A Predictive Model Based on Inflammatory and Coagulation Indicators for Sepsis-Induced Acute Kidney Injury

Abstract

Background Sepsis-induced acute kidney injury (S-AKI) is associated with systemic inflammatory responses and coagulation system dysfunction, and it is associated with an increased risk of mortality. However, there was no study to explore the predictive value of inflammatory and coagulation indicators for S-AKI. Methods In this retrospective study, 1051 sepsis patients were identified and divided into a training cohort (75%, n = 787) and a validation cohort (25%, n = 264) in chronological order according to the date they were admitted. Univariate analyses and multivariate logistic regression analyses were performed to identify the independent predictors of S-AKI. The logistic regression analyses (enter methods) were used to conducted the prediction models. The ROC curves were used to determine the predictive value of the constructed models on S-AKI. To test whether the increase in the AUC is significant, we used a two-sided test for ROC curves available online ( http://vassarstats.net/roc_comp.html ). The secondary outcome was different AKI stages and major adverse kidney events within 30 days (MAKE30). Stage 3B of S-AKI was defined as both meeting the stage 3 criteria [increase of Cr level by > 300% (≥ 4.0 mg/dL with an acute increase of ≥ 0.5 mg/dL) and/or UO < 0.3 mL/kg/h for > 24 h or anuria for > 12 h and/or acute kidney replacement therapy] and having cystatin C positive. MAKE30 were a composite of death, new renal replacement therapy (RRT), or persistent renal dysfunction (PRD). Results We discovered that cardiovascular disease, white blood cell (WBC), mean arterial pressure (MAP), platelet (PLT), serum procalcitonin (PCT), prothrombin time activity (PTA), and thrombin time (TT) were independent predictors for S-AKI. The predictive value (AUC = 0.855) of the simplest model 3 (constructed with PLT, PCT, and PTA), with a sensitivity of 77.6% and a specificity of 82.4%, had a similar predictive value comparing with the model 1 (AUC = 0.872) and the model 2 (AUC = 0.864) in the training cohort (P > 0.05). Compared with the model 1 (AUC = 0.888) and the model 2 (AUC = 0.887), the model 3 (AUC = 0.887) had a similar predictive value in the validation cohort. Moreover, model 3 had the best predictive power for predicting S-AKI in the stage 3 (AUC = 0.777), especially in stage 3B (AUC = 0.771). Finally, the model 3 (AUC = 0.843) had perfect predictive power for predicting MAKE30 in sepsis patients. Conclusion Within 24 hours after admission, the simplest model 3 (constructed with PLT, PCT, and PTA) might be a robust predictor of the S-AKI in sepsis patients, providing information for timely and efficient intervention.

Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study

“…Xin et al. [ 35 ] observed that PLT and PTA were independent predictors of SA-AKI and then constructed a prediction model (AUC = 0.887) combined with PCT. Moreover, Xu et al.…”

Relationship between serum sodium level and sepsis-induced coagulopathy

“…Therefore, we speculate that Cr may be associated with sepsis complicated by acute kidney loss. One study showed that coagulation function was significantly abnormal in patients with SAKI compared with patients without AKI, as evidenced by thrombocytopenia, elevated INR, and prolonged PT ( 55 ). This may be related to the fact that coagulation activation crosstalks with an inflammatory response to form extensive microthrombi, resulting in renal ischemic injury ( 56 ).…”

Impact of early heparin therapy on mortality in critically ill patients with sepsis associated acute kidney injury: a retrospective study from the MIMIC-IV database

“…Severe changes in systemic microvascular distribution caused by sepsis include a significant decrease in capillary density, a decrease in the proportion of capillaries with continuous flow, and an increase in the proportion of capillaries with intermittent or no flow. Based on Inflammatory and Coagulation Indicators (platelet, serum procalcitonin, prothrombin time activity) may be a robust predictor for the SAKI in patients, which providing information for timely and efficient intervention ( Xin et al, 2022 ).…”