ObjectiveWe investigated the association between lactate clearance or serum lactate levels and neurologic outcomes or in-hospital mortality in cardiac arrest survivors who were treated with targeted temperature management (TTM).MethodsA retrospective analysis of data from cardiac arrest survivors treated with TTM between 2012 and 2015 was conducted. Serum lactate levels were measured on admission and at 12, 24, and 48 hours following admission. Lactate clearance at 12, 24, and 48 hours was also calculated. The primary outcome was neurologic outcome at discharge. The secondary outcome was in-hospital mortality.ResultsThe study included 282 patients; 184 (65.2%) were discharged with a poor neurologic outcome, and 62 (22.0%) died. Higher serum lactate levels at 12 hours (odds ratio [OR], 1.157; 95% confidence interval [CI], 1.006 to 1.331), 24 hours (OR, 1.320; 95% CI, 1.084 to 1.607), and 48 hours (OR, 2.474; 95% CI, 1.459 to 4.195) after admission were associated with a poor neurologic outcome. Furthermore, a higher serum lactate level at 48 hours (OR, 1.459; 95% CI, 1.181 to 1.803) following admission was associated with in-hospital mortality. Lactate clearance was not associated with neurologic outcome or in-hospital mortality at any time point after adjusting for confounders.ConclusionIncreased serum lactate levels after admission are associated with a poor neurologic outcome at discharge and in-hospital mortality in cardiac arrest survivors treated with TTM. Conversely, lactate clearance is not a robust surrogate marker of neurologic outcome or in-hospital mortality.

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In order to obtain precise kinematic global positioning systems (GPS) in medium to large scale networks, the atmospheric effects from tropospheric and ionospheric delays need to be properly modeled and estimated. It is also preferable to use multiple reference stations to improve the reliability of the solutions. In this study, GPS kinematic positioning algorithms are developed for the medium to large-scale network based on the position-velocity-acceleration model. Hence, the algorithm can perform even in cases where the near-constant velocity assumption does not hold. In addition, the estimated kinematic accelerations can be used for the airborne gravimetry. The proposed algorithms are implemented using Kalman filter and are applied to the in situ airborne GPS data. The performance of the proposed algorithms is validated by analyzing and comparing the results with those from reference values. The results show that reliable and comparable solutions in both position and kinematic acceleration levels can be obtained using the proposed algorithms.