Early lactate kinetics predicts survival and neurological outcomes after out-of-hospital cardiac arrest: a retrospective cohort study
摘要
Lactate dynamics reflects restoration of tissue perfusion and has prognostic value in critical illness. However, the temporal evolution of lactate and its prognostic implications after out-of-hospital cardiac arrest (OHCA) have not been fully elucidated.
MethodsWe retrospectively analyzed adult patients with OHCA with sustained return of spontaneous circulation (ROSC) at a tertiary medical center in Taiwan between 2016 and 2022. Serial lactate levels were measured at 2, 6, 12, and 24 h after ROSC. The primary outcome was a favorable neurological status at discharge, and a key secondary outcome was survival to discharge. Linear mixed-effects models were applied to evaluate longitudinal lactate trajectories and their associations with outcomes, accounting for repeated measures and adjusting for age, initial rhythm, bystander cardiopulmonary resuscitation (CPR), CPR duration, public location, witnessed status, targeted temperature management, percutaneous coronary intervention, and extracorporeal membrane oxygenation.
ResultsOf the 496 included patients, 90 (18.1%) achieved a favorable neurological outcome and 241 (48.6%) survived to discharge. Lactate levels declined significantly over time in both groups (p < 0.001), but patients with favorable neurological outcomes and survivors showed a steeper decline between 12 and 24 h. Interaction analyses revealed significant time × outcome effects for neurological outcome at 12 h (β = − 0.33, 95% CI − 0.65 to − 0.001, p = 0.049) and 24 h (β = − 0.61, 95% CI − 0.91 to − 0.30, p < 0.001), and for survival at 12 h (β = − 0.38, 95% CI − 0.64 to − 0.13, p = 0.002) and 24 h (β = − 0.48, 95% CI − 0.72 to − 0.24, p < 0.001).
ConclusionSerial lactate trajectories within the first 24 h after ROSC were strongly associated with survival and neurological recovery in OHCA patients. Modeling lactate as a continuous dynamic biomarker using LMM captured prognostic information beyond single time points and may help guide individualized post-resuscitation management.