Optimization Program Research Articles

Influenza Hospitalization Burden by Subtype, Age, Comorbidity, and Vaccination Status: 2012-2013 to 2018-2019 Seasons, Quebec, Canada.

Influenza immunization programs aim to reduce the risk and burden of severe outcomes. To inform optimal program strategies, we monitored influenza hospitalizations over 7 seasons, stratified by age, comorbidity, and vaccination status. We assembled data from 4 hospitals involved in an active surveillance network with systematic collection of nasal samples and polymerase chain reaction testing for influenza virus in all patients admitted through the emergency department with acute respiratory infection during the 2012-2013 to 2018-2019 influenza seasons in Quebec, Canada. We estimated seasonal, population-based incidence of influenza-associated hospitalizations by subtype predominance, age, comorbidity, and vaccine status, and derived the number needed to vaccinate to prevent 1 hospitalization per stratum. The average seasonal incidence of influenza-associated hospitalization was 89/100 000 (95% confidence interval, 86-93), lower during A(H1N1) (49-82/100 000) than A(H3N2) seasons (73-143/100 000). Overall risk followed a J-shaped age pattern, highest among infants 0-5 months and adults ≥75 years old. Hospitalization risks were highest for children <5 years old during A(H1N1) but for highest adults aged ≥75 years during A(H3N2) seasons. Age-adjusted hospitalization risks were 7-fold higher among individuals with versus without comorbid conditions (214 vs 30/100 000, respectively). The number needed to vaccinate to prevent hospitalization was 82-fold lower for ≥75-years-olds with comorbid conditions (n = 1995), who comprised 39% of all hospitalizations, than for healthy 18-64-year-olds (n = 163 488), who comprised just 6% of all hospitalizations. In the context of broad-based influenza immunization programs (targeted or universal), severe outcome risks should be simultaneously examined by subtype, age, comorbidity, and vaccine status. Policymakers require such detail to prioritize promotional efforts and expenditures toward the greatest and most efficient program impact.

Development of an automatic optimization program for the weight of multi-field irradiation in BNCT based on a genetic algorithm

ABSTRACT Boron Neutron Capture Therapy (BNCT) is a promising radiotherapy technique for cancer treatment. Optimizing dose distribution in tumors is a significant challenge for BNCT, especially for tumors that are deep-seated. The method of optimizing dose distribution through multi-field irradiation holds great potential for clinical applications in BNCT. This study presents an optimization program that couples genetic algorithm and Monte Carlo simulation, which can automatically optimize irradiation field weights for multi-field irradiation, ensuring optimal dose distribution for cancer patients. Using this program, a glioblastoma case was used to analyze the optimization results for different numbers of irradiation fields. The results demonstrate that the program possesses an efficient and accurate automatic optimization feature for multi-field irradiation weights. The minimum dose, mean dose, and D80% of tumor dose improve with the increase in irradiation fields, while the homogeneity index (HI) of tumor dose decreases, without exceeding dose constraints for organs at risk. The developed optimization program provides a method for determining irradiation field weights in BNCT treatment planning systems, which has the potential to improve treatment outcomes.

Impact of pay-for-performance for stroke unit access on mortality in Queensland, Australia: an interrupted time series analysis

Stroke unit care provides substantial benefits for all subgroups of patient with stroke, but consistent access has been difficult to achieve in many healthcare systems. Pay-for-performance incentives have been introduced widely in attempt to improve quality and efficiency in healthcare, but there is limited evidence of positive impact when they are targeted at hospitals. In 2012, a pay-for-performance program targeting stroke unit access was co-designed and implemented within a clinical quality improvement network across public hospitals in Queensland, Australia. We assessed the impact on access to specialist care and mortality following stroke. We used interrupted time series analysis on linked hospital and death registry data to compare changes in level (absolute proportions) and trends in outcomes (stroke/coronary care unit admission, 6-month mortality) for stroke, and a control condition of myocardial infarction (MI) without pay-for-performance incentive, from 2009 before, to 2017 after introduction of the pay-for-performance scheme in2012. We included 23,572 patients with stroke and 39,511 with MI. Following pay-for-performance introduction, stroke unit access increased by an absolute 35% (95% CI 29, 41) more than historical trend prediction, with greater impact for regional/rural residents (41% vs major city 24%) where baseline access was lowest (18% vs major city residents 53%). Historical upward 6-month mortality trends following stroke (+0.11%/month) reversed to a downward slope (-0.05%/month) with pay-for-performance; difference-0.16%/month (95% CI-0.29,-0.03). In contrast, access to coronary care and mortality trends for MI controls were unchanged, difference-in-difference for mortality-0.18%, (95% CI-0.34,-0.02). This clinician led pay-for-performance incentive stimulated significant improvements in stroke unit access, reduced regional disparities; and resulted in a sustained decline in 6-month mortality. As our findings contrast with lack of effect in most hospital directed pay-for-performance programs, differences in design and context provide insights for optimal program design. Queensland Advancing Clinical Research Fellowship, National Health and Medical Research Council Senior Research Fellowship.

Perioperative Blood Management Programme in Jehovah’s Witnesses Undergoing Total Hip Arthroplasty

Total hip arthroplasties aim to improve quality of life and reduce pain in patients suffering from late-stage hip osteoarthritis. On the other hand, it may represent a risky surgical procedure in people who refuse blood products because of religious beliefs, such as Jehovah’s Witnesses (JW). Preoperative optimisation protocols of these patients allow medical professionals to perform arthroplasties in a safer manner, avoiding allogeneic blood transfusion. In our retrospective study, two groups of patients were evaluated. Group 1 included JW patients who underwent a preoperative Hb optimisation program; Group 2 included non-JW patients authorizing transfusion in case of necessity. Differences in Hb levels were as follows: before surgery (JW 14.24 ± 1.10 vs. non-JW 12.48 ± 1.00, p-value ≤ 0.05), and after surgery (day 1 Hb: JW 12.88 ± 0.90 vs. non-JW 10.04 ± 1.30, p-value ≤ 0.05; day 3 Hb: JW 14.65 ± 0.80 vs. non-JW 9.10 ± 0.90 p-value ≤ 0.05). Moreover, cost-effectiveness strategies were evaluated in both groups. Our findings support that patient blood management programs are a safe and good strategy in hip prosthetic surgery, decreasing risks and transfusion overuse.

Using Artificial Intelligence in Education

The article discusses e-learning tools as a fundamental step in implementing digital learning technologies as a factor in the development of teachers’ creativity today. Recently, higher education has been developing with the help of digital technologies that significantly enhance education quality. The integration of complex software-hardware systems into the educational process lies in incorporating big data analytics, robotics, neural networks and artificial intelligence. The objectives of reforming the educational system require corresponding changes in teaching methods, as well as in higher education, through informatization and digitalization. This has led to the emergence of open education which allows for unrestricted access to educational resources. Open education also contributes to the development of digital pedagogy. Indeed, every student can choose an optimal educational programme to follow a personalized learning trajectory online which considers one’s characteristics and interests. After all, the development of digital pedagogy requires addressing various organizational, methodical and practical issues. They range from implementing digital learning technologies in the context of open education and culminating in the creation of a long-term strategic plan that will gradually transform the educational process and, in turn, encourage teachers to think creatively.

The dispersive art gallery problem

We introduce a new variant of the art gallery problem that comes from safety issues. In this variant we are not interested in guard sets of smallest cardinality, but in guard sets with largest possible distances between these guards. To the best of our knowledge, this variant has not been considered before. We call it the Dispersive Art Gallery Problem. In particular, in the dispersive art gallery problem we are given a polygon P and a real number ℓ, and want to decide whether P has a guard set such that every pair of guards in this set is at least a distance of ℓ apart.In this paper, we study the vertex guard variant of this problem for the class of polyominoes. We consider rectangular visibility and distances as geodesics in the L1-metric. Our results are as follows. We give a (simple) thin polyomino such that every guard set has minimum pairwise distances of at most 3. On the positive side, we describe an algorithm that computes guard sets for simple polyominoes that match this upper bound, i.e., the algorithm constructs worst-case optimal solutions. We also study the computational complexity of computing guard sets that maximize the smallest distance between all pairs of guards within the guard sets. We prove that deciding whether there exists a guard set realizing a minimum pairwise distance for all pairs of guards of at least 5 in a given polyomino is NP-complete.We also present an optimal dynamic programming approach that computes a guard set that maximizes the minimum pairwise distance between guards in tree-shaped polyominoes, i.e., computes optimal solutions. Because the shapes constructed in the NP-hardness reduction are thin as well (but have holes), this result completes the case for thin polyominoes.

An Optimal Operation Strategy for Collaborative Flexibility Provision of a Carbon Capture and Utilization Process With Wind Energy

Improving power system flexibility by responsive demand is essential for integrating wind energy with a high level of variability in power systems. Carbon dioxide-based chemical processes as energy-intensive industrial loads may offer a vast potential of new forms of flexible operation due to their existing control infrastructure and storage capabilities. However, a collaborative decision model is needed for optimal energy sharing among the chemical plant and the grid under the variations and uncertainties of wind power. This study develops an optimal two-stage stochastic programming model for a novel flexible operation strategy of the chemical process coupled with wind turbines. In the proposed control scheme, a small-scale wind farm provides the power input of a chemical plant. Wind turbines are connected to the grid and actively participate in the day-ahead energy and reserve markets, considering the chemical plant as a source of flexibility. An equivalent scenario-based model of the proposed optimization problem is suggested using the Group Method of Data Handling (GMDH) for a data-driven prediction of stochastic variables. Simulation results demonstrate the effectiveness and significance of the proposed approach for an optimal and collaborative contribution in ancillary market of a carbon dioxide-based chemical plant supplied by wind energy.

The Role of a Registered Dietitian Nutritionist in Improving Weight Loss Prior to Elective Total Joint Arthroplasty in Overweight or Obese Adults Among Participants in MODIFY, a Pre-surgical Optimization Program

The Role of a Registered Dietitian Nutritionist in Improving Weight Loss Prior to Elective Total Joint Arthroplasty in Overweight or Obese Adults Among Participants in MODIFY, a Pre-surgical Optimization Program

Time-Optimal Path Tracking for Dual-Arm Free-Floating Space Manipulator System Using Convex Programming

In this paper, the time-optimal path tracking problem of the dual-arm free-floating space manipulator (FFSM) system is solved by using convex programming. Firstly, we construct a continuous nonlinear optimization problem that considers system dynamics and constraints along the path, including torque, velocity and acceleration. Subsequently, the original optimization problem is transformed into a convex optimal control problem by using scalar path coordinate and nonlinear variables. To characterize the problem in a more manageable form, we apply the direct transcription method and discretize the problem into a sparse convex optimization program. Numerical results demonstrate that the proposed approach efficiently determines the trajectory of the FFSM along the preset path. Meanwhile, the base's velocity can be limited to a small region, which is an essential requirement for ensuring the stability of the satellite. Furthermore, an experiment is conducted using GLUON robots. The tracking error between the actual and preset pose of the target verifies the feasibility of the proposed method.

Graph deep learning accelerated efficient crystal structure search and feature extraction

Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, which includes a symmetry-based combinatorial crystal optimization program (SCCOP) and a feature additive attribution model, to significantly reduce computational costs and to extract property-related structural features. Our method is highly accurate and predictive, and extracts structural features from desired structures to guide materials design. We first test SCCOP on 35 typical compounds to demonstrate its generality. As a case study, we apply our approach to a two-dimensional B-C-N system, which identifies 28 previously undiscovered stable structures out of 82 compositions; our analysis further establishes the structural features that contribute most to energy and bandgap. Compared to conventional approaches, SCCOP is about 10 times faster while maintaining a comparable accuracy. Our framework is generally applicable to all types of systems for precise and efficient structural search, providing insights into the relationship between ML-extracted structural features and physical properties.

Research on Multi-Objective Optimization Model of Foundation Pit Dewatering Based on NSGA-II Algorithm

This study focuses on optimizing the foundation pit dewatering scheme using the foundation pit dewatering theory and the principles of multi-objective optimization. It explores the development of a multi-objective optimization model and efficient solution technology for foundation pit dewatering. This research focuses on the foundation pit dewatering project at the inverted siphon section of Xixiayuan canal head, specifically from pile number XZ0+326 to XZ0+500. It establishes an optimized mathematical model for foundation pit dewatering that incorporates three objectives. Additionally, a dewatering optimization program is developed by utilizing the MATLAB optimization toolbox and the multi-objective optimization algorithm program based on the NSGA-II algorithm (Gamultiobj). The multi-objective optimization mathematical model is solved, and a Pareto-optimal solution set with uniform distribution is obtained. The multi-objective optimization evaluation system based on AHP is constructed from the three aspects of dewatering cost, the impact of settlement on the environment, and the safety and stability of the foundation pit. The optimization scheme of the Pareto-optimal solution set is selected as the decision result to provide multiple feasible schemes for the dewatering construction of foundation pits. The optimization scheme is verified by using the GMS software. The simulation results demonstrate that the optimization scheme fulfills the requirements for water level and settlement control. Moreover, the developed optimization program efficiently solves the multi-objective optimization problem associated with foundation pit dewatering. Lastly, an evaluation system incorporating the NSGA-II algorithm and AHP is developed and utilized in the context of dewatering engineering in order to offer multiple viable optimal dewatering schemes.

Preoperative Optimization for Orthopaedic Surgery: Steps to Reduce Complications.

As the population ages and patients maintain higher levels of activity, the incidence of major and minor orthopaedic procedures continues to rise. At the same time, health policies are incentivizing efforts to improve the quality and value of musculoskeletal health services. As such, orthopaedic surgeons play a key role in directing the optimization of patients before surgery by assessing patient risk factors to inform risk/benefit discussions during shared decision-making and designing optimization programs to address modifiable risks. These efforts can lead to improved health outcomes, reduced costs, and preference-congruent treatment decisions. In this review, we (1) summarize the evidence on factors known to affect outcomes after common orthopaedic procedures, (2) identify which factors are considered modifiable and amenable to preoperative intervention, and (3) provide guidance for preoperative optimization.

Compound learning adaptive neural network optimal backstepping control of uncertain fractional-order predator–prey systems

Reinforcement learning as an effective strategy is widely utilized in optimal control. However, when updating critic–actor weight vectors based on the square of Bellman residual, it often leads to substantial computational complexity. This paper formulates a compound learning optimal backstepping control programme that can efficaciously reduce the computational burden for fractional-order predator–prey systems (FOPPS) with uncertainties. To economize resource, a reinforcement learning technology is adopted to realize the optimal control in view of neural networks under identifier–critic–actor structure. To address the computational complexity issue raised above, a simple positive definite function is proposed to update critic–actor weight vectors. Fractional-order filters are utilized to estimate virtual signals and their fractional-order derivatives for tackling the “explosion of complexity” problem existing in the conventional backstepping technology. Simultaneously, to enhance the approximation accuracy of uncertainties in FOPPS, a compound learning updating law is built by using tracking error and prediction error. In accordance with the stability analysis, the formulated scheme ensures that the output of FOPPS can track the reference signal with the expected accuracy and all signals are bounded. Eventually, a numerical simulation is presented to validate the effectiveness of the proposed control strategy.

Optimization of breeding program design through stochastic simulation with kernel regression.

In recent years, breeding programs have increased significantly in size and complexity, with various highly interdependent parameters and many contrasting breeding goals. As a result, resource allocation in these programs has become more complex, and deriving an optimal breeding strategy has become increasingly challenging. To address this, a common practice is to reduce the optimization problem to a set of scenarios that differ only in a few parameters and can therefore be analyzed in detail. The goal of this article is to provide a framework for the numerical optimization of breeding programs that goes beyond the simple comparison of scenarios. For this, we first determine the space of potential breeding programs only limited by basic constraints like the budget and housing capacities. Subsequently, the goal is to identify the optimal breeding program by finding the parametrization that maximizes the target function by combining different breeding goals. To assess the value of the target function for a parametrization, we propose using stochastic simulations and the subsequent use of a kernel regression method to cope with the stochasticity of simulation outcomes. This procedure is performed iteratively to narrow down the most promising areas of the search space and perform more and more simulations in these areas of interest. In a simplified example applied to a dairy cattle program, our proposed framework has shown its ability to identify an optimal breeding strategy that aligns with a target function aiming at genetic gain and genetic diversity conservation limited by budget constraints.

Resilience Enhancement with Transportable Storage Systems and Repair Crews in Coupled Transportation and Distribution Networks

The significance of resilience within power system infrastructures has been progressively acknowledged. To address this challenge, this paper introduces an innovative framework to enhance distribution network (DN) resilience by incorporating transportable storage systems (TSSs). To do so, we take into account the interconnection between the DN and the transportation network (TN) to optimize the trajectory planning of TSSs. To account for real-world features, the traffic flow of TN is modeled according to traffic counter cameras of Sioux Falls city. In addition, the effects of blocked roads on TSSs' movements through microgrids (MGs), and clearing operation of obstructions by repair crews are considered as these factors have a great impact on system operational cost. Further, an optimal reconfiguration program is proposed to add more flexibility to the system. The developed model is formulated as mixed-integer linear programming (MILP) and various case studies are presented including analyzing TSSs and repair crews’ operations, both individually and concurrently. Different case studies reveal the positive impact of TSSs, reducing the total cost of the system by approximately 13.11%. In post-event, blocked roads directly increase the operation costs by 17.02%; however, repair crews' cooperation with TSSs, achieved a remarkable 12.75% reduction in operation costs.

Water production function and optimal irrigation programme for drip irrigated castor (Ricinus communis L.)

Water shortage for crop production necessitates adequate knowledge of the crop water production function to make decisions on water allocation for optimum returns from castor crop under surface drip irrigation. The study was conducted in rabi 2009-10 at Agricultural college farm, Rajendranagar, Hyderabad. The different drip irrigation schedules at 40, 60 and 80 per cent pan evaporation replenishment levels either kept constant throughout the crop life or combinations at vegetative, flowering and development stages were compared with surface check basin at (0.8 IW/CPE ratio) irrigation. The results showed that drip irrigation at 0.6 Epan replenishment up to flowering and 0.8 Epan later on registered comparable seed yields with drip irrigation scheduled at either at 0.6 or 0.8 Epan and was significantly superior over other treatments and the best fit water production function was quadratic (R2= 0.99). The optimum quantity of water (ETc) for maximum net returns under prevailing prices (water and castor @ `10 and 30 ha/mm and kg/ha, respectively) was 381.4 ha mm with a resultant seed yield of 3611 kg/ha. The gross returns, net returns and net return/rupee invested varied from ` 72194 to 144447/ha, ` 36808 to 108018/ha and 1.040 to 3.049, respectively.

Dukungan Keluarga dan Peran Pengawas Minum Obat dengan Kepatuhan Minum Obat pada Penderita TB Paru

Tuberculosis (TB) is a bacterial infection caused by Mycobacterium Tuberculosis. The most deadly infectious illness on the globe is still pulmonary tuberculosis. Southeast Asia accounts for up to 58% of all TB cases. India, Indonesia, and China have the greatest number of TB cases in the globe, accounting for 23%, 10%, and 10% of global incidence, respectively. The overall goal of this study is to examine the association between family support and the function of medication supervisors in pulmonary tuberculosis patients at the Putri Ayu Health Center in Jambi City. This is a cross-sectional quantitative study. The investigation was conducted in the operating area of the Putri Ayu Health Center in Jambi City in August 2022. This study included all active tuberculosis patients at the Putri Ayu Health Center, a total of 66 individuals. The study's sample size was 60 people (after deducting 6 respondents of the initial survey). Total sampling is the sampling technique used. The analysis performed is both univariate and bivariate. The chi square test was applied, with a meaningfulness threshold of 95% (alpha = 0.05). The study revealed a link between family support (p-value: 0.021) and the role of supervisors taking medications (p-value: 0.010) and adherence to taking drugs in pulmonary TB patients at the Putri Ayu Health Center in Jambi City. It is expected that the Puskesmas Putri Ayu Jambi City would also educate families and emphasise the need of family support for active Tb patients' rehabilitation and develop a PMO optimization program to prevent drop out.

Is Morbid Obesity a Modifiable Risk Factor in Patients Who Have Severe Knee Osteoarthritis and do Not Have a Formal Perioperative Optimization Program?

BackgroundObesity is considered a modifiable risk factor prior to total knee arthroplasty (TKA); however, little data support this hypothesis. Our purpose was to evaluate patients who have a body mass index (BMI) >40 presenting for TKA to determine the incidence of: (1) patients who achieved successful weight loss through nutritional modification or bariatric surgery and (2) patients who underwent TKA over the study period without the presence of a formal optimization program. MethodsThis was a retrospective, single-center analysis. Inclusion criteria included: Kellgren and Lawrence grade 3 or 4 knee osteoarthritis, BMI >40 at presentation, and minimum 1-year follow-up (mean 45 months) (N = 624 patients). Demographics, weight loss interventions, pursuit of TKA, maximum BMI change, and Patient-Reported Outcomes Measurement Information System scores were collected. Multivariable logistic and linear regressions evaluated associations of underlying demographic and treatment characteristics with outcomes. ResultsThere were 11% of patients who ended up pursuing TKA over the study period. Bariatric surgery was 3.7 times more likely to decrease BMI by minimum 10 compared to nonsurgical intervention (95% confidence interval [CI] [1.7, 8.1]; P = .001). Bariatric surgery resulted in mean BMI change of −3.3 (range, 0 to 22) compared to nonsurgical interventions (−2.6 [range, 0 to 12]) and no intervention (0.4 [range, 0 to 15]; P < .0001). Bariatric surgery patients were 3.1 times more likely to undergo TKA (95% CI [1.3, 7.1]; P = .008), and nonsurgical interventions were 2.4 times more likely to undergo TKA (95% CI [1.3, 4.5]; P = .006) compared to no intervention. Non-White patients across all interventions were less likely to experience loss >5 BMI compared to White patients (95% CI [0.2, 0.9]; P = .018). ConclusionsMost patients were unable to reduce BMI more than 5 to 10 over a mean 4-year period without a formal weight optimization program. Utilization of bariatric surgery was most successful compared to nonsurgical interventions, although ultimate pursuit of TKA remained low in all cohorts.

Sequential mediation of early temperament and eating behaviors in the pathways from feeding practices to childhood overweight and obesity.

Childhood eating behaviors and temperament may have important implication for constructing the pathways from maternal feeding practices to childhood overweight and obesity (OW/OB). Examining multiple feeding styles simultaneously to childhood OW/OB is critical through the mediators of early childhood temperament and eating behaviors. This cross-sectional study recruited mothers mainly responsible for child care from two hospitals and two healthcare centers in eastern China. Sociodemographic characteristics, and data from the Infant Feeding Style Questionnaire (IFSQ), the short form of Children Behavior Questionnaire [Revised (IBQ-RSF)], and the Child Eating Behavior Questionnaire for toddler (CEBQ-T) were collected. Weight and recumbent length were measured to calculate the age- and sex-specific body mass index (BMI) z-scores (BMIz). The structural equation modeling (SEM) approach was used to examine direct and indirect pathways from five maternal feeding styles to childhood OW/OB through temperament and eating behaviors. A total of 486 children were recruited, 73 (15.02%) children were OW/OB; the age of the children was 14.55 (SD = 5.14) months, and the age of the mothers was 29.90 (SD = 3.63) years. The responsive feeding exerted significant direct (β = -0.098), indirect (β = -0.136) and total (β = -0.234) effects on childhood OW/OB. Restrictive feeding had significant direct (β = 0.222), indirect (β = 0.102) and total (β = 0.324) effects on childhood OW/OB. Indulgent feeding had significant direct (β = 0.220), indirect (β = 0.063), and total (β = 0.283) effects on childhood OW/OB. Pressuring feeding had significant direct (β = -0.116), indirect (β = -0.096) and total (β = -0.212) effects on childhood OW/OB. There was a direct effect of feeding practices on childhood OW/OB; feeding practices indirectly predicted childhood OW/OB through temperament and eating behaviors in children aged 6-23 months. This study could help governments agencies, policymakers, and healthcare workers to establish optimal intervention programs targeting feeding practices through childhood eating behaviors and temperament to prevent childhood OW/OB.

Perioperative blood management programme in Jehovah’s witnesses undergoing total knee arthroplasty: clinical results and cost-benefit analysis

Introduction Total knee arthroplasties have the purpose to improve quality of life from joint-related pain. On the other hand, it may represent a risky surgical procedure in people who refuse blood products due to their religious beliefs, such as Jehovah’s Witnesses (JW). Preoperative optimization protocols of these patients allow performing arthroplasties in a safer manner avoiding allogeneic blood transfusion. Methods In our retrospective study, 2 groups of patients were evaluated. Group 1, included JW patients who underwent a preoperative Hb optimisation programme; group 2 included non-JW patients authorizing transfusion in case of necessity. Results Differences in Hb levels were: before surgery (JW 13,63±1,26 vs. non-JW 12,83±1,21, p-value=0,22), after surgery (day 1 Hb: JW 12,26±1,47 vs. non-JW 10,88±1,87, p-value=0,1; day 3 Hb: JW 10±2,97 vs. non-JW 9,23±1,07 p-value=0,64). In the JW group the mean Hb concentration decreased from 13,63±1,26 g/dL preoperatively to 12,26±1,47 g/dL (day 1) while in non-JW group fell from 12,83± g/dL to 10,88± g/dL. There was no statistically-difference between the 2 groups. Moreover, cost-effectiveness strategies were evaluated in both groups. Conclusion Our findings support that PBM programs represent a safe and good strategy in knee prosthetic surgery, decreasing costs, risks and transfusion overuse.