Incomplete immune reconstitution and its predictors in people living with HIV in Wuhan, China

Incomplete immune reconstitution and its predictors in people living with HIV in Wuhan, China | 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 Incomplete immune reconstitution and its predictors in people living with HIV in Wuhan, China Wenyuan Zhang, Jisong Yan, Hong Luo, Xianguang Wang, Lianguo Ruan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2790359/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Sep, 2023 Read the published version in BMC Public Health → Version 1 posted 8 You are reading this latest preprint version Abstract Objective This study aimed to build and validate a nomogram model to predict the risk of incomplete immune reconstitution in people living with HIV (PLWH). Methods Totally 3783 individuals with a confirmed diagnosis of HIV/AIDS were included. A predictive model was developed based on a retrospective set (N = 2678) and was validated using the remaining cases (N = 1105). Univariable and multivariable logistic regression analyses were performed to determine valuable predictors among the collected clinical and laboratory variables. The predictive model was presented as a nomogram, and internally validated using another independent dataset. The predictive value of the model was evaluated by determining the area under the curve (AUC). Besides, calibration curve and decision curve (DCA) analyses were performed in both the training and test sets. Results The final model comprised 5 predictors, including baseline CD4, age at ART initiation, BMI, HZ and TBIL. The AUC of the nomogram model was 0.902 in the training cohort, versus 0.926 in the validation cohort. The calibration accuracy and diagnostic performance were satisfactory in both the training and test sets. Conclusions This predictive model based on a retrospective study was internally validated using 5 readily available clinical indicators. It showed high performance in predicting the risk of incomplete immune reconstitution. HIV/AIDS immune reconstitution nomogram predictive model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The use of antiretroviral therapy (ART) suppresses viral replication and increases CD4 + T cell counts[ 1 – 3 ], improving the prognosis of the majority of people living with HIV (PLWH) and dramatically decreasing both morbidity and mortality in acquired immunodeficiency syndrome (AIDS)[ 4 , 5 ]. However, up to 10–40% of patients may fail to achieve a sufficient immunologic response, as assessed by CD4 + T cell count, despite HIV virologic suppression, and are referred to as “immunologic non responders” (INRs)[ 6 , 7 ]. Compared with PLWH achieving good immune reconstitution, these patients show a greater risk of AIDS-defining diseases and non-AIDS-defining events (nADE), which is associated with high mortality[ 8 – 10 ]. Incomplete immune reconstitution is pathophysiologically thought to be associated with decreased bone marrow hematopoiesis, thymic dysfunction, residual viral replication, altered gut microbiota, and coinfections, particularly persistent inflammation and abnormal immune activation, significantly decreasing CD4 production and persistent CD4 destruction[ 7 , 11 – 13 ]. Several therapeutics, e.g., immunosuppressive agents[ 14 ] and cytokines[ 15 , 16 ], have been used to limit and restore chronic insufficient immune reconstitution for a long time, however, with marginal success. To date, no effective treatment could recover CD4 + T cells, especially in INRs. At present, it is particularly important to assess patient condition earlier, especially at the initial examination, and to adopt a timely and individualized treatment plan. It is commonly admitted that several factors can predict immunological function recovery and disease progression, e.g., CD4 + T cell count, CD4/CD8 ratio, viral load (VL) and IFN-γ[ 17 – 22 ]. Furthermore, it is essential to identify additional markers for improved assessment. Scherpenisse et al.[ 23 ] found a potential predictive marker of immunological failure, the cell-associated HIV-1 unspliced-to-multiply-spliced (US/MS) RNA ratio, which was positively correlated with markers of CD4 + T cell activation and apoptosis during ART treatment; the higher the US/MS RNA ratio the higher the frequency of HIV-infected cells, leading to sustained immune activation and apoptosis, resulting in decreased immune response to ART. In clinic, a single index is often inadequate to independently predict disease progression with satisfactory results. However, the combination of several single indexes may greatly improve the predictive effect. Medical nomograms based on various markers have been increasingly used in oncology and other areas of medicine in recent years. In addition, multiple prognostic models for PLWH have been established[ 24 , 25 ]. However, scoring models for predicting the risk of incomplete immune reconstitution in China have not been reported. Since several risk factors have been identified for INRs, a specific model is needed to predict poor immune reconstitution in advance. Thus, this study aimed to select potential indicators to construct a predictive model based on multivariate logistic regression analysis, providing improved prevention and individualized treatment in PLWH who are at high risk of poor immune reconstitution at the time of primary treatment. Then, a unique scoring system was created using the primary predictive model's modified nomogram for easy clinical application. Additionally, in a retrospective analysis, we internally verified the diagnostic capabilities of the improved scoring model. Materials And Methods Study design This was a retrospective study of data collected from the Wuhan Center for Disease Prevention and Control (CDC)’s Information System. Patients with HIV/AIDS treated at Wuhan Jinyintan Hospital from December 2006 to October 2022 were included. Inclusion criteria were: (1) complete laboratory test confirming HIV infection; (2) treatment with a combination ART regimen containing at least three drugs; (3) ART duration ≥ 24 months; (4) at least one visit during this period; (5) age > 15 years. Exclusion criteria were: (1) previous exposure to ART; (2) VL > 400 copies/mL, indicating virologic treatment failure. At the end of follow-up, 3783 participants meeting the above criteria were included. Data collection Demographic characteristics, clinical data and laboratory indexes were collected, including age at the time of diagnosis, sex, body mass index (BMI) calculated as weight/height 2 (kg/m 2 ), infection route, marital status, interval from diagnosis to ART, WHO clinical stage of the HIV disease, opportunistic infection (OI), coinfection with other bacteria or virus, several clinical symptoms, tumors, ART regimens, CD4 + T cells, VL, white blood cells (WBCs), platelets (PLTs), hemoglobin (HB), alanine aminotransferase (ALT), aspartate transaminase (AST), total bilirubin (TBIL), serum creatinine (Scr), triglycerides (TG), serum total cholesterol (TC) and blood glucose (BG). These parameters were obtained by trained professionals every 3 months. This study was approved by the ethics review board of Wuhan Jinyintan Hospital. Statistical analysis Data processing There is no straightforward way to determine the right sample size for a multifactor regression model. A predictive component requires at least 10 effective outcomes, according to previous reports, based on a cautious estimate. Given there were 583 instances with successful results, less than 58 predictors are needed. Multiple imputations were used to acquire suitable values for missing data before data analysis since directly discarding data with missing values might cause selection bias or decrease the power of a test. The outcomes were depicted in Fig. 1 . Thus, a sensitivity analysis was performed to determine how the missing data filled in the gaps (sTable 1). Construction and validation of the predictive model In this study, we aimed to set up a predictive model for forecasting the risk of becoming an INR. To evaluate the repeatability and extrapolation of this model, we randomly split participants in a ratio of 7:3 to establish one training and one test sets. Variables in these two datasets were described as number (percentage) or median (interquartile range, IQR), as appropriate. Continuous variables among groups were compared by the Mann-Whitney U test. Meanwhile, categorical variables were compared by the chi-square test, the fisher’s exact test or Wilcoxon rank sum test. Univariate logistic regression analysis (ULRA) was carried out to select factors in the training set. Then, 34 potential variables with P < 0.1 were retained for further analysis. After multivariate logistic regression, 15 candidate predictors were retained. Variables were further selected considering statistically significant parameters and medically important parameters such as availability at first assessment and objectivity of the metric. Finally, the five above variables, extracted by experienced physicians, were included in the predictive model with the highest predictive performance. Presentation of the nomogram Based on the five most significant variables, a nomogram model with an appropriate predictive ability was developed. The discrimination and calibration of the predictive model was evaluated to test the effectiveness of the model. In both the training and validation sets, receiver operating characteristic (ROC) curve analysis was utilized to quantify the discriminative value of the model, and a calibration curve was used to evaluate the calibration. Finally, decision curve analysis (DCA) was used to evaluate the predictive ability of the model in two independent data sets. Data analysis used SPSS version 26.0 (IBM Inc., Chicago, IL, USA) and R-Studio for windows (version 4.2.0) ( http://cran.r-project.org ). Two-sided p < 0.05 was considered statistically significant. Results According to the above inclusion and exclusion criteria, 3783 participants were confirmed and followed up in Wuhan Jinyintan Hospital from 2003 to 2020. We divided them into two groups, 2678 in the training set and 1150 in the validation set, and the characteristics of the both sets were similar in all variables (Table 1 ). Table 1 Variables Validation cohort Derivation cohort P-value Age at HIV diagnosis (years) 32 (25,48) 32(25,49) 0.698 Age at ART initiation (years) 32 (25,48) 33 (25,49) 0.685 Sex Male 1005(91.0) 2429(90.7) 0.810 Female 100(9.0) 249(9.3) Marital status Married 312(28.2) 786(29.4) 0.492 Unmarried 793(71.8) 1892(70.6) Route of HIV exposure MSM 764(69.1) 1785(66.7) 0.138 Heterosexual transmission 334(30.2) 851(31.8) 0.350 Injection drug use 3(0.3) 18(0.7) 0.205 Blood transfusion 0(0) 3(0.1) 0.633 Others 4(0.4) 21(0.8) 0.216 Coinfection HBsAg+ 105(9.5) 234(8.7) 0.454 Anti HCV+ 27(2.4) 54(2.0) 0.409 Herpes Zoster 45(4.1) 108(4.0) 0.955 PCP 27(2.4) 72(2.7) 0.668 Pulmonary infection 62(5.6) 138(5.2) 0.567 tumor 3(0.3) 12(0.4) 0.616 Symptoms Fever > 1month 77(7.0) 211(7.9) 0.337 Diarrhea > 1month 56(5.1) 118(4.4) 0.377 Fever 107(9.7) 254(9.5) 0.850 Diarrhea 76(6.9) 163(6.1) 0.363 Cough 145(13.1) 349(13.0) 0.940 Night sweats 81(7.3) 214(8.0) 0.491 Rash 59(5.3) 150(5.6) 0.749 Lymphnode swelling 84(7.6) 216(8.1) 0.631 WHO stage 1 222(20.1) 574(21.4) 0.665 2 559(50.6) 1306(48.8) 3 245(22.2) 616(23.0) 4 79(7.1) 182(6.8) ART initiation regimen AZT + 3TC + NVP/EFV 375(33.9) 986(36.8) 0.093 D4T + 3TC + NVP/EFV 10(0.9) 25(0.9) 0.934 TDF + 3TC + NVP/EFV 674(61.0) 1570(58.6) 0.177 TDF + 3TC + LPV/r 10(0.9) 18(0.7) 0.447 Other 36(3.3) 79(2.9) 0.616 ART initiation year,n(%) 2008–2011 58(5.2) 184(6.9) 0.098 2012–2015 408(36.9) 923(34.5) 2016–2020 639(57.8) 1571(58.7) ART delay 1.4(0.9,2.7) 1.4(0.9,2.6) 0.106 BMI, kg/m 21.3(19.6,23.4) 21.5(19.6,23.4) 0.683 Laboratory indicators CD4 cell count, cells/ µ L 272.00(150.00,393.50) 264.00(149.00,383.00) 0.308 HIV viral load, copies/mL 38102.00(10573.50, 110000.00) 39274.00(9044.25, 123559.75) 0.918 HIV viral load (log) 4.60(4.00, 5.00) 4.60(4.00,5.10) 0.917 WBC, 109/L 4.89(4.00,5.92) 4.87(3.94,5.98) 0.948 PLT, 109/L 191.00(155.00,230.50) 190.00(156.00,229.00) 0.987 Hb, g/L 143.00(129.00,152.00) 143.00(129.00,152.00) 0.913 Scr, µ mol/L 71.90(64.05,81.85) 72.90(64.00,82.20) 0.480 TG, mmol/L 1.32(0.92,1.94) 1.28(0.90,1.85) 0.262 TC, mmol/L 3.87(3.35,4.39) 3.85(3.34,4.41) 0.709 BG, mmol/L 5.40(5.00,6.00) 5.40(4.97,6.00) 0.897 AST, U/L 24.00(20.00,31.00) 24.00(20.00,31.00) 0.657 ALT, U/L 21.00(15.00,32.00) 22.00(15.00,33.00) 0.737 TBIL, µ mol/L 11.40(8.61,15.05) 11.10(8.50,14.60) 0.067 Abbreviations : MSM , men who have sex with men; PCP , pneumocystis carinii pneumonia; BMI , body mass index; AZT , zidovudine; 3TC , lamivudine; NVP , nevirapine; EFV , efavirenz; D4T , stavudine; TDF , tenofovir disoproxil; LPV/r , lopinavir/ritonavir; WBC : white blood cell; PLT : platelet; Hb : hemoglobin; Scr : serum creatinine; TG : triglyceride; TC : total cholesterol; BG : blood glucose; ALT : alanine aminotransferase; AST : aspartate aminotransferase; TBIL : total bilirubin. Of all PLWH, 21.8% (826/3782) were INRs, including 21.8% (583/2678) in the training set and 22.0% (243/1105) in the validation set. Based on the ULRA of the training set, 34 factors, including age at HIV diagnosis, age at the initiation of ART, marital status, MSM (men who have sex with men) status, OIs, skin lesions, fever, herpes zoster (HZ), pneumocystis pneumonia (PCP), cough, WHO clinical stage, BMI, baseline CD4, baseline VL, coinfection with HBsAg, WBC, PLT, HB and TBIL were significantly associated with the INR status (sTable 2). Variables with p < 0.1 were selected by experienced physicians for further multivariate logistic regression analysis. Finally, five predictors (baseline CD4, age at the initiation of ART, BMI, HZ and TBIL) were selected as independent risk factors for the INR status (Table 2 ). Hence, utilizing these five predictors, we developed a nomogram model (Fig. 2 ), tested the discriminative power and calibration of the predictive model, and extensively analyzed the individual and combined abilities of these five predictors by ROC analysis. In the training set (Fig. 3 a), the AUCs for age, BMI, CD4, HZ and TBIL were 0.902, 0.654, 0.611, 0.891, 0.532 and 0.598, respectively. These AUCs in the validation set were 0.926, 0.690, 0.664, 0.918, 0.552 and 0.632, respectively, as anticipated (Fig. 3 b). The calibration curves for both sets showed no statistically significant variation from a perfect match between the predicted and actual values (Fig. 4 ). Table 2 Variable β Coefficient Standard Error OR (95%CI) P-value Age at ART initiation 0.028 0.004 1.028(1.020–1.037) < 0.001 BMI -0.083 0.022 0.921(0.883–0.960) < 0.001 Baseline CD4 -0.014 0.001 0.986(0.985–0.988) < 0.001 HZ 0.894 0.271 2.446(1.437–4.161) 0.001 TBIL 0.040 0.012 1.041(1.017–1.066) 0.001 Abbreviations : BMI , body mass index; CD4 : CD4 + T lymphocyte; HZ : Herpes zoster; TBIL : total bilirubin. The decision curve analysis also indicated that the nomogram was feasible to make valuable and profitable judgments. As depicted in Fig. 5 a-b, clinical interventions using the developed nomograms yielded better clinical benefits within a threshold probability of 0.1 to 0.8, both in the training and validation sets. Furthermore, to facilitate the application of the predictive model in clinic, dynamic nomograms were constructed as online scoring systems, which are available at https://husteryjs.shinyapps.io/INRs_prediction/ . Discussion Despite virological response, INRs have significantly decreased peripheral CD4 + T cell count and functionality after at least 1 ~ 2 years of ART[ 6 , 26 ]. Patients with poor immune status experience chronic immune activation, resulting in higher risks of opportunistic infections (OIs), malignancies and other nADE[ 27 ]. Among all participants, the number of INRs was 826, accounting for 21.8%, including 21.8% and 22.0% in the training and validation sets, respectively. These outcomes corroborated a previous study that found a percentage of INRs in PLWH of 15–30%[ 28 ]. For early diagnosis and treatment, in this study, we developed and validated a feasible and simple visual nomogram as a new approach for predicting the development of immune recovery. The novel approach combines several prominent parameters to create a predictive model for improved diagnosis. This predictive model was constructed based on the derivation and validation cohorts, in which risk factors were selected though logistic regression and their risk scores were evaluated based on the stepwise regression model. A predictive model was developed in the derivation cohort, containing 5 variables: baseline CD4, age at the initiation of ART, BMI, HZ and TBIL. Then, the validation set was applied to assess the efficacy of the predictive model. In the training and validation sets, the developed nomogram demonstrated good calibration and had high diagnostic performance and clinical utility. We transformed the nomogram into an online calculator to help physicians in clinical practice. The five parameters utilized in this study were significantly associated with higher risks of failed acquisition of sufficient immune restoration in PLWH, and have important clinical implications. CD4 + T cell count and VL are commonly considered the important markers of treatment outcomes, with associations with long-term prognosis, as well as influencing indicators of immune reconstitution. Indeed, mounting evidence[ 22 , 29 – 31 ] shows low baseline CD4 + T cell count negatively impacts long-term CD4 + T cell recovery in both amount and functionality, affecting the progression of HIV infection. In addition, Jiang et al. [32] found that baseline HIV VL is significantly associated with CD4 + T cell restoration among PLWH. However, no matched conclusion was obtained from our data. Previous findings[ 33 – 35 ] indicate older age may be a risk factor for incomplete CD4 + T cell recovery in PLWH, suggesting age may exert a strong effect on long-term recovery of CD4 + T cells. This was also found in the newly developed model, showing that median maximal CD4 + T cell count is higher in patients aged 16-32.5 years than in patients aged 32.5 years after ART treatment. Furthermore, this study suggested that male patients have higher risk of poor immune reconstitution and mortality than females, which may be traced back to differences in life customs, metabolism and adherence between males and females[ 36 , 37 ]. Kroeze et al.[ 38 ] corroborated the above literature data. In addition, some opportunistic infections (OIs) can also be considered predictors of immune reconstitution. Herpes zoster (HZ) is caused by a variety of diseases that affect immunity, and its incidence increases with decreasing immune levels[ 39 , 40 ]. Therefore, we speculate that HZ represents a manifestation of immune suppression, to some extent. It has been shown that patients with high pre-treatment body mass index (BMI) have a substantial gain in CD4 + T lymphocyte recovery independently[ 41 , 42 ]. This may be because BMI contributes to some extent to drug metabolism, thus affecting the efficacy of cART. TBIL is mostly produced by destroyed red blood cells, somewhat reflecting the liver function of an individual[ 43 ]. In the present study, a negative correlation was found between TBIL and immune recovery in PLWH. Therefore, we hypothesized that abnormal liver function affects the metabolism and absorption of ART drugs, which may decrease treatment efficacy and affect disease progression. However, further investigation is warranted to test the above hypothesis. Previous findings[ 44 ] indicate that the timing of ART initiation also affects long-term immune recovery, regardless of the selected ART regimen. Since 2016, WHO recommends that once diagnosed, all HIV-infected patients should start ART, regardless of CD4 count[ 45 ]. Engsig et al. suggested that prolonged immunological suppression is a risk factor for incomplete CD4 + cell recovery in patients with otherwise successful HAART[ 29 ]. However, we did not observe the same outcome in the current cohort, which might be because the participants examined were recently diagnosed cases. Jain et al. proposed that immune restoration may enhance the rate of HBsAg clearance in HIV patients [46] . This means co-infection with other viruses such as Hepatitis B virus (HBV) is another strong risk factor for suboptimal immune recovery, although the underlying mechanism is not fully elucidated, and this notion was not confirmed by our current data. The present study had several advantages. First, the above model was based on a retrospective cohort with a large sample size, as the first predictive model assessing the risk of becoming INR in an early stage, which showed good performance in an independent validation dataset, and rigorously adhered to known guidelines (TRIPOD) for model construction and validation. Secondly, this model performed well in the validation set, which suggests its potential generalizability. Thirdly, this model can more accurately help clinicians make decisions, with a high AUC. Fourthly, we developed a user-friendly online calculator that only requires the input of a few parameters, and all data conversions and computations are built right into the system, in order to decrease the difficulties imposed by model complexity in clinical application. We developed and validated a model consisting of 5 clinical and laboratory variables for accurate prediction of the risk of poor immune reconstitution at the time of primary diagnosis. This model can help predict disease progression and regression, providing efficient and precise treatments to improve the life expectancy and quality of life of patients. This study also had several limitations. First, CD8 + T cell count was not included as a candidate predictor in our model due to its high percentage of missing values. This was largely caused by the inherent drawback of retrospective data collection. Next, there was a bias in the predicted accuracy. Even though the majority of indicators in the developed model may be assessed objectively, the route of HIV acquisition is reported by the patients themselves, which could be biased. Finally, the model's generalizability should be further confirmed because it is based on cohorts from a single hospital in an upper-middle-income nation. Therefore, this model should be validated in external cohorts in other contexts as well as by independent research teams. This available and novel scoring system for identifying patients at high risk of becoming INR should be further validated in multicenter prospective studies to determine its significance and for better implementation. In conclusion, we recommend the widespread application of the novel scoring model to identify patients at high risk of becoming INR quickly and effectively, as this system is based on five readily accessible clinical parameters and shows an excellent diagnostic performance and favorable calibration in detecting the possibility of becoming INR. Declarations Acknowledgements Not applicable. Author contributions LR and XW conceived and designed the experiments; WZ and JY wrote the artcle; WZ, JY, HL collected and analyzed the data. All authors approved the final version. Funding There is no funding to report. Data availability The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality reasons. Data can be made available upon reasonable request from Lianguo Ruan ( [email protected] ). Ethics approval and consent to participate The Declaration of Helsinki was followed in the conduct of this study. The Huazhong University of Science and Technology's Tongji Medical College's ethics committee at Wuhan Jinyintan Hospital gave its approval to the study protocol (KY-2022-13). All participants provided informed consent to take part at the beginning of the process as part of the online survey. Consent for publication Not Applicable. Competing interests The authors declare that they have no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Autran B, Carcelain G, Li TS, Blanc C, Mathez D, Tubiana R, Katlama C, Debré P, Leibowitch J: Positive effects of combined antiretroviral therapy on CD4+ T cell homeostasis and function in advanced HIV disease . Science 1997, 277 (5322):112-116. 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Xiao Q, Yu F, Yan L, Zhao H, Zhang F: Alterations in circulating markers in HIV/AIDS patients with poor immune reconstitution: Novel insights from microbial translocation and innate immunity . Frontiers In Immunology 2022, 13 :1026070. Engsig FN, Gerstoft J, Kronborg G, Larsen CS, Pedersen G, Røge B, Jensen J, Nielsen LN, Obel N: Long-term mortality in HIV patients virally suppressed for more than three years with incomplete CD4 recovery: a cohort study . BMC infectious diseases 2010, 10 :318. Carvalho-Silva WHV, Andrade-Santos JL, Guedes MCDS, Crovella S, Guimarães RL: CCR5 genotype and pre-treatment CD4+ T-cell count influence immunological recovery of HIV-positive patients during antiretroviral therapy . Gene 2020, 741 :144568. Li K, Chen H, Li J, Feng Y, Lan G, Liang S, Liu M, Rashid A, Xing H, Shen Z et al : Immune reconstruction effectiveness of combination antiretroviral therapy for HIV-1 CRF01_AE cluster 1 and 2 infected individuals . Emerg Microbes Infect 2022, 11 (1):158-167. Jiang TY, Hou JH, Su B, Zhang T, Yang Y, Liu ZY, Wang W, Guo CP, Dai LL, Sun LJ et al : Demographic and clinical factors associated with immune reconstitution in HIV/HBV co-infected and HIV mono-infected patients: a retrospective cohort study . HIV Med 2020, 21 (11):722-728. Engsig FN, Gerstoft J, Kronborg G, Larsen CS, Pedersen G, Røge B, Jensen J, Nielsen LN, Obel N: Long-term mortality in HIV patients virally suppressed for more than three years with incomplete CD4 recovery: a cohort study . BMC infectious diseases 2010, 10 :318. Chen J, Titanji K, Sheth AN, Gandhi R, McMahon D, Ofotokun I, Weitzmann MN, De Paris K, Dumond JB: The effect of age on CD4+ T-cell recovery in HIV-suppressed adult participants: a sub-study from AIDS Clinical Trial Group (ACTG) A5321 and the Bone Loss and Immune Reconstitution (BLIR) study . Immun Ageing 2022, 19 (1):4. Burgos J, Moreno-Fornés S, Reyes-Urueña J, Bruguera A, Martín-Iguacel R, Raventos B, Llibre JM, Imaz A, Peraire J, Orti A-J et al : Mortality and immunovirological outcomes in patients with advanced HIV disease on their first antiretroviral treatment: differential impact of antiretroviral regimens . The Journal of Antimicrobial Chemotherapy 2022. Bastard M, Soulinphumy K, Phimmasone P, Saadani AH, Ciaffi L, Communier A, Phimphachanh C, Ecochard R, Etard J-F: Women experience a better long-term immune recovery and a better survival on HAART in Lao People's Democratic Republic . BMC infectious diseases 2013, 13 :27. Tiendrebeogo T, Messou E, Arikawa S, Ekouevi DK, Tanon A, Kwaghe V, Balestre E, Zannou MD, Poda A, Dabis F et al : Ten-year attrition and antiretroviral therapy response among HIV-positive adults: a sex-based cohort analysis from eight West African countries . J Int AIDS Soc 2021, 24 (5):e25723. Kroeze S, Ondoa P, Kityo CM, Siwale M, Akanmu S, Wellington M, de Jager M, Ive P, Mandaliya K, Stevens W et al : Suboptimal immune recovery during antiretroviral therapy with sustained HIV suppression in sub-Saharan Africa . AIDS (London, England) 2018, 32 (8):1043-1051. Habel LA, Ray GT, Silverberg MJ, Horberg MA, Yawn BP, Castillo AL, Quesenberry CP, Li Y, Sadier P, Tran TN: The epidemiology of herpes zoster in patients with newly diagnosed cancer . Cancer Epidemiol Biomarkers Prev 2013, 22 (1):82-90. Da Silva AMPDS, De Moraes-Pinto MI, Succi RCM, Terreri MT, Machado DM: Clinical and Laboratory Characteristics of Herpes Zoster in Patients With HIV/AIDS and Those With Juvenile Systemic Lupus Erythematosus . Pediatr Infect Dis J 2020, 39 (7):624-627. Han WM, Jiamsakul A, Jantarapakde J, Yunihastuti E, Choi JY, Ditangco R, Chaiwarith R, Sun LP, Khusuwan S, Merati TP et al : Association of body mass index with immune recovery, virological failure and cardiovascular disease risk among people living with HIV . HIV Med 2021, 22 (4):294-306. Li X, Ding H, Geng W, Liu J, Jiang Y, Xu J, Zhang Z, Shang H: Predictive effects of body mass index on immune reconstitution among HIV-infected HAART users in China . BMC infectious diseases 2019, 19 (1):373. Vítek L: Bilirubin as a signaling molecule . Med Res Rev 2020, 40 (4):1335-1351. Ambrosioni J, Farrera J, de Lazzari E, Nicolás D, Manzardo C, Hernández-Meneses MM, Mosquera MM, Ligero C, Marcos MA, Sánchez-Palomino S et al : Immunological and virological efficacy of different antiretroviral regimens initiated during acute/recent HIV infection . AIDS (London, England) 2020, 34 (15):2269-2274. World Health O: Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV . Geneva: World Health Organization; 2015. Jain MK, Vigil KJ, Parisot P, Go G, Vu T, Li X, Hansen L, Taylor BS: Incidence and Predictors of Hepatitis B Surface Antigen Clearance in HIV Patients: A Retrospective Multisite Study . Open Forum Infect Dis 2021, 8 (7):ofab116. Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.docx Cite Share Download PDF Status: Published Journal Publication published 16 Sep, 2023 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Major revision 18 Jul, 2023 Reviews received at journal 05 Jul, 2023 Reviewers agreed at journal 25 Jun, 2023 Reviewers invited by journal 23 Jun, 2023 Editor assigned by journal 23 Jun, 2023 Editor invited by journal 18 Apr, 2023 Submission checks completed at journal 18 Apr, 2023 First submitted to journal 07 Apr, 2023 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. We do this by developing innovative software and high quality services for the global research community. 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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-2790359","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":192822828,"identity":"3e830f08-9119-43a4-a70e-e58f209b1a7f","order_by":0,"name":"Wenyuan Zhang","email":"","orcid":"","institution":"Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology; Hubei Clinical Research Center for Infectious Diseases; Wuhan Research Center for Communicable Diseas","correspondingAuthor":false,"prefix":"","firstName":"Wenyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":192822829,"identity":"cd99fc21-1820-407d-8b64-83c954f054f8","order_by":1,"name":"Jisong Yan","email":"","orcid":"","institution":"Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology; Hubei Clinical Research Center for Infectious Diseases; Wuhan Research Center for Communicable Diseas","correspondingAuthor":false,"prefix":"","firstName":"Jisong","middleName":"","lastName":"Yan","suffix":""},{"id":192822830,"identity":"e46a730f-c162-416f-9dd5-335d29c1a23a","order_by":2,"name":"Hong Luo","email":"","orcid":"","institution":"Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology; Hubei Clinical Research Center for Infectious Diseases; Wuhan Research Center for Communicable Diseas","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Luo","suffix":""},{"id":192822831,"identity":"3980655d-a895-4fd8-bd65-d7717b984800","order_by":3,"name":"Xianguang Wang","email":"","orcid":"","institution":"Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology; Hubei Clinical Research Center for Infectious Diseases; Wuhan Research Center for Communicable Diseas","correspondingAuthor":false,"prefix":"","firstName":"Xianguang","middleName":"","lastName":"Wang","suffix":""},{"id":192822832,"identity":"3a748b3c-c3e2-41ef-9579-02c32d13665c","order_by":4,"name":"Lianguo Ruan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3RMUvEMBTA8RcCuSVH1hcK/QyBQkEQ7qu0CJ1ucHI+EHKLt9/id9BFHSuBTDnnbhaETh1uEg4ymLtzcGnrKJj/EGjJj6YvALHYH0xQWtcIyBkY+/2uGCdyrcsW4TIVxFbhWU0T5VwWtlWZvHX57wg0RY4X2pQP1n1+cO9BzJYKDi/DgmyLCuWRuN1zNtcK5F2vyMYNE4qFPZNm95TMVwpUs1SU6GHCsNRn8t53CfcKFlOEc0OV1OH3V44lnIWv4ATBmSYtvoUhg83lvc44uu76dTNCFkbsDd6crrLD3qepWF89tocRchqBZD9OelzqcRAGvfdTW2KxWOxf9wUfulOnDyljuwAAAABJRU5ErkJggg==","orcid":"","institution":"Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology; Hubei Clinical Research Center for Infectious Diseases; Wuhan Research Center for Communicable Diseas","correspondingAuthor":true,"prefix":"","firstName":"Lianguo","middleName":"","lastName":"Ruan","suffix":""}],"badges":[],"createdAt":"2023-04-07 14:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2790359/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2790359/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-023-16738-w","type":"published","date":"2023-09-16T15:02:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":36063599,"identity":"f72dfbaf-02b3-4e12-a6bc-5ffa53726255","added_by":"auto","created_at":"2023-04-20 13:25:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1474317,"visible":true,"origin":"","legend":"\u003cp\u003eProportion and distribution pattern of missing values in training set.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: BMI\u003c/strong\u003e, body mass index; \u003cstrong\u003eCD4\u003c/strong\u003e: CD4\u003csup\u003e+\u003c/sup\u003e T lymphocyte; \u003cstrong\u003eVL\u003c/strong\u003e: viral load; \u003cstrong\u003eWBC\u003c/strong\u003e: white blood cell; \u003cstrong\u003ePLT\u003c/strong\u003e: platelet; \u003cstrong\u003eHb\u003c/strong\u003e: hemoglobin; \u003cstrong\u003eScr\u003c/strong\u003e: serum creatinine; \u003cstrong\u003eTG\u003c/strong\u003e: triglyceride;\u003cstrong\u003e TC\u003c/strong\u003e: total cholesterol; \u003cstrong\u003eFBG\u003c/strong\u003e: fasting blood-glucose; \u003cstrong\u003eALT\u003c/strong\u003e: alanine aminotransferase; \u003cstrong\u003eAST\u003c/strong\u003e: aspartate aminotransferase; \u003cstrong\u003eTBIL\u003c/strong\u003e: total bilirubin.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2790359/v1/8ddf249dee3f1feb37d87b25.jpg"},{"id":36064864,"identity":"7684d111-c5c9-4b6a-ad93-a346d264b173","added_by":"auto","created_at":"2023-04-20 13:33:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":589874,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram of predictors for predicting incomplete immune reconstitution of PLHIV. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: \u003cstrong\u003eBMI\u003c/strong\u003e, body mass index; \u003cstrong\u003eCD4\u003c/strong\u003e: CD4\u003csup\u003e+\u003c/sup\u003e T lymphocyte; \u003cstrong\u003eHZ\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eHerpes zoster;\u003cstrong\u003e TBIL\u003c/strong\u003e: total bilirubin.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2790359/v1/b33ed6e1fd061f4902b3dd05.jpg"},{"id":36064863,"identity":"b682420f-1726-4354-acfb-0fc7b62a8d40","added_by":"auto","created_at":"2023-04-20 13:33:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":786324,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of age, baseline CD4, BMI, HZ, TBIL and nomogram in the training set (a)\u0026nbsp;and the validation set (b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: \u003cstrong\u003eBMI\u003c/strong\u003e, body mass index; \u003cstrong\u003eCD4\u003c/strong\u003e: CD4\u003csup\u003e+\u003c/sup\u003e T lymphocyte; \u003cstrong\u003eHZ\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eHerpes zoster;\u003cstrong\u003e TBIL\u003c/strong\u003e: total bilirubin.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2790359/v1/923082ac177dedb19ee04473.jpg"},{"id":36063600,"identity":"9da78daa-e6f6-4c07-857b-a100963b0588","added_by":"auto","created_at":"2023-04-20 13:25:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":441322,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for predicting incomplete immune reconstitution of PLHIV in the training set(a) and the validation set (b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: \u003cstrong\u003eBMI\u003c/strong\u003e, body mass index; \u003cstrong\u003eCD4\u003c/strong\u003e: CD4\u003csup\u003e+\u003c/sup\u003e T lymphocyte; \u003cstrong\u003eHZ\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eHerpes zoster;\u003cstrong\u003e TBIL\u003c/strong\u003e: total bilirubin.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2790359/v1/d59f190d5ceb720c560b8f5d.jpg"},{"id":36063601,"identity":"354051ee-d9f9-4fd8-adcb-2935c5c90dbe","added_by":"auto","created_at":"2023-04-20 13:25:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":607088,"visible":true,"origin":"","legend":"\u003cp\u003eThe\u003cstrong\u003e \u003c/strong\u003eDCA curves of age, baseline CD4, BMI, HZ, TBIL and nomogram in the training set(a) and the validation set (b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: \u003cstrong\u003eBMI\u003c/strong\u003e, body mass index; \u003cstrong\u003eCD4\u003c/strong\u003e: CD4\u003csup\u003e+\u003c/sup\u003e T lymphocyte; \u003cstrong\u003eHZ\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eHerpes zoster;\u003cstrong\u003e TBIL\u003c/strong\u003e: total bilirubin.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2790359/v1/26946699988859aa78268c75.jpg"},{"id":43301591,"identity":"67fe0ed6-42fb-4304-8895-b39c36bbe938","added_by":"auto","created_at":"2023-09-18 15:11:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1889341,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2790359/v1/068ff07e-5753-4496-ac24-b6c2ecc98945.pdf"},{"id":36063602,"identity":"b060d08b-01b2-476e-8588-c283c06f0a1a","added_by":"auto","created_at":"2023-04-20 13:25:08","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":28758,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-2790359/v1/74435d8410a4c57eff1b0cbc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Incomplete immune reconstitution and its predictors in people living with HIV in Wuhan, China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe use of antiretroviral therapy (ART) suppresses viral replication and increases CD4\u003csup\u003e+\u003c/sup\u003e T cell counts[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], improving the prognosis of the majority of people living with HIV (PLWH) and dramatically decreasing both morbidity and mortality in acquired immunodeficiency syndrome (AIDS)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, up to 10\u0026ndash;40% of patients may fail to achieve a sufficient immunologic response, as assessed by CD4\u003csup\u003e+\u003c/sup\u003e T cell count, despite HIV virologic suppression, and are referred to as \u0026ldquo;immunologic non responders\u0026rdquo; (INRs)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Compared with PLWH achieving good immune reconstitution, these patients show a greater risk of AIDS-defining diseases and non-AIDS-defining events (nADE), which is associated with high mortality[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIncomplete immune reconstitution is pathophysiologically thought to be associated with decreased bone marrow hematopoiesis, thymic dysfunction, residual viral replication, altered gut microbiota, and coinfections, particularly persistent inflammation and abnormal immune activation, significantly decreasing CD4 production and persistent CD4 destruction[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Several therapeutics, e.g., immunosuppressive agents[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and cytokines[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], have been used to limit and restore chronic insufficient immune reconstitution for a long time, however, with marginal success.\u003c/p\u003e \u003cp\u003eTo date, no effective treatment could recover CD4\u003csup\u003e+\u003c/sup\u003e T cells, especially in INRs. At present, it is particularly important to assess patient condition earlier, especially at the initial examination, and to adopt a timely and individualized treatment plan. It is commonly admitted that several factors can predict immunological function recovery and disease progression, e.g., CD4\u003csup\u003e+\u003c/sup\u003e T cell count, CD4/CD8 ratio, viral load (VL) and IFN-γ[\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, it is essential to identify additional markers for improved assessment. Scherpenisse et al.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] found a potential predictive marker of immunological failure, the cell-associated HIV-1 unspliced-to-multiply-spliced (US/MS) RNA ratio, which was positively correlated with markers of CD4\u003csup\u003e+\u003c/sup\u003e T cell activation and apoptosis during ART treatment; the higher the US/MS RNA ratio the higher the frequency of HIV-infected cells, leading to sustained immune activation and apoptosis, resulting in decreased immune response to ART.\u003c/p\u003e \u003cp\u003eIn clinic, a single index is often inadequate to independently predict disease progression with satisfactory results. However, the combination of several single indexes may greatly improve the predictive effect. Medical nomograms based on various markers have been increasingly used in oncology and other areas of medicine in recent years. In addition, multiple prognostic models for PLWH have been established[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, scoring models for predicting the risk of incomplete immune reconstitution in China have not been reported. Since several risk factors have been identified for INRs, a specific model is needed to predict poor immune reconstitution in advance. Thus, this study aimed to select potential indicators to construct a predictive model based on multivariate logistic regression analysis, providing improved prevention and individualized treatment in PLWH who are at high risk of poor immune reconstitution at the time of primary treatment.\u003c/p\u003e \u003cp\u003eThen, a unique scoring system was created using the primary predictive model's modified nomogram for easy clinical application. Additionally, in a retrospective analysis, we internally verified the diagnostic capabilities of the improved scoring model.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was a retrospective study of data collected from the Wuhan Center for Disease Prevention and Control (CDC)\u0026rsquo;s Information System. Patients with HIV/AIDS treated at Wuhan Jinyintan Hospital from December 2006 to October 2022 were included. Inclusion criteria were: (1) complete laboratory test confirming HIV infection; (2) treatment with a combination ART regimen containing at least three drugs; (3) ART duration\u0026thinsp;\u0026ge;\u0026thinsp;24 months; (4) at least one visit during this period; (5) age\u0026thinsp;\u0026gt;\u0026thinsp;15 years. Exclusion criteria were: (1) previous exposure to ART; (2) VL\u0026thinsp;\u0026gt;\u0026thinsp;400 copies/mL, indicating virologic treatment failure. At the end of follow-up, 3783 participants meeting the above criteria were included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eDemographic characteristics, clinical data and laboratory indexes were collected, including age at the time of diagnosis, sex, body mass index (BMI) calculated as weight/height\u003csup\u003e2\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e), infection route, marital status, interval from diagnosis to ART, WHO clinical stage of the HIV disease, opportunistic infection (OI), coinfection with other bacteria or virus, several clinical symptoms, tumors, ART regimens, CD4\u003csup\u003e+\u003c/sup\u003e T cells, VL, white blood cells (WBCs), platelets (PLTs), hemoglobin (HB), alanine aminotransferase (ALT), aspartate transaminase (AST), total bilirubin (TBIL), serum creatinine (Scr), triglycerides (TG), serum total cholesterol (TC) and blood glucose (BG). These parameters were obtained by trained professionals every 3 months.\u003c/p\u003e \u003cp\u003e This study was approved by the ethics review board of Wuhan Jinyintan Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData processing\u003c/h2\u003e \u003cp\u003eThere is no straightforward way to determine the right sample size for a multifactor regression model. A predictive component requires at least 10 effective outcomes, according to previous reports, based on a cautious estimate. Given there were 583 instances with successful results, less than 58 predictors are needed.\u003c/p\u003e \u003cp\u003eMultiple imputations were used to acquire suitable values for missing data before data analysis since directly discarding data with missing values might cause selection bias or decrease the power of a test. The outcomes were depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Thus, a sensitivity analysis was performed to determine how the missing data filled in the gaps (sTable 1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the predictive model\u003c/h2\u003e \u003cp\u003eIn this study, we aimed to set up a predictive model for forecasting the risk of becoming an INR. To evaluate the repeatability and extrapolation of this model, we randomly split participants in a ratio of 7:3 to establish one training and one test sets. Variables in these two datasets were described as number (percentage) or median (interquartile range, IQR), as appropriate. Continuous variables among groups were compared by the Mann-Whitney U test. Meanwhile, categorical variables were compared by the chi-square test, the fisher\u0026rsquo;s exact test or Wilcoxon rank sum test.\u003c/p\u003e \u003cp\u003eUnivariate logistic regression analysis (ULRA) was carried out to select factors in the training set. Then, 34 potential variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were retained for further analysis. After multivariate logistic regression, 15 candidate predictors were retained. Variables were further selected considering statistically significant parameters and medically important parameters such as availability at first assessment and objectivity of the metric. Finally, the five above variables, extracted by experienced physicians, were included in the predictive model with the highest predictive performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePresentation of the nomogram\u003c/h2\u003e \u003cp\u003eBased on the five most significant variables, a nomogram model with an appropriate predictive ability was developed. The discrimination and calibration of the predictive model was evaluated to test the effectiveness of the model. In both the training and validation sets, receiver operating characteristic (ROC) curve analysis was utilized to quantify the discriminative value of the model, and a calibration curve was used to evaluate the calibration. Finally, decision curve analysis (DCA) was used to evaluate the predictive ability of the model in two independent data sets.\u003c/p\u003e \u003cp\u003eData analysis used SPSS version 26.0 (IBM Inc., Chicago, IL, USA) and R-Studio for windows (version 4.2.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cran.r-project.org\u003c/span\u003e\u003cspan address=\"http://cran.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAccording to the above inclusion and exclusion criteria, 3783 participants were confirmed and followed up in Wuhan Jinyintan Hospital from 2003 to 2020. We divided them into two groups, 2678 in the training set and 1150 in the validation set, and the characteristics of the both sets were similar in all variables (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eValidation cohort\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDerivation cohort\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge at HIV diagnosis (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (25,48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32(25,49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.698\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge at ART initiation (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (25,48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (25,49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.685\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1005(91.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2429(90.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.810\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100(9.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e249(9.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarital status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e312(28.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e786(29.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.492\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnmarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e793(71.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1892(70.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRoute of HIV exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMSM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e764(69.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1785(66.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.138\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeterosexual transmission\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e334(30.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e851(31.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.350\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInjection drug use\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(0.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18(0.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.205\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlood transfusion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(0.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.633\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21(0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.216\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoinfection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHBsAg+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e105(9.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234(8.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.454\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnti HCV+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27(2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54(2.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHerpes Zoster\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45(4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108(4.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.955\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27(2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72(2.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.668\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePulmonary infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62(5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138(5.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.567\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003etumor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3(0.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12(0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.616\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSymptoms\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFever\u0026thinsp;\u0026gt;\u0026thinsp;1month\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77(7.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e211(7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.337\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiarrhea\u0026thinsp;\u0026gt;\u0026thinsp;1month\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56(5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e118(4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.377\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107(9.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e254(9.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.850\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiarrhea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76(6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163(6.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.363\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145(13.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e349(13.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.940\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNight sweats\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81(7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e214(8.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.491\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRash\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59(5.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150(5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.749\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLymphnode swelling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84(7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e216(8.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.631\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e222(20.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e574(21.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.665\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e559(50.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1306(48.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e245(22.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e616(23.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79(7.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e182(6.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eART initiation regimen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAZT\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;NVP/EFV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e375(33.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e986(36.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD4T\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;NVP/EFV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10(0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.934\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTDF\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;NVP/EFV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e674(61.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1570(58.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.177\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTDF\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;LPV/r\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10(0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18(0.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.447\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36(3.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79(2.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.616\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eART initiation year,n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2008\u0026ndash;2011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58(5.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184(6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2012\u0026ndash;2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e408(36.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e923(34.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2016\u0026ndash;2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e639(57.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1571(58.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eART delay\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4(0.9,2.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4(0.9,2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.106\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI, kg/m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.3(19.6,23.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.5(19.6,23.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.683\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLaboratory indicators\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD4 cell count, cells/\u003cem\u003e\u0026micro;\u003c/em\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e272.00(150.00,393.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e264.00(149.00,383.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.308\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHIV viral load, copies/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38102.00(10573.50, 110000.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39274.00(9044.25, 123559.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.918\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHIV viral load (log)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.60(4.00, 5.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.60(4.00,5.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.917\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC, 109/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.89(4.00,5.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.87(3.94,5.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.948\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLT, 109/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e191.00(155.00,230.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e190.00(156.00,229.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.987\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHb, g/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143.00(129.00,152.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143.00(129.00,152.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.913\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eScr, \u003cem\u003e\u0026micro;\u003c/em\u003emol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.90(64.05,81.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.90(64.00,82.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.480\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.32(0.92,1.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.28(0.90,1.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.262\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTC, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.87(3.35,4.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.85(3.34,4.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.709\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBG, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.40(5.00,6.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.40(4.97,6.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.897\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAST, U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.00(20.00,31.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.00(20.00,31.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.657\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALT, U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.00(15.00,32.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.00(15.00,33.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.737\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTBIL, \u003cem\u003e\u0026micro;\u003c/em\u003emol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.40(8.61,15.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.10(8.50,14.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: \u003cstrong\u003eMSM\u003c/strong\u003e, men who have sex with men; \u003cstrong\u003ePCP\u003c/strong\u003e, pneumocystis carinii pneumonia; \u003cstrong\u003eBMI\u003c/strong\u003e, body mass index; \u003cstrong\u003eAZT\u003c/strong\u003e, zidovudine; \u003cstrong\u003e3TC\u003c/strong\u003e, lamivudine; \u003cstrong\u003eNVP\u003c/strong\u003e, nevirapine; \u003cstrong\u003eEFV\u003c/strong\u003e, efavirenz; \u003cstrong\u003eD4T\u003c/strong\u003e, stavudine; \u003cstrong\u003eTDF\u003c/strong\u003e, tenofovir disoproxil; \u003cstrong\u003eLPV/r\u003c/strong\u003e, lopinavir/ritonavir; \u003cstrong\u003eWBC\u003c/strong\u003e: white blood cell; \u003cstrong\u003ePLT\u003c/strong\u003e: platelet; \u003cstrong\u003eHb\u003c/strong\u003e: hemoglobin; \u003cstrong\u003eScr\u003c/strong\u003e: serum creatinine; \u003cstrong\u003eTG\u003c/strong\u003e: triglyceride; \u003cstrong\u003eTC\u003c/strong\u003e: total cholesterol; \u003cstrong\u003eBG\u003c/strong\u003e: blood glucose; \u003cstrong\u003eALT\u003c/strong\u003e: alanine aminotransferase; \u003cstrong\u003eAST\u003c/strong\u003e: aspartate aminotransferase; \u003cstrong\u003eTBIL\u003c/strong\u003e: total bilirubin.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf all PLWH, 21.8% (826/3782) were INRs, including 21.8% (583/2678) in the training set and 22.0% (243/1105) in the validation set.\u003c/p\u003e\n\u003cp\u003eBased on the ULRA of the training set, 34 factors, including age at HIV diagnosis, age at the initiation of ART, marital status, MSM (men who have sex with men) status, OIs, skin lesions, fever, herpes zoster (HZ), pneumocystis pneumonia (PCP), cough, WHO clinical stage, BMI, baseline CD4, baseline VL, coinfection with HBsAg, WBC, PLT, HB and TBIL were significantly associated with the INR status (sTable 2). Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were selected by experienced physicians for further multivariate logistic regression analysis. Finally, five predictors (baseline CD4, age at the initiation of ART, BMI, HZ and TBIL) were selected as independent risk factors for the INR status (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Hence, utilizing these five predictors, we developed a nomogram model (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), tested the discriminative power and calibration of the predictive model, and extensively analyzed the individual and combined abilities of these five predictors by ROC analysis. In the training set (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea), the AUCs for age, BMI, CD4, HZ and TBIL were 0.902, 0.654, 0.611, 0.891, 0.532 and 0.598, respectively. These AUCs in the validation set were 0.926, 0.690, 0.664, 0.918, 0.552 and 0.632, respectively, as anticipated (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). The calibration curves for both sets showed no statistically significant variation from a perfect match between the predicted and actual values (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta; Coefficient\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard Error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR (95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge at ART initiation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.028(1.020\u0026ndash;1.037)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.083\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.921(0.883\u0026ndash;0.960)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline CD4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.986(0.985\u0026ndash;0.988)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.894\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.446(1.437\u0026ndash;4.161)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTBIL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.041(1.017\u0026ndash;1.066)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: \u003cstrong\u003eBMI\u003c/strong\u003e, body mass index; \u003cstrong\u003eCD4\u003c/strong\u003e: CD4\u003csup\u003e+\u003c/sup\u003e T lymphocyte; \u003cstrong\u003eHZ\u003c/strong\u003e: Herpes zoster; \u003cstrong\u003eTBIL\u003c/strong\u003e: total bilirubin.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe decision curve analysis also indicated that the nomogram was feasible to make valuable and profitable judgments. As depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea-b, clinical interventions using the developed nomograms yielded better clinical benefits within a threshold probability of 0.1 to 0.8, both in the training and validation sets. Furthermore, to facilitate the application of the predictive model in clinic, dynamic nomograms were constructed as online scoring systems, which are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://husteryjs.shinyapps.io/INRs_prediction/\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite virological response, INRs have significantly decreased peripheral CD4\u003csup\u003e+\u003c/sup\u003e T cell count and functionality after at least 1\u0026thinsp;~\u0026thinsp;2 years of ART[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Patients with poor immune status experience chronic immune activation, resulting in higher risks of opportunistic infections (OIs), malignancies and other nADE[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Among all participants, the number of INRs was 826, accounting for 21.8%, including 21.8% and 22.0% in the training and validation sets, respectively. These outcomes corroborated a previous study that found a percentage of INRs in PLWH of 15\u0026ndash;30%[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For early diagnosis and treatment, in this study, we developed and validated a feasible and simple visual nomogram as a new approach for predicting the development of immune recovery.\u003c/p\u003e \u003cp\u003eThe novel approach combines several prominent parameters to create a predictive model for improved diagnosis. This predictive model was constructed based on the derivation and validation cohorts, in which risk factors were selected though logistic regression and their risk scores were evaluated based on the stepwise regression model. A predictive model was developed in the derivation cohort, containing 5 variables: baseline CD4, age at the initiation of ART, BMI, HZ and TBIL. Then, the validation set was applied to assess the efficacy of the predictive model.\u003c/p\u003e \u003cp\u003eIn the training and validation sets, the developed nomogram demonstrated good calibration and had high diagnostic performance and clinical utility. We transformed the nomogram into an online calculator to help physicians in clinical practice.\u003c/p\u003e \u003cp\u003eThe five parameters utilized in this study were significantly associated with higher risks of failed acquisition of sufficient immune restoration in PLWH, and have important clinical implications.\u003c/p\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e T cell count and VL are commonly considered the important markers of treatment outcomes, with associations with long-term prognosis, as well as influencing indicators of immune reconstitution. Indeed, mounting evidence[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] shows low baseline CD4\u003csup\u003e+\u003c/sup\u003e T cell count negatively impacts long-term CD4\u003csup\u003e+\u003c/sup\u003e T cell recovery in both amount and functionality, affecting the progression of HIV infection. In addition, Jiang et al.\u003csup\u003e[32]\u003c/sup\u003e found that baseline HIV VL is significantly associated with CD4\u0026thinsp;+\u0026thinsp;T cell restoration among PLWH. However, no matched conclusion was obtained from our data.\u003c/p\u003e \u003cp\u003ePrevious findings[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] indicate older age may be a risk factor for incomplete CD4\u003csup\u003e+\u003c/sup\u003e T cell recovery in PLWH, suggesting age may exert a strong effect on long-term recovery of CD4\u003csup\u003e+\u003c/sup\u003e T cells. This was also found in the newly developed model, showing that median maximal CD4\u003csup\u003e+\u003c/sup\u003e T cell count is higher in patients aged 16-32.5 years than in patients aged 32.5 years after ART treatment.\u003c/p\u003e \u003cp\u003eFurthermore, this study suggested that male patients have higher risk of poor immune reconstitution and mortality than females, which may be traced back to differences in life customs, metabolism and adherence between males and females[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Kroeze et al.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] corroborated the above literature data.\u003c/p\u003e \u003cp\u003eIn addition, some opportunistic infections (OIs) can also be considered predictors of immune reconstitution. Herpes zoster (HZ) is caused by a variety of diseases that affect immunity, and its incidence increases with decreasing immune levels[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, we speculate that HZ represents a manifestation of immune suppression, to some extent.\u003c/p\u003e \u003cp\u003eIt has been shown that patients with high pre-treatment body mass index (BMI) have a substantial gain in CD4\u003csup\u003e+\u003c/sup\u003e T lymphocyte recovery independently[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This may be because BMI contributes to some extent to drug metabolism, thus affecting the efficacy of cART.\u003c/p\u003e \u003cp\u003eTBIL is mostly produced by destroyed red blood cells, somewhat reflecting the liver function of an individual[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In the present study, a negative correlation was found between TBIL and immune recovery in PLWH. Therefore, we hypothesized that abnormal liver function affects the metabolism and absorption of ART drugs, which may decrease treatment efficacy and affect disease progression. However, further investigation is warranted to test the above hypothesis.\u003c/p\u003e \u003cp\u003ePrevious findings[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] indicate that the timing of ART initiation also affects long-term immune recovery, regardless of the selected ART regimen. Since 2016, WHO recommends that once diagnosed, all HIV-infected patients should start ART, regardless of CD4 count[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Engsig et al. suggested that prolonged immunological suppression is a risk factor for incomplete CD4\u003csup\u003e+\u003c/sup\u003e cell recovery in patients with otherwise successful HAART[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, we did not observe the same outcome in the current cohort, which might be because the participants examined were recently diagnosed cases. Jain et al. proposed that immune restoration may enhance the rate of HBsAg clearance in HIV patients\u003csup\u003e[46]\u003c/sup\u003e. This means co-infection with other viruses such as Hepatitis B virus (HBV) is another strong risk factor for suboptimal immune recovery, although the underlying mechanism is not fully elucidated, and this notion was not confirmed by our current data.\u003c/p\u003e \u003cp\u003eThe present study had several advantages. First, the above model was based on a retrospective cohort with a large sample size, as the first predictive model assessing the risk of becoming INR in an early stage, which showed good performance in an independent validation dataset, and rigorously adhered to known guidelines (TRIPOD) for model construction and validation. Secondly, this model performed well in the validation set, which suggests its potential generalizability. Thirdly, this model can more accurately help clinicians make decisions, with a high AUC. Fourthly, we developed a user-friendly online calculator that only requires the input of a few parameters, and all data conversions and computations are built right into the system, in order to decrease the difficulties imposed by model complexity in clinical application.\u003c/p\u003e \u003cp\u003eWe developed and validated a model consisting of 5 clinical and laboratory variables for accurate prediction of the risk of poor immune reconstitution at the time of primary diagnosis. This model can help predict disease progression and regression, providing efficient and precise treatments to improve the life expectancy and quality of life of patients.\u003c/p\u003e \u003cp\u003eThis study also had several limitations. First, CD8\u003csup\u003e+\u003c/sup\u003e T cell count was not included as a candidate predictor in our model due to its high percentage of missing values. This was largely caused by the inherent drawback of retrospective data collection. Next, there was a bias in the predicted accuracy. Even though the majority of indicators in the developed model may be assessed objectively, the route of HIV acquisition is reported by the patients themselves, which could be biased. Finally, the model's generalizability should be further confirmed because it is based on cohorts from a single hospital in an upper-middle-income nation. Therefore, this model should be validated in external cohorts in other contexts as well as by independent research teams. This available and novel scoring system for identifying patients at high risk of becoming INR should be further validated in multicenter prospective studies to determine its significance and for better implementation.\u003c/p\u003e \u003cp\u003eIn conclusion, we recommend the widespread application of the novel scoring model to identify patients at high risk of becoming INR quickly and effectively, as this system is based on five readily accessible clinical parameters and shows an excellent diagnostic performance and favorable calibration in detecting the possibility of becoming INR.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLR and XW conceived and designed the experiments; WZ and JY wrote the artcle; WZ, JY, HL collected and analyzed the data. All authors approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to confidentiality reasons. Data can be made available upon reasonable request from Lianguo Ruan (\u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Declaration of Helsinki was followed in the conduct of this study. The Huazhong University of Science and Technology\u0026apos;s Tongji Medical College\u0026apos;s ethics committee at Wuhan Jinyintan Hospital gave its approval to the study protocol (KY-2022-13). All participants provided informed consent to take part at the beginning of the process as part of the online survey.\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFootnotes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAutran B, Carcelain G, Li TS, Blanc C, Mathez D, Tubiana R, Katlama C, Debr\u0026eacute; P, Leibowitch J: \u003cstrong\u003ePositive effects of combined antiretroviral therapy on CD4+ T cell homeostasis and function in advanced HIV disease\u003c/strong\u003e. \u003cem\u003eScience \u003c/em\u003e1997, \u003cstrong\u003e277\u003c/strong\u003e(5322):112-116.\u003c/li\u003e\n\u003cli\u003eSaag MS, Gandhi RT, Hoy JF, Landovitz RJ, Thompson MA, Sax PE, Smith DM, Benson CA, Buchbinder SP, Del Rio C\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAntiretroviral Drugs for Treatment and Prevention of HIV Infection in Adults: 2020 Recommendations of the International Antiviral Society-USA Panel\u003c/strong\u003e. \u003cem\u003eJAMA \u003c/em\u003e2020, \u003cstrong\u003e324\u003c/strong\u003e(16):1651-1669.\u003c/li\u003e\n\u003cli\u003eYounes S-A, Talla A, Pereira Ribeiro S, Saidakova EV, Korolevskaya LB, Shmagel KV, Shive CL, Freeman ML, Panigrahi S, Zweig S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCycling CD4+ T cells in HIV-infected immune nonresponders have mitochondrial dysfunction\u003c/strong\u003e. \u003cem\u003eThe Journal of Clinical Investigation \u003c/em\u003e2018, \u003cstrong\u003e128\u003c/strong\u003e(11):5083-5094.\u003c/li\u003e\n\u003cli\u003ePalella FJ, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, Aschman DJ, Holmberg SD: \u003cstrong\u003eDeclining morbidity and mortality among patients with advanced human immunodeficiency virus infection. 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Geneva: World Health Organization; 2015.\u003c/li\u003e\n\u003cli\u003eJain MK, Vigil KJ, Parisot P, Go G, Vu T, Li X, Hansen L, Taylor BS: \u003cstrong\u003eIncidence and Predictors of Hepatitis B Surface Antigen Clearance in HIV Patients: A Retrospective Multisite Study\u003c/strong\u003e. \u003cem\u003eOpen Forum Infect Dis \u003c/em\u003e2021, \u003cstrong\u003e8\u003c/strong\u003e(7):ofab116.\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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV/AIDS, immune reconstitution, nomogram, predictive model","lastPublishedDoi":"10.21203/rs.3.rs-2790359/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2790359/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study aimed to build and validate a nomogram model to predict the risk of incomplete immune reconstitution in people living with HIV (PLWH).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTotally 3783 individuals with a confirmed diagnosis of HIV/AIDS were included. A predictive model was developed based on a retrospective set (N\u0026thinsp;=\u0026thinsp;2678) and was validated using the remaining cases (N\u0026thinsp;=\u0026thinsp;1105). Univariable and multivariable logistic regression analyses were performed to determine valuable predictors among the collected clinical and laboratory variables. The predictive model was presented as a nomogram, and internally validated using another independent dataset. The predictive value of the model was evaluated by determining the area under the curve (AUC). Besides, calibration curve and decision curve (DCA) analyses were performed in both the training and test sets.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe final model comprised 5 predictors, including baseline CD4, age at ART initiation, BMI, HZ and TBIL. The AUC of the nomogram model was 0.902 in the training cohort, versus 0.926 in the validation cohort. The calibration accuracy and diagnostic performance were satisfactory in both the training and test sets.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis predictive model based on a retrospective study was internally validated using 5 readily available clinical indicators. It showed high performance in predicting the risk of incomplete immune reconstitution.\u003c/p\u003e","manuscriptTitle":"Incomplete immune reconstitution and its predictors in people living with HIV in Wuhan, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-20 13:25:03","doi":"10.21203/rs.3.rs-2790359/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-07-18T09:55:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-05T09:38:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a9959f8c-1c52-4483-a002-e50b6f6a46e6","date":"2023-06-26T01:49:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-06-23T16:56:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-23T16:41:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-04-18T07:03:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-04-18T06:59:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2023-04-07T14:34:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e1bab92-2b9a-4f3f-9437-dca9062c3dc0","owner":[],"postedDate":"April 20th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-18T15:10:03+00:00","versionOfRecord":{"articleIdentity":"rs-2790359","link":"https://doi.org/10.1186/s12889-023-16738-w","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2023-09-16 15:02:21","publishedOnDateReadable":"September 16th, 2023"},"versionCreatedAt":"2023-04-20 13:25:03","video":"","vorDoi":"10.1186/s12889-023-16738-w","vorDoiUrl":"https://doi.org/10.1186/s12889-023-16738-w","workflowStages":[]},"version":"v1","identity":"rs-2790359","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2790359","identity":"rs-2790359","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}