Pre-defined Performance Research Articles

Who benefits from increased service utilisation? Examining the distributional effects of payment for performance in Tanzania

BackgroundPayment for performance (P4P) strategies, which provide financial incentives to health workers and/or facilities for reaching pre-defined performance targets, can improve healthcare utilisation and quality. P4P may also reduce inequalities in healthcare use and access by enhancing universal access to care, for example, through reducing the financial barriers to accessing care. However, P4P may also enhance inequalities in healthcare if providers cherry-pick the easier-to-reach patients to meet their performance targets. In this study, we examine the heterogeneity of P4P effects on service utilisation across population subgroups and its implications for inequalities in Tanzania.MethodsWe used household data from an evaluation of a P4P programme in Tanzania. We surveyed about 3000 households with women who delivered in the last 12 months prior to the interview from seven intervention and four comparison districts in January 2012 and a similar number of households in 13 months later. The household data were used to generate the population subgroups and to measure the incentivised service utilisation outcomes. We focused on two outcomes that improved significantly under the P4P, i.e. institutional delivery rate and the uptake of antimalarials for pregnant women. We used a difference-in-differences linear regression model to estimate the effect of P4P on utilisation outcomes across the different population subgroups.ResultsP4P led to a significant increase in the rate of institutional deliveries among women in poorest and in middle wealth status households, but not among women in least poor households. However, the differential effect was marginally greater among women in the middle wealth households compared to women in the least poor households (p = 0.094). The effect of P4P on institutional deliveries was also significantly higher among women in rural districts compared to women in urban districts (p = 0.028 for differential effect), and among uninsured women than insured women (p = 0.001 for differential effect). The effect of P4P on the uptake of antimalarials was equally distributed across population subgroups.ConclusionP4P can enhance equitable healthcare access and use especially when the demand-side barriers to access care such as user fees associated with drug purchase due to stock-outs have been reduced.

\xa0An assessment of the interoperability of PPP-AR network products

Integer ambiguity resolution of carrier-phase measurements from a single receiver can be implemented by applying additional satellite corrections (products) to mitigate unmodelled satellite equipment delays. Interoperability of different PPP-AR products would allow the PPP user to transform independently generated PPP-AR products to obtain multiple fixed solutions of comparable precision and accuracy with limited changes required to user PPP measurement processing software. The ability to provide multiple solutions would increase the reliability of the solution for, e.g., real-time processing; if there were an outage in the generation of one set of PPP-AR products, the user could instantly switch streams to a different provider.There are currently three main public providers of real-time products that enable PPP-AR. These include School of Geodesy and Geomatics at Wuhan University (SGG-WHU), Natural Resources Canada (NRCan) and Centre National d’Etudes Spatiales (CNES). The presented research examines the PPP-AR products generated from the FCB (Fractional Cycle Bias) model and IRC (Integer Recovery Clock) model that have been transformed into the DC (Decoupled Clock) format and applied within the PPP user solution. Interoperability of the different PPP-AR products is a challenging task due to the public availability of different quality of products, limited literature documenting the conventions adopted within the network solution of the providers and unclear definitions of the corrections. The novelty of the research is in the analysis of using the transformed products. The convergence time (time to first fix and time to a pre-defined performance level), position precision (repeatability), position accuracy and solution outliers are examined. Equivalent performance was noted utilizing the different methods. Of the four solutions, FCB products had the highest accuracy. This is attributed to the products being generated using final IGS orbit and clock products. To confirm this, FCBs generated using GRG orbit and clock products were also examined and comparable performance was observed between the FCBs and IRC (GRG) products. The least accurate solution was obtained using the IRC (CNT) products, which was due to the products being archived real time products.

Performance evaluation of waste electrical and electronic equipment disassembly layout configurations using simulation

Recycling of waste electrical and electronic equipment (WEEE) is crucially important since it handles hazardous waste according to ever tightening laws and regulations and it adds benefits to economy and sustainable environment. Disassembly is one of the most important processes performed during the recovery of WEEE. The overall goal of disassembly is to maximize the retrieval of various metals and plastics contained in WEEE in order to reduce their negative effects on human health and environmental sustainability and to increase economic gains. This study aims to evaluate alternative layout configurations for WEEE disassembly systems (WDS). In this context, various configurations were compared in terms of pre-defined performance criteria, such as the total number of disassembled WEEE and the total revenue from sales, using simulation models. The results of this study show that the performance of a WDS was significantly affected by output transfer systems along with the specialization of operators on certain types of WEEE.

Robust LS-SVM-based adaptive constrained control for a class of uncertain nonlinear systems with time-varying predefined performance

This paper focuses on robust adaptive control for a class of uncertain nonlinear systems subject to input saturation and external disturbance with guaranteed predefined tracking performance. To reduce the limitations of classical predefined performance control method in the presence of unknown initial tracking errors, a novel predefined performance function with time-varying design parameters is first proposed. Then, aiming at reducing the complexity of nonlinear approximations, only two least-square-support-vector-machine-based (LS-SVM-based) approximators with two design parameters are required through norm form transformation of the original system. Further, a novel LS-SVM-based adaptive constrained control scheme is developed under the time-vary predefined performance using backstepping technique. Wherein, to avoid the tedious analysis and repeated differentiations of virtual control laws in the backstepping technique, a simple and robust finite-time-convergent differentiator is devised to only extract its first-order derivative at each step in the presence of external disturbance. In this sense, the inherent demerit of backstepping technique–“explosion of terms” brought by the recursive virtual controller design is conquered. Moreover, an auxiliary system is designed to compensate the control saturation. Finally, three groups of numerical simulations are employed to validate the effectiveness of the newly developed differentiator and the proposed adaptive constrained control scheme.

Application of Data Driven Control to Dynamic Positioning

The paper presents the results of applying a data driven model free control strategy, built upon unfalsified control techniques, to the problem of Dynamic Positioning (DP) of marine vessels subjected to environmental forces. The unfalsified control technique builds on the construction of a bank of controllers for a given plant, in which there exists at least one controller that meets desired performance specification and a falsification unit. The latter is implemented using a cost function that directly evaluates the performance of the controllers (in and out of the feedback loop) using measured input and output data. At each sampling time, the performance of the controllers is assessed and the controllers that do not meet the pre-defined performance specification criteria will be falsified and removed from the bank of the controllers after which an active controller will be selected among the unfalsified ones. The proposed adaptive structure of the controller enables the DP system to operate under different environmental conditions by automatically falsifying controllers that do not meet the performance criteria, and eventually selecting the controller that provides the requested performance, leaving the human decision to the top of the hierarchical DP control structure. Numerical simulations show how a destabilizing controller is detected and replaced by one that is able to meet the designed performance specification.

A survey of case studies in production scheduling: Analysis and perspectives

The problems of production scheduling and sequencing refer to decision making regarding the designation of jobs to available resources and their subsequent order to optimize pre-defined performance measures. From the early days of research in this area until this last decade, the publication of case studies has been scarce, with their frequency only increasing very recently. This survey aims to highlight practical research and case studies published in the literature in the scheduling area, identifying the main characteristics of the problems treated, trends in this research and also gaps showing potential areas for future study.

Adaptive neural prescribed performance control for a class of strict-feedback stochastic nonlinear systems with hysteresis input

This paper studies an adaptive neural tracking control problem for a class of strict-feedback stochastic nonlinear systems with guaranteed predefined performance subject to unknown backlash-like hysteresis input. First, utilizing the prescribed performance control, the predefined tracking control performance can be guaranteed via exploiting a new performance function without considering the accurate initial error. Second, by integrating neural network approximation capability into the backstepping technique, a robust adaptive neural control scheme is developed to deal with unknown nonlinear functions, stochastic disturbances and unknown hysteresis input. The designed controller overcomes the problem of the over-parameterization. Under the proposed controller, all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB), and the prespecified transient and steady tracking control performance are guaranteed. Simulation studies are performed to demonstrate and verify the effectiveness of the proposed method.

Development of performance and error metrics for ultrasound-guided axillary brachial plexus block

PurposeChange in the landscape of medical education coupled with a paradigm shift toward outcome-based training mandates the trainee to demonstrate specific predefined performance benchmarks in order to progress through training. A valid and reliable assessment tool is a prerequisite for this process. The objective of this study was to characterize ultrasound-guided axillary brachial plexus block to develop performance and error metrics and to verify face and content validity using a modified Delphi method.MethodsA metric group (MG) was established, which comprised three expert regional anesthesiologists, an experimental psychologist and a trained facilitator. The MG deconstructed ultrasound-guided axillary brachial plexus block to identify and define performance and error metrics. Experts reviewed five video recordings of the procedure performed by anesthesiologists with different levels of expertise to aid task deconstruction. Subsequently, the MG subjected the metrics to “stress testing”, a process to ascertain the extent to which the performance and error metrics could be scored objectively, either occurring or not occurring with a high degree of reliability. Ten experienced regional anesthesiologists used a modified Delphi method to reach consensus on the metrics.ResultsFifty-four performance metrics, organized in six procedural phases and characterizing ultrasound-guided axillary brachial plexus block and 32 error metrics (nine categorized as critical) were identified and defined. Based on the Delphi panel consensus, one performance metric was modified, six deleted and three added.ConclusionIn this study, we characterized ultrasound-guided axillary brachial plexus block to develop performance and error metrics as a prerequisite for outcome-based training and assessment. Delphi consensus verified face and content validity.

Abstract 89: Differences in Acute Stroke Care in Primary and Comprehensive Stroke Centers in Florida: An Analysis of the Florida-Puerto Rico Collaboration to Reduce Stroke Disparities (FL-PR CReSD) Study- the NINDS Stroke Prevention Intervention Research Program

Introduction: Primary stroke center (PSC) and comprehensive stroke center (CSC) designation in Florida aims to improve delivery of care and outcomes for stroke patients. In line with the goals of the NINDS funded Florida-Puerto Rico Collaboration to Reduce Stroke Disparities (FL-PR CReSD) Study, we sought to compare ischemic stroke performance metrics by stroke center designation in participating Florida hospitals. Methods: We analyzed 74,623 cases with acute ischemic stroke from 26 CSC and 40 PSC from January 2010-April 2016. We described patient demographics, comorbidities and Get With The Guidelines-Stroke performance metrics of defect free care (compliance with 7 pre-defined performance core measures), door to CT time (DTCT) ≤25 mins and door to needle time (DTN) ≤60 mins. Results: Compared with PSC patients, CSC patients were younger (70 ± 15 vs. 71 ± 14 years, p<.0001), more likely male (51% vs. 50%, p=.0008), more likely Hispanic (17% vs. 10%, p<.0001) and Black (21% vs. 17%, p<.0001), had more severe strokes (NIHSS median 5 (IQR 2-12) vs. 4 (IQR 1-9); NIHSS ≥16, 12% vs. 9%, p <.0001), were more likely to have atrial fibrillation (19% vs. 17%, p<.0001), and were more likely to arrive by EMS (55% vs. 46%, p<.0001). CSC cases were more likely to have faster DTCT (44 vs. 48 mins, p=.0124 ; < 25 mins 33% vs. 31%, p<.0001). More patients in CSC received thrombolysis (12% vs. 9%, p<.0001), with faster DTN (59 vs. 71 min, p <.0001; ≤60 minutes 53% vs. 37%, p <.0001). Patients in CSC had greater rates of defect free care (85% vs. 82.4%, p<.0001). Blacks had longer median DTCT than Whites and Hispanics in both CSC (56 mins Blacks vs. 41 mins Whites and Hispanics) and PSC (60 mins Blacks, 44 mins Whites, 57 mins Hispanics). Blacks in CSC had longer median DTN (63 mins) than Whites (60 mins) and Hispanics (53 mins). Hispanics had longer median DTN (73 mins) in PSC than Blacks (70 mins) and Whites (70 mins). Conclusion: Patients treated in CSC, compared with those treated in PSC, received better defect-free care and had lower DTCT and DTN times. Race-ethnic disparities in performance metrics are still evident in both CSC and PSC. Identification of these disparities is important to design interventions to reduce disparities and improve stroke quality of care for all.

State estimation for recurrent neural networks with unknown delays: A robust analysis approach

In this paper, the state estimation problem is investigated for recurrent neural networks with unknown delays both in state equation and output equation. By constructing the Taylor series and linear matrix inequality (LMI) technique, the sufficient conditions of state estimation for a kind of recurrent neural networks with unknown delays are presented, therefore, the error system is globally asymptotically stable with L∞ performance. The design observer can attenuate the effect of the unknown delays on a pre-defined performance output, and the observer gain can be obtained by solving a set of linear matrix inequalities. Some remarks and examples are given to show the effectiveness of the proposed method in comparison with some existing results.

Doing it twice, getting it right? The effects of grade retention and course repetition in higher education

Many students who enter college are insufficiently prepared to follow a demanding college-level curriculum. Thus, higher education institutions often require low-performing students to repeat failed courses, a full term, or even a full year. This paper is the first to investigate the effects of such a “(grade) retention” policy on student performance in higher education. We study a setting where first-year undergraduates who fall short of a pre-defined performance requirement have to repeat all first-year courses before they can proceed to the second year. To determine the causal effect of retention and repetition on student performance, we apply a sharp regression discontinuity design to administrative data from a Swiss university. Based on a sample of 5000 students, we find that grade retention increases dropout probabilities after the first year by about 10 percentage points. Repetition of a full year persistently boosts grade point averages by about 0.5 standard deviations, but does not affect study pace and major choices.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Optimization of Air Quality Monitoring Network Using GIS Based Interpolation Techniques

This paper proposes a simple method of optimizing Air Quality Monitoring Network (AQMN) using Geographical Information System (GIS), interpolation techniques and historical data. Existing air quality stations are systematically eliminated and the missing data are filled in using the most appropriate interpolation technique. The interpolated data are then compared with the observed data. Pre-defined performance measures root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r) were used to check the accuracy of the interpolated data. An algorithm was developed in GIS environment and the process was simulated for several sets of measurements conducted in different locations in Riyadh, Saudi Arabia. This methodology proves to be useful to the decision makers to find optimal numbers of stations that are needed without compromising the coverage of the concentrations across the study area.

Dynamic Learning From Neural Control for Strict-Feedback Systems With Guaranteed Predefined Performance.

This paper focuses on dynamic learning from neural control for a class of nonlinear strict-feedback systems with predefined tracking performance attributes. To reduce the number of neural network (NN) approximators used and make the convergence of neural weights verified easily, state variables are introduced to transform the state-feedback control of the original strict-feedback systems into the output-feedback control of the system in the normal form. Then, using the output error transformation based on performance functions, the constrained tracking control problem of the normal systems is transformed into the stabilization problem of an equivalent unconstrained one. By combining the backstepping method, a high-gain observer with radial basis function (RBF) NNs, a novel adaptive neural control (ANC) scheme is proposed to guarantee the predefined tracking error performance as well as the ultimate boundedness of all other closed-loop signals. In particular, only one NN is employed to approximate the lumped unknown system dynamics during the controller design. Under the satisfaction of the partial persistent excitation condition for RBF NNs, the proposed stable ANC scheme is shown to be capable of achieving knowledge acquisition, expression, and storage of unknown system dynamics. The stored knowledge is reused to develop a neural learning controller for improving the control performance of the closed-loop system. When the initial condition satisfies the predefined performance, the proposed neural learning control can still guarantee the predefined tracking performance. Simulation results on a third-order one-link robot are given to show the effectiveness of the proposed method.

One-year outcome of a prospective trial stopping dual antiplatelet therapy at 3months after everolimus-eluting cobalt-chromium stent implantation: ShortT and OPtimal duration of Dual AntiPlatelet Therapy after everolimus-eluting cobalt-chromium stent (STOPDAPT) trial.

There has been no previous prospective study evaluating dual antiplatelet therapy (DAPT) duration shorter than 6 months after cobalt-chromium everolimus-eluting stent (CoCr-EES) implantation. STOPDAPT trial is a prospective multi-center single-arm study evaluating 3-month DAPT duration after CoCr-EES implantation. The primary endpoint was a composite of cardiovascular death, myocardial infarction (MI), stroke, definite stent thrombosis (ST) and TIMI major/minor bleeding at 1 year. Between September 2012 and October 2013, a total of 1525 patients were enrolled from 58 Japanese centers, with complete 1-year follow-up in 1519 patients (99.6 %). Thienopyridine was discontinued within 4 months in 1444 patients (94.7 %). The event rates beyond 3 months were very low (cardiovascular death: 0.5 %, MI: 0.1 %, ST: 0 %, stroke: 0.7 %, and TIMI major/minor bleeding: 0.8 %). Cumulative 1-year incidence of the primary endpoint was 2.8 % [upper 97.5 % confidence interval (CI) 3.6 %], which was lower than the pre-defined performance goal of 6.6 % (P < 0.0001). Using the CoCr-EES group in the RESET trial as a historical comparison group, where nearly 90 % of patients had continued DAPT at 1 year, cumulative incidence of the primary endpoint tended to be lower in the STOPDAPT than in the RESET (2.8 versus 4.0 %, P = 0.06) and adjusted hazard ratio was 0.64 (95 % CI 0.42–0.95, P = 0.03). The cumulative incidence of definite/probable ST was lower in the STOPDAPT than in the RESET [0 patient (0 %) versus 5 patients (0.3 %), P = 0.03]. In conclusion, stopping DAPT at 3 months in selected patients after CoCr-EES implantation was at least as safe as the prolonged DAPT regimen adopted in the historical control group.Electronic supplementary materialThe online version of this article (doi:10.1007/s12928-015-0366-9) contains supplementary material, which is available to authorized users.

Synergy of Dynamic Frequency Scaling and Demotion on DRAM Power Management: Models and Optimizations

Main memory (or DRAM) is one of the most significant components to the computer system's performance and energy consumption. Dynamic frequency scaling (DFS) and DRAM low-power states (Demotion) are two main-stream techniques for DRAM power management. DFS reduces the operation frequency of memory channels and DRAM devices when the memory bandwidth is under-utilized, whereas demotion transits individual memory ranks to low-power states during long idle periods. Despite that there have been fruitful research work for DFS and demotion separately, little attention has been paid to the synergy between these two techniques. To bridge this gap, this paper conducts a comprehensive study on the synergy between DFS and demotion. In particular, we leverage queuing theory to develop analytical models for the energy consumption and performance of DRAM systems with DFS and demotion. These models provide valuable insights into the synergy between DFS and demotion. We further attempt to minimize the energy consumption by considering both DFS and demotion while keeping a pre-defined performance penalty budget. To reduce the optimization complexity, we develop simple yet effective heuristics to search near-optimum DFS-demotion configurations. We experimentally compare our design with other state-of-the-art DRAM energy saving policies using detailed simulations of a large set of workloads. Experimental results show the accuracy of our analytical models and the effectiveness of our optimizations.

Multi-objective optimization for rapid and robust optimal oilfield development under geological uncertainty

Optimal well placement is critical to oil and gas field development. Typical workflows involve procedures to place a new well or a group of new wells in a reservoir in order to maximize some pre-defined reservoir performance metric. However, there are two main drawbacks with these traditional optimization approaches: First, the impact of geological uncertainty is often neglected or there may be no framework to include geological uncertainty. Second, traditional optimization techniques normally cannot meet the requirement of optimizing two or more conflicting objectives simultaneously—this may be useful when maximizing oil recovery while also minimizing water production. Consequently, in recent years, multiple objective optimization to obtain robust solutions that minimize the decision risk under geological uncertainty becomes a topic of renewed interest. Therefore, in this work, we develop a new work flow for well placement optimization while considering geological uncertainty in reservoir models. In general, when considering geological uncertainty, the primary goal is to maximize the mean net present value (NPV) over all realizations. However, restricting the search to simply maximizing the mean NPV may be inappropriate or inadequate for decision-making. A more reasonable choice is to maximize the mean NPV while minimizing the spread of the optimal NPV’s obtained for each realization. Therefore, in this work, we apply multi-objective optimization techniques to maximize the mean and minimize the variance of NPV values over all geological realizations to provide robust well placement solutions for decision-makers to select according to their risk attitude towards field development plans.

A candidate reference measurement procedure for quantifying serum concentrations of 25-hydroxyvitamin D₃ and 25-hydroxyvitamin D₂ using isotope-dilution liquid chromatography-tandem mass spectrometry.

The inaccuracy of routine serum 25-hydroxyvitamin D measurements hampers the interpretation of data in patient care and public health research. We developed and validated a candidate reference measurement procedure (RMP) for highly accurate quantitation of two clinically important 25-hydroxyvitamin D metabolites in serum, 25-hydroxyvitamin D2 [25(OH)D2] and 25-hydroxyvitamin D3 [25(OH)D3]. The two compounds of interest together with spiked deuterium-labeled internal standards [d 3-25(OH)D2 and d 6-25(OH)D3] were extracted from serum via liquid-liquid extraction. The featured isotope-dilution LC-MS/MS method used reversed-phase chromatography and atmospheric pressure chemical ionization in positive ion mode. A pentafluorophenylpropyl-packed UHPLC column together with isocratic elution allowed for complete baseline resolution of 25(OH)D2 and 25(OH)D3 from their structural C-3 isomers within 12 min. We evaluated method trueness, precision, potential interferences, matrix effects, limits of quantitation, and measurement uncertainty. Calibration materials were, or were traceable to, NIST Standard Reference Materials 2972. Within-day and total imprecision (CV) averaged 1.9 and 2.0% for 25(OH)D3, respectively, and 2.4 and 3.5% for 25(OH)D2, respectively. Mean trueness was 100.3% for 25(OH)D3 and 25(OH)D2. The limits of quantitation/limits of detection were 4.61/1.38 nmol/L for 25(OH)D3 and 1.46/0.13 nmol/L for 25(OH)D2. When we compared our RMP results to an established RMP using 40 serum samples, we found a nonsignificant mean bias of 0.2% for total 25(OH)D. This candidate RMP for 25(OH)D metabolites meets predefined method performance specifications (≤5% total CV and ≤1.7% bias) and provides sufficient sample throughput to meet the needs of the Centers for Disease Control and Prevention Vitamin D Standardization Certification Program. Graphical abstract Bias assessment using NIST standard reference materials. Legend CDC mean mass fractions (ng/g) ± U 95 (6 replicates per mean). NIST-certified mass fractions (ng/g) ± U 95 from the Certificates of Analysis.

Impacts of rainfall variability and expected rainfall changes on cost-effective adaptation of water systems to climate change

Stormwater drainage and other water systems are vulnerable to changes in rainfall and runoff and need to be adapted to climate change. This paper studies impacts of rainfall variability and changing return periods of rainfall extremes on cost-effective adaptation of water systems to climate change given a predefined system performance target, for example a flood risk standard. Rainfall variability causes system performance estimates to be volatile. These estimates may be used to recurrently evaluate system performance. This paper presents a model for this setting, and develops a solution method to identify cost-effective investments in stormwater drainage adaptations. Runoff and water levels are simulated with rainfall from stationary rainfall distributions, and time series of annual rainfall maxima are simulated for a climate scenario. Cost-effective investment strategies are determined by dynamic programming. The method is applied to study the choice of volume for a storage basin in a Dutch polder. We find that ‘white noise’, i.e. trend-free variability of rainfall, might cause earlier re-investment than expected under projected changes in rainfall. The risk of early re-investment may be reduced by increasing initial investment. This can be cost-effective if the investment involves fixed costs. Increasing initial investments, therefore, not only increases water system robustness to structural changes in rainfall, but could also offer insurance against additional costs that would occur if system performance is underestimated and re-investment becomes inevitable.

Abstract T P261: Stroke Care Improvement Trends in Puerto Rico Hospitals Participating in the Florida-Puerto Rico Collaboration to Reduce Stroke Disparities (FL-PR CReSD) Study - the NINDS Stroke Prevention Intervention Research Program

Background: Racial and ethnic disparities in stroke care are known to contribute to inequality in stroke outcomes. The goal of the FL-PR CReSD Study is to assess and evaluate Get With The Guidelines-Stroke (GWTG-S) quality improvement data collected among Florida and Puerto Rico hospitals to evaluate for race-ethnic, sex, and regional disparities in stroke performance metrics. We sought to analyze the temporal trends in stroke performance metrics among the 9 hospitals located in Puerto Rico from 2010 to 2013. Methods: Age-adjusted temporal trends were evaluated in the following GWTG-S pre-defined performance measures (IV tPA treatment within 3 hours among eligible patients arriving in 2 hours, in-hospital antithrombotic therapy, DVT prophylaxis, antithrombotic therapy at discharge, anticoagulation therapy, statin medication at discharge, and smoking cessation counseling) and defect-free care (compliance with all 7 performance measures). Results: The mean age of 3,094 registered stroke cases was 69.5 ± 14.0 years, 50% were men, and 2,184 (71%) were diagnosed as ischemic strokes. Defect-free care occurred in 60% of ischemic stroke patients. Lower performance metrics were found for IV tPA treatment (78%), DVT prophylaxis (64%), and statin medication at discharge (83%). An additional IV tPA measure of treatment by 4.5 hours when arriving by 3.5 hours was observed in 65% of patients. The performance metrics that significantly improved over time from 2010 to 2013 included: IV tPA treatment within 3 hours (64% to 87%, p=0.04), DVT prophylaxis (33% to 87%, p&lt;0.0001), statin medication at discharge (78% to 87%, p=0.01), and defect-free care (37% to 79%, p&lt;0.0001). Conclusions: In only four years, Puerto Rico hospitals participating in GWTG-S showed significant improvement across all pre-defined acute stroke performance measures. Wider implementation of quality improvement programs like GWTG-S among the 3.7 million inhabitants of Puerto Rico could further improve acute stroke care.