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Posts Tagged ‘Clinical and Translational Science Award’


The Stanford Center for Clinical and Translational Research and Education, or Spectrum – NIH Awards Stanford $45.3M

Reporter: Aviva Lev-Ari, PhD, RN

NIH Awards Stanford $45.3M for Translational Research

October 07, 2013

NEW YORK (GenomeWeb News) – The Stanford Center for Clinical and Translational Research and Education, or Spectrum, is being awarded $45.3 million over four and a half years by the National Institutes of Health to push forward translational research in medicine.

Spectrum is one of 15 institutions to receive such an award being funded as part of the Clinical and Translational Sciences Awards, which were launched in 2006 by NIH “to help meet the nation’s urgent need to provide better healthcare to more people for less money,” the Stanford School of Medicine said.

Stanford won a first round of CTSA funding in 2008 of $30 million.

The new funding will be used to support two new programs at Stanford, one in disease diagnostics and one in population health sciences.

The diagnostics program seeks to develop new methods of testing and preventing disease through advances in omics, immune monitoring, molecular imaging, single-cell analysis, computation, and informatics, the school said. Atul Butte, chief of systems medicine and associate professor of pediatrics and genetics, will lead the program.

The Population Health Sciences Initiative will design systems to serve as a new source of practice-based evidence. The systems will be based on the daily experiences of practicing physicians and information drawn from clinical data warehouses, Stanford said.

This initiative is led by Robert Harrington, professor and chair of medicine; Mark Cullen, professor of medicine and chief of the Division of General Medical Disciplines; and Douglas Owens, professor of medicine and director of the Stanford Center for Primary Care and Outcomes Research and the Center for Health Policy.

The new CTSA award also will be used to address the shortage of qualified clinical and translational researchers across the US by funding new training programs and online courses in clinical research, Stanford said.

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http://www.genomeweb.com//node/1290106?utm_source=SilverpopMailing&utm_medium=email&utm_campaign=Stanford%20Nabs%20$45M%20NIH%20Award;%20Signal%20Genetics’%20BCBS%20Coverage;%20$1.4M%20Approval%20for%20Geisinger;%20More%20-%2010/07/2013%2003:45:00%20PM

 

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State of Cardiology on Wall Stress, Ventricular Workload and Myocardial Contractile Reserve: Aspects of Translational Medicine (TM)

Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC

and

Article Curator, Aviva Lev-Ari, PhD, RN

This article is based on and all citations are from the following two articles that have appeared in Journal of Translational Medicine in 2013

#1:

Identifying translational science within the triangle of biomedicine

http://www.translational-medicine.com/content/11/1/126

Griffin M Weber

Journal of Translational Medicine 2013, 11:126 (24 May 2013)

 #2:

Integrated wall stress: a new methodological approach to assess ventricular

workload and myocardial contractile reserve

http://www.translational-medicine.com/content/11/1/183

Dong H, Mosca H, Gao E, Akins RE, Gidding SS and Tsuda T

Journal of Translational Medicine 2013, 11:183 (7 August 2013)

In this article we expose the e-Reader to

A. The State of Cardiology on

  • wall stress
  • ventricular workload and
  • myocardial contractile reserve

B. Innovations in a Case Study in Cardiology Physiological Research on above subjects

C. Prevailing Models in Translational Medicine

D. Mapping of One Case Study in Cardiology Physiological Research onto Weber’s Triangle of Biomedicine.

The mapping facilitate e-Reader’s effort to capture the complexity of aspects of Translational Medicine and visualization of the distance on this Triangle between where the results of this case study are and the Human Corner — the Roadmap of the “bench-to-bedside” research, or the “translation” of physiological and basic science research into practical clinical applications.

This article has the following sections:

Introduction

Author:  Justin Pearlman, MD, PhD, FACC

Translational medicine aims to fast track the pathway from scientific discovery to clinical applications and assessment of benefits. Cardiovascular examples include novel biomarkers of disease, new heart assist devices, new technologies for catheter intervention, and new medications. The Institute of Medicine’s Clinical Research Roundtable describes translation medicine in two fundamental blocks:  “…the transfer of new understandings of disease mechanisms gained in the laboratory into the development of new methods for diagnosis, therapy, and prevention [with] first testing in humans…”, and  “…the translation of results from clinical studies into everyday clinical practice and health decision making…” [2].

Identifying where contributions are achieving translation has been addressed by the biometric tool called the triangle of biomedine [3].

REFERENCES:

  1. Jiang F, Zhang J, Wang X, Shen X: Important steps to improve translation from medical research to health policy.J Trans Med 2013, 11:33. BioMed Central Full Text OpenURL
  2. Sung NS, Crowley WF Jr, Genel M, Salber P, Sandy L, Sherwood LM, Johnson SB, Catanese V, Tilson H, Getz K, Larson EL, Scheinberg D, Reece EA, Slavkin H, Dobs A, Grebb J, Martinez RA, Korn A, Rimoin D: Central challenges facing the national clinical research enterprise.JAMA 2003, 289:1278-1287. PubMed Abstract | Publisher Full Text
  3. Identifying translational science within the triangle of biomedicineGriffin M WeberJournal of Translational Medicine 2013, 11:126 (24 May 2013)
  4. Woolf SH: The meaning of translational research and why it matters.JAMA 2008, 299(2):211-213. PubMed Abstract | Publisher Full Text OpenURL
  5. Chiappelli F: From translational research to translational effectiveness: the “patient-centered dental home” model.Dental Hypotheses 2011, 2:105-112. Publisher Full Text OpenURL
  6. Maida C: Building communities of practice in comparative effectiveness research.In Comparative effectiveness and efficacy research and analysis for practice (CEERAP): applications for treatment options in health care. Edited by Chiappelli F, Brant X, Cajulis C. Heidelberg: Springer–Verlag; 2012.
  7. Agency for Healthcare Research and QualityBudget estimates for appropriations committees, fiscal year (FY) 2008: performance budget submission for congressional justification. 
    http://www.ahrq.gov/about/cj2008/cjweb08a.htm#Statement webcite. Accessed 11 May 2013OpenURL
  8. Westfall JM, Mold J, Fagnan L: Practice-based research—“blue highways” on the NIH roadmap.JAMA 2007, 297:403-406. PubMed Abstract | Publisher Full Text OpenURL
  9. Chiappelli F, Brant X, Cajulis C: Comparative effectiveness and efficacy research and analysis for practice (CEERAP) applications for treatment options in health care. Heidelberg: Springer–Verlag; 2012. OpenURL
  10. Dousti M, Ramchandani MH, Chiappelli F: Evidence-based clinical significance in health care: toward an inferential analysis of clinical relevance.Dental Hypotheses 2011, 2:165-177. Publisher Full Text
  11. CRD: Systematic Reviews: CRD’s guidance for undertaking reviews in health care. National Institute for Health Research (NIHR). University of York, UK: Center for reviews and dissemination; 2009. PubMed Abstract | Publisher Full Text OpenURL
  12. Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, Savovic J, Schulz KF, Weeks L, Sterne JA, Cochrane Bias Methods Group; Cochrane Statistical Methods Group:The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials.British Med J 2011, 343:d5928. Publisher Full Text OpenURL
  13. Bartolucci AA, Hillegas WB: Overview, strengths, and limitations of systematic reviews and meta-analyses. In Understanding evidence-based practice: toward optimizing clinical outcomes. Edited by Chiappelli F, Brant XMC, Oluwadara OO, Neagos N, Ramchandani MH. Heidelberg: Springer–Verlag; 2010.
  14. Jüni P, Altman DG, Egger M: Systematic reviews in health care: assessing the quality of controlled clinical trials.British Med J 2001, 323(7303):42-46. Publisher Full Text OpenURL
  15. Chiappelli F, Arora R, Barkhordarian B, Ramchandani M: Evidence-based clinical research: toward a New conceptualization of the level and the quality of the evidence.Annals Ayurvedic Med 2012, 1:60-64. OpenURL
  16. Chiappelli F, Barkhordarian A, Arora R, Phi L, Giroux A, Uyeda M, Kung K, Ramchandani M:Reliability of quality assessments in research synthesis: securing the highest quality bioinformation for HIT.Bioinformation 2012, 8:691-694. PubMed Abstract | Publisher Full Text |PubMed Central Full Text OpenURL
  17. Shavelson RJ, Webb NM: Generalizability theory: 1973–1980.Br J Math Stat Psychol 1981, 34:133-166. Publisher Full Text OpenURL
  18. Chiappelli F, Navarro AM, Moradi DR, Manfrini E, Prolo P: Evidence-based research in complementary and alternative medicine III: treatment of patients with Alzheimer’s disease.Evidence-Based Comp Alter Med 2006, 3:411-424. Publisher Full Text OpenURL
  19. Montgomery C: Statistical quality control: a modern introduction. Chichester, West Sussex, UK: Johm Wiley & sons; 2009. OpenURL

 This article has the following EIGHT Sections:

I. Key Explanation Models for the Translational Process in BioMedicine, aka Translational Medicine (TM)

II. TM Model selection in this article, for mapping the fit of a Case Study in Cardiology Physiological Research, within the TM Model selected

III. Limitations of the TM Model to explain the Translational Process in BioMedicine

IV. Mapping the fit of a Case Study in Cardiology Physiological Research, within the TM Model selected

V. Clinical Implications of the Case Study in Cardiology Physiological Research

VI. Limitations of the Case Study in Cardiology Physiological Research

VII. The State of Cardiology on

  • wall stress
  • ventricular workload and
  • myocardial contractile reserve

VIII. What are the Innovations of the Case Study in Cardiology Physiological Research

I. Key Explanation Models for the Translational Process in BioMedicine, aka Translational Medicine (TM)

The National Institutes of Health (NIH) Roadmap places special emphasis on “bench-to-bedside” research, or the “translation” of basic science research into practical clinical applications. The Clinical and Translational Science Awards (CTSA) Consortium is one example of the large investments being made to develop a national infrastructure to support translational science, which involves reducing regulatory burdens, launching new educational initiatives, and forming partnerships between academia and industry. However, while numerous definitions have been suggested for translational science, including the qualitative T1-T4 classification, a consensus has not yet been reached. This makes it challenging to measure the impact of these major policy changes.

BASIC DISCOVERY -T1->  CLINICAL INSIGHTS -T2-> IMPLICATIONS FOR PRACTICE -T3-> IMPLICATIONS FOR POPULATION HEALTH -T4-> IMPLICATIONS FOR GLOBAL HEALTH

Model A: QUALTITATIVE T1-T4 CLASSIFICATION [(7) & (8-10) in Weber’s list of Reference, below]

In biomedicine, translational science is research that has gone from “bench” to “bedside”, resulting in applications such as drug discovery that can benefit human health  [16]. However, this is an imprecise description. Numerous definitions have been suggested, including the qualitative T1-T4 classification [7].

Several bibliometric techniques have been developed to quantitatively place publications in the translational spectrum. Narin assigned journals to fields, and then grouped these fields into either “Basic Research” or “Clinical Medicine” [8-10]. Narin also developed another classification called research levels, in which journals are assigned to “Clinical Observation” (Level 1), “Clinical Mix” (Level 2), “Clinical Investigation” (Level 3), or “Basic Research” (Level 4) [8]. He combines Levels 1 and 2 into “Clinical Medicine” and Levels 3 and 4 to “Biomedical Research”.

Model B: Average research level of a collection of articles as the mean of the research levels of those articles

Lewison developed methods to score the translational research level of individual articles from keywords within the articles’ titles and addresses. He defines the average research level of a collection of articles as the mean of the research levels of those articles [1113] .  For validity, one must assume that the keywords reflect content fairly and without bias. If the government adapts such a scoring system to influence funding in order to promote translational research, that will create a bias.

Model C:  “Translatability” of drug development projects 

A multidimensional scoring system has been developed to assess the “translatability” of drug development projects [29,30]. This requires manual review of the literature which poses difficulties for scalability and consistency across reviewers and over time.

Model D: Fontelo’s  59 words and phrases suggesting that the article is Translational 

Fontelo identified 59 words and phrases, which when present in the titles or abstracts of articles, suggest that the article is translational [31]. It is an interesting sampling method, but it may present a bias to particular styles of presentation.

Model E:  The triangle of biomedicine by Griffin M Weber – This Model is the main focus of this article

http://www.translational-medicine.com/content/11/1/126

Methods

The Triangle of Biomedicine uses a bibliometric approach to map PubMed articles onto a graph. The corners of the triangle represent research related to animals, to cells and molecules. The position of a publication on the graph is based on its topics, as determined by its Medical Subject Headings (MeSH). Translation is defined as movement of a collection of articles, or the articles that cite those articles, towards the human corner.

Results

The Triangle of Biomedicine provides a quantitative way of determining if an individual scientist, research organization, funding agency, or scientific field is producing results that are relevant to clinical medicine. Validation of the method examined examples that have been previously described in the literature, comparing it to other methods of measuring translational science.

Conclusions

The Triangle of Biomedicine is a novel way to identify translational science and track changes over time. This is important to policy makers in evaluating the impact of the large investments being made to accelerate translation. The Triangle of Biomedicine also provides a simple visual way of depicting this impact, which can be far more powerful than numbers alone. As with any metric, its limitations and potential biases should always be kept in mind. As a result, it should be used to supplement rather than replace alternative methods of measuring or defining translational science. What is unique, though, to the Triangle of Biomedicine, is its simple visual way of depicting translation, which can be far more powerful to policy makers than numbers alone.

Keywords:

Translational science; Bibliometric analysis; Medical subject headings; Data visualization; Citation analysis

II. TM Model selection in this article, for mapping the fit of a Case Study in Cardiology Physiological Research, within the TM Model selected

Model E:  The triangle of biomedicine by Griffin M Weber

In this study, we analyze the 20 million publications in the National Library of Medicine’s PubMed database by extending these bibliometric approaches in three ways: (1) We divide basic science into two subcategories, research done on animals or other complex organisms and research done on the cellular or molecular level. We believe it is important to make this distinction due to the rapid increase in “-omics” research and related fields in recent years. (2) We classify articles using their Medical Subject Headings (MeSH), which are assigned based on the content of the articles. Journal fields, title keywords, and addresses only approximate an article’s content. (3) We map the classification scheme onto a graphical diagram, which we call the Triangle of Biomedicine, which makes it possible to visualize patterns and identify trends over time.

Article classification technique

Using a simple algorithm based on an article’s MeSH descriptors, we determined whether each article in PubMed contained research related to three broad topic areas—animals and other complex organisms (A), cells and molecules (C), or humans (H). An article can have more than one topic area. Articles about both animals and cells are classified as AC, articles about both animals and humans are AH, articles about cells and humans are CH, and articles about all three are ACH. Articles that have none of these topic areas are unclassified by this method.

In order to identify translational research, we constructed a trilinear graph [21], where the three topic areas are placed at the corners of an equilateral triangle, with A on the lower-left, C on the top, and H on the lower-right. The midpoints of the edges correspond to AC, AH, and CH articles, and the center of the triangle corresponds to ACH articles.

An article can be plotted on the Triangle of Biomedicine according to the MeSH descriptors that have been assigned to it. For example, if only human descriptors, and no animal or cell descriptors have been assigned to an article, then it is classified as an H article and placed at the H corner. An article with both animal and cell descriptors, and no human descriptors, is classified as an AC article and placed at the AC point. A collection of articles is represented by the average position of its articles. Although an individual article can only be mapped to one of seven points, a collection of articles can be plotted anywhere in the triangle.

An imaginary line, the Translational Axis, can be drawn from the AC point to the H corner. The position of one or more articles when projected onto this axis is the Translational Index (TI). By distorting the Triangle of Biomedicine by bringing the A and C corners together at the AC point, the entire triangle can be collapsed down along the Translational Axis to the more traditional depiction of translational science being a linear path from basic to clinical research. In other words, the Triangle of Biomedicine does not replace the traditional linear view, but rather provides additional clarity into the path research takes towards translation.

Summary of categories

Mapping A-C-H categories to Narin’s basic-clinical classification scheme

The National Library of Medicine (NLM) classifies journals into different disciplines, such as microbiology, pharmacology, or neurology, with the use of Broad Journal Headings. We used Narin’s mappings to group these disciplines into basic research or clinical medicine. Individual articles were given a “basic research” score of 1 if they were in a basic research journal and 0 if they were in a “clinical medicine” journal. For each A-C-H category, a weighted average of its articles’ scores was calculated, with the weights being the inverse of the total number of basic research (4,316,495) and clinical medicine (11,689,341) articles in PubMed. That gives a numeric value for the fraction of articles within a category that are basic research, which is corrected for the fact that PubMed as a whole has a greater number of clinical medicine articles.

Mapping A-C-H categories to Narin’s four-level classification scheme

For each of his four research levels, Narin selected a prototype journal to conduct his analyses:The Journal of the American Medical Association (JAMA, Level 1), The New England Journal of Medicine (NEJM, Level 2), The Journal of Clinical Investigation (JCI, Level 3), and The Journal of Biological Chemistry (JBC, Level 4). Each is widely considered a leading journal and has over 25,000 articles spanning more than 50 years. For each A-C-H category, we determined the number of articles from each of these four journals and calculated a weighted average of their research levels, with the weights being the inverse of the total number of articles each journal has in PubMed.

III. Limitations of the TM Model to explain the Translational Process in BioMedicine:  The triangle of biomedicine by Griffin M Weber

This work is limited in several ways. It takes at least a year for most articles to be assigned MeSH descriptors. During that time the articles cannot be classified using the method described in this paper. Also, our classification method is based on a somewhat arbitrary set of MeSH descriptors—different descriptors could have been used to map articles to A-C-H categories. However, the ones we used seemed intuitive and they produced results that were consistent with Narin’s classification schemes. Finally, any metric based on citation analysis is dependent on the particular citation database used, and there are significant differences among the leading databases [22]. In this study, we used citations in PubMed that are derived from PubMed Central because they are freely available in their entirety, and therefore our method can be used without subscriptions to commercial citation databases, such as Scopus and Web of Science, which are cost-prohibitive to most people. However, because these commercial databases have a greater number of citations and index different journals than PubMed, they might show shorter or alternative paths towards translation (i.e., fewer citation generations or less time). Though, as described in our Methods, there is evidence that suggests these differences might be relatively small. Selecting the best citation database for identifying translational research is a topic for future research.

Another area of future research could attempt to identify a subset of H articles that truly reflect changes in health practice and create a separate category P for these articles. This might be possible, for example, by using Khoury’s approach of using PubMed’s “publication type” categorization of each article to select for those that are clinical trials or practice guidelines [7]. This could be visualized in the Triangle of Biomedicine by moving H articles to the center of the triangle and placing P articles in the lower-right corner, thereby highlighting research that has translated beyond H into health practice.

IV. Mapping the fit of a Case Study in Cardiology Physiological Research, within the TM Model selected

The triangle of biomedicine by Griffin M Weber

Weber

Figure 1. Disciplines mapped onto the Triangle of Biomedicine.The corners of the triangle correspond to animal (A), cellular or molecular (C), and human (H) research. The dashed blue line indicates the Translational Axis from basic research to clinical medicine. The position of each circle represents the average location of the articles in a discipline. The size of the circle is proportional to the number of articles in that discipline. The color of the circle indicates the Translational Distance (TD)—the average number of citation generations needed to reach an H article. The position of the light blue box connected to each discipline represents the average location of articles citing publications in that discipline. To provide clarity, not all disciplines are shown. Note however, that if authors knew this measurement would be applied and could affect their funding, then they might increase human study citation of basic research to game the “translational distance.”

For this article we selected A Case Study in Cardiology Physiological Research

Integrated wall stress: a new methodological approach to assess ventricular workload and myocardial contractile reserve  

Hailong Dong124Heather Mosca1Erhe Gao3Robert E Akins1Samuel S Gidding2and Takeshi Tsuda12*

This study appeared in 2013 in the Journal of Translational Medicine. It studied mice, creating heart attacks in order to evaluate the physiologic significance of “integrated wall stress” (IWS) as a marker of total ventricular workload. The measure IWS was obtained by integrating continuous wall stress curve by accumulating wall stress values at millisecond sampling intervals over one minute, in order to include in  wall stress effects of heart rate and contractility (inotropic status of the myocardium). As an example of translational medicine, it raises numerous issues. As a mouse study, it qualifies as basic science. It examines the impact of heart attack on changes inducible by the inotropic agent dobutamine. If the concept were to influence clinical care and outcomes, it would qualify as translational. All of the tools applied to the mice are applicable to patients: heart attacks (albeit not purposefully induced), the echocardiography measurements, and the dobutamine impact. That enables citation of human studies in the references, and ready application to human studies in the future. Mice however have much faster heart rates, so the choice of one minute for the integral may have different significance for humans. Gene expression was also measured. The authors conclude IWS represents  a balance between external ventricular workload and intrinsic myocardial contractile reserve. The fact that the Journal has the word “translational” may represent a bias. Many of the links between animal and human focused references occur electively in the discussion section. The authors propose the measurement might help identify pre-clinical borderline failing of contractility. If so, the full axis of translational value will require that IWS can improve outcomes. Currently, blood levels of brain naturetic peptide are used as a marker of myocardial strain that may help identify early failing contractility. Presumably, early recognition could identify a population that might benefit from early intervention to forestall progression. Evidence based medicine will have difficulties. First, it is biased by the “Will Roger’s Effect” whereby early recognition of a disease subdivides the lowest class, inherently shifting the apparent status of each half of the subdivision (Will Roger’s made a joke that when Oklahoma residents moved to California for the gold rush, they improved the average intelligence of both groups, an observation adapted to explain a redefinition bias). Second, the actual basis for a change in clinical application will be complex, with political as well as scientific influences. Third, it will be even more difficult to discern its impact on outcomes, even if targeted therapy for patients with distinctive IWS is associated with an apparent improvement in outcomes. Convincing documentation would require extensive comparisons and controlled studies, but once a method is clinically adapted, it is commonly considered unethical to perform a controlled study in which the “preferred method” is not applied to a group.

V. Clinical Implications of the Case Study in Cardiology Physiological Research

Background

Wall stress is a useful concept to understand the progression of ventricular remodeling. We measured cumulative LV wall stress throughout the cardiac cycle over unit time and tested whether this “integrated wall stress (IWS)” would provide a reliable marker of total ventricular workload.

Methods and results

We applied IWS to mice after experimental myocardial infarction (MI) and sham-operated mice, both at rest and under dobutamine stimulation. Small infarcts were created so as not to cause subsequent overt hemodynamic decompensation. IWS was calculated over one minute through simultaneous measurement of LV internal diameter and wall thickness by echocardiography and LV pressure by LV catheterization. At rest, the MI group showed concentric LV hypertrophy pattern with preserved LV cavity size, LV systolic function, and IWS comparable with the sham group. Dobutamine stimulation induced a dose-dependent increase in IWS in MI mice, but not in sham mice; MI mice mainly increased heart rate, whereas sham mice increased LV systolic and diastolic function. IWS showed good correlation with a product of peak-systolic wall stress and heart rate. We postulate that this increase in IWS in postMI mice represents limited myocardial contractile reserve.

Conclusion

We hereby propose that IWS provides a useful estimate of total ventricular workload in the mouse model and that increased IWS indicates limited LV myocardial contractile reserve.

Keywords:

Wall stress; Ventricular workload; Myocardial contractile reserve; Ventricular remodeling

Clinical implications

IWS can be estimated by obtaining IWS index, which is calculated non-invasively by simultaneous M-mode echocardiogram and cuff blood pressure measurement, i.e., PS-WS instead of ES-WS and heart rate. This will provide a sensitive way to detect subclinical borderline failing myocardium in which the decline in LV myocardial contractile reserve precedes apparent LV dysfunction. This method may be clinically useful to address LV myocardial reserve in those patients who are not amenable to perform on exercise stress test, such as immediate post-operative patients under mechanical ventilation, critically ill patients with questionable LV dysfunction, and patients with primary muscular disorders and general muscular weakness (i.e., Duchenne muscular dystrophy).

VI. Limitations of the Case Study in Cardiology Physiological Research

There are certain limitations in this study.

  • First, wall stress measurement is reliable when there is an equal wall thickness with symmetrical structure. Obviously, with the creation of small MI, there is an asymmetry of LV myocardium in both structure and consistency (myocardium vs. scar tissue). However, the scar tissue is small and restricted to the LV apex (approximately 14% of entire LV myocardium [5]). In fact, most of LV wall was thickened after induction of this small experimental MI. Nevertheless, we acknowledge that this is our major limitation.
  • Secondly, there is an individual variability in response to dobutamine stimulation even in sham mice. Although the average sham mice (n = 5) showed only a modest increase in HR, PS-WS, and IWS during dobutamine stimulation, one mouse presented in Figure 1 showed a notable increase in HR and PS-WS in response to dobutamine. Nevertheless, even with increased HR and PS-WS, the calculated IWS remained relatively unchanged in the sham-operated mice.
  • Lastly, the reliability of IWS index is based upon the stipulation that ED-WS is significantly low compared with the systolic wall stress. Thus, IWS index may not be accurate in obvious volume overload cases and/or dilated hearts with LV dysfunction where ED-WS is significantly higher than that in normal condition. Of note, ED-WS in human is higher than that in mice in relation to PS-WS, probably around 15 to 20% of PS-WS [12].

VII. State of Cardiology on

  • wall stress
  • ventricular workload and
  • myocardial contractile reserve

Ventricular remodeling is a chronic progressive pathological process that results in heart failure after myocardial infarction (MI) or persistent unrelieved biomechanical overload [1,2]. Persistent and unrelieved biomechanical overload in combination with activation of inflammatory mediators and neurohormones is thought to be responsible for progressive ventricular remodeling after MI [3,4], but studies to investigate specific mechanisms in animals are hampered by the difficulty involved in quantifying biomechanical workload in vivo. The magnitude of ventricular remodeling advances in line with progressive ventricular geometric changes including myocardial hypertrophy and chamber dilatation with accompanying functional deterioration [1,2]. Previously, we proposed that post-ischemic ventricular remodeling is a pathological spectrum ranging from benign myocardial hypertrophy to progressive heart failure in the mouse model in which the prognosis is primarily determined by the magnitude of residual hemodynamic effects [5]. However, there has been no optimum quantitative measurement of ventricular workload as a contributory indicator of ventricular remodeling other than wall stress theory to explain how ventricular dilatation and hypertrophy develop after loss of viable working myocardium [6,7].

The concept of ventricular wall stress was introduced by Strauer et al. as a primary determinant of myocardial oxygen demand [8]. They indicated that overall myocardial energy demand depends upon intramyocardial wall tension, inotropic state of the myocardium, and heart rate. Wall stress theory is commonly introduced to explain development of concentric hypertrophy in chronic pressure overload and progressive ventricular dilatation in the failing heart. One study argued that peak-systolic wall stress increased as LV function worsened in a chronic volume overloaded status [9], and another suggested that peak-systolic wall stress closely reflected LV functional reserve during exercise [10]. However, the effect of heart rate or myocardial contractility was not considered in either study. Heart rate has been shown to be one of several important factors contributing to myocardial oxygen consumption [11].

Herein, we introduce a novel concept of “integrated wall stress (IWS)” to assess its significance as a marker of total ventricular workload and to validate its physiological relevance in the mouse model. The concept of continuous LV wall stress measurement was reported previously, but authors did not address the overall effects of changing wall stress during the cardiac cycle on the working myocardium [12]. We have defined IWS as cumulative wall stress over unit time: IWS was obtained by integrating continuous wall stress curve by accumulating wall stress values at millisecond sampling intervals over 1 min. By calculating IWS, we were able to incorporate the effects of not only systolic wall stress, but also of heart rate and inotropic status of the myocardium. These data were analyzed against conventional hemodynamic parameters in animals with and without MI in conjunction with incremental dobutamine stress. We hypothesize that unchanged IWS represents stable ventricular myocardial contractile reserve and that increase in IWS implies an early sign of mismatch between myocardial reserve and workload imposed on ventricular myocardium.

VIII. What are the Innovations of the Case Study in Cardiology Physiological Research

IWS measures total wall stress throughout the cardiac cycle over a unit time (= 1 min) including the effect of heart rate and inotropic state of the ventricular myocardium, whereas one-spot measurement of PS-WS and ED-WS only reflects maximum and minimum wall stress during a cardiac cycle, respectively. We hypothesized that increase in IWS indicates failure of myocardium to counteract increased ventricular workload. We have measured IWS in the mouse model in various physiological and pathological conditions to validate this hypothesis. Unchanged IWS observed in sham operated mice may imply that the contractile reserve of ventricular myocardium can absorb the increased cardiac output, whereas increased IWS after MI suggests that ventricular workloads exceeds intrinsic myocardial contractile reserve. Thus, we postulate that IWS is a reliable physiological marker in indicating a balance between external ventricular workload and intrinsic myocardial contractile reserve.

#1

IWS and myocardial reserve

“Wall stress theory” is an important concept in understanding the process of cardiac hypertrophy in response to increased hemodynamic loading [16]. When the LV myocardium encounters biomechanical overload, either pressure overload or volume overload, cardiac hypertrophy is naturally induced to normalize the wall stress so that myocardium can minimize the increase in myocardial oxygen demand; myocardial oxygen consumption depends mainly on systolic wall stress, heart rate, and contractility [8,17]. A question arises whether this hypertrophic response is a compensatory physiological adaptation to stabilize the wall stress or a pathological process leading to ventricular remodeling and heart failure. Physiological hypertrophy as seen in trained athletes reveals increased contractile reserve, whereas pathological hypertrophy shows a decrease in contractile reserve in addition to molecular expression of ventricular remodeling [1820]. However, what regulates the transition from compensatory adaptation to maladaptive process is not well understood.

Systolic wall stress has been studied extensively as a clinical marker for myocardial reserve. Systolic wall stress reflects the major determinants of the degree of LV hypertrophy and plays a predominant role in LV function and myocardial energy balance [17]. It has been shown that increased systolic wall stress inversely correlates with systolic function and myocardial reserve in patients with chronic volume overload [9,10,21], chronic pressure overload [22,23], and dilated cardiomyopathy [24]. However, one-point measurement of systolic wall stress does not encompass the effect of heart rate and contractile status, the other critical factors that affect myocardial oxygen demand [11]. The idea of IWS has been proposed to incorporate wall stress throughout the cardiac cycle and reflects the effects of heart rate and contractile status.

Myocardial oxygen consumption is determined mainly by ventricular wall stress, heart rate and contractility [17], which are all incorporated in IWS measurement. Continuous measurement of LV wall stress was previously reported in humans [12,15] and dogs [11] with a similar method, but not in mice. By integrating the continuous WS over one minute, we estimated the balance between myocardial contractile reserve and total external ventricular workload and examined its trend in relation to inotropic stimulation in the mouse heart in vivo. In this study, we have proposed unchanged IWS as a marker of sufficient myocardial contractile reserve, since increased wall stress demands higher myocardial oxygen consumption. Indeed, systolic wall stress does not increase with strenuous isometric exercise in healthy young athletes [25]. Thus, we propose that increase in IWS indicates diminished myocardial contractile reserve.

#2

Small MI model as a unique model to study early phase of progressive ventricular remodeling

A complex series of protective and damaging events takes place after MI, resulting in increased ventricular workload [26]. Initial ventricular geometric change is considered as a primary compensatory response to counteract an abrupt loss of contractile tissue. In classical theories of wall stress, which rely on the law of Laplace, the mechanisms of progressive ventricular dilatation and functional deterioration of the LV are attributed to the increased wall stress that is not compensated by the intrinsic compensatory mechanisms [2,16]. Although this theory is obvious in advanced stage of heart failure, the subclinical ventricular remodeling following borderline cases such as following small MI with initial full compensatory response is not well explained.

Study shown that our small MI model induced concentric hypertrophy without LV dilatation as if initial myocardial damage was completely compensated (Figure 2[5]. Although LV hypertrophy is induced initially to normalize the wall stress and to prevent ventricular dilatation, this hypertrophy is not altogether a physiological one because of decreased inotrophic and lusitropic reserve when stimulated with dobutamine (Figure 4) and because of simultaneous molecular and histological evidence of remodeling in the remote nonischemic LV myocardium (Figure 3). IWS and PS-WS become normalized in small MI at rest under anesthesia as a result of reactive hypertrophy accompanied by increased ANP and BNP mRNA level. Borderline maladaptive LVH is characterized by maintained LV performance at the expense of limited myocardial contractile reserve, and this abnormality can be unmasked by inotropic stimulation [18]. The trend of IWS at rest and with dobutamine stimulation suggests that MI mice were likely exposed to higher IWS during usual awake and active condition than sham-operated mice. In contrast, systolic wall stress in the pressure overload-induced LV hypertrophy showed a level comparable to that of sham both at rest and under stimulation by β1 adrenergic agonist, prenalterol, with comparable heart rate changes [27]. For this reason, IWS assessment by measuring cumulative WS in a unit time with and without inotropic stimuation should serve as a sensitive marker to assess whether induced LV hypertrophy is a compensatory physiological adaptation process or a pathological maladaptation process. Increased IWS that indicates imposed workload surpassing myocardial contractile reserve is likely to become a major driving factor in inducing progressive ventricular remodeling or initiating deleterious maladaptive processes after MI.

#3

IWS represents myocardial oxygen demand that can be estimated non-invasively

Study demonstrated a very good correlation between IWS and the product of PS-WS and HR (“IWS index”) in both MI and sham-operated hearts (Figure 6). This formula appears physiologically acceptable provided that ED-WS is sufficiently low compared with the PS-WS (approximately 10%, as is shown in Figures 4B and C). ES-WS was previously introduced as a useful tool for assessing myocardial loading status and myocardial oxygen consumption, but its measurement requires complicated preparation [28,29]. Because there is an excellent correlation between PS-WS and ES-WS, it has been demonstrated that ES-WS can be substituted by PS-WS [28], which can be easily obtained non-invasively [30]. ES-WS was previously determined as a useful marker to quantify LV afterload and contractility that can be simply and accurately measured non-invasively [15]. As myocardial oxygen consumption is mainly dependent upon systolic wall stress, contractility, and heart rate, it seems reasonable to propose that IWS and IWS index represent the status of myocardial contractile reserve.

Conclusions & Next Phases in Translational Medicine and Cardiology Physiological Research

Author: Justin Pearlman, MD, PhD, FACC 

Visual and numeric scores that assess the commitment to translation of basic discoveries to measured impact on human outcomes followed by increased prevalence of the benefits is of course desirable, but fraught with challenges.  Metrics of translational medicine may lead to rewards that can “game” the system by promoting choices of MeSH codes that augment the score for individual articles and/or clusters of work from a center of research without correlation to the actual impact of the body of work. The fairness of a metric also must account for division of labor whereby one group of researchers achieves major basic discoveries that ferment useful applications to improved outcomes in patient care, while others focus on applications or application assessments that may have widely disparate degrees of impact on the reduction to practice, validation and dissemination of improved care.

Thus in order to promote useful metrics of translational medicine progress, we propose a set of metrics on the metrics:

1. impact of reviewer skill/bias

2. impact of author coding/bias

3. ability to assess an impact factor independent of author word choices

4. ability to credit basic research for its downstream impact on other researchers culminating in clinical applications, validation, and dissemination of human benefits

5. ability to discern pioneering advances from “me too” duplications of effort and minor variations on work of the same group or others

6. ability to assess cost effectiveness, including the occurrences of subsequent re-investigations to clarify issues that could have been addressed in the instance study

7. ability to compute contribution to quality life year gain per dollar of added care

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http://www.translational-medicine.com/content/11/1/126

Griffin M Weber

Journal of Translational Medicine 2013, 11:126 (24 May 2013)

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Integrated wall stress: a new methodological approach to assess ventricular

workload and myocardial contractile reserve

http://www.translational-medicine.com/content/11/1/183

Dong H, Mosca H, Gao E, Akins RE, Gidding SS and Tsuda T

Journal of Translational Medicine 2013, 11:183 (7 August 2013)

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