for the BaSICS investigators and the BRICNet members IMPORTANCE Slower intravenous fluid infusion rates could reduce the formation of tissue edema and organ dysfunction in critically ill patients; however, there are no data to support different infusion rates during fluid challenges for important outcomes such as mortality.OBJECTIVE To determine the effect of a slower infusion rate vs control infusion rate on 90-day survival in patients in the intensive care unit (ICU). DESIGN, SETTING, AND PARTICIPANTS Unblinded randomized factorial clinical trial in 75 ICUs in Brazil, involving 11 052 patients requiring at least 1 fluid challenge and with 1 risk factor for worse outcomes were randomized from May 29, 2017, to March 2, 2020. Follow-up was concluded on October 29, 2020. Patients were randomized to 2 different infusion rates (reported in this article) and 2 different fluid types (balanced fluids or saline, reported separately).INTERVENTIONS Patients were randomized to receive fluid challenges at 2 different infusion rates; 5538 to the slower rate (333 mL/h) and 5514 to the control group (999 mL/h). Patients were also randomized to receive balanced solution or 0.9% saline using a factorial design.
MAIN OUTCOMES AND MEASURESThe primary end point was 90-day survival.RESULTS Of all randomized patients, 10 520 (95.2%) were analyzed (mean age, 61.1 years [SD, 17.0 years]; 44.2% were women) after excluding duplicates and consent withdrawals. Patients assigned to the slower rate received a mean of 1162 mL on the first day vs 1252 mL for the control group. By day 90, 1406 of 5276 patients (26.6%) in the slower rate group had died vs 1414 of 5244 (27.0%) in the control group (adjusted hazard ratio, 1.03; 95% CI, 0.96-1.11; P = .46). There was no significant interaction between fluid type and infusion rate (P = .98).CONCLUSIONS AND RELEVANCE Among patients in the intensive care unit requiring fluid challenges, infusing at a slower rate compared with a faster rate did not reduce 90-day mortality. These findings do not support the use of a slower infusion rate.
The
application of economic model predictive control (EMPC) techniques
in bioprocesses is scarce due to limitations in obtaining accurate
dynamic models. Simplified unstructured models (e.g., Monod) can be
easily developed, but their prediction capacity is poor. On the other
hand, models based on dynamic flux balance analysis (dFBA) of the
detailed metabolic network appear as a promising alternative. However,
using dFBA inside the controller leads to a bilevel optimization problem,
which could require excessive computational effort. In the present
work, a control approach is proposed to tackle this limitation. By
combining mass balances with a surrogate model for the metabolic network,
our approach provides a significant reduction in online computation
while still accurately describing the microorganism of interest. A
case study of a fed-batch bioreactor of Saccharomyces
cerevisiae for ethanol maximization was chosen to
test the new approach. First, flux balance analysis simulations with
Yeast 8.30 genome-scale model were performed, then a simple polynomial
model was fitted to these data by partial least-squares regression
(PLSR). The identification step showed that only 11 PLS components
were necessary to allow the FBA to be replaced by the surrogate model
with a good accuracy. The surrogate model was coupled to the EMPC,
and the results were similar to those presented in the literature,
where the bilevel optimization problem is explicitly solved. The EMPC
was able to compensate for structural errors in the identification
process, and it provided a higher ethanol titer in comparison to the
open-loop operation. The results showed that applying surrogate models
to dFBA is a viable strategy to the solution of a bilevel optimization
problem.
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