Predicting death and lost to follow-up among adults initiating antiretroviral therapy in resource-limited settings: Derivation and external validation of a risk score in Haiti

A Risk Prediction Model to Identify People Living with HIV Who are High-risk for Disengagement from Care after HIV Diagnosis in South Africa

The provision of ART in South Africa has transformed the HIV epidemic, resulting in an increase in life expectancy by over 10 years. Despite this, nearly 2 million people living with HIV are not on treatment. The objective of this study was to develop and externally validate a practical risk assessment tool to identify people with HIV (PWH) at highest risk for attrition from care after testing. A machine learning model incorporating clinical and psychosocial factors was developed in a primary cohort of 498 PWH. LASSO regression analysis was used to optimize variable selection. Multivariable logistic regression analysis was applied to build a model using 80% of the primary cohort as a training dataset and validated using the remaining 20% of the primary cohort and data from an independent cohort of 96 participants. The risk score was developed using the Sullivan and D'Agostino point based method. Of 498 participants with mean age 35.7 years, 192 (38%) did not initiate ART after diagnosis. Controlling for site, factors associated with non-engagement in care included being < 35 years, feeling abandoned by God, maladaptive coping strategies using alcohol or other drugs, no difficulty concentrating, and having high levels of confidence in one's ability to handle personal challenges. An effective risk score can enable clinicians and implementers to focus on tailoring care for those most in need of ongoing support. Further research should focus on potential strategies to enhance the generalizability and evaluate the implementation of the proposed risk prediction model in HIV treatment programs.

Prognostic prediction models for treatment experienced people living with HIV: a protocol for systematic review and meta-analysis

INTRODUCTION

Despite the favourable efficacy of antiretroviral therapy (ART), HIV/AIDS continues to impose significant disease burdens worldwide. This study aims to systematically review published prognostic prediction models for survival outcomes of treatment experienced people living with HIV (TE-PLHIV), to describe their characteristics, compare their performance and assess the risk of bias and real-world clinical utility.

METHODS AND ANALYSIS

Studies will be identified through a comprehensive search in PubMed, EMBASE, Scopus, the Cochrane Library, and OpenGrey databases. Two reviewers will independently conduct a selection of eligible studies, data extraction and critical appraisal. Included studies will be systematically summarised using appropriate tools designed for prognostic prediction modelling studies. Where applicable, evidence will be summarised with meta-analyses.

ETHICS AND DISSEMINATION

Ethical approval is not required because only available published data will be analysed. The results of this work will be published in a peer-reviewed journal.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number CRD42023412118.

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis

BACKGROUND

Early prediction of mortality in individuals with HIV (PWH) has perpetually posed a formidable challenge. With the widespread integration of machine learning into clinical practice, some researchers endeavor to formulate models predicting the mortality risk for PWH. Nevertheless, the diverse timeframes of mortality among PWH and the potential multitude of modeling variables have cast doubt on the efficacy of the current predictive model for HIV-related deaths. To address this, we undertook a systematic review and meta-analysis, aiming to comprehensively assess the utilization of machine learning in the early prediction of HIV-related deaths and furnish evidence-based support for the advancement of artificial intelligence in this domain.

METHODS

We systematically combed through the PubMed, Cochrane, Embase, and Web of Science databases on November 25, 2023. To evaluate the bias risk in the original studies included, we employed the Predictive Model Bias Risk Assessment Tool (PROBAST). During the meta-analysis, we conducted subgroup analysis based on survival and non-survival models. Additionally, we utilized meta-regression to explore the influence of death time on the predictive value of the model for HIV-related deaths.

RESULTS

After our comprehensive review, we analyzed a total of 24 pieces of literature, encompassing data from 401,389 individuals diagnosed with HIV. Within this dataset, 23 articles specifically delved into deaths during long-term follow-ups outside hospital settings. The machine learning models applied for predicting these deaths comprised survival models (COX regression) and other non-survival models. The outcomes of the meta-analysis unveiled that within the training set, the c-index for predicting deaths among people with HIV (PWH) using predictive models stands at 0.83 (95% CI: 0.75-0.91). In the validation set, the c-index is slightly lower at 0.81 (95% CI: 0.78-0.85). Notably, the meta-regression analysis demonstrated that neither follow-up time nor the occurrence of death events significantly impacted the performance of the machine learning models.

CONCLUSIONS

The study suggests that machine learning is a viable approach for developing non-time-based predictions regarding HIV deaths. Nevertheless, the limited inclusion of original studies necessitates additional multicenter studies for thorough validation.

Toward a Prognostic Model for Mortality Risk in Older People Living With HIV: A Prospective Cohort Study From Southwestern China

OBJECTIVE

The existing prognostic models for mortality risk in people living with HIV (PLWH) may not be applicable for older PLWH because the risk factors were confined to biomarkers and clinical variables. We developed and validated a nomogram for the prognosis of all-cause mortality in older PLWH based on comprehensive predictors.

DESIGN

Prospective cohort study.

SETTING AND PARTICIPANTS

We included 824 participants aged ≥50 years (mean age, 64.0 ± 7.6 years) from 30 study sites in Sichuan, China, and followed up from Nov 2018 to Mar 2021.

METHODS

Data on demographics, biomarkers, and clinical indicators were extracted from the registry; mental and social factors were assessed by a survey. Elastic net was used to select predictors. A nomogram was developed based on Cox proportional hazards regression model to visualize the relative effect size (points) of the selected predictors. The prognostic index (PI) was calculated by summing points of all predictors to quantify mortality risk.

RESULTS

Predictive performance of PI from the nomogram was good, with area under the curve of 0.76 for the training set, and 0.77 for the validation set. Change in CD4 count, virological failure in antiretroviral therapy, and living with comorbidities were robust predictors. Depressive symptoms were an important predictor in men, those aged ≥65 years, and those with time of diagnosis <1 year; low social capital was an additional predictor in people aged <65. Mortality risk increased approximately 10-fold among participants whose PI was in the fourth quartile compared with those in the first quartile (hazard ratio, 9.5; 95% CI, 2.9-31.5).

CONCLUSION AND IMPLICATIONS

Although biological and clinical factors are crucial predictors, mental and social predictors are essential for specific groups. The developed nomogram is useful for identifying risk factors and groups at risk of mortality in older PLWH.

Development and validation of a risk prediction model for lost to follow-up among adults on active antiretroviral therapy in Ethiopia: a retrospective follow-up study

Background

Over 420,000 people have initiated life-saving antiretroviral therapy (ART) in Ethiopia; however, lost-to-follow-up (LTFU) rates continues to be high. A clinical decision tool is needed to identify patients at higher risk for LTFU to provide individualized risk prediction to intervention. Therefore, this study aimed to develop and validate a statistical risk prediction tool that predicts the probability of LTFU among adult clients on ART.

Methods

A retrospective follow-up study was conducted among 432 clients on ART in Gondar Town, northwest, Ethiopia. Prognostic determinates included in the analysis were determined by multivariable logistic regression. The area under the receiver operating characteristic (AUROC) and calibration plot were used to assess the model discriminative ability and predictive accuracy, respectively. Individual risk prediction for LTFU was determined using both regression formula and score chart rule. Youden index value was used to determine the cut-point for risk classification. The clinical utility of the model was evaluated using decision curve analysis (DCA).

Results

The incidence of LTFU was 11.19 (95% CI 8.95–13.99) per 100-persons years of observation. Potential prognostic determinants for LTFU were rural residence, not using prophylaxis (either cotrimoxazole or Isoniazid or both), patient on appointment spacing model (ASM), poor drug adherence level, normal Body mass index (BMI), and high viral load (viral copies > 1000 copies/ml). The AUROC was 85.9% (95% CI 82.0–89.6) for the prediction model and the risk score was 81.0% (95% CI 76.7–85.3) which was a good discrimination probability. The maximum sensitivity and specificity of the probability of LTFU using the prediction model were 72.07% and 83.49%, respectively. The calibration plot of the model was good (p-value = 0.350). The DCA indicated that the model provides a higher net benefit following patients based on the risk prediction tool.

Conclusion

The incidence of LTFU among clients on ART in Gondar town was high (> 3%). The risk prediction model presents an accurate and easily applicable prognostic prediction tool for clients on ART. A prospective follow-up study and external validation of the model is warranted before using the model.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07691-x.

Factors Associated with Retention of HIV Patients on Antiretroviral Therapy in Care: Evidence from Outpatient Clinics in Two Provinces of the Democratic Republic of the Congo (DRC)

Interruptions in the continuum of care for HIV can inadvertently increase a patient’s risk of poor health outcomes such as uncontrolled viral load and a greater likelihood of developing drug resistance. Retention of people living with HIV (PLHIV) in care and determinants of attrition, such as adherence to treatment, are among the most critical links strengthening the continuum of care, reducing the risk of treatment failure, and assuring viral load suppression. Objective: To analyze the variation in, and factors associated with, retention of patients enrolled in HIV services at outpatient clinics in the provinces of Kinshasa and Haut-Katanga, Democratic Republic of the Congo (DRC). Methods: Data for the last visit of 51,286 patients enrolled in Centers for Disease Control (CDC)-supported outpatient HIV clinics in 18 health zones in Haut-Katanga and Kinshasa, DRC were extracted in June 2020. Chi-square tests and multivariable logistic regressions were performed. Results: The results showed a retention rate of 78.2%. Most patients were classified to be at WHO clinical stage 1 (42.1%), the asymptomatic stage, and only 3.2% were at stage 4, the severest stage of AIDS. Odds of retention were significantly higher for patients at WHO clinical stage 1 compared to stage 4 (adjusted odds ratio (AOR), 1.325; confidence interval (CI), 1.13−1.55), women as opposed to men (AOR, 2.00; CI, 1.63−2.44), and women who were not pregnant (vs. pregnant women) at the start of antiretroviral therapy (ART) (AOR, 2.80; CI, 2.04−3.85). Odds of retention were significantly lower for patients who received a one-month supply rather than multiple months (AOR, 0.22; CI, 0.20−0.23), and for patients in urban health zones (AOR, 0.75; CI, 0.59−0.94) rather than rural. Compared to patients 55 years of age or older, the odds of retention were significantly lower for patients younger than 15 (AOR, 0.35; CI, 0.30−0.42), and those aged 15 and <55 (AOR, 0.75; CI, 0.68−0.82). Conclusions: Significant variations exist in the retention of patients in HIV care by patient characteristics. There is evidence of strong associations of many patient characteristics with retention in care, including clinical, demographic, and other contextual variables that may be beneficial for improvements in HIV services in DRC.

Construction and validation of a prognostic nomogram for predicting the survival of HIV/AIDS adults who received antiretroviral therapy: a cohort between 2003 and 2019 in Nanjing

BACKGROUND

Great achievements have been achieved by free antiretroviral therapy (ART). A rapid and accurate prediction of survival in people living with HIV/AIDS (PLHIV) is needed for effective management. We aimed to establish an effective prognostic model to forecast the survival of PLHIV after ART.

METHODS

The participants were enrolled from a follow-up cohort over 2003-2019 in Nanjing AIDS Prevention and Control Information System. A nested case-control study was employed with HIV-related death, and a propensity-score matching (PSM) approach was applied in a ratio of 1:4 to allocate the patients. Univariable and multivariable Cox proportional hazards analyses were performed based on the training set to determine the risk factors. The discrimination was qualified using the area under the curve (AUC) and concordance index (C-Index). The nomogram was calibrated using the calibration curve. The clinical benefit of prognostic nomogram was assessed by decision curve analysis (DCA).

RESULTS

Predictive factors including CD4 cell count (CD4), body mass index (BMI) and hemoglobin (HB) were determined and incorporated into the nomogram. In the training set, AUC and C-index (95% CI) were 0.831 and 0.798 (0.758, 0.839), respectively. The validation set revealed a good discrimination with an AUC of 0.802 and a C-index (95% CI) of 0.786 (0.681, 0.892). The calibration curve also exhibited a high consistency in the predictive power (especially in the first 3 years after ART initiation) of the nomogram. Moreover, DCA demonstrated that the nomogram was clinically beneficial.

CONCLUSION

The nomogram is effective and accurate in forecasting the survival of PLHIV, and beneficial for medical workers in health administration.

Development and external validation of a prognostic model for survival of people living with HIV/AIDS initiating antiretroviral therapy

Background: Most existing prognostic models for people living with HIV/AIDS (PLWHA) were derived from cohorts in high-income settings established a decade ago and may not be applicable for contemporary patients, especially for patients in developing settings. The aim of this study was to develop and externally validate a prognostic model for survival in PLWHA initiating ART based on a large population-based cohort in China.

Methods: We obtained data for patients from the Chinese National Free Antiretroviral Treatment Program database. The derivation cohort consisted of PLWHA treated between February 2004 and December 2019 in a tertiary center in Guangzhou, South China, and validation cohort of patients treated between February 2004 to December 2018 in another tertiary hospital in Shenyang, Northeast China. We included ART-naive patients aged above 16 who initiated a combination ART regimen containing at least three drugs and had at least one follow-up record. We assessed 20 candidate predictors including patient characteristics, disease characteristics, and laboratory tests for an endpoint of death from all causes. The prognostic model was developed from a multivariable cox regression model with predictors selected using the least absolute shrinkage and selection operator (Lasso). To assess the model's predictive ability, we quantified the discriminative power using the concordance (C) statistic and calibration accuracy by comparing predicted survival probabilities with observed survival probabilities estimated with the Kaplan–Meier method.

Findings: The derivation cohort included 16481 patients with a median follow-up of 3·41 years, among whom 735 died. The external validation cohort comprised 5751 participants with a median follow-up of 2·71 years, of whom 185 died. The final model included 10 predictors: age, body mass index, route of HIV acquisition, coinfection with tuberculosis, coinfection with hepatitis C virus, haemoglobin, CD4 cell count, platelet count, aspartate transaminase, and plasma glucose. The C-statistic was 0·84 (95% confidence interval 0·82–0·85) in internal validation after adjustment of optimism and 0·84 (0·82–0·87) in external validation, which remained consistently above 0·75 in all landmark time points within five years of follow up when using time-updated laboratory measurements. The calibration accuracy was satisfactory in both derivation and validation cohorts.

Interpretation: We have developed and externally validated a model to predict long-term survival in PLWHA on ART. This model could be applied to individualized patient counseling and management during treatment, and future innovative trial design.

Funding: Natural Science Foundation of China Excellent Young Scientists Fund, Natural Science Foundation of China International/Regional Research Collaboration Project, Natural Science Foundation of China Young Scientist Fund, the National Science and Technology Major Project of China,National Special Research Program of China for Important Infectious Diseases, 13th Five-Year Key Special Project of Ministry of Science and Technology, and the Joint-innovation Program in Healthcare for Special Scientific Research Projects of Guangzhou.

A Prognostic Model to Assess Long-Term Survival of Patients on Antiretroviral Therapy: A 15-Year Retrospective Cohort Study in Southwestern China

BACKGROUND

Because there is no assessment tool for survival of people with human immunodeficiency virus (PWH) who received antiretroviral therapy (ART) in rural southwestern China, we aimed to formulate and validate a simple-to-use model to predict long-term overall survival at the initiation of ART.

METHODS

In total, 36 268 eligible participants registered in the Guangxi autonomous region between December 2003 and December 2018 were enrolled and randomized into development and validation cohorts. Predictive variables were determined based on Cox hazard models and specialists' advice. Discrimination, calibration, and clinical utility were measured, respectively.

RESULTS

The prognostic combined 14 variables: sex, age, marital status, infectious route, opportunistic infection, acquired immunodeficiency syndrome (AIDS)-related symptoms, body mass index, CD4+ T lymphocyte count, white blood cell, platelet, hemoglobin, serum creatinine, aspartate transaminase, and total bilirubin. Age, aspartate transaminase, and serum creatinine were assigned higher risk scores than that of CD4+ T lymphocytopenia count and having opportunistic infections or AIDS-related symptoms. At 3 time points (1, 3, and 5 years), the area under the curve ranged from 0.75 to 0.81 and the Brier scores ranged from 0.03 to 0.07. The decision curve analysis showed an acceptable clinical net benefit.

CONCLUSIONS

The prognostic model incorporating routine baseline data can provide a useful tool for early risk appraisal and treatment management in ART in rural southwestern China. Moreover, our study underscores the role of non-AIDS-defining events in long-term survival in ART.

Critical appraisal and external validation of a prognostic model for survival of people living with HIV/AIDS who underwent antiretroviral therapy

BACKGROUND

HIV/AIDS remains a leading cause of death worldwide. Recently, a model has been developed in Wenzhou, China, to predict the survival of people living with HIV/AIDS (PLWHA) who underwent antiretroviral therapy (ART). We aimed to evaluate the methodological quality and validate the model in an external population-based cohort.

METHODS

Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the Wenzhou model. Data were from the National Free Antiretroviral Treatment Program database. We included PLWHA treated between February 2004 and December 2019 in a tertiary hospital in Guangzhou city, China. The endpoint was all-cause deaths and assessed until January 2020. We assessed the discrimination performance of the model by Harrell's overall C-statistics and time-dependent C-statistics and calibration by comparing observed survival probabilities estimated with the Kaplan-Meier method versus predicted survival probabilities. To assess the potential prediction value of age and gender which were precluded in developing the Wenzhou model, we compared the discriminative ability of the original model with an extended model added with age and gender.

RESULTS

Based on PROBAST, the Wenzhou model was rated as high risk of bias in three out of the four domains (selection of participants, definition of outcome, and methods for statistical analysis) mainly because of the misuse of nested case-control design and propensity score matching. In the external validation analysis, 16758 patients were included, among whom 743 patients died (mortality rate 11.41 per 1000 person-years) during follow-up (median 3.41 years, interquartile range 1.64-5.62). The predictor of HIV viral load was missing in 14361 patients (85.7%). The discriminative ability of the Wenzhou model decreased in the external dataset, with the Harrell's overall C-statistics being 0.76, and time-dependent C-statistics dropping from 0.81 at 6 months to 0.48 at 10 years after ART initiation. The model consistently underestimated the survival, and the level was 6.23%, 10.02%, and 14.82% at 1, 2, and 3 years after ART initiation, respectively. The overall and time-dependent discriminative ability of the model improved after adding age and gender to the original model.

CONCLUSION

The Wenzhou prognostic model is at high risk of bias in model development, with inadequate model performance in external validation. Thereby, we could not confirm the validity and extended utility of the Wenzhou model. Future prediction model development and validation studies need to comply with the methodological standards and guidelines specifically developed for prediction models.

Risk scores for predicting early antiretroviral therapy mortality in sub-Saharan Africa to inform who needs intensification of care: a derivation and external validation cohort study

BACKGROUND

Clinical scores to determine early (6-month) antiretroviral therapy (ART) mortality risk have not been developed for sub-Saharan Africa (SSA), home to 70% of people living with HIV. In the absence of validated scores, WHO eligibility criteria (EC) for ART care intensification are CD4 < 200/μL or WHO stage III/IV.

METHODS

We used Botswana XPRES trial data for adult ART enrollees to develop CD4-independent and CD4-dependent multivariable prognostic models for 6-month mortality. Scores were derived by rescaling coefficients. Scores were developed using the first 50% of XPRES ART enrollees, and their accuracy validated internally and externally using South African TB Fast Track (TBFT) trial data. Predictive accuracy was compared between scores and WHO EC.

RESULTS

Among 5553 XPRES enrollees, 2838 were included in the derivation dataset; 68% were female and 83 (3%) died by 6 months. Among 1077 TBFT ART enrollees, 55% were female and 6% died by 6 months. Factors predictive of 6-month mortality in the derivation dataset at p < 0.01 and selected for the CD4-independent score included male gender (2 points), ≥ 1 WHO tuberculosis symptom (2 points), WHO stage III/IV (2 points), severe anemia (hemoglobin < 8 g/dL) (3 points), and temperature > 37.5 °C (2 points). The same variables plus CD4 < 200/μL (1 point) were included in the CD4-dependent score. Among XPRES enrollees, a CD4-independent score of ≥ 4 would provide 86% sensitivity and 66% specificity, whereas WHO EC would provide 83% sensitivity and 58% specificity. If WHO stage alone was used, sensitivity was 48% and specificity 89%. Among TBFT enrollees, the CD4-independent score of ≥ 4 would provide 95% sensitivity and 27% specificity, whereas WHO EC would provide 100% sensitivity but 0% specificity. Accuracy was similar between CD4-independent and CD4-dependent scores. Categorizing CD4-independent scores into low (< 4), moderate (4-6), and high risk (≥ 7) gave 6-month mortality of 1%, 4%, and 17% for XPRES and 1%, 5%, and 30% for TBFT enrollees.

CONCLUSIONS

Sensitivity of the CD4-independent score was nearly twice that of WHO stage in predicting 6-month mortality and could be used in settings lacking CD4 testing to inform ART care intensification. The CD4-dependent score improved specificity versus WHO EC. Both scores should be considered for scale-up in SSA.

Risk factors for loss to follow-up from antiretroviral therapy programmes in low-income and middle-income countries

INTRODUCTION

Loss to follow-up (LTFU) rates from antiretroviral treatment (ART) programmes in low- and middle-income countries (LMIC) are high, leading to poor treatment outcomes and onward transmission of HIV. Knowledge of risk factors is required to address LTFU. In this systematic review, risk factors for LTFU are identified and meta-analyses performed.

METHODS

PubMed, Embase, Psycinfo and Cochrane were searched for studies that report on potential risk factors for LTFU in adults who initiated ART in LMICs. Meta-analysis was performed for risk factors evaluated by at least five studies. Pooled effect estimates and their 95% confidence intervals (95% CI) were calculated using random effect models with inverse variance weights. Risk of bias was assessed and sensitivity analyses performed.

RESULTS

Eighty studies were included describing a total of 1 605 320 patients of which 87.4% from sub-Saharan Africa. The following determinants were significantly associated with an increased risk of LTFU in meta-analysis: male sex, older age, being single, unemployment, lower educational status, advanced WHO stage, low weight, worse functional status, poor adherence, nondisclosure, not receiving cotrimoxazole prophylactic therapy when indicated, receiving care at secondary level and more recent year of initiation. No association was seen for CD4 cell count, tuberculosis at baseline, regimen, and geographical setting.

CONCLUSION

There are several sociodemographic, clinical, patient behaviour, treatment-related and system level risk factors for LTFU from ART programs. Knowledge of risk factors should be used to better target retention interventions and develop tools to identify high-risk patients.

Developing a triage tool for use in identifying people living with HIV who are at risk for non-retention in HIV care

Introduction:

Identifying PLHIV in HIV care who are at particular risk of non-retention in care is an important element in improving their HIV care outcomes. The purpose of this study was to develop a risk prediction tool to identify PLHIV at risk of non-retention in care over the course of the next year.

Method:

We used stepwise logistic regression to assess sociodemographic, clinical and behavioral predictors of non-retention in HIV care. Retention in care was defined as having evidence of at least two encounters with an HIV care provider (or CD4 or viral load lab tests as a proxy measure for the encounter), at least 3 months apart within a year. We validated the risk prediction tool internally using the bootstrap method.

Results:

The risk prediction tool included a total of six factors: age group, race, poverty level, homelessness, problematic alcohol/drug use and viral suppression status. The total risk score ranged from 0 to 17. Compared to those in the lowest quartile (0 risk score), those who were in the middle two quartiles (score 1–4) and those in the upper quartile (>4 risk score) were more likely not to be retained in care (odds ratio [OR] 1.63 [CI; 1.39–1.92] and OR 4.82 [CI; 4.04–5.78] respectively). The discrimination ability for the prediction model was 0.651.

Conclusion:

We found that increased risk for non-retention in care can be predicted with routinely available variables. Since the discrimination of the tool was low, future studies may need to include more prognostic factors in the risk prediction tool.

Development and validation of a prognostic nomogram for HIV/AIDS patients who underwent antiretroviral therapy: Data from a China population-based cohort

BACKGROUND

Accurate forecast of the death risk is crucial to the administration of people living with HIV/AIDS (PLHIV). We aimed to establish and validate an effective prognosis nomogram in PLHIV receiving antiretroviral therapy (ART).

METHODS

All the data were obtained from 2006 to 2018 in the Wenzhou area from China AIDS prevention and control information system. Factors included in the nomogram were determined by univariate and multiple Cox proportional hazard analysis based on the training set. The receiver operating characteristic (ROC) and calibration curves were used to assess its predictive accuracy and discriminative ability. Its clinical utility was also evaluated using decision curve analysis (DCA), X-tile analysis and Kaplan-Meier curve, respectively in an independent validation set.

FINDINGS

Independent prognostic factors including haemoglobin, viral load and CD4+ T-cell count were determined and contained in the nomogram. Good agreement between the prediction by nomogram and actual observation could be detected in the calibration curve for mortality, especially in the first year. In the training cohort, AUC (95% CI) and C-index (95% CI) were 0.93 (0.90, 0.96) and 0.90 (0.85, 0.96), respectively. In the validation set, the nomogram still revealed excellent discriminations [AUC (95% CI): 0.95 (0.91, 1.00)] and good calibration [C-index (95% CI): 0.92 (0.82-1.00)]. Moreover, DCA also demonstrated that the nomogram was clinical beneficial. Additionally, participants could be classified into three distinct (low, middle and high) risk groups by the nomogram.

INTERPRETATION

The nomogram presents accurate and favourable prognostic prediction for PLHIV who underwent ART.

FUNDING

This work was supported by Zhejiang Basic Public Welfare Research Project (LGF19H260011), Wenzhou Basic Public Welfare Research Project (Y20180201), the Initial Scientific Research Fund (KYQD170301), the Major Project of the Eye Hospital Wenzhou the Major Project of the Eye Hospital Wenzhou Medical University (YNZD201602). Part of this work was also funded by National Natural Science Foundation of China (81670777) and Science and Technology Innovation Activity Plan and New Talents Plan for College Students in Zhejiang Province (2019R413073). The funders had no roles in study design, data collection, data analysis, interpretation and writing of the report.