OBJECTIVES:Fluid volume optimization guided by stroke volume measurements reduces complications of colorectal and high-risk surgeries. We studied whether dehydration or a strong hemodynamic response to general anesthesia increases the probability of fluid responsiveness before surgery begins.METHODS:Cardiac output, stroke volume, central venous pressure and arterial pressures were measured in 111 patients before general anesthesia (baseline), after induction and stepwise after three bolus infusions of 3 ml/kg of 6% hydroxyethyl starch 130/0.4 (n = 86) or Ringer's lactate (n = 25). A subgroup of 30 patients who received starch were preloaded with 500 ml of Ringer's lactate. Blood volume changes were estimated from the hemoglobin concentration and dehydration was estimated from evidence of renal water conservation in urine samples.RESULTS:Induction of anesthesia decreased the stroke volume to 62% of baseline (mean); administration of fluids restored this value to 84% (starch) and 68% (Ringer's). The optimized stroke volume index was clustered around 35-40 ml/m2/beat. Additional fluid boluses increased the stroke volume by ≥10% (a sign of fluid responsiveness) in patients with dehydration, as suggested by a low cardiac index and central venous pressure at baseline and by high urinary osmolality, creatinine concentration and specific gravity. Preloading and the hemodynamic response to induction did not correlate with fluid responsiveness. The blood volume expanded 2.3 (starch) and 1.8 (Ringer's) times over the infused volume.CONCLUSIONS:Fluid volume optimization did not induce a hyperkinetic state but ameliorated the decrease in stroke volume caused by anesthesia. Dehydration, but not the hemodynamic response to the induction, was correlated with fluid responsiveness.

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BackgroundThe infusion of large amounts of Ringer’s lactate prolongs the functional gastrointestinal recovery time and increases the number of complications after open abdominal surgery. We performed an open-labelled clinical trial to determine whether hydroxyethyl starch or Ringer’s lactate exerts these adverse effects when the surgery is performed by laparoscopy.MethodsEighty-eight patients scheduled for major abdominal cancer surgery (83% by laparoscopy) received a first-line fluid treatment with 9 ml/kg of either 6% hydroxyethyl starch 130/0.4 (Voluven) or Ringer’s lactate, just after induction of anaesthesia; this was followed by a second-line infusion with 12 ml/kg of either starch or Ringer’s lactate over 1 hour. Further therapy was managed at the discretion of the attending anaesthetist. Outcome data consisted of postoperative gastrointestinal recovery time, complications and length of hospital stay.ResultsThe order of the infusions had no impact on the outcome. Both the administration of ≥ 2 L of Ringer’s lactate and the development of a surgical complication were associated with a longer time period of paralytic ileus and food intolerance (two-way ANOVA, P < 0.02), but only surgical complications prolonged the length of hospital stay (P < 0.001). The independent effect of Ringer’s lactate and complications of food intolerance time amounted to 2 days each. The infusion of ≥ 1 L of hydroxyethyl starch did not adversely affect gastrointestinal recovery.ConclusionsRinger’s lactate, but not hydroxyethyl starch, prolonged the gastrointestinal recovery time in patients undergoing laparoscopic cancer surgery. Surgical complications prolonged the hospital stay.

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High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.