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At least 289 records · Page 16Linked to original sources

PubMed2005

Structural optimization of dental restorations using the principle of adaptive growth.

OBJECTIVES: In a restored tooth, the stresses that occur at the tooth-restoration interface during loading could become large enough to fracture the tooth and/or restoration and it has been estimated that 92% of fractured teeth have been previously restored. The tooth preparation process for a dental restoration is a classical optimization problem: tooth reduction must be minimized to preserve tooth tissue whilst stress levels must be kept low to avoid fracture of the restored unit. The objective of the present study was to derive alternative optimized designs for a second upper premolar cavity preparation by means of structural shape optimization based on the finite element method and biological adaptive growth. METHODS: Three models of cavity preparations were investigated: an inlay design for preparation of a premolar tooth, an undercut cavity design and an onlay preparation. Three restorative materials and several tooth/restoration contact conditions were utilized to replicate the in vitro situation as closely as possible. The optimization process was run for each cavity geometry. RESULTS: Mathematical shape optimization based on biological adaptive growth process was successfully applied to tooth preparations for dental restorations. Significant reduction in stress levels at the tooth-restoration interface where bonding is imperfect was achieved using optimized cavity or restoration shapes. In the best case, the maximum stress value was reduced by more than 50%. SIGNIFICANCE: Shape optimization techniques can provide an efficient and effective means of reducing the stresses in restored teeth and hence has the potential of prolonging their service lives. The technique can easily be adopted for optimizing other dental restorations.

Bicuspid↗

PubMed2006

Prospective comparison of echocardiographic atrioventricular delay optimization methods for cardiac resynchronization therapy.

BACKGROUND: Atrioventricular (AV) delay optimization can be an important determinant of the response to cardiac resynchronization therapy (CRT) in patients with medically refractory heart failure and a ventricular conduction delay. OBJECTIVES: The purpose of this study was to compare two Doppler echocardiographic methods of AV delay optimization after CRT. METHODS: Forty consecutive patients (age 59 +/- 12 years) with severe heart failure, New York Heart Association class 3.1 +/- 0.4, QRS duration 177 +/- 23 ms, and left ventricular ejection fraction 26% +/- 6% referred for CRT were studied using two-dimensional Doppler echocardiography. In each patient, the acute improvement in stroke volume with CRT in response to two methods of AV delay optimization was compared. In the first method, the AV delay that produced the largest increase in the aortic velocity time integral (VTI) derived from continuous-wave Doppler (aortic VTI method) was measured. In the second method, the AV delay that optimized the timing of mitral valve closure to occur simultaneously with the onset of left ventricular systole was calculated from pulsed Doppler mitral waveforms at a short and long AV delay interval (mitral inflow method). RESULTS: The optimized AV delay determined by the aortic VTI method resulted in an increase in aortic VTI of 19% +/- 13% compared with an increase of 12% +/- 12% by the mitral inflow method (P <.001). The optimized AV delay by the aortic VTI method was significantly longer than the optimized AV delay calculated from the mitral inflow method (119 +/- 34 ms vs 95 +/- 24 ms, P <.001). There was no correlation in the AV delay determined by the two methods (r = 0.03). CONCLUSION: AV delay optimization by Doppler echocardiography for patients with severe heart failure treated with a CRT device yields a greater systolic improvement when guided by the aortic VTI method compared with the mitral inflow method.

Atrioventricular Node↗

PubMed2006

Immediate and chronic effects of AV-delay optimization in patients with cardiac resynchronization therapy.

BACKGROUND: Acute changes of the AV-delay in CRT patients have a significant impact on hemodynamics. However, the chronic functional effects of AV-delay optimization have not been systematically examined despite of their potential role for chronic functional improvement. METHODS: Therefore, in this study we investigated whether optimization of AV-delay in CRT patients as assessed by echocardiographic measurement of the velocity time integral of the left ventricular outflow tract (LVOT-VTI) chronically changes (1) echocardiographic parameters of systolic and diastolic left ventricular function, (2) walking distance in the 6-min walk test, (3) levels of NT-proBNP and (4) quality of life as assessed by a standard questionnaire. 33 patients underwent optimization of AV-delay 31+/-8 weeks after initiation of CRT. Follow up (FU) was conducted 43+/-5 days later. RESULTS: E/Ea, the ratio of peak E-wave of mitral inflow and of TDI of the mitral annulus, significantly decreased immediately post-optimization (11+/-1 vs. 14+/-1 at baseline, p<0.05) and further decreased at FU (8+/-1, p<0.05 vs. immediately post-optimization) indicating improvement of diastolic function, while traditional parameters of diastolic function derived from pulse wave Doppler remained unchanged. There was a slight increase of LV-ejection fraction as assessed by echocardiography acutely after optimization (baseline: 25+/-2%, optimized: 28+/-1%, p<0.05), while LV-ejection fraction at FU did not differ from baseline. 6-min walk test improved from 449+/-17 m (baseline) to 475+/-17 m at FU (p<0.05). During this period NT-proBNP significantly decreased from 3193+/-765 ng/l to 2593+/-675 ng/l (p<0.05). Quality of life was unchanged at FU. CONCLUSION: This study demonstrates for the first time chronic functional improvement due to AV-delay optimization in patients with CRT.

Acute Disease↗

PubMed2006

Risk-adaptive optimization: selective boosting of high-risk tumor subvolumes.

BACKGROUND AND PURPOSE: A tumor subvolume-based, risk-adaptive optimization strategy is presented. METHODS AND MATERIALS: Risk-adaptive optimization employs a biologic objective function instead of an objective function based on physical dose constraints. Using this biologic objective function, tumor control probability (TCP) is maximized for different tumor risk regions while at the same time minimizing normal tissue complication probability (NTCP) for organs at risk. The feasibility of risk-adaptive optimization was investigated for a variety of tumor subvolume geometries, risk-levels, and slopes of the TCP curve. Furthermore, the impact of a correlation parameter, delta, between TCP and NTCP on risk-adaptive optimization was investigated. RESULTS: Employing risk-adaptive optimization, it is possible in a prostate cancer model to increase the equivalent uniform dose (EUD) by up to 35.4 Gy in tumor subvolumes having the highest risk classification without increasing predicted normal tissue complications in organs at risk. For all tumor subvolume geometries investigated, we found that the EUD to high-risk tumor subvolumes could be increased significantly without increasing normal tissue complications above those expected from a treatment plan aiming for uniform dose coverage of the planning target volume. We furthermore found that the tumor subvolume with the highest risk classification had the largest influence on the design of the risk-adaptive dose distribution. The parameter delta had little effect on risk-adaptive optimization. However, the clinical parameters D(50) and gamma(50) that represent the risk classification of tumor subvolumes had the largest impact on risk-adaptive optimization. CONCLUSIONS: On the whole, risk-adaptive optimization yields heterogeneous dose distributions that match the risk level distribution of different subvolumes within the tumor volume.

Dose-Response Relationship, Radiation↗

PubMed2006

Strategies in preflight for an optimal Yurchenko layout vault.

An optimal Yurchenko layout vault of an elite female gymnast was identified by Koh et al. [2003. A predicted optimal performance of the Yurchenko layout vault in women's artistic gymnastics. Journal of Applied Biomechanics 19, 187-204] to require a combination of an increased body angle at horse contact and increased angular momentum for postflight than was recorded experimentally. However, the individual effect of each of these variables to producing the optimal vault is not known. The purpose of the study was to determine an appropriate strategy to teaching the optimal Yurchenko layout vault. Separate optimisations were carried out to investigate how each of these variables would change in order to produce the optimal vault identified by Koh et al. (2003). A combined optimal parameter selection and optimal control approach was used. The results suggest that when the body angle of attack at horse impact was kept low, pre-flight angular momentum had to be increased, with further gains during horse impact, to produce an optimal vault. This strategy of increasing solely the level of angular momentum needed for optimum postflight may not be attainable realistically. On the other hand, employing a larger body angle of attack required an increase in angular momentum during impact but which was attainable. Both optimisations show that increasing the vertical CM horse takeoff velocity is essential for postflight height and distance. The strategy to enhance performance should thus focus on maintaining an appropriate CM pre-flight velocity, a high level of angular momentum during pre-flight and to contact the horse with a large body angle of attack.

Adult↗

PubMed2001

ROC optimization may improve risk stratification of prostate cancer patients.

OBJECTIVES: Rational treatment decision requires accurate projection of the clinical course of a patient. Current methods in clinical outcome analysis mostly focus on population data. We investigated the applicability and optimization of the widely used actuarial method to project individual clinical outcomes. METHODS: We designed and implemented a Clinical Outcome Prediction Expert (COPE) that performs, assesses, and optimizes actuarial prediction on individual cases. We analyzed a post-prostatectomy database, consisting of 1043 patients. Sixty percent of the database was used for training and 40% for validation. Stratified actuarial curves are used to project individual outcomes. The prostate-specific antigen (PSA) level, the Gleason score, and the clinical American Joint Commission on Cancer Staging T-stage before treatment were used as predictors. The area under the receiver operator characteristic (ROC) curve was used to measure predictive performance. RESULTS: We obtained simple optimized stratification of pretreatment PSA level of 10 ng/mL or less, or more than 10 ng/mL; Gleason score of 6 or lower, or higher than 6; and clinical AJCC T-stage of T2a or lower, or higher. The optimized univariate risk scores were used to generate a multivariate score. After optimization, we found the higher risk group consisted of patients with PSA more than 10 ng/mL, or with PSA of 10 ng/mL or less and Gleason score higher than 6 and clinical AJCC T-stage higher than T2a. The optimized multivariate risk score has the highest ROC area of 0.77 among all predictors. CONCLUSIONS: The best conditions to perform actuarial prediction on individual cases are not known a priori and require optimization. This study shows that ROC optimization simplifies risk stratification and may improve the accuracy of clinical outcome prediction.

Data Interpretation, Statistical↗

PubMed1998

3D conformal intensity-modulated radiotherapy planning: interactive optimization by constrained matrix inversion.

BACKGROUND AND PURPOSE: This paper presents a method for interactive optimization of 3D conformal intensity-modulated radiotherapy plans employing a quadratic objective that also contains dose limitations in the organs at risk. This objective function is minimized by constrained matrix inversion (CMI) that follows the same approach as the gradient technique using matrix notation. MATERIALS AND METHODS: Sherouse's GRATIS radiotherapy design system is used to determine the outlines of the target volume and the organs at risk and to input beam segments which are given by the beam segmentation technique. This technique defines the beam incidences and the beam segmentation. The weights of the segments are then calculated using a quadratic objective function and CMI. The objective function to be minimized consists of two components based on the planning target volume (PTV) and the organ at risk (OAR) with an importance factor w associated with the OAR. RESULTS: Optimization is tested for concave targets in the head and neck region wrapping around the spinal cord. For a predefined w-value, segment weights are optimized within a few seconds on a DEC Alpha 3000. In practice, 5-10 w-values have to be tested, making optimization a less than 5 min procedure. This optimization procedure predicts the possibility of target dose escalation for a tumour in the lower neck to 120-150 Gy without exceeding the spinal cord tolerance, whereas human planners could not increase the dose above 65-80 Gy. CONCLUSIONS: Treatment plans optimized using a quadratic objective function and the CMI algorithm are superior to those which are generated by human planners. The optimization algorithm is very fast and allows interactive use. Quadratic optimization by CMI is routinely used by clinicians at the Division of Radiotherapy, U.Z.-Gent.

Equipment Design↗

PubMed2002

Integrated process optimization: lessons from retrovirus and virus-like particle production.

The optimization of production and purification processes is usually approached by engineers from a strictly biotechnological point of view. The present paper envisages the definition and application of an optimization model that takes into account the impact of both biological and technological issues upon the optimization protocols and strategies. For this purpose, the optimization of three analogous but different systems comprising animal cell growth and bioparticle production is presented. These systems were: human immunodeficiency 1 (HIV-1) and porcine parvovirus (PPV) virus-like particles (VLPs) produced in insect cells and retrovirus produced in mammalian cells. For the systematization of the optimization process four levels of optimization were defined-product, technology, design and integration. In this paper, the limits of each of the optimization levels defined are discussed by applying the concept to the systems described. This analysis leads to decisions regarding the production of VLPs and retrovirus as well as on the points relevant for further process development. Finally, the definition of the objective function or performance index, the possible strategies and tools for bioprocess optimization are described. Although developed from the three described processes, this approach can, based on the recent literature evidence reviewed here, be applied more universally for the process development of complex biopharmaceuticals.

Animals↗

PubMed2003

Outcomes of optimal or "stent-like"balloon angioplasty in acutemyocardial infarction: the CADILLAC trial.

OBJECTIVES: We sought to compare outcomes between patients with acute myocardial infarction (AMI) undergoing percutaneous transluminal coronary angioplasty (PTCA) with an optimal or "stent-like" result versus patients who underwent routine stent placement. BACKGROUND: Recent studies in patients with AMI undergoing stent implantation have suggested that PTCA may no longer be a relevant treatment modality for stent eligible lesions. However, whether routine stent placement is superior or necessary when an optimal PTCA or "stent-like" result is achieved is unknown. METHODS: In the Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications (CADILLAC) trial, 2,082 patients with AMI were randomly assigned to undergo PTCA alone, PTCA + abciximab, stenting alone, or stenting + abciximab. Outcomes were compared in patients achieving an optimal acute PTCA result (residual core laboratory diameter stenosis <30% without significant dissection) versus those assigned to routine stenting. RESULTS: Optimal PTCA was achieved in 40.7% of patients randomized to balloon angioplasty, including 38.5% and 42.7% assigned to PTCA alone and PTCA + abciximab, respectively. Ischemic target vessel revascularization (TVR) at 30 days occurred more frequently after optimal PTCA than routine stenting (5.1% vs. 2.3%, p = 0.007). The one-year composite adverse event rate (death, reinfarction, disabling stroke, or TVR) was greater after optimal PTCA than routine stenting (21.9% vs. 13.8%, p < 0.001), driven largely by increased rates of ischemic TVR (19.1% vs. 9.1%, p < 0.001); no significant differences were present in the rates of death, reinfarction, or disabling stroke between the two groups. Angiographic restenosis also was more common with optimal PTCA than routine stenting (36.2% vs. 22.2%, p = 0.003). Even a post-PTCA diameter stenosis of <20% (realized in 12% of patients) did not result in outcomes equivalent to stenting. CONCLUSIONS: Even if an optimal result is achieved after primary PTCA in AMI, early and late outcomes can be further improved with routine stent implantation.

Abciximab↗

PubMed2003

Intelligent optimal control with dynamic neural networks.

The application of neural networks technology to dynamic system control has been constrained by the non-dynamic nature of popular network architectures. Many of difficulties are-large network sizes (i.e. curse of dimensionality), long training times, etc. These problems can be overcome with dynamic neural networks (DNN). In this study, intelligent optimal control problem is considered as a nonlinear optimization with dynamic equality constraints, and DNN as a control trajectory priming system. The resulting algorithm operates as an auto-trainer for DNN (a self-learning structure) and generates optimal feed-forward control trajectories in a significantly smaller number of iterations. In this way, optimal control trajectories are encapsulated and generalized by DNN. The time varying optimal feedback gains are also generated along the trajectory as byproducts. Speeding up trajectory calculations opens up avenues for real-time intelligent optimal control with virtual global feedback. We used direct-descent-curvature algorithm with some modifications (we called modified-descend-controller-MDC algorithm) for the optimal control computations. The algorithm has generated numerically very robust solutions with respect to conjugate points. The adjoint theory has been used in the training of DNN which is considered as a quasi-linear dynamic system. The updating of weights (identification of parameters) are based on Broyden-Fletcher-Goldfarb-Shanno BFGS method. Simulation results are given for an intelligent optimal control system controlling a difficult nonlinear second-order system using fully connected three-neuron DNN.

Artificial Intelligence↗

PubMed2003

Statistical optimization for immobilized metal affinity purification of secreted human erythropoietin from Drosophila S2 cells.

We used a novel approach to affinity purify human erythropoietin (hEPO) following its secretion from Drosophila melanogaster S2 cells. Immobilized metal affinity purification of hEPO was optimized using a two-step serial statistical optimization strategy. After determining the elution conditions (based on preliminary batch-type purification experiments), the first optimization step considered three purification factors; resin, equilibrium, and washing. The results of this analysis showed that the resin amount was the major factor influencing yield and purity in both model equations and the washing factor lowered the confidence limits of the acquired model equations. The washing conditions were then set based on the results of the first step optimization and the second step then optimized three factors; resin, equilibrium, and elution. The yield and purity of hEPO were then compared following purification using three different approaches; batch-type purification based upon the conditions determined by serial statistical optimization, batch-type purification performed in preliminary experiments, and FPLC column chromatography-type purification. We found that the serial statistical optimization approach provided the best combination of yield and purity. These findings indicate that serial statistical optimization strategies can be successfully employed for immobilized metal affinity protein purification using either batch-type or column approaches.

Animals↗

PubMedDate not supplied

Optimization of ion-exchange protein separations using a vector quantizing neural network.

In this work, a previously proposed methodology for the optimization of analytical scale protein separations using ion-exchange chromatography is subjected to two challenging case studies. The optimization methodology uses a Doehlert shell design for design of experiments and a novel criteria function to rank chromatograms in order of desirability. This chromatographic optimization function (COF) accounts for the separation between neighboring peaks, the total number of peaks eluted, and total analysis time. The COF is penalized when undesirable peak geometries (i.e., skewed and/or shouldered peaks) are present as determined by a vector quantizing neural network. Results of the COF analysis are fit to a quadratic response model, which is optimized with respect to the optimization variables using an advanced Nelder and Mead simplex algorithm. The optimization methodology is tested on two case study sample mixtures, the first of which is composed of equal parts of lysozyme, conalbumin, bovine serum albumin, and transferrin, and the second of which contains equal parts of conalbumin, bovine serum albumin, tranferrin, beta-lactoglobulin, insulin, and alpha -chymotrypsinogen A. Mobile-phase pH and gradient length are optimized to achieve baseline resolution of all solutes for both case studies in acceptably short analysis times, thus demonstrating the usefulness of the empirical optimization methodology.

Algorithms↗

PubMedDate not supplied

On-line optimization of recombinant product in a fed-batch bioreactor.

In this paper, an efficient scheme for on-line optimization of a recombinant product in a fed-batch bioreactor is presented. This scheme is based on the parametrization of the system states and the elimination of a subset of the dynamic equations in the mathematical model of the fed-batch bioreactor. The fed-batch bioreactor considered here involves the production of chloramphenicol acetyltransferase (CAT) in a genetically modified E. coli. The optimal inducer and the glucose feed rates are obtained using the proposed optimization approach. This approach is compared with the traditional optimization approach, where all the states and the manipulated variables are parametrized. The approach presented in this paper results in a 5-fold improvement in the computational time for the recombinant product optimization. The optimization technique is employed in an on-line optimization scheme, when parametric drift and a disturbance in the manipulated variable is present. Feedback from the process is introduced through resetting the initial conditions of the model and through an observer for estimating the time varying parameter. The simulation results indicated improvement in the amount of product formed, when the optimal profile is regenerated during the course of the batch.

Algorithms↗

PubMed2001

Optimal design of a population pharmacodynamic experiment for ivabradine.

PURPOSE: To design a parsimonious population pharmacodynamic experiment that has the same or greater efficiency than that provided by two phase I studies. METHODS: The design was based on optimization of the population Fisher information matrix. Options for optimization were (1) determination of the optimal sampling times for each group ("group" represents a group of subjects that have identical design characteristics), (2) determination of the optimal doses for each group, and (3) determination of the optimal group structure. RESULTS: (1) Optimizing the sampling times, while retaining only four unique times per group, provided a more parsimonious experiment with the same efficiency as the original "study" that involved on average 10 samples per subject. Splitting sampling times between the first dose and a steady-state dose gave the most informative design. (2) The optimal dose was the same in all groups and was the upper bound of the dose range. (3) The optimal population design consisted of only one group with four unique sampling times that are the same for all subjects. CONCLUSION: A population pharmacodynamic trial design is presented that is more parsimonious than the original study and would be appropriate for inclusion in a premarketing clinical study.

Benzazepines↗

PubMed1997

Comparison of ED, EID, and API criteria for the robust optimization of sampling times in pharmacokinetics.

Optimization of the sampling schedule can be used in pharmacokinetic (PK) experiments to increase the accuracy and the precision of parameter estimation or to reduce the number of samples required. Several optimization criteria that formally incorporate prior parameter uncertainty have been proposed earlier. These criteria consist in finding the sampling schedule that maximizes the expectation (over a given parameter distribution) of det F (ED-optimality) or Log(det F) (API-optimality), or minimizes the expectation of 1/det F (EID-optimality), where F is the Fisher information matrix. The precision and the accuracy of parameter estimation after having fitted a PK model to a small number of optimal data points (determined according to D, ED, EID, and API criteria) or to a naive sampling schedule were compared in a Monte Carlo simulation study. A one-compartment model with first-order absorption rate (3 parameters) and a two-compartment model with zero-order infusion rate (4 parameters) were considered. Data were simulated for 300 subjects with both structural models, combined with several residual error models (homoscedastic, heteroscedastic with constant or variable coefficient of variation). Interindividual variabilities in PK parameters ranged from 25-66%. ED-, EID-, and API-optimal sampling times were calculated using the software OSP-Fit. Three or five samples were allowed for parameter estimation by extended least-squares. Performances of each design criterion were evaluated in terms of mean prediction error, root mean squared error, and number of acceptable estimates (i.e., with a SE less than 30%). Compared to the D-optimal design, the EID and API designs reduced the bias and the imprecision of the estimation of the parameters having a large interindividual variability. Moreover, the API design resulted in some cases in a higher number of acceptable estimates.

Models, Biological↗

PubMed2004

Determining optimal nursing intensity: the RAFAELA method.

BACKGROUND: RAFAELA is a modern system of patient classification. In the last few years the system has become widely used in Finland and has aroused international interest. It comprises three parts: (1) The Oulu Patient Classification (OPC) instrument and (2) a file on nurse resources. Using these, the daily nursing care intensity, expressed as OPC points per nurse, can be calculated. The existing nursing care intensity can then be compared with the optimal by using the third instrument, (3) the Professional Assessment of Optimal Nursing Care Intensity Level (PAONCIL). This is a daily questionnaire that nurses complete in a 2-month period at intervals every few years. The daily workload is scored from -3 to +3, where zero is the optimal level. The optimal nursing care intensity per nurse is then defined by using linear regression analysis. No expensive time studies are needed. AIMS: This paper reports on a study which aimed to identify the minimum requirements for determining optimal nursing care intensity that allow the results to be accepted as correct, in terms of: length of the PAONCIL examination period, PAONCIL questionnaire response rate, explanatory power of the regression analysis and mean values of the OPC and PAONCIL instruments. DESIGN: The results of analyses of optimal nursing care intensity from 61 wards in eight Finnish hospitals for the period 1997-2001 are presented. The data do not contain any information about the identity of the patients. METHODS: Linear regression analysis, one-way analysis of variance, t-tests and correlation analysis were used, as well as parameters of distribution of the data. RESULTS: The results of the analysis of optimal nursing care intensity can be regarded as reliable if the PAONCIL response rate is above 70%, the period of examination is at least 3-4 weeks, the mean PAONCIL value is below 0.65 and the explanatory power is above 25%. CONCLUSION: On the basis of the RAFAELA system, the optimal nursing care intensity of a ward can be reliably determined. The prerequisites for achieving reliable results were clear and mostly fulfilled. Because a study period shorter than that which has previously been the practice is enough, the use of this system will be easier than before. The credibility and usefulness of the RAFAELA system have thus received considerable additional confirmation.

Analysis of Variance↗

PubMed1999

Optimization of phenytoin therapy in adults with epilepsy in the Western Cape, South Africa.

OBJECTIVE: To assess the extent to which adults with epilepsy were optimized and individualized on phenytoin monotherapy in the Western Cape, South Africa and to estimate the average optimized dose and serum phenytoin concentration, and the therapeutic range for this patient group. METHODS: Patients were considered to be optimized on phenytoin if they were seizure-free or the best compromise was achieved between seizure reduction and side-effects. RESULTS: 538 (233 black and 305 coloured) adult people with epilepsy were treated at nine epilepsy clinics as outpatients. Of these patients, 332 (226 male and 106 female, 149 black and 183 coloured) were included in the data analysis as they were considered to have reliable phenytoin levels. Phenytoin doses and steady-state serum concentrations were predicted using the Michaelis-Menten equation. Patients attended a clinical pharmacokinetic service for 7.7+/-5.3 (range 1-22) months. The average optimized dose was 305.8 (range 100-500) mg/day and the average optimized level was 62.7+/-23.9 (range 15-133) micromol/l. Most patients (61.9%) were optimized in the therapeutic range 40-79 micromol/l; 21.1% were optimized above and 17% below this range. In 1.6% of patients serum concentrations above 120 micromol/l were required. Dosage adjustments were made in 47.0% of patients, increased in 31.9% and reduced in 15.1%. CONCLUSION: These findings indicate that many patients (47%) attending outpatient clinics were not optimized on phenytoin therapy.

Adolescent↗

PubMed2001

Doppler index and plasma level of atrial natriuretic hormone are improved by optimizing atrioventricular delay in atrioventricular block patients with implanted DDD pacemakers.

Doppler index is the sum of isovolumetric contraction time and isovolumetric relaxation time divided by ejection time and has clinical value as an index of combined systolic and diastolic myocardial performance. This crossover study compared the Doppler index and atrial natriuretic hormone (atrial natriuretic peptide) [ANP] between optimal (AV) delay and prolonged AV delay in patients with DDD pacemakers. The study included 14 patients (6 men, 8 women, age 78.4+/-9.3 [SD] years) with AV block with an implanted DDD pacemaker. AV delay was prolonged in a 25-ms, stepwise fashion starting from 125 ms to 250 ms. Pacing rate was set at 70 beats/min. Cardiac output (CO) was assessed by pulsed Doppler echocardiography, and optimal AV delay was defined as the AV delay at which CO was maximum, and an AV delay setting of 250 ms as prolonged AV delay. Plasma level of ANP and Doppler index determined by echocardiography were measured 1 week after programming. AV delay was switched to another AV delay and measurements were repeated after 1 week. Optimal AV delay was 159+/-19 ms. Doppler index was significantly lower at optimal AV delay than at prolonged AV delay (0.68+/-0.26 vs 0.92+/-0.30, P < 0.05). The plasma ANP level was significantly lower at optimal AV delay than at prolonged AV delay (29.0+/-30.7 vs 52.6+/-44.9 pg/mL, P < 0.05). In conclusion, the Doppler index and the plasma ANP level were significantly lower at optimal AV delay than at prolonged AV delay. This study shows the importance of the optimal AV delay setting in patients with an implanted DDD pacemaker, the Doppler index and plasma ANP levels are good indicators for optimizing AV delay.

Aged↗