Establishment and validation of a predictive model for immune reconstitution in people with HIV after antiretroviral therapy

Establishment and validation of a predictive model for immune reconstitution in people with HIV after antiretroviral therapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Establishment and validation of a predictive model for immune reconstitution in people with HIV after antiretroviral therapy Na Li, Rui Li, Hong-Yi Zheng, Wen-Qiang He, Ru-Fei Duan, Xia Li, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4883942/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Feb, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 10 You are reading this latest preprint version Abstract Background Achieving complete immune reconstitution (CIR) in people with human immunodeficiency virus (PWH) following antiretroviral therapy (ART) is essential for preventing acquired immunodeficiency syndrome (AIDS) progression and improving survival. However, there is a paucity of robust prediction models for determining the likelihood of CIR in PWH after ART. We aimed to develop and validate a CIR prediction model utilizing baseline data. Methods Data including demographic information, immunological profiles, and routine laboratory test results, were collected from PWH in Yunnan, China. The participants were divided into training and validation sets (7:3 ratio). To construct the model and accompanying nomogram, univariate and multivariate Cox regression analyses were performed. The model was evaluated using the C-index, time-dependent receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curves to assess discrimination, calibration, and clinical applicability. Results 5 408 PWH were included, with a CIR of 38.52%. Cox regression analysis revealed various independent factors associated with CIR, including infection route, marital status, baseline CD4 + T cell count, and baseline CD4/CD8 ratio. A nomogram was formulated to predict the probability of achieving CIR at years 4, 5, and 6. The model demonstrated good performance, as evidenced by an AUC of 0.8 for both sets. Calibration curve analysis demonstrated a high level of agreement, and decision curve analysis revealed a significant positive yield. Conclusions This study successfully developed a prediction model with robust performance. This model has considerable potential to aid clinicians in tailoring treatment strategies, which could enhance outcomes and quality of life for PWH. HIV ART immune reconstitution predictive model nomogram model evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Human immunodeficiency virus (HIV) infection remains a significant global health challenge [1]. The World Health Organization estimated that by the end of 2022, the population of people living with HIV (PWH) would reach approximately 38 million, including the addition of 1.7 million new cases. The widespread implementation of "treat-all" antiretroviral therapy (ART) has greatly improved both the clinical prognosis and life expectancy of PWH [2, 3]. ART is known to effectively suppress viral replication, restore immune function, and reduce the risk of acquired immunodeficiency syndrome (AIDS)-related complications [4–6]. However, despite these advances, a considerable proportion of PWH (15–30%) experience suboptimal recovery of immune function, referred to as immune unresponsiveness or incomplete immune reconstitution (IIR) [7]. There is still a lack of consensus and debate continues regarding the immunomodulatory mechanisms and therapeutic approaches for managing IIR. This condition, closely linked to diminished CD4 + T cell production in the bone marrow, reduced thymic output, persistent HIV replication, abnormal immune activation, disruptions in cytokine secretion, and specific genetic or metabolic characteristics in PWH, increases their susceptibility to various complications, including both AIDS and non-AIDS events and is associated with elevated mortality rates [8, 9]. Therefore, identifying PWH at risk of immune nonresponse is essential for personalized management and improved clinical outcomes. Clinical prediction models are widely utilized in both medical research and practice to assess the likelihood of a specific clinical outcome within a study population. Typically, these models utilize various variables or predictors for comprehensive evaluation [10, 11]. However, there remains a notable scarcity of published research on models that effectively integrate multiple variables for accurately identifying complete immune reconstitution (CIR) in China. Most existing studies have focused on short-term outcomes, without in-depth analysis of long-term changes and trends in immune recovery following ART [12]. Studies with extended follow-up periods could provide a more accurate assessment of the CIR and the progression of adverse risks, especially given the existence of a plateau phase in immune recovery [13]. This lack of comprehensive long-term prediction models underscores the urgent need for the development of reliable, long-term clinical prediction models. The specific objective of this study was to develop and validate a prediction model to determine the likelihood of CIR in PWH after 4, 5, and 6 years of ART. Univariate and multivariate Cox regression analyses were conducted to identify CIR-associated signatures. A nomogram was constructed based on the multivariate Cox regression coefficients. The performance of the nomogram was validated through discrimination, calibration, and decision curve analyses (DCA). The model incorporated demographic, clinical, and immunological variables to offer clinicians a straightforward and reliable tool for accurately identifying PWH who may need additional monitoring and interventions, especially during the initiation of ART, thereby enabling targeted and timely clinical intervention and personalized care. Methods The Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist was used for the validation of the prediction model [14]. Study design and participants This study involved participants who receiving ART at the Antiviral Outpatient Department of Yunnan Provincial Hospital of Infectious Diseases in Yunnan, China, between October 2004 and December 2020. Participant records were obtained by professional physicians from the National AIDS Integrated Prevention and Control Information System, and the data quality was thoroughly assessed. The inclusion criteria for participants were PWH who were aged 18 years or older, had confirmed positive results for HIV antibodies in both primary screening and confirmatory tests, had undergone ART for a minimum of 3 years, and exhibited a viral load below the detection limit (≤ 50 copies/mL) at the most recent follow-up. The exclusion criteria included participants who had died, were lost to follow-up, discontinued medication, or lacked available demographic data. Study outcomes The primary outcome of interest was the rate of CIR in virologically suppressed PWH after ART. The CIR was determined using binary indicators, specifically a CD4 + T cell count of ≥ 500 cells/µL and a CD4/CD8 ratioof ≥ 0.8, which are considered accurate measures of immune system function and status [15]. The determination of immunological reconstitution was based on the results from the latest recorded follow-up visit in the electronic case system. Candidate predictor variables Potential predictor variables for the model were identified from the literature, and relevant variables were collected from the electronic medical records, including demographics, baseline immunological and laboratory tests, coinfections, and ART regimens. Baseline referred to the first recorded results before starting ART but after HIV diagnosis. The demographic data included age, sex, date of diagnosis, infection route, and marital status, with the latter two obtained from patient-initiated information or doctor‒patient communication. Baseline immunological outcomes were assessed based on CD4 + and CD8 + T cell counts and the CD4/CD8 ratio. The CD4 + and CD8 + T cell counts were measured using Truecount™ Tubes (BD Biosciences, San Jose, CA, USA) and a FACSCalibur™ flow cytometer (BD Biosciences, San Jose, CA, USA). Baseline laboratory test indicators included white blood cell count (WBC) hemoglobin (HGB), platelet (PLT), total bilirubin (TBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and creatinine (Crea) levels; the creatinine clearance rate (Ccr); and blood glucose (Glu), triglyceride (TG), and total cholesterol (CHO) levels. These parameters were analyzed using a DxH 800 hematology analyzer (Beckman Coulter, Miami, Florida, USA) and an automatic biochemical detector (Hitachi 7180, Tokyo, Japan). Coi nfection examination primarily focused on hepatitis B and C virus (HBV and HCV) infections, which were detected using HBV and HCV antibody diagnostic kits via enzyme-linked immunosorbent assay (ELISA) (Wantai BioPharm, Beijing, China). A positive result for anti-HBV or HCV surface antigen (HBsAg) indicated the presence of HBV or HCV infection. ART regimens, including both initial and currently used regimens, typically consist of a combination of two nucleoside reverse transcriptase inhibitors (NRTIs) along with a nonnucleoside reverse transcriptase inhibitors (NNRTIs), a protease inhibitors (PIs), or an integrase strand transfer inhibitor (INSTIs) [16]. Thus, these regimens were categorized into three groups: NNRTIs or NRTIs, PIs, and INSTIs-containing regimens. Sample size and missing data Although no formal calculation of sample size was performed, the study adhered to the TRIPOD guidelines by including a minimum of 10 events per variable [14]. This necessitated the inclusion of at least 410 cases of CIR. To ensure adequate test efficiency, all 5,408 eligible participants who met the criteria during the study period were included. Among them, 2,083 belonged to the CIR category, exceeding the estimated minimum sample size. As shown in Figure S1 A , missing data (with missing rates below 15%) were addressed through multiple imputation using the MICE package in R, assuming that missing data were random. Comparative analysis revealed that the distribution of the imputed data was similar to that of the original data ( Figure S1 B) . Statistical methods Data collection and management were performed using Microsoft Office Excel 2011. Statistical analyses were performed using the R program (v 4.3.2). The R package "caret" was used to randomly divide participants into training and validation sets (at a 7:3 ratio). Continuous variables were transformed into categorical variables using X-tile software to determine optimal cutoff values. Categorical variables were compared using the chi-square test or Fishers exact probability method with the "tableone" package and are presented as frequencies (n) and proportions (%). Univariate and multivariate Cox regression analyses (“survival” package) were conducted to construct the model. Variables with P < 0.05 in univariate analyses were included in the multivariate analysis. The best model was selected using backward stepwise regression with the Akaike information criterion (AIC). Finally, the nomogram was created using the "rms" package. Model performance was assessed for discrimination, calibration, and clinical utility. Discrimination was evaluated using concordance statistics (C statistics) and time-dependent receiver operating characteristic (ROC) curves (“survival ROC” and "rms" packages). Internal verification and calibration curves were generated using the bootstrap resampling method, with 1,000 bootstrap repetitions. Clinical net benefit and DCA results were evaluated and plotted using the "rmda" package. Additionally, risk stratification analysis of all PWH was conducted based on the prognosis index using Kaplan‒Meier survival analysis and compared with the log-rank test, with the optimal cutoff value calculated using the "survminer" package. A statistical significance level of P < 0.05 was applied. Results Characteristics and clinical features of participants Between October 2022 and June 2023, a retrospective screening was conducted on 7 201 PWH. Among these participants, 1710 were excluded from the study due to various factors, such as age, length of treatment, viral load, and unavailability of data. Ultimately, a total of 5 408 PWH were enrolled based on the inclusion criteria ( Fig. 1 ) . The cohort predominantly consisted of males (66.79%), with a majority being middle-aged (59.93% aged 42 years or older) and unmarried (51.31%). The primary mode of HIV transmission was sexual contact. Approximately 47.95% of participants had initial CD4 + T cell counts ranging from 187 to 459 cells/µL and CD8 + T cell counts ranging from 751 to 1 499 cells/µL. As a result, 40.16% of participants had a CD4/CD8 ratio ≤ 0.2. NNRTIs and NRTIs were the main components, accounting for 92.86% of the initial regimens and 60.67% of the current regimens. The cohort also included 2083 participants with CIR and 3325 participants with IIR. The overall rate of CIR, as defined, was 38.52%. Significant differences were observed between the CIR and IIR cohorts in certain baseline data, including sex, age, and interval from diagnosis to treatment ( P < 0.001). Among the study participants, 3 788 were randomly assigned to the training cohort, and 1 620 were randomly assigned to the validation cohort. Baseline characteristics were well balanced between the two cohorts, as shown in Table 1 . Table 1 Characteristics of the 5408 patients in the study according to IIR/CIR and randomization to the training and validation sets. Total patient cohort (n = 5408) IIR (n = 3325) CIR (n = 2083) P value Training set (n = 3788) Validation set (n = 1620) P value Gender Male 3612 (66.79) 2377 (71.49) 1235 (59.29) < 0.001 2538 (67.00) 1074 (66.30) 0.637 Female 1796 (33.21) 948 (28.51) 848 (40.71) 1250 (33.00) 546 (33.70) Current age (year) ≤ 35 1105 (20.43) 637 (19.16) 468 (22.47) < 0.001 784 (20.70) 321 (19.81) 0.758 35–41 1062 (19.64) 617 (18.56) 445 (21.36) 740 (19.54) 322 (19.88) ≥ 42 3241 (59.93) 2071 (62.29) 1170 (56.17) 2264 (59.77) 977 (60.31) Age at diagnosis (year) ≤ 46 4422 (81.77) 2653 (79.79) 1769 (84.93) 46 986 (18.23) 672 (20.21) 314 (15.07) 654 (17.27) 332 (20.49) Age at initial ART (year) ≤ 45 4241 (78.42) 2545 (76.54) 1696 (81.42) 45 1167 (21.58) 780 (23.46) 387 (18.58) 784 (20.70) 383 (23.64) Marital status Married 2101 (38.85) 1300 (39.10) 801 (38.45) 0.649 1481 (39.10) 620 (38.27) 0.830 Unmarried 2775 (51.31) 1691 (50.86) 1084 (52.04) 1938 (51.16) 837 (51.67) Divorced or widowed 532 (9.84) 334 (10.05) 198 (9.51) 369 (9.74) 163 (10.06) Infection route Heterosexual 2881 (53.27) 1763 (53.02) 1118 (53.67) 0.067 2006 (52.96) 875 (54.01) 0.506 Homosexual 1272 (23.52) 766 (23.04) 506 (24.29) 892 (23.55) 380 (23.46) Intravenous drug use 515 (9.52) 344 (10.35) 171 (8.21) 355 (9.37) 160 (9.88) Other/Unclear* 740 (13.68) 452 (13.59) 288 (13.83) 535 (14.12) 205 (12.65) Weight (kg) ≤ 54 1902 (35.17) 1183 (35.58) 719 (34.52) 0.501 1350 (35.64) 552 (34.07) 0.116 55–64 1975 (36.52) 1219 (36.66) 756 (36.29) 1397 (36.88) 578 (35.68) ≥ 65 1531 (28.31) 923 (27.76) 608 (29.19) 1041 (27.48) 490 (30.25) Anti-HBsAg Negative 5129 (94.84) 3150 (94.74) 1979 (95.01) 0.708 3591 (94.80) 1538 (94.94) 0.885 Positive 279 (5.16) 175 (5.26) 104 (4.99) 197 (5.20) 82 (5.06) Anti-HCV Negative 4803 (88.81) 2936 (88.30) 1867 (89.63) 0.143 3365 (88.83) 1438 (88.77) 0.980 Positive 605 (11.19) 389 (11.70) 216 (10.37) 423 (11.17) 182 (11.23) Diagnosis treatment interval (month) ≤ 0.7 1978 (36.58) 1245 (37.44) 733 (35.19) 0.001 1353 (35.72) 625 (38.58) 0.135 0.8–1.6 1340 (24.78) 860 (25.86) 480 (23.04) 951 (25.11) 389 (24.01) ≥ 1.7 2090 (38.65) 1220 (36.69) 870 (41.77) 1484 (39.18) 606 (37.41) CD4 + T cell counts (cells/µL) ≤ 186 2051 (37.93) 1652 (49.68) 399 (19.16) < 0.001 1454 (38.38) 597 (36.85) 0.292 187–459 2593 (47.95) 1416 (42.59) 1177 (56.51) 1790 (47.25) 803 (49.57) ≥ 460 764 (14.13) 257 (7.73) 507 (24.34) 544 (14.36) 220 (13.58) CD8 + T cell counts (cells/µL) ≤ 750 1966 (36.35) 1233 (37.08) 733 (35.19) < 0.001 1359 (35.88) 607 (37.47) 0.498 751–1499 2547 (47.10) 1494 (44.93) 1053 (50.55) 1802 (47.57) 745 (45.99) ≥ 1500 895 (16.55) 598 (17.98) 297 (14.26) 627 (16.55) 268 (16.54) CD4/CD8 ≤ 0.20 2172 (40.16) 1755 (52.78) 417 (20.02) < 0.001 1541 (40.68) 631 (38.95) 0.475 0.21–0.39 2027 (37.48) 1197 (36.00) 830 (39.85) 1411 (37.25) 616 (38.02) ≥ 0.40 1209 (22.36) 373 (11.22) 836 (40.13) 836 (22.07) 373 (23.02) WBC (×109/L) ≤ 3.7 1280 (23.67) 897 (26.98) 383 (18.39) < 0.001 886 (23.39) 394 (24.32) 0.320 3.8–4.7 1361 (25.17) 844 (25.38) 517 (24.82) 975 (25.74) 386 (23.83) ≥ 4.8 2767 (51.16) 1584 (47.64) 1183 (56.79) 1927 (50.87) 840 (51.85) PLT (×109/L) ≤ 186 2473 (45.73) 1630 (49.02) 843 (40.47) 186 2935 (54.27) 1695 (50.98) 1240 (59.53) 2058 (54.33) 877 (54.14) HGB (g/L) ≤ 134 1850 (34.21) 1209 (36.36) 641 (30.77) 0.000 1271 (33.55) 579 (35.74) 0.257 135–154 1656 (30.62) 1002 (30.14) 654 (31.40) 1179 (31.12) 477 (29.44) ≥ 155 1902 (35.17) 1114 (33.50) 788 (37.83) 1338 (35.32) 564 (34.81) Crea (µmol/L) ≤ 58 1480 (27.37) 813 (24.45) 667 (32.02) < 0.001 1043 (27.53) 437 (26.98) 0.533 59–65 735 (13.59) 455 (13.68) 280 (13.44) 502 (13.25) 233 (14.38) ≥ 65 3193 (59.04) 2057 (61.86) 1136 (54.54) 2243 (59.21) 950 (58.64) Ccr (mL/min) ≤ 140 4723 (87.33) 2958 (88.96) 1765 (84.73) 140 685 (12.67) 367 (11.04) 318 (15.27) 487 (12.86) 198 (12.22) CHO (mmol/L) ≤ 4 1588 (29.36) 1043 (31.37) 545 (26.16) < 0.001 1099 (29.01) 489 (30.19) 0.642 4.01–5.62 2543 (47.02) 1549 (46.59) 994 (47.72) 1795 (47.39) 748 (46.17) ≥ 5.63 1277 (23.61) 733 (22.05) 544 (26.12) 894 (23.60) 383 (23.64) TG (mmol/L) ≤ 2.6 4635 (85.71) 2866 (86.20) 1769 (84.93) 0.208 3245 (85.67) 1390 (85.80) 0.929 > 2.6 773 (14.29) 459 (13.80) 314 (15.07) 543 (14.33) 230 (14.20) Glu (mmol/L) ≤ 4.5 975 (18.03) 579 (17.41) 396 (19.01) 0.147 677 (17.87) 298 (18.40) 0.675 > 4.5 4433 (81.97) 2746 (82.59) 1687 (80.99) 3111 (82.13) 1322 (81.60) ALT (U/L) < 23 2268 (41.94) 1359 (40.87) 909 (43.64) 0.048 1599 (42.21) 669 (41.30) 0.552 ≥ 23 3140 (58.06) 1966 (59.13) 1174 (56.36) 2189 (57.79) 951 (58.70) AST (U/L) < 25 2696 (49.85) 1635 (49.17) 1061 (50.94) 0.217 1912 (50.48) 784 (48.40) 0.170 ≥ 25 2712 (50.15) 1690 (50.83) 1022 (49.06) 1876 (49.52) 836 (51.60) TBIL (µmol/L) < 8.5 2232 (41.27) 1405 (42.26) 827 (39.70) 0.068 1551 (40.95) 681 (42.04) 0.473 ≥ 8.5 3176 (58.73) 1920 (57.74) 1256 (60.30) 2237 (59.05) 939 (57.96) Initial ART regimen NNRTIs 5022 (92.86) 3090 (92.93) 1932 (92.75) < 0.001 3533 (93.27) 1489 (91.91) 0.077 PIs 306 (5.66) 169 (5.08) 137 (6.58) 197 (5.20) 109 (6.73) INSTIs 80 (1.48) 66 (1.98) 14 (0.67) 58 (1.53) 22 (1.36) Current ART regimen NNRTIs 3281 (60.67) 1983 (59.64) 1298 (62.31) 0.143 2318 (61.19) 963 (59.44) 0.303 PIs 747 (13.81) 469 (14.11) 278 (13.35) 526 (13.89) 221 (13.64) INSTIs 1380 (25.52) 873 (26.26) 507 (24.34) 944 (24.92) 436 (26.91) Categorical data are presented as n (%). *Others/Unclear included blood transfusion, mother-to-child transmission, and unknown. IIR: incomplete immune reconstitution, CIR: complete immune reconstitution, ART: antiretroviral treatment, HBsAg: hepatitis B surface antigen, Anti-HCV: antibodies against the hepatitis C virus, WBC: white blood cell count, PLT: platelet, HGB: hemoglobin, CR: creatinine, Ccr: creatinine clearance rate, CHO: cholesterol, TG: triglycerides, Glu: blood glucose, ALT: alanine transaminase, AST: aspartate transaminase, TBIL: total bilirubin, NRTIs: nucleoside reverse transcriptase inhibitors, NNRTIs: nonnucleoside reverse transcriptase inhibitors, PIs: protease inhibitors, INSTI: integrase strand transfer inhibitors. Construction of a prediction model for the CIR in PWH based on the training set Univariate and multivariate Cox regression analyses were performed to identify potential predictors of CIR in PWH. Table S1 presents the results of the univariate analysis of all 26 candidate predictors individually. Based on these results, 20 predictors were included in the multivariate Cox regression analyses. Six factors with P ≥ 0.05 were excluded from the analysis: CHO, age at HIV diagnosis, age at initiation of ART, sex, coinfection with hepatitis B, and TG. Multivariate Cox regression analysis identified several predictors, including age, interval from diagnosis to initial ART, marital status, infection route, baseline CD4 + T cell count, CD4/CD8 ratio, initial ART regimen, current ART regimen, and the PLT, Glu, Crea, HGB, and ALT. Consequently, a nomogram was constructed using the multivariable analysis results, as shown in Fig. 2 . Total points were obtained by summing the corresponding points of each index value and then converted into the probability of CIR incidence at 4, 5, and 6 years according to the nomogram. Evaluation and validation of the prediction models The discrimination capacity of the prediction model was evaluated using C-indices and time-ROC curves. The model achieved a C-index of 0.78 (95% CI, 0.768–0.791), and internal validation yielded a similar C-index of 0.765 (95% CI, 0.747–0.782). The time-dependent ROC curves for predicting the CIR at 4, 5, and 6 years are shown in Fig. 3 . The areas under the ROC curve (AUCs) reached 0.799 at 4 years, 0.824 at 5 years, and 0.830 at 6 years in the training cohort. The AUCs of the validation set reached 0.799 at 4 years, 0.817 at 5 years, and 0.809 at 6 years. These results indicate that the model achieved good predictive performance. Calibration curves are used to compare observed outcomes with model predictions, providing a measure of model accuracy in estimating absolute risk. The agreement between the predicted and observed values is evaluated by the degree of alignment between the calibration curve and diagonal line [17]. In this study, the prediction results strongly agreed with the actual observations in both the training and validation sets, suggesting that the model accurately and effectively captured the actual values ( Fig. 4 ) . Furthermore, DCA was employed to evaluate the clinical utility of the prediction model [18], including both the training and validation sets. As shown in Fig. 5 , the model provided a net benefit, ranging from approximately 5–50%, in both the training and validation sets. These results indicated that the model is advantageous for making decisions in clinical settings, particularly for scenarios in the sixth year. To assess the predictive effectiveness of the model, the study participants were divided into two risk groups based on their calculated risk scores from the nomogram: a low-scoring group (total score < 20.6) and a high-scoring group (total score ≥ 20.6). As shown in Fig. 6 , the Kaplan‒Meier curves for both the training and validation sets clearly demonstrated that the model effectively distinguished between the high- and low-risk groups (log-rank test, P < 0.05). Discussion Accurate assessment of the potential for CIR following ART is crucial for improving prognosis and guiding treatment decisions for PWH. This study aimed to develop and validate a prediction model to determine the likelihood of PWH achieving CIR at years 4, 5, and 6 after initiating ART. Participant data for model development were derived from initial routine laboratory tests performed post-HIV diagnosis, selected for their affordability, ease of collection, and broad applicability. Direct comparison of our model with others is challenging due to differences in CIR definitions. In contrast to the findings of Zhang et al. [12], our CIR criteria were more stringent, defining the CIR as a CD4 + T cell count ≥ 500 cells/µL and a CD4/CD8 ratio ≥ 0.8, which has been proven to more accurately evaluate the extent of immune restoration in the "treat all" era [15]. Longitudinal research has suggested a gradual CIR process following ART, often exhibiting a prolonged plateau phase. Studies have indicated that total CD4 + T and CD8 + T cell turnover rates tend to stabilize after 12–36 months of ART and reach a plateau after 3–4 years of suppressive treatment [19, 20]. The Multicenter AIDS Cohort Study, which involved 314 PWH, revealed no increase in CD4 + T cell counts after 2–3 years of ART [21]. Similarly, the AIDS Clinical Trials Group (ACTG) study revealed that most changes in CD4 + T cell counts occur within the first year of ART, with no significant increases in the second or third year of therapy [22]. Based on these findings, our model was constructed using data from PWH who had undergone ART for a minimum of 3 years, aiming to forecast CIR at 4, 5, and 6 years post-ART initiation. In this study, we identified several factors influencing the CIR, including age, diagnosis-treatment interval, marital status, infection route, baseline CD4 + T cell count, CD4/CD8 ratio, treatment regimen, and various hematological and biochemical parameters. The significance of baseline CD4 + T cell counts and the CD4/CD8 ratio in immune recovery has been extensively studied [15, 23–27]. For instance, among PWH with baseline CD4 + T cell counts of less than 50, 50–199, 200–349, and 350–499 cells/µL, the probabilities of achieving CD4 + T cell counts of 500 cells/µL or higher after ART vary considerably, ranging from 1.97%, 7.84%, 62.85%, and 71.07%, respectively [15]. Similarly, among PWH who started cART with less than 200 cells/µL, 57% did not reach 600 cells/µL after 7 years, while those with baseline counts of 200–349 CD4 + T cells/µL achieved this count in less than 2 years [23]. Previous studies have also demonstrated that the time required to achieve a 90% probability of CIR after two years of ART is significantly longer for PWH with a CD4/CD8 ratio less than 0.5 compared to those with a ratio greater than 0.5 [28]. These studies consistently demonstrated that higher baseline CD4 + T cell counts and CD4/CD8 ratioplay a crucial role in determining the rate and extent of immune recovery after ART, underscoring the importance of initiating ART at higher CD4 + T cell counts. Additionally, homosexual transmission and initial treatment regimens containing INSTIs were significantly associated with a lower rate of CIR. The impact of different infection routes on immune reconstitution is complex, with factors such as viral tropism, intestinal flora, and coreceptor switching linked to CIR in men who have sex with men (MSM) [29–31]. Research has indicated that only 60.5% of Chinese MSM have undergone HIV testing [32], which is attributed to limited awareness, social discrimination, and concealment of sexual orientation. Consequently, approximately 50% of Chinese MSM living with HIV are unaware of their serostatus, impacting timely diagnosis and access to treatment, which are crucial for effective immune reconstitution [32, 33]. In deciding on medication use, physicians consider various factors, including CD4 + T cell counts, virological efficacy, and drug tolerability. Preference is often given to more potent ART regimens when CD4 + T cell counts are low. In our cohort, participants who were initially prescribed INSTI-based regimens had average baseline CD4 + T cell counts of only 230 cells/µL. In contrast, those on NNRTI-based regimens had average baseline CD4 + T cell counts of 265 cells/µL, while those on PI-based regimens had average baseline CD4 + T cell counts of 320 cells/µL. Regarding other influential factors, both a longer interval between HIV diagnosis and ART initiation and older age were associated with lower baseline CD4 + T cell counts. An extended interval between diagnosis and treatment can lead to increased HIV replication, more severe immune system damage, and an increased incidence of opportunistic infections. Previous studies have shown positive correlations between the length of the diagnosis-to-treatment interval and the progression of AIDS and mortality [34]. Additionally, older age may be associated with thymic atrophy, abnormal immune activation, and immunosenescence, all of which can accelerate HIV/AIDS progression and are closely associated with IIR [35–37]. Moreover, older PWH tend to receive a diagnosis at a later stage than younger individuals, further hindering immune recovery [38]. This study also revealed associations between immune reconstitution and various hematological and blood biochemical parameters, including HGB, Crea, PLT, Glu, ALT, and AST. Previous research has demonstrated that PWH with suboptimal CD4 + T cell recovery often have elevated platelet counts [39]. This could be attributed to the ability of platelets to directly interact with HIV, facilitating viral binding and entry into cells, with platelets further acting as a reservoir for the virus during chronic infection. In addition, platelet-CD4 + T cell aggregates display increased levels of activation, depletion, and apoptotic markers, suggesting a potential role for platelets in CD4 + T cell depletion [40]. However, further research is needed to clarify the relationships between the CIR and other blood biochemical parameters. Given the complexity and importance of immune reconstitution, various studies have examined adjuvant therapeutic strategies to enhance CIR, including interleukin 2 (IL-2), methotrexate, statins, metformin, and other immunomodulators [41–43]. Furthermore, various immunotherapeutic approaches, such as broadly neutralizing antibodies, stem cell transplants, and therapeutic vaccines, are currently under investigation [44–46]. Recent clinical trials have shown the potential of (5R)-5-hydroxytriptolide to promote CD4 + T cell recovery and reduce inflammation in PWH, suggesting that this is a new approach for the treatment of IIR [47]. Despite these developments, many current clinical trials and research initiatives have yet to achieve success, spurring ongoing efforts to develop more efficient immunomodulators and therapeutic strategies. Our study is distinguished by a relatively large sample size (N = 5 408) and extended follow-up period, with 85.11% of participants monitored for more than 5 years. However, several limitations should be considered. First, the retrospective nature of the study inherently limited the scope of the analysis and may have introduced biases. Furthermore, as the study was conducted at a single center without external validation, the generalizability of the findings may be limited. Therefore, caution should be exercised when extrapolating the results to other populations or regions. Second, the study primarily focused on PWH examination results at baseline and at the most recent follow-up. The predictors were derived from the initial test results following diagnosis, while the outcome indicators were based on the most recent test results. It is important to note that all immunological, virological, blood biochemical, and relevant indicators of routine laboratory tests in PWH change over time. Our study did not consider these dynamic changes during ART, which are crucial for a comprehensive understanding of the CIR, potentially leading to inaccurate estimations of the true risk of IIR in PWH. Finally, the absence of data on baseline viral load, viral load rebound, comorbidities, coinfections, treatment adherence, drug resistance, adverse effects, and changes in ART regimens for most participants is a significant limitation, as these factors could differentially impact the CIR but were not considered in our analysis. Recent advances in medical research have deepened our understanding of HIV and the human immune system. Novel indicators, such as the percentage of naïve CD4 + T cells prior to ART in PWH, the ratio of naïve/effective memory CD4 + T cells [48], and the activity of the immunomodulatory kynurenine pathway in tryptophan catabolism, have emerged. These metrics show promise in predicting the normalization of CD4 + T cell counts. Although the current evidence remains inconclusive, these findings offer new possibilities for diagnosis and prediction. Additionally, advancements in artificial intelligence and machine learning are expected to yield new models and algorithms that can adapt to the dynamic shifts in PWH metrics, aiding healthcare professionals in making more informed decisions and improving longevity and quality of life for PWH. Conclusion The prediction model developed in this study, derived from baseline data, effectively predicted the probability of CIR in PWH undergoing ART. The model demonstrated high accuracy and discriminatory power during internal validation, and the clinical utility of the nomogram was evaluated and confirmed using DCA. Based on our findings, we recommend the adoption of the model as a diagnostic tool to facilitate timely provision of appropriate therapeutic interventions, adjustment of ART regimens, precise and individualized management of PWH, and optimization of cost-effectiveness. Abbreviations CIR Complete immune reconstitution PWH People with human immunodeficiency virus ART Antiretroviral therapy AIDS Acquired immunodeficiency syndrome ROC Receiver operating characteristic HIV Human immunodeficiency virus IRR Incomplete immune reconstitution DCA Decision curve analyses TRIPOD Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis WBC White blood cell count HGB Hemoglobin PLT Platelet TBIL Total bilirubin ALT Alanine aminotransferase AST Aspartate aminotransferase Crea Creatinine Ccr Creatinine clearance rate Glu Blood glucose TG Triglyceride CHO Total cholesterol HBV Hepatitis B virus HCV Hepatitis C virus ELISA Enzyme-linked immunosorbent assay HBsAg Anti-HBV or HCV surface antigen NRTIs Nucleoside reverse transcriptase inhibitors NNRTIs Nonnucleoside reverse transcriptase inhibitors PIs Protease inhibitors INSTIs Integrase strand transfer inhibitor AIC Akaike information criterion C statistics Concordance statistics AUCs Areas under the ROC curve ACTG AIDS Clinical Trials Group MSM Men who have sex with men IL-2 Interleukin 2 Declarations Ethics approval consent to participate This study was approved by the Ethics Committee of Yunnan Infectious Disease Hospital (approval No. Ke201927), and informed consent from the participants was waived due to the use of anonymous data. Consent for publication Not applicable. Data Availability Statement Data requests can be addressed to the corresponding author. Competing interests The authors declare no competing interests. Authors contributions The authors' contributions in this study are as follows: Profs. Z-Q Shen and Y-T Zheng proposed the initial conception and design of the study and provided important guidance throughout its design and implementation. Profs. X-Q Dong and H-Q Li managed the study, including data collection and coordination, and provided the source of clinical data. Na Li and Rui Li were responsible for the data analysis, graphical presentation of the results, and writing of the first draft of the paper. Authors H-Y Zheng and R-R Tian conducted a comprehensive literature review and made substantial revisions to the paper. W-Q He, R-F Duan, and Xia Li carefully checked the clinical data and the reasonableness of the corresponding statistical methods. All authors read and approved the final manuscript. Funding This work was supported by grants from the National Key R & D Program of China (2023YFC2306700), the National Natural Science Foundation of China (U23A20473), the Yunnan Key R & D Program (202403AC100011), and the Key Laboratory of Bioactive Peptides of Yunnan Province (HXDT-2022-3). Acknowledgment. We are sincerely grateful for the support and assistance received for this study. We would like to thank all the medical staff and colleagues who were involved in the data collection and analysis for their support and assistance. Importantly, we express our gratitude to all the anonymous participants who participated in this study. Furthermore, our sincere gratitude goes to Yunnan Infectious Disease Hospital for providing valuable data resources that enabled us to conduct this study. References Bekker L-G, Alleyne G, Baral S, Cepeda J, Daskalakis D, Dowdy D, et al. 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Increased Platelet-CD4+ T Cell Aggregates Are Correlated With HIV-1 Permissiveness and CD4+ T Cell Loss. Frontiers in Immunology. 2021;12. Zhang Y, Jiang T, Li A, Li Z, Hou J, Gao M, et al. Adjunct Therapy for CD4+ T-Cell Recovery, Inflammation and Immune Activation in People Living With HIV: A Systematic Review and Meta-Analysis. Frontiers in Immunology. 2021;12. Heyckendorf J, Aries SP, Greinert U, Richter E, Schultz H, Lange C. Functional Immune Reconstitution by Interleukin-2 Adjunctive Therapy for HIV/Mycobacterial Co-infection. Emerging Infectious Diseases. 2015;21:1685–7. Zaongo SD, Chen Y. Metformin may be a viable adjunctive therapeutic option to potentially enhance immune reconstitution in HIV-positive immunological non-responders. Chinese Medical Journal. 2023;136:2147–55. Zhang W, Ruan L. Recent advances in poor HIV immune reconstitution: what will the future look like? Frontiers in Microbiology. 2023;14:1236460. Asaf Yanir, Schulz A, Lawitschka A, Nierkens S, Eyrich M. 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Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 24 Feb, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 28 Jan, 2025 Reviews received at journal 28 Jan, 2025 Reviewers agreed at journal 17 Jan, 2025 Reviews received at journal 17 Dec, 2024 Reviewers agreed at journal 02 Dec, 2024 Reviewers agreed at journal 18 Aug, 2024 Reviewers invited by journal 16 Aug, 2024 Editor assigned by journal 13 Aug, 2024 Submission checks completed at journal 12 Aug, 2024 First submitted to journal 08 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4883942","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350571418,"identity":"ac79ea24-8cd3-4f0c-b49c-617dab1092d8","order_by":0,"name":"Na Li","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Li","suffix":""},{"id":350571419,"identity":"676f83ff-289c-4973-8eb3-a9e3022e1a60","order_by":1,"name":"Rui Li","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Li","suffix":""},{"id":350571420,"identity":"1d109aca-7880-4878-90ed-b5f293b22568","order_by":2,"name":"Hong-Yi Zheng","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hong-Yi","middleName":"","lastName":"Zheng","suffix":""},{"id":350571421,"identity":"d141188e-ff1b-4033-9309-d88b4858315a","order_by":3,"name":"Wen-Qiang He","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wen-Qiang","middleName":"","lastName":"He","suffix":""},{"id":350571422,"identity":"ac59b9cb-b104-48ea-bdeb-77bc917202c1","order_by":4,"name":"Ru-Fei Duan","email":"","orcid":"","institution":"The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Ru-Fei","middleName":"","lastName":"Duan","suffix":""},{"id":350571423,"identity":"da2c41d1-7d6b-41f1-907f-3bc88ba8cebf","order_by":5,"name":"Xia Li","email":"","orcid":"","institution":"Yunnan Provincial Hospital of Infectious Disease","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Li","suffix":""},{"id":350571424,"identity":"b968bc88-a4fe-480a-9627-664e6732c5ee","order_by":6,"name":"Ren-Rong Tian","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ren-Rong","middleName":"","lastName":"Tian","suffix":""},{"id":350571425,"identity":"050913cd-3387-452a-a645-95e75a5af836","order_by":7,"name":"Hui-Qin Li","email":"","orcid":"","institution":"Yunnan Provincial Hospital of Infectious Disease","correspondingAuthor":false,"prefix":"","firstName":"Hui-Qin","middleName":"","lastName":"Li","suffix":""},{"id":350571426,"identity":"2c6670a1-f8f7-4c93-8677-fcd9436eb02c","order_by":8,"name":"Xing-Qi Dong","email":"","orcid":"","institution":"Yunnan Provincial Hospital of Infectious Disease","correspondingAuthor":false,"prefix":"","firstName":"Xing-Qi","middleName":"","lastName":"Dong","suffix":""},{"id":350571427,"identity":"cf3be085-7b80-4c62-929b-dbad9e1fdb76","order_by":9,"name":"Zhi-Qiang Shen","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhi-Qiang","middleName":"","lastName":"Shen","suffix":""},{"id":350571428,"identity":"a424e56e-2eb7-4f26-af5e-b6f1f1a449f0","order_by":10,"name":"Yong-Tang Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACCQglx8DA2HiA4YAE0VoMjIFaGkjTktgAJIFaiNAhP7v52MOvbX/S17YfBtpyxsKegf3wA4afO3BrYZxzLN1Yts0gd9uZRKCWGxLMDDxpBoy9Z3BrYZbIMZOWBGk5ANLyQYKNgSGHgZmxDbcWNon8byAt6WbnH4K18DDwv8GvhUcih03yY5tBgtkNiMMkGCQI2CIhkWYmzXDO2HDbDaAtCWckDNgknhkc7MWjRX5G8jPJH2Vy8mbn0x8++HCszp6fP/nhg594tICDgJcNykoA+Y6BgXD0MP74Q0jJKBgFo2AUjGgAAP7LUEt80RvRAAAAAElFTkSuQmCC","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yong-Tang","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2024-08-09 02:56:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4883942/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4883942/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-10673-4","type":"published","date":"2025-02-24T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66120270,"identity":"9828f292-31fc-4f14-98da-834b391d8243","added_by":"auto","created_at":"2024-10-08 01:35:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study\u003c/strong\u003e. Flowchart of study selection for the present study.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4883942/v1/d9f4285ac7e13d44dd7ee37b.jpg"},{"id":66120014,"identity":"2c3624cb-aee6-4974-9103-7bf477c29ead","added_by":"auto","created_at":"2024-10-08 01:27:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58672,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe nomogram for predicting the CIR of PLWH. \u003c/strong\u003eA nomogram\u003cstrong\u003e \u003c/strong\u003ewas established based on multivariate Cox regression models.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4883942/v1/5eaea4e96f04439ec53f2133.jpg"},{"id":66120017,"identity":"5ae28936-9467-4917-b28b-9a37735e9616","added_by":"auto","created_at":"2024-10-08 01:27:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83186,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime‐dependent ROC analysis for 5, 6 and 7 years of ART.\u003c/strong\u003e Training set (A) and the validation set (B).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4883942/v1/6f8f9ac724124e627013c29b.jpg"},{"id":66120271,"identity":"13c8bed2-708c-4701-bbdf-b900eef480d2","added_by":"auto","created_at":"2024-10-08 01:35:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":107196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves of the prognostic nomogram for 5-, 6- and 7-year survival after ART.\u003c/strong\u003e Training set (A-C) and the validation set (D-F).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4883942/v1/d9abb0185e7f340ffccd0958.jpg"},{"id":66120016,"identity":"5980fe6e-ad7d-4310-9c8d-14deaa7d751d","added_by":"auto","created_at":"2024-10-08 01:27:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis (DCA) curve of the nomogram.\u003c/strong\u003e (A) DCA curve for the training group. (B) DCA curve for the validation group.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4883942/v1/d7080a8cdcd3bd4b13661724.jpg"},{"id":66120044,"identity":"e18eb06e-0680-43e7-836a-ffdbed6fbfcc","added_by":"auto","created_at":"2024-10-08 01:28:09","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":71749,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eK‒M curve of the nomogram model. \u003c/strong\u003e(A) Kaplan‒Meier curve of the training set. (B) Kaplan‒Meier curve of the validation set.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4883942/v1/e9f5641f42aa34ca83cb9ade.jpg"},{"id":77622538,"identity":"b06d1611-81bc-4287-b618-d6e9990c3f71","added_by":"auto","created_at":"2025-03-03 16:08:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2156112,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4883942/v1/ff69af69-8e58-4073-9168-a326a6ba96ff.pdf"},{"id":66120019,"identity":"41ded78a-2022-43d7-a0bb-2e1c4f8bb2bb","added_by":"auto","created_at":"2024-10-08 01:27:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":486195,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4883942/v1/81e267e871bfa3686252dfc4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Establishment and validation of a predictive model for immune reconstitution in people with HIV after antiretroviral therapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman immunodeficiency virus (HIV) infection remains a significant global health challenge [1]. The World Health Organization estimated that by the end of 2022, the population of people living with HIV (PWH) would reach approximately 38\u0026nbsp;million, including the addition of 1.7\u0026nbsp;million new cases. The widespread implementation of \"treat-all\" antiretroviral therapy (ART) has greatly improved both the clinical prognosis and life expectancy of PWH [2, 3]. ART is known to effectively suppress viral replication, restore immune function, and reduce the risk of acquired immunodeficiency syndrome (AIDS)-related complications [4\u0026ndash;6]. However, despite these advances, a considerable proportion of PWH (15\u0026ndash;30%) experience suboptimal recovery of immune function, referred to as immune unresponsiveness or incomplete immune reconstitution (IIR) [7].\u003c/p\u003e \u003cp\u003eThere is still a lack of consensus and debate continues regarding the immunomodulatory mechanisms and therapeutic approaches for managing IIR. This condition, closely linked to diminished CD4\u003csup\u003e+\u003c/sup\u003e T cell production in the bone marrow, reduced thymic output, persistent HIV replication, abnormal immune activation, disruptions in cytokine secretion, and specific genetic or metabolic characteristics in PWH, increases their susceptibility to various complications, including both AIDS and non-AIDS events and is associated with elevated mortality rates [8, 9]. Therefore, identifying PWH at risk of immune nonresponse is essential for personalized management and improved clinical outcomes.\u003c/p\u003e \u003cp\u003eClinical prediction models are widely utilized in both medical research and practice to assess the likelihood of a specific clinical outcome within a study population. Typically, these models utilize various variables or predictors for comprehensive evaluation [10, 11]. However, there remains a notable scarcity of published research on models that effectively integrate multiple variables for accurately identifying complete immune reconstitution (CIR) in China. Most existing studies have focused on short-term outcomes, without in-depth analysis of long-term changes and trends in immune recovery following ART [12]. Studies with extended follow-up periods could provide a more accurate assessment of the CIR and the progression of adverse risks, especially given the existence of a plateau phase in immune recovery [13]. This lack of comprehensive long-term prediction models underscores the urgent need for the development of reliable, long-term clinical prediction models.\u003c/p\u003e \u003cp\u003eThe specific objective of this study was to develop and validate a prediction model to determine the likelihood of CIR in PWH after 4, 5, and 6 years of ART. Univariate and multivariate Cox regression analyses were conducted to identify CIR-associated signatures. A nomogram was constructed based on the multivariate Cox regression coefficients. The performance of the nomogram was validated through discrimination, calibration, and decision curve analyses (DCA). The model incorporated demographic, clinical, and immunological variables to offer clinicians a straightforward and reliable tool for accurately identifying PWH who may need additional monitoring and interventions, especially during the initiation of ART, thereby enabling targeted and timely clinical intervention and personalized care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist was used for the validation of the prediction model [14].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003e This study involved participants who receiving ART at the Antiviral Outpatient Department of Yunnan Provincial Hospital of Infectious Diseases in Yunnan, China, between October 2004 and December 2020. Participant records were obtained by professional physicians from the National AIDS Integrated Prevention and Control Information System, and the data quality was thoroughly assessed. The inclusion criteria for participants were PWH who were aged 18 years or older, had confirmed positive results for HIV antibodies in both primary screening and confirmatory tests, had undergone ART for a minimum of 3 years, and exhibited a viral load below the detection limit (\u0026le;\u0026thinsp;50 copies/mL) at the most recent follow-up. The exclusion criteria included participants who had died, were lost to follow-up, discontinued medication, or lacked available demographic data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy outcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome of interest was the rate of CIR in virologically suppressed PWH after ART. The CIR was determined using binary indicators, specifically a CD4\u003csup\u003e+\u003c/sup\u003e T cell count of \u0026ge;\u0026thinsp;500 cells/\u0026micro;L and a CD4/CD8 ratioof\u0026thinsp;\u0026ge;\u0026thinsp;0.8, which are considered accurate measures of immune system function and status [15]. The determination of immunological reconstitution was based on the results from the latest recorded follow-up visit in the electronic case system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCandidate predictor variables\u003c/h2\u003e \u003cp\u003ePotential predictor variables for the model were identified from the literature, and relevant variables were collected from the electronic medical records, including demographics, baseline immunological and laboratory tests, coinfections, and ART regimens. Baseline referred to the first recorded results before starting ART but after HIV diagnosis. The demographic data included age, sex, date of diagnosis, infection route, and marital status, with the latter two obtained from patient-initiated information or doctor‒patient communication. Baseline immunological outcomes were assessed based on CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cell counts and the CD4/CD8 ratio. The CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cell counts were measured using Truecount\u0026trade; Tubes (BD Biosciences, San Jose, CA, USA) and a FACSCalibur\u0026trade; flow cytometer (BD Biosciences, San Jose, CA, USA).\u003c/p\u003e \u003cp\u003eBaseline laboratory test indicators included white blood cell count (WBC) hemoglobin (HGB), platelet (PLT), total bilirubin (TBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and creatinine (Crea) levels; the creatinine clearance rate (Ccr); and blood glucose (Glu), triglyceride (TG), and total cholesterol (CHO) levels. These parameters were analyzed using a DxH 800 hematology analyzer (Beckman Coulter, Miami, Florida, USA) and an automatic biochemical detector (Hitachi 7180, Tokyo, Japan).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCoi\u003c/strong\u003e \u003cp\u003enfection examination primarily focused on hepatitis B and C virus (HBV and HCV) infections, which were detected using HBV and HCV antibody diagnostic kits via enzyme-linked immunosorbent assay (ELISA) (Wantai BioPharm, Beijing, China). A positive result for anti-HBV or HCV surface antigen (HBsAg) indicated the presence of HBV or HCV infection.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eART regimens, including both initial and currently used regimens, typically consist of a combination of two nucleoside reverse transcriptase inhibitors (NRTIs) along with a nonnucleoside reverse transcriptase inhibitors (NNRTIs), a protease inhibitors (PIs), or an integrase strand transfer inhibitor (INSTIs) [16]. Thus, these regimens were categorized into three groups: NNRTIs or NRTIs, PIs, and INSTIs-containing regimens.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample size and missing data\u003c/h2\u003e \u003cp\u003e Although no formal calculation of sample size was performed, the study adhered to the TRIPOD guidelines by including a minimum of 10 events per variable [14]. This necessitated the inclusion of at least 410 cases of CIR. To ensure adequate test efficiency, all 5,408 eligible participants who met the criteria during the study period were included. Among them, 2,083 belonged to the CIR category, exceeding the estimated minimum sample size. As shown in \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e, missing data (with missing rates below 15%) were addressed through multiple imputation using the MICE package in R, assuming that missing data were random. Comparative analysis revealed that the distribution of the imputed data was similar to that of the original data (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e B)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eData collection and management were performed using Microsoft Office Excel 2011. Statistical analyses were performed using the R program (v 4.3.2). The R package \"caret\" was used to randomly divide participants into training and validation sets (at a 7:3 ratio). Continuous variables were transformed into categorical variables using X-tile software to determine optimal cutoff values. Categorical variables were compared using the chi-square test or Fishers exact probability method with the \"tableone\" package and are presented as frequencies (n) and proportions (%). Univariate and multivariate Cox regression analyses (\u0026ldquo;survival\u0026rdquo; package) were conducted to construct the model. Variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analyses were included in the multivariate analysis. The best model was selected using backward stepwise regression with the Akaike information criterion (AIC). Finally, the nomogram was created using the \"rms\" package. Model performance was assessed for discrimination, calibration, and clinical utility. Discrimination was evaluated using concordance statistics (C statistics) and time-dependent receiver operating characteristic (ROC) curves (\u0026ldquo;survival ROC\u0026rdquo; and \"rms\" packages). Internal verification and calibration curves were generated using the bootstrap resampling method, with 1,000 bootstrap repetitions. Clinical net benefit and DCA results were evaluated and plotted using the \"rmda\" package. Additionally, risk stratification analysis of all PWH was conducted based on the prognosis index using Kaplan‒Meier survival analysis and compared with the log-rank test, with the optimal cutoff value calculated using the \"survminer\" package. A statistical significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics and clinical features of participants\u003c/h2\u003e \u003cp\u003eBetween October 2022 and June 2023, a retrospective screening was conducted on 7 201 PWH. Among these participants, 1710 were excluded from the study due to various factors, such as age, length of treatment, viral load, and unavailability of data. Ultimately, a total of 5 408 PWH were enrolled based on the inclusion criteria \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The cohort predominantly consisted of males (66.79%), with a majority being middle-aged (59.93% aged 42 years or older) and unmarried (51.31%). The primary mode of HIV transmission was sexual contact. Approximately 47.95% of participants had initial CD4\u003csup\u003e+\u003c/sup\u003e T cell counts ranging from 187 to 459 cells/\u0026micro;L and CD8\u003csup\u003e+\u003c/sup\u003e T cell counts ranging from 751 to 1 499 cells/\u0026micro;L. As a result, 40.16% of participants had a CD4/CD8 ratio\u0026thinsp;\u0026le;\u0026thinsp;0.2. NNRTIs and NRTIs were the main components, accounting for 92.86% of the initial regimens and 60.67% of the current regimens. The cohort also included 2083 participants with CIR and 3325 participants with IIR. The overall rate of CIR, as defined, was 38.52%. Significant differences were observed between the CIR and IIR cohorts in certain baseline data, including sex, age, and interval from diagnosis to treatment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the study participants, 3 788 were randomly assigned to the training cohort, and 1 620 were randomly assigned to the validation cohort. Baseline characteristics were well balanced between the two cohorts, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the 5408 patients in the study according to IIR/CIR and randomization to the training and validation sets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal patient cohort (n\u0026thinsp;=\u0026thinsp;5408)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIIR\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3325)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCIR\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2083)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTraining set (n\u0026thinsp;=\u0026thinsp;3788)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eValidation set (n\u0026thinsp;=\u0026thinsp;1620)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3612 (66.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2377 (71.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1235 (59.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2538 (67.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1074 (66.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1796 (33.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e948 (28.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e848 (40.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1250 (33.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e546 (33.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent age (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1105 (20.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e637 (19.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e468 (22.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e784 (20.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e321 (19.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1062 (19.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e617 (18.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e445 (21.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e740 (19.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e322 (19.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3241 (59.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2071 (62.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1170 (56.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2264 (59.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e977 (60.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at diagnosis (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4422 (81.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2653 (79.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1769 (84.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3134 (82.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1288 (79.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e986 (18.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e672 (20.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e314 (15.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e654 (17.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e332 (20.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at initial ART (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4241 (78.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2545 (76.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1696 (81.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3004 (79.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1237 (76.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1167 (21.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e780 (23.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e387 (18.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e784 (20.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e383 (23.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2101 (38.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1300 (39.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e801 (38.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1481 (39.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e620 (38.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2775 (51.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1691 (50.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1084 (52.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1938 (51.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e837 (51.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e532 (9.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e334 (10.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198 (9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e369 (9.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e163 (10.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfection route\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeterosexual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2881 (53.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1763 (53.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1118 (53.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2006 (52.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e875 (54.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomosexual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1272 (23.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e766 (23.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e506 (24.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e892 (23.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e380 (23.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntravenous drug use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e515 (9.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e344 (10.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171 (8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e355 (9.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e160 (9.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/Unclear*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e740 (13.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e452 (13.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e288 (13.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e535 (14.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e205 (12.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1902 (35.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1183 (35.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e719 (34.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1350 (35.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e552 (34.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1975 (36.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1219 (36.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e756 (36.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1397 (36.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e578 (35.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1531 (28.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e923 (27.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e608 (29.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1041 (27.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e490 (30.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-HBsAg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5129 (94.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3150 (94.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1979 (95.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3591 (94.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1538 (94.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e279 (5.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175 (5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104 (4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e197 (5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e82 (5.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-HCV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4803 (88.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2936 (88.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1867 (89.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3365 (88.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1438 (88.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e605 (11.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e389 (11.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e216 (10.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e423 (11.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e182 (11.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnosis treatment interval (month)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1978 (36.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1245 (37.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e733 (35.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1353 (35.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e625 (38.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.8\u0026ndash;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1340 (24.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e860 (25.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e480 (23.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e951 (25.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e389 (24.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2090 (38.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1220 (36.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e870 (41.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1484 (39.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e606 (37.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD4\u0026thinsp;+\u0026thinsp;T cell counts (cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2051 (37.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1652 (49.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e399 (19.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1454 (38.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e597 (36.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e187\u0026ndash;459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2593 (47.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1416 (42.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1177 (56.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1790 (47.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e803 (49.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e764 (14.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e257 (7.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e507 (24.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e544 (14.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e220 (13.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD8\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eT cell counts (cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1966 (36.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1233 (37.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e733 (35.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1359 (35.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e607 (37.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e751\u0026ndash;1499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2547 (47.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1494 (44.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1053 (50.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1802 (47.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e745 (45.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e895 (16.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e598 (17.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e297 (14.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e627 (16.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e268 (16.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD4/CD8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2172 (40.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1755 (52.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e417 (20.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1541 (40.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e631 (38.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.21\u0026ndash;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2027 (37.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1197 (36.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e830 (39.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1411 (37.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e616 (38.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1209 (22.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e373 (11.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e836 (40.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e836 (22.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e373 (23.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC (\u0026times;109/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1280 (23.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e897 (26.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e383 (18.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e886 (23.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e394 (24.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.8\u0026ndash;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1361 (25.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e844 (25.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e517 (24.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e975 (25.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e386 (23.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2767 (51.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1584 (47.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1183 (56.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1927 (50.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e840 (51.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLT (\u0026times;109/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2473 (45.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1630 (49.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e843 (40.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1730 (45.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e743 (45.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2935 (54.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1695 (50.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1240 (59.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2058 (54.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e877 (54.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHGB (g/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1850 (34.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1209 (36.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e641 (30.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1271 (33.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e579 (35.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e135\u0026ndash;154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1656 (30.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1002 (30.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e654 (31.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1179 (31.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e477 (29.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1902 (35.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1114 (33.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e788 (37.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1338 (35.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e564 (34.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCrea (\u0026micro;mol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1480 (27.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e813 (24.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e667 (32.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1043 (27.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e437 (26.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e59\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e735 (13.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e455 (13.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e280 (13.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e502 (13.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e233 (14.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3193 (59.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2057 (61.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1136 (54.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2243 (59.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e950 (58.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCcr (mL/min)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4723 (87.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2958 (88.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1765 (84.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3301 (87.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1422 (87.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e685 (12.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e367 (11.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e318 (15.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e487 (12.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e198 (12.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHO (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1588 (29.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1043 (31.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e545 (26.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1099 (29.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e489 (30.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.01\u0026ndash;5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2543 (47.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1549 (46.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e994 (47.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1795 (47.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e748 (46.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1277 (23.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e733 (22.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e544 (26.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e894 (23.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e383 (23.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4635 (85.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2866 (86.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1769 (84.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3245 (85.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1390 (85.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e773 (14.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e459 (13.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e314 (15.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e543 (14.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e230 (14.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlu (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e975 (18.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e579 (17.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e396 (19.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e677 (17.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e298 (18.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4433 (81.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2746 (82.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1687 (80.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3111 (82.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1322 (81.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT (U/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2268 (41.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1359 (40.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e909 (43.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1599 (42.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e669 (41.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3140 (58.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1966 (59.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1174 (56.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2189 (57.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e951 (58.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST (U/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2696 (49.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1635 (49.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1061 (50.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1912 (50.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e784 (48.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2712 (50.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1690 (50.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1022 (49.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1876 (49.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e836 (51.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTBIL (\u0026micro;mol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2232 (41.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1405 (42.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e827 (39.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1551 (40.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e681 (42.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3176 (58.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1920 (57.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1256 (60.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2237 (59.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e939 (57.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial ART regimen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNNRTIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5022 (92.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3090 (92.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1932 (92.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3533 (93.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1489 (91.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e306 (5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169 (5.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137 (6.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e197 (5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e109 (6.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINSTIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80 (1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58 (1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22 (1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent ART regimen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNNRTIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3281 (60.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1983 (59.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1298 (62.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2318 (61.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e963 (59.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e747 (13.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e469 (14.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e278 (13.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e526 (13.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e221 (13.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINSTIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1380 (25.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e873 (26.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e507 (24.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e944 (24.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e436 (26.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCategorical data are presented as n (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Others/Unclear included blood transfusion, mother-to-child transmission, and unknown.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eIIR: incomplete immune reconstitution, CIR: complete immune reconstitution, ART: antiretroviral treatment, HBsAg: hepatitis B surface antigen, Anti-HCV: antibodies against the hepatitis C virus, WBC: white blood cell count, PLT: platelet, HGB: hemoglobin, CR: creatinine, Ccr: creatinine clearance rate, CHO: cholesterol, TG: triglycerides, Glu: blood glucose, ALT: alanine transaminase, AST: aspartate transaminase, TBIL: total bilirubin, NRTIs: nucleoside reverse transcriptase inhibitors, NNRTIs: nonnucleoside reverse transcriptase inhibitors, PIs: protease inhibitors, INSTI: integrase strand transfer inhibitors.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a prediction model for the CIR in PWH based on the training set\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate Cox regression analyses were performed to identify potential predictors of CIR in PWH. \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e presents the results of the univariate analysis of all 26 candidate predictors individually. Based on these results, 20 predictors were included in the multivariate Cox regression analyses. Six factors with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05 were excluded from the analysis: CHO, age at HIV diagnosis, age at initiation of ART, sex, coinfection with hepatitis B, and TG. Multivariate Cox regression analysis identified several predictors, including age, interval from diagnosis to initial ART, marital status, infection route, baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell count, CD4/CD8 ratio, initial ART regimen, current ART regimen, and the PLT, Glu, Crea, HGB, and ALT. Consequently, a nomogram was constructed using the multivariable analysis results, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Total points were obtained by summing the corresponding points of each index value and then converted into the probability of CIR incidence at 4, 5, and 6 years according to the nomogram.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation and validation of the prediction models\u003c/h2\u003e \u003cp\u003eThe discrimination capacity of the prediction model was evaluated using C-indices and time-ROC curves. The model achieved a C-index of 0.78 (95% CI, 0.768\u0026ndash;0.791), and internal validation yielded a similar C-index of 0.765 (95% CI, 0.747\u0026ndash;0.782). The time-dependent ROC curves for predicting the CIR at 4, 5, and 6 years are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The areas under the ROC curve (AUCs) reached 0.799 at 4 years, 0.824 at 5 years, and 0.830 at 6 years in the training cohort. The AUCs of the validation set reached 0.799 at 4 years, 0.817 at 5 years, and 0.809 at 6 years. These results indicate that the model achieved good predictive performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCalibration curves are used to compare observed outcomes with model predictions, providing a measure of model accuracy in estimating absolute risk. The agreement between the predicted and observed values is evaluated by the degree of alignment between the calibration curve and diagonal line [17]. In this study, the prediction results strongly agreed with the actual observations in both the training and validation sets, suggesting that the model accurately and effectively captured the actual values \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, DCA was employed to evaluate the clinical utility of the prediction model [18], including both the training and validation sets. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the model provided a net benefit, ranging from approximately 5\u0026ndash;50%, in both the training and validation sets. These results indicated that the model is advantageous for making decisions in clinical settings, particularly for scenarios in the sixth year.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess the predictive effectiveness of the model, the study participants were divided into two risk groups based on their calculated risk scores from the nomogram: a low-scoring group (total score\u0026thinsp;\u0026lt;\u0026thinsp;20.6) and a high-scoring group (total score\u0026thinsp;\u0026ge;\u0026thinsp;20.6). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the Kaplan‒Meier curves for both the training and validation sets clearly demonstrated that the model effectively distinguished between the high- and low-risk groups (log-rank test, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccurate assessment of the potential for CIR following ART is crucial for improving prognosis and guiding treatment decisions for PWH. This study aimed to develop and validate a prediction model to determine the likelihood of PWH achieving CIR at years 4, 5, and 6 after initiating ART. Participant data for model development were derived from initial routine laboratory tests performed post-HIV diagnosis, selected for their affordability, ease of collection, and broad applicability.\u003c/p\u003e \u003cp\u003eDirect comparison of our model with others is challenging due to differences in CIR definitions. In contrast to the findings of Zhang et al. [12], our CIR criteria were more stringent, defining the CIR as a CD4\u003csup\u003e+\u003c/sup\u003e T cell count\u0026thinsp;\u0026ge;\u0026thinsp;500 cells/\u0026micro;L and a CD4/CD8 ratio\u0026thinsp;\u0026ge;\u0026thinsp;0.8, which has been proven to more accurately evaluate the extent of immune restoration in the \"treat all\" era [15]. Longitudinal research has suggested a gradual CIR process following ART, often exhibiting a prolonged plateau phase. Studies have indicated that total CD4\u003csup\u003e+\u003c/sup\u003e T and CD8\u003csup\u003e+\u003c/sup\u003e T cell turnover rates tend to stabilize after 12\u0026ndash;36 months of ART and reach a plateau after 3\u0026ndash;4 years of suppressive treatment [19, 20]. The Multicenter AIDS Cohort Study, which involved 314 PWH, revealed no increase in CD4\u003csup\u003e+\u003c/sup\u003e T cell counts after 2\u0026ndash;3 years of ART [21]. Similarly, the AIDS Clinical Trials Group (ACTG) study revealed that most changes in CD4\u003csup\u003e+\u003c/sup\u003e T cell counts occur within the first year of ART, with no significant increases in the second or third year of therapy [22]. Based on these findings, our model was constructed using data from PWH who had undergone ART for a minimum of 3 years, aiming to forecast CIR at 4, 5, and 6 years post-ART initiation.\u003c/p\u003e \u003cp\u003eIn this study, we identified several factors influencing the CIR, including age, diagnosis-treatment interval, marital status, infection route, baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell count, CD4/CD8 ratio, treatment regimen, and various hematological and biochemical parameters. The significance of baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell counts and the CD4/CD8 ratio in immune recovery has been extensively studied [15, 23\u0026ndash;27]. For instance, among PWH with baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell counts of less than 50, 50\u0026ndash;199, 200\u0026ndash;349, and 350\u0026ndash;499 cells/\u0026micro;L, the probabilities of achieving CD4\u003csup\u003e+\u003c/sup\u003e T cell counts of 500 cells/\u0026micro;L or higher after ART vary considerably, ranging from 1.97%, 7.84%, 62.85%, and 71.07%, respectively [15]. Similarly, among PWH who started cART with less than 200 cells/\u0026micro;L, 57% did not reach 600 cells/\u0026micro;L after 7 years, while those with baseline counts of 200\u0026ndash;349 CD4\u003csup\u003e+\u003c/sup\u003e T cells/\u0026micro;L achieved this count in less than 2 years [23]. Previous studies have also demonstrated that the time required to achieve a 90% probability of CIR after two years of ART is significantly longer for PWH with a CD4/CD8 ratio less than 0.5 compared to those with a ratio greater than 0.5 [28]. These studies consistently demonstrated that higher baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell counts and CD4/CD8 ratioplay a crucial role in determining the rate and extent of immune recovery after ART, underscoring the importance of initiating ART at higher CD4\u003csup\u003e+\u003c/sup\u003e T cell counts. Additionally, homosexual transmission and initial treatment regimens containing INSTIs were significantly associated with a lower rate of CIR. The impact of different infection routes on immune reconstitution is complex, with factors such as viral tropism, intestinal flora, and coreceptor switching linked to CIR in men who have sex with men (MSM) [29\u0026ndash;31]. Research has indicated that only 60.5% of Chinese MSM have undergone HIV testing [32], which is attributed to limited awareness, social discrimination, and concealment of sexual orientation. Consequently, approximately 50% of Chinese MSM living with HIV are unaware of their serostatus, impacting timely diagnosis and access to treatment, which are crucial for effective immune reconstitution [32, 33].\u003c/p\u003e \u003cp\u003eIn deciding on medication use, physicians consider various factors, including CD4\u003csup\u003e+\u003c/sup\u003e T cell counts, virological efficacy, and drug tolerability. Preference is often given to more potent ART regimens when CD4\u003csup\u003e+\u003c/sup\u003e T cell counts are low. In our cohort, participants who were initially prescribed INSTI-based regimens had average baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell counts of only 230 cells/\u0026micro;L. In contrast, those on NNRTI-based regimens had average baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell counts of 265 cells/\u0026micro;L, while those on PI-based regimens had average baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell counts of 320 cells/\u0026micro;L.\u003c/p\u003e \u003cp\u003eRegarding other influential factors, both a longer interval between HIV diagnosis and ART initiation and older age were associated with lower baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell counts. An extended interval between diagnosis and treatment can lead to increased HIV replication, more severe immune system damage, and an increased incidence of opportunistic infections. Previous studies have shown positive correlations between the length of the diagnosis-to-treatment interval and the progression of AIDS and mortality [34]. Additionally, older age may be associated with thymic atrophy, abnormal immune activation, and immunosenescence, all of which can accelerate HIV/AIDS progression and are closely associated with IIR [35\u0026ndash;37]. Moreover, older PWH tend to receive a diagnosis at a later stage than younger individuals, further hindering immune recovery [38].\u003c/p\u003e \u003cp\u003eThis study also revealed associations between immune reconstitution and various hematological and blood biochemical parameters, including HGB, Crea, PLT, Glu, ALT, and AST. Previous research has demonstrated that PWH with suboptimal CD4\u003csup\u003e+\u003c/sup\u003e T cell recovery often have elevated platelet counts [39]. This could be attributed to the ability of platelets to directly interact with HIV, facilitating viral binding and entry into cells, with platelets further acting as a reservoir for the virus during chronic infection. In addition, platelet-CD4\u003csup\u003e+\u003c/sup\u003e T cell aggregates display increased levels of activation, depletion, and apoptotic markers, suggesting a potential role for platelets in CD4\u003csup\u003e+\u003c/sup\u003e T cell depletion [40]. However, further research is needed to clarify the relationships between the CIR and other blood biochemical parameters.\u003c/p\u003e \u003cp\u003eGiven the complexity and importance of immune reconstitution, various studies have examined adjuvant therapeutic strategies to enhance CIR, including interleukin 2 (IL-2), methotrexate, statins, metformin, and other immunomodulators [41\u0026ndash;43]. Furthermore, various immunotherapeutic approaches, such as broadly neutralizing antibodies, stem cell transplants, and therapeutic vaccines, are currently under investigation [44\u0026ndash;46]. Recent clinical trials have shown the potential of (5R)-5-hydroxytriptolide to promote CD4\u003csup\u003e+\u003c/sup\u003e T cell recovery and reduce inflammation in PWH, suggesting that this is a new approach for the treatment of IIR [47]. Despite these developments, many current clinical trials and research initiatives have yet to achieve success, spurring ongoing efforts to develop more efficient immunomodulators and therapeutic strategies.\u003c/p\u003e \u003cp\u003e Our study is distinguished by a relatively large sample size (N\u0026thinsp;=\u0026thinsp;5 408) and extended follow-up period, with 85.11% of participants monitored for more than 5 years. However, several limitations should be considered. First, the retrospective nature of the study inherently limited the scope of the analysis and may have introduced biases. Furthermore, as the study was conducted at a single center without external validation, the generalizability of the findings may be limited. Therefore, caution should be exercised when extrapolating the results to other populations or regions. Second, the study primarily focused on PWH examination results at baseline and at the most recent follow-up. The predictors were derived from the initial test results following diagnosis, while the outcome indicators were based on the most recent test results. It is important to note that all immunological, virological, blood biochemical, and relevant indicators of routine laboratory tests in PWH change over time. Our study did not consider these dynamic changes during ART, which are crucial for a comprehensive understanding of the CIR, potentially leading to inaccurate estimations of the true risk of IIR in PWH. Finally, the absence of data on baseline viral load, viral load rebound, comorbidities, coinfections, treatment adherence, drug resistance, adverse effects, and changes in ART regimens for most participants is a significant limitation, as these factors could differentially impact the CIR but were not considered in our analysis.\u003c/p\u003e \u003cp\u003eRecent advances in medical research have deepened our understanding of HIV and the human immune system. Novel indicators, such as the percentage of na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells prior to ART in PWH, the ratio of na\u0026iuml;ve/effective memory CD4\u003csup\u003e+\u003c/sup\u003e T cells [48], and the activity of the immunomodulatory kynurenine pathway in tryptophan catabolism, have emerged. These metrics show promise in predicting the normalization of CD4\u003csup\u003e+\u003c/sup\u003e T cell counts. Although the current evidence remains inconclusive, these findings offer new possibilities for diagnosis and prediction. Additionally, advancements in artificial intelligence and machine learning are expected to yield new models and algorithms that can adapt to the dynamic shifts in PWH metrics, aiding healthcare professionals in making more informed decisions and improving longevity and quality of life for PWH.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe prediction model developed in this study, derived from baseline data, effectively predicted the probability of CIR in PWH undergoing ART. The model demonstrated high accuracy and discriminatory power during internal validation, and the clinical utility of the nomogram was evaluated and confirmed using DCA. Based on our findings, we recommend the adoption of the model as a diagnostic tool to facilitate timely provision of appropriate therapeutic interventions, adjustment of ART regimens, precise and individualized management of PWH, and optimization of cost-effectiveness.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCIR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Complete immune reconstitution\u003c/p\u003e\n\u003cp\u003ePWH \u0026nbsp; \u0026nbsp; \u0026nbsp; People with human immunodeficiency virus\u003c/p\u003e\n\u003cp\u003eART \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Antiretroviral therapy\u003c/p\u003e\n\u003cp\u003eAIDS \u0026nbsp; \u0026nbsp; \u0026nbsp; Acquired immunodeficiency syndrome\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eHIV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Human immunodeficiency virus\u003c/p\u003e\n\u003cp\u003eIRR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Incomplete immune reconstitution\u003c/p\u003e\n\u003cp\u003eDCA \u0026nbsp; \u0026nbsp; \u0026nbsp; Decision curve analyses\u003c/p\u003e\n\u003cp\u003eTRIPOD \u0026nbsp; \u0026nbsp;Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis\u003c/p\u003e\n\u003cp\u003eWBC \u0026nbsp; \u0026nbsp; \u0026nbsp; White blood cell count\u003c/p\u003e\n\u003cp\u003eHGB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hemoglobin\u003c/p\u003e\n\u003cp\u003ePLT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Platelet\u003c/p\u003e\n\u003cp\u003eTBIL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total bilirubin\u003c/p\u003e\n\u003cp\u003eALT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eAST \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eCrea \u0026nbsp; \u0026nbsp; \u0026nbsp; Creatinine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCcr \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Creatinine clearance rate\u003c/p\u003e\n\u003cp\u003eGlu \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Blood glucose\u003c/p\u003e\n\u003cp\u003eTG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Triglyceride\u003c/p\u003e\n\u003cp\u003eCHO \u0026nbsp; \u0026nbsp; \u0026nbsp; Total cholesterol\u003c/p\u003e\n\u003cp\u003eHBV \u0026nbsp; \u0026nbsp; \u0026nbsp; Hepatitis B virus\u003c/p\u003e\n\u003cp\u003eHCV \u0026nbsp; \u0026nbsp; \u0026nbsp; Hepatitis C virus\u003c/p\u003e\n\u003cp\u003eELISA \u0026nbsp; \u0026nbsp; \u0026nbsp; Enzyme-linked immunosorbent assay\u003c/p\u003e\n\u003cp\u003eHBsAg \u0026nbsp; \u0026nbsp; \u0026nbsp;Anti-HBV or HCV surface antigen\u003c/p\u003e\n\u003cp\u003eNRTIs \u0026nbsp; \u0026nbsp; \u0026nbsp; Nucleoside reverse transcriptase inhibitors\u003c/p\u003e\n\u003cp\u003eNNRTIs \u0026nbsp; \u0026nbsp; Nonnucleoside reverse transcriptase inhibitors\u003c/p\u003e\n\u003cp\u003ePIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Protease inhibitors\u003c/p\u003e\n\u003cp\u003eINSTIs \u0026nbsp; \u0026nbsp; \u0026nbsp;Integrase strand transfer inhibitor\u003c/p\u003e\n\u003cp\u003eAIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Akaike information criterion\u003c/p\u003e\n\u003cp\u003eC statistics \u0026nbsp;Concordance statistics\u003c/p\u003e\n\u003cp\u003eAUCs \u0026nbsp; \u0026nbsp; \u0026nbsp; Areas under the ROC curve\u003c/p\u003e\n\u003cp\u003eACTG \u0026nbsp; \u0026nbsp; \u0026nbsp; AIDS Clinical Trials Group\u003c/p\u003e\n\u003cp\u003eMSM \u0026nbsp; \u0026nbsp; \u0026nbsp; Men who have sex with men\u003c/p\u003e\n\u003cp\u003eIL-2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interleukin 2\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Yunnan Infectious Disease Hospital (approval No. Ke201927), and informed consent from the participants was waived due to the use of anonymous data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData requests can be addressed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026apos; contributions in this study are as follows: Profs. Z-Q Shen and Y-T Zheng proposed the initial conception and design of the study and provided important guidance throughout its design and implementation. Profs. X-Q\u0026nbsp;Dong and H-Q Li managed the study, including data collection and coordination, and provided the source of clinical data. Na Li and Rui Li were responsible for the data analysis, graphical presentation of the results, and writing of the first draft of the paper. Authors H-Y Zheng and R-R Tian conducted a comprehensive literature review and made substantial revisions to the paper. W-Q He, R-F Duan, and Xia Li carefully checked the clinical data and the reasonableness of the corresponding statistical methods.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Key R \u0026amp; D Program of China (2023YFC2306700), the National Natural Science Foundation of China (U23A20473), the Yunnan Key R \u0026amp; D Program (202403AC100011), and the Key Laboratory of Bioactive Peptides of Yunnan Province (HXDT-2022-3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are sincerely grateful for the support and assistance received for this study. We would like to thank all the medical staff and colleagues who were involved in the data collection and analysis for their support and assistance. Importantly, we express our gratitude to all the anonymous participants who participated in this study. Furthermore, our sincere gratitude goes to Yunnan Infectious Disease Hospital for providing valuable data resources that enabled us to conduct this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBekker L-G, Alleyne G, Baral S, Cepeda J, Daskalakis D, Dowdy D, et al. Advancing global health and strengthening the HIV response in the era of the Sustainable Development Goals: the International AIDS Society\u0026mdash;Lancet Commission. The Lancet. 2018;392:312\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eSmiley CL, Rebeiro PF, Cesar C, Belaunzaran-Zamudio PF, Crabtree-Ramirez B, Padgett D, et al. Estimated life expectancy gains with antiretroviral therapy among adults with HIV in Latin America and the Caribbean: a multisite retrospective cohort study. 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Archives of Virology. 2015;160:1953\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003eZheng H-Y, Wang X-H, He X-Y, Chen M, Zhang M-X, Lian X-D, et al. Aging induces severe SIV infection accompanied by an increase in follicular CD8+ T cells with overactive STAT3 signaling. Cellular \u0026amp; Molecular Immunology. 2022;19:1042\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eZheng H-Y, Zhang M-X, Chen M, Jiang J, Song J-H, Lian X-D, et al. Accelerated disease progression and robust innate host response in aged SIVmac239-infected Chinese rhesus macaques is associated with enhanced immunosenescence. Scientific Reports. 2017;7.\u003c/li\u003e\n\u003cli\u003eMay M, Gompels M, Delpech V, Porter K, Post F, Johnson M, et al. Impact of late diagnosis and treatment on life expectancy in people with HIV-1: UK Collaborative HIV Cohort (UK CHIC) Study. 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Vaccines. 2023;11:896.\u003c/li\u003e\n\u003cli\u003eCao W, Liu X, Han Y, Song X, Lu L, Li X, et al. (5R)-5-hydroxytriptolide for HIV immunological non-responders receiving ART: a randomized, double-blinded, placebo-controlled phase II study. The Lancet Regional Health - Western Pacific. 2023;34:100724\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eMirte Scherpenisse, Kootstra NA, Bakker M, Berkhout B, Pasternak AO. Cell-Associated HIV-1 Unspliced-to-Multiply-Spliced RNA Ratio at 12 Weeks of ART Predicts Immune Reconstitution on Therapy. mBio. 2021;12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV, ART, immune reconstitution, predictive model, nomogram, model evaluation","lastPublishedDoi":"10.21203/rs.3.rs-4883942/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4883942/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAchieving complete immune reconstitution (CIR) in people with human immunodeficiency virus (PWH) following antiretroviral therapy (ART) is essential for preventing acquired immunodeficiency syndrome (AIDS) progression and improving survival. However, there is a paucity of robust prediction models for determining the likelihood of CIR in PWH after ART. We aimed to develop and validate a CIR prediction model utilizing baseline data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData including demographic information, immunological profiles, and routine laboratory test results, were collected from PWH in Yunnan, China. The participants were divided into training and validation sets (7:3 ratio). To construct the model and accompanying nomogram, univariate and multivariate Cox regression analyses were performed. The model was evaluated using the C-index, time-dependent receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curves to assess discrimination, calibration, and clinical applicability.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e5 408 PWH were included, with a CIR of 38.52%. Cox regression analysis revealed various independent factors associated with CIR, including infection route, marital status, baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell count, and baseline CD4/CD8 ratio. A nomogram was formulated to predict the probability of achieving CIR at years 4, 5, and 6. The model demonstrated good performance, as evidenced by an AUC of 0.8 for both sets. Calibration curve analysis demonstrated a high level of agreement, and decision curve analysis revealed a significant positive yield.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study successfully developed a prediction model with robust performance. This model has considerable potential to aid clinicians in tailoring treatment strategies, which could enhance outcomes and quality of life for PWH.\u003c/p\u003e","manuscriptTitle":"Establishment and validation of a predictive model for immune reconstitution in people with HIV after antiretroviral therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 01:27:54","doi":"10.21203/rs.3.rs-4883942/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-29T01:11:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-28T16:02:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236958053399401416493751044843900548468","date":"2025-01-17T09:31:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-17T15:48:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246000462040005735685354419714003721888","date":"2024-12-02T12:18:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120012829748507872796541508621819179452","date":"2024-08-18T14:35:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-16T12:12:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-13T18:27:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-12T23:44:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-08-09T02:55:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e5ae1a6e-8c8d-4bcb-852a-d54be3ab67a8","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T16:02:56+00:00","versionOfRecord":{"articleIdentity":"rs-4883942","link":"https://doi.org/10.1186/s12879-025-10673-4","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-02-24 15:58:07","publishedOnDateReadable":"February 24th, 2025"},"versionCreatedAt":"2024-10-08 01:27:54","video":"","vorDoi":"10.1186/s12879-025-10673-4","vorDoiUrl":"https://doi.org/10.1186/s12879-025-10673-4","workflowStages":[]},"version":"v1","identity":"rs-4883942","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4883942","identity":"rs-4883942","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}