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PubMed2005

Individual optimization of pacing sensors improves exercise capacity without influencing quality of life.

INTRODUCTION: Programmable pacemaker sensor features are frequently used in default setting. Limited data are available about the effect of sensor optimization on exercise capacity and quality of life (QOL). Influence of individual optimization of sensors on QOL and exercise tolerance was investigated in a randomized, single blind study in patients with VVIR, DDDR, or AAIR pacemakers. METHODS: Patients with > or =75% pacing were randomized to optimized sensor settings (OSS) or default sensor setting (DSS). Standardized optimization was performed using three different exercise tests. QOL questionnaires (QOL-q: Hacettepe, Karolinska, and RAND-36) were used for evaluation of the sensor optimization. One month before and after optimization, exercise capacity using chronotropic assessment exercise protocol and the three QOL-q were assessed. RESULTS: Fifty-four patients (26 male, 28 female) with a mean age of 65 +/- 16 years were enrolled in the study. In each group (OSS and DSS) 27 patients were included. One month after sensor optimization, the achieved maximal heart rate (HR) and metabolic workload (METS) were significantly higher in OSS when compared with DSS (124 +/- 28 bpm vs 108 +/- 20 bpm, P = 0.036; 7.3 +/- 4 METS vs 4.9 +/- 4 METS, P = 0.045). Highest HR and METS were achieved in patients with pacemakers with accessible sensor algorithms. In patients with automatic slope settings (33%), exercise capacity did not improve after sensor optimization. QOL did not improve in OSS compared with DSS. CONCLUSION: After 1 month of individual optimization of rate response pacemakers, exercise capacity was improved and maximum HR increased, although QOL remained unchanged. Accessible pacemaker sensor algorithms are mandatory for individual optimization.

Adult↗

PubMedDate not supplied

Reducing long-term remedial costs by transport modeling optimization.

The Department of Defense (DoD) Environmental Security Technology Certification Program and the Environmental Protection Agency sponsored a project to evaluate the benefits and utility of contaminant transport simulation-optimization algorithms against traditional (trial and error) modeling approaches. Three pump-and-treat facilities operated by the DoD were selected for inclusion in the project. Three optimization formulations were developed for each facility and solved independently by three modeling teams (two using simulation-optimization algorithms and one applying trial-and-error methods). The results clearly indicate that simulation-optimization methods are able to search a wider range of well locations and flow rates and identify better solutions than current trial-and-error approaches. The solutions found were 5% to 50% better than those obtained using trial-and-error (measured using optimal objective function values), with an average improvement of approximately 20%. This translated into potential savings ranging from 600,000 dollars to 10,000,000 dollars for the three sites. In nearly all cases, the cost savings easily outweighed the costs of the optimization. To reduce computational requirements, in some cases the simulation-optimization groups applied multiple mathematical algorithms, solved a series of modified subproblems, and/or fit "meta-models" such as neural networks or regression models to replace time-consuming simulation models in the optimization algorithm. The optimal solutions did not account for the uncertainties inherent in the modeling process. This project illustrates that transport simulation-optimization techniques are practical for real problems. However, applying the techniques in an efficient manner requires expertise and should involve iterative modification to the formulations based on interim results.

Algorithms↗

PubMed2003

An evaluation of population D-optimal designs via pharmacokinetic simulations.

One goal of large scale clinical trials is to determine how a drug is processed by, and cleared from, the human body [i.e., its pharmacokinetic (PK) properties] and how these PK properties differ between individuals in a population (i.e., its population PK properties). Due to the high cost of these studies and the limited amount of data (e.g., blood samples) available from each study subject, it would be useful to know how many measurements are needed and when those measurements should be taken to accurately quantify population PK model parameters means and variances. Previous studies have looked at optimal design strategies of population PK experiments by developing an optimal design for an individual study (i.e., no interindividual variability was considered in the design), and then applying that design to each individual in a population study (where interindividual variability is present). A more algorithmically and informationally intensive approach is to develop a population optimal design, which inherently includes the assessment of interindividual variability. We present a simulation-based evaluation of these two design methods based on nonlinear Gaussian population PK models. Specifically, we compute standard individual and population D-optimal designs and compare population PK model parameter estimates based on simulated optimal design measurements. Our results show that population and standard D-optimal designs are not significantly different when both designs have the same number of samples per individual. However, population optimal designs allow for sampling schedules where the number of samples per individual is less than the number of model parameters, the theoretical limit allowed in standard optimal design. These designs with a low number of samples per individual are shown to be nearly as robust in parameter estimation as standard D-optimal designs. In the limit of just one sample per individual, however, population D-optimal designs are shown to be inadequate.

AIDS Vaccines↗

PubMed2003

Beam orientation optimization for IMRT by a hybrid method of the genetic algorithm and the simulated dynamics.

We have developed a new method for beam orientation optimization in intensity-modulated radiation therapy (IMRT). The problem of beam orientation optimization in IMRT is solved by a decoupled two-step iterative process: (1) optimization of the intensity profiles for given beam configurations; (2) selection of optimal beam configurations based on the ranking by an objective function score for the results of the intensity profile optimization. The simulated dynamics algorithm is used for the intensity profile optimization. This algorithm enforces both the hard constraints and dose-volume constraints. A genetic algorithm is used to select beam orientation configurations. The method has been tested for both a simulated and clinical case, and the results show that beam orientation optimization significantly improved IMRT plans within a time period that is clinically acceptable. The results also show the dependence of the optimal orientation configurations on the prescribed constraints. In addition, beam orientation optimization by the method described here can provide multiple plans with similar dose distributions. This degeneracy characteristic can be exploited to our advantage in introducing additional planning objectives, e.g., the smoothness of intensity profiles, for the selection of the optimal plan among the degenerate configurations for treatment delivery.

Algorithms↗

PubMed2004

Determining optimal two-beam axial orientations for heart sparing in left-sided breast cancer patients.

BACKGROUND AND PURPOSE: The optimal intensity fluence profile of a beam depends on the profiles of other beams but most optimizations assume fixed beam orientations, a priori. Breast cancer radiotherapy attempts to cover the target and to spare critical structures such as the heart and lungs. The study aims are (1) to determine and document the optimal two-beam orientation that best spares the heart for left-sided breast cancer patients and (2) to investigate the influence of the treatment technique (i.e., conformal versus intensity modulation) on the optimal objective cost function. MATERIAL AND METHODS: Ten left-sided breast cancer patients were planned using a conformal (3DCRT) and a simplified intensity modulated (sIMRT) technique using predefined segments and different two-beam orientations. Optimal segment weights were determined exhaustively for all axial two-beam combinations, in 5 degree increments, by minimizing a quadratic objective cost function. The resulting objective cost function was analyzed with respect to target geometry and treatment technique. RESULTS: The sIMRT plans are generally less sensitive to beam orientation compared to 3DCRT plans. Optimal two-beam orientations for 3DCRT and sIMRT plans exist and they correspond to a hinge angle of approximately 188 degrees and 160 degrees or 210 degrees (the latter is bimodal), respectively. CONCLUSIONS: The optimization software is a useful tool that can test many different beam combinations and estimate their associated objective cost values. Afterwards, the most promising beam orientations could be re-optimized under the TPS to fine-tune and verify the dose distributions. Optimal uniform two-beam orientations for the breast consist of opposing tangential medial and lateral beams. Optimal nonuniform two-beam orientations for left-sided breast cancers are bimodal, containing hinge angles around 160 degrees and 210 degrees. Nonuniform beam techniques are less sensitive to beam orientation compared to uniform beam techniques and result in significantly improved heart sparing but at a cost of slightly compromised planning target volume coverage.

Breast↗

PubMed2004

IMRT optimization: variability of solutions and its radiobiological impact.

We aim at (1) defining and measuring a "complexity" index for the optimization process of an intensity modulated radiation therapy treatment plan (IMRT TP), (2) devising an efficient approximate optimization strategy, and (3) evaluating the impact of the complexity of the optimization process on the radiobiological quality of the treatment. In this work, for a prostate therapy case, the IMRT TP optimization problem has been formulated in terms of dose-volume constraints. The cost function has been minimized in order to achieve the optimal solution, by means of an iterative procedure, which is repeated for many initial modulation profiles, and for each of them the final optimal solution is recorded. To explore the complexity of the space of such solutions we have chosen to minimize the cost function with an algorithm that is unable to avoid local minima. The size of the (sub)optimal solutions distribution is taken as an indicator of the complexity of the optimization problem. The impact of the estimated complexity on the probability of success of the therapy is evaluated using radiobiological indicators (Poissonian TCP model [S. Webb and A. E. Nahum, Phys. Med. Biol. 38(6), 653-666 (1993)] and NTCP relative seriality model [Kallman et al., Int. J. Radiat. Biol. 62(2), 249-262 (1992)]). We find in the examined prostate case a nontrivial distribution of local minima, which has symmetry properties allowing a good estimate of near-optimal solutions with a moderate computational load. We finally demonstrate that reducing the a priori uncertainty in the optimal solution results in a significant improvement of the probability of success of the TP, based on TCP and NTCP estimates.

Algorithms↗

PubMed2004

Penalized likelihood fluence optimization with evolutionary components for intensity modulated radiation therapy treatment planning.

A novel iterative penalized likelihood algorithm with evolutionary components for the optimization of beamlet fluences for intensity modulated radiation therapy (IMRT) is presented. This algorithm is designed to be flexible in terms of the objective function and automatically escalates dose, as long as the objective function increases and all constraints are met. For this study, the objective function employed was the product of target equivalent uniform dose (EUD) and fraction of target tissue within set homogeneity constraints. The likelihood component of the algorithm iteratively attempts to minimize the mean squared error between a homogeneous dose prescription and the actual target dose distribution. The updated beamlet fluences are then adjusted via a quadratic penalty function that is based on the dose-volume histogram (DVH) constraints of the organs at risk. The evolutionary components were included to prevent the algorithm from converging to a local maximum. The algorithm was applied to a prostate cancer dataset, with especially difficult DVH constraints on bladder, rectum, and femoral heads. Dose distributions were generated for manually selected sets of three-, four-, five-, and seven-field treatment plans. Additionally, a global search was performed to find the optimal orientations for an axial three-beam plan. The results from this optimal orientation set were compared to results for manually selected orientation (gantry angle) sets of 3- (0 degrees, 90 degrees, 270 degrees), 4- (0 degrees, 90 degrees, 180 degrees, 270 degrees), 5- (0 degrees, 50 degrees, 130 degrees, 230 degrees, 310 degrees), and 7- (0 degrees, 40 degrees, 90 degrees, 140 degrees, 230 degrees, 270 degrees, 320 degrees) field axial treatment plans. For all the plans generated, all DVH constraints were met and average optimization computation time was approximately 30 seconds. For the manually selected orientations, the algorithm was successful in providing a relatively homogeneous target dose distribution, while simultaneously satisfying dose-volume limits by diverting dose away from proximal critical structures. The global search for an optimal three-beam orientation set yielded gantry angles of 70 degrees, 170 degrees, and 320 degrees. The EUD for this orientation set was 58 Gy, with 96% of the target within the set upper and lower limits. In comparison, optimized EUDs for the manually selected orientation sets of three, four, five and seven beams were 52.3, 52.6, 56.9, and 61.3 Gy, respectively. The orientation optimized three-beam plan yielded higher EUDs than the manually selected three-, four-, and five-beam plans, but lower EUDs than the seven-beam plan. In conclusion, a novel penalized likelihood algorithm with evolutionary components has successfully been implemented to optimize beamlet fluences for IMRT. Initial results are promising for dose conformity and uniformity of dose to target. When combined with optimal beam orientation selection for prostate cancer treatment planning, the results indicate that plans with a small number of optimized beam orientations achieve results comparable to those with a larger number of conventionally oriented beams.

Bone and Bones↗

PubMed1998

A new genetic algorithm technique in optimization of permanent 125I prostate implants.

Real time optimized treatment planning at the time of the implant is desirable for ultrasound-guided transperineal 125I permanent prostate implants. Currently available optimization algorithms are too slow to be used in the operating room. The goal of this work is to develop a robust optimization algorithm, which is suitable for such application. Three different genetic algorithms (sGA, sureGA and securGA) were developed and compared in terms of the number of function evaluations and the corresponding fitness. The optimized dose distribution was achieved by searching the best seed distribution through the minimization of a cost function. The cost function included constraints on the periphery dose of the planned target volume, the dose uniformity within the target volume, and the dose to the critical structure. Adjustment between the peripheral dose, the dose uniformity and critical structure dose can be achieved by varying the weighting factors in the cost function. All plans were evaluated in terms of the dose nonuniformity ratio, the conformation number and the dose volume histograms. Among these three GA algorithms, the securGA provided the best performance. Within 2500 function evaluations, the near optimum results were obtained. For a large target volume (5 cm x 4 cm x 4.5 cm) including urethra with 20 needles, the computer time needed for the optimization was less than 5 min on a HP735 workstation. The results showed that once the best set of parameters was found, they were applicable for all sizes of prostate volume. For a fixed needle geometry, the optimized plan showed much better dose distribution than that of nonoptimized plan. If the critical structure was considered in the optimization, the dose to the critical structure could be minimized. In the cases of irregular and skewed needle geometry, the optimized treatment plans were almost as good as ideal needle geometry. It is concluded that this new genetic algorithm (securGA) allows for an efficient and rapid optimization of dose distribution, which is suitable for real time treatment planning optimization for ultrasound-guided prostate implant.

Algorithms↗

PubMedDate not supplied

Super-optimal CO2 reduces seed yield but not vegetative growth in wheat.

Although terrestrial atmospheric CO2 levels will not reach 1000 micromoles mol-1 (0.1%) for decades, CO2 levels in growth chambers and greenhouses routinely exceed that concentration. CO2 levels in life support systems in space can exceed 10000 micromoles mol-1(1%). Numerous studies have examined CO2 effects up to 1000 micromoles mol-1, but biochemical measurements indicate that the beneficial effects of CO2 can continue beyond this concentration. We studied the effects of near-optimal (approximately 1200 micromoles mol-1) and super-optimal CO2 levels (2400 micromoles mol-1) on yield of two cultivars of hydroponically grown wheat (Triticum aestivum L.) in 12 trials in growth chambers. Increasing CO2 from sub-optimal to near-optimal (350-1200 micromoles mol-1) increased vegetative growth by 25% and seed yield by 15% in both cultivars. Yield increases were primarily the result of an increased number of heads per square meter. Further elevation of CO2 to 2500 micromoles mol-1 reduced seed yield by 22% (P < 0.001) in cv. Veery-10 and by 15% (P < 0.001) in cv. USU-Apogee. Super-optimal CO2 did not decrease the number of heads per square meter, but reduced seeds per head by 10% and mass per seed by 11%. The toxic effect of CO2 was similar over a range of light levels from half to full sunlight. Subsequent trials revealed that super-optimal CO2 during the interval between 2 wk before and after anthesis mimicked the effect of constant super-optimal CO2. Furthermore, near-optimal CO2 during the same interval mimicked the effect of constant near-optimal CO2. Nutrient concentration of leaves and heads was not affected by CO2. These results suggest that super-optimal CO2 inhibits some process that occurs near the time of seed set resulting in decreased seed set, seed mass, and yield.

Biomass↗

PubMed2006

Impact of biological and economic variables on optimal parity for replacement in swine breed-to-wean herds.

Voluntary and involuntary culling practices determine the average parity when sows are replaced in a herd. Underlying these practices is the economic effect of replacing a sow at different parities. A dynamic programming model was used to find the optimal parity and net present value in breed-to-wean swine herds. The model included income and costs per parity weighted by the discount rate and sow removal rate. Three scenarios that reflect a wide range of cases were considered: low removal rates per parity with no salvage value (LRNS), high removal rates per parity with no salvage value (HRNS), and high removal rates per parity with a percentage of the sows having a salvage value (HRYS). The optimal parity of replacement for the base biological and economic conditions was 4 and 5 parities in the high and low removal scenarios, respectively. Sensitivity analyses identified the variables influencing the optimal replacement parity. Optimal parity of replacement ranged from 3 to 7 parities in the low replacement scenario, compared with 1 to 5 parities in the high replacement scenarios. Sow replacement cost and salvage value had the greatest impact on optimal parity of replacement followed by revenues per piglet weaned. The discount rate and number of parities per year generally had little influence on optimal parity. For situations with high sow costs, low salvage values, and low revenues per piglet, the optimal parity at removal was as high as 6 to 10 parities, and for situations with low sow cost, high salvage values, and high revenues per piglet, the optimal parity at removal was as low as 1 to 2 parities depending on removal rates. The modified internal rate of return suggested that, for most LRNS and HRYS scenarios considered, investment in a swine breed-to-wean enterprise was favored over other investments involving a similar risk profile. Our results indicate that in US breeding herds, sows are culled on average near the optimal parity of 4. However, the optimization process should be a dynamic one that adapts to changes in replacement rates, salvage value, replacement cost, and revenues per piglet.

Agriculture↗

PubMed1992

Optimizing model: insemination, replacement, seasonal production, and cash flow.

Dynamic programming to solve the Markov decision process problem of optimal insemination and replacement decisions was adapted to address large dairy herd management decision problems in the US. Expected net present values of cow states (151,200) were used to determine the optimal policy. States were specified by class of parity (n = 12), production level (n = 15), month of calving (n = 12), month of lactation (n = 16), and days open (n = 7). Methodology optimized decisions based on net present value of an individual cow and all replacements over a 20-yr decision horizon. Length of decision horizon was chosen to ensure that optimal policies were determined for an infinite planning horizon. Optimization took 286 s of central processing unit time. The final probability transition matrix was determined, in part, by the optimal policy. It was estimated iteratively to determine post-optimization steady state herd structure, milk production, replacement, feed inputs and costs, and resulting cash flow on a calendar month and annual basis if optimal policies were implemented. Implementation of the model included seasonal effects on lactation curve shapes, estrus detection rates, pregnancy rates, milk prices, replacement costs, cull prices, and genetic progress. Other inputs included calf values, values of dietary TDN and CP per kilogram, and discount rate. Stochastic elements included conception (and, thus, subsequent freshening), cow milk production level within herd, and survival. Validation of optimized solutions was by separate simulation model, which implemented policies on a simulated herd and also described herd dynamics during transition to optimized structure.

Animals↗

PubMed2006

Association of optimism-pessimism with quality of life in patients with head and neck and thyroid cancers.

OBJECTIVE: To examine the relationship between optimism-pessimism and quality of life (QOL) in survivors of head and neck and thyroid cancers. PATIENTS AND METHODS: Between 1963 and 2000, 190 patients completed both the Minnesota Multiphasic Personality Inventory (MMPI), used to assess explanatory style (optimism-pessimism), and either the 12-Item or 36-Item Short-Form Health Survey (SF-12 or SF-36), used to assess QOL. The MMPIs were completed an average of 13.4 years before the QOL assessment. The QOL measures were completed an average of 12.5 years after cancer diagnosis. Patients were divided into quartiles based on their MMPI Optimism-Pessimism scale score. Analysis was performed for all patients, those with head and neck cancer, and those with thyroid cancer. Adjustments were made for age, sex, and disease stage. RESULTS: For all 190 patients, optimism was associated with a higher QOL on both the mental and the physical component scales and 6 of 8 subscales of the SF-12 and SF-36. For patients with head and neck cancer, optimism was associated with higher QOL on 3 subscales but neither component scale. For patients with thyroid cancer, optimism was associated with higher QOL on both component scales and 6 subscales. After adjusting for age, sex, and disease stage, optimism was not associated with QOL in the head and neck cancer group. CONCLUSIONS: Optimism was associated with a higher QOL in survivors of thyroid cancer compared with survivors of head and neck cancer. After adjusting for age, sex, and disease stage, optimism was not associated with QOL for survivors of head and neck cancer. Optimism was more associated with the mental rather than physical QOL subscales.

Adult↗

PubMed2002

An optimization algorithm that incorporates IMRT delivery constraints.

An intensity-modulated beam optimization algorithm is presented which incorporates the delivery constraints into the optimization cycle. The optimization algorithm is based on the quasi-Newton method of iteratively solving minimization problems. The developed algorithm iteratively corrects the incident, pencil-beam-like, fluence to incorporate the delivery constraints. In the present study, the goal of the optimization algorithm is to achieve the best deliverable radiotherapy plan, subject to the constraints of the delivery technique described by a leaf-sequencing algorithm being applied concurrently. In general, if they are applied after, rather than during, the optimization cycle, the delivery constraints associated with the IMRT technique can produce local variations up to 6% in the 'optimized' dose (i.e., distribution without applied constraints) and reduce the degree of conformity, of the dose, to the PTV region. The optimization method has been applied to three IMRT delivery techniques: dynamic multileaf (DMLC), multiple-static-field (MSF) and slice-by-slice tomotherapy (NOMOS MIMiC). The beam profiles were generated for a prostate tumour with organs at risk being the rectum, bladder and femoral heads. The optimization method described was shown to generate optimum and deliverable IMRT plans for these three delivery techniques. In the case of the DMLC and MSF the optimization converged within 3-5 iterations to a mean PTV dose of 69.60 +/- 1.34 Gy and 69.71 +/- 1.34 Gy, respectively, while for NOMOS MIMiC approximately 10 iterations were needed to obtain 69.68 +/- 1.55 Gy. In addition to this, the IMRT optimization also yielded optimum fluence profiles when clustering was performed concurrently with the leaf-sequencer. An optimum between 8 and 15 clusters of equal fluence 'intensity' was shown to establish the best compromise between the number of fluence levels and the PTV dose coverage.

Algorithms↗

PubMed1996

Development of a fast optimization preview in radiation treatment planning.

PURPOSE: Having the capability of fast previewing an expected optimization result for a defined combination of optimization parameters is essential to reach a better optimization result. In order to achieve this goal, the well-tried Cimmino optimization algorithm requires modification. MATERIALS AND METHODS: Usually, the speed of convergence of some iterative algorithms is restricted to a bounded relaxation parameter. Exploiting the special altering behavior of the weighting factors at each step, many iteration steps are avoided by overrelaxing this relaxation parameter. Therefore, the relaxation parameter is increased as long as the optimization result is improved. This can be performed without loss of accuracy. RESULTS: Our optimization technique is demonstrated by the case of a right lung carcinoma. The solution space for this case is 36 isocentric X-ray beams evenly spaced at 10 degree. Each beam is restricted to 23 MV X-ray fields with a planning target volume matched by irregular field shapes, similar to that produced by a multileaf collimator. Four organs at risk plus the planning target volume are considered in the optimization process. The convergence behavior of the optimization algorithm is shown by overrelaxing the relaxation parameter in comparison to conventional relaxation parameter control. CONCLUSION: The new approach offers the ability to get a fast preview of the expected final result. If the clinician is in agreement with the preview, the algorithm is continued and achieves the result proven by the Cimmino optimization algorithm. In the other case, if the clinician doesn't agree with the preview, he will be able to change the optimization parameters (e.g., field entry points) and to restart the algorithm.

Algorithms↗

PubMed2002

Perioperative fluid volume optimization following proximal femoral fracture.

BACKGROUND: Proximal Femoral Fracture (PFF) or 'hip fracture' is a frequent injury, and adverse outcomes are common. Many patients are elderly, with significant comorbidity. Several factors suggest the importance of developing appropriate techniques to optimize intravascular fluid volume. These may include protocols that enhance the efficacy of clinicians' assessments, invasive techniques such as oesophageal Doppler or central venous pressure monitoring, or advanced non-invasive techniques such as plethysmographic pulse volume determination. OBJECTIVES: To determine the optimal method of fluid volume optimization for adult patients undergoing surgical repair of hip fracture. Comparisons of fluid types (e.g. crystalloid vs. colloid) or of blood transfusion strategies or of other pharmacological interventions (e.g. inotropes) are not considered in this review. SEARCH STRATEGY: Randomized controlled trials (RCTs) since 1985 were identified by searching MEDLINE, EMBASE, the Cochrane Library, the Cochrane Anaesthesia Group's specialized Controlled Trials Register and bibliographies of retrieved articles. Relevant journals and conference proceedings were handsearched. SELECTION CRITERIA: RCTs comparing a fluid optimization intervention with normal practice (control) or with another fluid optimization intervention, in patients following PFF undergoing surgery of any type under anaesthesia of any type. DATA COLLECTION AND ANALYSIS: Searches and exclusion of clearly irrelevant articles were performed by one reviewer. Two reviewers examined independently the remaining studies, extracting study quality and results data. A wide range of short- and long-term outcome data was sought. Study quality was assessed using a ten-point instrument and studies were excluded if they did not meet the study criteria or if results were likely to be biased. Due to a lack of consistency in reporting, combination of data was not generally possible. MAIN RESULTS: Searches identified only four trials, of which two studies, randomizing a total of 130 patients, were of adequate quality and addressed the review question. Both studies were of invasive advanced haemodynamic monitoring during the intraoperative period only. One study randomized patients to 'normal care' or optimization using oesophageal Doppler; the second study randomized patients to 'normal care', oesophageal Doppler or central venous pressure monitoring. In each study, invasive monitoring led to a significant increase in volume of fluid infused and a reduction in length of hospital stay. The pooled Peto odds ratio for in-hospital fatality was 1.44 (95% confidence interval 0.45-4.62). Neither study followed patients beyond hospital discharge or assessed functional outcomes (for example return to previous accommodation). No serious complications were reported to be directly attributable to the monitoring interventions. There were no studies of protocol-guided fluid optimization or of advanced non-invasive techniques. REVIEWER'S CONCLUSIONS: Invasive methods of fluid optimization during surgery may shorten hospital stay, but their effects on other important, patient-centred, longer-term outcomes are uncertain. An adverse effect on fatality cannot be excluded. Other fluid optimization techniques have not been evaluated. The lack of randomized studies of adequate quality addressing this important question is disappointing given the high incidence and frequently adverse outcome of PFF. More research is needed.

Blood Volume↗

PubMed2002

Acetopyruvate hydrolase production by Pseudomonas putida O1--optimization of batch and fed-batch fermentations.

The main objective of this work was the optimization of the production of the beta-ketolase, acetopyruvate hydrolase, from Pseudomonas putida O1. Orcinol was used as an inducer for enzyme production. The growth medium was optimized in two steps. In the first step, screening for optimal glucose concentration was performed. In the second step, a central composite design was used to optimize carbon and nitrogen sources in the medium. After this optimization procedure, a medium was obtained which produced seven times more biomass than the initial medium. Acetopyruvate hydrolase enzyme production was optimized by determining the optimal time of feed and amount of orcinol, using statistical methods. In a subsequent step, the maximal orcinol-degradation rate was determined. The results obtained were used to find an optimal feeding profile for enzyme production. By using the optimized fed-batch process, acetopyruvate hydrolase activity was enhanced from 10 units l(-1)to 400 units l(-1), in comparison with previously reported fermentation experiments. Productivity could even be increased by a factor of 75, to a value of 20 units l(-1 )h(-1).

Fermentation↗

PubMed2004

Fatigue and optimal conditions for short-term work capacity.

There is an optimal load and corresponding velocity at which peak power output occurs. It is reasonable to expect that these conditions will change as a result of fatigue during 30 s of all-out cycling. This study evaluated optimal velocity after 30 s of maximal isokinetic cycle ergometer exercise and tested the hypothesis that progressive adjustment of velocity (optimized) during 30 s of all-out cycling would permit greater short-term work capacity (STWC). Non-fatigued optimal cadence [NF(OC), 109.6 (2.5) rpm] was determined for ten males on an SRM ergometer using regression analysis of the torque-angular velocity relation during a 7-s maximal acceleration. Fatigued optimal cadence [73.4 (2.4) rpm] was determined in the same way, immediately after a 30-s isokinetic test at NF(OC). A subsequent trial with cadence decreasing in steps from NF(OC) to a conservative estimate of fatigued optimal cadence [83.9 (2.8) rpm] was completed to see if more work could be done with a more optimal cadence during the test. STWC was not different ( P=0.50) between the constant [23,681 (764) J] and optimized [23,679 (708) J] conditions. Another more radical progressive change in cadence with four subjects yielded the same result (no increase in STWC). Extraneous factors apparently contribute more to variability in STWC than differences between constant and adjusted optimization of conditions.

Adult↗

PubMed2006

A global optimization method for prediction of muscle forces of human musculoskeletal system.

Inverse dynamic optimization is a popular method for predicting muscle and joint reaction forces within human musculoskeletal joints. However, the traditional formulation of the optimization method does not include the joint reaction moment in the moment equilibrium equation, potentially violating the equilibrium conditions of the joint. Consequently, the predicted muscle and joint reaction forces are coordinate system-dependent. This paper presents an improved optimization method for the prediction of muscle forces and joint reaction forces. In this method, the location of the rotation center of the joint is used as an optimization variable, and the moment equilibrium equation is formulated with respect to the joint rotation center to represent an accurate moment constraint condition. The predicted muscle and joint reaction forces are independent of the joint coordinate system. The new optimization method was used to predict muscle forces of an elbow joint. The results demonstrated that the joint rotation center location varied with applied loading conditions. The predicted muscle and joint reaction forces were different from those predicted by using the traditional optimization method. The results further demonstrated that the improved optimization method converged to a minimum for the objective function that is smaller than that reached by using the traditional optimization method. Therefore, the joint rotation center location should be involved as a variable in an inverse dynamic optimization method for predicting muscle and joint reaction forces within human musculoskeletal joints.

Biomechanical Phenomena↗