We report two cases of dolutegravir (DTG) resistance in highly treatment experienced patients. Monitoring for treatment failure and adherence support is important in highly treatment experienced patients taking DTG.Dolutegravir is the mainstay of HIV treatment programmes and emergence of drug resistance to DTG is of public health relevance. Dolutegravir is co-formulated with other antiretroviral medicines, well tolerated, has a high genetic barrier to resistance and is available in generic formulations. To date, few cases of emergent DTG resistance in integrase-naïve patients have been reported,We describe two cases of highly treatment experienced, InSTI-naïve people living with HIV from Newlands Clinic, Harare, Zimbabwe, who developed InSTI drug-resistance mutations (DRMs) within 14 months of InSTI initiation.Written informed consent was obtained from the patient described in this case report. The patient gave his consent to have his clinical and demographic data to be used as well as his images.Ethical approval to conduct a case report on these patients was obtained from the Medical Research Council of Zimbabwe. Clearance number: E/316.An 18-year-old male patient was commenced on ART in September 2010, at seven years of age. His baseline CD4 count was 145 cells/μL. His first-line ART regimens were stavudine/lamivudine (3TC)/nevirapine, followed by stavudine/3TC/efavirenz (EFV), followed by zidovudine/3TC/EFV. Viral load (VL) results were not available until 2014, at which time he was noted to have virological treatment failure. An HIV genotypic resistance test done in January 2015 showed significant nucleoside reverse transcriptase inhibitor (NRTI) and non-nucleoside reverse transcriptase inhibitor (NNRTI) DRMs with no protease inhibitor resistance (see Virological rebound occurred 11 months after initiation of the InSTI regimen. The patient reported a history of suboptimal adherence to TDF/3TC/DTG; no other risk factors for virological failure were identified. Despite adherence counselling his VL remained high, and an HIV genotypic resistance test taken 14 months after TDF/3TC/DTG initiation showed persistent NRTI and NNRTI DRMs, and extensive InSTI DRMs (see Following the genotypic resistance test, the patient was switched to TDF/3TC/boosted darunavir and adherence support was reinforced. Eight weeks after the switch, the VL was 400 copies/mL and at 24 weeks, the viral load was undetectable. He continues this regimen with 3-monthly VL monitoring and adherence support.A 29-year-old female patient was diagnosed with HIV infection in June 2017 and commenced on TDF/3TC/EFV. Her baseline CD4 count and VL are unknown. Three years later, she was switched to ABC/3TC/ATZ/r following virological failure. Four weeks after treatment switch, she developed pulmonary tuberculosis and in error was given rifampicin with ATZ/r for two months. For treatment optimisation reasons, the ART regimen was switched to ABC/3TC/DTG, but DTG was dosed at 50 mg daily with rifampicin for four months instead of twice daily according to current guidelines. The VL at switch to ABC/3TC/DTG is unknown. The clinical response to treatment was poor and she was referred to Newlands Clinic. A VL taken after seven months on ABC/3TC/DTG was 6540 copies/mL, despite adherence counselling. A genotypic resistance test taken 10 months after initiation of DTG showed extensive NRTI DRMs and one major InSTI DRM (see The patient was retreated for tuberculosis with adherence monitoring and the clinical response was good with weight gain and symptom resolution. On completion of six months of tuberculosis therapy, her integrase resistance data was available, but the initial sequencing of reverse transcriptase and protease had failed and was being repeated. Due to the long history of poor adherence, it was assumed that backbone NRTI resistance was likely; therefore, a third-line regimen consisting of TDF/3TC, darunavir in addition to twice daily DTG was considered the most robust regimen. After receiving weekly enhanced adherence support her VL at week 12 was 98 copies/mL and at week 24 was 71 copies/mL.1 A similar case from South Africa has been described, of a treatment experienced patient who developed resistance to DTG after taking DTG 50 mg once a day with rifampicin, instead of the recommended twice daily dosing. Rifampicin is an inducer of the UGT1A1 and CYP3A4 pathways by which DTG is metabolised, resulting in reduced serum DTG concentrations. The current World Health Organization recommendation is to dose DTG at 50 mg 12-hourly for patients taking both medications concurrently.We report two cases of treatment experienced, InSTI-naïve patients who developed InSTI resistance within 14 months of starting DTG. The first patient had significant NRTI resistance prior to switching to TDF/3TC/DTG and suboptimal adherence post treatment switch. The second patient also reported suboptimal adherence and may have had sub-therapeutic drug levels of DTG due to a drug interaction between DTG and rifampicin. Known risk factors for incident drug resistance include suboptimal medication adherence, drug interactions, a high baseline VL and active opportunistic infections.1 As increasing numbers of cases of DTG resistance are reported, the question arises as to whether patients who are treatment experienced may be at higher risk of InSTI resistance. The use of DTG in patients with compromised NRTI backbones has raised concerns regarding the efficacy and durability of these regimens.2 However, emerging evidence from the NADIA, VISEND and DAWNING trials appears reassuring that viral suppression despite the presence of NRTI mutations is achievable.5 Despite the good virological suppression rates achieved in these trials, it is of concern that a small percentage of participants developed treatment emergent DTG resistance; 4.0% of NADIA partcipants at week 96 and 0.6% of DAWNING participants. Although the risk of InSTI resistance was modest in these clinical trials, it is likely to be increased in real-world settings particularly those with failing health systems.Integrase strand transferase inhibitor resistance in those with previous exposure to first-generation InSTI has been well described; however, the development of InSTI resistance in patients taking DTG is rare in ART-naïve patients. In a systematic review, Cevik et al. report 15 cases of emergent InSTI resistance in patients on DTG: 5 cases in ART-naïve patients and 10 cases in ART experienced patients.Despite the high barrier to resistance of second-generation InSTIs, emergent DRMs can occur in treatment experienced InSTI-naïve patients. Evaluation of background resistance, avoidance of drug interactions and adherence support could prevent the development of InSTI resistance.


Objective: To assess the state of the retention in care of HIV patients in three health settings in Morrumbene, a rural district of Inhambane Province, Mozambique. We evaluated potential factors associated with early loss to follow‐up (LTFU), retention in care and ART adherence during the first year of follow‐up.Material and methods: Retrospective, cross‐sectional, observational study. We collected data on patients diagnosed with HIV infection in 2017 in two permanent clinics and one mobile clinic. Demographic, clinical, immunological and therapeutic data were retrieved up to December 31st, 2018. Data on follow‐up were collected at 6 and 12 months for medical visits and for ART adherence and analysed for factors associated with LTFU, retention in care and adherence to ART by Stata Version 14 and univariate and stepwise multiple unconditional logistic regression models.Results: In 2017, 960 patients were diagnosed with HIV infection. At 6‐month follow‐up, 49% attended the medical visit and 157 (25%) adhered to ART. After one year, 34% of patients were available for follow‐up, and only 72 patients adhered to ART. In multivariate analysis, factors associated with early LTFU were male sex (p = 0.036) and immediate prescription of ART (p = 0.064). Older age (p < 0.001) and being followed in the mobile clinic (p = 0.001) favoured retention in care. Advanced WHO status (p = 0.005) and being pregnant or breastfeeding showed a negative correlation with adherence to treatment (p = 0.068).Conclusions: Only one‐third of patients were available for follow‐up after one year, and only 13% adhered to ART. Young individuals, men and pregnant/breastfeeding women seem to be particularly at risk for LTFU and non‐adherence to treatment. Serviços Distritais de Saúde Mulher e Ação Social e Direcção Provincial de Saúde) designed a strategy called “TARV Movel” service (TM) in the district of Morrumbene, Province of Inhambane, Southern Mozambique. Taking advantage of the long multi‐year experience of MMI on supporting the Mobile Clinics strategy (Brigadas Móveis), that usually offers primary healthcare services for more distant rural areas, populous communities were selected to receive the HIV outpatient package, in full compatibility with the protocols of the Mozambican Ministry of Health (MISAU). By the time of our analysis, TM served the remote locations of Chicungussa, Mucambe Feha and Bie. The package included counselling and clinical visits, ART dispensation and laboratory analysis, and was offered once a month, all fully integrated with other primary health services, such as vaccinations and general visits. With this strategy, HIV patients had the opportunity to being attended in a health centre or in the mobile clinic, with the same standard of care and specific tailored services, such as lymphocytes CD4+ count on‐site. MMI offered coordination of the activity and technical supports to all the phases of the activity. In the framework of the collaboration between the University of Brescia UNESCO Chair and MMI, we evaluated the current state of the HIV cascade of care in the TMs and in two other types of health service delivery: At the first level, central Health Center of Morrumbene City (CS); and at the second level, the peripheral Health Center of Mahangue (MAH). We then performed two analyses, with two different but interconnected aims: first, a snapshot of the HIV continuum of care in the three settings in the rural district of Morrumbene, to identify potential weaknesses and strengths of the three different strategies. Secondly, we evaluated whether there were any epidemiological, social, clinical and therapeutic factors correlated with LTFU, retention in care and the adherence to antiretroviral treatment in the overall population, to identify risk groups on which future strategies should focus.To date, 37.7 million people live with HIV infection worldwide . Sub‐SahThe study was conducted in accordance with the guidelines of the Declaration of Helsinki and the principles of Good Clinical Practice. The study protocol was approved by the local authorities that released an ethical and formal authorisation on collecting and analysing data (Nota no. 67/001.5/DPSI/NIOI/2021). Written or oral informed consent was obtained from all patients at the time of enrolment, as part of the routinely activities of health centres of the National Health System, according to local regimentations. Data from this study were approved for publication (Nota no. 67/001.5/DPSI/NIOI/2021).We performed a retrospective, observational study. The study took place in the rural Morrumbene district in the province of Inhambane, southern Mozambique. In 2017, Morrumbene was home to 152,517 people, 56% of whom were women [‐The first level Centro de Saúde de Morrumbene (CSM)‐The second level Centro de Saúde de Mahangue (MAH)‐the TARV Móvel service (TM).We collected data on all adult patients aged ≥14 years old with a new diagnosis of HIV infection performed between the 1st of January and the 31st of December 2017 in three health facilities of the Morrumbene district:For the retrospective nature of this work, the three populations hailing from the three health centres were not matched for any characteristics. As described in another study , the smaData were collected manually through paper and digital medical records available on Open Medical Record System (OpenMRS®). Digital records were available only for patients followed by CSM and TM. We retrieved data on age, sex, education, pregnancy, breastfeeding by the moment of HIV diagnosis, date of HIV diagnosis, date of entry to care, site and centre of diagnosis of HIV infection, CD4+ cell count and WHO status at baseline, date of ART prescription, presence at visit, death, transfer to other health centre and adherence to ART.We evaluated three steps of the cascade of HIV care. Regarding timing and prescription of ART, the criteria for eligibility for ART were consistent with the national guidelines  Table 11. In theImmediate initiation of ART was defined as starting an antiretroviral regimen on the same day of the HIV diagnosis. As for retention in care, we evaluated the adherence to the scheduled follow‐up at 6 and 12 months after HIV diagnosis , median with interquartile range, were obtained as appropriate for all variables; for comparison, chi‐square test or Fisher's exact test for categorical variables, Student's p‐value for entrance = 0.15 and for exit = 0.10. In the latter analysis, the site of follow‐up was retained obligatory independently from the existence of a significant correlation in the univariate analysis.The time pattern of cART prescription by site and by gender was studied using Kaplan–Meier method and by log‐rank test. In order to evaluate the factors associated with the outcomes of early loss at follow‐up, retention in care and adherence to ART, we performed a univariate logistic regression with the following variables: age, sex, pregnancy/breastfeeding, level of education, immunological and clinical status, timing and prescription of ART, being followed in one of the three health centres. For the same outcomes, a stepwise multivariable unconditional logistic regression model was applied, with a It must be said that, for many patients, medical and therapeutic records were incomplete due to incomplete collection of data at the moment of the visit or to the decay, loss or transfer of the paper medical/therapeutic record. Hence, we specify how many patients we were able to take into account in the analysis in each section.p < 0.001), a lower median count of CD4+ T cells  and a more advanced WHO status , whereas females tended to have a lower level of education (Table p = 0.02). In TM, one‐third (29%) of the patients had no educational at all, especially females . CD4+ T‐cell count at baseline was not significantly different among the three centres, although the proportion of patients with <100 CD4+ T cells/ml was larger in CSM (p = 0.015)  were diagnosed with HIV infection. Females constituted 73% and 14% were pregnant. At baseline, median age was 36 years (28–75 IQR), median CD4+ T‐cell count was 309 cells/ml (155–510 cells/ml IQR). Two hundred and eighty‐three (32%) patients had a CD4+ T‐cell count <200 cells/ml. Males had a higher median age at diagnosis  by health service and by sex  at CSM, 54% (95%CI45–63) at TM and 21% (95%CI15–28) at MAH. Among females, pregnant women were more likely to be prescribed ART on the same day than other women . Using stepwise logistic regression adjusted for sex, we found that patients followed in MAH , elderly patients  and those with higher CD4+ cell count  were less likely to be prescribed ART on the day of HIV diagnosis. Immediate prescription of ART increased during the study period .After 6 months, 76% and by the end of 2018, 83% of the enrolled patients were prescribed ART (784/938) . Overall, 11 deaths (1.25%) were recorded: seven in CSM, one in MAH and three in TM, with no significant differences between the services.When we explored how many patients were adherent at scheduled follow‐up, we counted only 297/880 (33.8%) present at both the 6‐ and 12‐month visits, with no differences among the centres. Among the 469 patients not attending the 6‐month visit, only 51 (11%) showed up at the 12‐month visit. After one year, patients registered as referred to other centres accounted for 27% of the cohort in TM, 4% in MAH and 2% in CSM  regularly collected drugs for six consecutive months, with no significant differences among the three health centres. Continuous adherence to the prescribed ART was recorded for just 76/599 patients (13%). No statistical difference was shown between CSM and TM. Conversely, patients in follow‐up in MAH were less likely to adhere to ART Figures  and 4b.p < 0.001 and p = 0.001 respectively), while male sex showed a positive correlation (p = 0.001).The univariate analysis for factors correlated to early loss to follow‐up (418/916) is shown in Table immediate vs. early, where early is ART prescription within one month) was added to the model (Table p = 0.064), where CSM remained another independent risk factor for early loss .When prescription approach , even if not when followed at CSM  and in those with a low level of education .When only women were considered in the analysis, pregnancy or breastfeeding preserved from early abandonment , while male sex and a low BMI were negatively correlated with retention in care . Being followed in TM gave significant better results in compliance at the medical visit than MAH   Table .p = 0,068), and advanced WHO status   Table .p = 0.015), probably because this is the biggest health centre in the area and has an in‐patient ward for the most severe cases. Whether the fact that only 4–5% of patients in MAH and TM were clinically diagnosed with an advanced WHO status despite the high percentages of late presenters was due to a lack of recognition of WHO status III or IV defining diseases should be further investigated.Despite a good rate of ART prescription over one year of observation, HIV patients in rural Morrumbene in Mozambique display poor compliance to follow‐up and even worse adherence to antiviral treatment. In our study population, three of four patients were females. Besides, men were older than women, and tended to present with a more advanced stage of disease by the time of HIV diagnosis. Females are more represented than men in other African cohorts , 14, 15,p < 0.001), whereas older patients and patients with higher T‐CD4+ cell counts tended to be prescribed ART later .By the end of the observational period, 83% of all patients were prescribed ART. This is a much higher ART prescription rate than in other studies from Mozambique , 5, 20. At 6 months, 49% of patients were present at the follow‐up visit, with 25% continuously adherent to ART. When we evaluated the proportion of patients who regularly attended all of their follow‐up visits after one year, we observed that only 34% were present at both visits and just 13% adhered to treatment.These data are hardly comparable with other studies, mostly because the study design is highly heterogeneous among similar works in literature. In the SEARCH study , 88.6% wAmong centres, TM had the highest adherence at follow‐up at six months (59%) and MAH recorded the worst ART adherence in the overall period. Factors influencing these results may be multiple, such as distance from the health centre, relationship between health practitioners and patients, awareness of patients about HIV treatment and follow‐up, perception of stigma and many others. Although the presence of MMI supporting the mobile clinic strategy could justify a better data register and a “motivational input” for rural populations, it is to be noted that the TM population presents peculiar characteristics, such as lower education and socio‐geographical isolation, justifying a lower probability to perceive the importance of a chronic therapy. These positive and negative factors may somehow offset each other. Exploring these matters in the near future may help in understanding these differences, while each centre may become more aware of its current situation and work on its weaknesses in the next years.Among the possible factors influencing the retention into the HIV continuum of care, older age was protective against loss to follow‐up. This result is consistent with other studies, which found the same association , 22, 24.Starting ART on the day of diagnosis of HIV infection was independently associated with early loss to follow‐up. In the Rapid Initiation of Treatment (RapIT) trial in South Africa, 64% of patients in the rapid arm (initiation of ART ≤90 days) were virally suppressed vs. 51% receiving the standard of care . In the p = 0.068) with adherence to ART after one year. This result is consistent with other studies showing lower rates of retention in care in women who started therapy during pregnancy and/or breastfeeding than in women who undertook ART for their own health [Being pregnant or breastfeeding at the moment of HIV diagnosis was protective against loss to follow‐up, but showed a negative correlation , this strategy should probably be complemented with other interventions. Men need to be especially addressed in future strategies to diagnose them at an earlier stage. Particular attention must be paid to younger patients, pregnant and breastfeeding women, especially long‐term, when the reasons of the losses to follow‐up in these populations should be understood and addressed. We hope that routine testing of HIV viral load will be soon available to help manage HIV‐positive patients in this area and to understand the efficacy of ART in this population.The data supporting the findings of this study are available from the corresponding author upon reasonable request. PM received financial support from MMI for travel to and living in Mozambique during data collection. LRT and ST declare no conflicts of interest. FC reports acting as a principal investigator of company‐sponsored clinical trials in the field of HIV infection . CC and ADG were working as health providers for MMI by the time of the study. The study was conducted in the context of the health cooperation project "CAREvolution: Innovation of community health services in Inhambane Province" (AID Code 11492), in which MMI activities were financially supported by the Italian Agency for International Development Cooperation (AICS).Supplementary MaterialClick here for additional data file.


HIV treatment programs face challenges in identifying patients at risk for loss-to-follow-up and uncontrolled viremia. We applied predictive machine learning algorithms to anonymised, patient-level HIV programmatic data from two districts in South Africa, 2016–2018. We developed patient risk scores for two outcomes: (1) visit attendance ≤ 28 days of the next scheduled clinic visit and (2) suppression of the next HIV viral load (VL). Demographic, clinical, behavioral and laboratory data were investigated in multiple models as predictor variables of attending the next scheduled visit and VL results at the next test. Three classification algorithms  were evaluated for building predictive models. Data were randomly sampled on a 70/30 split into a training and test set. The training set included a balanced set of positive and negative examples from which the classification algorithm could learn. The predictor variable data from the unseen test set were given to the model, and each predicted outcome was scored against known outcomes. Finally, we estimated performance metrics for each model in terms of sensitivity, specificity, positive and negative predictive value and area under the curve (AUC). In total, 445,636 patients were included in the retention model and 363,977 in the VL model. The predictive metric (AUC) ranged from 0.69 for attendance at the next scheduled visit to 0.76 for VL suppression, suggesting that the model correctly classified whether a scheduled visit would be attended in 2 of 3 patients and whether the VL result at the next test would be suppressed in approximately 3 of 4 patients. Variables that were important predictors of both outcomes included prior late visits, number of prior VL tests, time since their last visit, number of visits on their current regimen, age, and treatment duration. For retention, the number of visits at the current facility and the details of the next appointment date were also predictors, while for VL suppression, other predictors included the range of the previous VL value. Machine learning can identify HIV patients at risk for disengagement and unsuppressed VL. Predictive modeling can improve the targeting of interventions through differentiated models of care before patients disengage from treatment programmes, increasing cost-effectiveness and improving patient outcomes. Despite wide-scale HIV prevention efforts and the adoption of a ‘treat all’ strategy2, where treatment also provides a prevention benefit, South Africa still saw 230,000 new infections in 2020 alone3. While the implementation of the treat-all policy has increased the uptake of antiretroviral therapy (ART) with recent estimates indicating ART coverage levels of approximately 70%4, the challenge of patient retention within public sector ART programs in South Africa remains of concern5. In 2020, only 66% of PLWH in South Africa were virally suppressed3. To optimize South Africa’s HIV response and reach targets of 95% of PLHIV tested, 95% of those on ART and 95% of ART patients virally suppressed, the numbers of patients initiating and successfully maintaining viral suppression on antiretroviral therapy must increase7.Globally, it is estimated that in 2019, there were more than 38 million people living with HIV (PLHIV), of which 8 million (20%) were living in South Africa10. These approaches are costly in terms of human capital and do not optimize resource allocation according to risk prioritization and are thus not universally provided. If we could more accurately identify patient groups at risk of disengagement and how to target available interventions to these groups while still engaged in care, resources could be allocated more efficiently and precisely, addressing higher risk groups while simultaneously reducing health care worker burden, a major driver of cost in health care.Current interventions to address disengagement from care are typically reactive in nature; attempting to identify, trace and return to care those who have already disengaged from treatment programs11, we aimed to determine whether machine learning applied to routinely collected longitudinal HIV phenotypic and clinical outcome data in South African programmes could consistently identify patients at risk of poor outcomes in terms of two key programmatic outcomes: (1) attendance at next scheduled clinic visit and (2) suppression of next HIV viral load (VL).Borrowing from techniques widely utilized in credit scoringWe utilized routinely collected, anonymised, longitudinal data from patients accessing HIV care and treatment at public sector treatment sites in Mpumalanga and the Free State between January 2016 and December 2018. Data were used to train two predictive models: (1) retention model and (2) VL suppression model. Data included demographic, clinical and antiretroviral treatment records indicating clinical visits, laboratory test results and other key HIV indicators. For this study, we included records from patients who had accessed HIV care (observed in routine HIV electronic records) and, during the period 2016–2018, had at least one recorded clinic visit . Each visit and VL test were considered unique events, such that a single patient would have each visit and VL test outcome measured and a patient’s outcomes (missed/attended and suppressed/unsuppressed) could change across subsequent visits and tests.We defined two For the VL model, we included patient records with at least one VL test recorded between 2016 and 2018, while for the retention model, visit data were included only where a valid next scheduled visit date was recorded (between 14 and 99 days after the current visit). Patients without any recorded visit to a recognized three-drug highly active antiretroviral treatment regimen were excluded from analysis. For both models, we included patients commencing ART prior to 2016 provided that we observed visit or viral load data for that patient during the period 2016–2018.In addition, for the VL outcome, we excluded any VL tests from analysis that followed a high VL value (> 1000 copies/mL) due to the majority of these being repeat confirmatory tests and thus having a high probability of also being unsuppressed. We also removed any VL observation that had a recent prior VL (within the previous 6 months) again due to the high probability of concordance between these results  into one best breed result. Their random nature often limits overfitting while controlling error, making them attractive modeling tools for complex hyperspaces with nonlinear separations in classes. Like random forest, AdaBoost (“Adaptive Boosting”) classifier is a meta-estimator that fits a classifier on the original dataset and then fits additional copies of the same classifier on the same dataset; in this case with decision trees as the classifier. However, it uses boosting to evaluate the performance of subtrees sequentially as the training develops instead of waiting to the end as per the random forest method. These three algorithms were selected to test the diverse approaches to learning the input data space that each algorithm represents and to evaluate the fit of each algorithm to the study dataset with respect to the different strengths, weaknesses and applications of each.Both ensemble methods are designed to predict a binary outcomes based on various inputs but do not have the linear separation limitation of logistic regression. Both ensemble learning algorithms have the benefit of upweighting the combination of variables at certain thresholds on how they correlate with an output variable—thus often being more sensitive to minority classes or multiple subgroups that may emerge in the data.class imbalance problem14 and as such should be interpreted as the primary analytic output. An additional model was tested with unbalanced class membership (60–40) in the training data; we present the results of these for comparison and demonstration of this principle.In total, 75 predictor variables (raw and engineered) were investigated for inclusion in the models. Laboratory, visit and patient demographic data were linked and deduplicated, and invalid or implausible fields were removed, as outlined in Fig. The classifier algorithm was trained using the training data set, consisting of input of predictor variables as well as the specified target outcomes per visit. This training produces an optimal configuration (predictive model) such that the predictor variables correspond to the specified target outcomes as often as possible. After the model was trained, we separated the unseen test set into predictor variable data and outcome data. The model then was tested on the unseen data set by generating predicted outcomes using the predictor variables from the unseen test set as input for each observation. In this case, observations were each scheduled visit for the retention outcome and each viral load test for the VL outcome. The predictions were then scored for accuracy against the known outcomes in the unseen test set .The model’s performance was evaluated in terms of accuracy , sensitivity , positive predictive value also known as precision  and specificity (the proportion of known negative outcomes in the unseen test set that are correctly identified as such). Next, we utilized the area under the curve (AUC) of a receiver operating characteristic (ROC) curve to evaluate the broad predictive classification performance of the model. A range of 0.5 indicates no predictive power, while 1.0 indicates perfect predictive power.Finally, we repeated the fitting of the model after looking individually at the highest- and lowest-ranked patients and each of their characteristics. We manually checked for areas of obvious commonality and where the algorithm might be choosing obvious answers or making erroneous assumptions.No experiments on animal or human tissues were conducted as part of this study. All methods relating to the analysis of de-identified data from human subjects were approved by and carried out in accordance with relevant guidelines and regulations as set out by the Human Research Ethics Committee of the University of the Witwatersrand . This study involved secondary analysis of deidentified data collected as part of routine care, and the requirement for individual patient consent was waived by the Human Research Ethics Committee of the University of the Witwatersrand for protocols M140201 and M210472 during the study approval.In total, 445,636 patients were included in the retention model and 363,977 in the VL model. Nearly one-third (30%) of patients were male, with a median age of 39 years (IQR 31–49 years) at the time of visit. In the retention dataset, patients had a median of 18 (IQR 10–25) visits since entering care and had been in care for a median of 31 (IQR 15–43) months. The vast majority (91%) of patients visited a single facility during the period under analysis.15 binary classification algorithms, respectively, from the scikit-learn16 open source project and tested against unseen data to evaluate predictive performance.We generated 75 potential predictor variables per visit and 42 predictor variables per VL test. The retention and VL suppression models were built using the AdaBoost and random forest17. For the VL suppression model, the dataset was split into training and testing sets, with the test set consisting of 30%  of the original unseen tests randomly selected from across the study period. In the VL test set, there were 21,679 unsuppressed (> 400 copies/mL) viral load results for a baseline prevalence of unsuppressed VL results of 10.3%.For the retention model, the test set consisted of 1,399,145 unseen visits randomly selected from across 2016–2018. The test set’s baseline prevalence of missed visits was 10.5% , consistent with the LTFU prevalence observed in both the full data set and the training set. This observed baseline was comparable with meta-studies of LTFU at 1 year in South Africa 2011–2015missed or not missed visits) in the training set. Using the test set, the retention model correctly classified 926,814 of the test set (~ 1.4 m visits) correctly, yielding an accuracy of 66.2% ; and second, with an unbalanced sample—60% not missed and 40% missed visits). The AdaBoost classifier was trained with a 50:50 balanced sample of the modeling set, which resulted in 343,078 of each visit classification , for an accuracy of 78.6% Table B. Howeve18. However, the predictive power or utility of the model to separate between classes—represented by the AUC metric—remained consistent across models. The two ROC curves are depicted in Fig. 20.The two models demonstrated the potential trade-off in accuracy, precision and sensitivity that can be manipulated in the training of the modelsFor the suppressed VL model, the final training set was down sampled to 101,976 tests, such that it had a 50:50 balanced sample. The model correctly classified 153,183 VL results out of the test set of 211,315 correctly, yielding an accuracy of 72.5%  were reduced to a more practical number through feature selection using a Random Forest algorithm on all inputs. Random Forest permutes the inputs into trees of different groups of predictors, and the change in predictive power (as measured by AUC) of the model for each permutation was calculated. This process prioritises groups of predictor variables that together improve predictive power and deprioritises those that contribute little or no improvement to AUC. Random Forest was able to rank the relative feature importance of the total input set for each model. Figure Our results indicated that prior patient behavior and treatment history were extremely important in predicting both visit attendance and viral load results in these datasets and that traditional demographic predictor variables were less useful than behavioral indicators. These more powerful predictor variables can also be used to further stratify populations by risk and segment more granularly for targeted interventions and differentiated care.During feature selection we investigated overfitting to particular features through comparative tests of features permutation importances with the goal of identifying any overfitted but erroneous highly correlated features in the training set that weren’t a reflected phenomenon in the test set , holds promise to offer substantial advancements not only in the health of those living with HIV22 but also in the country’s efforts to initiate and retain 95% of all people living with HIV (PLWH) who know their status on antiretroviral therapy (ART) as part of UNAIDS’ global 95-95-95 strategy. However, implementation of a policy such as UTT requires a rapid scale-up and expansion of the ART program on a country-wide level, a shift that is often challenging in communities with the most cases of HIV and the largest ARV programs globally.In September 2016, the National Department of Health revised its treatment guidelines to extend the availability of ART to all people living with HIV, irrespective of CD4 cell count and stage of diseaseAs South Africa increases efforts toward 95-95-95 goals, knowing which patients require additional services and interventions to achieve successful treatment outcomes at each step of the care cascade is critical. As national health monitoring systems expand to collect large volumes of increasingly detailed data, the application of data science technology and methodology to these and other data sources holds the potential to improve individual health across population groups. Traditional approaches to estimating the risk of losses from the care cascade and designing interventions to address those losses have not realized the improvements in outcomes they promise. Reasons for disengagement differ widely, yet studies investigating these typically look at individual or combinations of baseline characteristics  and then treat large groups  as homogenous. These approaches make two potentially flawed assumptions: (1) it assumes that large subpopulation groups are homogenous and should be addressed as a single entity , and (2) it assumes predictive characteristics are fixed from a random baseline point of assessment through to specific endpoints ranging anywhere from 6 months to several years after assessment of these features. Behavioral science, however, tells us that behavioral drivers of health seeking patterns not only differ by patient but also over time for individuals.To address these challenges, we applied machine learning to routinely collected demographic, visit and laboratory data and built a predictive model able to effectively separate high-risk from low-risk patients using a combination of routinely available clinical, laboratory and behavioral (visit patterns) data. With continuous challenges in funding to run and operationalize HIV/TB programmes in South Africa, precision programming pivoted by targeted cost-effective, data driven interventions in an almost near to real-time fashion are critical. This approach is enhanced by the implementation of predictive analytics and machine learning within existing programmes.Our results suggest some important implications for the application of this methodology in clinical practice. First, utilizing only routinely collected demographic, visit and laboratory data, and depending on the training method, predictive models have the ability to correctly predict attendance in a range of 66–79% of scheduled clinic visits and VL suppression in 76% of viral load test results, two key predictors of UNAID’s 95-95-95 targets. Second, the shape of the ROC curve allows us to identify thresholds and ranges of differing group risk profiles (groups that represent an important share of those with each outcome). For example, in the unsuppressed VL model, 20% of the population accounted for > 50% of the unsuppressed VL results, indicating different risk strata within population groups. Thresholds can be identified to segment the population into risk groups . This stratification of risk groups can be leveraged for gains in predictive power estimated through population segments. Targeting at-risk patients before they disengage from care or virally unsuppressed allows for greater resource use efficiencies.23, “all happy patients are alike; each unhappy patient is unhappy in its own way”. While the models cannot provide the underlying reason for disengagement from care, the ability to identify low-risk patients with high accuracy could prove to be of immense value for health systems, as it allows health care workers to prioritise patients not classified as low risk who may be at higher risk of disengagement, prioritizing time and resources toward these patients for targeted intervention as appropriate to their individual context.Third, we demonstrated high negative predictive value  and sensitivity > 60% for both models. This, combined with a low positive predictive value (PPV ~ 20%), potentially indicates that low-risk or so-called “green” patients are readily and accurately identified by the model and have common traits. In contrast, patients at high risk of poor outcomes or so-called “red” patients can be readily identified as “not green” but demonstrated much heterogeneity in terms of presentation and underlying reason for disengaging from care. Or, to paraphrase the Anna Karenina principle24 Third, one of the recognized limitations in the nature of black-box machine learning methods such as classification trees is that while these predictors contribute to differences in risk, we cannot yet fully explain how or why they contribute within the context of an individual patient. The next step is to understand which values or ranges of the predictor’s spectrum in combination with the other variables at certain levels may correlate with a certain outcome. For example, it is unclear whether the risk relationship between visits and age and poor outcomes is with younger people coming on time or older people being late or vice versa. Additional analysis and modeling activities are underway to provide interpretable descriptions of how the algorithm is able to segment the populations of observations. Finally, much of the understanding as to why certain visit or demographic predictors are important may lie in more subtle social nuances related to an individual’s social circumstances and health-seeking behaviors 25. To this end, our approach can be utilized and further refined by applying it to different data sources with richer social and behavioral predictors.Our results should be interpreted in light of some limitations. First, as anonymised routinely collected facility-level data were used to fit the models, it was not possible to trace missing data or correct erroneously linked visits and laboratory data. Second, facility-level data used to define the outcome in each of the models do not account for all silent transfers to another facility, so to the extent that this outcome misclassification has occurred, we will overestimate missed visits and blur the predictive efforts of the model. Future models would benefit from being fit to national cohorts where a system-wide view accounts for the effect of silent transfers26. Accurately identifying those at risk for poor treatment outcomes will allow for health care services to better triage patients, improving efficiency and resource utilization. By prioritizing those most at-risk, clinics can realize better health outcomes without additional investments in data collection and staff. In addition, the results of the algorithm can also be aggregated and used to risk score population subgroups at the facility level to identify where programs need to target specific interventions.Despite these limitations, the analysis has several strengths. First, our findings highlight the importance of better understanding the risk profile of individuals, supporting recent calls for advancing data science toward precision public health models29.Second, while most retention interventions are directed at tracing patients disengaged from care and then returning them to care, our model offers the opportunity to shift the focus and resources directed at retention efforts to a period while the at-risk patient is still engaged at the point of care. By anticipating future issues before any visible clinical signs are present , clinics can intervene proactively while patients are still accessible, engaged in health services and provide targeted services earlier. Early detection of patients at high risk of becoming virologically unsuppressed has implications not only for the individual patient’s health but also for the risk of onward transmission of the virus and impact on breaking transmission chainsPredictive models and machine learning can identify and target HIV patients at risk for disengaging from care and not being virally suppressed. Our approach could enable anticipation of future outcomes before any visible signs and/or poor outcomes occur  and, most importantly, while the patient is still engaged in care. This affords the opportunity to take a proactive approach to patient management—specific targeted interventions can be designed on identified subsets of the treatment cohorts, allowing for cost-effective differentiated models on care and treatment to be applied across the cascade. This approach could also be extended to other key HIV outcomes, allowing for the use of a cost-effective and precision programming approach.Supplementary Information.


Before starting the second-line ART, there was no difference in CD4 count between the two groups (p = 0.28). At the end of the first year of second-line ART, the difference was statistically significant between the two groups with regard to CD4% (p < 0.001) and deaths (p = 0.001). The children disclosed to also reported fewer depressive symptoms post-disclosure and had three times fewer clinic visits. HIV status disclosure to children is an important determinant of ART compliance and a child’s psychological well-being.HIV status disclosure to children remains a challenge in sub-Saharan Africa. For sociocultural reasons, parents often delay disclosure with subsequent risks to treatment compliance and the child’s psychological well-being. This article assesses the effects of HIV disclosure on second-line ART compliance after first-line failure. We conducted a retrospective study of 52 HIV-positive children at Kalembelembe Pediatric Hospital in Kinshasa who were unaware of their HIV status and had failed to respond to the first-line ART. Before starting second-line ART, some parents agreed to disclosure. All children were followed before and during the second-line ART. Conventional usual descriptive statistics were used. For analysis, the children were divided into two groups: disclosed to ( Africa remains the continent most heavily affected by HIV/AIDS, accounting for 68% of people living with HIV (PLHIV) in 2021 . StudiesA systematic review of studies on HIV status disclosure to children in sub-Saharan Africa showed that the prevalence of HIV disclosure ranged from as low as 9% to 72% . The decAccording to the 2021 annual report of the AIDS National Program, the prevalence of HIV in the Democratic Republic of Congo (DRC) was 1.2% of the general population, and the number of PLHIV was about 506,000, of which 72,000 were children aged under 15. Of these children, only 33% were on ART, and very few had their HIV status disclosed to them.In 2007, a four-stage HIV disclosure process was created and implemented in the DRC and later described in the “WHO guideline on HIV disclosure counselling for children up to 12 years of age” in 2011 . HoweverThe main objective of this study was to assess the effects of HIV disclosure on compliance with the second-line antiretroviral therapy (ART2) after first-line ART (ART1) failure, measured by (a) the evolution of CD4+ T-cell count/percentage and viral load before and after disclosure, (b) deaths, and (c) depressive symptoms.A retrospective hospital-based study was conducted using anonymous information from patient records followed at Kalembelembe Pediatric Hospital in Kinshasa, DRC.Kalembelembe Pediatric Hospital (KLLPH) is a public health facility specializing in managing children and adolescents with infectious diseases such as HIV and was the first centre in the country (in 2002) to provide ART and follow-up to HIV-exposed children.This study included children living with HIV aged from 6 to 17 years at KLLPH between December 2004 and November 2008, whose HIV-positive status was undisclosed to them and who were non-responsive to first-line ART. Those with unknown HIV disclosure status were excluded. All medical records of the study population containing relevant information on ART and disclosure were listed.For children who were failing on first-line ART and where the parents had initially refused disclosure, a meeting was held with parents to try to convince them to approve disclosure before initiating the second-line ART. The parents of 39 children consented to the disclosure, and others did not (13 children). Eventually—after around two years—these children were also disclosed to.During the study period, the process of HIV status disclosure to children was handled by healthcare workers (HCWs). The full disclosure was done by caregivers or HCWs, involving or not “peer educators” , after cAfter disclosure, the level of acceptance was assessed, and the child was screened for possible symptoms of depression. After disclosure, the patient was encouraged to join a children’s self-support group (SSG) .Data were collected between October and December 2017 on a structured data extraction form, which was specially designed to collect information from the medical records of the patients and the database of KLLPH. Information on the disclosure of HIV status procedure, medical history, socio-demographic characteristics, the clinical stage, the CD4 count/percentage and their evolution on first-line ART, and the viral load (VL) were collected. The health status of the children was evaluated through a review of the chart for comments regarding the clinical examination and comments on the mental health of the child.The clinical stage of HIV was recorded in accordance with the WHO classification . In addiPatients who had failed first-line ART were started on second-line ART. The CD4 count/percentage was evaluated at 6, 12, 24, 36, and 48 months. The VL data were assessed to confirm ART1 failure and again after 4 years on ART2 for long-term follow-up. Patient records with missing data on one or more of the covariates were censored from the multivariable analysis. All biological analyses were conducted at the National Laboratory of Reference for HIV-AIDS in Kinshasa, using the FACSCalibur,  for CD4 count and the Abbott m 2000rt Real-time HIV-1 assay  for VL.A total of 564 patients aged 6 to 17 years old were followed up with and treated on first-line ART from 2004 to 2008 at KLLPH. Of them, 52 (9.2%) were non-responsive to first-line ART and constituted our study population.The main outcome variables were the effects of disclosure or non-disclosure of the child’s HIV status on compliance to second-line therapy evaluated by CD4 count/percentage, viral load, survival, and depressive symptoms.Disclosure: The process by which a child is incrementally made aware of their HIV status in an age-appropriate manner .Full disclosure: When the caregiver or HCW announces to a child that they are infected and uses the term HIV in their explanation during the disclosure process.Partial disclosure: When the child is informed that they have chronic health problems but not told specifically that the disease is HIV.Non-disclosure: When the caregiver or HCW reported telling the child nothing about their illness.Loss to follow-up: Children not attending the scheduled follow-up visits for three consecutive visits, and all attempts to retrieve the patient for follow-up failed without unscheduled visits noted during this period ,13.3 while on treatment [Immunologic failure: Fall of CD4 count to below baseline in the absence of concurrent infections, a fall of more than 50% from the peak value, or persistent CD4 below 100 cells/mmreatment .Immunologic recovery: An increase in CD4 count of ≥ 25% from pre-therapy baseline levels ,15.Virologic suppression: Plasma HIV-1 RNA < 1000 copies/mL after 12 months of ART .Virologic failure: Plasma HIV-RNA over 1000 copies/mL after two consecutive viral load measurements in 3 months, with adherence support following the first viral load test ,16.Depression: According to the American Psychological Association, depression or major depressive disorder is a common and serious medical illness that negatively affects how you feel, the way you think, and how you act.Evaluation of depressive symptoms after HIV disclosure:The Hamilton Depression Rating Scale (HDRS)  was used® SPSS® Statistics for Microsoft Windows version 23.0  and Stata 14  were used for statistical analysis.Data were captured on Microsoft Excel, and the IBMThe categorical variables were presented in the form of proportions with a 95% confidence interval. The average and standard deviation were calculated for continuous variables with a symmetric distribution. The median, with extremes, was calculated for continuous variables with the asymmetric distribution.p-value < 0.05 was considered as statistically significant for treatment failure.The Kolmogorov-Smirnov and Shapiro-Wilk tests, as well as the -Q plots, were used to test and appreciate the distribution of the numerical variables. Repeated-measures ANOVA was used to compare the means of different CD4 groups. Variables with skewed distribution were standardized to a standard normal distribution of a mean 0 and standard deviation of 1 to fulfil the condition of the normality of the variables to be introduced into the repeated-measures ANOVA test model. All parents of the patients signed an informed consent for participation in the HIV care program before enrolment. Children older than 12 years of age provided their assent.Ethical approval was obtained from the DRC National Health Ethics Committee at the Ministry of Health  and the Norwegian Regional Committee for Medical and Health Research Ethics (code: 2018/1525/REK Vest).A total of 564 patients aged 6 to 17 years old were followed up with and treated under ART1 from 2004 to 2008 at KLLPH. After applying the inclusion and exclusion criteria, 90.8% were either responsive to ART1 or the patient records had missing data and were excluded from the analysis. The remaining 9.2% were non-responsive to ART1 and constituted our study population .n = 39, 75%) and the group whose parents refused it   were boys. The mean age was 10.8 years , with an average treatment duration of 3.4 years (SD: 1.2). In the analysis, the children were divided into two groups: the group whose parents accepted the subsequent disclosure  .p = 0.12), There was no statistically significant difference in parental status between the two groups  of the 52 children were classified in the WHO clinical stage 1 or 2, and 37 (71%) were in stage 3 or 4. All had previously been provided first-line ART in accordance with the national guidelines. The median CD4% following initiation of first-line therapy increased from 5% (95% CI 2–10%) to 13% (95% CI 7.5–20.5%) after one year and then to 11% (95% CI 5–17.5%) after two years. None of the 52 children had been disclosed to or benefited from any disclosure process. After 3.3 years, the average CD4% fell to 5.6% (range: 0 to 31%), and the average VL was high at 255,000  RNA copies/mL, thus fulfilling the criteria for immunological and virological failure, and therefore considered as a first-line ART treatment failure.Before initiating second-line ART, 39 (75%) parents/guardians accepted to immediately start the disclosure process for partial or full disclosure of HIV status. Meanwhile, 13 (25%) parents/guardians refused to disclose HIV status to their children.Among the 39 children whose parents/guardians accepted the disclosure, 23 (59%) aged over 12 years were disclosed to within 2 to 4 weeks, and 16 (41%), aged between 6 and 11 years, started the disclosure process in line with the protocol in place.p = 0.005) at the end of the first year on second-line ART  in the first year following the initiation of second-line therapy.Among the children that were disclosed to, there was one death  and in the group not disclosed to almost half died   for the children immediately disclosed to and 144,000 RNA copies/mL  for the children disclosed to late. After four years on second-line ART, 71% (27/38) of the children immediately disclosed to had a suppressed VL versus 50% (2/4) of the children that were disclosed to late.Of the 32 children disclosed to early, depressive symptoms were documented in 23 (71.9%) of them. At the end of the first month on second-line ART, severe or very severe depression symptoms were present in 13 out of 23 children (56.5%) who had had their HIV status fully disclosed to them. Three months later, after integration into a self-support group, 1 child presented severe symptoms, and 22 children had mild, or no symptoms. At 6 months, one child presented severe symptoms.In the group not disclosed to before initiating ART2, the ones still alive were eventually disclosed to. The average age for disclosure was 15 years, and 6 out of 7 children attended an SSG. After disclosure, three children presented symptoms of depression, and two amongst them were lost to follow-up. The third had refused to take ARVs and was eventually lost to follow-up.This study is the first in DR Congo to assess the effects of HIV disclosure on ART compliance among children after first-line ART failure. Our main finding is that adolescents failing on first-line ART and moving to second-line ART do much better if they are disclosed to before the shift. This is in line with previous studies that have shown that early and full disclosure is strongly associated with improved adherence amongst ART-initiated adolescents ,19,20,21In our study, a 9.2% failure rate was recorded in patients with a low CD4% (on average 5.5%). These data are consistent with most studies in Africa where children are often diagnosed with advanced diseases. Previous studies suggest a relationship between the advanced clinical stage at therapy initiation and treatment failure ,29,30,31In our study, most registered deaths happened in the group of children whose parents had refused disclosure. Therapeutic failure and death are very often seen as part of the normal course of the disease in HIV-infected adolescents. Many families are reluctant to discuss the nature of the illness with their infected child or adolescent. Delaying disclosure of HIV infection may potentially result in negative consequences. Lack of disclosure may impair treatment understanding and participation and increase psychological and behavioural problems ,33,34,35The lessons learned are that children disclosed to had significantly better ART adherence and psychological well-being compared to children not disclosed to.Early and full disclosure is strongly associated with improved adherence in our study.In fact, the disclosure provides the adolescent with appropriate and truthful explanations of HIV, dissipates concerns or fears, clarifies misconceptions about HIV, and prepares the child or adolescent to deal with stigma. This allows them to accept their diagnosis, adhere to ART, and be more engaged with HIV care, resulting in better HIV treatment outcomes, higher self-esteem, and improved long-term mental health outcomes compared with children and adolescents not disclosed to ,36,37.However, full disclosure, even with good preparation, may be followed by mental health reactions such as sadness, anger, rebellion, fear, perceived stigma, internalized stigma, perceived shortened future, and fear of death ,36.Therefore, it is important for caregivers and HCWs to change the practice of non-disclosure of HIV status to children and to incrementally fully disclose, taking into account their cognitive skills and emotional maturity.This study is the first in DR Congo that assesses the effects of HIV disclosure on children after first-line ART failure. However, our findings require careful interpretation due to the limitations, which include: (1) the small sample size and the single study site; (2) the retrospective nature of the work that possibly led to missing data and bias; and (3) other factors that may explain poor adherence or therapeutic failure, such as primary resistance to first-line ART, were not considered.However, despite the fact that the information collected in the present study is dated, we believe that our findings are of great importance in this context as it provides useful information on current challenges faced in the process of HIV status disclosure among adolescents and its impact on monitoring.HIV disclosure to children living with HIV is important as disclosure is likely to improve both ART compliance and the overall well-being of the children.


To analyze the clinical characteristics and risk factors of viral shedding time in mildly symptomatic and asymptomatic patients with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (BA.2 and BA2.2) infection in Shanghai, and the effect of traditional Chinese medicine (TCM) treatment, so as to provide a reference basis for epidemic prevention, control and clinical treatment.A total of 6,134 asymptomatic or mildly symptomatic Omicron-infected patients admitted to Tianhua Road fangcang shelter hospital in Jinshan, Shanghai, between April 2022 and May 2022 were included. Demographic characteristics and clinical histories were collected and compared in subgroups according to the different durations of viral shedding. Spearman's correlation analysis was performed to explore the association between virus shedding time and clinical variables. Multiple linear regression was used to evaluate the risk factors for viral shedding time.Most patients with asymptomatic and mildly symptomatic Omicron infection were male, and more than half of patients had a viral shedding time of 8–15 days. The patients were divided into three groups according to the time of viral shedding: short-duration (≤ 7 days), intermediate-duration (8–15 days) and long-duration group (≥16 days). The proportion of patients aged ≤ 29 years was the highest in the short-duration group (30.2%), whereas the proportion of patients aged 50–64 yeas was the highest in the long-duration group (37.9%). The proportion of patients with the chronic non-communicable diseases among the short-, intermediate- and long-duration groups was 6.2, 9.4, and 14.9%, respectively. Among them, hypertension was the most found . By multivariate analyses, we identified that viral shedding time of Omicron variants was independently negatively correlated with male patients, TCM treatment, and manual laborers, while it was independently positively associated with age and hypertension. Additionally, TCM treatment could significantly shorten the length of viral shedding time, especially for men, age ≥30 years, comorbid chronic non-communicable diseases, unemployed people and manual worker.Our results suggested that age and hypertension were independent risk factors for the duration of viral shedding in asymptomatic and mildly symptomatic omicron infected patients. TCM can effectively shorten viral shedding time. Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, there have been several variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), one of which is the Omicron variant . The OmiIn early March 2022, a major outbreak of SARS-CoV-2 Omicron variant spread rapidly throughout Shanghai, China. This SARS-CoV-2 epidemic was the Omicron subtype BA.2 and BA.2.2. To effectively implement the “dynamic zeroing” policy, China established a large number of fangcang shelter hospitals to manage asymptomatic and mildly symptomatic patients infected with Omicron BA.2 and BA2.2 , 11. RecA key infected population in this outbreak was the asymptomatic and mildly symptomatic patients, as Omicron tended to infect primarily the upper respiratory tract, not the lungs , 14. TheGiven the serious public health threat posed by Omicron variants and the potential to undermine global efforts to control the COVID-19 pandemic, in-depth research and comprehensive understanding of Omicron is urgently needed. However, information on the characterization of the duration of nucleic acid turning negative  in the Omicron outbreak in Shanghai remains limited. Therefore, we aimed to provide a detailed description of the clinical characteristics of asymptomatic and mildly symptomatic patients with SARS-CoV-2 Omicron variants infection (both BA.2 and BA.2.2) during the epidemic period in Shanghai, as well as to analyze the risk factors associated with the timing of viral shedding. In addition, we investigated the efficacy of TCM in order to develop more effective treatment strategies in the future.The study was approved by the Ethics Committee of Tongji Hospital, Tongji University School of Medicine (K-W-2022-010). In this retrospective study, we included a total of 6,134 asymptomatic or mildly symptomatic patients admitted to Tianhua Road fangcang shelter hospital in Jinshan, Shanghai, between April 2022 and May 2022.The definitions of confirmed COVID-19 cases and SARS-CoV-2 infections were based on the Clinical Guidance for the Diagnosis and Treatment of COVID-19 Pneumonia  published by the National Health Commission of China . SARS-CoParticipants received an oropharyngeal swab for SARS-CoV-2 RT-PCR test once daily during hospitalization, and were considered to be virus cleared when two consecutive SARSCoV-2 RT-PCR tests for nucleic acid were negative [cycle thresholds >35 for both the nucleocapsid protein (N) gene and open reading frame (ORF) 1ab gene], tested at intervals of at least 24 h. The duration of viral shedding was defined as the first day of positive nucleic acid test to the date of the first negative test of consecutive negative results.Baseline information , clinical manifestations, comorbidities , TCM treatment or not, type of occupation, vaccination status, and nucleic acid test results were recorder. The kinds of TCM treatment include, Jinhua Qinggan Granule, Lianhua Qingwen Capsule, Shufeng Jiedu Capsule, Huoxiang Zhengqi Capsule, Jingyin gubiao Tang, Longyi Fangyi Tang, Longfei zhengqi Tang and Maxing Zhiliao Tang. The main source of information was medical records of the patients (both electronic and paper).t-test was used for comparison between two groups. Categorical data were expressed as number of cases (percentage), and compared using Pearson's chi-Square test. Spearman's correlation analysis was performed to explore the association between virus shedding time and clinical variables. We evaluated factors affecting viral clearance using multiple linear regression analysis. Covariates included sex, age, different comorbidities, vaccination status, TCM treatment, and type of occupation. P < 0.05 was considered to be statistically significant.All statistical analyses were performed using SPSS software . Duration of viral shedding was a continuous variable, conforming to a normal distribution, and expressed as mean ± standard deviation. Independent samples A total of 6,134 asymptomatic and mildly symptomatic patients were included in the present study. Some of the infected patients received TCM treatment. Among them, 274 (21.9%) infections were treated with TCM in the short-duration group, 767 (20.0%) cases in the intermediate-duration group, and 128 (12.1%) cases in the long-duration group. Different types of occupation were present among asymptomatic and mildly ill patients, with manual workers predominating, followed by mental workers and finally worker with no employment. Interestingly, the type of occupation may also influence the time of viral shedding. Although the proportion of manual workers was higher than that of mental workers and workers with no employment within the three viral shedding time groups, however, the proportion of manual workers decreased in the long-duration group (71.5%) compared to the short-duration group (76.3%), while the proportion of jobless people increased in the long-duration group (10.6%) compared to the short-duration group (4.2%).In addition, the largest proportion of patients in all three groups had received a full vaccination plus booster , followed by those who had received a full vaccination . Although the proportion of vaccination status was not statistically different between the three groups, numerically the highest proportion of unvaccinated in the long-duration group (22%), which was higher than the short-duration group (20.3%) and the intermediate group (18.3%). Mildly symptomatic and asymptomatic patients had received COVID-19 vaccine produced by five Chinese companies, including Sinovac Life Science (CoronaVac), Sinopharm Beijing Bio-Institute of Biological Products (BBIBP), Sinopharm Wuhan Institute of Biological Products (WIBP), CanSinoBio and Zhifei Longcom. Of all three groups, the largest proportion of patients received the CoronaVac vaccine , followed by those who received the BBIBP vaccine  .We included 6,134 patients with viral shedding time into analysis. Spearman's correlation analysis suggested that the duration of viral shedding in asymptomatic and mildly infected patients with Omicron was negatively correlated with sex, TCM therapy and occupation type, and positively correlated with age, hypertension, type 2 diabetes mellitus and vaccination status .P < 0.001. TCM therapy: β, −1.167 (95%CI: −1.466 to −0.868); P < 0.001. Manual worker: β, −0.753 (95%CI: −1.293 to −0.213); P = 0.006]. More importantly, we found an independent positive association between age, hypertension and the duration of viral shedding: age 30–39 years , 40–49 years , 50–64 years , ≥ 65 years , hypertension  . More importantly, the duration of viral shedding was obviously shorter in asymptomatic and mildly symptomatic Omicron infected patients who were male, age ≥30 years, accompanied by chronic non-communicable diseases, workers with no employment and manual workers in the treatment group compared to the non-treatment group (P < 0.05). Notably, as the age of the infected patients increased, the TCM-treated group showed a more pronounced reduction in viral clearance time compared to the untreated group.To investigate the effect of TCM therapy on the duration of viral shedding in asymptomatic and mildly symptomatic patients with Omicron infection, we classified patients into non-treatment, and TCM treatment groups according to whether they received TCM therapy or not. As shown in This study provides a comprehensive analysis of the characteristics of the timing of viral shedding and associated risk factors in asymptomatic and mildly symptomatic patients infected with the Omicron variant of SARS-CoV-2. Our findings revealed an independent negative correlation between the duration of viral shedding and men, TCM treatment and manual workers, and an independent positive correlation with age and hypertension. Furthermore, TCM treatment significantly reduced the time to viral shedding in asymptomatic and mildly symptomatic patients with Omicron infection, especially in male, aged ≥30 years, with chronic non-communicable diseases, workers with no employment and manual workers. These findings may help health professionals to better understand the characteristics of the Omicron variant and to adjust treatment regimens.Our study found a higher proportion of males than females were infected with Omicron variant in mildly symptomatic and asymptomatic patients, and men was independently and negatively correlated with the duration of viral shedding. In addition, the duration of viral clearance increased with age, and age was independently and positively correlated with time to virus shedding. We speculate that these differences may be due to the differential effects of age and sex on the immune system. Using single-cell transcriptome sequencing, one study has found that females have more plasma cells in the circulation and a stronger B-cell-activating factor of the tumor necrosis factor family (BAFF)/proliferation-inducing ligand (APRIL) system, which is consistent with a stronger adaptive immune response. In contrast, males have a higher percentage of natural killer (NK) cells in their blood and higher expression of certain pro-inflammatory genes . During in vivo, thereby increasing the risk of SARS-CoV-2 infection  of the asymptomatic and mildly symptomatic patients in this study received TCM treatment. The results showed that the time to viral shedding was significantly shorter in patients who received TCM treatment compared to those without TCM treatment, and there was an independent negative correlation between TCM treatment and time to viral shedding. Notably, as the age of the infected patients increased, the reduction in time to viral shedding was more pronounced in TCM-treated than untreated patients. These demonstrate the effectiveness and importance of TCM treatment for mildly symptomatic and asymptomatic patients infected with Omicron. Clinical evidence has previously shown that TCM can significantly alleviate the clinical symptoms, effectively reduce the incidence of severe illness and decrease all-cause mortality in patients with COVID-19 . The mecSeveral limitations should be mentioned in our study. Firstly, we only included patients from one fangcang shelter hospital in Shanghai, which is not fully representative of the overall population of Omicron-infected patients. Secondly, due to the retrospective design of this study and the lack of relevant laboratory tests (such as blood indicators), the analysis of risk factors for time to viral shedding may not be comprehensive. Thirdly, the choice of TCM therapies is mainly based on the patient's wish to use the medicine and the experience of the clinician who sees them. As a result, there may be artificial bias in the choice of TCM treatment. Finally, as only mildly symptomatic and asymptomatic infected patients were admitted to this shelter hospital, we were unable to analyze the time to viral shedding and associated risk factors in moderate, severe and critical cases.Our study describes in detail the clinical characteristics of the period of viral shedding and associated risk factors in mildly symptomatic and asymptomatic patients infected with SARS-CoV-2 Omicron variant (including BA.2 and BA.2.2). We found that the duration of viral shedding was 8–15 days in most patients. Age and hypertension were positively associated with time to viral shedding and were independent risk factors for it. However, males and manual workers were independently and negatively related to the duration of viral shedding. Additionally, TCM treatment was effective in shortening the duration of viral shedding time, especially in males, ≥30 years of age, with chronic non-communicable diseases, workers with no employment and manual workers. This study provides a theoretical basis for clinicians to more rationally determine the time to isolation and provide effective treatment for mildly symptomatic and asymptomatic patients with Omicron infection.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Ethics Committee of Tongji Hospital, Tongji University School of Medicine (K-W-2022-010). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.RL, CJ, and LZ analyzed the patient data and drafted the manuscript. DK and KH contributed to data interpretation. MX coordinated the research. QL and SL contributed to data interpretation and critical revision of the manuscript. YX and KZ designed the study, revised, and prepared the final version of the manuscript. All authors read and approved the final manuscript.


P < 0.001). The subtypes and drug resistance mutation sites are consistent in CSF and matched plasma samples of HIV-1 patients, and there is a correlation between ARV treatment and possible drug resistance, especially in CSF reservoirs. These findings highlight the concern about CSF drug resistance in HIV patients.The emergence of HIV drug resistance seriously affects the quality of life of patients. However, there has been no extensive study of CSF resistance. The aim of this study is to evaluate common HIV-1 resistance in CSF and compare it with resistance in matched plasma, and analyse the influencing factors of cerebrospinal fluid drug resistance. The matched CSF and plasma samples of 62 HIV-1 patients were tested at one study site in China . HIV genotyping and drug resistance was evaluated using the Stanford v8.7 algorithm. The diagnosis and treatment data and basic information were collected from the clinical case system, and the influencing factors of drug resistance mutations in CSF was obtained by variance analysis. CSF and matched plasma HIV-1 subtypes were confirmed in 62 patients, and the most frequent recombinant form was CRF07-BC (64.5%). Thirteen patients (21.0%) were detected with drug-resistant mutations, and the sites were consistent in both CSF and matched plasma. The drug-resistant ratios of untreated patients and treated patients were 5/51 (9.8%) and 8/11 (72.7%), respectively. The type with the highest mutation frequency was NNRTI, and no mutation was found in INSTI. Multivariate analysis indicated that ARV treatment was associated with CSF resistance ( With the development of anti-HIV drugs and the widespread application of antiretroviral (ARV) therapy, the opportunistic infection rate and mortality rate of AIDS patients have been continuously reduced, and the quality of life has been significantly improved. However, the problem of ARV drug resistance has become increasingly prominent . In highMeanwhile, studies have been shown that although ARV treatment can successfully inhibit HIV-1 in plasma, HIV-1 invading the central nervous system (CNS) can establish viral neural reservoirs through macrophages, microglia and lymphocytes ,7. The rSince the replication and mutation of HIV-1 in the CNS are relatively independent, and the concentration of ARV drugs in different tissues varies, the drug concentration decreases after passing through the blood-brain barrier ,12, as wWe retrospectively collected matched pairs of 68 HIV-1 patients who were admitted to Chongqing Public Health Medical Center from June 2020 to June 2022 and completed plasma CD4+ T cell counts, HIV RNA viral load, and CSF biochemical tests. CSF and plasma samples were tested for drug resistance of HIV genotypes. Among them, 6 patients had low CSF viral load and could not be tested for drug resistance. As a result, a total of 62 patients were included in this study, and all test reports were recorded. Demographic characteristics  and clinical data  of these patients were collected and anonymously analysed. Thus, the usual requirement of written or oral informed consent is waived. This study has been approved by the Medical Ethics Committee of Chongqing Public Health Medical Center.https://hivdb.stanford.edu/hivdb/by-sequences/) for HIV-1 subtypes and resistance mutation analysis. PCR primers are listed in Supplementary Table 1.We amplified the nucleotide sequences of HIV-1 protease, reverse transcriptase and integrase using nested polymerase chain reaction (nest-pcr) method. A viral nucleic acid extraction kit  was used to extract RNA from 00 μL plasma and CFS. The first round of PCR was performed using the HiScript® II One-Step RT-PCR Kit , followed by the second round of nested PCR using Ace Taq kit . One-step RT-PCR and nested PCR was performed on a GeneAmp®9700PCR instrument . The target bands were subjected to 1% agarose gel electrophoresis for validation, and the amplified product was sequenced. Sequences were then spliced and edited using the Seqman pro and Megalign7.0V software, and were submitted to the drug resistance database of Stanford University . The influencing factors  related to drug resistance were selected for one-way analysis of variance. Variables with significant differences in one-way ANOVA were included in multi-factor ANOVA.3, respectively. All patients were infected with non-b subtype, and the most common recombinant form was CRF07-BC (64.5%). Nucleotide reverse transcriptase inhibitors (NRTIs) were the main drugs used in ARV-treated patients  age of 51 (38–64) years  were analysed . CSF propatients .Table 1n = 5), and the mutation sites were consistent, and most of the patient had more than one mutation site. The mutation subtypes were mainly nonnucleoside reverse transcriptase inhibitor (NNRTI), nucleotide reverse transcriptase inhibitor (NRTI) and protease inhibitor (PI), and no mutation was found in integrase strand transfer inhibitor (INSTI). Among ARV-non-treated patients, 5/51 (9.8%) developed HIV-1 resistance mutations in both plasma and CSF: PI (n = 2), NNRTI (n = 3). The PI-related drug resistance mutation site was Q58E; the NNRTI-related mutation sites were mainly E138A/G and V179E (n = 1), NRTI (n = 2), NNRTI (n = 1), NRTI+NNRTI (n = 3), NRTI+NNRTI+PI (n = 1). The PI-related drug resistance mutation sites are mainly Q58E, M46I, 54 V, V82A; the NRTI-related mutation sites are mainly 184 V/I, 65 R, Y115F, K70E/Q, 41 L, D67N, K219N; the NNRTI-related mutation sites were mainly E138A/G, V179E/D, K101E/Q, 106 M/I, Y181C, G190A, K103N, and 227 L , we found that ARV treatment/non-treatment and plasma CD4 counts were significant different in between groups. Therefore, we only performed multivariate analysis of variance with the above two factors, and the P values were 0.000 for ARV treatment and 0.370 for plasma CD4 count, suggesting receiving ARV treatment contributes significantly.We assessed the association of CSF resistance with demographic and clinical factors . Age, geThis study compared the PI, NRTI, NNRTI, and INSTI resistance profiles in CSF and plasma of HIV-1 patients for the first time, and demonstrated that CSF and plasma have consistent drug resistance mutations. In addition, the statistical analysis of the factors associated with drug resistance in CSF confirmed that whether or not to receive ARV treatment was significantly associated with the resistance.−4 to 10−5, which makes it almost impossible to determine the fidelity of a single round of replication [Due to the lack of correction function of HIV reverse transcriptase at replication, physiological fluctuations of dNTP pools and asymmetric error repair, the mutation rate of replication reaches 10lication . Althouglication ,15. Thislication ,17, therCurrently, the international recommended ARV treatment regimen is combination of NRTIs and third-class drugs, and the third-class drugs can be NNRTIs or enhanced PIs or INSTIs; or compound single-tablet regimens (STR) . Long-teEmerging evidence indicate that certain antiretroviral drugs have low penetration in the CNS ,26, and In conclusion, the present study demonstrates consistent PI, NRTI, NNRTI and INSTI resistance profiles in the CNS and matched plasma of HIV-1 patients. Meanwhile, the evaluation on the influencing factors of CSF drug resistance shows that receiving ARV treatment may increase the incidence of HIV-1 drug resistance in CSF.Click here for additional data file.


Visceral leishmaniasis (VL) is an endemic parasitic disease in Latin America, and its clinical picture is aggravated in coinfections with the human immunodeficiency virus (HIV). The objective of this study was to investigate clinical factors and laboratory variables associated with VL relapse and death in VL/HIV coinfected patients.A prospective longitudinal study was conducted from January 2013 to July 2020 among 169 patients coinfected with VL and HIV. The outcomes investigated were the occurrence of VL relapse and death. Chi-square test, Mann–Whitney test and logistic regression models were used for statistical analysis.p = .005) and creatinine (p < .001). Patients who died had lower red blood cell counts (p = .012), hemoglobin (p = .017) and platelets (p < .001). The adjusted model showed that antiretroviral therapy for more than 6 months was associated with a decrease in VL relapse, and adenomegaly was associated with an increase in VL relapse. In addition, edema, dehydration, poor general health status, and paleness were associated with an increase in hospital death.The occurrence rates were 41.4% for VL relapse and 11.2% for death. Splenomegaly and adenomegaly were associated with the increased risk of VL relapse. Patients with VL relapse had higher levels of urea . Leishmania [Visceral leishmaniasis (VL) is an infectious disease caused by protozoa of the genus ishmania . It occuishmania , 3.VL cases are becoming urbanized as people leave rural areas. Meanwhile, other diseases such as the Human Immunodeficiency Virus (HIV) are advancing, as they are leaving large cities and also becoming incident in rural areas. Therefore, reports of cases of VL/HIV coinfection are starting to emerge , 5. BetwVL/HIV coinfection induces an increase in the lethality rate and an increase in the number of visceral leishmaniasis relapses, in proportions of approximately threefold and fivefold respectively, compared with HIV-negative groups. In addition, it favors the appearance of unusual clinical manifestations . Some evIn this context, understanding the clinical features and serum markers associated with VL relapses and deaths in VL/HIV coinfection may help to identify patients more susceptible to adverse outcomes and guide therapy and monitoring. Therefore, the present study aimed to investigate predictive factors for VL relapse and hospital death among patients coinfected with VL/HIV.A prospective longitudinal study was conducted with the coinfected patients who were admitted from the year 2013 to 2019 in the hospital unit where the research was conducted. These patients were treated for the coinfection, being reassessed after 12 months for analysis of the outcome. Clinical evaluation was performed along with the search for laboratory information in the medical record. It should be noted that from the year 2020 to 2022, due to the pandemic situation, it was not possible to continue the study. The present study was conducted in a reference hospital for the treatment of infectious-parasitic diseases in the city of São Luís, state of Maranhão, Brazil. The present study was approved by the Research Ethics Committee of the University Hospital of Maranhão.The study sample included 169 patients of both sexes, older than 18 years of age, who were serologically positive for HIV and had a parasitological diagnosis of VL, determined from bone marrow aspirates, according to Brazilian Ministry of Health guidance .Clinical and laboratory data were collected at the time of admission of the patient with a confirmed diagnosis of HIV and strong suspicion of VL. The outcomes investigated in this study were VL relapse and death during treatment follow-up. The definitions adopted for cure, relapse, and death followed the guidelines stipulated by the Brazilian Ministry of Health. Clinical recovery was considered when parasite suppression was maintained for a period longer than 12 months. Relapse was defined as the return of symptoms after clinical recovery. Death was defined as death after confirmation of the VL diagnosis of the patients . All patExposure and severity  variables  were colThe presence of hepatomegaly was evaluated by palpating the liver and measuring its size using the Adams Classification. The occurrences of hepatomegaly were considered mild if the liver length exceeded 1 to 2 cm, were considered moderate if there was a 3 to 7 cm increase, and were considered as major increase if the liver length was greater than 7 cm increase .The spleen was evaluated by palpation and percussion. The occurrences of splenomegaly were considered when the spleen was palpable or with the presence of flank bulging. Changes in the spleen were considered grade I , grade II , and grade III . To qualify the state of pallor, the color of the mucous membranes was examined through quantitative evaluation using a scale from 1 to 4, such that + represented mild or discrete pallor; +  + and +  +  + , moderate pallor; and +  +  +  + severe pallor .Clinical and laboratory scores were calculated by adding up the scores obtained for the presence of clinical and laboratory findings Table . For thiThe data analysis was performed using STATA version 16  and GraphPad Prism version 9.0 . The frequencies of categorical variables were compared using the chi-square test or Fisher's exact test, as appropriate. Continuous data were analyzed using the Mann–Whitney U test. Odds ratio (OR) and 95% confidence intervals (95% CI) were calculated to investigate associations of signs and symptoms with outcomes.Multiple logistic regression analysis was performed to estimate adjusted odds ratios and 95% confidence intervals. The dependent variables were VL relapse (Model 1) and hospital death (Model 2). The independent variables included in logistic model 1 were age, sex, duration of antiretroviral use, splenomegaly, and adenomegaly. The independent variables included in the logistic model 1 were age, sex, duration of antiretroviral use, emaciation, edema, dehydration, general status, and paleness. The significance level adopted was 5%.The Data on the general characterization of the sample are presented in Table P = 0.007). The patients on antiretroviral therapy for up to 6 months had higher relapse frequency (P < 0.001). In addition, VL relapse was higher in patients who had previous relapse episodes .Regarding antiretroviral therapy, most of the patients was under treatment with lamivudine, tenofovir and efavirenz (46.2%) or lamivudine, tenofovir and lopinavir/ritonavir (33.1%) schemes. It was noticed that 72.8% of the sample were under antiretroviral therapy in the period up to 6 months. Association analysis showed that the patients on the lamivudine, didanosine and lopinavir/ritonavir regimen had higher relapse frequency  and creatinine (p < 0.001). Patients who died had statistically lower counts for red blood cells (p = 0.012), hemoglobin (p = 0.017) and platelets (p < 0.001).Table p = 0.001) and laboratory scores (p < 0.001) were statistically significantly higher among patients who died.Figure The multiple logistic regression analysis is shown in Table The main findings from the present study indicated that the percentage of VL relapse was 41.4% among VL/HIV coinfected patients in this city in northeastern Brazil. In a study conducted in the state of Ceará, Brazil, 29% of coinfected patients had an unfavorable outcome, which was recurrence in 21.4% . A studyThe data from the present study showed that there were higher frequencies of coinfected men and individuals in the age group of 30 to 39 years. These results were in agreement with other data in the literature, which may reflect the fact that HIV and leishmaniasis are proportionally more prevalent among the male population than in the general population .L. donovani uptake by macrophages, thereby increasing the infection rates and levels of these cells in vitro, which suggested that this hormone had a direct influence on increasing the level of infected cells [A study has shown that male sex was a potential risk factor for seroprevalence, seroconversion and incidence . This sted cells . Other sed cells .Regarding VL treatment, it was observed in the present study that liposomal amphotericin B was the most prescribed drug therapy (72.8%), which is in line with the guidelines of the World Health Organization , 20 and In this study, liposomal amphotericin B was the drug that had the highest number of relapses and deaths. Studies show that liposomal amphotericin B has some side effects, including decreased potassium and transient changes in creatinine levels . In BrazMost of the sample was receiving highly active antiretroviral therapy (HAART) consisting of a regimen of either lamivudine + tenofovir + efavirenz (3TC + TDF + EFZ) (46.2%) or lamivudine + tenofovir + lopinavir/ritonavir (3TC + TDF + LPV/RTV) (33.1%), and it was noticed that 72.8% had only started the therapy within the last six months. In this study, patients on antiretroviral therapy for up to six months had statistically higher relapse frequency. This may be explained by the lack of complete recovery of CD4 + counts in these individuals. Even after antiretroviral therapy has been started, a certain degree of stabilization is still required for the immune system to be able to respond adequately again . Davi-Me3) have higher frequency of VL clinical manifestations [Parasitic infection together with viral infection induces chronic immune activation, which promotes increased HIV load, accelerated progression to AIDS and presence of immunological disturbances that propitiate uncontrolled parasite multiplication. Patients with decreased CD4 + cell counts (< 200 cells/mmstations , 15, 23.It was observed that 11.2% of the patients died during the follow-up of this study. This proportion was considered low in comparison with the mortality rates among coinfected patients described in other studies. It more closely resembled the mortality rates of HIV-positive people without coinfection . HoweverSantos et al.  found a Some statistically significant associations were observed with regard to the physical examination variables. Splenomegaly and adenomegaly were associated with a greater chance of VL relapse. Hurissa et al.  studied Detection of weight loss and edema in the physical examination was associated with occurrence of death. Although some studies have shown an atypical clinical manifestation in coinfected patients, other studies corroborate the present study through showing classic symptoms such as fever, weight loss and splenomegaly , 27. On In addition, present data showed that previous episodes of VL relapse were associated with new relapse. This finding can be supported by some studies that have suggested that Th2-mediated immune disorders could affect later infection control , 30.In the present study, patients with relapsing VL had statistically significantly higher levels of urea and creatinine. Other authors have also found abnormal creatinine levels in their studies with VL/HIV coinfected patients , 28. TheThe present findings showed that patients who died had statistically lower counts for red blood cells, hemoglobin and platelets. Anemia has already been reported among HIV-negative VL patients: its cause is probably multifactorial and may include immune-mediated mechanisms, changes to red blood cell membrane permeability, hypersplenism and hemolysis. Henn et al.  found siThe study showed a high frequency of relapses among VL/HIV coinfected patients, being associated with splenomegaly, adenomegaly and elevated urea and creatinine levels. The clinical variables of weight loss and edema and the laboratory variables of anemia and thrombocytopenia were associated with the outcome death. These data demonstrate the need to include other variables in the predictive models of prognosis when we talk about coinfected patients, with the need to consider the physical examination in these models focusing on the search for signs and symptoms associated with severe outcomes such as splenomegaly, adenomegaly, weight loss, and edema. These data are even more important since coinfection is more prevalent in low- and middle-income countries where resources to perform complex exams are scarcer and where physical examination becomes a powerful tool to reduce recurrence and mortality in this population.