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.

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.

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.

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.

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.

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.

Power and sample size for health researchers: uma ferramenta para cálculo de tamanho amostral e poder do teste voltado a pesquisadores da área da saúde / Power and sample size for health researchers: a tool for calculating sample size and statistical power designed for health researchers

Nas próximas edições da seção de Bioestatística da revistaClinical & Biomedical Researchuma nova série de artigos será publicada abordando um assunto de grande importância ao planejar uma pesquisa: o tamanho de amostra mínimo necessário para atingir os objetivos do estudo. Nessa série será apresentado como calcular o tamanho de uma amostra usando a ferramenta PSSHealth(Power and Sample Size for Health Researchers), construído em linguagem R por meio do pacote Shiny, para diferentes tipos e objetivos de estudo, direcionado à pesquisadores da área da saúde, utilizando termos e conceitos comumente utilizados nesta área. Além disso, o pacote fornece uma sugestão de texto com as informações consideradas no cálculo, e como devem ser descritas, com a finalidade de minimizar problemas de interpretação por parte dos pesquisadores. Neste primeiro artigo será apresentada essa ferramenta desenvolvida pela Unidade de Bioestatística do Grupo de Pesquisa e Pós-Graduação do Hospital de Clínicas de Porto Alegre, que permite calcular não apenas o tamanho de amostra, mas também o poder de um teste de hipóteses. (AU)

In the next issues ofClinical and Biomedical Research, the Biostatistics section will introduce a new series of articles addressing a very important subject for research planning: the minimum sample size to achieve the aim of a study. This series will show how to calculate sample size using PSS Health (Power and Sample Size for Health Researchers). This tool was built using R language through the Shiny package. It can be used for different types of study and is designed for health researchers by using terms and concepts commonly used in this area. PSS Health also suggests a text with information considered in the calculation to minimize problems of interpretation by the researchers. In this first article, a general overview of PSS Health will be presented. This tool, which was developed by the Research and Graduate Group Biostatistics Unit of the Hospital de Clínicas de Porto Alegre, is useful not only to calculate sample size but also to determine power of a hypothesis test. (AU)

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.

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.

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.

COMETS2: An advanced MATLAB toolbox for the numerical analysis of electric fields generated by transcranial direct current stimulation.

BACKGROUND: Since there is no way to measure electric current generated by transcranial direct current stimulation (tDCS) inside the human head through in vivo experiments, numerical analysis based on the finite element method has been widely used to estimate the electric field inside the head. In 2013, we released a MATLAB toolbox named COMETS, which has been used by a number of groups and has helped researchers to gain insight into the electric field distribution during stimulation. The aim of this study was to develop an advanced MATLAB toolbox, named COMETS2, for the numerical analysis of the electric field generated by tDCS. NEW METHOD: COMETS2 can generate any sizes of rectangular pad electrodes on any positions on the scalp surface. To reduce the large computational burden when repeatedly testing multiple electrode locations and sizes, a new technique to decompose the global stiffness matrix was proposed. RESULTS: As examples of potential applications, we observed the effects of sizes and displacements of electrodes on the results of electric field analysis. The proposed mesh decomposition method significantly enhanced the overall computational efficiency. COMPARISON WITH EXISTING METHODS: We implemented an automatic electrode modeler for the first time, and proposed a new technique to enhance the computational efficiency. CONCLUSIONS: In this paper, an efficient toolbox for tDCS analysis is introduced (freely available at http://www.cometstool.com). It is expected that COMETS2 will be a useful toolbox for researchers who want to benefit from the numerical analysis of electric fields generated by tDCS.

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.

The Movement Tracker: A Flexible System for Automated Movement Analysis in Invertebrate Model Organisms.

Phenotyping strategies in simple model organisms such as D. melanogaster and C. elegans are often broadly limited to growth, aging, and fitness. Recently, a number of physical setups and video tracking software suites have been developed to allow for accurate, quantitative, and high-throughput analysis of movement in flies and worms. However, many of these systems require precise experimental setups and/or fixed recording formats. We report here an update to the Parallel Worm Tracker software, which we termed the Movement Tracker. The Movement Tracker allows variable experimental setups to provide cross-platform automated processing of a variety of movement characteristics in both worms and flies and permits the use of simple physical setups that can be readily implemented in any laboratory. This software allows high-throughput processing capabilities and high levels of flexibility in video analysis, providing quantitative movement data on C. elegans and D. melanogaster in a variety of different conditions. © 2016 by John Wiley & Sons, Inc.

Using the Electronic Health Record in Nursing Research: Challenges and Opportunities.

Changes in the patient record from the paper to the electronic health record format present challenges and opportunities for the nurse researcher. Current use of data from the electronic health record is in a state of flux. Novel data analytic techniques and massive data sets provide new opportunities for nursing science. Realization of a strong electronic data output future relies on meeting challenges of system use and operability, data presentation, and privacy. Nurse researchers need to rethink aspects of proposal development. Joining ongoing national efforts aimed at creating usable data output is encouraged as a means to affect system design. Working to address challenges and embrace opportunities will help grow the science in a way that answers important patient care questions.

Novel integrative genomic tool for interrogating lithium response in bipolar disorder.

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery.