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Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios as Prognostic Inflammatory Biomarkers in HIV, HCV, and HIV/HCV Coinfection

Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios as Prognostic Inflammatory Biomarkers... Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios as Prognostic Inflammatory Biomarkers in HIV, HCV, and HIV/HCV Coinfection Authors: Jennifer S. Hanberg(1), Matthew S. Freiberg(2), Matthew B. Goetz(3), Maria C. Rodriguez-Barradas(4), Cynthia Gibert (5), Kris Ann Oursler (6), Amy C. Justice (1), Janet P. Tate (1), and the VACS Project Team (1) Department of Medicine, Yale University School of Medicine, New Haven, CT, 06511 and VA Connecticut Healthcare System, West Haven, CT, 06510; (2) Department of Medicine and Epidemiology, Vanderbilt University School of Medicine, Nashville, TN, 37232; (3) Division of Infectious Diseases, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, CA, 90095 and Greater Los Angeles VA Healthcare Center, Los Angeles, CA, 90073; (4) Department of Medicine and Infectious Disease, Baylor College of Medicine, Houston, TX, 77030 and Michael E. DeBakey VA Medical Center, Houston, TX, 77030; (5) Division of Infectious Diseases and Medicine, George Washington University, School of Medicine and Health Sciences, Washington, DC, 20052; (6) Department of Medicine, Virginia Tech Carilion School of Medicine, Salem, VA, 24016 and University of Maryland School of Medicine, Baltimore, MD, 21201 Corresponding author: Amy C. Justice PI, Veterans Aging Cohort Study Professor of Medicine VA Connecticut Healthcare System 950 Campbell Ave West Haven, CT 06516 Phone: (203) 932-5711 ext. 3541 Fax: 203-937-4926 Email: amy.justice2@va.gov Published by Oxford University Press on behalf of Infectious Diseases Society of America 2019. This work is written by (a) US Government employee(s) and is in the public domain in the US. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Background: Inflammation in HIV-infected patients is associated with poorer health outcomes. Whether inflammation as measured by the neutrophil-to-lymphocyte ratio (NLR) and platelet-to- lymphocyte ratio (PLR) adds information to existing prognostic indices is not known. Methods: We analyzed data from 2000-2012 in the Veterans Aging Cohort Study (VACS), overall and stratified by HIV/HCV status (n=89,786). We randomly selected a visit date at which all labs of interest were available within 180 days; participants with HIV received at least one year of antiretroviral therapy. We followed patients for (1) mortality and (2) hepatic decompensation (HD) and analyzed associations using Cox regression, adjusted for a validated mortality risk index (VACS Index 2.0). In VACS Biomarker Cohort (VACS-BC), we considered correlation with biomarkers of inflammation: IL-6, D-dimer and sCD-14. Results: NLR and PLR demonstrated strong unadjusted associations with mortality (p<0.0001) and hepatic decompensation (p<0.0001) and were weakly correlated with other inflammatory biomarkers. While NLR remained statistically independent for mortality, as did PLR for HD, the addition of NLR and PLR to the VACS Index 2.0 did not result in significant improvement in discrimination compared to VACS Index 2.0 alone for mortality (c-statistic 0.767 vs 0.758) or for HD (c-statistic 0.805 vs 0.801). Conclusions: NLR and PLR were strongly associated with mortality and HD, and weakly correlated with inflammatory biomarkers. However, most of their association was explained by VACS Index 2.0. Addition of NLR and PLR to VACS 2.0 did not substantially improve discrimination for either outcome. Key words: Inflammation, NLR, PLR, HCV, hepatic decompensation Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Background Human immunodeficiency virus (HIV) infection is associated with chronic pro- inflammatory states, even in patients receiving antiretroviral therapy (ART).[1, 2] Inflammation in HIV, even when viral loads are low or undetectable, is associated with worsened outcomes including mortality, development of acquired immunodeficiency syndrome, and cirrhosis (in hepatitis C virus (HCV) coinfection).[3-8] Most inflammatory biomarkers are not measured in standard practice and may be expensive or impractical to monitor routinely. Two established markers of subclinical inflammation, the neutrophil-to-lymphocyte ratio (NLR) and platelet-to- lymphocyte ratio (PLR), are easily determined from a complete blood count. NLR and PLR are associated with mortality in populations with and without HIV.[9-13] PLR is also associated with hepatic fibrosis in HCV.[14, 15] However, the clinical impact of these biomarkers is not clear. If they are useful for prognosis, they may eventually be incorporated into a weighted risk index, though it is unknown whether they add information to existing indices of this type (such as the VACS Index and its successor, VACS 2.0).[16] Further, if NLR and PLR correlate with known inflammatory pathways, they could be applied in large- scale research as a surrogate for less widely available biomarkers.[16] There has been substantial interest in inflammatory biomarkers in HIV, including IL-6, a marker of systemic inflammation and neutrophil activation, D-dimer, a marker of altered coagulation activity, and sCD-14, a marker of monocyte activation.[3, 17, 18] We sought to elucidate the importance of NLR and PLR in patients with HIV on antiretroviral therapy, with or without HCV coinfection. Our primary aim was to assess the prognostic significance of NLR and PLR, with respect to both mortality and hepatic decompensation (HD). Of particular interest was whether these markers improve discrimination of the VACS Index 2.0, a validated risk index. Our second aim was to further investigate the Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 relationship between NLR, PLR and three alternative inflammatory biomarkers (IL-6, D-dimer, and sCD-14). Methods: We analyzed data from the Veterans Aging Cohort Study (VACS), a prospective longitudinal observational study of HIV-infected veterans that includes age, race and site- matched HIV-negative veterans in care as controls.[19] The institutional review boards associated with participating VA sites and the coordinating center approved the VACS. We randomly selected a visit date between 2002 to 2012, at which all results of interest were available (allowing values to carry forward 180 days); participants with HIV had received at least one year of ART therapy. We assessed whether NLR and PLR added prognostic information to the VACS Index 2.0 using multivariate adjustment. VACS 2.0 is a validated risk score incorporating age, CD4 count, HIV-1 RNA, hemoglobin, FIB-4 (a composite marker of liver fibrosis), estimated glomerular filtration rate, HCV coinfection, body mass index, albumin and white blood cell count. [20, 21] [22] Active HCV infection was defined as (1) a positive HCV RNA, (2) a positive HCV genotype, or (3) one inpatient or two outpatient HCV ICD-9 codes. HCV-negative status was defined as a negative HCV antibody test. All other patients were considered to have unknown HCV status. Those with positive HCV status at any timepoint during follow-up were classified as HCV positive. Primary endpoints were mortality and HD. Death dates were determined from the VA vital status file, which combines information from inpatient mortality, social security data and national death benefits data; this method has been shown to provide excellent mortality Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 ascertainment.[23] HD was defined by ascites, spontaneous bacterial peritonitis or esophageal varices; one inpatient or two outpatient ICD-9 codes for any of these conditions were required. We created a separate dataset using the VACS Biomarker Cohort (VACS BC) sub-study to query associations between NLR, PLR and other inflammatory biomarkers. Interleukin-6 (IL- 6), soluble CD-14, and D-dimer, were measured in banked specimens from blood drawn between 2005 and 2007. Assay methods have been previously described.[8] Patients with HIV in our subset of VACS BC had also received at least one year of ART. To analyze associations between NLR, PLR and these biomarkers, we used NLR and PLR values closest to blood draw date (within 90 days). Statistical analysis: Mortality was assessed at the first of 5 years of follow-up or September 30, 2016. HD status was determined at the first of 5 years of follow-up, the last date of care at a Veteran’s Administration, or September 30, 2015, due to changes in ICD coding after this date. We analyzed mortality and HD risk using Cox regression. Initial models assessing NLR and PLR as covariates were age-adjusted. We then adjusted for VACS 2.0 to evaluate whether NLR and PLR added prognostic information. NLR and PLR were analyzed as categorical covariates using five levels. For mortality, these levels were defined to have equal number of deaths in each. For HD, levels were defined to have equal number of HD events. We stratified patients into four strata by HIV/HCV infection status for most analyses. The NLR or PLR level with the lowest age-adjusted association in HIV/HCV coinfected patients was selected as the referent in each analysis. Patients experiencing the endpoint within 180 days of the random visit date were excluded to avoid undue influence of imminent events which might have prompted ordering of a particular test. Given the association between NLR, PLR and mortality in patients with cancer, we performed a sensitivity analysis excluding patients with malignancy. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 We determined discrimination of combinations of covariates using Harrell’s c, with 95% confidence intervals (CIs) from the somersd package in Stata. Model fit was assessed using Akaike’s information criterion (AIC). In the VACS BC we assessed the association between NLR, PLR and IL-6, D-dimer and sCD-14, using Spearman’s rank correlation. Statistical significance was defined as a two-tailed p<0.05. Statistical analysis was performed in SAS version 9.4 (SAS Institute, Cary, NC) and Stata version 14 (StataCorp, College Station, TX). Results: Our sample included 89,786 veterans, 72,119 of whom had confirmed HCV status. The most common reason for a missing HCV status was lack of testing. The components of the NLR and PLR were distributed differently among strata defined by HCV and HIV infection, although frank thrombocytopenia, neutropenia and leukopenia were uncommon (Table 1). As expected, HIV-/HCV- patients had the highest median levels of platelets, lymphocytes and neutrophils. By contrast, HCV and HIV infection were each associated with decreased platelet and neutrophil count, with a synergistic effect in coinfection. HIV infection was associated with decreased lymphocyte counts. Consistent with these findings, NLR was overall lower in patients with HIV than those without. Likewise, PLR was lower in patients with HCV compared to those without. Associations with Mortality Over 5 years, 14,737 patients died, with 77% of these deaths occurring at least 180 days after the random visit date (Table 1, Supplementary Table 1). Of the deaths occurring after 180 days, 8,582 were included in analysis of the relationship between NLR, PLR and mortality; those that were excluded lacked documentation of HCV status. Adjusting only for age, we found a J- Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 shaped relationship between PLR and mortality and a more linear relationship between NLR and mortality (Figure 1, Supplementary Table 2). After adjustment for VACS 2.0, NLR remained associated with mortality. The association between mortality and NLR or PLR was similar across strata by HIV and HCV status for lower values of NLR or PLR, though hazard ratios (HRs) tended to be higher for uninfected controls than for patients with HIV and HCV among higher levels of NLR or PLR (Figure 1, Supplementary Table 2). Comparing highest to lowest NLR level, the VACS Index 2.0-adjusted HR associated with high NLR was 1.7 for HIV-/HCV- [95% CI 1.5-1.9] and 1.3 for HIV+/HCV+ patients (95% CI 1.1-1.6). A similar trend was observed for PLR, comparing highest to second PLR level [uninfected: HR=1.8 (95% CI 1.6-1.9), coinfected: HR=1.2 (95% CI 0.98-1.5)]. Sensitivity analysis excluding patients with a history of malignancy did not significantly alter these results (data not shown). Addition of NLR, PLR, or both to Cox models using VACS 2.0 did not improve discrimination or model fit; c-statistic improvements did not meet statistical significance in any subgroup, though point estimates increased by 0.003- 0.009 (Table 2, Figure 3). Hepatic Decompensation Hepatic decompensation occurred in 3,727 patients, with 30% of these events occurring at least 180 days after NLR and PLR were measured (Table 1; Supplementary Table 3). Of the HD events occurring at least 180 days after cohort entry, 980 had documented HCV status and were therefore included in this analysis. HD was more common among patients with HCV than without. Low PLR was a significant predictor of incident HD among all levels, with PLR in the lowest level conferring a VACS 2.0-adjusted HR of 2.1 (95% CI 1.3-3.4) among coinfected patients and 2.3 (95% CI 1.6-3.5) among uninfected patients (Figure 2D). PLR in the highest level also predicted HD in uninfected patients (HR=1.9, 95% CI 1.4-2.6), but not in any other Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 stratum. NLR was independently predictive of HD in uninfected patients only (Supplementary Table 4, Figure 2A and 2B). Addition of NLR, PLR or both to the VACS 2.0 model resulted in point estimate c-statistic increases of 0.004 in the overall cohort and 0.016 among HIV+/HCV+ patients; however, these increases did not meet statistical significance (Table 2, Figure 4). Association with Other Biomarkers of Inflammation In VACS BC (n=1475; Table 3; Supplementary Table 5), NLR was correlated with IL-6 (r=0.15, p<0.0001) but PLR was not (r=0.049, p=0.06). NLR and PLR were correlated with D- dimer (both r=0.12, p<0.0001). sCD-14 was correlated with NLR (r=0.10, p=0.0002) and PLR (r=0.06, p=0.03). Patterns were similar across HIV/HCV strata. Discussion The ideal role for NLR and PLR in research and practice has yet to be determined, despite numerous analyses demonstrating their prognostic relevance. In this analysis, we evaluated two possible applications: (1) incorporation into a risk index and (2) use as a surrogate for a pathway to end-organ disease (immune dysfunction and systemic inflammation).[16] We found modest differences in hazard ratios associated with level of PLR and NLR across strata, most of which were attenuated after adjustment for VACS Index 2.0. In particular, individuals with higher NLR or PLR seemed to be at greater relative mortality risk in age- adjusted analyses if they were HIV/HCV-negative than their uninfected counterparts. This trend should be interpreted in light of overall higher death rate in HIV/HCV coinfection, independent of NLR or PLR; though a high NLR or PLR may be a slightly less ominous sign in an HIV/HCV coinfected patient than an uninfected patient, the absolute 5-year mortality rate for coinfected patients with high NLR was 44% compared to 22% in uninfected patients, with a similar trend Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 for high levels of PLR. This suggests that individuals in the highest levels of NLR and PLR were likely characterized by higher illness severity overall, but that this risk was mostly captured by in the VACS Index, such as FIB-4 score. Despite an independent association between NLR and PLR with mortality and HD, NLR and PLR failed to improve discrimination when added to models containing VACS Index 2.0. As c-statistic changes can be relatively insensitive, it is possible that inflammatory state as measured by NLR and PLR may have an impact on outcome apart from the variables measured in the VACS Index 2.0. However, the lack of improvement in discrimination suggests these markers lack additional prognostic value beyond existing indices, and thus their clinical utility remains unclear. We observed statistically significant but very weak correlations between most combinations of PLR and NLR with D-dimer, sCD-14 and IL-6. The strongest correlation was between IL-6 and NLR, which is plausible given the documented role of IL-6 in neutrophil differentiation as well as known correlations between IL-6 and NLR in HCV monoinfection.[24- 26] However, the use of these ratios as surrogates for systemic inflammation is somewhat confounded in HIV and HCV. Specific considerations include the effects of HIV on neutrophil and lymphocyte counts as well HIV-associated pancytopenia. By adjusting for VACS 2.0, we have controlled for the expected effect of HIV severity (i.e., CD4 count and HIV viral load) on mortality; however, VACS Index 2.0 was not designed to predict HD. The mortality-specific weighting of variables incorporated into VACS Index 2.0 to create a single number may oversimplify the relative independent risks of the component variables with respect to a different outcome (e.g., FIB-4 score may be more strongly associated with HD than with mortality). Additionally, liver fibrosis causes thrombocytopenia, and therefore may affect the interpretation of PLR as a pure inflammatory marker. Based on weak correlations and possible confounding of Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 interpretation of these ratios, it is unlikely that NLR and PLR can reliably supplant IL-6, sCD-14 or D-dimer as measurements of clinically significant virally-induced inflammation. Limitations of this analysis include its retrospective nature. In particular, though we found an association between NLR, PLR and outcomes independent of VACS Index, our analysis does not rule out residual confounding, such as the possibility that NLR and PLR may reflect by additional comorbidities or clinical factors not accounted for in the VACS Index. Our dataset included a large number of deaths but fewer HD events, which limits the power of analyses using HD as an outcome. Further, the use of NLR and PLR as biomarkers for HD risk in patients with versus without HCV is limited by the different biological pathways that may lead to HD in HCV-related versus non-related liver disease. Our data include participants in care between 2000-2012, with outcomes out to 2015 and 2016; it is possible that the increasing number of patients with HCV and sustained viral response may affect the interpretation of NLR and PLR in HCV. Though the exclusion of patients who experienced endpoints within 180 days may introduce bias, sensitivity analyses including events occurring within 180 days of cohort entry found no substantive differences. Finally, we tested correlation between NLR and PLR with IL-6, soluble CD-14 and D-dimer, while many additional inflammatory biomarkers exist. NLR and PLR may be more strongly correlated with other biomarkers. Conclusions Both NLR and PLR were statistically independent of the VACS Index 2.0 for mortality prediction, as was PLR for the prediction of HD. Both ratios demonstrated moderate correlation with inflammatory biomarkers. However, neither NLR nor PLR added meaningfully to the discriminative ability of a validated risk index for mortality. Further research may be indicated to determine the usefulness of NLR and PLR in (1) prognostication for liver fibrosis and Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 hepatocellular carcinoma, especially in HIV/HCV-coinfected patients and (2) surrogacy for alternative inflammatory biomarkers. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Funding: This work was supported by the National Institute on Alcohol Abuse and Alcoholism at the National Institutes of Health [Grants U24 AA020794, U01 AA020790 and U10 AA013566]; the National Heart, Lung and Blood Institute at the National Institutes of Health [Grant T35HL007649]; and in kind by the United States Department of Veterans Affairs. Conflicts of Interest: The authors declare no conflicts of interest related to this manuscript. Acknowledgments: None. This work has not been previously presented in any format. Corresponding author: Janet P. Tate Director, Biostatistics Core, Veterans Aging Cohort Study Assistant Professor of Medicine VA Connecticut Healthcare System 950 Campbell Ave West Haven, CT 06516 Phone: (203) 932-5711 ext. 5371 Fax: 203-937-4926 Email: janet.tate2@va.gov Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 References: 1. Hunt PW. HIV and inflammation: mechanisms and consequences. Curr HIV/AIDS Rep 2012; 9:139-47. 2. Armah KA, McGinnis K, Baker J, et al. HIV status, burden of comorbid disease, and biomarkers of inflammation, altered coagulation, and monocyte activation. Clin Infect Dis 2012; 55:126-36. 3. Kuller LH, Tracy R, Belloso W, et al. Inflammatory and coagulation biomarkers and mortality in patients with HIV infection. PLoS Med 2008; 5:e203. 4. Boulware DR, Hullsiek KH, Puronen CE, et al. 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Tate J BS, Gibert C, Goetz M, Marconi V, Oursler KA, Rimland D, Rodriguez-Barradas M, Justice AC. . White blood count, albumin, and BMI enhance VACS Index prognostic model, but nadir CD4 and CD8 metrics do not. In: Infectious Diseases Society of America (IDSA) ID Week. (San Diego, CA). 23. Cowper DC, Kubal JD, Maynard C, Hynes DM. A primer and comparative review of major US mortality databases. Ann Epidemiol 2002; 12:462-8. 24. Abdel-Razik A, Mousa N, Besheer TA, et al. Neutrophil to lymphocyte ratio as a reliable marker to predict insulin resistance and fibrosis stage in chronic hepatitis C virus infection. Acta Gastroenterol Belg 2015; 78:386-92. 25. Suda T, Yamaguchi Y, Suda J, Miura Y, Okano A, Akiyama Y. Effect of interleukin 6 (IL-6) on the differentiation and proliferation of murine and human hemopoietic progenitors. Exp Hematol 1988; 16:891-5. 26. Hashizume M, Higuchi Y, Uchiyama Y, Mihara M. IL-6 plays an essential role in neutrophilia under inflammation. Cytokine 2011; 54:92-9. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Tables Table 1: Characteristics of veterans randomly selected from VACS 2.0 measurements between 2000-2012, with NLR and PLR measured within 180 days of VACS 2.0, and receiving ART for ≥1 year (if HIV+) Overall HIV-/HCV- HIV+/HCV- HIV-/HCV+ HIV+/HCV+ N 89,786 47,570 11,881 8,955 3,713 Demographics Age (y) 52.9±10.4 52.4±10.4 51.5±11.1 53.5±6.2 54.0±6.9 Male sex (%) 97.3 97. 97.1 98.9 98.6 Race (%) White 39.7 39.6 43.3 29.2 25.9 Black 48.5 48.0 45.5 61.1 62.6 Hispanic 8.8 9.8 7.2 8.6 9.5 Other 3.0 2.7 4.0 1.2 2.1 BMI (kg/m ) 28.1 (24.5-32.2) 29.2 (25.6-33.4) 25.7 (22.9-29.1) 27.1 (23.9-30.8) 24.5 (21.8-27.6) Prevalent comorbidities CAD 14.3 15.3 9.8 12.4 9.5 CHF 4.9 5.0 3.5 4.7 4.9 BMI>30 33.5 39.2 19.8 27.0 12.9 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Alcohol abuse or 26.0 24.4 20.4 52.7 47.8 dependence Alcohol-related 2.2 1.8 0.8 7.1 3.0 complications Cirrhosis 2.1 1.0 0.9 9.3 7.6 Diabetes mellitus 21.7 23.9 11.7 23.7 15.2 COPD 15.2 15.0 13.4 17.7 20.2 Cancer 3.2 3.0 4.1 2.6 2.8 CD4 (cells/mm ) ≥500 40.6 -- 43.5 -- 30.8 350-499 21.5 -- 21.3 -- 22.8 300-349 21.1 -- 19.9 -- 25.3 100-299 9.5 -- 8.5 -- 12.3 50-99 3.4 -- 3.0 -- 4.6 <50 4.0 -- 3.8 -- 4.3 HIV-1 RNA (copies/mL) ≤500 90.0 -- 82.8 -- 76.2 500-10,000 14.6 -- 13.3 -- 18.4 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 ≥10,000 4.4 -- 3.9 -- 5.4 Hemoglobin (g/dL) ≥14 60.4 63.0 56.5 55.9 42.6 12-13.9 29.2 28.6 31.3 29.3 35.1 10-11.9 8.1 6.6 9.3 11.5 17.1 <10 2.3 1.8 2.9 3.3 5.3 FIB-4 <1.45 68.4 76.1 65.8 41.1 27.6 1.45-3.25 25.9 21.1 29.8 39.9 45.6 >3.25 5.8 2.8 4.5 19.1 26.8 eGFR (mL/min/1.73 m ) ≥60 90.3 91.0 90.0 90.5 86.9 45-59.9 5.5 5.2 6.2 3.9 6.0 30-44.9 2.0 1.9 1.8 1.9 2.3 <30 2.2 1.9 2.1 3.7 4.7 VACS Index 2.0 34 (26-45) 30 (24-37) 46 (36-58) 42 (36-51) 65 (54-79) Died (%) Within 180 days 3.7 2.4 4.1 5.0 8.4 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 No 83.6 88.1 83.7 76.7 66.4 After 180 days 12.7 9.6 12.3 18.3 25.2 HD (%) Pre-existing (within 180 days) 2.9 1.9 2.2 8.7 9.0 No 95.9 97.5 97.0 87.1 86.4 Yes 1.2 0.7 0.8 4.2 4.6 White Blood Cells (x10 /µL) 5.2 (4.6-8.3) 6.8 (5.5-8.6) 5.5 (4.4-7.0) 6.4 (4.8-8.3) 5.0 (3.8-6.6) Neutrophils (x10 /µL) 3.8 (2.7-5.2) 4.0 (2.9-5.5) 2.9 (2.1-4.1) 3.4 (2.3-5.0) 2.5 (1.7-3.8) Platelets (x10 /µL) 230 (189-276) 237 (199-281) 219 (180-263) 206 (157-258) 188 (140-240) Lymphocytes (x10 /µL) 1.9 (1.4-2.4) 1.9 (1.5-2.4) 1.8 (1.3-2.3) 1.9 (1.4-2.5) 1.6 (1.1-2.2) 2.0 (1.4-3.0) 2.1 (1.5-3.1) 1.7 (1.1-2.5) 1.7 (1.1-2.7) 1.6 (1.0-2.6) NLR PLR 122 (92-165) 125 (95-166) 122 (92-169) 104 (75-147) 113 (79-166) Albumin (g/dL) 4.1 (3.8-4.4) 4.1 (3.9-4.4) 4.1 (3.8-4.4) 3.9 (3.6-4.2) 3.8 (3.4-4.1) Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 CAD: coronary artery disease. CHF: congestive heart failure. COPD: chronic obstructive pulmonary disease. eGFR: estimated glomerular filtration rate. HCV: hepatitis C virus. BMI: body mass index. NLR: neutrophil-to-lymphocyte ratio. PLR: platelet-to- lymphocyte ratio. ART: antiretroviral therapy. Values presented are median (Q1-Q3), mean±standard deviation or column percentage. Patients were followed for death and hepatic decompensation for up to five years. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Table 2: Model fit and discrimination Endpoint: Death OVERALL HIV-/HCV- HIV+/HCV- HIV-/HCV+ HIV+/HCV+ (N=86444) (N=46447) (N=11395) (N=8505) (N=3400) C-statistic AIC C-statistic AIC C-statistic AIC C-statistic AIC C-statistic AIC VACS Index 2.0 0.758 247201 0.773 92309 0.784 25254 0.725 28035 0.735 14189 VACS 2.0, NLR 0.766 246270 0.778 92150 0.786 25227 0.732 27997 0.738 14183 VACS 2.0, PLR 0.764 246825 0.779 92143 0.787 25239 0.728 28018 0.736 14193 VACS 2.0, NLR, PLR 0.767 246157 0.780 92094 0.788 25217 0.735 27976 0.738 14188 Endpoint: HD OVERALL HIV-/HCV- HIV+/HCV- HIV-/HCV+ HIV+/HCV+ (N=84584) (N=45750) (N=11247) (N=7978) (N=3195) C-statistic AIC C-statistic AIC C-statistic AIC C-statistic AIC C-statistic AIC 0.801 23452 0.778 6808 0.707 1704 0.765 6329 0.766 2541 VACS Index 2.0 VACS 2.0, NLR 0.802 23449 0.786 6798 0.713 1709 0.764 6322 0.7667 2545 VACS 2.0, PLR 0.803 23294 0.791 6785 0.705 1701 0.769 6279 0.7775 2531 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 0.805 23247 0.793 6778 0.715 1705 0.776 6256 0.782 2533 VACS 2.0, NLR, PLR NLR: neutrophil-to-lymphocyte ratio. PLR: Platelet-to-lymphocyte ratio. AIC: Akaike’s Information Criterion. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Table 3: Correlations of NLR and PLR with inflammatory biomarkers in VACS Biomarker Cohort Overall HIV-/HCV- HIV-/HCV+ HIV+/HCV- HIV+/HCV+ (N=1475) (N=337) (N=118) (N=496) (N=367) r p-value r p-value r p-value r p-value r p-value IL-6 0.15 <0.0001 0.11 0.05 0.22 0.02 0.21 <0.0001 0.21 <0.0001 NLR PLR 0.049 0.06 0.08 0.14 0.06 0.55 0.08 0.07 0.05 0.31 sCD- 14 0.095 0.0002 0.11 0.04 0.17 0.07 0.15 0.001 0.13 0.01 NLR PLR 0.055 0.03 0.14 0.01 -0.14 0.12 0.10 0.02 0.03 0.51 D-dim er NLR 0.12 <0.0001 0.05 0.36 0.27 0.003 0.08 0.09 0.11 0.01 0.12 <0.0001 0.07 0.21 0.08 0.36 0.14 0.001 0.03 0.51 PLR NLR: neutrophil-to-lymphocyte ratio. PLR: platelet-to-lymphocyte ratio. IL-6: interleukin-6. sCD-14: soluble CD-14. Correlations are Spearman’s rho. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Figure 1: Hazard ratios for all-cause mortality associated with NLR and PLR NLR: neutrophil-to-lymphocyte ratio. PLR: platelet-to-lymphocyte ratio. Levels defined by equal numbers of deaths. Error bars represent 95% CIs. Panels A and C are age-adjusted; panels B and D are adjusted for VACS 2.0. Panels A and B analyze NLR as an independent variable; panels C and D analyze PLR as an independent variable. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Figure 2: Hazard ratios for hepatic decompensation associated with NLR and PLR NLR: neutrophil-to-lymphocyte ratio. PLR: platelet-to-lymphocyte ratio. Levels defined by equal numbers of HD events. Error bars represent 95% CIs. Panels A and C are age-adjusted; panels B and D are adjusted for VACS 2.0. Panels A and B analyze NLR as an independent variable; panels C and D analyze PLR as an independent variable. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Figure 3: Forest plot of c-statistics (death outcome) VACS 2.0 VACS 2.0 + NLR Overall Cohort VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV-/HCV- VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV+/HCV- VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV-/HCV+ VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV+/HCV+ VACS 2.0 + PLR VACS 2.0 + NLR + PLR 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 Center dots represent Harrell’s c; whiskers represent 95% CI. Leftmost column denotes subgroup; second column indicates covariates in each model. Covariates included categorical NLR and PLR, and continuous VACS 2.0. Overall cohort includes individuals without documentation of HCV status. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Figure 4: Forest plot of c-statistics (Hepatic decompensation outcome) VACS 2.0 VACS 2.0 + NLR Overall Cohort VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV-/HCV- VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV+/HCV- VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV-/HCV+ VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV+/HCV+ VACS 2.0 + PLR VACS 2.0 + NLR + PLR 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 Center dots represent Harrell’s c; whiskers represent 95% CIs. Leftmost column denotes subgroup; second column indicates covariates in each model. Covariates included categorical NLR and PLR, and continuous VACS 2.0. HD: hepatic decompensation. Overall cohort includes individuals without documentation of HCV status. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Accepted Manuscript http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Forum Infectious Diseases Oxford University Press

Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios as Prognostic Inflammatory Biomarkers in HIV, HCV, and HIV/HCV Coinfection

Open Forum Infectious Diseases , Volume Advance Article – Jan 9, 32

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Published by Oxford University Press on behalf of Infectious Diseases Society of America 2019. This work is written by (a) US Government employee(s) and is in the public domain in the US.
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Abstract

Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios as Prognostic Inflammatory Biomarkers in HIV, HCV, and HIV/HCV Coinfection Authors: Jennifer S. Hanberg(1), Matthew S. Freiberg(2), Matthew B. Goetz(3), Maria C. Rodriguez-Barradas(4), Cynthia Gibert (5), Kris Ann Oursler (6), Amy C. Justice (1), Janet P. Tate (1), and the VACS Project Team (1) Department of Medicine, Yale University School of Medicine, New Haven, CT, 06511 and VA Connecticut Healthcare System, West Haven, CT, 06510; (2) Department of Medicine and Epidemiology, Vanderbilt University School of Medicine, Nashville, TN, 37232; (3) Division of Infectious Diseases, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, CA, 90095 and Greater Los Angeles VA Healthcare Center, Los Angeles, CA, 90073; (4) Department of Medicine and Infectious Disease, Baylor College of Medicine, Houston, TX, 77030 and Michael E. DeBakey VA Medical Center, Houston, TX, 77030; (5) Division of Infectious Diseases and Medicine, George Washington University, School of Medicine and Health Sciences, Washington, DC, 20052; (6) Department of Medicine, Virginia Tech Carilion School of Medicine, Salem, VA, 24016 and University of Maryland School of Medicine, Baltimore, MD, 21201 Corresponding author: Amy C. Justice PI, Veterans Aging Cohort Study Professor of Medicine VA Connecticut Healthcare System 950 Campbell Ave West Haven, CT 06516 Phone: (203) 932-5711 ext. 3541 Fax: 203-937-4926 Email: amy.justice2@va.gov Published by Oxford University Press on behalf of Infectious Diseases Society of America 2019. This work is written by (a) US Government employee(s) and is in the public domain in the US. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Background: Inflammation in HIV-infected patients is associated with poorer health outcomes. Whether inflammation as measured by the neutrophil-to-lymphocyte ratio (NLR) and platelet-to- lymphocyte ratio (PLR) adds information to existing prognostic indices is not known. Methods: We analyzed data from 2000-2012 in the Veterans Aging Cohort Study (VACS), overall and stratified by HIV/HCV status (n=89,786). We randomly selected a visit date at which all labs of interest were available within 180 days; participants with HIV received at least one year of antiretroviral therapy. We followed patients for (1) mortality and (2) hepatic decompensation (HD) and analyzed associations using Cox regression, adjusted for a validated mortality risk index (VACS Index 2.0). In VACS Biomarker Cohort (VACS-BC), we considered correlation with biomarkers of inflammation: IL-6, D-dimer and sCD-14. Results: NLR and PLR demonstrated strong unadjusted associations with mortality (p<0.0001) and hepatic decompensation (p<0.0001) and were weakly correlated with other inflammatory biomarkers. While NLR remained statistically independent for mortality, as did PLR for HD, the addition of NLR and PLR to the VACS Index 2.0 did not result in significant improvement in discrimination compared to VACS Index 2.0 alone for mortality (c-statistic 0.767 vs 0.758) or for HD (c-statistic 0.805 vs 0.801). Conclusions: NLR and PLR were strongly associated with mortality and HD, and weakly correlated with inflammatory biomarkers. However, most of their association was explained by VACS Index 2.0. Addition of NLR and PLR to VACS 2.0 did not substantially improve discrimination for either outcome. Key words: Inflammation, NLR, PLR, HCV, hepatic decompensation Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Background Human immunodeficiency virus (HIV) infection is associated with chronic pro- inflammatory states, even in patients receiving antiretroviral therapy (ART).[1, 2] Inflammation in HIV, even when viral loads are low or undetectable, is associated with worsened outcomes including mortality, development of acquired immunodeficiency syndrome, and cirrhosis (in hepatitis C virus (HCV) coinfection).[3-8] Most inflammatory biomarkers are not measured in standard practice and may be expensive or impractical to monitor routinely. Two established markers of subclinical inflammation, the neutrophil-to-lymphocyte ratio (NLR) and platelet-to- lymphocyte ratio (PLR), are easily determined from a complete blood count. NLR and PLR are associated with mortality in populations with and without HIV.[9-13] PLR is also associated with hepatic fibrosis in HCV.[14, 15] However, the clinical impact of these biomarkers is not clear. If they are useful for prognosis, they may eventually be incorporated into a weighted risk index, though it is unknown whether they add information to existing indices of this type (such as the VACS Index and its successor, VACS 2.0).[16] Further, if NLR and PLR correlate with known inflammatory pathways, they could be applied in large- scale research as a surrogate for less widely available biomarkers.[16] There has been substantial interest in inflammatory biomarkers in HIV, including IL-6, a marker of systemic inflammation and neutrophil activation, D-dimer, a marker of altered coagulation activity, and sCD-14, a marker of monocyte activation.[3, 17, 18] We sought to elucidate the importance of NLR and PLR in patients with HIV on antiretroviral therapy, with or without HCV coinfection. Our primary aim was to assess the prognostic significance of NLR and PLR, with respect to both mortality and hepatic decompensation (HD). Of particular interest was whether these markers improve discrimination of the VACS Index 2.0, a validated risk index. Our second aim was to further investigate the Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 relationship between NLR, PLR and three alternative inflammatory biomarkers (IL-6, D-dimer, and sCD-14). Methods: We analyzed data from the Veterans Aging Cohort Study (VACS), a prospective longitudinal observational study of HIV-infected veterans that includes age, race and site- matched HIV-negative veterans in care as controls.[19] The institutional review boards associated with participating VA sites and the coordinating center approved the VACS. We randomly selected a visit date between 2002 to 2012, at which all results of interest were available (allowing values to carry forward 180 days); participants with HIV had received at least one year of ART therapy. We assessed whether NLR and PLR added prognostic information to the VACS Index 2.0 using multivariate adjustment. VACS 2.0 is a validated risk score incorporating age, CD4 count, HIV-1 RNA, hemoglobin, FIB-4 (a composite marker of liver fibrosis), estimated glomerular filtration rate, HCV coinfection, body mass index, albumin and white blood cell count. [20, 21] [22] Active HCV infection was defined as (1) a positive HCV RNA, (2) a positive HCV genotype, or (3) one inpatient or two outpatient HCV ICD-9 codes. HCV-negative status was defined as a negative HCV antibody test. All other patients were considered to have unknown HCV status. Those with positive HCV status at any timepoint during follow-up were classified as HCV positive. Primary endpoints were mortality and HD. Death dates were determined from the VA vital status file, which combines information from inpatient mortality, social security data and national death benefits data; this method has been shown to provide excellent mortality Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 ascertainment.[23] HD was defined by ascites, spontaneous bacterial peritonitis or esophageal varices; one inpatient or two outpatient ICD-9 codes for any of these conditions were required. We created a separate dataset using the VACS Biomarker Cohort (VACS BC) sub-study to query associations between NLR, PLR and other inflammatory biomarkers. Interleukin-6 (IL- 6), soluble CD-14, and D-dimer, were measured in banked specimens from blood drawn between 2005 and 2007. Assay methods have been previously described.[8] Patients with HIV in our subset of VACS BC had also received at least one year of ART. To analyze associations between NLR, PLR and these biomarkers, we used NLR and PLR values closest to blood draw date (within 90 days). Statistical analysis: Mortality was assessed at the first of 5 years of follow-up or September 30, 2016. HD status was determined at the first of 5 years of follow-up, the last date of care at a Veteran’s Administration, or September 30, 2015, due to changes in ICD coding after this date. We analyzed mortality and HD risk using Cox regression. Initial models assessing NLR and PLR as covariates were age-adjusted. We then adjusted for VACS 2.0 to evaluate whether NLR and PLR added prognostic information. NLR and PLR were analyzed as categorical covariates using five levels. For mortality, these levels were defined to have equal number of deaths in each. For HD, levels were defined to have equal number of HD events. We stratified patients into four strata by HIV/HCV infection status for most analyses. The NLR or PLR level with the lowest age-adjusted association in HIV/HCV coinfected patients was selected as the referent in each analysis. Patients experiencing the endpoint within 180 days of the random visit date were excluded to avoid undue influence of imminent events which might have prompted ordering of a particular test. Given the association between NLR, PLR and mortality in patients with cancer, we performed a sensitivity analysis excluding patients with malignancy. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 We determined discrimination of combinations of covariates using Harrell’s c, with 95% confidence intervals (CIs) from the somersd package in Stata. Model fit was assessed using Akaike’s information criterion (AIC). In the VACS BC we assessed the association between NLR, PLR and IL-6, D-dimer and sCD-14, using Spearman’s rank correlation. Statistical significance was defined as a two-tailed p<0.05. Statistical analysis was performed in SAS version 9.4 (SAS Institute, Cary, NC) and Stata version 14 (StataCorp, College Station, TX). Results: Our sample included 89,786 veterans, 72,119 of whom had confirmed HCV status. The most common reason for a missing HCV status was lack of testing. The components of the NLR and PLR were distributed differently among strata defined by HCV and HIV infection, although frank thrombocytopenia, neutropenia and leukopenia were uncommon (Table 1). As expected, HIV-/HCV- patients had the highest median levels of platelets, lymphocytes and neutrophils. By contrast, HCV and HIV infection were each associated with decreased platelet and neutrophil count, with a synergistic effect in coinfection. HIV infection was associated with decreased lymphocyte counts. Consistent with these findings, NLR was overall lower in patients with HIV than those without. Likewise, PLR was lower in patients with HCV compared to those without. Associations with Mortality Over 5 years, 14,737 patients died, with 77% of these deaths occurring at least 180 days after the random visit date (Table 1, Supplementary Table 1). Of the deaths occurring after 180 days, 8,582 were included in analysis of the relationship between NLR, PLR and mortality; those that were excluded lacked documentation of HCV status. Adjusting only for age, we found a J- Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 shaped relationship between PLR and mortality and a more linear relationship between NLR and mortality (Figure 1, Supplementary Table 2). After adjustment for VACS 2.0, NLR remained associated with mortality. The association between mortality and NLR or PLR was similar across strata by HIV and HCV status for lower values of NLR or PLR, though hazard ratios (HRs) tended to be higher for uninfected controls than for patients with HIV and HCV among higher levels of NLR or PLR (Figure 1, Supplementary Table 2). Comparing highest to lowest NLR level, the VACS Index 2.0-adjusted HR associated with high NLR was 1.7 for HIV-/HCV- [95% CI 1.5-1.9] and 1.3 for HIV+/HCV+ patients (95% CI 1.1-1.6). A similar trend was observed for PLR, comparing highest to second PLR level [uninfected: HR=1.8 (95% CI 1.6-1.9), coinfected: HR=1.2 (95% CI 0.98-1.5)]. Sensitivity analysis excluding patients with a history of malignancy did not significantly alter these results (data not shown). Addition of NLR, PLR, or both to Cox models using VACS 2.0 did not improve discrimination or model fit; c-statistic improvements did not meet statistical significance in any subgroup, though point estimates increased by 0.003- 0.009 (Table 2, Figure 3). Hepatic Decompensation Hepatic decompensation occurred in 3,727 patients, with 30% of these events occurring at least 180 days after NLR and PLR were measured (Table 1; Supplementary Table 3). Of the HD events occurring at least 180 days after cohort entry, 980 had documented HCV status and were therefore included in this analysis. HD was more common among patients with HCV than without. Low PLR was a significant predictor of incident HD among all levels, with PLR in the lowest level conferring a VACS 2.0-adjusted HR of 2.1 (95% CI 1.3-3.4) among coinfected patients and 2.3 (95% CI 1.6-3.5) among uninfected patients (Figure 2D). PLR in the highest level also predicted HD in uninfected patients (HR=1.9, 95% CI 1.4-2.6), but not in any other Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 stratum. NLR was independently predictive of HD in uninfected patients only (Supplementary Table 4, Figure 2A and 2B). Addition of NLR, PLR or both to the VACS 2.0 model resulted in point estimate c-statistic increases of 0.004 in the overall cohort and 0.016 among HIV+/HCV+ patients; however, these increases did not meet statistical significance (Table 2, Figure 4). Association with Other Biomarkers of Inflammation In VACS BC (n=1475; Table 3; Supplementary Table 5), NLR was correlated with IL-6 (r=0.15, p<0.0001) but PLR was not (r=0.049, p=0.06). NLR and PLR were correlated with D- dimer (both r=0.12, p<0.0001). sCD-14 was correlated with NLR (r=0.10, p=0.0002) and PLR (r=0.06, p=0.03). Patterns were similar across HIV/HCV strata. Discussion The ideal role for NLR and PLR in research and practice has yet to be determined, despite numerous analyses demonstrating their prognostic relevance. In this analysis, we evaluated two possible applications: (1) incorporation into a risk index and (2) use as a surrogate for a pathway to end-organ disease (immune dysfunction and systemic inflammation).[16] We found modest differences in hazard ratios associated with level of PLR and NLR across strata, most of which were attenuated after adjustment for VACS Index 2.0. In particular, individuals with higher NLR or PLR seemed to be at greater relative mortality risk in age- adjusted analyses if they were HIV/HCV-negative than their uninfected counterparts. This trend should be interpreted in light of overall higher death rate in HIV/HCV coinfection, independent of NLR or PLR; though a high NLR or PLR may be a slightly less ominous sign in an HIV/HCV coinfected patient than an uninfected patient, the absolute 5-year mortality rate for coinfected patients with high NLR was 44% compared to 22% in uninfected patients, with a similar trend Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 for high levels of PLR. This suggests that individuals in the highest levels of NLR and PLR were likely characterized by higher illness severity overall, but that this risk was mostly captured by in the VACS Index, such as FIB-4 score. Despite an independent association between NLR and PLR with mortality and HD, NLR and PLR failed to improve discrimination when added to models containing VACS Index 2.0. As c-statistic changes can be relatively insensitive, it is possible that inflammatory state as measured by NLR and PLR may have an impact on outcome apart from the variables measured in the VACS Index 2.0. However, the lack of improvement in discrimination suggests these markers lack additional prognostic value beyond existing indices, and thus their clinical utility remains unclear. We observed statistically significant but very weak correlations between most combinations of PLR and NLR with D-dimer, sCD-14 and IL-6. The strongest correlation was between IL-6 and NLR, which is plausible given the documented role of IL-6 in neutrophil differentiation as well as known correlations between IL-6 and NLR in HCV monoinfection.[24- 26] However, the use of these ratios as surrogates for systemic inflammation is somewhat confounded in HIV and HCV. Specific considerations include the effects of HIV on neutrophil and lymphocyte counts as well HIV-associated pancytopenia. By adjusting for VACS 2.0, we have controlled for the expected effect of HIV severity (i.e., CD4 count and HIV viral load) on mortality; however, VACS Index 2.0 was not designed to predict HD. The mortality-specific weighting of variables incorporated into VACS Index 2.0 to create a single number may oversimplify the relative independent risks of the component variables with respect to a different outcome (e.g., FIB-4 score may be more strongly associated with HD than with mortality). Additionally, liver fibrosis causes thrombocytopenia, and therefore may affect the interpretation of PLR as a pure inflammatory marker. Based on weak correlations and possible confounding of Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 interpretation of these ratios, it is unlikely that NLR and PLR can reliably supplant IL-6, sCD-14 or D-dimer as measurements of clinically significant virally-induced inflammation. Limitations of this analysis include its retrospective nature. In particular, though we found an association between NLR, PLR and outcomes independent of VACS Index, our analysis does not rule out residual confounding, such as the possibility that NLR and PLR may reflect by additional comorbidities or clinical factors not accounted for in the VACS Index. Our dataset included a large number of deaths but fewer HD events, which limits the power of analyses using HD as an outcome. Further, the use of NLR and PLR as biomarkers for HD risk in patients with versus without HCV is limited by the different biological pathways that may lead to HD in HCV-related versus non-related liver disease. Our data include participants in care between 2000-2012, with outcomes out to 2015 and 2016; it is possible that the increasing number of patients with HCV and sustained viral response may affect the interpretation of NLR and PLR in HCV. Though the exclusion of patients who experienced endpoints within 180 days may introduce bias, sensitivity analyses including events occurring within 180 days of cohort entry found no substantive differences. Finally, we tested correlation between NLR and PLR with IL-6, soluble CD-14 and D-dimer, while many additional inflammatory biomarkers exist. NLR and PLR may be more strongly correlated with other biomarkers. Conclusions Both NLR and PLR were statistically independent of the VACS Index 2.0 for mortality prediction, as was PLR for the prediction of HD. Both ratios demonstrated moderate correlation with inflammatory biomarkers. However, neither NLR nor PLR added meaningfully to the discriminative ability of a validated risk index for mortality. Further research may be indicated to determine the usefulness of NLR and PLR in (1) prognostication for liver fibrosis and Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 hepatocellular carcinoma, especially in HIV/HCV-coinfected patients and (2) surrogacy for alternative inflammatory biomarkers. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Funding: This work was supported by the National Institute on Alcohol Abuse and Alcoholism at the National Institutes of Health [Grants U24 AA020794, U01 AA020790 and U10 AA013566]; the National Heart, Lung and Blood Institute at the National Institutes of Health [Grant T35HL007649]; and in kind by the United States Department of Veterans Affairs. Conflicts of Interest: The authors declare no conflicts of interest related to this manuscript. Acknowledgments: None. This work has not been previously presented in any format. Corresponding author: Janet P. Tate Director, Biostatistics Core, Veterans Aging Cohort Study Assistant Professor of Medicine VA Connecticut Healthcare System 950 Campbell Ave West Haven, CT 06516 Phone: (203) 932-5711 ext. 5371 Fax: 203-937-4926 Email: janet.tate2@va.gov Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 References: 1. Hunt PW. HIV and inflammation: mechanisms and consequences. Curr HIV/AIDS Rep 2012; 9:139-47. 2. Armah KA, McGinnis K, Baker J, et al. HIV status, burden of comorbid disease, and biomarkers of inflammation, altered coagulation, and monocyte activation. Clin Infect Dis 2012; 55:126-36. 3. Kuller LH, Tracy R, Belloso W, et al. Inflammatory and coagulation biomarkers and mortality in patients with HIV infection. PLoS Med 2008; 5:e203. 4. Boulware DR, Hullsiek KH, Puronen CE, et al. Higher levels of CRP, D-dimer, IL-6, and hyaluronic acid before initiation of antiretroviral therapy (ART) are associated with increased risk of AIDS or death. J Infect Dis 2011; 203:1637-46. 5. Marquez M, Romero-Cores P, Montes-Oca M, et al. Immune activation response in chronic HIV- infected patients: influence of Hepatitis C virus coinfection. PLoS One 2015; 10:e0119568. 6. So-Armah KA, Tate JP, Chang CH, et al. Do Biomarkers of Inflammation, Monocyte Activation, and Altered Coagulation Explain Excess Mortality Between HIV Infected and Uninfected People? J Acquir Immune Defic Syndr 2016; 72:206-13. 7. Mooney S, Tracy R, Osler T, Grace C. Elevated Biomarkers of Inflammation and Coagulation in Patients with HIV Are Associated with Higher Framingham and VACS Risk Index Scores. PLoS One 2015; 10:e0144312. 8. Justice AC, Freiberg MS, Tracy R, et al. Does an index composed of clinical data reflect effects of inflammation, coagulation, and monocyte activation on mortality among those aging with HIV? Clin Infect Dis 2012; 54:984-94. 9. Raffetti E, Donato F, Casari S, et al. Systemic inflammation-based scores and mortality for all causes in HIV-infected patients: a MASTER cohort study. BMC Infect Dis 2017; 17:193. 10. Guthrie GJ, Charles KA, Roxburgh CS, Horgan PG, McMillan DC, Clarke SJ. The systemic inflammation- based neutrophil-lymphocyte ratio: experience in patients with cancer. Crit Rev Oncol Hematol 2013; 88:218-30. 11. Wang X, Zhang G, Jiang X, Zhu H, Lu Z, Xu L. Neutrophil to lymphocyte ratio in relation to risk of all- cause mortality and cardiovascular events among patients undergoing angiography or cardiac revascularization: a meta-analysis of observational studies. Atherosclerosis 2014; 234:206-13. 12. Ishizuka M, Nagata H, Takagi K, Iwasaki Y, Kubota K. Combination of platelet count and neutrophil to lymphocyte ratio is a useful predictor of postoperative survival in patients with colorectal cancer. Br J Cancer 2013; 109:401-7. 13. Pinato DJ, Merli M, Dalla Pria A, et al. Systemic Inflammatory Response Is a Prognostic Marker in HIV- Infected Patients with Hepatocellular Carcinoma. Oncology 2017. 14. Meng X, Wei G, Chang Q, et al. The platelet-to-lymphocyte ratio, superior to the neutrophil-to- lymphocyte ratio, correlates with hepatitis C virus infection. Int J Infect Dis 2016; 45:72-7. 15. Alsebaey A, Elhelbawy M, Waked I. Platelets-to-lymphocyte ratio is a good predictor of liver fibrosis and insulin resistance in hepatitis C virus-related liver disease. Eur J Gastroenterol Hepatol 2018; 30:207- 16. Justice AC, Erlandson KM, Hunt PW, Landay A, Miotti P, Tracy RP. Can Biomarkers Advance HIV Research and Care in the Antiretroviral Therapy Era? J Infect Dis 2018; 217:521-8. 17. Borges AH, O'Connor JL, Phillips AN, et al. Factors associated with D-dimer levels in HIV-infected individuals. PLoS One 2014; 9:e90978. 18. Borges AH, O'Connor JL, Phillips AN, et al. Factors Associated With Plasma IL-6 Levels During HIV Infection. J Infect Dis 2015; 212:585-95. 19. Fultz SL, Skanderson M, Mole LA, et al. Development and verification of a "virtual" cohort using the National VA Health Information System. Med Care 2006; 44:S25-30. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 20. Akgun KM, Tate JP, Pisani M, et al. Medical ICU admission diagnoses and outcomes in human immunodeficiency virus-infected and virus-uninfected veterans in the combination antiretroviral era. Crit Care Med 2013; 41:1458-67. 21. Tate JP, Justice AC, Hughes MD, et al. An internationally generalizable risk index for mortality after one year of antiretroviral therapy. AIDS 2013; 27:563-72. 22. Tate J BS, Gibert C, Goetz M, Marconi V, Oursler KA, Rimland D, Rodriguez-Barradas M, Justice AC. . White blood count, albumin, and BMI enhance VACS Index prognostic model, but nadir CD4 and CD8 metrics do not. In: Infectious Diseases Society of America (IDSA) ID Week. (San Diego, CA). 23. Cowper DC, Kubal JD, Maynard C, Hynes DM. A primer and comparative review of major US mortality databases. Ann Epidemiol 2002; 12:462-8. 24. Abdel-Razik A, Mousa N, Besheer TA, et al. Neutrophil to lymphocyte ratio as a reliable marker to predict insulin resistance and fibrosis stage in chronic hepatitis C virus infection. Acta Gastroenterol Belg 2015; 78:386-92. 25. Suda T, Yamaguchi Y, Suda J, Miura Y, Okano A, Akiyama Y. Effect of interleukin 6 (IL-6) on the differentiation and proliferation of murine and human hemopoietic progenitors. Exp Hematol 1988; 16:891-5. 26. Hashizume M, Higuchi Y, Uchiyama Y, Mihara M. IL-6 plays an essential role in neutrophilia under inflammation. 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Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Tables Table 1: Characteristics of veterans randomly selected from VACS 2.0 measurements between 2000-2012, with NLR and PLR measured within 180 days of VACS 2.0, and receiving ART for ≥1 year (if HIV+) Overall HIV-/HCV- HIV+/HCV- HIV-/HCV+ HIV+/HCV+ N 89,786 47,570 11,881 8,955 3,713 Demographics Age (y) 52.9±10.4 52.4±10.4 51.5±11.1 53.5±6.2 54.0±6.9 Male sex (%) 97.3 97. 97.1 98.9 98.6 Race (%) White 39.7 39.6 43.3 29.2 25.9 Black 48.5 48.0 45.5 61.1 62.6 Hispanic 8.8 9.8 7.2 8.6 9.5 Other 3.0 2.7 4.0 1.2 2.1 BMI (kg/m ) 28.1 (24.5-32.2) 29.2 (25.6-33.4) 25.7 (22.9-29.1) 27.1 (23.9-30.8) 24.5 (21.8-27.6) Prevalent comorbidities CAD 14.3 15.3 9.8 12.4 9.5 CHF 4.9 5.0 3.5 4.7 4.9 BMI>30 33.5 39.2 19.8 27.0 12.9 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Alcohol abuse or 26.0 24.4 20.4 52.7 47.8 dependence Alcohol-related 2.2 1.8 0.8 7.1 3.0 complications Cirrhosis 2.1 1.0 0.9 9.3 7.6 Diabetes mellitus 21.7 23.9 11.7 23.7 15.2 COPD 15.2 15.0 13.4 17.7 20.2 Cancer 3.2 3.0 4.1 2.6 2.8 CD4 (cells/mm ) ≥500 40.6 -- 43.5 -- 30.8 350-499 21.5 -- 21.3 -- 22.8 300-349 21.1 -- 19.9 -- 25.3 100-299 9.5 -- 8.5 -- 12.3 50-99 3.4 -- 3.0 -- 4.6 <50 4.0 -- 3.8 -- 4.3 HIV-1 RNA (copies/mL) ≤500 90.0 -- 82.8 -- 76.2 500-10,000 14.6 -- 13.3 -- 18.4 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 ≥10,000 4.4 -- 3.9 -- 5.4 Hemoglobin (g/dL) ≥14 60.4 63.0 56.5 55.9 42.6 12-13.9 29.2 28.6 31.3 29.3 35.1 10-11.9 8.1 6.6 9.3 11.5 17.1 <10 2.3 1.8 2.9 3.3 5.3 FIB-4 <1.45 68.4 76.1 65.8 41.1 27.6 1.45-3.25 25.9 21.1 29.8 39.9 45.6 >3.25 5.8 2.8 4.5 19.1 26.8 eGFR (mL/min/1.73 m ) ≥60 90.3 91.0 90.0 90.5 86.9 45-59.9 5.5 5.2 6.2 3.9 6.0 30-44.9 2.0 1.9 1.8 1.9 2.3 <30 2.2 1.9 2.1 3.7 4.7 VACS Index 2.0 34 (26-45) 30 (24-37) 46 (36-58) 42 (36-51) 65 (54-79) Died (%) Within 180 days 3.7 2.4 4.1 5.0 8.4 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 No 83.6 88.1 83.7 76.7 66.4 After 180 days 12.7 9.6 12.3 18.3 25.2 HD (%) Pre-existing (within 180 days) 2.9 1.9 2.2 8.7 9.0 No 95.9 97.5 97.0 87.1 86.4 Yes 1.2 0.7 0.8 4.2 4.6 White Blood Cells (x10 /µL) 5.2 (4.6-8.3) 6.8 (5.5-8.6) 5.5 (4.4-7.0) 6.4 (4.8-8.3) 5.0 (3.8-6.6) Neutrophils (x10 /µL) 3.8 (2.7-5.2) 4.0 (2.9-5.5) 2.9 (2.1-4.1) 3.4 (2.3-5.0) 2.5 (1.7-3.8) Platelets (x10 /µL) 230 (189-276) 237 (199-281) 219 (180-263) 206 (157-258) 188 (140-240) Lymphocytes (x10 /µL) 1.9 (1.4-2.4) 1.9 (1.5-2.4) 1.8 (1.3-2.3) 1.9 (1.4-2.5) 1.6 (1.1-2.2) 2.0 (1.4-3.0) 2.1 (1.5-3.1) 1.7 (1.1-2.5) 1.7 (1.1-2.7) 1.6 (1.0-2.6) NLR PLR 122 (92-165) 125 (95-166) 122 (92-169) 104 (75-147) 113 (79-166) Albumin (g/dL) 4.1 (3.8-4.4) 4.1 (3.9-4.4) 4.1 (3.8-4.4) 3.9 (3.6-4.2) 3.8 (3.4-4.1) Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 CAD: coronary artery disease. CHF: congestive heart failure. COPD: chronic obstructive pulmonary disease. eGFR: estimated glomerular filtration rate. HCV: hepatitis C virus. BMI: body mass index. NLR: neutrophil-to-lymphocyte ratio. PLR: platelet-to- lymphocyte ratio. ART: antiretroviral therapy. Values presented are median (Q1-Q3), mean±standard deviation or column percentage. Patients were followed for death and hepatic decompensation for up to five years. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Table 2: Model fit and discrimination Endpoint: Death OVERALL HIV-/HCV- HIV+/HCV- HIV-/HCV+ HIV+/HCV+ (N=86444) (N=46447) (N=11395) (N=8505) (N=3400) C-statistic AIC C-statistic AIC C-statistic AIC C-statistic AIC C-statistic AIC VACS Index 2.0 0.758 247201 0.773 92309 0.784 25254 0.725 28035 0.735 14189 VACS 2.0, NLR 0.766 246270 0.778 92150 0.786 25227 0.732 27997 0.738 14183 VACS 2.0, PLR 0.764 246825 0.779 92143 0.787 25239 0.728 28018 0.736 14193 VACS 2.0, NLR, PLR 0.767 246157 0.780 92094 0.788 25217 0.735 27976 0.738 14188 Endpoint: HD OVERALL HIV-/HCV- HIV+/HCV- HIV-/HCV+ HIV+/HCV+ (N=84584) (N=45750) (N=11247) (N=7978) (N=3195) C-statistic AIC C-statistic AIC C-statistic AIC C-statistic AIC C-statistic AIC 0.801 23452 0.778 6808 0.707 1704 0.765 6329 0.766 2541 VACS Index 2.0 VACS 2.0, NLR 0.802 23449 0.786 6798 0.713 1709 0.764 6322 0.7667 2545 VACS 2.0, PLR 0.803 23294 0.791 6785 0.705 1701 0.769 6279 0.7775 2531 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 0.805 23247 0.793 6778 0.715 1705 0.776 6256 0.782 2533 VACS 2.0, NLR, PLR NLR: neutrophil-to-lymphocyte ratio. PLR: Platelet-to-lymphocyte ratio. AIC: Akaike’s Information Criterion. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Table 3: Correlations of NLR and PLR with inflammatory biomarkers in VACS Biomarker Cohort Overall HIV-/HCV- HIV-/HCV+ HIV+/HCV- HIV+/HCV+ (N=1475) (N=337) (N=118) (N=496) (N=367) r p-value r p-value r p-value r p-value r p-value IL-6 0.15 <0.0001 0.11 0.05 0.22 0.02 0.21 <0.0001 0.21 <0.0001 NLR PLR 0.049 0.06 0.08 0.14 0.06 0.55 0.08 0.07 0.05 0.31 sCD- 14 0.095 0.0002 0.11 0.04 0.17 0.07 0.15 0.001 0.13 0.01 NLR PLR 0.055 0.03 0.14 0.01 -0.14 0.12 0.10 0.02 0.03 0.51 D-dim er NLR 0.12 <0.0001 0.05 0.36 0.27 0.003 0.08 0.09 0.11 0.01 0.12 <0.0001 0.07 0.21 0.08 0.36 0.14 0.001 0.03 0.51 PLR NLR: neutrophil-to-lymphocyte ratio. PLR: platelet-to-lymphocyte ratio. IL-6: interleukin-6. sCD-14: soluble CD-14. Correlations are Spearman’s rho. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Figure 1: Hazard ratios for all-cause mortality associated with NLR and PLR NLR: neutrophil-to-lymphocyte ratio. PLR: platelet-to-lymphocyte ratio. Levels defined by equal numbers of deaths. Error bars represent 95% CIs. Panels A and C are age-adjusted; panels B and D are adjusted for VACS 2.0. Panels A and B analyze NLR as an independent variable; panels C and D analyze PLR as an independent variable. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Figure 2: Hazard ratios for hepatic decompensation associated with NLR and PLR NLR: neutrophil-to-lymphocyte ratio. PLR: platelet-to-lymphocyte ratio. Levels defined by equal numbers of HD events. Error bars represent 95% CIs. Panels A and C are age-adjusted; panels B and D are adjusted for VACS 2.0. Panels A and B analyze NLR as an independent variable; panels C and D analyze PLR as an independent variable. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Figure 3: Forest plot of c-statistics (death outcome) VACS 2.0 VACS 2.0 + NLR Overall Cohort VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV-/HCV- VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV+/HCV- VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV-/HCV+ VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV+/HCV+ VACS 2.0 + PLR VACS 2.0 + NLR + PLR 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 Center dots represent Harrell’s c; whiskers represent 95% CI. Leftmost column denotes subgroup; second column indicates covariates in each model. Covariates included categorical NLR and PLR, and continuous VACS 2.0. Overall cohort includes individuals without documentation of HCV status. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Figure 4: Forest plot of c-statistics (Hepatic decompensation outcome) VACS 2.0 VACS 2.0 + NLR Overall Cohort VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV-/HCV- VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV+/HCV- VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV-/HCV+ VACS 2.0 + PLR VACS 2.0 + NLR + PLR VACS 2.0 VACS 2.0 + NLR HIV+/HCV+ VACS 2.0 + PLR VACS 2.0 + NLR + PLR 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 Center dots represent Harrell’s c; whiskers represent 95% CIs. Leftmost column denotes subgroup; second column indicates covariates in each model. Covariates included categorical NLR and PLR, and continuous VACS 2.0. HD: hepatic decompensation. Overall cohort includes individuals without documentation of HCV status. Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Accepted Manuscript Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofz347/5539018 by Ed 'DeepDyve' Gillespie user on 13 August 2019 Accepted Manuscript

Journal

Open Forum Infectious DiseasesOxford University Press

Published: Jan 9, 32

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