Open Access

Factors associated with pre-treatment HIV RNA: application for the use of abacavir and rilpivirine as the first-line regimen for HIV-infected patients in resource-limited settings

  • Sasisopin Kiertiburanakul1Email author,
  • David Boettiger2,
  • Oon Tek Ng3,
  • Nguyen Van Kinh4,
  • Tuti Parwati Merati5,
  • Anchalee Avihingsanon6,
  • Wing-Wai Wong7,
  • Man Po Lee8,
  • Romanee Chaiwarith9,
  • Adeeba Kamarulzaman10,
  • Pacharee Kantipong11,
  • Fujie Zhang12,
  • Jun Yong Choi13,
  • Nagalingeswaran Kumarasamy14,
  • Rossana Ditangco15,
  • Do Duy Cuong16,
  • Shinichi Oka17,
  • Benedict Lim Heng Sim18,
  • Winai Ratanasuwan19,
  • Penh Sun Ly20,
  • Evy Yunihastuti21,
  • Sanjay Pujari22,
  • Jeremy L. Ross23,
  • Matthew Law2,
  • Somnuek Sungkanuparph1 and
  • on behalf of the TREAT Asia HIV Observational Databases (TAHOD)
AIDS Research and Therapy201714:27

https://doi.org/10.1186/s12981-017-0151-1

Received: 15 January 2017

Accepted: 26 April 2017

Published: 5 May 2017

Abstract

Background

Abacavir and rilpivirine are alternative antiretroviral drugs for treatment-naïve HIV-infected patients. However, both drugs are only recommended for the patients who have pre-treatment HIV RNA <100,000 copies/mL. In resource-limited settings, pre-treatment HIV RNA is not routinely performed and not widely available. The aims of this study are to determine factors associated with pre-treatment HIV RNA <100,000 copies/mL and to construct a model to predict this outcome.

Methods

HIV-infected adults enrolled in the TREAT Asia HIV Observational Database were eligible if they had an HIV RNA measurement documented at the time of ART initiation. The dataset was randomly split into a derivation data set (75% of patients) and a validation data set (25%). Factors associated with pre-treatment HIV RNA <100,000 copies/mL were evaluated by logistic regression adjusted for study site. A prediction model and prediction scores were created.

Results

A total of 2592 patients were enrolled for the analysis. Median [interquartile range (IQR)] age was 35.8 (29.9–42.5) years; CD4 count was 147 (50–248) cells/mm3; and pre-treatment HIV RNA was 100,000 (34,045–301,075) copies/mL. Factors associated with pre-treatment HIV RNA <100,000 copies/mL were age <30 years [OR 1.40 vs. 41–50 years; 95% confidence interval (CI) 1.10–1.80, p = 0.01], body mass index >30 kg/m2 (OR 2.4 vs. <18.5 kg/m2; 95% CI 1.1–5.1, p = 0.02), anemia (OR 1.70; 95% CI 1.40–2.10, p < 0.01), CD4 count >350 cells/mm3 (OR 3.9 vs. <100 cells/mm3; 95% CI 2.0–4.1, p < 0.01), total lymphocyte count >2000 cells/mm3 (OR 1.7 vs. <1000 cells/mm3; 95% CI 1.3–2.3, p < 0.01), and no prior AIDS-defining illness (OR 1.8; 95% CI 1.5–2.3, p < 0.01). Receiver-operator characteristic (ROC) analysis yielded area under the curve of 0.70 (95% CI 0.67–0.72) among derivation patients and 0.69 (95% CI 0.65–0.74) among validation patients. A cut off score >25 yielded the sensitivity of 46.7%, specificity of 79.1%, positive predictive value of 67.7%, and negative predictive value of 61.2% for prediction of pre-treatment HIV RNA <100,000 copies/mL among derivation patients.

Conclusion

A model prediction for pre-treatment HIV RNA <100,000 copies/mL produced an area under the ROC curve of 0.70. A larger sample size for prediction model development as well as for model validation is warranted.

Keywords

Abacavir HIV RNA Model Prediction Rilpivirine

Background

Antiretroviral therapy (ART) for the treatment of human immunodeficiency virus (HIV) infection has dramatically reduced HIV-associated morbidity and mortality and has transformed HIV infection into a manageable chronic condition [1, 2]. Furthermore, early ART is highly effective in preventing HIV transmission to sexual partners [3]. More than 25 antiretroviral drugs (ARV) in 6 classes are approved for treatment of HIV infection [4]. Selection of an ARV regimen should be individualized on the basis of efficacy, adverse effects, pill burden, dosing frequency, drug–drug interactions, comorbid conditions, and cost [4, 5].

The initial ARV regimen for a treatment-naïve HIV-infected patient generally consists of 2 nucleoside/nucleotide reverse transcriptase inhibitors, usually abacavir (ABC) plus lamivudine (3TC) or tenofovir disoproxil fumarate plus emtricitabine (TDF/FTC), plus a drug from 1 of 3 drug classes: an integrase strand transfer inhibitor, a non-nucleoside reverse transcriptase inhibitor (NNRTIs), or a boosted protease inhibitor [4, 5]. ABC is usually preferred over TDF for individuals with chronic kidney disease and/or those at risk of osteoporosis and fractures [4, 5]. However, ABC is recommended for patients who are HLA-B*5701 allele negative and have a pre-treatment HIV RNA <100,000 copies/mL [6], except when used with dolutegravir (DTG) and 3TC in the same regimen [4, 5].

Rilpivirine (RPV) is a recently approved NNRTI available at relatively low cost in Thailand (7 USD per month) and other countries. The advantages of RPV are once-daily dosing and very small pill size. In addition, RPV is associated with fewer treatment discontinuations for central nervous system adverse effects, fewer lipid effects, and fewer rashes when compared with efavirenz (EFV) [7, 8]. Nevertheless, RPV has a higher rate of virological failure when compared to EFV, especially in the first 48 weeks of treatment [7]. RPV is thus recommended as an alternative option for treatment naïve HIV-infected patients with a pre-treatment HIV RNA <100,000 copies/mL and CD4 count >200 cells/mm3 [4, 5].

Testing of HIV RNA levels is recommended during initial patient visits by treatment guidelines in developed countries [4, 5]. In resource-limited settings, pre-treatment HIV RNA is not routinely performed and not widely available [9, 10]. This limits the use of ABC and RPV as a component of the first-line ARV regimen. If a clinical prediction tool based on routinely collected data could accurately predict whether pre-treatment HIV RNA was <100,000 copies/mL, this could be applied into clinical practice. The aims of this study are to determine factors associated with pre-treatment HIV RNA <100,000 copies/mL and to construct prediction tools that predict a pre-treatment HIV RNA <100,000 copies/mL. This prediction tool might support the use of ABC and RPV as part of first-line regimens for selected treatment-naïve HIV-infected individuals in resource-limited settings with limited access to HIV RNA testing.

Patients and methods

Our study population consisted of HIV-infected patients enrolled in the TREAT (Therapeutics Research, Education, and AIDS Training) Asia HIV Observational Database (TAHOD). The characteristics of this cohort have been described previously. Briefly, TAHOD is a prospective multi-center, observational study of patients with HIV and aims to assess HIV disease natural history in treated and untreated patients in the Asia and Pacific region [11]. We included patients enrolled in the cohort from 23 clinical sites throughout 13 countries in the Asia Pacific region since September 2003. The date of data censoring for the analysis of this study was 31 March 2015.

HIV-infected adults enrolled in TAHOD were eligible if they had an HIV RNA measurement documented at or around the time of ART initiation (pre-treatment HIV RNA). The window period of pre-treatment HIV RNA measurement was between 3 months prior to 1 day after the date of starting ART. ART was defined as a regimen containing ≥3 ARVs. Those exposed to mono or dual therapy prior to starting combination ART were excluded. Baseline was defined as the date of ART initiation. At baseline, co-variables included age, sex, HIV exposure, hepatitis B and C serology (ever positive), time since diagnosis of HIV infection, HIV subtype, and AIDS diagnosis prior to baseline. The window period of the following co-variables was between 3 months prior to 3 months after the date of ART initiation; body mass index (BMI), anemia (hemoglobin <13 g/dL for men, <12 g/dL for women), total lymphocyte count, CD4 count, CD8 count, CD4:CD8 ratio, and syphilis serology [Rapid plasma reagin (RPR), Venereal Disease Research Laboratory (VDRL) or Treponema pallidum particle agglutination assay (TPHA)].

Statistical analysis

The dataset was randomly split into a derivation data set (containing data from 75% of all eligible patients) and validation data set (containing data from 25% of all eligible patients) using the PROC SURVEYSELECT command in SAS version 9.4 (SAS Institute Inc., Cary, North Carolina, USA). The study endpoint was pre-treatment HIV RNA <100,000 copies/mL. Factors associated with this endpoint were evaluated by logistic regression adjusted for study site. Co-variables were considered for inclusion in the multivariate model if one or more categories exhibited a p-value <0.1. They were retained in the multivariate model if one or more categories exhibited a p-value <0.05. Missing categories, where present, were included in all models but odds ratios (OR) were not shown.

Prediction scores were created by multiplying the OR for each multivariate co-variable category by 10 and subtracting 1 [12]. Scores were rounded to the nearest 0.5 points. Some categories among the variables including in the multivariate model gave similar OR and were therefore collapsed together for the prediction tool.

The discrimination was evaluated using the area under the receiver-operator characteristic (AUROC) curve [13]. We used data of patients that had data available on all variables including in the prediction model. The optimum cut-off point for the score was evaluated by sensitivity, specificity, positive predictive value, and negative predictive value. Stata version 14.1 (StataCorp, College Station, Texas, USA) was used for all statistical analysis.

Results

A total of 2592 patients were included in our derivation analysis. Median [interquartile range (IQR)] age was 35.8 (29.9–42.5) years, 56.2% had heterosexual HIV exposure, median (IQR) BMI was 21.1 (19.0–23.4) kg/m2, median duration of HIV diagnosis was 4.3 (1.4–29.2) months, and 34.5% had prior AIDS-defining illness. Median CD4 count was 147 (50–248) cells/mm3 and median pre-treatment HIV RNA was 100,000 (34,045–301,075) copies/mL. For other laboratory investigations, 49.3% had anemia, 10.8% had positive HBsAg, 8.3% had positive anti-HCV, 19.6% had positive syphilis serology, and 75.1% had HIV infection with CRF01_AE subtype. Baseline characteristics of the patients are shown in Table 1.
Table 1

Baseline characteristics of 2592 HIV-infected patients

Baseline characteristics

Valuea

Median (IQR) age, years

35.8 (29.9–42.5)

Male

1883 (72.6)

HIV exposure

 Heterosexual

1456 (56.2)

 Homosexual

778 (30.0)

 Intravenous drug use

93 (3.6)

 Other

265 (10.2)

Median (IQR) body mass index, kg/m2

21.1 (19.0–23.4)

  Missing

683 (26.4)

Anemia

 No, n (% tested)

1208 (50.7)

 Yes, n (% tested)

1176 (49.3)

 Unknown

208 (8.0)

Hepatitis B surface antigen

 Negative, n (% tested)

1925 (89.2)

 Positive, n (% tested)

232 (10.8)

 Unknown

435 (16.8)

Hepatitis C antibody

 Negative, n (% tested)

1844 (91.7)

 Positive, n (% tested)

168 (8.3)

 Unknown

580 (22.4)

Syphilis serology

 Negative, n (% tested)

825 (80.4)

 Positive, n (% tested)

201 (19.6)

 Unknown

1566 (60.4)

Median (IQR) duration of HIV diagnosis, months

4.3 (1.4–29.2)

  Missing

29 (1.1)

HIV subtype

 CRF01_AE, n (% tested)

796 (75.1)

 B, n (% tested)

173 (16.3)

 Other, n (% tested)

91 (8.6)

 Unknown

1532 (59.1)

Median (IQR) HIV RNA, copies/mL

100,000 (34,045–301,075)

Median (IQR) CD4 count, cells/mm3

147 (50–248)

  Missing

106 (4.1)

Median (IQR) CD8 count, cells/mm3

753 (485–1103)

  Missing

1268 (48.9)

Median (IQR) CD4:CD8 ratio

0.19 (0.09–0.32)

  Missing

1268 (48.9)

Median (IQR) total lymphocyte count, cells/mm3

1472 (1000–2005)

  Missing

286 (11.0)

Prior AIDS illness

 No

1698 (65.5)

 Yes

894 (34.5)

IQR interquartile range

a Values are n (% total) unless otherwise specified

Factors that statistically significantly associated with pre-treatment HIV RNA <100,000 copies/mL in the derivation patients by multivariate logistic regression, were age <30 years [OR 1.40 vs. 41–50 years; 95% confidence interval (CI) 1.10–1.80, p = 0.01], body mass index >30 kg/m2 (OR 2.4 vs. <18.5 kg/m2; 95% CI 1.1–5.1, p = 0.02), anemia (OR 1.70; 95% CI 1.40–2.10, p < 0.01], CD4 count >350 cells/mm3 (OR 3.9 vs. <100 cells/mm3; 95% CI 2.0–4.1, p < 0.01), total lymphocyte count >2000 cells/mm3 (OR 1.7 vs. <1000 cells/mm3; 95% CI 1.3–2.3, p < 0.01), and no prior AIDS-defining illness (OR 1.8; 95% CI 1.5–2.3, p < 0.01) (Table 2).
Table 2

Factors associated pre-treatment HIV RNA <100,000 copies/mL in derivation population

Factors

Number of patients

Patients (% total) with HIV RNA <100,000 copies/mL

Univariate OR (95% CI)

p-value

Multivariate OR (95% CI)

p- value

Years of ageb

 

 ≤30

656

360 (54.9)

1.6 (1.2–2.0)

<0.01

1.4 (1.1–1.8)

0.01

 31–40

1057

514 (48.6)

1.2 (0.9–1.4)

0.15

1.1 (0.9–1.4)

0.40

 41–50

600

273 (45.5)

1.0

 

1.0

 

 >50

279

131 (47.0)

1.0 (0.8–1.4)

0.85

1.0 (0.7–1.4)

0.96

Sexb

 Male

1883

908 (48.2)

1.0

   

 Female

709

370 (52.2)

1.2 (1.0–1.5)

0.02

  

HIV exposure

 Heterosexual

1456

688 (47.3)

1.0

   

 Homosexual

778

422 (54.2)

1.3 (1.0–1.6)

0.03

  

 Intravenous drug use

93

41 (44.1)

0.9 (0.6–1.4)

0.54

  

 Other

265

127 (47.9)

0.9 (0.7–1.3)

0.70

  

Body mass index (kg/m2)b

 <18.5

366

134 (36.6)

1.0

 

1.0

 

 18.5–24.9

1289

648 (50.3)

1.7 (1.3–2.2)

<0.01

1.3 (1.0–1.7)

0.07

 25.0–29.9

213

112 (52.6)

1.8 (1.3–2.6)

<0.01

1.2 (0.8–1.7)

0.47

 ≥30.0

41

29 (70.7)

4.1 (2.0–8.4)

<0.01

2.5 (1.2–5.2)

0.02

 Unknown

683

355 (52.0)

 

 

Anemiab

 No

1208

741 (61.3)

2.7 (2.3–3.3)

<0.01

1.7 (1.4–2.1)

<0.01

 Yes

1176

442 (37.6)

1.0

 

1.0

 

 Unknown

208

95 (45.7)

 

 

Hepatitis C antibody

 Negative

1844

916 (49.7)

1.0

   

 Positive

168

73 (43.5)

0.8 (0.6–1.1)

0.15

  

 Unknown

580

289 (49.8)

   

Month since HIV diagnosis

 <6

1384

614 (44.4)

1.0

   

 6–18

338

189 (55.9)

1.6 (1.3–2.1)

<0.01

  

 >18

841

461 (54.8)

1.5 (1.3–1.8)

<0.01

  

 Unknown

29

14 (48.3)

   

CD4 count (cells/mm3)b

 ≥350

219

147 (67.1)

4.8 (3.4–6.7)

<0.01

2.9 (2.0–4.1)

<0.01

 200–349

694

456 (65.7)

4.2 (3.4–5.3)

<0.01

2.7 (2.1–3.4)

<0.01

 100–199

617

308 (49.9)

2.1 (1.7–2.6)

<0.01

1.6 (1.2–2.0)

<0.01

 <100

956

318 (33.3)

1.0

 

1.0

 

 Unknown

106

49 (46.2)

 

 

Total lymphocyte count (cells/mm3)a

 ≥2000

593

325 (54.8)

2.9 (2.2–3.7)

<0.01

1.7 (1.3–2.3)

 

 1500–1999

529

303 (57.3)

2.9 (2.3–3.7)

<0.01

1.8 (1.4–2.4)

 

 1000–1499

627

331 (52.8)

2.3 (1.8–2.9)

<0.01

1.6 (1.3–2.1)

<0.01

 <1000

557

180 (32.3)

1.0

 

1.0

 

 Unknown

286

139 (48.6)

 

 

Prior AIDS-defining illnessa

 None known

1698

987 (58.1)

3.0 (2.5–3.6)

<0.01

1.8 (1.5–2.3)

<0.01

 Yes

894

291 (32.6)

1.0

 

1.0

 

OR odds ratio, CI confidence interval

a Multivariate result shows effect size when replacing CD4 count

b Included in the final model

Clinical prediction tool scores for pre-treatment HIV RNA <100,000 copies/mL are shown in Table 3. Scores were +3.5 for age <30 years, +2.5 for BMI of 18.5–29.9 kg/m2 or +14.5 for BMI of >30 kg/m2, +7.0 for non-anemia, +17.0 for CD4 count >200 cells/mm3 or +5.5 for 100–199 cells/mm3, and +8.5 for no prior AIDS-defining illness. The possible maximum score was 50.5.
Table 3

Clinical prediction tool scores for each variable for pre-treatment HIV RNA <100,000 copies/mL

Variables

Score

Age ≤30 years

+3.5

Age >30 years

0

Body mass index <18.5 kg/m2

0

Body mass index 18.5–29.9 kg/m2

+2.5

Body mass index ≥30 kg/m2

+14.5

Anemic

0

Non-anemic

+7.0

CD4 count ≥200 cells/mm3

+17.0

CD4 count 100–199 cells/mm3

+5.5

CD4 count <100 cells/mm3

0

No prior AIDS-defining illness

+8.5

Prior AIDS-defining illness

0

Maximum score

50.5

AUROC analysis was 0.70 (95% CI 0.67–0.72) among the derivation patients (Fig. 1) and 0.69 (95% CI 0.65–0.74) among validation patients.
Fig. 1

Receiver-operator characteristic curve for predicting pre-treatment HIV RNA <100,000 copies/mL among derivation patients with data on all included variables (n = 1757)

A cut off total score >25 yielded sensitivity of 46.7 and 47.4%, specificity of 79.1 and 77.1%, positive predictive value of 67.7 and 64.2%, and negative predictive value of 61.2 and 63.0% for pre-treatment HIV RNA <100,000 copies/mL among the derivation patients and validation patients, respectively (Tables 4, 5). In contrast a cut off score >5 yielded the highest sensitivity of 91.1 and 91.9% and lowest specificity of 24.8 and 24.1% among derivation patients and validation patients, respectively (Tables 4, 5). We also conducted a sensitivity analysis using other prediction models, e.g. using total lymphocyte count instead of CD4 count and restriction analysis only among patients with CD4 count >200 cells/mm3, however these models did not perform better.
Table 4

Sensitivities, specificities, positive predictive values, and negative predictive values of clinical prediction tool for pre-treatment HIV RNA <100,000 copies/mL among derivation patients with data on all included variables (n = 1757)

CPT score

N (%)

N (%) tests avoided

Sensitivity (%)

Specificity (%)

PPV (%)

NPV (%)

>25.0

586 (37.7)

1171 (75.3)

46.7

79.1

67.7

61.2

>20.0

764 (49.1)

993 (63.8)

58.0

70.2

64.7

64.0

>15.0

1018 (65.4)

739 (47.5)

72.3

55.5

60.4

68.1

>10.0

1239 (79.6)

518 (33.3)

81.3

39.6

55.9

69.3

>5.0

1456 (93.6)

301 (19.3)

91.1

24.8

53.2

74.8

CPT clinical prediction tool, PPV positive predictive value, NPV negative predictive value

Table 5

Sensitivities, specificities, positive predictive values, and negative predictive values of clinical prediction tool for pre-treatment HIV RNA <100,000 copies/mL among validation patients with data on all included variables (n = 587)

CPT score

N (%)

N (%) tests avoided

Sensitivity (%)

Specificity (%)

PPV (%)

NPV (%)

>25.0

201 (39.5)

386 (75.8)

47.4

77.1

64.2

63.0

>20.0

264 (51.9)

323 (63.5)

61.0

68.9

62.9

67.2

>15.0

355 (69.7)

232 (45.6)

75.4

52.4

57.7

71.1

>10.0

421 (82.7)

166 (32.6)

84.6

39.4

54.6

74.7

>5.0

489 (96.1)

98 (19.3)

91.9

24.1

51.1

77.6

CPT clinical prediction tool, PPV positive predictive value, NPV negative predictive value

Discussion

Plasma HIV RNA is one laboratory test used to stage HIV disease and to assist in the selection of ARV drug regimens [4, 5]. If treatment-naïve HIV-infected patients have a pre-treatment HIV RNA >100,000 copies/mL, the following regimens are not recommended; ABC/3TC with EFV or atazanavir/ritonavir (ATV/r) or raltegravir (RAL), RPV-based regimens, and darunavir/r (DRV/r) plus RAL [4, 5]. The main reason is being the higher rates of virologic failure observed in patients who received these particular drugs [7]. In addition, patients with pre-treatment HIV RNA >100,000 copies/mL or CD4 count <200 cells/μL are a subset of patients who may experience suboptimal virologic suppression if the regimen consists of ABC or PRV [5].

To our knowledge, this is the first study on prediction tool of pre-treatment HIV RNA <100,000 copies/mL in treatment-naïve HIV-infected patients that aims to facilitate the use of ABC and RPV as one of ARV in the first-line ART in resource-limited settings. We found some clinical and laboratory factors statistically significantly associated with pre-treatment HIV RNA <100,000 copies/mL. Our prediction tool of pre-treatment HIV RNA <100,000 copies/mL performed AUROC curve of 0.70. A cut off score >25 yielded the highest specificity of 79.0% for predicting pre-treatment HIV RNA <100,000 copies/mL.

Few studies focus on the association between HIV RNA levels and HIV-related outcomes. The results from some previous studies showed that HIV RNA level is rarely directly associated with the type of opportunistic infection [14] or HIV disease progression [15]. One study demonstrated a significant correlation between HIV RNA level and wasting syndrome in naïve HIV-infected patients, with HIV RNA levels in patients with wasting syndrome, significantly higher than those without the condition [16].

We also found six independent factors associated with pre-treatment HIV RNA <100,000 copies/mL: age, BMI, anemia, CD4 count, total lymphocyte count, and prior AIDS-defining illness. For example, patients with age <30 years had higher odds of 1.4 of having pre-treatment HIV RNA <100,000 copies/mL compared to patients 41–50 years old. Furthermore, patients with baseline CD4 count 100–199 cells/mm3 had higher odds of 1.6 of having pre-treatment HIV RNA <100,000 copies/mL compared to patients with baseline CD4 count <100 cells/mm3. These factors might be easily applied in the assessment of patients in resource-limited settings because they are patients’ clinical characteristics and routine baseline laboratory investigations.

The AUROC curve is a single index for measuring the performance a test and can be used to estimate the discriminating power of a test. The AUROC of a ‘perfect’ test would be 1.00, that of a useless test, 0.50 [13, 17]. The AUROC for the pre-treatment HIV RNA model applied to the derivation population was 0.70. The AUROC curve when the model was applied to the validation population was 0.69, indicating some loss of discriminating power when applied to the new population. The score >5 showed the highest sensitivity but lowest specificity. With prediction of pre-treatment HIV RNA <100,000 copies/mL, higher specificity is required to minimize false positive results. Using a score >25 for prediction of pre-treatment HIV RNA yielded specificity approximately 80% and positive predictive value almost 70% and might be more appropriate. Additional data variables and/or an increased number of the patients might be needed to improve this prediction model and enhance its performance.

This study had some limitations. First, some patients must be excluded from the regression analysis and from the prediction tool due to missing data. Second, the performance of the model described by the AUROC of 0.70 might be associated with the small sample size of the study population among derivation and validation group.

In conclusion, in situations where HIV RNA cannot be obtained prior to ART initiation due to high costs or limited availability, certain risk factors and models for predicting pre-treatment HIV RNA <100,000 copies/mL might be useful to predict pre-treatment HIV RNA and afford opportunities for ABC and RPV initiation among naïve HIV-infected patients. A larger sample size with greater data variety would be warranted for prediction model construction as well as for model validation. Pre-treatment HIV RNA should be performed before ABC and RPV initiation if it is available and affordable.

Abbreviations

ART: 

antiretroviral therapy

HIV: 

human immunodeficiency virus

ARV: 

antiretroviral drugs

ABC: 

abacavir

3TC: 

lamivudine

TDF: 

tenofovir disoproxil fumarate

FTC: 

emtricitabine

NNRTI: 

non-nucleoside reverse transcriptase inhibitors

DTG: 

dolutegravir

RPV: 

rilpivirine

EFV: 

efavirenz

TREAT: 

Therapeutics Research, Education, and AIDS Training

TAHOD: 

TREAT Asia HIV Observational Database

BMI: 

body mass index

RPR: 

rapid plasma regain

VDRL: 

Venereal Disease Research Laboratory

TPHA: 

Treponema pallidum particle agglutination assay

OR: 

odds ratio

AUROC: 

area under the receiver-operator characteristic

IQR: 

interquartile range

CI: 

confidence interval

ATV/r: 

atazanavir/ritonavir

RAL: 

raltegravir

DRV/r: 

darunavir/r

CPT: 

clinical prediction tool

PPV: 

positive predictive value

NPV: 

negative predictive value

Declarations

Authors’ contributions

SK and SS initiated concept ideas. SK, OTN, NVK, TPM, AA, WWW, MPL, RC, AK, PK, FZ, JYC, NK, RD, DDC, SO, BS, WR, PSL, EY and SP contributed data for the analysis. DB performed the statistical analysis. All authors commented on the draft manuscript. All authors read and approved the final manuscript.

Acknowledgements

PS Ly* and V Khol, National Center for HIV/AIDS, Dermatology & STDs, Phnom Penh, Cambodia; FJ Zhang*, HX Zhao and N Han, Beijing Ditan Hospital, Capital Medical University, Beijing, China; MP Lee* , PCK Li, W Lam and YT Chan, Queen Elizabeth Hospital, Hong Kong, China; N Kumarasamy*, S Saghayam and C Ezhilarasi, Chennai Antiviral Research and Treatment Clinical Research Site (CART CRS), YRGCARE Medical Centre, VHS, Chennai, India; S Pujari*, K Joshi, S Gaikwad and A Chitalikar, Institute of Infectious Diseases, Pune, India; TP Merati*, DN Wirawan and F Yuliana, Faculty of Medicine Udayana University & Sanglah Hospital, Bali, Indonesia; E Yunihastuti*, D Imran and A Widhani, Faculty of Medicine Universitas Indonesia - Dr. Cipto Mangunkusumo General Hospital, Jakarta, Indonesia; S Oka*, J Tanuma and T Nishijima, National Center for Global Health and Medicine, Tokyo, Japan; JY Choi*, Na S and JM Kim, Division of Infectious Diseases, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea; BLH Sim*, YM Gani, and R David, Hospital Sungai Buloh, Sungai Buloh, Malaysia; A Kamarulzaman*, SF Syed Omar, S Ponnampalavanar and I Azwa, University Malaya Medical Centre, Kuala Lumpur, Malaysia; R Ditangco*, E Uy and R Bantique, Research Institute for Tropical Medicine, Manila, Philippines; WW Wong*, WW Ku and PC Wu, Taipei Veterans General Hospital, Taipei, Taiwan; OT Ng*, PL Lim, LS Lee and PS Ohnmar, Tan Tock Seng Hospital, Singapore; A Avihingsanon*, S Gatechompol, P Phanuphak and C Phadungphon, HIV-NAT/Thai Red Cross AIDS Research Centre, Bangkok, Thailand; S Kiertiburanakul*, S Sungkanuparph, L Chumla and N Sanmeema, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; R Chaiwarith*, T Sirisanthana, W Kotarathititum and J Praparattanapan, Research Institute for Health Sciences, Chiang Mai, Thailand; P Kantipong* and P Kambua, Chiangrai Prachanukroh Hospital, Chiang Rai, Thailand; W Ratanasuwan* and R Sriondee, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand; KV Nguyen*, HV Bui, DTH Nguyen and DT Nguyen, National Hospital for Tropical Diseases, Hanoi, Vietnam; DD Cuong*, NV An and NT Luan, Bach Mai Hospital, Hanoi, Vietnam; AH Sohn*, JL Ross* and B Petersen, TREAT Asia, amfAR—The Foundation for AIDS Research, Bangkok, Thailand; DA Cooper, MG Law*, A Jiamsakul* and DC Boettiger, The Kirby Institute, UNSW Australia, Sydney, Australia. *TAHOD Steering Committee member;Steering Committee Chair;co-Chair.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available in the absence of local ethics committee approvals for the disclosure of study data, but are available from the corresponding author on reasonable request and pending study steering committee approval. Interested parties who would like to access the data underlying the findings of the study analysis may also contact research@treatasia.org for further assistance.

Ethics approval and consent to participate

Ethics approvals were obtained from institutional review boards at each of the participating clinical sites where study patient enrolment took place, as well as by separate review boards for the coordinating center (TREAT Asia, Bangkok) and the data management and analysis center (The Kirby Institute, University of New South Wales, Sydney). All patients have their data stored in both the site-level and centralized study databases for the purposes of research.

Funding

The TREAT Asia HIV Observational Database is an initiative of TREAT Asia, a program of amfAR, The Foundation for AIDS Research, with support from the US National Institutes of Health’s National Institute of Allergy and Infectious Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Cancer Institute, the National Institute of Mental Health, and the National Institute on Drug Abuse, as part of the International Epidemiologic Databases to Evaluate AIDS (IeDEA; U01AI069907). The Kirby Institute is funded by the Australian Government Department of Health and Ageing, and is affiliated with the Faculty of Medicine, UNSW Australia. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the governments or institutions mentioned above.

Presentation

Parts of this manuscript were presented at the 27th European Congress of Clinical Microbiology and Infectious Diseases (Vienna), May 22–25, 2017 [abstract 784].

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis 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.

Authors’ Affiliations

(1)
Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University
(2)
The Kirby Institute, University of New South Wales
(3)
Tan Tock Seng Hospital
(4)
National Hospital of Tropical Diseases
(5)
Department of Medicine, Faculty of Medicine, Udayana University & Sanglah Hospital
(6)
HIV-NAT/Thai Red Cross AIDS Research Centre
(7)
Taipei Veterans General Hospital
(8)
Queen Elizabeth Hospital
(9)
Research Institute for Health Sciences
(10)
University Malaya Medical Centre
(11)
Chiangrai Prachanukroh Hospital
(12)
Beijing Ditan Hospital, Capital Medical University
(13)
Division of Infectious Diseases, Department of Internal Medicine, Yonsei University College of Medicine
(14)
Chennai Antiviral Research and Treatment Clinical Research Site (CART CRS), YRGCARE Medical Centre, VHS
(15)
Research Institute for Tropical Medicine
(16)
Bach Mai Hospital
(17)
National Center for Global Health and Medicine
(18)
Hospital Sungai Buloh
(19)
Faculty of Medicine, Siriraj Hospital, Mahidol University
(20)
National Center for HIV/AIDS, Dermatology & STDs, University of Health Sciences
(21)
Faculty of Medicine Universitas Indonesia, Dr. Cipto Mangunkusumo General Hospital
(22)
Institute of Infectious Diseases
(23)
TREAT Asia, amfAR, The Foundation for AIDS Research

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Copyright

© The Author(s) 2017

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