Although the Shock–Lactate Index (SLI) has been increasingly explored as a composite physiological indicator, its association with outcomes in patients with sepsis-associated acute kidney injury (SA-AKI) remains unclear. This retrospective observational study included 9,873 patients with SA-AKI from the MIMIC-IV database (2008–2019). Sepsis and AKI were defined according to Sepsis-3 and KDIGO criteria, respectively. The association between SLI and 28-day all-cause mortality was evaluated using Kaplan–Meier survival analysis and multivariable Cox proportional hazards models. Restricted cubic splines with four knots were applied to explore potential non-linear relationships. Models were adjusted for clinically relevant covariates, including Sequential Organ Failure Assessment (SOFA) score, APACHE II score, organ support measures, and selected laboratory variables. Prespecified subgroup analyses were conducted to assess effect modification across clinically relevant strata. Among patients with SA-AKI, higher SLI levels were significantly associated with increased 28-day all-cause mortality ( P < 0.001). Kaplan–Meier analysis demonstrated progressively lower survival rates across increasing SLI quartiles, with the highest quartile exhibiting the poorest survival (log-rank P < 0.0001). In multivariable Cox models, elevated SLI remained independently associated with higher mortality risk. Restricted cubic spline analysis indicated a non-linear association between SLI and mortality (P for non-linearity < 0.001). Subgroup analyses showed generally consistent associations across patient subgroups, with significant interactions observed in strata defined by age, pneumonia, chronic kidney disease, ischemic heart disease, SOFA score and APACHE II score (all P for interaction < 0.05). Higher SLI levels are independently associated with increased 28-day mortality in patients with sepsis-associated acute kidney injury. SLI shows promise as a prognostic tool for assessing mortality risk in SA-AKI patients, providing clinicians with more accurate information to optimize treatment strategies and improve patient outcomes.
Ren et al. (Fri,) studied this question.