Lower health-related quality of life predicts all-cause hospitalization among HIV-infected individuals

Lower health-related quality of life predicts all-cause hospitalization among HIV-infected... Background: Health-related quality of life (HRQOL) is a patient-centered outcome measure used in assessing the individual’s overall functional health status but studies looking at HRQOL as a predictive tool are few. This work examines whether summary scores of HRQOL are predictive of all-cause hospitalization in the US Military HIV Natural History Study (NHS) cohort. Methods: The Short Form 36 (SF-36) was administered between 2006 and 2010 to 1711 NHS cohort members whose hospitalization records we had also obtained. Physical component summary scores (PCSS) and mental component summary scores (MCSS) were computed based on standard algorithms. Terciles of PCSS and MCSS were generated with the upper terciles (higher HRQOL) as referent groups. Proportional hazards multivariate regression models were used to estimate the hazard of hospitalization for PCSS and MCSS separately (models 1 and 2, respectively) and combined (model 3). Results: The hazard ratios (HR) of hospitalization were respectively 2.12 times (95% CI: 1.59–2.84) and 1.59 times (95% CI: 1.19–2.14) higher for the lower and middle terciles compared to the upper PCSS tercile. The HR of hospitalization was 1.33 times (95% CI: 1.02–1.73) higher for the lower compared to the upper MCSS tercile. Other predictors of hospitalization were CD4 count < 200 cells/mm (HR = 2.84, 95% CI: 1.96, 4.12), CD4 count 200–349 3 3 cells/mm (HR = 1.67, 95% CI: 1.24, 2.26), CD4 count 350–499 cells/mm (HR = 1.41, 95% CI: 1.09, 1.83), plasma viral load > 50 copies/mL (HR = 1.82, 95% CI: 1.46, 2.26), and yearly increment in duration of HIV infection (HR = 0.94, 95% CI: 0.93, 0.96) (model 3). Conclusion: After controlling for factors associated with hospitalization among those with HIV, both PCSS and MCSS were predictive of all-cause hospitalization in the NHS cohort. HRQOL assessment using the SF-36 may be useful in stratifying hospitalization risk among HIV-infected populations. Keywords: HIV, Human immunodeficiency virus, HRQOL, Health-related quality of life, HAART, Highly active antiretroviral therapy, PCSS, Physical component summary scores, MCSS, Mental component summary scores, Hospitalization * Correspondence: leoemuren@yahoo.com; idcrp@idcrp.org Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 2 of 11 Background different instrument, the EuroQol [5], and adjusted for Health-related quality of life (HRQOL) is primarily used only CD4 count and HIV plasma viral load. In this re- as a patient-centered outcome measure to assess the indi- search, we investigate the usefulness of the RAND SF-36 vidual’s overall functional health status and for evaluating in predicting hospitalization in the NHS cohort. Because therapeutic interventions in chronic diseases including HRQOL reflects an individual’s overall physical and human immunodeficiency virus (HIV) infection and mental functional health status, we hypothesize that par- acquired immune deficiency syndrome (AIDS) [1, 2]. ticipants with lower HRQOL are more likely to be hos- However, a few studies have also utilized HRQOL as a pitalized compared to participants with higher HRQOL prognostic tool for predicting survival in people living over the period of follow-up. with HIV/AIDS (PLWHA) [3–6], showing that HRQOL is useful as a risk stratification tool in HIV-infected indivi- Methods duals both in clinical trials and observational studies. Study cohort While HIV remains incurable, successful treatment The NHS is a prospective multicenter continuous enroll- with highly active antiretroviral therapy (HAART) has ment observational cohort of HIV-infected active duty resulted in prolonged survival among PLWHA [7–9] military personnel and other beneficiaries from the and with the steady incidence of HIV in the United Army, Navy/Marines and Air Force enrolled since 1986 States [9], the prevalence of the disease and, by exten- [14–17]. Participants are followed at six medical centers sion, the burden of the disease on the healthcare system in the U.S. Demographic data are collected at baseline will continue to rise. To mitigate the increasing burden and updated while medical and medication histories and of the disease on the healthcare system and improve the standard laboratory studies are collected biannually. quality of life of infected individuals, it is important that Blood samples obtained from participants in this cohort PLWHA are clinically stable and in optimal functional from scheduled visits are stored in a repository. All NHS health, free from medical/mental comorbidities or op- participants provided informed consent, and approval portunistic infections, and have minimal hospitalizations. for this research was obtained from the institutional Poor HRQOL measures have been associated with review board at each participating site. higher utilization of healthcare resources among patients with other chronic diseases [10–12]. Among Study participants HIV-infected individuals, HRQOL has been shown to be The SF-36 questionnaire was administered to NHS partic- associated with hospitalization and emergency depart- ipants at every other study visit (approximately 12-month ment utilization [5]. The rate of hospitalization in the intervals) from April 2006 to September 2010. For these U.S. Military HIV Natural History Study (NHS) cohort analyses, one SF-36 response per calendar year was cap- was previously reported to be as high as 137 per 1000 tured, using the last measurement if more than one survey person years (PYs) [13]. Given this high rate of was completed in a calendar year. Baseline was defined as hospitalization, it is important to evaluate factors that the earliest measure meeting these criteria. We used the may predict hospitalization such as HRQOL, in the hope CD4 count and viral load values closest in time to the that appropriate interventions directed at modifiable risk HRQOL measure, usually the same visit. factors such as CD4 count and medical/mental comor- bidities that impact HRQOL can be instituted with the Definitions and variable selections ultimate goal of reducing the high hospitalization rate. Hospitalization While HRQOL may be measured using various instru- Participants’ dates of hospitalization, diagnoses at ments, the Research and Development (RAND) Short hospitalization, and number of days of hospitalization were Form 36 (SF-36) is one of the more commonly used retrieved from their medical records and through coordin- instruments both in clinical trials and observational ator interviews. Furthermore, the military healthcare system studies. Although the Medical Outcome Studies for HIV operates a centralized electronic health records system that questionnaire has been used in previous studies to pre- enables investigators to track participants’ records and hos- dict mortality [4, 6], this instrument is disease-specific pitalizations. Hospitalization was the outcome of interest, and its measured health dimensions are slightly different and we considered only the first admission of participants from that of the SF-36. Furthermore, an instrument’s between April 2006 and September 2010 for the purposes ability in predicting mortality may not necessarily prove of this study. To establish a temporal relationship, we en- its usefulness in predicting other relevant clinical sured that date of completed questionnaire preceded the end-points such as hospitalization. To the best of our date of hospitalization. Hospitalization was coded as ‘yes’ if knowledge there has been only one study that has spe- participant was hospitalized after the first completed SF-36 cifically looked at how HRQOL predicts hospitalization questionnaire and ‘no’ if participant was not hospitalized in HIV-infected individuals, but this study used a after the first completed questionnaire for the duration of Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 3 of 11 follow-up. Some common causes of hospitalization include or more comorbidity. Mental comorbidity was classified bacterial, viral, fungal and parasitic infections, cancers, similarly. Common mental comorbidities in the NHS were psychiatric conditions such as major depressive disorders, major depressive disorder, generalized anxiety disorder, alcohol abuse, gastroenterological disorders such as gastro- bipolar disorder and alcohol abuse. Except for gender and esophageal reflux disease and peptic ulcers, and cardiovas- race, all other variables were treated as time-varying co- cular conditions such as myocardial infarction and variates. Baseline was earliest SF-36 captured according to pericarditis. the above criteria; follow-up continued to hospitalization, loss to follow-up, or Sept 30, 2010, whichever came first. Health-related quality of life scores Participants were deemed lost to follow up for this The norm-based physical component summary scores HRQOL analysis if they did not have a completed SF-36 (PCSS) and mental component summary scores questionnaire for a given year and none thereafter. (MCSS) were computed according to the recom- Participants lost to follow-up were censored at 6 months mended scoring algorithm for the RAND 36-item after the last completed SF-36 or at Sept 30, 2010. All par- health survey 1.0 [18, 19]. PCSS and MCSS, the main ticipants aged 18 years and above who were enrolled into explanatory variables, were categorized into terciles the HRQOL sub-study between 2006 and 2010 were with the upper terciles (highest HRQOL) as referent eligible for this analysis. groups. Terciles were established separately for PCSS and MCSS using all available HRQOL scores and we Statistical analyses verified an approximately even distribution of the We summarized the characteristics of the participants number of participants in each tercile at baseline. based on their frequency distribution for categorical vari- Participants could move from one tercile to the other ables and the median and interquartile range (IQR) for con- during the period of follow up based on their scores. tinuous variables. We plotted the Kaplan-Meier curves to estimate the survivor functions by group using the log-rank Covariates test; the Tukey-Kramer adjustment was used to assess Highly active antiretroviral therapy (HAART) was defined between-group differences for variables with more than as a combination of at least three full dose antiretroviral two categories. Cox regression modeling was used to esti- agents similar to previous investigations for this cohort mate the hazard of hospitalization for participants while [15]. In light of prior reports that use of protease inhibi- adjusting for covariates. As above, all covariates were time tors (PI) is associated with poorer HRQOL [20–22], varying, with the exceptions of gender and race. Because HAART was divided into four groups: protease separate multivariate models are traditionally used for PCSS inhibitor-based HAART (PI-HAART), for HAART with and MCSS when these variables are the outcome variables at least one protease inhibitor in the HAART regimen; in research settings, we also used them separately as inde- non-protease-inhibitor-based HAART (NPI-HAART), for pendent variables in two different models while controlling HAART regimens with no protease inhibitor; for the same set of covariates (models 1 and 2, respectively). HAART-naïve group (HAART-N) for those who had Furthermore, we constructed a third model with both never been on HAART, and Off-HAART/Non-HAART PCSS and MCSS included (model 3). To be eligible antiretroviral (ART) group, made up of those who were for inclusion into the multivariate models, the covari- previously treated, but off HAART or who were on ate must achieve a significance level of < 0.2 in the non-HAART ART. Other covariates were gender (male/ univariate model. Missing data were handled using female), age (in increments of 5 years), military rank (offi- the last-observation-carried-forward method. In line cer/warrant officer, enlisted, and civilian/retired), marital with the model specifications, we verified the propor- status (married, not married), race/ethnicity (Caucasian, tional hazard assumptions using both graphical and African-American, and others), HIV plasma viral load formal diagnostic tests including covariate-time inter- ([pVL], ≤50 copies/mL, > 50 copies/mL), CD4 count (< action effects [23–25]. All statistical analyses and 3 3 3 200 cells/mm ,200–349 cells/mm ,350–499 cells/mm graphs were performed using SAS 9.3 [SAS Institute and > 499 cells/mm ), medical comorbidity (yes/no), men- Inc., Cary, NC]. tal comorbidity (yes/no), AIDS diagnosis (yes/no), and time since HIV diagnosis (in years). AIDS was defined in Results accordance with the 1993 Centers for Disease Control and Out of the 1730 eligible participants at baseline, 13 did Prevention revised criteria, except for an isolated CD4 cell not completely answer the HRQOL questionnaire and count < 200 cells/mL, as CD4 was analyzed separately. were excluded. Another 6 participants with missing Medical co-morbidity referred to chronic medical condi- values for one or more covariates at baseline were also tions such as diabetes mellitus, hypertension or cancer excluded. Of the remaining 1711 participants included and was classified as having no comorbidity or having one in this analysis, 366 (21%) were hospitalized at least once Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 4 of 11 Table 1 Demographic and Clinical Characteristics of Participants Table 1 Demographic and Clinical Characteristics of Participants at Baseline at Baseline (Continued) Categorical Variables Mental Component Summary Scores (MCSS) Characteristics N (%) Lower Tercile 39.18 (31.99–43.88) Hospitalized Middle Tercile 50.69 (49.02–51.82) Yes 366 (21.39) Upper Tercile 55.22 (54.04–57.29) No 1345 (78.61) Gender Male 1594 (93.16) Female 117 (6.84) (Table 1). Participants were predominantly male (93%), Race with equal representation from non-Hispanic Whites Non-Hispanic White 719 (42.02) and African-Americans (42% each). 17% of participants Non-Hispanic African 722 (42.20) had a medical comorbidity while 29% had a mental co- Others 270 (15.78) morbidity, and about 12% had an AIDS diagnosis. Rank Slightly over 5% of the cohort had CD4 count < 200 Officer/Warrant Officer 126 (7.36) cells/mm and over 56% had CD4 count > 499 cells/ mm . 35% of participants had pVL > 50 copies/mL. Enlisted 893 (52.19) About 34% of patients were on a PI-based HAART while Others (Retired/Civilians) 692 (40.44) 44% were on a non-PI-based HAART. 14% of partici- Marriage, Yes 556 (32.50) pants were HAART naïve at baseline and another 7% Medical Comorbidity, Yes 285 (16.66) were off HAART at baseline. Only 1% of participants Mental Comorbidity, Yes 493 (28.81) (n = 18) were on a non-HAART antiretroviral therapy AIDS Diagnosis, Yes 202 (11.81) and were combined with the Off-HAART group for HAART the subsequent analyses. PI-Based 575 (33.61) The terciles for the PCSS at baseline had 570, 572 and Non-PI-Based 756 (44.18) 569 participants respectively in the lower, middle and HAART-Naïve 241 (14.09) upper terciles. The median score of the lower PCSS ter- Off-HAART 121 (7.07) cile was 41.78 (IQR, 35.88–46.13) compared to 54.56 Non-HAART ART 18 (1.05) (IQR, 52.78–55.87) for the middle tercile and 58.81 (IQR, 57.87–59.76) for the upper tercile. The Plasma Viral Load > 50 copies/mL Kaplan-Meier product-limit survival estimates for hospi- Yes 600 (35.07) talizations among the PCSS terciles showed statistically No 1111 (64.93) significant differences among all terciles of PCSS (Fig. 1). CD4 Count In the unadjusted Cox regression model, the hazard < 200 cells/mm3 92 (5.38) ratio (HR) of hospitalization for participants in the lower 200–349 cells/mm3 242 (14.14) PCSS tercile was 2.52 times higher compared to partici- 350–499 cells/mm3 411 (24.02) pants in the upper PCSS tercile (95% confidence interval > 499 cells/mm3 966 (56.46) [CI] 1.92–3.32; Table 2), while the hazard of Continuous Variables hospitalization was 1.74 times higher in the middle ter- Characteristics Median (Interquartile Range) cile compared to the upper tercile (95% CI: 1.31–2.33). Age (years) 42.00 (34.00–49.00) After adjustment, the hazards of hospitalization were CD4 Count (cells/mm3) 538.00 (389.00–721.00) 2.12 (95% CI: 1.59–2.84) and 1.59 (95% CI: 1.19–2.14) times higher respectively in the lower and middle ter- Plasma Viral Load (Log10) 1.70 (1.68–2.82) ciles compared to the upper tercile (Table 3, model 3 Time Since HIV Diagnosis (years) 10.00 (4.00–17.00) [after this, we report model 3, which is the combined Duration of Follow-Up from Baseline (Years, Overall) 2.71 (1.04–3.80) PCSS and MCSS model, unless there are obvious differ- Hospitalized 1.23 (0.53–2.37) ences in the results of the three models]). Not Hospitalized 3.13 (1.53–3.95) The terciles for the MCSS at baseline had 569, 571, Physical Component Summary Scores (PCSS) and 571 participants respectively for the lower, middle Lower Tercile 41.78 (35.88–46.13) and upper terciles. The median score of the lower MCSS Middle Tercile 54.56 (52.78–55.87) tercile was 39.18 (IQR, 31.99–43.88) compared to 50.69 Upper Tercile 58.81 (57.87–59.76) (IQR, 49.02–51.82) for the middle tercile and 55.22 (IQR, 54.04–57.29) for the upper tercile. The Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 5 of 11 Fig. 1 Kaplan-Meier Survival Curve for Physical Component Summary Score (PCSS). Legend: Lower Tercile of PCSS, Middle Tercile of PCSS, Upper Tercile of PCSS 3 3 Kaplan-Meier product-limit survival estimates for hospi- 349 cell/mm and 350–499 cell/mm by 2.84 (95% CI: talizations among the MCSS terciles showed statistically 1.96–4.12), 1.67 (95% CI: 1.24–2.26), and 1.41 (95% CI: significant differences between the lower tercile and 1.09–1.83) times respectively when compared to those other two MCSS terciles, but not between the middle with CD4 count > 499 cells/mm . Having pVL > 50 cop- and upper terciles (Fig. 2). In the unadjusted Cox regres- ies/mL was significantly associated with 82% increase in sion model (Table 2), participants in the lower MCSS the hazard of hospitalization (HR: 1.82; 95% CI: 1.46– tercile were 79% at increased hazard of being hospita- 2.26) while being retired/civilian was significantly associ- lized compared to those in the upper MCSS tercile (HR: ated with over a 100% increase in hazard of 1.79; 95% CI: 1.39–2.30). After adjustment (model 3), hospitalization (HR: 2.04; 95% CI: 1.25–3.34) (Table 3). the hazard of hospitalization in the lower tercile was Every year increment in time from HIV diagnosis led to 33% higher than that of the upper tercile (HR: 1.33; 95% a 5.6% reduced hazard of hospitalization (HR: 0.94; 95% CI 1.02–1.73). CI: 0.93–0.96). Factors that were not significantly associ- The first-hospitalization rate among the 1711 participants ated with hospitalization in the combined model but sig- was 8.7 per 100 person-years (PYs). As previously noted, nificant in the PCSS and/or MCSS models (Table 3) the number of participants at baseline in the lower, middle were mental comorbidity (both PCSS and MCSS and upper PCSS terciles were 570, 572 and 569 and the models) and AIDS diagnosis (MCSS model only). total duration of follow up in years were respectively HAART, on the other hand, was only significant in the 1312.34, 1414.88 and 1471.44. There were 168 hospitaliza- univariate model but not in any of the multivariate tions in lower terciles for the period of follow up, 124 in the models and therefore not shown in our most parsimoni- middle tercile and 74 in the upper tercile, therefore making ous models. the rates of hospitalization 12.8, 8.8 and 5.0 per 100 PYs for the lower, middle and upper terciles respectively (Table 4). Discussion Similarly, the rates of hospitalization were 11.8, 8.3 and 6.5 Our study shows that both PCSS and MCSS were inde- per 100 PYs for the lower, middle and upper terciles of pendently predictive of hospitalization among partici- MCSS respectively (Table 4). pants in the NHS cohort. To the best of our knowledge, The HRs of hospitalization were significantly increased this is the first study to use the SF-36 to evaluate in participants with CD4 count < 200 cells/mm ,200– whether HRQOL predicts hospitalization in an Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 6 of 11 Table 2 Univariate Cox Regression Model for Hazard of hospitalization compared to the upper PCSS tercile. Par- Hospitalization ticipants in the lower MCSS tercile had a 33% increased Variable Hazard Ratio 95% CI P-Value hazard of hospitalization compared to the upper MCSS tercile. These findings support the use of HRQOL in risk Physical Component Summary Score (PCSS) assessment for hospitalization in clinical and research Lower Tercile of PCSS 2.52 1.92–3.32 <.0001 settings. Although clinical factors such as mental comor- Middle Tercile of PCSS 1.74 1.31–2.33 0.0002 bidity and AIDS diagnosis were not significantly associ- Upper Tercile of PCSS 1.0 –– ated with hospitalization in the combined model (model Mental Component Summary Score (MCSS) 3, Table 3), mental comorbidity was significant in the Lower Tercile of MCSS 1.79 1.39–2.30 <.0001 separate PCSS and MCSS models while AIDS diagnosis was significant in the MCSS model and was nearly sig- Middle Tercile of MCSS 1.27 0.97–1.67 0.08 nificant in the PCSS model. These covariates should Upper Tercile of MCSS 1.0 –– therefore be included in studies examining clinical inter- Age (Years, Increment of 5 Years) 0.98 0.94–1.03 0.5 ventions directed at reducing the risk of hospitalization Gender (Male) 1.06 0.70–1.62 0.8 in PLWHA considering that they contribute to both the Marital Status (Married) 1.13 0.91–1.40 0.3 independent and outcome variables. Race/Ethnicity The overall rate of hospitalization of 8.7 per 100 PYs in our current study is much lower than that reported Non-Hispanic African American 0.96 0.77–1.19 0.7 earlier for the NHS by Crum-Cianflone et al. [13], which Hispanic/Others 0.86 0.63–1.18 0.3 reported a hospitalization rate of 137 per 1000 PYs or Non-Hispanic Caucasian 1.0 –– 13.7 per 100 PYs. This difference may be partly ex- Rank plained by the fact that we only considered the first Civilian/Retired 1.47 0.93–2.35 0.1 hospitalization per individual as against all hospitaliza- Enlisted 1.22 0.77–1.95 0.4 tions used in the study by Crum-Cianflone et al. Indeed, in that study 47% of all hospitalizations were repeat in- Officers 1.0 –– patient admissions. Furthermore, the general decline in CD4 Count hospitalization rates in HIV-infected populations over CD4 Count < 200 cells/mm3 3.89 2.78–5.44 <.0001 the years may partly explain the differences in CD4 Count 200–349 cells/mm3 2.16 1.63–2.86 <.0001 hospitalization rates noted in both studies. The rates of CD4 Count 350–499 cells/mm3 1.55 1.20–2.00 0.0008 hospitalizations were also markedly different by terciles CD4 Count > 499 cells/mm3 1.0 –– of PCSS and MCSS. These rates were 5.0, 8.8 and 12.8 per 100 PYs for the upper, middle and lower PCSS ter- Plasma Viral Load > 50 Copies/mL 2.36 1.92–2.89 <.0001 ciles respectively, and 6.5, 8.3 and 11.7 per 100 PYs for Medical Comorbidity 1.05 0.81–1.36 0.7 the upper, middle and lower terciles of MCSS respect- Mental Comorbidity 1.43 1.16–1.76 0.0009 ively. This clear pattern of less hospitalization with bet- AIDS Diagnosis 1.67 1.27–2.18 0.0002 ter physical and mental functional health status suggests Time Since HIV (Per year) 0.98 0.96–0.99 0.002 that applying innovative research to delineate inter- HAART Treatment ventions that will improve HRQOL may be strategically significant in reducing hospitalization in our population HAART-Naïve 1.80 1.33–2.43 0.0002 and likely more broadly among PLWHA. Off-HAART/Non-HAART ART 1.75 1.26–2.44 0.0008 Our findings on the association among CD4 count and Non-PI Based HAART 0.70 0.55–0.87 0.003 hospitalization were similar to and reinforced prior work. PI Based HAART 1.0 –– Compared to CD4 count > 499 cells/mm , CD4 count All variables are time-varying except for race and gender 3 3 < 200 cells/mm , CD4 count 200–349 cells/mm ,and CD4 count 350–499 cells/mm were respectively associated with increased hazard of hospitalization by 184, 67 and HIV-infected population, and perhaps the second study 41%. Somewhat similar to our findings, Crum-Cianflone for any HRQOL instrument [5]. Our study therefore et al. [13], in an earlier work on this cohort, had found that adds to the growing literature on the role of HRQOL in CD4 count > 499 cells/mm reduced the hazard of predicting hospitalization in chronic diseases [11, 26– hospitalization when compared to CD4 < 350 cells/mm 28]. In our current study, those in the middle PCSS ter- but not 350–499 cells/mm . The reason for the difference cile were at 59% greater hazard of hospitalization com- with our current study may be related to differences in the pared to the upper PCSS tercile while those in the lower time frame studied (2006–2010 [current study] versus PCSS tercile were at 112% greater hazard of 1999–2007 [previous study]) or in study design (first Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 7 of 11 Table 3 Multivariate Cox Regression Model for Hazard of Hospitalization for Terciles of PCSS and MCSS Variable Model 1: PCSS Model Model 2: MCSS Model Model 3: Combined PCSS and MCSS Model HR 95% CI HR 95% CI HR 95% CI PCSS Lower Tercile 2.18 1.64–2.90 2.12 1.59–2.84 Middle Tercile 1.62 1.21–2.17 1.59 1.19–2.14 Upper Tercile 1.0 – 1.0 – MCSS Lower Tercile 1.44 1.10–1.87 1.33 1.02–1.73 Middle Tercile 1.14 0.87–1.49 1.20 0.91–1.57 Upper Tercile 1.0 – 1.0 – CD4 Count < 200 cells/mm3 2.84 1.96–4.12 2.99 2.06–4.33 2.84 1.96–4.12 200–349 cells/mm3 1.69 1.25–2.27 1.67 1.24–2.25 1.67 1.24–2.26 350–499 cells/mm3 1.41 1.09–1.82 1.37 1.05–1.77 1.41 1.09–1.83 > 499 cells/mm3 1.0 – 1.0 – 1.0 – Plasma Viral Load > 50 Copies/mL 1.83 1.47–2.28 1.88 1.51–2.34 1.82 1.46–2.26 Mental Comorbidity 1.31 1.04–1.63 1.30 1.04–1.64 1.23 0.98–1.55 AIDS Diagnosis 1.35 1.00–1.83 1.47 1.09–1.99 1.34 0.99–1.81 Rank Civilian/Retired 2.06 1.26–3.37 2.16 1.32–3.52 2.04 1.25–3.34 Enlisted 1.18 0.74–1.88 1.17 0.73–1.86 1.19 0.74–1.89 Officer 1.0 – 1.0 –– – Time Since HIV (Per year) 0.94 0.92–0.96 0.94 0.93–0.96 0.94 0.93–0.96 All variables are time-varying Fig. 2 Kaplan-Meier Survival Curve for Mental Component Summary Score (MCSS). Legend: Lower Tercile of MCSS, Middle Tercile of MCSS, Upper Tercile of MCSS Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 8 of 11 Table 4 Overall Rates of Hospitalization in the NHS and by Terciles of PCSS and MCSS HRQOL Terciles Total Participants Total No. Hospitalized Duration of Rate of Hospitalization of HRQOL at Baseline during Follow Up Follow Up (Years) Per 100 Person - Years PCSS and MCSS Overall 1711 366 4201.67 8.71 PCSS Lower 570 168 1312.34 12.80 Middle 572 124 1414.88 8.76 Upper 569 74 1474.44 5.02 MCSS Lower 569 152 1293.77 11.75 Middle 571 116 1397.09 8.30 Upper 571 98 1510.8 6.49 Participants could move from one tercile to the other during the period of follow up based on their scores hospitalization [current study] versus all hospitalizations admission is warranted. Survival bias may also play a [previous study]) [13]. Other investigators have also shown role as those who live longer due to their innate ability that lower CD4 count is associated with hospitalization, to cope with the disease are less likely to be hospitalized especially when CD4 count falls below 200 [29–34]. because of their relatively healthier state. The finding HIV pVL > 50 copies/mL was also associated with that being civilian/retired was associated with over 100% hospitalization in our cohort. Although the levels of increased hazard of hospitalization is not totally unex- dichotomization differed, Fielden et al. [34]alsofound that pected as this group is older, and age has been associ- higher pVL was associated with hospitalization while ated with higher inpatient admissions [35]. However, the Mocroftetal.[29]demonstratedthatinthe lastof three fact that age was not independently associated with time points in their study, there was an increased odds of hospitalization in our current study may partly reflect hospitalization for every log unit increase in pVL. the relatively young age of our cohort (median age Interestingly, a longer time since HIV diagnosis was 42 years; IQR 34 to 49 years) but may also be suggestive predictive of a reduced hazard of hospitalization. One of the fact that other factors associated with non-active plausible explanation for this finding may be that duty status, such as health related behaviors, played a individuals with longer disease duration may be more more important role in this respect. Although being experienced with dealing with symptoms (including sub- civilian/retired has been shown to be associated with tle ones) associated with their infection and therefore lower PCSS in our cohort [36], its association with more likely to seek medical attention earlier before hospitalization in the current study is independent of Table 5 Multivariate Cox Regression Model for Hazard of Hospitalization (PCSS and MCSS Numeric) Variable Model 4: PCSS Model Model 5: MCSS Model Model 6: Combined PCSS and MCSS Model HR 95% CI HR 95% CI HR 95% CI PCSS, 5 Unit Increments 0.87 0.83–0.92 0.88 0.84–0.93 MCSS, 5 Unit Increments 0.91 0.87–0.96 0.94 0.89–0.99 CD4 Count < 200 cells/mm3 2.76 1.90–4.01 2.96 2.04–4.29 2.73 1.88–3.97 200–349 cells/mm3 1.66 1.23–2.23 1.67 1.24–2.25 1.65 1.22–2.23 350–499 cells/mm3 1.38 1.07–1.80 1.37 1.05–1.77 1.38 1.07–1.79 > 499 cells/mm3 1.0 – 1.0 – 1.0 – Plasma Viral Load > 50 Copies/mL 1.86 1.49–2.32 1.89 1.52–2.36 1.86 1.49–2.31 Mental Comorbidity 1.31 1.05–1.64 1.28 1.02–1.61 1.23 0.97–1.54 AIDS Diagnosis 1.34 0.99–1.82 1.48 1.09–2.00 1.34 0.99–1.82 Rank Civilian/Retired 2.21 1.35–3.61 2.14 1.31–3.49 2.15 1.32–3.52 Enlisted 1.28 0.80–2.04 1.16 0.73–1.85 1.27 0.79–2.02 Officer 1.0 – 1.0 –– – Time Since HIV (Per year) 0.94 0.92–0.96 0.95 0.93–0.96 0.94 0.93–0.96 All variables are time-varying Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 9 of 11 PCSS (Tables 3 and 5). Finally, while HAART was increase in PCSS the hazard of hospitalization was re- significant in the unadjusted model, it was no longer sig- duced by 12% (Table 5). MCSS, on the other hand, re- nificant after adjustment showing that its effect may duced the hazard of hospitalization by 6% for every have been captured by other variables. 5-unit increase in MCSS. Our study therefore adds to Potential limitations of our study include the predom- the predictive utility of the HRQOL measures in inantly male patients in the NHS cohort which may limit HIV-infected persons as assessed by the SF-36. Other its generalizability to females. The regimented lifestyle of important strengths of our study include its large sample our military population and the requirements of physical size, open access to healthcare and medications, racial and mental fitness may also mean that they may diversity, and the heterogeneity of the cohort with promptly seek medical attention, a behavior that may regards to the range of values for HIV disease indicators not necessarily be seen in the general population. We (CD4 count and pVL), and other clinical parameters, may not have captured hospital admissions outside the such as medical and mental comorbidities with respect- military settings; however, with the use of trained coor- ive prevalence of 17 and 29% of the cohort at baseline. dinators to conduct participants’ interviews, the number The heterogeneity of the cohort (Table 1) is further of missed admissions outside the single payer electronic reflected by the wide range of PCSS (16.7 to 70.7) and health records systems of the U.S. military is expected to MCSS (8.8 to 67.8), a finding that is rare in other pre- be small. Because we administratively censored partici- dictive studies using the SF-36 that tend to be with per- pants after September 2010, follow-up was limited for sons with relatively very low scores [3, 4, 6, 28, 38]. those subjects surveyed in 2010; however, as subjects Finally, HRQOL summary scores may be useful in prog- were censored 6 months after their last survey, this nostic studies in HIV-infected populations because they affected only those enrolled in 2010 (n = 99) [36]. In sen- capture information beyond HIV disease-specific indica- sitivity analysis we excluded these 99 participants and tors, such as CD4 count and pVL. The causes of our results remained essentially the same. In this current hospitalization in HIV-infected individuals are now be- study, we did not specifically evaluate whether yond disease-related factors but include both medical within-person difference in HRQOL score may predict and surgical conditions, especially in the HAART era hospitalization. While such difference in score may be [13]. HRQOL is also reflective of perceptions that may more meaningful to the clinician interested in individual potentially affect subsequent health-seeking behaviors risk of inpatient admission over time, we believe that and utilization of healthcare resources including knowing the actual scores that predict hospitalization is preventive services [4, 39]. a useful starting point. More so, within-person difference in score may be dependent on the baseline HRQOL Conclusion score. Finally, our findings that PCSS and MCSS were After controlling for factors associated with hospitalization predictive of hospitalization should not be interpreted as among those with HIV, both PCSS and MCSS were predict- implying causation but instead that physical and mental ive of all-cause hospitalization in the NHS cohort with functional health may be surrogates for the actual causes similar effect sizes for PCSS and low CD4. Applying in- of hospitalization. Yet, the fact that these measures were novative strategies to improve modifiable risk factors that statistically significant in predicting hospitalization in all influence the physical and mental functional status of multivariate models, the magnitude of the effect sizes, HIV-infected individuals has the potential to reduce the and the dose response relationships support their utility rate of hospitalization in this patient population. We, there- in clinical practice. fore, recommend the use of HRQOL in stratifying Our study had major advantages. There was clearly an hospitalization risk among HIV-infected individuals. Finally, established temporal relationship between the HRQOL future research to evaluate whether within-person differ- scores and hospitalization. Because hospitalization may ence in HRQOL scores is predictive of hospitalization is also negatively impact HRQOL [37], we further con- needed, as this may even increase the utility of HRQOL ducted sensitivity analyses to rule out reverse causation measures in clinical settings. by excluding participants whose scores were taken Abbreviations within 1 week hospitalization but our findings remained HAART: Highly Active Antiretroviral Therapy; HIV: Human Immunodeficiency essentially the same. This temporal pattern was also true Virus; HRQOL: Health-Related Quality of Life; MCSS: Mental Component Summary Scores; NPI-HAART: Non-Protease Inhibitor HAART; PCSS: Physical for the other time-dependent covariates included in the Component Summary Scores; PI-HAART: Protease Inhibitor HAART; study. The clear dose-response relationship between pVL: Plasma Viral Load; SF-36: Short Form 36 PCSS and hospitalization in our study strongly supports Acknowledgements its utility as a predictive tool. To further demonstrate Support for this work (IDCRP-000-24) was provided by the Infectious Disease the discriminatory quality of PCSS we used it as a con- Clinical Research Program (IDCRP), a Department of Defense (DoD) program tinuous variable, and it showed that for every 5-unit executed through the Uniformed Services University of the Health Sciences. Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 10 of 11 This project has been funded in whole, or in part, with federal funds from Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, the National Institute of Allergy and Infectious Diseases, National Institutes of MD, USA. Henry M. Jackson Foundation for the Advancement of Military Health (NIH), under Inter-Agency Agreement Y1-AI-5072. Medicine, Inc., Bethesda, MD, USA. Members of the Infectious Disease Clinical Research Program HIV Working Group include the following: Received: 25 October 2017 Accepted: 10 May 2018 Madigan Army Medical Center, Tacoma, WA: S. Chambers; COL (Ret) M. Fairchok; LTC A. Kunz; C. Schofield. National Institute of Allergy and Infectious Diseases, Bethesda, MD: J. Powers; COL (Ret) E. Tramont. References Naval Medical Center, Portsmouth, VA: S. Banks; CDR K. Kronmann; T. Lalani; 1. Gakhar H, Kamali A, Holodniy M. Health-related quality of life assessment after LCDR K. St. Clair; R. Tant. antiretroviral therapy: a review of the literature. Drugs. 2013;73(7):651–72. Naval Medical Center, San Diego, CA: CAPT M. Bavaro; R. Deiss; A. Diem; N. 2. Degroote S, Vogelaers D, Vandijck DM. What determines health-related Kirkland; CDR R. Maves. quality of life among people living with HIV: an updated review of the San Antonio Military Medical Center, San Antonio, TX: S. Merritt; T. O’Bryan; literature. Arch Public Health. 2014;72(1):40. 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Lower health-related quality of life predicts all-cause hospitalization among HIV-infected individuals

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Abstract

Background: Health-related quality of life (HRQOL) is a patient-centered outcome measure used in assessing the individual’s overall functional health status but studies looking at HRQOL as a predictive tool are few. This work examines whether summary scores of HRQOL are predictive of all-cause hospitalization in the US Military HIV Natural History Study (NHS) cohort. Methods: The Short Form 36 (SF-36) was administered between 2006 and 2010 to 1711 NHS cohort members whose hospitalization records we had also obtained. Physical component summary scores (PCSS) and mental component summary scores (MCSS) were computed based on standard algorithms. Terciles of PCSS and MCSS were generated with the upper terciles (higher HRQOL) as referent groups. Proportional hazards multivariate regression models were used to estimate the hazard of hospitalization for PCSS and MCSS separately (models 1 and 2, respectively) and combined (model 3). Results: The hazard ratios (HR) of hospitalization were respectively 2.12 times (95% CI: 1.59–2.84) and 1.59 times (95% CI: 1.19–2.14) higher for the lower and middle terciles compared to the upper PCSS tercile. The HR of hospitalization was 1.33 times (95% CI: 1.02–1.73) higher for the lower compared to the upper MCSS tercile. Other predictors of hospitalization were CD4 count < 200 cells/mm (HR = 2.84, 95% CI: 1.96, 4.12), CD4 count 200–349 3 3 cells/mm (HR = 1.67, 95% CI: 1.24, 2.26), CD4 count 350–499 cells/mm (HR = 1.41, 95% CI: 1.09, 1.83), plasma viral load > 50 copies/mL (HR = 1.82, 95% CI: 1.46, 2.26), and yearly increment in duration of HIV infection (HR = 0.94, 95% CI: 0.93, 0.96) (model 3). Conclusion: After controlling for factors associated with hospitalization among those with HIV, both PCSS and MCSS were predictive of all-cause hospitalization in the NHS cohort. HRQOL assessment using the SF-36 may be useful in stratifying hospitalization risk among HIV-infected populations. Keywords: HIV, Human immunodeficiency virus, HRQOL, Health-related quality of life, HAART, Highly active antiretroviral therapy, PCSS, Physical component summary scores, MCSS, Mental component summary scores, Hospitalization * Correspondence: leoemuren@yahoo.com; idcrp@idcrp.org Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 2 of 11 Background different instrument, the EuroQol [5], and adjusted for Health-related quality of life (HRQOL) is primarily used only CD4 count and HIV plasma viral load. In this re- as a patient-centered outcome measure to assess the indi- search, we investigate the usefulness of the RAND SF-36 vidual’s overall functional health status and for evaluating in predicting hospitalization in the NHS cohort. Because therapeutic interventions in chronic diseases including HRQOL reflects an individual’s overall physical and human immunodeficiency virus (HIV) infection and mental functional health status, we hypothesize that par- acquired immune deficiency syndrome (AIDS) [1, 2]. ticipants with lower HRQOL are more likely to be hos- However, a few studies have also utilized HRQOL as a pitalized compared to participants with higher HRQOL prognostic tool for predicting survival in people living over the period of follow-up. with HIV/AIDS (PLWHA) [3–6], showing that HRQOL is useful as a risk stratification tool in HIV-infected indivi- Methods duals both in clinical trials and observational studies. Study cohort While HIV remains incurable, successful treatment The NHS is a prospective multicenter continuous enroll- with highly active antiretroviral therapy (HAART) has ment observational cohort of HIV-infected active duty resulted in prolonged survival among PLWHA [7–9] military personnel and other beneficiaries from the and with the steady incidence of HIV in the United Army, Navy/Marines and Air Force enrolled since 1986 States [9], the prevalence of the disease and, by exten- [14–17]. Participants are followed at six medical centers sion, the burden of the disease on the healthcare system in the U.S. Demographic data are collected at baseline will continue to rise. To mitigate the increasing burden and updated while medical and medication histories and of the disease on the healthcare system and improve the standard laboratory studies are collected biannually. quality of life of infected individuals, it is important that Blood samples obtained from participants in this cohort PLWHA are clinically stable and in optimal functional from scheduled visits are stored in a repository. All NHS health, free from medical/mental comorbidities or op- participants provided informed consent, and approval portunistic infections, and have minimal hospitalizations. for this research was obtained from the institutional Poor HRQOL measures have been associated with review board at each participating site. higher utilization of healthcare resources among patients with other chronic diseases [10–12]. Among Study participants HIV-infected individuals, HRQOL has been shown to be The SF-36 questionnaire was administered to NHS partic- associated with hospitalization and emergency depart- ipants at every other study visit (approximately 12-month ment utilization [5]. The rate of hospitalization in the intervals) from April 2006 to September 2010. For these U.S. Military HIV Natural History Study (NHS) cohort analyses, one SF-36 response per calendar year was cap- was previously reported to be as high as 137 per 1000 tured, using the last measurement if more than one survey person years (PYs) [13]. Given this high rate of was completed in a calendar year. Baseline was defined as hospitalization, it is important to evaluate factors that the earliest measure meeting these criteria. We used the may predict hospitalization such as HRQOL, in the hope CD4 count and viral load values closest in time to the that appropriate interventions directed at modifiable risk HRQOL measure, usually the same visit. factors such as CD4 count and medical/mental comor- bidities that impact HRQOL can be instituted with the Definitions and variable selections ultimate goal of reducing the high hospitalization rate. Hospitalization While HRQOL may be measured using various instru- Participants’ dates of hospitalization, diagnoses at ments, the Research and Development (RAND) Short hospitalization, and number of days of hospitalization were Form 36 (SF-36) is one of the more commonly used retrieved from their medical records and through coordin- instruments both in clinical trials and observational ator interviews. Furthermore, the military healthcare system studies. Although the Medical Outcome Studies for HIV operates a centralized electronic health records system that questionnaire has been used in previous studies to pre- enables investigators to track participants’ records and hos- dict mortality [4, 6], this instrument is disease-specific pitalizations. Hospitalization was the outcome of interest, and its measured health dimensions are slightly different and we considered only the first admission of participants from that of the SF-36. Furthermore, an instrument’s between April 2006 and September 2010 for the purposes ability in predicting mortality may not necessarily prove of this study. To establish a temporal relationship, we en- its usefulness in predicting other relevant clinical sured that date of completed questionnaire preceded the end-points such as hospitalization. To the best of our date of hospitalization. Hospitalization was coded as ‘yes’ if knowledge there has been only one study that has spe- participant was hospitalized after the first completed SF-36 cifically looked at how HRQOL predicts hospitalization questionnaire and ‘no’ if participant was not hospitalized in HIV-infected individuals, but this study used a after the first completed questionnaire for the duration of Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 3 of 11 follow-up. Some common causes of hospitalization include or more comorbidity. Mental comorbidity was classified bacterial, viral, fungal and parasitic infections, cancers, similarly. Common mental comorbidities in the NHS were psychiatric conditions such as major depressive disorders, major depressive disorder, generalized anxiety disorder, alcohol abuse, gastroenterological disorders such as gastro- bipolar disorder and alcohol abuse. Except for gender and esophageal reflux disease and peptic ulcers, and cardiovas- race, all other variables were treated as time-varying co- cular conditions such as myocardial infarction and variates. Baseline was earliest SF-36 captured according to pericarditis. the above criteria; follow-up continued to hospitalization, loss to follow-up, or Sept 30, 2010, whichever came first. Health-related quality of life scores Participants were deemed lost to follow up for this The norm-based physical component summary scores HRQOL analysis if they did not have a completed SF-36 (PCSS) and mental component summary scores questionnaire for a given year and none thereafter. (MCSS) were computed according to the recom- Participants lost to follow-up were censored at 6 months mended scoring algorithm for the RAND 36-item after the last completed SF-36 or at Sept 30, 2010. All par- health survey 1.0 [18, 19]. PCSS and MCSS, the main ticipants aged 18 years and above who were enrolled into explanatory variables, were categorized into terciles the HRQOL sub-study between 2006 and 2010 were with the upper terciles (highest HRQOL) as referent eligible for this analysis. groups. Terciles were established separately for PCSS and MCSS using all available HRQOL scores and we Statistical analyses verified an approximately even distribution of the We summarized the characteristics of the participants number of participants in each tercile at baseline. based on their frequency distribution for categorical vari- Participants could move from one tercile to the other ables and the median and interquartile range (IQR) for con- during the period of follow up based on their scores. tinuous variables. We plotted the Kaplan-Meier curves to estimate the survivor functions by group using the log-rank Covariates test; the Tukey-Kramer adjustment was used to assess Highly active antiretroviral therapy (HAART) was defined between-group differences for variables with more than as a combination of at least three full dose antiretroviral two categories. Cox regression modeling was used to esti- agents similar to previous investigations for this cohort mate the hazard of hospitalization for participants while [15]. In light of prior reports that use of protease inhibi- adjusting for covariates. As above, all covariates were time tors (PI) is associated with poorer HRQOL [20–22], varying, with the exceptions of gender and race. Because HAART was divided into four groups: protease separate multivariate models are traditionally used for PCSS inhibitor-based HAART (PI-HAART), for HAART with and MCSS when these variables are the outcome variables at least one protease inhibitor in the HAART regimen; in research settings, we also used them separately as inde- non-protease-inhibitor-based HAART (NPI-HAART), for pendent variables in two different models while controlling HAART regimens with no protease inhibitor; for the same set of covariates (models 1 and 2, respectively). HAART-naïve group (HAART-N) for those who had Furthermore, we constructed a third model with both never been on HAART, and Off-HAART/Non-HAART PCSS and MCSS included (model 3). To be eligible antiretroviral (ART) group, made up of those who were for inclusion into the multivariate models, the covari- previously treated, but off HAART or who were on ate must achieve a significance level of < 0.2 in the non-HAART ART. Other covariates were gender (male/ univariate model. Missing data were handled using female), age (in increments of 5 years), military rank (offi- the last-observation-carried-forward method. In line cer/warrant officer, enlisted, and civilian/retired), marital with the model specifications, we verified the propor- status (married, not married), race/ethnicity (Caucasian, tional hazard assumptions using both graphical and African-American, and others), HIV plasma viral load formal diagnostic tests including covariate-time inter- ([pVL], ≤50 copies/mL, > 50 copies/mL), CD4 count (< action effects [23–25]. All statistical analyses and 3 3 3 200 cells/mm ,200–349 cells/mm ,350–499 cells/mm graphs were performed using SAS 9.3 [SAS Institute and > 499 cells/mm ), medical comorbidity (yes/no), men- Inc., Cary, NC]. tal comorbidity (yes/no), AIDS diagnosis (yes/no), and time since HIV diagnosis (in years). AIDS was defined in Results accordance with the 1993 Centers for Disease Control and Out of the 1730 eligible participants at baseline, 13 did Prevention revised criteria, except for an isolated CD4 cell not completely answer the HRQOL questionnaire and count < 200 cells/mL, as CD4 was analyzed separately. were excluded. Another 6 participants with missing Medical co-morbidity referred to chronic medical condi- values for one or more covariates at baseline were also tions such as diabetes mellitus, hypertension or cancer excluded. Of the remaining 1711 participants included and was classified as having no comorbidity or having one in this analysis, 366 (21%) were hospitalized at least once Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 4 of 11 Table 1 Demographic and Clinical Characteristics of Participants Table 1 Demographic and Clinical Characteristics of Participants at Baseline at Baseline (Continued) Categorical Variables Mental Component Summary Scores (MCSS) Characteristics N (%) Lower Tercile 39.18 (31.99–43.88) Hospitalized Middle Tercile 50.69 (49.02–51.82) Yes 366 (21.39) Upper Tercile 55.22 (54.04–57.29) No 1345 (78.61) Gender Male 1594 (93.16) Female 117 (6.84) (Table 1). Participants were predominantly male (93%), Race with equal representation from non-Hispanic Whites Non-Hispanic White 719 (42.02) and African-Americans (42% each). 17% of participants Non-Hispanic African 722 (42.20) had a medical comorbidity while 29% had a mental co- Others 270 (15.78) morbidity, and about 12% had an AIDS diagnosis. Rank Slightly over 5% of the cohort had CD4 count < 200 Officer/Warrant Officer 126 (7.36) cells/mm and over 56% had CD4 count > 499 cells/ mm . 35% of participants had pVL > 50 copies/mL. Enlisted 893 (52.19) About 34% of patients were on a PI-based HAART while Others (Retired/Civilians) 692 (40.44) 44% were on a non-PI-based HAART. 14% of partici- Marriage, Yes 556 (32.50) pants were HAART naïve at baseline and another 7% Medical Comorbidity, Yes 285 (16.66) were off HAART at baseline. Only 1% of participants Mental Comorbidity, Yes 493 (28.81) (n = 18) were on a non-HAART antiretroviral therapy AIDS Diagnosis, Yes 202 (11.81) and were combined with the Off-HAART group for HAART the subsequent analyses. PI-Based 575 (33.61) The terciles for the PCSS at baseline had 570, 572 and Non-PI-Based 756 (44.18) 569 participants respectively in the lower, middle and HAART-Naïve 241 (14.09) upper terciles. The median score of the lower PCSS ter- Off-HAART 121 (7.07) cile was 41.78 (IQR, 35.88–46.13) compared to 54.56 Non-HAART ART 18 (1.05) (IQR, 52.78–55.87) for the middle tercile and 58.81 (IQR, 57.87–59.76) for the upper tercile. The Plasma Viral Load > 50 copies/mL Kaplan-Meier product-limit survival estimates for hospi- Yes 600 (35.07) talizations among the PCSS terciles showed statistically No 1111 (64.93) significant differences among all terciles of PCSS (Fig. 1). CD4 Count In the unadjusted Cox regression model, the hazard < 200 cells/mm3 92 (5.38) ratio (HR) of hospitalization for participants in the lower 200–349 cells/mm3 242 (14.14) PCSS tercile was 2.52 times higher compared to partici- 350–499 cells/mm3 411 (24.02) pants in the upper PCSS tercile (95% confidence interval > 499 cells/mm3 966 (56.46) [CI] 1.92–3.32; Table 2), while the hazard of Continuous Variables hospitalization was 1.74 times higher in the middle ter- Characteristics Median (Interquartile Range) cile compared to the upper tercile (95% CI: 1.31–2.33). Age (years) 42.00 (34.00–49.00) After adjustment, the hazards of hospitalization were CD4 Count (cells/mm3) 538.00 (389.00–721.00) 2.12 (95% CI: 1.59–2.84) and 1.59 (95% CI: 1.19–2.14) times higher respectively in the lower and middle ter- Plasma Viral Load (Log10) 1.70 (1.68–2.82) ciles compared to the upper tercile (Table 3, model 3 Time Since HIV Diagnosis (years) 10.00 (4.00–17.00) [after this, we report model 3, which is the combined Duration of Follow-Up from Baseline (Years, Overall) 2.71 (1.04–3.80) PCSS and MCSS model, unless there are obvious differ- Hospitalized 1.23 (0.53–2.37) ences in the results of the three models]). Not Hospitalized 3.13 (1.53–3.95) The terciles for the MCSS at baseline had 569, 571, Physical Component Summary Scores (PCSS) and 571 participants respectively for the lower, middle Lower Tercile 41.78 (35.88–46.13) and upper terciles. The median score of the lower MCSS Middle Tercile 54.56 (52.78–55.87) tercile was 39.18 (IQR, 31.99–43.88) compared to 50.69 Upper Tercile 58.81 (57.87–59.76) (IQR, 49.02–51.82) for the middle tercile and 55.22 (IQR, 54.04–57.29) for the upper tercile. The Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 5 of 11 Fig. 1 Kaplan-Meier Survival Curve for Physical Component Summary Score (PCSS). Legend: Lower Tercile of PCSS, Middle Tercile of PCSS, Upper Tercile of PCSS 3 3 Kaplan-Meier product-limit survival estimates for hospi- 349 cell/mm and 350–499 cell/mm by 2.84 (95% CI: talizations among the MCSS terciles showed statistically 1.96–4.12), 1.67 (95% CI: 1.24–2.26), and 1.41 (95% CI: significant differences between the lower tercile and 1.09–1.83) times respectively when compared to those other two MCSS terciles, but not between the middle with CD4 count > 499 cells/mm . Having pVL > 50 cop- and upper terciles (Fig. 2). In the unadjusted Cox regres- ies/mL was significantly associated with 82% increase in sion model (Table 2), participants in the lower MCSS the hazard of hospitalization (HR: 1.82; 95% CI: 1.46– tercile were 79% at increased hazard of being hospita- 2.26) while being retired/civilian was significantly associ- lized compared to those in the upper MCSS tercile (HR: ated with over a 100% increase in hazard of 1.79; 95% CI: 1.39–2.30). After adjustment (model 3), hospitalization (HR: 2.04; 95% CI: 1.25–3.34) (Table 3). the hazard of hospitalization in the lower tercile was Every year increment in time from HIV diagnosis led to 33% higher than that of the upper tercile (HR: 1.33; 95% a 5.6% reduced hazard of hospitalization (HR: 0.94; 95% CI 1.02–1.73). CI: 0.93–0.96). Factors that were not significantly associ- The first-hospitalization rate among the 1711 participants ated with hospitalization in the combined model but sig- was 8.7 per 100 person-years (PYs). As previously noted, nificant in the PCSS and/or MCSS models (Table 3) the number of participants at baseline in the lower, middle were mental comorbidity (both PCSS and MCSS and upper PCSS terciles were 570, 572 and 569 and the models) and AIDS diagnosis (MCSS model only). total duration of follow up in years were respectively HAART, on the other hand, was only significant in the 1312.34, 1414.88 and 1471.44. There were 168 hospitaliza- univariate model but not in any of the multivariate tions in lower terciles for the period of follow up, 124 in the models and therefore not shown in our most parsimoni- middle tercile and 74 in the upper tercile, therefore making ous models. the rates of hospitalization 12.8, 8.8 and 5.0 per 100 PYs for the lower, middle and upper terciles respectively (Table 4). Discussion Similarly, the rates of hospitalization were 11.8, 8.3 and 6.5 Our study shows that both PCSS and MCSS were inde- per 100 PYs for the lower, middle and upper terciles of pendently predictive of hospitalization among partici- MCSS respectively (Table 4). pants in the NHS cohort. To the best of our knowledge, The HRs of hospitalization were significantly increased this is the first study to use the SF-36 to evaluate in participants with CD4 count < 200 cells/mm ,200– whether HRQOL predicts hospitalization in an Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 6 of 11 Table 2 Univariate Cox Regression Model for Hazard of hospitalization compared to the upper PCSS tercile. Par- Hospitalization ticipants in the lower MCSS tercile had a 33% increased Variable Hazard Ratio 95% CI P-Value hazard of hospitalization compared to the upper MCSS tercile. These findings support the use of HRQOL in risk Physical Component Summary Score (PCSS) assessment for hospitalization in clinical and research Lower Tercile of PCSS 2.52 1.92–3.32 <.0001 settings. Although clinical factors such as mental comor- Middle Tercile of PCSS 1.74 1.31–2.33 0.0002 bidity and AIDS diagnosis were not significantly associ- Upper Tercile of PCSS 1.0 –– ated with hospitalization in the combined model (model Mental Component Summary Score (MCSS) 3, Table 3), mental comorbidity was significant in the Lower Tercile of MCSS 1.79 1.39–2.30 <.0001 separate PCSS and MCSS models while AIDS diagnosis was significant in the MCSS model and was nearly sig- Middle Tercile of MCSS 1.27 0.97–1.67 0.08 nificant in the PCSS model. These covariates should Upper Tercile of MCSS 1.0 –– therefore be included in studies examining clinical inter- Age (Years, Increment of 5 Years) 0.98 0.94–1.03 0.5 ventions directed at reducing the risk of hospitalization Gender (Male) 1.06 0.70–1.62 0.8 in PLWHA considering that they contribute to both the Marital Status (Married) 1.13 0.91–1.40 0.3 independent and outcome variables. Race/Ethnicity The overall rate of hospitalization of 8.7 per 100 PYs in our current study is much lower than that reported Non-Hispanic African American 0.96 0.77–1.19 0.7 earlier for the NHS by Crum-Cianflone et al. [13], which Hispanic/Others 0.86 0.63–1.18 0.3 reported a hospitalization rate of 137 per 1000 PYs or Non-Hispanic Caucasian 1.0 –– 13.7 per 100 PYs. This difference may be partly ex- Rank plained by the fact that we only considered the first Civilian/Retired 1.47 0.93–2.35 0.1 hospitalization per individual as against all hospitaliza- Enlisted 1.22 0.77–1.95 0.4 tions used in the study by Crum-Cianflone et al. Indeed, in that study 47% of all hospitalizations were repeat in- Officers 1.0 –– patient admissions. Furthermore, the general decline in CD4 Count hospitalization rates in HIV-infected populations over CD4 Count < 200 cells/mm3 3.89 2.78–5.44 <.0001 the years may partly explain the differences in CD4 Count 200–349 cells/mm3 2.16 1.63–2.86 <.0001 hospitalization rates noted in both studies. The rates of CD4 Count 350–499 cells/mm3 1.55 1.20–2.00 0.0008 hospitalizations were also markedly different by terciles CD4 Count > 499 cells/mm3 1.0 –– of PCSS and MCSS. These rates were 5.0, 8.8 and 12.8 per 100 PYs for the upper, middle and lower PCSS ter- Plasma Viral Load > 50 Copies/mL 2.36 1.92–2.89 <.0001 ciles respectively, and 6.5, 8.3 and 11.7 per 100 PYs for Medical Comorbidity 1.05 0.81–1.36 0.7 the upper, middle and lower terciles of MCSS respect- Mental Comorbidity 1.43 1.16–1.76 0.0009 ively. This clear pattern of less hospitalization with bet- AIDS Diagnosis 1.67 1.27–2.18 0.0002 ter physical and mental functional health status suggests Time Since HIV (Per year) 0.98 0.96–0.99 0.002 that applying innovative research to delineate inter- HAART Treatment ventions that will improve HRQOL may be strategically significant in reducing hospitalization in our population HAART-Naïve 1.80 1.33–2.43 0.0002 and likely more broadly among PLWHA. Off-HAART/Non-HAART ART 1.75 1.26–2.44 0.0008 Our findings on the association among CD4 count and Non-PI Based HAART 0.70 0.55–0.87 0.003 hospitalization were similar to and reinforced prior work. PI Based HAART 1.0 –– Compared to CD4 count > 499 cells/mm , CD4 count All variables are time-varying except for race and gender 3 3 < 200 cells/mm , CD4 count 200–349 cells/mm ,and CD4 count 350–499 cells/mm were respectively associated with increased hazard of hospitalization by 184, 67 and HIV-infected population, and perhaps the second study 41%. Somewhat similar to our findings, Crum-Cianflone for any HRQOL instrument [5]. Our study therefore et al. [13], in an earlier work on this cohort, had found that adds to the growing literature on the role of HRQOL in CD4 count > 499 cells/mm reduced the hazard of predicting hospitalization in chronic diseases [11, 26– hospitalization when compared to CD4 < 350 cells/mm 28]. In our current study, those in the middle PCSS ter- but not 350–499 cells/mm . The reason for the difference cile were at 59% greater hazard of hospitalization com- with our current study may be related to differences in the pared to the upper PCSS tercile while those in the lower time frame studied (2006–2010 [current study] versus PCSS tercile were at 112% greater hazard of 1999–2007 [previous study]) or in study design (first Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 7 of 11 Table 3 Multivariate Cox Regression Model for Hazard of Hospitalization for Terciles of PCSS and MCSS Variable Model 1: PCSS Model Model 2: MCSS Model Model 3: Combined PCSS and MCSS Model HR 95% CI HR 95% CI HR 95% CI PCSS Lower Tercile 2.18 1.64–2.90 2.12 1.59–2.84 Middle Tercile 1.62 1.21–2.17 1.59 1.19–2.14 Upper Tercile 1.0 – 1.0 – MCSS Lower Tercile 1.44 1.10–1.87 1.33 1.02–1.73 Middle Tercile 1.14 0.87–1.49 1.20 0.91–1.57 Upper Tercile 1.0 – 1.0 – CD4 Count < 200 cells/mm3 2.84 1.96–4.12 2.99 2.06–4.33 2.84 1.96–4.12 200–349 cells/mm3 1.69 1.25–2.27 1.67 1.24–2.25 1.67 1.24–2.26 350–499 cells/mm3 1.41 1.09–1.82 1.37 1.05–1.77 1.41 1.09–1.83 > 499 cells/mm3 1.0 – 1.0 – 1.0 – Plasma Viral Load > 50 Copies/mL 1.83 1.47–2.28 1.88 1.51–2.34 1.82 1.46–2.26 Mental Comorbidity 1.31 1.04–1.63 1.30 1.04–1.64 1.23 0.98–1.55 AIDS Diagnosis 1.35 1.00–1.83 1.47 1.09–1.99 1.34 0.99–1.81 Rank Civilian/Retired 2.06 1.26–3.37 2.16 1.32–3.52 2.04 1.25–3.34 Enlisted 1.18 0.74–1.88 1.17 0.73–1.86 1.19 0.74–1.89 Officer 1.0 – 1.0 –– – Time Since HIV (Per year) 0.94 0.92–0.96 0.94 0.93–0.96 0.94 0.93–0.96 All variables are time-varying Fig. 2 Kaplan-Meier Survival Curve for Mental Component Summary Score (MCSS). Legend: Lower Tercile of MCSS, Middle Tercile of MCSS, Upper Tercile of MCSS Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 8 of 11 Table 4 Overall Rates of Hospitalization in the NHS and by Terciles of PCSS and MCSS HRQOL Terciles Total Participants Total No. Hospitalized Duration of Rate of Hospitalization of HRQOL at Baseline during Follow Up Follow Up (Years) Per 100 Person - Years PCSS and MCSS Overall 1711 366 4201.67 8.71 PCSS Lower 570 168 1312.34 12.80 Middle 572 124 1414.88 8.76 Upper 569 74 1474.44 5.02 MCSS Lower 569 152 1293.77 11.75 Middle 571 116 1397.09 8.30 Upper 571 98 1510.8 6.49 Participants could move from one tercile to the other during the period of follow up based on their scores hospitalization [current study] versus all hospitalizations admission is warranted. Survival bias may also play a [previous study]) [13]. Other investigators have also shown role as those who live longer due to their innate ability that lower CD4 count is associated with hospitalization, to cope with the disease are less likely to be hospitalized especially when CD4 count falls below 200 [29–34]. because of their relatively healthier state. The finding HIV pVL > 50 copies/mL was also associated with that being civilian/retired was associated with over 100% hospitalization in our cohort. Although the levels of increased hazard of hospitalization is not totally unex- dichotomization differed, Fielden et al. [34]alsofound that pected as this group is older, and age has been associ- higher pVL was associated with hospitalization while ated with higher inpatient admissions [35]. However, the Mocroftetal.[29]demonstratedthatinthe lastof three fact that age was not independently associated with time points in their study, there was an increased odds of hospitalization in our current study may partly reflect hospitalization for every log unit increase in pVL. the relatively young age of our cohort (median age Interestingly, a longer time since HIV diagnosis was 42 years; IQR 34 to 49 years) but may also be suggestive predictive of a reduced hazard of hospitalization. One of the fact that other factors associated with non-active plausible explanation for this finding may be that duty status, such as health related behaviors, played a individuals with longer disease duration may be more more important role in this respect. Although being experienced with dealing with symptoms (including sub- civilian/retired has been shown to be associated with tle ones) associated with their infection and therefore lower PCSS in our cohort [36], its association with more likely to seek medical attention earlier before hospitalization in the current study is independent of Table 5 Multivariate Cox Regression Model for Hazard of Hospitalization (PCSS and MCSS Numeric) Variable Model 4: PCSS Model Model 5: MCSS Model Model 6: Combined PCSS and MCSS Model HR 95% CI HR 95% CI HR 95% CI PCSS, 5 Unit Increments 0.87 0.83–0.92 0.88 0.84–0.93 MCSS, 5 Unit Increments 0.91 0.87–0.96 0.94 0.89–0.99 CD4 Count < 200 cells/mm3 2.76 1.90–4.01 2.96 2.04–4.29 2.73 1.88–3.97 200–349 cells/mm3 1.66 1.23–2.23 1.67 1.24–2.25 1.65 1.22–2.23 350–499 cells/mm3 1.38 1.07–1.80 1.37 1.05–1.77 1.38 1.07–1.79 > 499 cells/mm3 1.0 – 1.0 – 1.0 – Plasma Viral Load > 50 Copies/mL 1.86 1.49–2.32 1.89 1.52–2.36 1.86 1.49–2.31 Mental Comorbidity 1.31 1.05–1.64 1.28 1.02–1.61 1.23 0.97–1.54 AIDS Diagnosis 1.34 0.99–1.82 1.48 1.09–2.00 1.34 0.99–1.82 Rank Civilian/Retired 2.21 1.35–3.61 2.14 1.31–3.49 2.15 1.32–3.52 Enlisted 1.28 0.80–2.04 1.16 0.73–1.85 1.27 0.79–2.02 Officer 1.0 – 1.0 –– – Time Since HIV (Per year) 0.94 0.92–0.96 0.95 0.93–0.96 0.94 0.93–0.96 All variables are time-varying Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 9 of 11 PCSS (Tables 3 and 5). Finally, while HAART was increase in PCSS the hazard of hospitalization was re- significant in the unadjusted model, it was no longer sig- duced by 12% (Table 5). MCSS, on the other hand, re- nificant after adjustment showing that its effect may duced the hazard of hospitalization by 6% for every have been captured by other variables. 5-unit increase in MCSS. Our study therefore adds to Potential limitations of our study include the predom- the predictive utility of the HRQOL measures in inantly male patients in the NHS cohort which may limit HIV-infected persons as assessed by the SF-36. Other its generalizability to females. The regimented lifestyle of important strengths of our study include its large sample our military population and the requirements of physical size, open access to healthcare and medications, racial and mental fitness may also mean that they may diversity, and the heterogeneity of the cohort with promptly seek medical attention, a behavior that may regards to the range of values for HIV disease indicators not necessarily be seen in the general population. We (CD4 count and pVL), and other clinical parameters, may not have captured hospital admissions outside the such as medical and mental comorbidities with respect- military settings; however, with the use of trained coor- ive prevalence of 17 and 29% of the cohort at baseline. dinators to conduct participants’ interviews, the number The heterogeneity of the cohort (Table 1) is further of missed admissions outside the single payer electronic reflected by the wide range of PCSS (16.7 to 70.7) and health records systems of the U.S. military is expected to MCSS (8.8 to 67.8), a finding that is rare in other pre- be small. Because we administratively censored partici- dictive studies using the SF-36 that tend to be with per- pants after September 2010, follow-up was limited for sons with relatively very low scores [3, 4, 6, 28, 38]. those subjects surveyed in 2010; however, as subjects Finally, HRQOL summary scores may be useful in prog- were censored 6 months after their last survey, this nostic studies in HIV-infected populations because they affected only those enrolled in 2010 (n = 99) [36]. In sen- capture information beyond HIV disease-specific indica- sitivity analysis we excluded these 99 participants and tors, such as CD4 count and pVL. The causes of our results remained essentially the same. In this current hospitalization in HIV-infected individuals are now be- study, we did not specifically evaluate whether yond disease-related factors but include both medical within-person difference in HRQOL score may predict and surgical conditions, especially in the HAART era hospitalization. While such difference in score may be [13]. HRQOL is also reflective of perceptions that may more meaningful to the clinician interested in individual potentially affect subsequent health-seeking behaviors risk of inpatient admission over time, we believe that and utilization of healthcare resources including knowing the actual scores that predict hospitalization is preventive services [4, 39]. a useful starting point. More so, within-person difference in score may be dependent on the baseline HRQOL Conclusion score. Finally, our findings that PCSS and MCSS were After controlling for factors associated with hospitalization predictive of hospitalization should not be interpreted as among those with HIV, both PCSS and MCSS were predict- implying causation but instead that physical and mental ive of all-cause hospitalization in the NHS cohort with functional health may be surrogates for the actual causes similar effect sizes for PCSS and low CD4. Applying in- of hospitalization. Yet, the fact that these measures were novative strategies to improve modifiable risk factors that statistically significant in predicting hospitalization in all influence the physical and mental functional status of multivariate models, the magnitude of the effect sizes, HIV-infected individuals has the potential to reduce the and the dose response relationships support their utility rate of hospitalization in this patient population. We, there- in clinical practice. fore, recommend the use of HRQOL in stratifying Our study had major advantages. There was clearly an hospitalization risk among HIV-infected individuals. Finally, established temporal relationship between the HRQOL future research to evaluate whether within-person differ- scores and hospitalization. Because hospitalization may ence in HRQOL scores is predictive of hospitalization is also negatively impact HRQOL [37], we further con- needed, as this may even increase the utility of HRQOL ducted sensitivity analyses to rule out reverse causation measures in clinical settings. by excluding participants whose scores were taken Abbreviations within 1 week hospitalization but our findings remained HAART: Highly Active Antiretroviral Therapy; HIV: Human Immunodeficiency essentially the same. This temporal pattern was also true Virus; HRQOL: Health-Related Quality of Life; MCSS: Mental Component Summary Scores; NPI-HAART: Non-Protease Inhibitor HAART; PCSS: Physical for the other time-dependent covariates included in the Component Summary Scores; PI-HAART: Protease Inhibitor HAART; study. The clear dose-response relationship between pVL: Plasma Viral Load; SF-36: Short Form 36 PCSS and hospitalization in our study strongly supports Acknowledgements its utility as a predictive tool. To further demonstrate Support for this work (IDCRP-000-24) was provided by the Infectious Disease the discriminatory quality of PCSS we used it as a con- Clinical Research Program (IDCRP), a Department of Defense (DoD) program tinuous variable, and it showed that for every 5-unit executed through the Uniformed Services University of the Health Sciences. Emuren et al. Health and Quality of Life Outcomes (2018) 16:107 Page 10 of 11 This project has been funded in whole, or in part, with federal funds from Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, the National Institute of Allergy and Infectious Diseases, National Institutes of MD, USA. Henry M. Jackson Foundation for the Advancement of Military Health (NIH), under Inter-Agency Agreement Y1-AI-5072. Medicine, Inc., Bethesda, MD, USA. Members of the Infectious Disease Clinical Research Program HIV Working Group include the following: Received: 25 October 2017 Accepted: 10 May 2018 Madigan Army Medical Center, Tacoma, WA: S. Chambers; COL (Ret) M. Fairchok; LTC A. Kunz; C. Schofield. National Institute of Allergy and Infectious Diseases, Bethesda, MD: J. Powers; COL (Ret) E. Tramont. References Naval Medical Center, Portsmouth, VA: S. Banks; CDR K. Kronmann; T. Lalani; 1. Gakhar H, Kamali A, Holodniy M. Health-related quality of life assessment after LCDR K. St. Clair; R. Tant. antiretroviral therapy: a review of the literature. Drugs. 2013;73(7):651–72. Naval Medical Center, San Diego, CA: CAPT M. Bavaro; R. Deiss; A. Diem; N. 2. Degroote S, Vogelaers D, Vandijck DM. What determines health-related Kirkland; CDR R. Maves. quality of life among people living with HIV: an updated review of the San Antonio Military Medical Center, San Antonio, TX: S. Merritt; T. O’Bryan; literature. Arch Public Health. 2014;72(1):40. 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Health and Quality of Life OutcomesSpringer Journals

Published: May 30, 2018

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