Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values.

BACKGROUND: A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. RESULTS: We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients. CONCLUSION: SOHPIE-DNA is the first attempt of introducing the regression framework for the DN analysis in microbiome data. This enables the prediction of characteristics of a connectivity of a network with the presence of additional covariate information in the regression. The R package with a vignette of our methodology is available through the CRAN repository ( https://CRAN.R-project.org/package=SOHPIE ), named SOHPIE (pronounced as Sofie). The source code and user manual can be found at https://github.com/sjahnn/SOHPIE-DNA .

A Behavioral Economic Examination of Sexual Behaviors in the Era of Pre-exposure Prophylaxis via Explanatory Sequential Mixed Methods.

Pre-exposure prophylaxis (PrEP) use may be associated with condom use decisions. The current investigation examined sexual decision-making in the context of PrEP among young adult men who have sex with men (MSM) between 18 and 30 years old, using an explanatory sequential mixed methods design. For the quantitative aim, 99 MSM currently taking PrEP (i.e., PrEP-experienced) and 140 MSM not currently taking PrEP (i.e., PrEP-naive) completed an online survey, including the Sexual Delay Discounting Task (SDDT), which captures likelihood of condom use. For the qualitative aim, 15 people from each group were interviewed about their (1) conceptualizations of risky sex and (2) ways they manage their sexual risk. Participants were, on average, 25.69 years old (SD = 3.07) and 64% White. Results from the quantitative aim revealed, controlling for covariates, PrEP-experienced participants exhibited significantly lower likelihood of (1) using an immediately available condom and (2) waiting for a delayed condom (i.e., sexual delay discounting) compared to PrEP-naive participants. Qualitative themes explaining what young adult MSM consider to be risky sex included: (1) any sex as risky sex, (2) risky sex as "sex without a conversation," and (3) risky sex as sex with risk for physical harm. Themes on ways young adult MSM manage sexual risk were classified as proactive, reactive, and passive. Results suggest that PrEP use is related to condom use decisions. Taken together, quantitative differences in sexual delay discounting, but qualitatively similar conceptualizations and management of risky sex, suggest that the SDDT may be a useful tool in sex research to capture processes (i.e., delay discounting) underlying sexual decision-making that may be missed by traditional self-reports. Implications of results, including potentially providing (good quality) condoms with every PrEP prescription, and future research topics are discussed.

SOHPIE: statistical approach via pseudo-value information and estimation for differential network analysis of microbiome data.

SUMMARY: The SOHPIE R package implements a novel functionality for "multivariable" differential co-abundance network (DN, hereafter) analyses of microbiome data. It incorporates a regression approach that adjusts for additional covariates for DN analyses. This distinguishes from previous prominent approaches in DN analyses such as MDiNE and NetCoMi which do not feature a covariate adjustment of finding taxa that are differentially connected (DC, hereafter) between individuals with different clinical and phenotypic characteristics. AVAILABILITY AND IMPLEMENTATION: SOHPIE with a vignette is available on CRAN repository https://CRAN.R-project.org/package=SOHPIE and published under General Public License (GPL) version 3 license.

PRANA: an R package for differential co-expression network analysis with the presence of additional covariates.

BACKGROUND: Advances in sequencing technology and cost reduction have enabled an emergence of various statistical methods used in RNA-sequencing data, including the differential co-expression network analysis (or differential network analysis). A key benefit of this method is that it takes into consideration the interactions between or among genes and do not require an established knowledge in biological pathways. As of now, none of existing softwares can incorporate covariates that should be adjusted if they are confounding factors while performing the differential network analysis. RESULTS: We develop an R package PRANA which a user can easily include multiple covariates. The main R function in this package leverages a novel pseudo-value regression approach for a differential network analysis in RNA-sequencing data. This software is also enclosed with complementary R functions for extracting adjusted p-values and coefficient estimates of all or specific variable for each gene, as well as for identifying the names of genes that are differentially connected (DC, hereafter) between subjects under biologically different conditions from the output. CONCLUSION: Herewith, we demonstrate the application of this package in a real data on chronic obstructive pulmonary disease. PRANA is available through the CRAN repositories under the GPL-3 license: https://cran.r-project.org/web/packages/PRANA/index.html .

Identification of shared biological features in four different lung cell lines infected with SARS-CoV-2 virus through RNA-seq analysis.

The COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of confirmed cases and deaths worldwide. Understanding the biological mechanisms of SARS-CoV-2 infection is crucial for the development of effective therapies. This study conducts differential expression (DE) analysis, pathway analysis, and differential network (DN) analysis on RNA-seq data of four lung cell lines, NHBE, A549, A549.ACE2, and Calu3, to identify their common and unique biological features in response to SARS-CoV-2 infection. DE analysis shows that cell line A549.ACE2 has the highest number of DE genes, while cell line NHBE has the lowest. Among the DE genes identified for the four cell lines, 12 genes are overlapped, associated with various health conditions. The most significant signaling pathways varied among the four cell lines. Only one pathway, "cytokine-cytokine receptor interaction", is found to be significant among all four cell lines and is related to inflammation and immune response. The DN analysis reveals considerable variation in the differential connectivity of the most significant pathway shared among the four lung cell lines. These findings help to elucidate the mechanisms of SARS-CoV-2 infection and potential therapeutic targets.

Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema.

In the current practice, an essential element of safety management systems, Job Hazard Analysis (JHA), is performed manually, relying on the safety personnel's experiential knowledge and observations. This research was conducted to create a new ontology that comprehensively represents the JHA knowledge domain, including the implicit knowledge. Specifically, 115 actual JHA documents and interviews with 18 JHA domain experts were analyzed and used as the source of knowledge for creating a new JHA knowledge base, namely the Job Hazard Analysis Knowledge Graph (JHAKG). To ensure the quality of the developed ontology, a systematic approach to ontology development called METHONTOLOGY was used in this process. The case study performed for validation purposes demonstrates that a JHAKG can operate as a knowledge base that answers queries regarding hazards, external factors, level of risks, and appropriate control measures to mitigate risks. As the JHAKG is a database of knowledge representing a large number of actual JHA cases previously developed and also implicit knowledge that has not been formalized in any explicit forms yet, the quality of JHA documents produced from queries to the database is expectedly higher than the ones produced by an individual safety manager in terms of completeness and comprehensiveness.

Differential Co-Abundance Network Analyses for Microbiome Data Adjusted for Clinical Covariates Using Jackknife Pseudo-Values.

A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients.

A pseudo-value regression approach for differential network analysis of co-expression data.

BACKGROUND: The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a pseudo-value regression approach for network analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information criteria, followed by pseudo-value calculations, which are then entered into a robust regression model. RESULTS: This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus database to identify DC genes that are associated with chronic obstructive pulmonary disease to demonstrate its utility. CONCLUSION: To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis.

Reducing Unnecessary 'Admission' Chest X-rays: An Initiative to Minimize Low-Value Care.

Introduction Internal medicine admission services often request a baseline admission chest X-ray (CXR) for patients already admitted to the emergency department (ED) and who are waiting for inpatient beds, despite rarely providing clinical value. Adverse consequences of such CXRs include unnecessary radiation exposure, cost, time, and false positives, which can trigger a diagnostic cascade. Extraneous CXRs performed on already-admitted ED patients can delay inpatient transfer, thereby increasing boarding and crowding, which in turn may affect mortality and satisfaction. In 2016, our ED and internal medicine hospitalist services implemented guidelines (reflecting those of the American College of Radiology) to reduce unnecessary admission CXRs. All relevant providers were educated on the guideline. The primary aim of this study was to determine if there were changes in the percentage of patients with pre-admission and admission CXRs following guideline implementation. Our secondary aim was to determine which patient characteristics predict getting a CXR. Methods All ED and internal medicine hospitalist providers were educated once about the guideline. We performed a retrospective analysis of pre- vs. post-guideline data. Patients were included if admitted to the internal medicine service during those timeframes with an admission diagnosis unrelated to the cardiac or pulmonary systems. A CXR performed during ED evaluation prior to the admission disposition time was recorded as "pre-admission," and if performed after disposition time it was recorded as "admission." A CXR was "unwarranted" if the admission diagnosis did not suggest a CXR was necessary. The numerator was the number of unnecessary admission CXRs ordered on patients with diagnoses unrelated to the cardiac or pulmonary systems (minus those with a pre-admission CXR); the denominator was the number of such admissions (minus those with a pre-admission CXR). Variables of interest that might influence whether a CXR was ordered were age, gender, respiratory rate ≥20, cardiac- or pulmonary-related chief complaint, ED diagnosis category, or past medical history.  Results Among admitted patients with diagnoses that did not suggest a CXR was warranted, there was no change in the percentage of admission CXRs (21.7% to 25.6%, p = 0.2678), whereas the percentage with pre-admission CXRs decreased (66.6% to 60.7%, p = 0.0152). This decrease was driven by fewer CXRs being performed on patients whose chief complaint did not suggest one was indicated (p = .0121). In multivariate analysis, risk factors for an unwarranted CXR were age >40 (risk ratio (RR) = 2.9) and past medical history of cardiovascular disease (e.g., myocardial infarction, atrial fibrillation), renal disease, or hyperkalemia.  Conclusion This educational initiative was not associated with the intended decrease in ordering unwarranted admission CXRs among ED boarding patients, though there was an unanticipated decrease in pre-admission CXRs. This decrease was driven by fewer CXRs being performed on patients whose chief complaint did not suggest one was indicated. Organizations interested in reducing processes with little clinical value might adopt a similar program while emphasizing the lack of benefit to admitted patients through iterative educational programs on hospital admitting services.

Transfusion in Root Replacement for Aortic Dissection: The STS Adult Cardiac Surgery Database Analysis.

BACKGROUND: Transfusion in acute aortic syndromes has been studied in a limited fashion. We sought to describe contemporary transfusion practice for root replacement in acute (Stanford) type A aortic dissection. METHODS: The Society of Thoracic Surgeons Adult Cardiac Surgery Database was interrogated to identify patients who underwent primary aortic root replacement for acute (Stanford) type A aortic dissection (July 2014 to June 2017). Patients (n = 1558) were stratified by type of root replacement. Multivariate regression was used to determine those variables associated with transfusion and postoperative morbidity. RESULTS: Transfusion was required in 90.5% of cases (n = 1410). Operative mortality for all patients was 17.3% (261 deaths). Intraoperative red blood cell transfusion portended reduced short-term survival (odds ratio [OR] 2.00, P = .025). Massive postoperative transfusion was associated with prolonged ventilation (OR 13.47, P < .001), sepsis (OR 4.13, P < .001), and new dialysis-dependent renal failure (OR 2.43, P < .001). Women were more likely to require transfusion (OR 3.03, P < .001), as were patients who had coronary artery bypass (OR 1.57, P = .009), and those in shock (OR 2.27, P < .001). Valve-sparing aortic root replacement was associated with reduced transfusion requirements vs composite roots. Institutional case volume was not appreciably correlated with transfusion. CONCLUSIONS: Most patients undergoing root replacement for aortic dissection require blood products. Composite root replacement is associated with a greater likelihood of transfusion than a valve-sparing operation. Transfusion independently foreshadows greater operative mortality.

Impact of Treatment Parameters on Racial Survival Differences in Oropharyngeal Cancer: National Cancer Database Study.

OBJECTIVE: To investigate how differences in treatment parameters account for survival differences between races of patients with oropharyngeal squamous cell carcinoma (OPSCC). STUDY DESIGN: Retrospective cohort study. SETTING: National Cancer Database. METHODS: Data of patients with OPSCC undergoing radiation therapy (RT) or concurrent chemoradiation therapy as primary treatment were obtained from the National Cancer Database from 2004 to 2016. We analyzed 4 treatment-related time intervals to determine their impact on survival between races when controlling for human papilloma virus (HPV) status. Cox proportional hazards models, stepwise logistic regressions, covariate adjustments, and propensity score matching were performed. RESULTS: A total of 3152 patients were identified (2877 White, 275 Black). In HPV- cases, Black patients with prolonged radiation duration had a significantly worse overall survival as compared with White patients (hazard ratio, 1.77; 95% CI, 1.03-3.05; P = .039). In a logistic regression model, the only covariate that was significantly associated with prolonged RT was facility type. When further adjusted for facility type, the survival difference between Black and White patients with HPV- status and prolonged RT times was no longer significant (hazard ratio, 1.55; 95% CI, 0.90-2.69; P = .116). CONCLUSIONS: There is a significant disparity in overall survival between Black and White patients with HPV- OPSCC when RT duration is prolonged. Clinicians should be aware of the negative impact of prolonged RT, especially in Black patients, so that they can attempt to decrease treatment-related time intervals. Facility type was also found to affect the outcomes of patients with OPSCC, and efforts should be made to improve patient access to well-equipped, high-volume facilities.

Reining in Unnecessary Admission EKGs: A Successful Interdepartmental High-Value Care Initiative.

Introduction Unnecessary "admission electrocardiograms (EKGs)" on admitted patients waiting ("boarding") in the emergency department (ED) are often ordered. We introduced evidence-based EKG ordering guidelines and determined changes in the percent of patients with "preadmission" and "admission" EKGs ordered before vs. after guideline introduction and which patient characteristics predicted EKG ordering. Methods In 2016, our ED, cardiology, and hospitalist services implemented EKG ordering guidelines to reduce unnecessary ED EKGs ordered after disposition. We compared pre- vs. post-guideline EKG ordering to determine whether guidelines were associated with changes in "preadmission" or "admission EKG" ordering. Patients with an admission diagnosis unrelated to cardiac or pulmonary systems were included. An EKG was "admission" if the order time was after disposition time. The numerator was the number of "admission EKGs" ordered; the denominator was the total number of such admissions; those with "preadmission EKGs" were excluded from this analysis. Variables that might influence EKG ordering were explored. The chi-square test with Bonferroni adjustment was used to compare 2015 vs. 2016 percentages of patients with an "admission EKG." Results There was a decrease in unwarranted "admission EKGs" among ED boarding patients (44.1% pre-implementation to 27.5% by two years post-implementation) and an increase in unwarranted "preadmission EKGs" (66.1% pre-implementation to 72.8% post-implementation). Age ≥40 and past medical history independently predicted EKG ordering. Discussion The decrease in the ordering of "admission EKGs" but "preadmission EKGs" suggests the decline reflects a true change in ordering and not a general environmental/ecologic decline in ordering. This highlights the importance of careful guideline development and implementation.

The Analysis of Gene Expression Data Incorporating Tumor Purity Information.

The tumor microenvironment is composed of tumor cells, stroma cells, immune cells, blood vessels, and other associated non-cancerous cells. Gene expression measurements on tumor samples are an average over cells in the microenvironment. However, research questions often seek answers about tumor cells rather than the surrounding non-tumor tissue. Previous studies have suggested that the tumor purity (TP)-the proportion of tumor cells in a solid tumor sample-has a confounding effect on differential expression (DE) analysis of high vs. low survival groups. We investigate three ways incorporating the TP information in the two statistical methods used for analyzing gene expression data, namely, differential network (DN) analysis and DE analysis. Analysis 1 ignores the TP information completely, Analysis 2 uses a truncated sample by removing the low TP samples, and Analysis 3 uses TP as a covariate in the underlying statistical models. We use three gene expression data sets related to three different cancers from the Cancer Genome Atlas (TCGA) for our investigation. The networks from Analysis 2 have greater amount of differential connectivity in the two networks than that from Analysis 1 in all three cancer datasets. Similarly, Analysis 1 identified more differentially expressed genes than Analysis 2. Results of DN and DE analyses using Analysis 3 were mostly consistent with those of Analysis 1 across three cancers. However, Analysis 3 identified additional cancer-related genes in both DN and DE analyses. Our findings suggest that using TP as a covariate in a linear model is appropriate for DE analysis, but a more robust model is needed for DN analysis. However, because true DN or DE patterns are not known for the empirical datasets, simulated datasets can be used to study the statistical properties of these methods in future studies.

Impact of early-onset sepsis guidelines on breastfeeding.

OBJECTIVE: To evaluate the impact of changes to neonatal early-onset sepsis (EOS) guidelines on in-hospital breastfeeding. STUDY DESIGN: Asymptomatic neonates admitted to the Neonatal Intensive Care Unit (NICU) for sepsis evaluations over a 2-year period were identified. A retrospective chart review was conducted as part of a larger quality initiative on antibiotic stewardship. RESULT: In Epoch 1, Epoch 2, and Epoch 3, there were 268 babies, 138 babies and 138 babies admitted to the NICU based on sepsis protocol, respectively. When comparing Epoch 1 to Epoch 3, there was a 14% increase in total breast milk consumption rates (p < 0.0001) and a 15% increase in exclusive breastfeeding at discharge (p < 0.002). CONCLUSION: By implementing new EOS protocols, we have decreased NICU length of stay. We suggest that the decrease in mother-infant separation time leads to an improvement in breastfeeding.

Influence of Framing: Recruitment to a Diabetes Disease Management Program From an Emergency Department Improves Enrollment and Outcomes.

Introduction Disease management programs (DMPs) provide education, self-management skills, care coordination, and frequent clinical assessment and medication adjustment. Our health system's diabetes mellitus (DM) DMP recruited patients from an emergency department (ED) and outpatient settings (primary care physicians' [PCP] and endocrinologists' offices; cold calling patients with poorly-controlled diabetes). We investigated whether recruitment to a DMP from an ED is feasible and effective, hypothesizing such patients would have better enrollment rates, future A1c control, and ED utilization because their receptiveness to change was "framed" by their ED visit. "Framing" is the notion that the same problem presented using a different context impacts response to the information. Being told in an acute-care ED setting one has newly-diagnosed or poorly-controlled DM, or DM-related complications may influence desire/commitment to enroll in the DMP and make lifestyle/medication changes. That is, acute illness or acute setting may influence/"frame" willingness to enroll and improve glycemic control.  Methods We captured all DMP recruitees' demographic, medical, insurance, A1c, and recruitment venue characteristics and evaluated future enrollment rates, A1c, and ED utilization from any ED in our health system. We analyzed pre- vs. post-recruitment changes in A1c and ED visit rates, comparing patients recruited from the ED who enrolled, patients recruited from the ED who did not enroll, patients recruited from outpatient settings who enrolled, and patients recruited from outpatient settings who did not enroll. Continuous enrollment predictor and outcome variables were compared using the Mann-Whitney test; categorical outcome variables were compared using Fisher's exact test. Results There were no statistically significant differences in characteristics (including mean baseline A1c [~11.4%]) among patients recruited from the ED, clinics, or cold calling. Twenty-five percent of all ED-recruited patients enrolled vs. 35% from outpatient settings. When a recruiter familiar with the DMP was in the ED, 41% of ED patients enrolled vs. 12% at other times (p=0.0001). Nearly 84% of ED visits were for direct DM-related causes (eg, diabetic ketoacidosis, hyperosmolar hyperglycemic state) or complications with a well-established link to diabetes (eg, acute coronary syndrome, stroke, wound infection); there was no statistically-significant difference in enrollment rates between patients whose ED visit was vs. was not for a DM-related complaint (53.8% vs. 60.0%, p=0.8018). No other variables, including whether the patient had newly diagnosed DM, were associated with enrollment. Enrollees with worse baseline glycemic control (A1c ≥11%) had a greater median A1c decrease (3.5% vs. 1.9%) vs. those with less-poor baseline glycemic control (A1c <11%) or those declining the program (p=0.05). Post-recruitment ED visits-per-patient-per-month decreased among patients recruited from the ED (-0.08), but not among those recruited from outpatient settings. (+0.08), p<0.0001). Conclusion ED recruitment to a diabetes DMP is feasible and effective. An ED-based diabetes DMP recruiter had enrollment rates substantially greater than a cold-calling DMP recruiter, comparable to enrollment rates from PCPs and endocrinologists, suggesting the importance of the recruitment framing/context. ED-recruited patients achieved substantial improvements in A1c and future ED visit rates.

Determinants of the lung microbiome in intubated premature infants at risk for bronchopulmonary dysplasia.

BACKGROUND: Airway dysbiosis in premature infants may be associated with bronchopulmonary dysplasia (BPD). Early oropharyngeal colostrum (OPC) administration alters the oral microbiome, which may impact the lung microbiome. We aim to compare the oral and tracheal microbiota during the first week of life, and to determine whether early OPC administration affects microbial diversity or leukocyte inflammatory activity in the lung. METHODS: Intubated premature infants (n = 42) were evaluated. The oral microbiome was characterized on day of life (DOL) 3, and the tracheal microbiome on DOL 3 and DOL 7, using 16S ribosomal DNA sequencing. Gene expression for inflammatory markers was quantified in airway leukocytes by real-time q-PCR. RESULTS: The oral and tracheal microbiota were significantly different on DOL 3, but the tracheal microbiome on DOL 7 was more similar to the oral from DOL 3. Tracheal bacterial diversity decreased from DOL 3 to DOL 7. Longer time to first OPC administration tended to be associated with lower bacterial diversity in the airways. CONCLUSIONS: The tracheal microbiome in intubated premature infants in the first week is likely determined, in part, by the composition of the oral microbiome. Bacterial diversity in intubated babies decreases during the first week of life, a pattern that could be consistent with risk for BPD. Decreased bacterial diversity and increased inflammatory activity in the lung may also be associated with delayed administration of OPC.

Transfusion in Elective Aortic Root Replacement: Analysis of the STS Adult Cardiac Surgery Database.

BACKGROUND: Data on blood use in proximal aortic surgery is limited. This study sought to establish quality benchmarks in the pattern of transfusion during elective aortic root replacement. METHODS: The Society of Thoracic Surgeons Adult Cardiac Surgery Database was queried to identify all patients who underwent primary elective aortic root replacement between July 2014 and June 2017. Multivariable negative binomial regressions were used to determine whether perioperative transfusion was associated with demographic or procedural factors. Multivariable logistic regression analysis was performed for clinical outcomes. RESULTS: Of 5559 patients analyzed, 38.95% (n = 2165) received no blood products. Patients who had a valve-sparing root replacement were less likely to undergo transfusion than those who received composite roots (bioprosthetic or mechanical valves) or homografts. The 30-day mortality for all patients was 2.57% (n = 143). Transfusion was associated with an increased risk of death at 30 days (odds ratio [OR], 1.833; P = .012), more frequent reoperation for bleeding (OR, 1.766; P < .001), prolonged ventilation (OR, 1.935; P < .001), a longer postoperative hospital stay (OR, 1.056; P < .001), and a higher incidence of new dialysis-dependent renal failure (OR, 2.088; P = .003). There was no correlation between institutional case volume and transfusion practice. CONCLUSIONS: Elective aortic root replacement can be performed with acceptable requirements for blood products. Composite root replacement has a greater likelihood of transfusion than does a valve-sparing procedure. Transfusion is independently associated with more complications after elective aortic root surgery, including 30-day mortality.

VR-Based investigation of forklift operator situation awareness for preventing collision accidents.

Forklifts are among the machines involved with the highest levels of occupational fatalities. As many accidents involved with a forklift can be attributed to the low situation awareness (SA) of the operator, it is essential to understand the factors influencing a forklift operator's SA for reducing forklift accidents, especially of collision type. Against this background, this research aims to investigate how a forklift operator's SA about other people around can be influenced by the type of subtasks they are carrying out. In this research, a virtual reality (VR) environment is used as the experiment environment, in which subjects perform a series of subtasks, such as driving, turning, reversing, loading and unloading, with a VR forklift simulation model. A SAGAT-an established SA measurement technique based on a series of queries targeting Level 1, 2, and 3 SA-is used as the main method to collect data about subjects' SA in the experiment. The analysis of the data reveals that a forklift operator is likely to have a reduced SA about the workers around when he/she is performing a loading or unloading task due to attention narrowing, which occurs when a person concentrates on a cognitively demanding task. The findings provide insights into how forklift operator SA could be improved through an SA-oriented safety training program and also how sensing technologies might assist forklift operators with maintaining a good SA.

The Use of Online Physician Training Can Improve Patient Experience and Physician Burnout.

The authors tested the efficacy of an integrated approach to improving patient experience and physician burnout using a 24-week online training program coupled with a physician engagement strategy. Physicians from different disciplines were randomized to intervention (n = 30) and control (n = 33) groups. Patient experience, physician burnout, and satisfaction data were assessed using patient and provider surveys. Comparisons were made pre and post intervention, and between the groups. Intervention group mean scores increased (+1.40 points) while control group scores dropped (-0.11 points; P = .039). Scores on physician burnout surveys for the intervention group improved in all areas and changes in 2 domains were statistically significant. In all, 73.5% of physicians felt the program was effective. This integrated intervention enhanced patient experience scores and positively affected physicians' level of burnout, and physicians believed this exercise was useful.