scholarly journals Hand Hygiene in the Era of Big Data: We Can Now See What We Have Been Missing

2020 ◽  
Vol 41 (S1) ◽  
pp. s445-s446
Author(s):  
Megan DiGiorgio ◽  
Lori Moore ◽  
Greg Robbins ◽  
Albert Parker ◽  
James Arbogast
Keyword(s):  
Hand Hygiene ◽  
Direct Observation ◽  
Fixed Effects ◽  
Small Sample Size ◽  
Compliance Rate ◽  
Final Analysis ◽  
Small Sample ◽  
Annual Number ◽  
Data Set ◽  
Cast Doubt

Background: Hand hygiene (HH) has long been a focus in the prevention of healthcare-associated infections. The limitations of direct observation, including small sample size (often 20–100 observations per month) and the Hawthorne effect, have cast doubt on the accuracy of reported compliance rates. As a result, hospitals are exploring the use of automated HH monitoring systems (AHHMS) to overcome the limitations of direct observation and to provide a more robust and realistic estimation of HH behaviors. Methods: Data analyzed in this study were captured utilizing a group-based AHHMS installed in a number of North American hospitals. Emergency departments, overflow units, and units with <1 year of data were excluded from the study. The final analysis included data from 58 inpatient units in 10 hospitals. Alcohol-based hand rub and soap dispenses HH events (HHEs) and room entries and exits (HH opportunities (HHOs) were used to calculate unit-level compliance rates. Statistical analysis was performed on the annual number of dispenses and opportunities using a mixed effects Poisson regression with random effects for facility, unit, and year, and fixed effects for unit type. Interactions were not included in the model based on interaction plots and significance tests. Poisson assumptions were verified with Pearson residual plots. Results: Over the study period, 222.7 million HHOs and 99 million HHEs were captured in the data set. There were an average of 18.7 beds per unit. The average number of HHOs per unit per day was 3,528, and the average number of HHEs per unit per day was 1,572. The overall median compliance rate was 35.2 (95% CI, 31.5%–39.3%). Unit-to-unit comparisons revealed some significant differences: compliance rates for medical-surgical units were 12.6% higher than for intensive care units (P < .0001). Conclusions: This is the largest HH data set ever reported. The results illustrate the magnitude of HHOs captured (3,528 per unit per day) by an AHHMS compared to that possible through direct observation. It has been previously suggested that direct observation samples between 0.5% to 1.7% of all HHOs. In healthcare, it is unprecedented for a patient safety activity that occurs as frequently as HH to not be accurately monitored and reported, especially with HH compliance as low as it is in this multiyear, multicenter study. Furthermore, hospitals relying on direct observation alone are likely insufficiently allocating and deploying valuable resources for improvement efforts based on the scant information obtained. AHHMSs have the potential to introduce a new era in HH improvement.Funding: GOJO Industries, Inc., provided support for this study.Disclosures: Lori D. Moore and James W. Arbogast report salary from GOJO.

scholarly journals Systematic Review and Meta-Analysis of Linezolid and Daptomycin for Treatment of Vancomycin-Resistant Enterococcal Bloodstream Infections

2013 ◽  
Vol 57 (10) ◽  
pp. 5013-5018 ◽  
Author(s):  
Donald W. Whang ◽  
Loren G. Miller ◽  
Neil M. Partain ◽  
James A. McKinnell
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Fixed Effects ◽  
Randomized Clinical Trials ◽  
Small Sample Size ◽  
Statistical Significance ◽  
Bloodstream Infections ◽  
Small Sample ◽  
Antibiotic Selection ◽  
Vancomycin Resistant

ABSTRACTBloodstream infections due to vancomycin-resistant enterococci (VRE-BSI) result in substantial patient mortality and cost. Daptomycin and linezolid are commonly prescribed for VRE-BSI, but there are no clinical trials to determine optimal antibiotic selection. We conducted a systematic review for investigations that compared daptomycin and linezolid for VRE-BSI. We searched Medline from 1966 through 2012 for comparisons of linezolid and daptomycin for VRE-BSI. We included searches of EMBASE, clinicaltrials.gov, and national meetings. Data were extracted using a standardized instrument. Pooled odds ratios (OR) and 95% confidence intervals (95% CI) were calculated using a fixed-effects model. Our search yielded 4,243 publications, of which 482 contained data on VRE treatment. Most studies (452/482) did not present data on BSI or did not provide information on linezolid or daptomycin. Among the remaining 30 studies, 9 offered comparative data between the two agents. None were randomized clinical trials. There was no difference in microbiologic (n= 5 studies, 517 patients; OR, 1.0; 95% CI, 0.4 to 1.7;P= 0.95) and clinical (n= 3 studies, 357 patients; OR, 1.2; 95% CI, 0.7 to 2.0;P= 0.7) cures between the two antibiotics. There was a trend toward increased survival with linezolid compared to daptomycin treatment (n= 9 studies, 1,074 patients; OR, 1.3; 95% CI, 1.1 to 1.8;I2= 0 [whereI2is a measure of inconsistency]), but this did not reach statistical significance (P= 0.054). There are limited data to inform clinicians on optimal antibiotic selection for VRE-BSI. Available studies are limited by small sample size, lack of patient-level data, and inconsistent outcome definitions. Additional research, including randomized clinical trials, is needed before conclusions can be drawn about treatment options for VRE therapy.

A Multi-Linear Statistical Method for Discriminant Analysis of 2D Frontal Face Images

2012 ◽  
pp. 18-33 ◽  
Author(s):  
Carlos Eduardo Thomaz ◽  
Vagner do Amaral ◽  
Gilson Antonio Giraldi ◽  
Edson Caoru Kitani ◽  
João Ricardo Sato ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Face Image ◽  
Small Sample ◽  
High Dimensional ◽  
Data Set ◽  
Linear Discriminant ◽  
Face Images ◽  
Linear Framework ◽  
Facial Changes ◽  
2D Data

This chapter describes a multi-linear discriminant method of constructing and quantifying statistically significant changes on human identity photographs. The approach is based on a general multivariate two-stage linear framework that addresses the small sample size problem in high-dimensional spaces. Starting with a 2D data set of frontal face images, the authors determine a most characteristic direction of change by organizing the data according to the patterns of interest. These experiments on publicly available face image sets show that the multi-linear approach does produce visually plausible results for gender, facial expression and aging facial changes in a simple and efficient way. The authors believe that such approach could be widely applied for modeling and reconstruction in face recognition and possibly in identifying subjects after a lapse of time.

scholarly journals SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data

2020 ◽  
Author(s):  
Xiaoyu Lu ◽  
Szu-Wei Tu ◽  
Wennan Chang ◽  
Changlin Wan ◽  
Jiashi Wang ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Cell Types ◽  
Small Sample ◽  
Training Data ◽  
Mouse Tissue ◽  
Marker Genes ◽  
Specific Cell ◽  
Cell Type ◽  
Data Set ◽  
Transcriptomics Data

Abstract Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD.

scholarly journals The CES1 Gene rs2244613 Minor Allele Impact on the Safety Profile of Dabigatran Etexilate: Meta-Analysis

2020 ◽  
Vol 16 (5) ◽  
pp. 699-705
Author(s):  
S. P. Abdullaev ◽  
K. B. Mirzaev ◽  
P. O. Bochkov ◽  
I. N. Sychev ◽  
D. A. Sychev
Keyword(s):  
Fixed Effects ◽  
Small Sample Size ◽  
Meta Analysis ◽  
Dabigatran Etexilate ◽  
Equilibrium Concentration ◽  
Small Sample ◽  
Heterogeneity Test ◽  
Russian Science Citation Index ◽  
Study Results ◽  
Ces1 Gene

Aim. A meta-analysis of studies on the CES1 gene c.1168-33A>C polymorphism (rs2244613) carriage influence on the equilibrium concentration and the risk of bleeding during dabigatran taking.Material and methods. The search was carried out in the Russian Science Citation Index, Google Academy, Medline PubMed, Embase databases. The meta-analysis included patients who according to the indications (atrial fibrillation, stroke, joint orthopedic surgery) were prescribed dabigatran in various doses. The association was identified in rs2244613 allele C carriers (genotypes AC and CC) and non-carriers (genotype AA). Quantitative synthesis was performed using OpenMetaAnalyst software. In statistical analysis the fixed effects model was used to estimate the influence of the allele C carriage on the any bleeding frequency and the random effects model was used to estimate the influence on the equilibrium plasma concentration level of dabigatran. The homogeneity of the analyzed studies was verified by Cochrane Q-test.Results. The analysis resulted in selection of 5 works matching all meta-analysis inclusion/exclusion criteria. All selected works included 2030 patients in total. The carriage of the rs2246613 allele C was associated with reduction of risk of any bleeding during dabigatran taking (risk ratio [RR] 0.732, 95% confidence interval [CI] 0.629-0.851; p<0.001). The heterogeneity test did not reveal any reliable differences between the study results (Q=2.183; p=0.535). The level of equilibrium residual concentration of dabigatran was not statistically significant lower for the carriers of C allele of the rs2244613 (mean difference -69.324, 95%CI -236.687-98.039; p=0.417). This might be related to the small sample size and the number of studies included in the meta-analysis. The heterogeneity test did not reveal statistically significant differences between studies (Q=0.388; I2=0%, p=0.534).Conclusion. The carriage of minor C allelic variant of rs2244613 reduces the risk of any bleeding during dabigatran taking, however, no significant association with decrease in dabigatran concentration was found.

scholarly journals Deep generative models in DataSHIELD

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Stefan Lenz ◽  
Moritz Hess ◽  
Harald Binder
Keyword(s):  
Genetic Variant ◽  
Small Sample Size ◽  
Synthetic Data ◽  
Routine Data ◽  
Original Data ◽  
Generative Models ◽  
Small Sample ◽  
Generative Adversarial Networks ◽  
Artificial Data ◽  
Data Set

Abstract Background The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients. Methods The DataSHIELD software provides an infrastructure and a set of statistical methods for joint, privacy-preserving analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. Generating artificial data is possible using so-called generative models, which are able to capture the distribution of given data. Here, we employ deep Boltzmann machines (DBMs) as generative models. For the implementation, we use the package “BoltzmannMachines” from the Julia programming language and wrap it for use with DataSHIELD, which is based on R. Results We present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Additionally, we compare DBMs, variational autoencoders, generative adversarial networks, and multivariate imputation as generative approaches by assessing the utility and disclosure of synthetic data generated from real genetic variant data in a distributed setting with data of a small sample size. Conclusions Our implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e.g., for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.

scholarly journals Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Zhihua Wang ◽  
Yongbo Zhang ◽  
Huimin Fu
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Small Sample Size ◽  
Computational Effort ◽  
Small Sample ◽  
Grey Theory ◽  
Data Set ◽  
Rolling Mechanism ◽  
Short Term Forecasting ◽  
Prediction Approach

Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR) prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.

scholarly journals Validity of WebCAAFE questionnaire for assessment of schoolchildren’s dietary compliance with Brazilian Food Guidelines

2016 ◽  
Vol 19 (13) ◽  
pp. 2347-2356 ◽  
Author(s):  
Emil Kupek ◽  
Maria Alice Altenburg de Assis ◽  
France Bellisle ◽  
Adriana Soares Lobo
Keyword(s):  
Public Schools ◽  
Food Intake ◽  
Sample Size ◽  
Direct Observation ◽  
Small Sample Size ◽  
Small Sample ◽  
Reporting Bias ◽  
Food Groups ◽  
Significant Difference ◽  
Moderate Bias

AbstractObjectiveTo estimate reporting bias of WebCAAFE, a web-based questionnaire for the assessment of food intake (recall of frequency of intake of thirty-two food items the day before) and physical activity in schoolchildren.DesignCross-sectional study. Self-reported food intake on WebCAAFE was compared with direct observation of school meals in five public schools. Additional data included school grade, sex, BMI, socio-economic status and access to Internet at home. Poisson regression was used to calculate the reporting bias (WebCAAFE v. direct observation) and the sample size necessary to detect a statistically significant difference between WebCAAFE reports and at least 75 % compliance with the recommendations for a healthy diet.SettingIntentional sample of five elementary public schools in Florianopolis, Brazil.SubjectsSchoolchildren (n 629) from 2nd to 5th grades.ResultsModerate bias magnitude was found for most food groups of interest. Frequency of consumption was not related to the bias. Sample sizes necessary to detect the compliance with dietary recommendations varied between four and seventy-four individuals for the different groups investigated.ConclusionsAfter adjusting for moderate bias, WebCAAFE may be used as a food questionnaire for evaluation of schoolchildren’s food compliance on a group level, even with a relatively small sample size.

scholarly journals Assessment of Business Interruption of Flood-Affected Companies Using Random Forests

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1049 ◽  
Author(s):  
Zakia Sultana ◽  
Tobias Sieg ◽  
Patric Kellermann ◽  
Meike Müller ◽  
Heidi Kreibich
Keyword(s):  
Random Forests ◽  
Small Sample Size ◽  
Economic Loss ◽  
Important Variable ◽  
Small Sample ◽  
Economic Losses ◽  
Data Set ◽  
Variable Importance Measures ◽  
Interruption Costs ◽  
Business Interruption

Losses due to floods have dramatically increased over the past decades, and losses of companies, comprising direct and indirect losses, have a large share of the total economic losses. Thus, there is an urgent need to gain more quantitative knowledge about flood losses, particularly losses caused by business interruption, in order to mitigate the economic loss of companies. However, business interruption caused by floods is rarely assessed because of a lack of sufficiently detailed data. A survey was undertaken to explore processes influencing business interruption, which collected information on 557 companies affected by the severe flood in June 2013 in Germany. Based on this data set, the study aims to assess the business interruption of directly affected companies by means of a Random Forests model. Variables that influence the duration and costs of business interruption were identified by the variable importance measures of Random Forests. Additionally, Random Forest-based models were developed and tested for their capacity to estimate business interruption duration and associated costs. The water level was found to be the most important variable influencing the duration of business interruption. Other important variables, relating to the estimation of business interruption duration, are the warning time, perceived danger of flood recurrence and inundation duration. In contrast, the amount of business interruption costs is strongly influenced by the size of the company, as assessed by the number of employees, emergency measures undertaken by the company and the fraction of customers within a 50 km radius. These results provide useful information and methods for companies to mitigate their losses from business interruption. However, the heterogeneity of companies is relatively high, and sector-specific analyses were not possible due to the small sample size. Therefore, further sector-specific analyses on the basis of more flood loss data of companies are recommended.

The Support Feature Machine: Classification with the Least Number of Features and Application to Neuroimaging Data

2013 ◽  
Vol 25 (6) ◽  
pp. 1548-1584 ◽  
Author(s):  
Sascha Klement ◽  
Silke Anders ◽  
Thomas Martinetz
Keyword(s):  
Feature Selection ◽  
Small Sample Size ◽  
Small Sample ◽  
Biological Data ◽  
Support Vector ◽  
Data Sets ◽  
Universal Method ◽  
Data Set ◽  
Separating Hyperplane ◽  
New Formulation

By minimizing the zero-norm of the separating hyperplane, the support feature machine (SFM) finds the smallest subspace (the least number of features) of a data set such that within this subspace, two classes are linearly separable without error. This way, the dimensionality of the data is more efficiently reduced than with support vector–based feature selection, which can be shown both theoretically and empirically. In this letter, we first provide a new formulation of the previously introduced concept of the SFM. With this new formulation, classification of unbalanced and nonseparable data is straightforward, which allows using the SFM for feature selection and classification in a large variety of different scenarios. To illustrate how the SFM can be used to identify both the smallest subset of discriminative features and the total number of informative features in biological data sets we apply repetitive feature selection based on the SFM to a functional magnetic resonance imaging data set. We suggest that these capabilities qualify the SFM as a universal method for feature selection, especially for high-dimensional small-sample-size data sets that often occur in biological and medical applications.

TIBIAL NERVE PERINEURAL INJECTIONS AT THE POSTERIOR TARSAL TUNNEL

2013 ◽  
Vol 16 (03) ◽  
pp. 1350014
Author(s):  
Oliver C. Joseph ◽  
Oleg Uryasev ◽  
John P. McNamara ◽  
Apostolos P. Dallas
Keyword(s):  
Patient Population ◽  
Tibial Nerve ◽  
Small Sample Size ◽  
Symptomatic Relief ◽  
Small Sample ◽  
Definitive Treatment ◽  
Tarsal Tunnel ◽  
Initial Attempt ◽  
Data Set ◽  
Us Guidance

Introduction: Posterior tarsal tunnel syndrome (PostTTS) refers to compression of the tibial nerve (TN) within this tunnel. PostTTS is most commonly secondary to entrapment with subsequent inflammation. As it is true with other entrapment-type neuropathies, corticosteroids could provide therapeutic relief. To the authors' knowledge, the feasibility of such injections using ultrasound guidance has not been described in the literature. We hypothesize that one can inject the TN perineural space immediately proximal to the posterior tarsal tunnel utilizing ultrasonography US-guidance. Methods: This research was a pilot study using four cadaveric models. US was utilized to image the proximal posterior tarsal tunnel. Perineural injections of methylene blue were performed with subsequent dissection. Injections were designated as accurate (referring to nerve staining) and precise (referring to dye localization). Results: One cadaver was precluded due to pronounced musculoskeletal abnormality. 5-of-6 (83%) injections were accurate and 6-of-6 (100%) precise. Conclusion: Initial attempt was inaccurate and precise, while later injections were both accurate and precise. The most apparent source of error was from one cadaver's pronounced musculoskeletal deformity, which precluded successful injections bilaterally. Of the three cadavers unaffected by musculoskeletal deformity, accuracy was 5-of-6 (83%) and precision was 6-of-6 (100%). While surgery is the definitive treatment for refractory PostTTS, therapeutic effect of corticosteroid injections has not been evaluated in this patient population. Such injections could provide symptomatic relief and postpone surgical intervention. Small sample size not withstanding the results suggest that TN perineural injections are feasible under US-guidance. This study suggests that US-guidance can increase accuracy and precision and is a potential adjunct to the treatment. Future study will expand the initial data set and categorize consistent protocol. Subsequent translational research will then be sought to evaluate therapeutic efficacy in this patient population.

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