Key Driver Analysis Research Articles

At the Intersection of Patient Experience Data, Outcomes Research, and Practice: Analysis of HCAHPS Scores in Neurology Patients.

ObjectiveTo assess variation in patient-reported experience in inpatient neurology patients.Patients and MethodsWe retrospectively identified 1045 patients 18 years and older admitted to a neurology service and discharged from January 1, 2013, through September 30, 2016, who completed Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) surveys. Multivariable logistic regression evaluated the associations of patient factors with HCAHPS measures. Key driver analysis identified associations between HCAHPS measures and the Global score (combination of 0-10 hospital rating and likelihood to recommend). Multivariable logistic regression compared HCAHPS scores between neurology patients and those admitted to a neurosurgery (n=2190) or internal medicine (n=3401) service during the same period.ResultsAmong patients admitted to a neurology service, overall (summary) scores did not vary significantly by diagnosis after adjustment for age, education, and overall health, but patients with neurologic diagnoses other than stroke, epilepsy, and neurodegenerative disease were more likely to report lower Pain Management scores compared with patients with cancer. Key driver analysis showed Care Transition scores as drivers of the Global score. After adjustment, general internal medicine service patients were more likely to report low Summary scores and neurosurgery service patients were significantly less likely to report low Summary scores compared with neurology service patients.ConclusionEfforts to improve how neurology patients experience their care should be aimed at targeting patients' perceptions of pain management, and improving care transitions is an important first-priority target for improvement. This analysis may help other institutions improve hospital rating, value-based payments, and patient-centered outcomes.

Identification of human age-associated gene co-expressions in functional modules using liquid association.

Aging is a major risk factor for age-related diseases such as certain cancers. In this study, we developed Age Associated Gene Co-expression Identifier (AAGCI), a liquid association based method to infer age-associated gene co-expressions at thousands of biological processes and pathways across 9 human tissues. Several hundred to thousands of gene pairs were inferred to be age co-expressed across different tissues, the genes involved in which are significantly enriched in functions like immunity, ATP binding, DNA damage, and many cancer pathways. The age co-expressed genes are significantly overlapped with aging genes curated in the GenAge database across all 9 tissues, suggesting a tissue-wide correlation between age-associated genes and co-expressions. Interestingly, age-associated gene co-expressions are significantly different from gene co-expressions identified through correlation analysis, indicating that aging might only contribute to a small portion of gene co-expressions. Moreover, the key driver analysis identified biologically meaningful genes in important function modules. For example, IGF1, ERBB2, TP53 and STAT5A were inferred to be key genes driving age co-expressed genes in the network module associated with function “T cell proliferation”. Finally, we prioritized a few anti-aging drugs such as metformin based on an enrichment analysis between age co-expressed genes and drug signatures from a recent study. The predicted drugs were partially validated by literature mining and can be readily used to generate hypothesis for further experimental validations.

Integrative transcriptomic analysis reveals key drivers of acute peanut allergic reactions

Mechanisms driving acute food allergic reactions have not been fully characterized. We profile the dynamic transcriptome of acute peanut allergic reactions using serial peripheral blood samples obtained from 19 children before, during, and after randomized, double-blind, placebo-controlled oral challenges to peanut. We identify genes with changes in expression triggered by peanut, but not placebo, during acute peanut allergic reactions. Network analysis reveals that these genes comprise coexpression networks for acute-phase response and pro-inflammatory processes. Key driver analysis identifies six genes (LTB4R, PADI4, IL1R2, PPP1R3D, KLHL2, and ECHDC3) predicted to causally modulate the state of coregulated networks in response to peanut. Leukocyte deconvolution analysis identifies changes in neutrophil, naive CD4+ T cell, and macrophage populations during peanut challenge. Analyses in 21 additional peanut allergic subjects replicate major findings. These results highlight key genes, biological processes, and cell types that can be targeted for mechanistic study and therapeutic targeting of peanut allergy.

OCDD: an obesity and co-morbid disease database

BackgroundObesity is a medical condition that is known for increased body mass index (BMI). It is also associated with chronic low level inflammation. Obesity disrupts the immune-metabolic homeostasis by changing the secretion of adipocytes. This affects the end-organs, and gives rise to several diseases including type 2 diabetes, asthma, non-alcoholic fatty liver diseases and cancers. These diseases are known as co-morbid diseases. Several studies have explored the underlying molecular mechanisms of developing obesity associated comorbid diseases. To understand the development and progression of diseases associated with obesity, we need a detailed scenario of gene interactions and the distribution of the responsible genes in human system.ResultsObesity and Co-morbid Disease Database (OCDD) is designed for relating obesity and its co-morbid diseases using literature mining, and computational and systems biology approaches. OCDD is aimed to investigate the genes associated with comorbidity. Several existing databases have been used to extract molecular interactions and functional annotations of each gene. The degree of co-morbid associations has been measured and made available to the users. The database is available at http://www.isical.ac.in/~systemsbiology/OCDD/home.phpConclusionsThe main objective of the database is to derive the relations among the genes that are involved in both obesity and its co-morbid diseases. Functional annotation of common genes, gene interaction networks and key driver analyses have made the database a valuable and comprehensive resource for investigating the causal links between obesity and co-morbid diseases.

Analysis of key value drivers for major mining companies for the period 2006–2015

The end of the 2000s commodity super cycle resulted in all-time high market values for most commodity based companies, followed by a rapid decline in the values post the Global Financial Crisis (GFC) of 2008. The period 2006–2015 was therefore an appropriate review period as it enabled reviewing changes in company value pre and post the GFC.In order to determine the key drivers of company value, financial and production data of four diversified and international mining companies was analyzed. Each of the four companies had a different commodity mix. Due to its suitability for comparison of company value, the market based valuation approach was selected as the appropriate company valuation technique, using enterprise value (EV) as the value metric.Eight potential value drivers were identified. These are production output; commodity price; revenue; earnings before interest, tax, depreciation and amortization (EBITDA) multiple; EBITDA margin; gearing ratio; net debt to EBITDA ratio; and return on capital employed (ROCE). The eight potential value drivers were analyzed firstly using graphical checks, followed by numerical determination of the degree of correlation between each potential value driver and EV using the Pearson correlation coefficient method to confirm the visual analysis. Hypothesis testing was finally done to investigate the strength of the relationships between the potential value drivers and EV.This paper notes some key findings. Revenue, commodity price and EBITDA multiple are primary drivers of value across all four companies, despite their different commodity mix. Of these three drivers revenue is the strongest value driver. It was also noted that the two debt metrics, gearing ratio and net debt to EBITDA, only correlated with EV in times of declining commodity prices and revenue, indicating that value drivers can change with changing economic conditions. It is therefore important for mining companies to periodically identify key value drivers of company value during different economic periods and ensure that they measure their performance based on these.

A functional genomics predictive network model identifies regulators of inflammatory bowel disease.

A major challenge in inflammatory bowel disease (IBD) is the integration of diverse IBD data sets to construct predictive models of IBD. We present a predictive model of the immune component of IBD that informs causal relationships among loci previously linked to IBD through genome-wide association studies (GWAS) using functional and regulatory annotations that relate to the cells, tissues, and pathophysiology of IBD. Our model consists of individual networks constructed using molecular data generated from intestinal samples isolated from three populations of patients with IBD at different stages of disease. We performed key driver analysis to identify genes predicted to modulate network regulatory states associated with IBD, prioritizing and prospectively validating 12 of the top key drivers experimentally. This validated key driver set not only introduces new regulators of processes central to IBD but also provides the integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD.

SKDA in Context

In contrast to the view that survey key driver analysis (SKDA) is a misused and blind empirical process, we suggest it is a reasonable, hypothesis-driven approach that builds on cumulative knowledge drawn from both the literature and practice, and requires reasoned judgment about the relationships of individual items to the constructs they represent and the criteria of interest. The logic of key driver analysis in applied settings is no different than the logic of its application in fundamental research regarding employee attitudes (e.g., Dalal, Baysinger, Brummel, & LeBreton, 2012). However, there are important survey design and analysis issues with respect to how key driver analyses are best conducted. Just some of these are discussed below.

With the Right Map, Survey Key Driver Analysis Can Help Get Organizations to the Right Destination

Cucina, Walmsley, Gast, Martin, and Curtin (2017) raise an important issue in evaluating whether our current approaches for key driver analysis on employee opinion survey data are indeed best practices. As has been argued elsewhere (Putka & Oswald, 2016; Scherbaum, Putka, Naidoo, & Youssefnia, 2010), there is and can be misalignment between current and best practices. We agree with Cucina et al. that our field should engage in larger discussion of these issues. That discussion is critical, as industrial and organizational (I-O) psychologists are competing with those outside our field who have either little knowledge of best practices in data analysis (but who have been empowered by technology that automates the analysis) or little knowledge of psychology (but a great deal of knowledge in big data analytical techniques). I-O psychologists are in the vanguard of survey data analysis (Ducey et al., 2015), and we have a responsibility to maintain the standards of our field as well as to wield our influence to guide other practitioners outside our field on sound theoretical and analytical approaches.

Best Practice Recommendations for Conducting Key Driver Analyses

In their critical review of survey key driver analyses (SKDA), Cucina, Walmsley, Gast, Martin, and Curtin (2017) contend that methodological issues limit the usefulness of SKDA and recommend that survey providers stop conducting SKDA until these issues can be overcome. I contend that many of these methodological issues are either overstated or able to be addressed through the proper application of the technique by a competent professional. In this commentary, I make recommendations for how SKDA should be applied so that methodological issues are addressed and the value of SKDA is maximized. Many of these recommendations were made in Lundby and Johnson (2006), who were cited by Cucina et al. but did not have much impact on their focal article.

Survey Key Driver Analysis: Our GPS to Navigating Employee Attitudes

Cucina, Walmsley, Gast, Martin, and Curtin (2017) started an important dialogue about survey key driver analysis (SKDA). We believe that promoting more useful and valid ways to understand survey data is critical not only for the organizations we serve, but also for advancing the relevancy of our field. We use the terms useful and valid quite intentionally. “Useful” is driven by our practitioner side, but “valid” is driven by our science side. It is the science that often sets industrial and organizational (I-O) psychology apart from other fields. But in some ways, it also holds us back from being timely and relevant. Overall, we believe that the focal article erred too much on the science side.

In Defense of Responsible Survey Key Driver Analysis

In their focal article, Cucina, Walmsley, Gast, Martin, and Curtin (2017) raise a number of concerns about survey key driver analysis (SKDA). Although many are valid, we think their critique ultimately goes too far and risks throwing the baby out with the bathwater. We have found that SKDA can be an effective way to generate insight and action when analysis is done correctly and results are used appropriately. In this response article, we first provide data that counters Cucina et al.’s findings. Then we describe the approach we take to survey key driver analytics and application. In doing so, we hope to address some of the core concerns raised in the focal article and identify ways for scientists and practitioners to conduct SKDA in a responsible way.

Survey Key Driver Analysis: Perhaps the Right Question Is, “Are We There Yet?”

In their focal article, Cucina, Walmsley, Gast, Martin, and Curtin (2017) raise an important and timely issue, but go too far in arguing that practitioners should abandon survey key driver analysis (SKDA) altogether. There are many valuable benefits to using SKDA, and rather than discontinuing the practice entirely, we suggest that a better approach is to focus on ways to make it even more valuable to organizations. It is true that this area is in need of further research, but instead of asking whether we are driving down the right road, perhaps the right question is, “Are we there yet?”

Does SKDA Make It Too Easy for Survey Practitioners and Clients to Avoid Harder (OD) Challenges?

A fair reading of Cucina, Walmsley, Gast, Martin, and Curtin's (2017) astute analysis of survey key driver analysis (SKDA) presents a good case that such analyses are driving down the wrong road and should slow down or come to a halt. In large part, the SKDA they describe seems to be a misleading set of exercises.

Let's Keep Looking for Other Roads: Improving Approaches to Identifying and Addressing Key Drivers

Cucina, Walmsley, Gast, Martin, and Curtin (2017) provide several valid criticisms of survey key driver analysis (SKDA), which is an approach used to identify causes of important employee and organizational outcomes, such as job satisfaction or employee engagement.

Survey Key Driver Analysis: Are We Driving Down the Right Road?

One of the typical roles of industrial–organizational (I-O) psychologists working as practitioners is administering employee surveys measuring job satisfaction/engagement. Traditionally, this work has involved developing (or choosing) the items for the survey, administering the items to employees, analyzing the data, and providing stakeholders with summary results (e.g., percentages of positive responses, item means). In recent years, I-O psychologists moved into uncharted territory via the use of survey key driver analysis (SKDA), which aims to identify the most critical items in a survey for action planning purposes. Typically, this analysis involves correlating (or regressing) a self-report criterion item (e.g., “considering everything, how satisfied are you with your job”) with (or on) each of the remaining survey items in an attempt to identify which items are “driving” job satisfaction/engagement. It is also possible to use an index score (i.e., a scale score formed from several items) as the criterion instead of a single item. That the criterion measure (regardless of being a single item or an index) is internal to the survey from which predictors are drawn distinguishes this practice from linkage research. This methodology is not widely covered in survey methodology coursework, and there are few peer-reviewed articles on it. Yet, a number of practitioners are marketing this service to their clients. In this focal article, a group of practitioners with extensive applied survey research experience uncovers several methodological issues with SKDA. Data from a large multiorganizational survey are used to back up claims about these issues. One issue is that SKDA ignores the psychometric reality that item standard deviations impact which items are chosen as drivers. Another issue is that the analysis ignores the factor structure of survey item responses. Furthermore, conducting this analysis each time a survey is administered conflicts with the lack of situational and temporal specificity. Additionally, it is problematic to imply causal relationships from the correlational data seen in most surveys. Most surprisingly, randomly choosing items out of a hat yields validities similar to those from conducting the analysis. Thus, we recommend that survey providers stop conducting SKDA until they can produce science that backs up this practice. These issues, in concert with the lack of literature examining the practice, make rigorous evaluations of SKDA a timely inquiry.

Manufacturing Value Added Development in North Africa: Analysis of Key Drivers

It is widely recognized that structural transformation lies at the heart of economic development of any nation. Recent research suggests that the industrial sector, especially manufacturing, is a key engine of growth in the development process, including that of North Africa. The necessity for structural transformation in North Africa arises from the fact that the sub-region needs high and sustained economic growth in order to make significant progress in generating increased productive and quality jobs and livelihood for its teeming population, especially the youth. Unfortunately, manufacturing development in North Africa has not improved over time. For example, its manufacturing value added (MVA) in world MVA accounted for only 0.10 percent of world MVA in 2013. Also, the share of North African MVA in GDP was just 16.00% in 2013 compared with Asia & Pacific?s 25%. This paper empirically assesses the key drivers of manufacturing value added in the sub-region using a time series cross-sectional data set of the countries for the period, 1990 to 2014. Two estimation techniques, the pooled panel OLS regression with year fixed effects and the IV-2SLS estimation procedure, were used. There is also a strong support for a non-monotonic, cubic (third degree polynomial) relationship between MVA with economic development. The following factors are found to exert significant positive effect on manufacturing added in North Africa: secondary education, agricultural land, domestic credit to the private sector, trade openness, inward stock of FDI, population size, and ICT infrastructure/technology. On the other hand, the results indicate that dependence on oil, mineral and natural gas rents, domestic investment rate, political globalization, institutionalized democracy, age dependency ratio (young), and civil violence have significant negative effect on MVA in the sub-region. The paper concludes with policy recommendations.

Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity

Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the sources of gene expression variability in 317 humaniPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retaina donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycombtargets contribute significantly to thenon-genetic variability seen within andacross individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation betweeniPSC lines and illustrate the potential for our dataset and other similar large-scale analyses toidentify underlying drivers relevant to iPSC applications.

Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration.

BackgroundHuman diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development.ResultsTo make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server (http://mergeomics.research.idre.ucla.edu/). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use.ConclusionsOur Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3057-8) contains supplementary material, which is available to authorized users.

Exploration of key driver analysis for outpatient satisfaction

Objective To learn key drivers for outpatients satisfaction for support of better care of quality of the hospital. Methods Outpatients satisfaction questionnaires were distributed at 19 municipal hospitals' outpatient department, and the importance matrix analysis method was used to identify key factors for outpatients satisfaction. Results These hospitals were found advantageous in such items as doctors' patience, medical staffs' politeness, doctors' medical competence, and communication capability; and disadvantageous in outpatient waiting time. Conclusions Clinical waiting time proves a significant factor on outpatient satisfaction, which is the key driver to be focused on in the future. Key words: Outpatient service; Patients' satisfaction; Importance matrix analysis; Key drivers; Impact

Achieving a 5-star rating: Analysis of Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores among patients undergoing elective colorectal operations

Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) is a publicly reported survey of patients' hospital experience. All inpatient, elective colorectal resections with completed HCAHPS surveys at a single institution between June 2012 and April 2015 were identified. HCAHPS measures were analyzed according to published methodologies. Univariate logistic regression evaluated associations of various HCAHPS measures with age, sex, ostomy, approach, diagnosis, and prolonged length of stay (PLOS; ≥7days). Key driver analysis demonstrated associations between the individual HCAHPS measures and the global hospital rating measure. We identified 755 patients. Younger age, inflammatory bowel disease, open approach, ostomy construction, and PLOS were associated with low quality of pain management. Patients with inflammatory bowel disease, open approach, and PLOS had a low overall star score (all P<.05). Care transitions and communication about medications received low scores but were associated highly with the global hospital rating measure. Efforts aimed at improving pain management among patients with colorectal resection should focus on patients with inflammatory bowel disease, open operations, ostomies, and PLOS. Improving care transitions and communication about medications are important targets for improvement to increase the overall hospital score. Considering the importance of improving patient-centered outcomes, we suggest that all institutions utilize their existing HCAHPS data in this manner.