Bedside estimates of dead space using end-tidal CO2 are independently associated with mortality in ARDS.

PURPOSE: In acute respiratory distress syndrome (ARDS), dead space fraction has been independently associated with mortality. We hypothesized that early measurement of the difference between arterial and end-tidal CO2 (arterial-ET difference), a surrogate for dead space fraction, would predict mortality in mechanically ventilated patients with ARDS. METHODS: We performed two separate exploratory analyses. We first used publicly available databases from the ALTA, EDEN, and OMEGA ARDS Network trials (N = 124) as a derivation cohort to test our hypothesis. We then performed a separate retrospective analysis of patients with ARDS using University of Chicago patients (N = 302) as a validation cohort. RESULTS: The ARDS Network derivation cohort demonstrated arterial-ET difference, vasopressor requirement, age, and APACHE III to be associated with mortality by univariable analysis. By multivariable analysis, only the arterial-ET difference remained significant (P = 0.047). In a separate analysis, the modified Enghoff equation ((PaCO2-PETCO2)/PaCO2) was used in place of the arterial-ET difference and did not alter the results. The University of Chicago cohort found arterial-ET difference, age, ventilator mode, vasopressor requirement, and APACHE II to be associated with mortality in a univariate analysis. By multivariable analysis, the arterial-ET difference continued to be predictive of mortality (P = 0.031). In the validation cohort, substitution of the arterial-ET difference for the modified Enghoff equation showed similar results. CONCLUSION: Arterial to end-tidal CO2 (ETCO2) difference is an independent predictor of mortality in patients with ARDS.

A Novel Method to Estimate the Full Knee Joint Kinematics Using Low Cost IMU Sensors for Easy to Implement Low Cost Diagnostics.

Traditional motion capture systems are the current standard in the assessment of knee joint kinematics. These systems are, however, very costly, complex to handle, and, in some conditions, fail to estimate the varus/valgus and internal/external rotation accurately due to the camera setup. This paper presents a novel and comprehensive method to infer the full relative motion of the knee joint, including the flexion/extension, varus/valgus, and internal/external rotation, using only low cost inertial measurement units (IMU) connected to the upper and lower leg. Furthermore, sensors can be placed arbitrarily and only require a short calibration, making it an easy-to-use and portable clinical analysis tool. The presented method yields both adequate results and displays the uncertainty band on those results to the user. The proposed method is based on an fixed interval smoother relying on a simple dynamic model of the legs and judicially chosen constraints to estimate the rigid body motion of the leg segments in a world reference frame. In this pilot study, benchmarking of the method on a calibrated robotic manipulator, serving as leg analogue, and comparison with camera-based techniques confirm the method's accurateness as an easy-to-implement, low-cost clinical tool.

Diagnosing Postpartum Hemorrhage: A New Way to Assess Blood Loss in a Low-Resource Setting.

Introduction Postpartum hemorrhage (PPH) is the leading cause of maternal mortality worldwide. The largest barriers to treating PPH are symptom recognition and timely diagnosis. The SAPHE (Signaling a Postpartum Hemorrhage Emergency) Mat was constructed so that each square on the Mat absorbs up to 50 mL of blood. The objective of this study was to evaluate the correlation of visually estimated blood loss (EBL) using the SAPHE Mat with actual blood loss. Methods Thirty-six patients gave birth via vaginal delivery using the SAPHE Mat. Visual estimation of blood loss using the SAPHE Mat was calculated by multiplying the number of blood- saturated squares or partial squares by 50 mL. The visual EBL was compared with the actual blood loss calculated based on Mat weight before and after use (volume blood loss). Results Visual blood loss estimations were within 100 mL of the volume blood loss 69 % of the time and within 200 mL 97 % of the time. The mean difference between the visual EBL and volume blood loss (Mat weight change) was 80.91 mL. The Pearson correlation coefficient for visual EBL and volume blood loss was positive at 0.96 (p < 0.001). Discussion The SAPHE Mat is able to provide a visual estimate of blood loss that is highly correlated with the actual blood loss on the mat. Future studies will assess the ability to deploy the SAPHE Mat in low-resource settings as a potential guide for estimating blood loss to assist in improved management of PPH.

Geospatial analysis and distribution patterns of home nursing care in a metropolitan area - a large-scale analysis.

AIMS: This study focuses on home nursing care distribution in an urban setting in Germany. BACKGROUND: A shortage of nursing care workforce is present in Germany. METHODS: A geospatial analysis was performed to examine distribution patterns at the district level in Frankfurt, Germany (n = 46 districts) and factors were analysed influencing the location choice of home nursing care providers (n = 151). Furthermore, within the analysis we focused on the population aged over 65 years to model the demand for nursing care. RESULTS: The analysis revealed a tendency of home nursing care providers to be located near the city centre (centripetal distribution pattern). However, the demand for care showed more inconsistent patterns. Still, a centripetal distribution pattern of demand could be stated. Compared with the control groups (e.g. acute hospitals and pharmacies) similar geographical distribution patterns were present. However, the location of home nursing care providers was less influenced by demand compared with the control groups. CONCLUSION: The supply of nursing care was unevenly distributed in this metropolitan setting, but still matched the demand for nursing care. IMPLICATION FOR NURSING MANAGEMENT: Due to the rapidly changing health care environments policy, regulations must be (re-)evaluated critically to improve the management and delivery of nursing care provision.

Modeling the Declining Positivity Rates for Human Immunodeficiency Virus Testing in New York State.

OBJECTIVE: New York health care providers have experienced declining percentages of positive human immunodeficiency virus (HIV) tests among patients. Furthermore, observed positivity rates are lower than expected on the basis of the national estimate that one-fifth of HIV-infected residents are unaware of their infection. We used mathematical modeling to evaluate whether this decline could be a result of declining numbers of HIV-infected persons who are unaware of their infection, a measure that is impossible to measure directly. DESIGN AND SETTING: A stock-and-flow mathematical model of HIV incidence, testing, and diagnosis was developed. The model includes stocks for uninfected, infected and unaware (in 4 disease stages), and diagnosed individuals. Inputs came from published literature and time series (2006-2009) for estimated new infections, newly diagnosed HIV cases, living diagnosed cases, mortality, and diagnosis rates in New York. MAIN OUTCOME MEASURES: Primary model outcomes were the percentage of HIV-infected persons unaware of their infection and the percentage of HIV tests with a positive result (HIV positivity rate). RESULTS: In the base case, the estimated percentage of unaware HIV-infected persons declined from 14.2% in 2006 (range, 11.9%-16.5%) to 11.8% in 2010 (range, 9.9%-13.1%). The HIV positivity rate, assuming testing occurred independent of risk, was 0.12% in 2006 (range, 0.11%-0.15%) and 0.11% in 2010 (range, 0.10%-0.13%). The observed HIV positivity rate was more than 4 times the expected positivity rate based on the model. CONCLUSIONS: HIV test positivity is a readily available indicator, but it cannot distinguish causes of underlying changes. Findings suggest that the percentage of unaware HIV-infected New Yorkers is lower than the national estimate and that the observed HIV test positivity rate is greater than expected if infected and uninfected individuals tested at the same rate, indicating that testing efforts are appropriately targeting undiagnosed cases.

A comprehensive method for GNSS data quality determination to improve ionospheric data analysis.

Global Navigation Satellite Systems (GNSS) are now recognized as cost-effective tools for ionospheric studies by providing the global coverage through worldwide networks of GNSS stations. While GNSS networks continue to expand to improve the observability of the ionosphere, the amount of poor quality GNSS observation data is also increasing and the use of poor-quality GNSS data degrades the accuracy of ionospheric measurements. This paper develops a comprehensive method to determine the quality of GNSS observations for the purpose of ionospheric studies. The algorithms are designed especially to compute key GNSS data quality parameters which affect the quality of ionospheric product. The quality of data collected from the Continuously Operating Reference Stations (CORS) network in the conterminous United States (CONUS) is analyzed. The resulting quality varies widely, depending on each station and the data quality of individual stations persists for an extended time period. When compared to conventional methods, the quality parameters obtained from the proposed method have a stronger correlation with the quality of ionospheric data. The results suggest that a set of data quality parameters when used in combination can effectively select stations with high-quality GNSS data and improve the performance of ionospheric data analysis.

A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti.

BACKGROUND: Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. METHODS: Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. RESULTS: Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these "hotspots". CONCLUSIONS: Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations.

Front-crawl instantaneous velocity estimation using a wearable inertial measurement unit.

Monitoring the performance is a crucial task for elite sports during both training and competition. Velocity is the key parameter of performance in swimming, but swimming performance evaluation remains immature due to the complexities of measurements in water. The purpose of this study is to use a single inertial measurement unit (IMU) to estimate front crawl velocity. Thirty swimmers, equipped with an IMU on the sacrum, each performed four different velocity trials of 25 m in ascending order. A tethered speedometer was used as the velocity measurement reference. Deployment of biomechanical constraints of front crawl locomotion and change detection framework on acceleration signal paved the way for a drift-free integration of forward acceleration using IMU to estimate the swimmers velocity. A difference of 0.6 ± 5.4 cm · s(-1) on mean cycle velocity and an RMS difference of 11.3 cm · s(-1) in instantaneous velocity estimation were observed between IMU and the reference. The most important contribution of the study is a new practical tool for objective evaluation of swimming performance. A single body-worn IMU provides timely feedback for coaches and sport scientists without any complicated setup or restraining the swimmer's natural technique.

Smart query answering for marine sensor data.

We review existing query answering systems for sensor data. We then propose an extended query answering approach termed smart query, specifically for marine sensor data. The smart query answering system integrates pattern queries and continuous queries. The proposed smart query system considers both streaming data and historical data from marine sensor networks. The smart query also uses query relaxation technique and semantics from domain knowledge as a recommender system. The proposed smart query benefits in building data and information systems for marine sensor networks.

Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study.

The accurate prediction of likely discharges and estimates of length of stay (LOS) aid in effective hospital administration and help to prevent access block. Machine learning (ML) may be able to help with these tasks. For consecutive patients admitted under General Medicine at the Royal Adelaide Hospital over an 8-month period, daily ward round notes and relevant discrete data fields were collected from the electronic medical record. These data were then split into training and testing sets (7-month/1-month train/test split) prior to use in ML analyses aiming to predict discharge within the next 2 days, discharge within the next 7 days and an estimated date of discharge (EDD). Artificial neural networks and logistic regression were effective at predicting discharge within 48 h of a given ward round note. These models achieved an area under the receiver operator curve (AUC) of 0.80 and 0.78, respectively. Prediction of discharge within 7 days of a given note was less accurate, with artificial neural network returning an AUC of 0.68 and logistic regression an AUC of 0.61. The generation of an exact EDD remains inaccurate. This study has shown that repeated estimates of LOS using daily ward round notes and mixed-data inputs are effective in the prediction of general medicine discharges in the next 48 h. Further research may seek to prospectively and externally validate models for prediction of upcoming discharge, as well as combination human-ML approaches for generating EDDs.

Improved individual and population-level HbA1c estimation using CGM data and patient characteristics.

Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by COVID-19.

Estimating Costs of an Implementation Intervention.

Health care systems frequently have to decide whether to implement interventions designed to reduce gaps in the quality of care. A lack of information on the cost of these interventions is often cited as a barrier to implementation. In this article, we describe methods for estimating the cost of implementing a complex intervention. We review methods related to the direct measurement of labor, supplies and space, information technology, and research costs. We also discuss several issues that affect cost estimates in implementation studies, including factor prices, fidelity, efficiency and scale of production, distribution, and sunk costs. We examine case studies for stroke and depression, where evidence-based treatments exist and yet gaps in the quality of care remain. Understanding the costs for implementing strategies to reduce these gaps and measuring them consistently will better inform decision makers about an intervention's likely effect on their budget and the expected costs to implement new interventions.

Assessing Bias in Population Size Estimates Among Hidden Populations When Using the Service Multiplier Method Combined With Respondent-Driven Sampling Surveys: Survey Study.

BACKGROUND: Population size estimates (PSEs) for hidden populations at increased risk of HIV, including female sex workers (FSWs), are important to inform public health policy and resource allocation. The service multiplier method (SMM) is commonly used to estimate the sizes of hidden populations. We used this method to obtain PSEs for FSWs at 9 sites in Zimbabwe and explored methods for assessing potential biases that could arise in using this approach. OBJECTIVE: This study aimed to guide the assessment of biases that arise when estimating the population sizes of hidden populations using the SMM combined with respondent-driven sampling (RDS) surveys. METHODS: We conducted RDS surveys at 9 sites in late 2013, where the Sisters with a Voice program (the program), which collects program visit data of FSWs, was also present. Using the SMM, we obtained PSEs for FSWs at each site by dividing the number of FSWs who attended the program, based on program records, by the RDS-II weighted proportion of FSWs who reported attending this program in the previous 6 months in the RDS surveys. Both the RDS weighting and SMM make a number of assumptions, potentially leading to biases if the assumptions are not met. To test these assumptions, we used convergence and bottleneck plots to assess seed dependence of RDS-II proportion estimates, chi-square tests to assess if there was an association between the characteristics of FSWs and their knowledge of program existence, and logistic regression to compare the characteristics of FSWs attending the program with those recruited to RDS surveys. RESULTS: The PSEs ranged from 194 (95% CI 62-325) to 805 (95% CI 456-1142) across 9 sites from May to November 2013. The 95% CIs for the majority of sites were wide. In some sites, the RDS-II proportion of women who reported program use in the RDS surveys may have been influenced by the characteristics of selected seeds, and we also observed bottlenecks in some sites. There was no evidence of association between characteristics of FSWs and knowledge of program existence, and in the majority of sites, there was no evidence that the characteristics of the populations differed between RDS and program data. CONCLUSIONS: We used a series of rigorous methods to explore potential biases in our PSEs. We were able to identify the biases and their potential direction, but we could not determine the ultimate direction of these biases in our PSEs. We have evidence that the PSEs in most sites may be biased and a suggestion that the bias is toward underestimation, and this should be considered if the PSEs are to be used. These tests for bias should be included when undertaking population size estimation using the SMM combined with RDS surveys.

The Pre-Hospital Initial Fluid Therapy Estimate in Early Nasty Burns (PHIFTEEN B, 15-B) Guideline applied to a retrospective cohort of Intensive Care Unit patients with major burns.

BACKGROUND: Appropriate fluid administration in severe burns is a cornerstone of early burns management. The American Burns Association's (ABA) recommendation is to administer 2 mL-4 mL × burnt Body Surface Area (BSA) × weight in the first 24 h with half administered in the first eight hours. Unfortunately, the calculations involved are complex and clinicians do not estimate the BSA or weight well, which can lead to errors in the amount of fluid administered. To simplify cognitive load to calculate the fluid resuscitation of early burns, the investigators derived the PHIFTEEN B (15-B) guideline. The 15-B guideline estimates the initial hourly fluid for adults ≥ 50 kg to be: 15 mL × BSA (to the nearest 10%) AIMS: To model and determine the accuracy of the 15-B calculated based on the characteristics of a retrospective cohort of patients admitted with ≥ 20% BSA to the Royal Brisbane and Women's Hospital (RBWH) Intensive Care Unit (ICU). METHODS: The 15-B formula was retrospectively calculated on the prehospital BSA estimate on patients admitted to the RBWH ICU. In addition, the 15-B guideline was modelled against a variety of weights and BSAs. The fluid volume was deemed to be clinically significant if it was greater than 250 mL/h outside the ABA's recommendations. RESULTS: The ICU cohort consisted of 107 patients (63.2% male, median age 37 years), with a median ICU estimated BSA of 40% and a median ICU weight estimation of 80 kg. In 43.9% of the cohort, the magnitude of the proportional difference between prehospital and ICU BSA estimate was greater than 25%. The 15-B formula accurately estimated the hourly fluid for all BSA (20%-100%) and weight combinations (50 kg-140 kg) in a BSA- weight matrix. When prehospital BSA estimate was utilized, 15-B guideline accurately estimated the fluid to be given within clinically significant limits for 97.2% of cases. CONCLUSIONS: The 15-B formula is a simple, easy to calculate guideline which approximates the early fluid estimates in severely burned patients despite inaccuracy in prehospital BSA estimates.

Web server suite for complex mixture analysis by covariance NMR.

Elucidation of the chemical composition of biological samples is a main focus of systems biology and metabolomics. Their comprehensive study requires reliable, efficient, and automatable methods to identify and quantify the underlying metabolites. Because nuclear magnetic resonance (NMR) spectroscopy is a rich source of molecular information, it has a unique potential for this task. Here we present a suite of public web servers (http://spinportal.magnet.fsu.edu), termed COLMAR, which facilitates complex mixture analysis by NMR. The COLMAR web portal presently consists of three servers: COLMAR covariance calculates the covariance NMR spectrum from an NMR input dataset, such as a TOCSY spectrum; COLMAR DemixC method decomposes the 2D covariance TOCSY spectrum into a reduced set of nonredundant 1D cross sections or traces, which belong to individual mixture components; and COLMAR query screens the traces against a NMR spectral database to identify individual compounds. Examples are presented that illustrate the utility of this web server suite for complex mixture analysis.

Relative Validity of a Method Based on a Smartphone App (Electronic 12-Hour Dietary Recall) to Estimate Habitual Dietary Intake in Adults.

BACKGROUND: Accurate dietary assessment is key to understanding nutrition-related outcomes and for estimating the dietary change in nutrition-based interventions. When researching the habitual consumption of selected food groups, it is essential to be aware of factors that could possibly affect reporting accuracy. OBJECTIVE: This study aimed to evaluate the relative validity of the current-day dietary recall, a method based on a smartphone app called electronic 12-hour dietary recall (e-12HR), to categorize individuals according to habitual intake, in the whole sample of adults and in different strata thereof. METHODS: University students and employees over 18 years recorded the consumption of 10 selected groups of food using e-12HR during 28 consecutive days. During this period, they also completed 4 dietary records. Once the period was finished, the subjects then completed a food frequency questionnaire (FFQ) and a usability-rating questionnaire for e-12HR. The food group intakes estimated by the e-12HR app, the dietary records, and the FFQ were categorized into sextiles: less than once a week, once or twice a week, 3-4 times a week, 5-6 times a week, once or twice a day, and 3 or more times a day. The 10 selected groups with e-12HR were compared with 4 dietary records and an FFQ reference method, in the whole sample and in different strata thereof: age (years): <25 and ≥25; gender: females and males; occupation: students and employees; smoking: no and yes; physical activity (minutes/week): ≥150 and <150; and body mass index (kg/m2): <25 and ≥25. The association between the different methods was assessed using Spearman correlation coefficient (SCC). Cross-classification and kappa statistic were used as a measure of agreement between the different methods. RESULTS: In total, 203 participants completed the study (56.7% [115/203] women, and 43.3% [88/203] men). For all food groups and all participants, the mean SCC for e-12HR versus FFQ was 0.67 (≥0.62 for all strata). On average, 50.7% of participants were classified into the same category (≥47.0% for all strata) and 90.2% within the nearest category (≥88.6% for all strata). Mean weighted kappa was 0.49 (≥0.44 for all strata). For e-12HR versus RDs, mean SCC was 0.65 (≥0.57 for all strata). On average, 50.0% of participants were classified into the same category (≥47.0% for all strata) and 88.2% within the nearest category (≥86.1% for all strata). Mean weighted kappa was 0.50 (≥0.44 for all strata). CONCLUSIONS: The results indicate that e-12HR generated categories of dietary intake highly comparable with the 2 reference methods in the whole sample and in different strata thereof. The inclusion of photographs to facilitate estimation of the servings consumed generated correlation/agreement data between e-12HR and the FFQ that were similar to a previous study using an older version of the app, which did not include photographs.

BSMART: a Matlab/C toolbox for analysis of multichannel neural time series.

We have developed a Matlab/C toolbox, Brain-SMART (System for Multivariate AutoRegressive Time series, or BSMART), for spectral analysis of continuous neural time series data recorded simultaneously from multiple sensors. Available functions include time series data importing/exporting, preprocessing (normalization and trend removal), AutoRegressive (AR) modeling (multivariate/bivariate model estimation and validation), spectral quantity estimation (auto power, coherence and Granger causality spectra), network analysis (including coherence and causality networks) and visualization (including data, power, coherence and causality views). The tools for investigating causal network structures in respect of frequency bands are unique functions provided by this toolbox. All functionality has been integrated into a simple and user-friendly graphical user interface (GUI) environment designed for easy accessibility. Although we have tested the toolbox only on Windows and Linux operating systems, BSMART itself is system independent. This toolbox is freely available (http://www.brain-smart.org) under the GNU public license for open source development.

Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring.

While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.

Using Technology to Support Data Analytics: A Chief Nursing Officer Accountability Scorecard.

Accountability for health system improvement is a requirement for nursing leadership in practice. However, to be accountable for organizational goals, nurse leaders need the tools to identify gaps in their microsystems of care. The purpose of this article was to provide a case study example of chief nursing officer (CNO) leadership in using a technology solution to develop a CNO accountability scorecard. This project highlights the HQ Essential for data analytics using an innovative technological approach to drive improvement at the front line of clinical care.

Setting strategy of delay-optimization-oriented SMAC contention window size.

Some frame components, such as SYNC (frame synchronization) and RTS/CTS (Ready to Send/Clear to Send), are not taken into consideration when the traditional setting strategies conduct the optimization of SMAC (Sensor MAC) contention window size. This paper proposes mathematical models that allow the analysis of data packets forwarding delay within one SMAC virtual cluster. Simulation results in OMNeT++ show good agreements with the proposed mathematical models, validating the models' correctness. The curve analyses of the models confirm the existence of delay-optimization-oriented contention window size that is closely related to the number of simultaneously contending nodes. Afterwards, it is shown that SYNC, RTS/CTS and EIFS (Extended InterFrame Space) have impacts on the optimal contention window size and expected delivery delay to various degrees, as well as throughput and energy efficiency. One ideal setting strategy of delay-optimization-oriented SMAC contention window size requires the combination of the network scale, SYNC, RTS/CTS and EIFS. Additionally, it is demonstrated that the proposed setting strategy makes contributions to the improvement in the existing SMAC extensions when they are integrated with each other, in terms of the end-to-end delay, throughput and energy consumption.