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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Acute kidney injury (AKI) is a critical complication in COVID-19 patients and associated with worse prognosis. Existing prediction tools predominantly rely on static parameters at admission or complex biomarkers but with limited application value in clinical practice. This study aimed to evaluate the predictive capacity of longitudinal dynamic changes in readily available peripheral blood cell indices for AKI risk by joint model.
A multicenter prospective cohort of COVID-19 inpatients from six Chinese medical centers (December 1, 2022 - January 31, 2023) was analyzed. Joint models were constructed incorporating lymphocyte, neutrophil, platelet counts, and derived ratios (NLR, PLR, NPR, NLPR) respectively from temporal complete blood count tests during hospitalization. Both "current value" (representing real-time biomarker levels) and "slope" (representing temporal change rate) of longitudinal indices were introduced as time-dependent covariates into survival models. AKI diagnosis during hospitalization, defined by KDIGO criteria, served as the primary endpoint. Furtherly, restricted cubic spline (RCS) analysis was employed to identify nonlinear risk thresholds on admission, and baseline-stratified dynamic effects of above markers were estimated by joint models.
Among 3,691 patiets, 768 (20.8%) developed AKI. Joint modeling revealed that elevated lymphocyte values (β = -0.54, 95%CI -0.83 to -0.24, P = 0.0007) and their upward trends (β = -6.30, 95%CI -11.65 to -0.52, P = 0.0327) were independently associated with reduced AKI risk. Conversely, increased neutrophil counts, NLR values/trends, and PLR trends demonstrated positive associations with AKI risk (P < 0.05). Elevated platelet values were protective (P < 0.0001), while NPR and NLPR values showed significant risk elevation (NPR: β=1.09, 95%CI 0.79-1.40; NLPR: β=0.72, 95%CI 0.50-0.94; both P < 0.0001). NLPR exhibited the most optimal predictive performance (AUC=0.771, Brier score=0.117). RCS-stratified analysis demonstrated differential predictive capacity across baseline thresholds: lymphocyte count ≤0.84×10⁹/L group showed protective effects with value elevation (P=0.0043), while NLPR >3.23 group exhibited significant increasing risk (P<0.0001).
A dynamic prediction framework for AKI risk was successfully constructed using routinely monitored peripheral blood parameters through joint modeling, overcoming limitations of static approaches. RCS-based stratification revealed intensity variations in predictive effects across baseline levels, enhancing individualized risk assessment.
