Eosinopenia as a diagnostic marker of bloodstream infection in a general internal medicine setting: A cohort study

doi:10.21203/rs.2.12730/v1

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Eosinopenia as a diagnostic marker of bloodstream infection in a general internal medicine setting: A cohort study

https://doi.org/10.21203/rs.2.12730/v1

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published 30 Jan, 2020

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Background

Little is known about the potential use of the eosinophil count as a predictive marker of bloodstream infection. In this study, we aimed to assess the reliability of eosinopenia as a predictive marker of bloodstream infection.

Methods

This study was a retrospective cohort study. The outpatient department and general internal medicine department of a tertiary university hospital in Japan. A total of 189 adult patients with at least 2 sets of blood cultures obtained during January 1–December 31, 2018, after excluding those with the use of antibiotic therapy within 2 weeks prior to blood culture, steroid therapy, a history of haematological cancer, or eosinophilia. The diagnostic accuracies of each univariate variable and the multivariable logistic regression models were assessed by calculating the areas under the receiver operating characteristic curves (AUROCs). The primary outcome was a positive blood culture indicating bloodstream infection.

Results

Severe eosinopenia (<10 cells/mm3) alone yielded little overall predictive ability (AUROC: 0.606, 95% confidence interval (CI): 0.502–0.710, P=0.035), and only moderate sensitivity (50%, 95%CI: 29–70%) and specificity (71%, 95%CI: 63–78%). The model comprising baseline variables (age, sex) and the C-reactive protein level yielded an AUROC of 0.7384, and the further addition of eosinopenia yielded a slight improvement, with an AUROC of 0.7547 (P=0.4297) and a statistically significant net reclassification improvement (NRI) (P=0.03). However, the integrated discrimination index (IDI) (P=0.282) remained non-significant.

Conclusions

Severe eosinopenia can be considered an inexpensive marker of bloodstream infection in a general internal medicine setting.

Infectious Diseases

eosinopenia

bloodstream infection

blood culture

Blood cultures are necessary for the diagnosis and management of patients with bloodstream infection(1). However, the usefulness of this test is limited except in special situations [e.g., inpatients with suspected infectious endocarditis(2) or meningitis(3)] because of its poor sensitivity in ambulatory outpatient (4), primary care and hospital internal medicine department settings(5). Additionally, many contaminants may lead to a false positive culture and, consequently, unnecessary therapy(6).

To date, no study has identified a highly sensitive and specific, easily measured, rapid and inexpensive marker of bloodstream infection that correlates with infection severity and prognosis. Although the presence of chills(7), the C-reactive protein (CRP) level(8, 9), and the quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) score (10) have been identified as potential predictors of bloodstream infection, none has been determined to have adequate specificity and sensitivity.

Previously eosinopenia, defined as a reduced serum eosinophil count, was identified as a good diagnostic marker of infection(11). Although some studies reported that the absence of peripheral blood eosinophils could not be used as a clinically reliable marker of bacteraemia in a hospital inpatient setting(12, 13), those studies included limited numbers of patients and were not restricted to general internal medicine departments. Therefore, the potential usefulness of eosinopenia as a predictor of bloodstream infection in patients presenting or admitted to a general internal medicine department remains unclear. In this study, therefore, we hypothesised that eosinopenia would be a reliable marker of bloodstream infection in adult patients treated in the general internal medicine department of a tertiary university hospital. We conducted a retrospective cohort study to assess the diagnostic accuracy of eosinopenia as a marker of bloodstream infection in comparison to usual conventional markers of infection, including the serum CRP concentration, white cell count, and neutrophil count.

Study Design and Patient Selection

This retrospective, single-centre cohort study included all consecutive patients who presented to the inpatient and outpatient in the general internal medicine department, excluding intensive care unit and emergency department, of Dokkyo Medical University Hospital, Mibu, Tochigi, Japan and underwent blood cultures during January 1–December 31, 2018. Dokkyo Medical University Hospital is a tertiary teaching hospital. This study was conducted in accordance with the current version of the Declaration of Helsinki. The study protocol was approved by the institutional ethics committee of Dokkyo Medical University (No. R–20–18J).

Patient population

From a total of 399 adult patients (age >15 years) who underwent blood culture in the general internal medicine department during the study period, 206 were excluded because of antibiotic use within 2 weeks prior to the blood culture (n = 178), steroid use (n = 25), haematological cancer (n = 2), or eosinophilia (>1,500 cells/mm³ for >6 months; n = 1)(14). Four other patients were excluded because of a lack of data. The remaining 189 patients were enrolled into the study. Nine of these 189 patients lacked vital sign data. A flow diagram of patient selection is shown in Figure 1. All blood cultures were drawn at the discretion of the treating physician.

Patient and Public Involvement

No patient involved.

Outcome and definition

The primary study outcome was a positive blood culture indicative of bloodstream infection. We defined a bloodstream infection as the detection of a pathogenic microorganism in at least one blood culture bottle. Episodes involving bacterial contaminants were counted as negative cultures. The contamination criteria included a single set of blood cultures containing multiplying coagulase-negative Staphylococcus species, Bacillus species, Propionibacterium acnes or Corynebacterium species, which were previously identified as frequent contaminants in a previous study(15). All such samples were excluded prior to the review of medical notes.

Absolute eosinopenia was defined as an eosinophilic count of <10 cells/mm³(13). The qSOFA, a recently developed measure for the rapid identification of infected patients at risk of mortality, was also applied(10, 16, 17). This bedside clinical score identifies adult patients with suspected infection and a higher risk of poor outcomes typical of sepsis as those who meet with at least 2 of the following clinical criteria: respiratory rate of ≥22/min, altered mentation or systolic blood pressure of ≤100 mm Hg (10).

Procedure

From each patient, the clinicians drew 10 mL of blood aseptically from a superficial vessel and inoculated the sample into both aerobic and anaerobic cultures. This procedure was then repeated using a different superficial vessel to yield 2 sets of blood cultures for each patient(18). The cultures were incubated in blood culture bottles containing BACTEC resin-beads (Bactec Plus Aerobic/23F and Anaerobic/22F bottles; Becton Dickinson Instrument Systems, Sparks, MD, USA). The bottles were incubated at 35℃, sub-cultured daily, and inspected for bacterial growth for 6 days.

A fully automated BACTEC-FX blood culture incubation system (Becton Dickinson) was used to isolate bacteria from the blood cultures. Significant isolates were identified and tested for antimicrobial susceptibility according to the National Committee for Clinical Laboratory Standards guidelines(19). Positive bottles were subjected to microbiologic analyses for bacterial identification and antibiotic sensitivity. No negative bottles were subjected to terminal subculture. All bacterial species isolated from blood culture bottles were confirmed using matrix-assisted laser desorption ionisation–time of flight mass spectrometry.

Data collection

The medical records of patients who underwent blood cultures were reviewed to ensure that 2 attending clinicians considered the detected micro-organisms to be pathological, rather than contaminants. All data in this study were collected by the treating clinicians in the context of clinical management, and included age, sex, presence of chills, and vital signs (mental status, respiratory rate, and systolic blood pressure) at the time of blood culture. The potential markers of bloodstream infection assessed in this study included the serum CRP concentration and total white blood cell, neutrophil, and eosinophil counts. All markers were measured within 1 day of blood culture sample collection. Eosinophil counts were determined using an automated method.

Analysis

Continuous variables are presented as medians and interquartile ranges [25^th–75^th percentiles] and were compared using the Mann–Whitney U test. Categorical or binary variables are presented as numbers (percentages) and were compared using the chi‐squared test or Fisher’s exact test. The diagnostic accuracies of each univariate variable and the multivariable logistic regression models were assessed by calculating the corresponding area under each receiver operating characteristic curve (AUROC). A P value of <0.05 was considered statistically significant. The 95% confidence intervals (CIs) were used to quantify uncertainty.

Previous studies identified the CRP level as a powerful predictive marker of bloodstream infection(11, 20). In this study, we calculated the integrated discrimination index (IDI) and net reclassification improvement (NRI)(21) to assess whether the inclusion of eosinopenia into a model involving the baseline variables (age + sex) and CRP level would improve the predictive value. All statistical tests were performed using the R 3.6.0 and pROC package(22) for MacOS X (The R foundation for Statistical Computing, Vienna, Austria).

Of the 189 patients enrolled in the final analysis, 26 and 61 patients with a positive blood culture or eosinopenia were identified during the study period, respectively. In 4 patients, multiple organisms were detected in the same blood culture specimen at the time of bloodstream infection diagnosis. Ten of the 26 identified bloodstream infections (38%) were due to Gram-positive organisms, while 12 (46%) were due to Gram-negative organisms. The baseline characteristics of infected and non-infected patients are shown in Table 1. Patients with a bloodstream infection had a significantly higher age and CRP concentration and a significantly lower eosinophil count than those without a bloodstream infection. All other comparisons yielded insignificant results. Other patient characteristics that might have affected the eosinophil count(23) are presented in the Supplementary Table.

Table 2 presents the results of univariable analyses. Eosinopenia (AUROC: 0.606, 95%CI: 0.502–0.710), neutrophil count (AUROC: 0.654, 95%CI: 0.535–0.772, cut-off = 9033 cells/mm³), and CRP concentration (AUROC: 0.705, 95%CI: 0.605–0.806, cut-off = 4.89 mg/dL) were all identified as significant predictive markers of bloodstream infection. In contrast, the eosinophil count, white cell count (P = 0.06), qSOFA (P = 0.476), and presence of chills (P = 0.26) were not identified as statistically significant predictive markers. A further analysis revealed that eosinopenia could predict bloodstream infection at a reasonable level of specificity (71%, 95%CI: 63–78%) but a low level of sensitivity (50%, 95%CI: 29–70%). The CRP concentration and neutrophil count were more sensitive (80%, 95%CI: 61–93% and 61%, 95%CI: 41–80%, respectively) but less specific (56%, 95%CI: 48–64% and 69%, 95%CI: 62–77%, respectively).

Table 3 presents the AUROCs of the predictive models for bloodstream infection. The addition of CRP to the baseline variables (age, sex) improved the AUROC (from 0.6439 to 0.7384; P = 0.03) and yielded a statistically significant IDI (P = 0.03) and NRI (P = 0.003). The further addition of eosinopenia to the model including the baseline variables and CRP led to a slight improvement, with an AUROC of 0.7547 (P = 0.4297) and a statistically significant NRI (P = 0.03). However, the IDI (P = 0.282) remained non-significant. The corresponding AUROC curves are shown in Figure 2.

According to our findings, eosinopenia alone yielded a reasonable specificity but a low sensitivity and overall predictive ability for bloodstream infection in a cohort of patients who presented or were admitted to the department of general internal medicine at our university hospital. However, we found that eosinopenia was a more useful predictor of bloodstream infection than the qSOFA score and presence of chills in general internal medicine setting, excluding intensive care unit and emergency department. Moreover, the inclusion of eosinopenia in a prediction model comprising the baseline variables and CRP led to a slight improvement in the AUROC. These results suggest that eosinopenia may be useful as an inexpensive predictor of bloodstream infections. However, further investigations would be needed to exclude bloodstream infection.

Our study can be distinguished from previous work by a notable strength, namely the collection of data from a general internal medicine department. Although chills or qSOFA were previously identified as useful predictors of bloodstream infection in an intensive care unit or emergency department setting(11, 20, 24), our study showed that neither factor was a significant predictor of bloodstream infection in our general internal medicine department setting. This inconsistency suggests that chills and qSOFA may only be useful predictors in patients with a severe acute condition, who often present to an emergency department or are admitted to intensive care unit, but not in patients with milder conditions who would present to a general internal medicine department. Additionally, our finding that eosinopenia is a predictor of bloodstream infection suggests that this marker may be a useful tool for predicting such infections in patients with mild general condition. We found that the addition of an elevated CRP level to the baseline variables yielded a stronger predictive measure. The further addition of severe eosinopenia to this model also led to a slight improvement in the predictive ability, even though the eosinophil count itself was not a sufficiently predictive marker.

This study had several limitations. First, it was conducted in a single department at a single centre, and therefore our results cannot be easily generalised. Second, we excluded 178 patients (44.6%) who used antibiotics within 2 weeks prior to blood culture from the original sample. This may be due to the tertiary nature of our institution, as patients with bloodstream infection may have initially visited a primary clinic and began to receive antibiotic treatment prior to referral to our department. Third, no clear criteria have been set to determine which patients would be subjected to blood culture. Rather, this decision was made by the treating physician on a case-by-case basis. Fourth, we excluded patients with haematological diseases, eosinophilia and steroid users, as such cases are rarely seen in our department. Accordingly, our findings are not generalisable to these patient groups or to areas with a high prevalence of these diseases. Fifth, not only severe chills, shaking chills, but also mild to moderate chills were included in this study. According to the original article, the more severe degree of chills, especially shaking chills, suggests the high risk of bacteremia(7). That would be one possibility to show insignificant results. Finally, procalcitonin was recently identified as a novel predictive marker of bloodstream infection, although one study identified it as a poor predictor of culture positivity(25). In our study, procalcitonin was not evaluated in most cases. We further note that we defined eosinopenia using a cut-off of <10 cells/mm³. Possibly, a more sensitive measurement might improve the diagnostic utility. However, the results of our AUROC analysis suggest that when eosinopenia was modelled as a continuous variable, its diagnostic utility as a marker of bloodstream infection was limited.

In summary, the presence of eosinopenia could be considered an inexpensive indicator of possible bloodstream infection in a general internal medicine setting. However, further testing would be needed to confirm infection.

Areas under the receiver operating characteristic curves: AUROCs
Net reclassification improvement: NRI
Integrated discrimination index: IDI
C-reactive protein: CRP
quick Sequential (Sepsis-Related) Organ Failure Assessment: qSOFA

Ethic approval and consent to participate: Ethics committee of Dokkyo Medical University (No. R–20–18J). The committee approved participation of patients was obtained through an opt-out methodology.

Consent for publication: Not applicable.

Availability of data and material: No data sharing statement can be made at this point.

Competing interests: None declared.

Funding: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Author’s contributions: TH, YH, KM, HT, MN and TS contributed to the study concept and design. TH and YH performed the statistical analyses. TH contributed to the drafting of the manuscript. YH, KM, HT, MN and TS contributed to the critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.

Acknowledgements: Not applicable.

1.Kirn TJ, Weinstein MP. Update on blood cultures: how to obtain, process, report, and interpret. Clin Microbiol Infect. 2013;19(6):513–20.

2.Baddour LM, Wilson WR, Bayer AS, Fowler VG, Jr., Tleyjeh IM, Rybak MJ, et al. Infective Endocarditis in Adults: Diagnosis, Antimicrobial Therapy, and Management of Complications: A Scientific Statement for Healthcare Professionals From the American Heart Association. Circulation. 2015;132(15):1435–86.

3.Tunkel AR, Hartman BJ, Kaplan SL, Kaufman BA, Roos KL, Scheld WM, et al. Practice guidelines for the management of bacterial meningitis. Clin Infect Dis. 2004;39(9):1267–84.

4.Laupland KB, Church DL, Gregson DB. Blood cultures in ambulatory outpatients. BMC Infect Dis. 2005;5:35.

5.Friedman ND, Braun T, Fallach N, Carmeli Y. Blood Culture Sampling Practices among Internal Medicine Inpatients. Clinical Microbiology and Infectious Diseases. 2017;2(1).

6.Bates DW CE, Goldman L, Lee TH. Predicting bacteremia in hospitalized patients. A prospectively validated model. Annals of internal medicine. 1990;113(7):6.

7.Tokuda Y, Miyasato H, Stein GH, Kishaba T. The degree of chills for risk of bacteremia in acute febrile illness. Am J Med. 2005;118(12):1417.

8.Black S, Kushner I, Samols D. C-reactive Protein. J Biol Chem. 2004;279(47):48487–90.

9.Mccabe RE, Remington JS. C-Reactive Protein in Patients with Bacteremia. Journal of Clinical Microbiology. 1984;20(3):317–9.

10.Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis–3). JAMA. 2016;315(8):801.

11.Abidi K, Khoudri I, Belayachi J, Madani N, Zekraoui A, Zeggwagh AA, et al. Eosinopenia is a reliable marker of sepsis on admission to medical intensive care units. Crit Care. 2008;12(2):R59.

12.Setterberg MJ, Newman W, Potti A, Smego RA, Jr. Utility of eosinophil count as predictor of bacteremia. Clin Infect Dis. 2004;38(3):460–1.

13.Wibrow BA, Ho KM, Flexman JP, Keil AD, Kohrs DL. Eosinopenia as a diagnostic marker of bloodstream infection in hospitalised paediatric and adult patients: a case-control study. Anaesthesia and intensive care. 2011;39(2):224–30.

14.Roufosse F, Weller PF. Practical approach to the patient with hypereosinophilia. J Allergy Clin Immunol. 2010;126(1):39–44.

15.Terradas R, Grau S, Blanch J, Riu M, Saballs P, Castells X, et al. Eosinophil Count and Neutrophil-Lymphocyte Count Ratio as Prognostic Markers in Patients with Bacteremia: A Retrospective Cohort Study. PLOS ONE. 2012;7(8):e42860.

16.Seymour CW, Gesten F, Prescott HC, Friedrich ME, Iwashyna TJ, Phillips GS, et al. Time to Treatment and Mortality during Mandated Emergency Care for Sepsis. N Engl J Med. 2017;376(23):2235–44.

17.Ferrer R, Martin-Loeches I, Phillips G, Osborn TM, Townsend S, Dellinger RP, et al. Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program. Crit Care Med. 2014;42(8):1749–55.

18.Weinstein MP. Current blood culture methods and systems: clinical concepts, technology, and interpretation of results. Clin Infect Dis. 1996;23(1):40–6.

19.Wayne P. CLSI. Performance Standards for Antimicrobial Susceptibility Testing CLSI Supplement M100S. 26th ed. Clinical and Laboratory Standards Institute; 2016. 2016.

20.Shaaban H, Daniel S, Sison R, Slim J, Perez G. Eosinopenia: Is it a good marker of sepsis in comparison to procalcitonin and C-reactive protein levels for patients admitted to a critical care unit in an urban hospital? J Crit Care. 2010;25(4):570–5.

21.Pencina MJ, D’Agostino RB, Sr., D’Agostino RB, Jr., Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72; discussion 207–12.

22.Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.

23.Karakonstantis S, Kalemaki D, Tzagkarakis E, Lydakis C. Pitfalls in studies of eosinopenia and neutrophil-to-lymphocyte count ratio. Infectious diseases (London, England). 2018;50(3):163–74.

24.Lavoignet CE, Le Borgne P, Slimani H, Forato M, Kam C, Kauffmann P, et al. [Relevance of eosinopenia as marker of sepsis in the Emergency Department]. Rev Med Interne. 2016;37(11):730–4.

25.Caffarini EM, DeMott J, Patel G, Lat I. Determining the Clinical Utility of an Absolute Procalcitonin Value for Predicting a Positive Culture Result. Antimicrob Agents Chemother. 2017;61(5).

Table 1. Comparison of characteristics between patients who underwent blood culture and were diagnosed with bloodstream infection or non-blood infection.

Variable	Bloodstream infection (n = 26)	No bloodstream infection (n = 163)	P value**
Age, y (SD) [median]	71.4 (15.8) [75.0]	61.7 (20.0) [67.0]	0.02
Male, n(%)	13 (50)	93 (57)	0.50
CRP, mg/l [median, IQR]	121.2 [108, 50.6-157.6]	63.4 [40, 8.8-86.0]	<0.001
Total white cell count, cells/mm3 [median, IQR]	11,800 [11,400, 8600-13,000]	9,800 [8,900, 6,700-11,800]	0.06
Eosinophil count, cells/mm3 [median, IQR]	30.8 [10.5, 0.00-37.6]	110.9 [37.6, 0.00-98.3]	0.008
Neutrophil count, cells/mm3 [median, IQR]	10,600 [9,600, 7,100-11,900]	7,900 [7,300, 4,800-9,900]	0.012
qSOFA* score 0-1	16	112	0.476
qSOFA* score 2-3	9	43
Chills, n(%)	8 (31)	34 (21)	0.26

SD = standard deviation, IQR=interquartile range, CRP=C-reactive protein. qSOFA= Quick Sequential (Sepsis-Related) Organ Failure Assessment.

*N= 180 because of no vital sign data.

** P values by chi-squared, Mann-Whitney U test, or Fisher’s exact test.

Table 2. Areas under the receiver operating characteristic curves of eosinopenia, eosinophil, total white cell count, neutrophil, CRP and qSOFA as potential markers of bloodstream infection identified through a univariable analysis

Variable	Cut-off value	AUROC curve (95% CI)	P value**
Eosinopenia, <10 cells/mm3		0.606 (0.502-0.710)	0.035
Eosinophil count	< 24.4 cells/mm3	0.660 (0.558-0.761)	0.055
White cell count	> 10950 cells/mm3	0.614 (0.490-0.739)	0.070
Neutrophil count	> 9033 cells/mm3	0.654 (0.535-0.772)	0.012
CRP*, mg/l	> 4.89 mg/dl	0.705 (0.605-0.806)	0.001
qSOFA**		0.541 (0.439-0.6436)	0.400
Chills		0.550 (0.454-0.645)	0.263

AUROC = Areas under the receiver operating characteristic curves.

CI = confidence interval, CRP=C-reactive protein.

qSOFA= Quick Sequential (Sepsis-Related) Organ Failure Assessment.

* N=188 ** N=180

Table 3. Areas under the receiver operating characteristic curves of the predictive models for bloodstream infection

Model	AUROC curve (95% CI)	P value	IDI	P value	NRI	P value
Baseline variables*	0.6439 (0.544-0.7437)
Baseline variables + CRP	0.7384 (0.6371-0.8396)	0.03**	0.0651**	0.03**	0.6106**	0.003**
Baseline variables + CRP + eosinopenia	0.7547 (0.6525-0.857)	0.4297***	0.0129***	0.282***	0.4321***	0.03***

AUROC = Areas under the receiver operating characteristic curves.

CI = confidence interval, CRP=C-reactive protein.

IDI = integrated discrimination index.

NRI = net reclassification improvement.

* Including age, sex

** Compared with the model with baseline variables.

***Compared with the model with baseline variables + CRP

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Reviewer #2 agreed at journal
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Review #2 received at journal
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Editorial decision: Major revision
06 Nov, 2019
Review #1 received at journal
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Reviewer #1 agreed at journal
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Reviewers invited by journal
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Submission checks completed at journal
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Editor invited by journal
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First submitted to journal
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Eosinopenia as a diagnostic marker of bloodstream infection in a general internal medicine setting: A cohort study

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Study Design and Patient Selection

Patient population

Patient and Public Involvement

Outcome and definition

Procedure

Data collection

Analysis

Results

Discussion

Conclusion

List of abbrevitations

Declarations

References

Tables

Supplementary Files

Status:

Journal Publication

Version 1