic(ROC) curve area under the curve = 0.983, 95% Confidence Interval ( CI ): 0.951-1.000) and good calibration (Hosmer-Lemeshow test: χ² = 1.784, df  = 8, P  = 0.987). Its clinical utility was validated by Decision Curve Analysis (DCA). Conclusion SOFA score, 28-day mortality, cTnI, A, e', E/e', E/A, and RWT are independent risk factors for diastolic dysfunction in septic patients. The constructed predictive model exhibits excellent performance and clinical applicability.