Mortality and its association with CD4 cell count and hemoglobin level among children on antiretroviral therapy in Ethiopia: a systematic review and meta-analysis

BackgroundPeople living with HIV/AIDS (PLWHA) are frequently confronted with severe social issues such as rejection, abandonment, criticism, and stigma. This would negatively affect their quality of life. Several studies have been conducted so far to assess factors affecting the health-related quality of life among people living with HIV/AIDS who are on antiretroviral therapy (ART) in Ethiopia. However, to our knowledge, there is no previous study that has summarized the results of the studies that investigated health-related quality of life (HRQOL) among PLWHA in Ethiopia. Therefore, the purpose of this review was to estimate the pooled prevalence of HRQOL and its association with social support among people living with HIV/AIDS (PLWHA) on ART in Ethiopia.MethodsA systematic search was carried out using several electronic databases (PubMed, Science Direct, Web of Science, and Cochrane electronic), Google Scholar, Google, and a manual search of the literature on health-related quality of life among people living with HIV/AIDS who are on ART. A Microsoft Excel data extraction sheet was used to extract pertinent data from an individual study. To assess the heterogeneity of primary articles, the Cochrane Q test statistics and the I2 test were carried out, and a random effects meta-analysis was used to estimate the pooled prevalence of HRQOL.ResultOut of the 493 articles reviewed, ten with a total of 3257 study participants were eligible for meta-analysis. The pooled prevalence of HRQOL among people living with HIV/AIDS who are on antiretroviral therapy in Ethiopia was 45.27%. We found that strong perceived social support was significantly associated with higher levels of subjectively perceived HRQOL. PLWHA who were on ART and had good social support were four times more likely to report higher HRQOL when compared to their counterparts [AOR = 4.01, 95% CI 3.07–5.23].ConclusionA substantial number of PLWHA had poor HRQOL in Ethiopia. Social support was significantly associated with HRQOL among people living with HIV/AIDS. Hence, it’s recommended to encourage suitable intervention at every follow-up visit, and psycho-social support is also warranted to improve the quality of life.

To investigate the incidence of anemia and evaluate the immune status among newly reported HIV/AIDS patients in Jiangsu Province in 2021, and to identify the risk factors of anemia among patients living with HIV infections. Newly reported HIV/AIDS patients in Jiangsu Province from January 1 to December 31, 2021 that were registered in China's National AIDS Comprehensive Control Information Management System were enrolled. Subjects' fresh whole blood samples were collected, and hemoglobin levels, CD4 and CD8 cell counts and HIV viral loads were measured. Anemia was defined according to hemoglobin levels, and the immunological parameters and HIV viral loads were compared between HIV-infected patients with and without anemia. The risk factors of anemia were identified among individuals living with HIV infections using univariate analysis and multivariate logistic regression analysis. In addition, subjects' CD4 cell counts one year following antiretroviral therapy (ART) were retrieved from China's National AIDS Comprehensive Control Information Management System, and compared between subjects with and without anemia. A total of 635 newly diagnosed HIV/AIDS patients were reported in Jiangsu Province in 2021, including 544 males (85.67%) and 91 females (14.33%), and with ages of 15 to 83 years, and the overall incidence of anemia was 5.51% (35/635) among the study subjects. Men, individuals at ages of 45 years and lower and workers had relatively higher hemoglobin levels, with median hemoglobin levels of 156 (interquartile range, 22), 154 (interquartile range, 23) g/L and 162 (interquartile range, 19) g/L, respectively. The median baseline HIV viral load was 40 500.00 (interquartile range, 119 735.00) copies/mL among HIV-infected individuals with anemia and 29 754.00 (69 183.00) copies/mL among those without anemia (Z = -0.91, P = 0.31), and the median baseline CD4 and CD8 cell counts were significantly lower among HIV-infected individuals with anemia [166 (interquartile range, 143) cells/μL and 755 (653) cells/μL] than those without anemia [308 (253) cells/μL and 892 (638) cells/μL] (Z = -4.36 and -2.37, both P values < 0.05). The median CD4 cell counts remained lower among HIV-infected individuals with anemia than those without anemia [296 (interquartile range, 229) cells/μL vs. 457 (interquartile range, 313) cells/μL; Z = -3.71, P < 0.05] one year following ART, and the proportions of moderate and severe immunosuppression were significantly higher among HIV-infected individuals with anemia (40.00% and 17.14%) than those without anemia (21.00% and 9.33%) (χ2 = 10.37 and 8.79, both P values < 0.01). Univariate analysis showed a higher detection rate of anemia among female HIV-infected individuals than among males [odds ratio (OR) = 4.528, 95% confidence interval (CI): (3.811, 5.245), P < 0.001], a higher rate among HIV-infected individuals at ages of 45 to < 60 years [OR = 3.415, 95% CI: (1.191, 9.788), P = 0.022] and 60 years and older [OR = 5.820, 95% CI: (2.013, 16.826), P < 0.001] than among those at ages of 15 to < 30 years, a higher rate among HIV-infected individuals through heterosexual transmission than among those through homogeneous transmission [OR = 3.015, 95% CI: (1.423, 6.387), P = 0.004], a lower rate among HIV-infected individuals with an educational level of college and above than among those with an educational level of primary school [OR = 0.103, 95% CI: (0.028, 0.386), P < 0.001], a higher rate among HIV-infected individuals with baseline CD4 cell counts of < 200 cells/μL than among those with baseline CD4 cell counts of 200 cells/μL and higher [OR = 4.340, 95% CI: (2.165, 8.702), P < 0.001], and lower detection rates among HIV-infected individuals with CD4/CD8 cell ratios of 0.208 to < 0.326 [OR = 0.232, 95% CI: (0.076, 0.711), P = 0.011] and 0.516 and higher [OR = 0.292, 95% CI: (0.104, 0.818), P = 0.019] than among those with CD4/CD8 cell ratios of < 0.208. Multivariate logistic regression analysis identified woman [OR = 4.945, 95% CI: (3.944, 5.946), P = 0.002], and CD4 cell counts of < 200 cells/μL [OR = 3.597, 95% CI: (1.448, 8.937), P = 0.006] as risk factors of anemia among newly reported HIV/AIDS patients. The incidence of anemia was low among newly reported HIV/AIDS patients in Jiangsu Province in 2021, and the immune status was poorer among HIV-infected individuals with anemia than those without anemia at baseline and one year following ART. Female and CD4 cell counts of < 200 cells/μL are risk factors of anemia among individuals living with HIV infections, and intensified surveillance, follow-up and precision interventions are recommended targeting female HIV-infected individuals and HIV-infected individuals with low CD4 cell counts.

Total lymphocyte counts (TLC) may be used as an alternative for CD4 cell counts to monitor HIV infection in resource-limited settings, where CD4 cell counts are too expensive or not available. We used prospectively collected patient data from an urban HIV clinic in Indonesia. Predictors of mortality were identified via Cox regression, and the relation between TLC and CD4 cell counts was calculated by linear regression. Receiver operating characteristics (ROC) curves were used to choose the cut-off values of TLC corresponding with CD4 cell counts <200 and ≤350 cells/μl. Based on these analyses, we designed TLC-based treatment algorithms. Of 889 antiretroviral treatment (ART)-naïve subjects included, 66% had CD4 cell counts <200 and 81% had 350 ≤ cells/μl at baseline. TLC and CD4 cell count were equally strong predictors of mortality in our population, where ART was started based on CD4 cell count criteria. The correlation coefficient (R) between TLC and √CD4 was 0.70. Optimal cut-off values for TLC to identify patients with CD4 cell counts <200 and ≤350 cells/μl were 1500 and 1700 cells/μl, respectively. Treatment algorithms based on a combination of TLC, gender, oral thrush, anaemia and body mass index performed better in terms of predictive value than WHO staging or TLC alone. In our cohort, such an algorithm would on average have saved $14.05 per patient. Total lymphocyte counts is a good marker for HIV-associated mortality. Simple algorithms including TLC can prioritize patients for HIV treatment in a resource-limited setting, until affordable CD4 cell counts will be universally available.

4.0 When to start

Introduction HAART has changed HIV infection from a life-threatening into a chronic condition, but the perspective of never ending HAART once it is started is daunting to many patients. As a cure for HIV infection is currently not in sight, many patients, who are looking to their physicians for advice on whether to stop HAART. New drugs and combinations with better safety and tolerability profiles have become available [1], but there will still be patients who experience toxicity [1–3]; therefore, being able to take a rest from HAART may be beneficial [3]. Worldwide, the cost of treating millions with HIV burdens fragile economies; intermittent treatment promises relief. Many scheduled treatment interruption (STI) studies have shown between 40 and 60% drug savings with STI [4–7]. For example, in Thailand, using the least expensive generic fixed dose combination of stavudine/lamivudine/nevirapine, which is US$27 per month, the yearly cost of treating 500 000 HIV-positive individuals would decrease from US$162 million to US$81 million if there is a 50% drug saving. Moreover, STI may provide the possibility that a potent and inexpensive drug such as stavudine may still be used without many side-effects, whereas continuous use would necessitate its replacement by a less toxic, but mostly more expensive, alternative. However, these potential benefits have to be weighed against the potential risks, which include the development of HIV-related illnesses, acute retroviral syndrome, thrombocytopenia, resistance and the transmission of HIV infection. Most individuals with HIV are diagnosed and treated during the chronic HIV infection phase. In those who have a good response to HAART and are able to maintain high CD4 cell counts and low viral loads (VL) for a period of time, STI have been explored with fixed time strategies (such as one week on–one week off, or 8 weeks on–8 weeks off) or with varying off times depending usually on the CD4 cell count. The goal here is to improve quality of life and HAART toxicity, and lower costs while maintaining patients' good health and a safe level of CD4 cells. Initial studies have been small and uncontrolled: later studies have enrolled several hundred patients and used a control group of continuously treated patients. On the whole, results have been quite encouraging [4,8–13], although a meta-analysis of STI studies published from January 1996 to March 2005 concluded that evidence to support STI was inconclusive [14]. In January 2006, the largest STI study that had recruited over 5000 patients, the Strategy for Management of Antiretroviral Therapy (SMART), showed that an STI strategy using CD4 cell counts as a guide led to an increase in HIV and non-HIV-related serious illnesses. As a result, recruitment was stopped prematurely [15]. In this article, we present the rationale for STI, review data on STI in chronic HIV infection, consider the effects of STI on the selection of resistance mutations, and finally provide a perspective on the future of STI. CD4 cell counts after stopping treatment CD4 cell counts decline when HAART is stopped. The most dramatic drop occurs within the first 4 weeks, with an average decline of 30 cells per week, followed by a more gradual drop of 3 cells per week (Fig. 1) [16]. The VL usually becomes detectable 7 days after stopping and peaks at approximately 4 weeks [9]. CD4 cell counts tend to drop faster in patients who have low pre-antiretroviral CD4 cell counts, high CD4 cell counts before stopping and high VL at the time of peak VL rebound [11,16,17].Fig. 1: Biphasic CD4 cell count decline after scheduled treatment interruption. The error bars show standard errors of the mean for scheduled treatment interruptions (STI) lasting 4 and 24 weeks. They have been deleted for STI lasting 8 and 12 weeks so as not to overload the figure. Data are from patients enrolled in the Staccato and the Swiss Spanish Intermittent Treatment Trial studies showing CD4 cell declines in patients who were able to stop HAART for different lengths of time until they reached the CD4 cell count of less than 350 cells/μl restart criteria. It shows that patients with lower baseline CD4 cell counts reached the CD4 cell count restart criteria (< 350 cells/μl) before week 24. Regardless of the STI time, the CD4 cell count dropped most dramatically during the first 4 weeks, after which a more gradual drop was seen [19].STI ≥ 24 weeks (112);STI 12 weeks (19);STI 8 weeks (43);STI 4 weeks (24).Scheduled treatment interruption studies in chronic HIV infection using CD4 cell counts as a guide Stopping and starting treatment based on the CD4 cell count makes sense as it is the best predictor of HIV disease progression. This is also a strategy that is tailored to each individual's immune status providing an opportunity for some to be off treatment for a long time, particularly those who have a high baseline CD4 cell count. The different randomized CD4 cell count-guided (referred to hereafter as CD4-guided) studies in VL-suppressed patients are summarized in Table 1. Because of space constraints, studies that are non-randomized or use non-standard HAART are not described [12,13,18–21].Table 1: Summary of randomized CD4 cell count-guided scheduled treatment interruption study results in virally suppressed chronic HIV-infected patients.Table 1: (Continued)The BASTA study randomly assigned 69 patients with CD4 cell counts greater than 800 cells/μl to either continuous or CD4 cell count-guided HAART with a CD4 cell count restart criteria of less than 400 cells/μl. They found that the CD4 arm was safe and most CD4 arm patients could stop for at least one year. After 64 weeks, 72% had CD4 cell counts greater than 400 cells/μl [11]. The Tibet study randomly assigned 201 patients with CD4 cell counts greater than 500 cells/μl and VL less than 50 copies/ml to CD4 cell count-guided HAART (stopping at CD4 cell counts ≥ 500 cells/μl and starting at CD4 cell counts < 350 cells/μl compared with continuous HAART). At 96 weeks, no one had AIDS-defining illness. The CD4 arm had more adverse events related to the antiretroviral syndrome after stopping treatment but had lower adverse events from antiretroviral drugs. The drug exposure was 68% less in the CD4-guided arm compared with the continuous arm [22]. The ACTG 5102 evaluated the use of IL-2 in combination with HAART to raise the CD4 cell count before stopping therapy. Almost all patients were able to stop for the 48-week duration and the HAART plus IL-2 arm had a higher CD4 cell count [23]. The HIV–NAT 001.4 study was a pilot study to Staccato but was performed in a different patient population: HIV–NAT 001.4 patients were dual nucleoside reverse transcriptase inhibitor (NRTI)-pretreated compared with patients newly treated with HAART in Staccato; nevertheless, the results were similar to those of Staccato [4]. Staccato evaluated a CD4 stop/start threshold of 350 cells/μl in 284 patients who had baseline CD4 cell counts greater than 350 cells/μl in comparison with 146 continuous HAART patients. After almost 2 years, there was no difference in AIDS or death [one death or 0.4 per 100 person-years (100PY) in the continuous versus one death or 0.2 per 100PY in the STI arms] and the VL responses to HAART retreatment were the same between the arms, with approximately 90% having VL of less than 50 copies/ml. The CD4 arm, however, had more minor HIV-related illnesses including oral and vaginal candidiasis (2.28/100PY in the CD4 versus 0.34/100PY in the continuous arms, P = 0.04). The CD4 arm had lower CD4 cell counts (median 374 cells/μl in the CD4 arm versus 601 cells/μl in the continuous arm), but the CD4 cell count rose rapidly after 12 weeks of HAART and there was a significant antiretroviral drug saving. In addition, the patients in the CD4 arm felt they had less lipodystrophy and the cholesterol level was lower, and more patients in the continuous arm had antiretroviral drug-related neuropathy and diarrhea [5]. Trivacan and SMART both enrolled patients with similar criteria as Staccato, baseline CD4 cell counts greater than 350 cells/μl and VL below the limits of detection (the latter for all patients in Trivacan and 72% in SMART), but the start/stop CD4 cell count criteria were different. Both of the studies stopped HAART at CD4 cell counts greater than 350 cells/μl and restarted at CD4 cell counts of less than 250 cells/μl, whereas in Staccato, treatment started again when the CD4 cell count declined below 350 cells/μl. In Trivacan and SMART, there was a significantly higher incidence of serious illnesses in the CD4-guided arm. In the Trivacan study, World Health Organization stage 3 and 4 illnesses were found in 17.6/100PY in the CD4 arm compared with 6.7/100PY in the continuous arm (P = 0.001). The most common serious illness was invasive bacteremia, which is not usually considered an HIV-related disease. Oral and vaginal candidiasis was also more common in the CD4 arm [7]. In the SMART study, the number of opportunistic infections (OI)/death events were 3.3/100PY in the STI arm compared with 1.3/100PY in the continuous arm (P < 0.0001). The excess risk of the CD4-guided arm was evident in all subcategories: death (1.5/100PY in CD4 arm versus 0.8/100PY in the continuous arm, P = 0.007), serious OI (0.4/100PY in the CD4 arm versus 0.1/100PY in the continuous arm, P = 0.01) and non-serious OI (1.7/100PY in the CD4 arm versus 0.5/100PY in the continuous arm, P < 0.001). The most common cause of death was cancer, and only 7% of death was due to OI. Esophageal candidiasis was the most common non-fatal OI in both arms. For non-OI events, major cardiovascular diseases and metabolic events were 1.8/100PY in the CD4 arm compared with 1.0/100PY in the continuous arm (P = 0.01) with events in the subgroups as follow: fatal or non-fatal cardiovascular diseases (1.3/100PY in the CD4 arm versus 0.8/100PY in the continuous arm, P = 0.06), fatal or non-fatal renal disease (0.2/100PY in the CD4 arm versus 0.1/100PY in the continuous arm, P = 0.05) and fatal or non-fatal liver disease (0.3/100PY in the CD4 arm versus 0.2/100PY in the continuous arm, P = 0.46). The grade 4 severe toxicities according to the Division of AIDS, National Institutes of Allergy and Infectious Diseases grading system were not different in the two arms (4.8/100PY in the CD4 arm versus 4.1/100PY in the continuous arm, P = 0.20; the SMART Study Group, in preparation). SMART patients had up to 4 years of follow-up, with a mean follow-up time of 16 months. Patients were able to stop for a long period: the median first period off HAART was 18 months (interquartile range 5.8–43.5 months). The hazard ratio of both OI/non-OI events appear to increase in the CD4 arm after 8 months in the study. Most patients stopped only once, with approximately 12 and 2% stopping two or three times. With the HAART re-initiation CD4 cell count criteria of less than 250 cells/μl, the time patients spent at CD4 cell counts below 200 cells/μl was only 3% of the total study time. The median CD4 cell count at re-initiation was 234 cells/μl. After HAART retreatment, the response was good, with a median time to VL suppression below 400 of 3.1 months and a CD4 cell increase of 164 cells by 8 months. On average, the CD4 arm had 206 less CD4 cells/μl than the continuous arm. The time on HAART was much less in the CD4 arm (33%) compared with the continuous arm (94%). In SMART, the following factors did not significantly affect the number of OI/non-OI deaths: sex, CD4 cell nadir, duration of previous HAART and baseline antiretroviral status (naive, experienced, on/off antiretroviral drugs). Older patients had a higher risk of AIDS/death events in both treatment groups. Being black increased the relative risk of events, comparing CD4-guided to continuous treatment. As expected, the superiority of the continuous suppression arm was mainly apparent in those patients whose viremia was undetectable at baseline. In the patients with non-suppressed viremia, the continuous and CD4-guided arms had similar outcomes: Non-suppressed viremia is evidence for ineffective treatment; whether such ineffective treatment is given continuously or intermittently doesn't matter. The analysis of SMART was based on the ‘latest’ CD4 cell counts and VL results. These are values, before and closest, to the OI/non-OI endpoint events. As expected, having a lower latest CD4 cell count and higher latest VL puts one at more risk of both OI (fatal and non-fatal) and non-OI deaths for both the CD4 and continuous arms. Those who developed OI had a latest CD4 cell count at least 75–100 cells lower than the whole group for both arms. The latest CD4 cell count before OI events tended to be lower than those before non-OI events in both arms. What is surprising is that the risk of both OI and non-OI events is statistically higher in the CD4 arm than the continuous arm only when the latest CD4 cell counts exceeds 350 cells/μl. Regarding the VL, the risk of OI (fatal and non-fatal) but not the non-OI deaths in both arms seems to be highest when the latest VL is greater than 50 000 copies/ml. But surprising again, the relative risk of both OI and non-OI deaths is higher in the CD4 arm compared with the continuous arm only when the latest VL is 400 copies/ml or less [15]. What does this all mean? The typical SMART patient had a high baseline CD4 cell count (the median pre-STI CD4 cell count in SMART was 597 cells/μl), a potentially large CD4 cell count drop to below 250 cells/μl and exposure to high VL over a long stop time (median time off after the first interruption in SMART was 18 months). In contrast, the typical STACCATO patient had lower CD4 cells at baseline, and the median time to starting treatment again was only 18 weeks. Do the OI and non-OI events occur while the patients are still off HAART, or do they occur after HAART has been restarted during the CD4 cell increase and VL decline period? This may be similar to an immune reconstitution syndrome model in which the inflammation occurs when there is a rapid decline in VL and a rise in the CD4 cell count, with an increased incidence of OI shortly after starting HAART. However, immune reconstitution syndrome occurs in patients who are more severely immune suppressed than those who participate in STI studies and indeed, analyses of SMART data presented in meetings are not in favor of this hypothesis. The results of SMART confounded expectations, as shown in Table 2.Table 2: Expectation and reality in SMART.In summary, of the six randomized studies, vour (BASTA, ACTG 5102, HIV–NAT 001.4 and Staccato) used a CD4 cell count restart threshold of 350–400 cells/μl and did not see increased serious morbidity, and two studies (Trivacan and SMART) used a CD4 cell count restart threshold of 250 cells/μl and saw an increase in serious morbidity. This suggests that the CD4 cell count restart threshold is important; however, all the studies except SMART did not have the statistical power to find a difference in serious morbidity. Therefore, at this time, it is unclear whether ‘safe’ CD4 cell count stop/start thresholds (thresholds at which the difference between an STI and continuous treatment strategies would disappear) can be defined. Other factors than the CD4 cell counts may be important. Compared with SMART, Staccato enrolled patients with lower CD4 cell counts. In the CD4 cell count-guided arm, HAART was restarted at 350 CD4 cells compared with 250 in SMART. Consequently, STI were much shorter in Staccato (median of 18 weeks compared with 18 months in SMART). Another study (Windows) mentioned below used STI with a fixed length of 8 weeks. Both Staccato and Windows showed no excess in OI/death events in the STI arms. Had the event rates of SMART applied to Staccato, 16 such events would have been expected in the CD4 arm, whereas only one was observed. On the basis of these data, one may speculate that the length of treatment interruptions matters; long, but not short STI increase the risk of OI/death. It will also be important to decide globally whether a strategy that has the potential of reducing drug costs and toxicity and improving quality of life should be abandoned altogether as a result of the 2.0/100PY difference in the AIDS/death rate compared with continuous HAART in SMART. Further information from SMART on the non-HIV-related illness occurrence such as cardiovascular complications in the CD4 arm and the CD4 dynamics in those who developed disease progression will help in understanding how best to proceed. Scheduled treatment interruption studies in chronic HIV infection using fixed time intervals Fixed time STIs aim to maintain a high CD4 cell count with a low risk of disease progression, and to achieve significant drug savings using a simple predictable strategy that a patient could easily follow. The different randomized studies in virally suppressed patients are shown in Table 3.Table 3: Summary of randomized fixed cycle scheduled treatment interruption study results in virally suppressed chronic HIV-infected patients.Dybul et al. [8] used a week on–week off schedule and showed that eight out of eight patients were able to maintain their VL at less than 50 copies/ml at the end of the week off for 68 weeks. However, the HIV–NAT 001.4 and the Staccato study, both randomized studies comparing one week on–one week off with continuous and CD4 cell count-guided HAART, showed virological failure rates of 46 and 53%, respectively, leading to premature termination of that arm [4,24]. Fortunately, no significant resistance mutations were found in patients who failed, and when retreated with continuous HAART using the same regimen the VL was resuppressed. The Five Days On, Two Days Off (FOTO) study, aim to give patients the weekend off of drugs, enrolled 30 subjects with CD4 > 200 cells/mm3 and undetectable VL who were on different HAART: 10 on NVP-based, 10 on EFV-based and 10 on PI-based. All those taking EFV had VL < 50 copies while 1 or 2 of those in the arms had VL This may be due to the long life of EFV level was at the end of interruption Another fixed time strategy that had to be prematurely of an increased risk of resistance mutations to and reverse transcriptase inhibitor used an 8 weeks weeks off After the of patients, out of in the STI arm had resistance Most of the patients were treated with before HAART and may have had mutations that to the resistance seen after STI at an 8 schedule but with an off time of 4 weeks. The study enrolled with a CD4 cell count greater than 100 cells/μl, VL less than 200 copies/ml and CD4 cell count of cells/μl or greater for months or to analysis showed no significant difference in and in the of patients with CD4 cell counts less than cells/μl at 96 in STI compared with in the continuous arms. The with VL of 400 copies/ml or less was lower in the STI compared with 90% in the continuous arm (P = but the incidence of virological failure and resistance were not different. was also a drug savings et al. a study to whether short interruptions before a long stop can the time to a VL greater than 5000 copies/ml. the first patients either three fixed interruptions of 4 and weeks, followed by HAART for up to weeks until the VL was less than 50 copies/ml or continuous HAART for 40 weeks. In both stopped HAART. The time to a VL of greater than 5000 copies/ml was approximately one in both groups. Treatment failure was more common in the continuous however, more resistance mutations during VL rebound were in the interruption arm, but almost all patients their VL after the same also evaluated a fixed time strategy with STI of 2 and 3 each followed by 3 months of compared with continuous HAART. The median of CD4 cell to end of follow-up was cells in the STI At 24 more than 90% in both arms had VL of less than 400 and time to failure as VL > 400 copies/ml after 12 weeks of were not different. mutations were found in out of STI patients, to a risk of resistance at 24 months of These were to be patients on their first regimen without evidence of however, many had resistance mutations in their baseline and In summary, the study did not from a with lengths of STI. The of Therapy in is an randomized to HIV disease progression with antiretroviral in by two compared with plus and STI weeks weeks off) compared with continuous HAART. The has enrolled patients in and data of patients followed up to 40 weeks showed that the STI arm had more HIV-related disease progression compared with the continuous arm most of the events were which were and did not these were considered important; therefore, the STI arm patients continuous HAART as of March concluded that the 12 weeks weeks off strategy be in patients who have had a short time before STI and had low CD4 cell counts and many illnesses before HAART, such as those who in the in scheduled treatment interruption studies After treatment is drug decrease at and have shorter than study in showed that after a dose of the median time to an undetectable level was days 10 to > in and days in is with the which is common in it could take more than days for to be the end of these drug HIV to potentially the stage for the selection of a dose of to transmission of resistance mutations occur This is the in most STI studies stopped between 3 and days before in an to the risk of resistance The selection of resistance mutations is particularly if mutations in before STI However, there is evidence that mutations are during STI, and it that the risk may be highest with and with In the study by et al. of the patients randomly assigned to the continuous arm but out of of the 8 weeks off arm had This was mainly treated with but the patients were at more risk as three out of eight patients had two with and one with The two patients were on and had resistance to one with and one with It is important to that most patients had a of previous antiretroviral treatment Table Data on resistance from scheduled treatment interruption the Swiss Spanish Intermittent Treatment Trial patients had of 2 weeks weeks and of the patients whose VL was 50 copies/ml after 8 weeks of retreatment had mutations, mostly In the Tibet study one CD4 arm patient and continuous arm patients had virological CD4 arm patients had performed on a viral rebound after and had the virological failure patients in the continuous arm, were and three had mutations were mostly but many of the mutations were newly In HIV–NAT 001.4 using there was no selection of major mutations in the CD4 arm, many patients had a of treatment failure on previous Staccato had out of 146 continuous arm and 10 out of 284 CD4 arm patients with at least one VL greater than 500 copies/ml after at least 12 weeks on HAART only one STI patient and three continuous treatment patients had mutations, mostly to resistance mutations were in patients who at least two STI only had mutations and almost all were to with patients also having or Compared with patients who the relative risk of resistance was for (P = and for (P = et al. more VL failure in the continuous arm out of compared with the fixed interruption arm out of In this 60% were on all the were patients had no VL but resistance mutations were during the long three out of in the continuous arm and out of in the fixed interruption arm. These were most mutations as the patients were drug out of 12 patients the same and viral saw a similar rate of virological failure in the STI compared with the continuous arms, but 40 out of STI subjects developed mutations, to a risk over 24 months. In the STI arm, baseline greater than copies/ml and mutations during STI with the risk of virological Most patients had mutations in at baseline, and this resistance selection after STI (P = 0.001). The use of also selection (P = These results should be with as resistance data on the continuous arm are In at the and of continuous and CD4 cell count-guided arms had resistance mutations to at least one drug (P = The is and there is no information at this time [7]. Other scheduled treatment interruption retroviral syndrome can occur with high VL rebound during STI. In Staccato, acute retroviral syndrome was seen in with VL greater than 250 000 copies/ml in [5]. The acute HIV infection. can occur Treatment is to HAART. can also occur during STI of a previous of HAART the count. The risk of transmission during VL rebound is a major health HIV increase in a with CD4 cell counts is not it was in of almost 5000 HIV-infected Swiss individuals of HIV transmission during treatment interruption has been in a who stopped safe after the VL undetectable on HAART, and to have during an STI cycle Therefore, an HAART interruption should be considered at a high risk of HIV during It is the of the to the of safe during treatment interruption. on the future of scheduled treatment interruption SMART many without at the present time. the most important of these is how to the between SMART and STI studies such as Had the event rates of SMART applied to Staccato, between and events would have whereas one event was observed. The most important between the two studies are the CD4 cell count at which treatment was started again cells/μl in SMART and 350 cells/μl in the length of time on HAART before the study months in SMART and months in the length of interruption months in SMART and 18 weeks in Staccato) and the of the patients (median 46 years in SMART and years in Staccato) It is but from that these particularly the duration of previous treatment and of interruption and the lower CD4 cell count threshold for HAART in SMART, to the The SMART study was large and with results that show that STI increase the risk of AIDS-defining OI and the benefits in of cardiovascular and were not it in a in the results of SMART have started HAART, never can stop is the future for quality of life is to many patients may that their of is by time off drugs, and the one in risk of is Moreover, the to for drug toxicities will an important in HIV treatment. is to STI will using higher CD4 cell count and stop criteria. the SMART however, it is that large study with will be data on fixed cycle STI with at least 8 weeks on HAART are and may provide an of a STI with a 50% in drug use and In the SMART study the of only after also that shorter STI be

Antiretroviral treatment programs in sub-Saharan Africa have high rates of early mortality and loss to follow-up. Switching to second-line regimens is often delayed because of limited access to laboratory monitoring. Retrospective analysis was performed of a cohort of adults who initiated a standard first-line antiretroviral treatment at 5 public sector sites in 3 African countries. Monitoring included routine CD4 cell counts, human immunodeficiency virus RNA measures, and records of whether appointments were kept. Incidence and predictors of death, loss to follow-up, and switch to second-line regimens were analyzed by time-to-event approaches. A total of 3749 patients were analyzed; at baseline, 37.1% were classified as having World Health Organization disease stage 3 or 4, and the median CD4 cell count was 192 cells/mL. First-line regimens were nevirapine based in 96.5% of patients; 17.7% of patients attended <95% of their drug pickup appointments. During 4545 person-years of follow-up, mortality was 8.6 deaths per 100 person-years and was predicted by lower baseline CD4 cell count, lower hemoglobin level, and lower body mass index (calculated as weight in kilograms divided by the square of height in meters); more-advanced clinical stage of infection; male sex; and more missed drug pickup appointments. Dropouts (which accrued at a rate of 2.1 dropouts per 100 person-years) were predicted by a lower body mass index, more missed visits and missed drug pickup appointments, and later calendar year. Incidence of switches to second-line regimens was 4.9 per 100 person-years; increased hazards were observed with lower CD4 cell count and earlier calendar year at baseline. In patients who switched, virological failure was predicted by combined clinical and CD4 criteria with 74% sensitivity and 30% specificity. In an antiretroviral treatment program employing comprehensive monitoring, the probability of switching to second-line therapy was limited. Regular pickup of medication was a predictor of survival and was also strongly predictive of patient retention.

ObjectiveTo examine the association between nutritional markers at initiation and during follow up in two different cohorts of HIV-infected adults initiating highly active antiretroviral therapy (HAART) in West Africa.MethodsThe ATARAO study was a one year prospective study carried in Mali. It consisted of a sample of consecutive patients initiating HAART in one of four participating centers during that period. Data were collected at time of treatment initiation (baseline) and every 3 months thereafter. The ANRS 1290 study followed Senegalese patients recruited in similar conditions. Bivariate analyses were used to identify nutritional and immunological covariates of malnutrition at baseline. Longitudinal trajectories of body mass index, hemoglobin and albumin, and their associated factors, were evaluated using mixed linear models.ResultsIn ATARAO, 250 participants were retained for analyses; of which, 36% had a BMI < 18.5 kg/m2, nearly 60% were anemic and 47.4% hypoalbuminemic at time of treatment initiation. At baseline, low hemoglobin, hypoalbuminemia and low CD4 levels were associated with a BMI < 18.5 kg/m2. Similarly, low BMI, low albumin and low CD4 counts were linked to anemia; while, hypoalbuminemia was associated with low hemoglobin levels and CD4 counts. In ANRS, out of the 372 participants retained for analyses, 31% had a low BMI and almost 70% were anemic. At baseline, low BMI was associated with low hemoglobin levels and CD4 counts, while anemia was associated with low CD4 counts and female sex. While treatment contributed to early gains in BMI, hemoglobin and albumin in the first 6 months of treatment, initial improvements plateaued or subsided thereafter. Despite HAART, malnutrition persisted in both cohorts after one year, especially in those who were anemic, hypoalbuminemic or had a low BMI at baseline.ConclusionIn ATARAO and ANRS, malnutrition was common across all indicators (BMI, hemoglobin, albumin) and persisted despite treatment. Low BMI, anemia and hypoalbuminemia were associated with attrition, and with a deficient nutritional and immunological status at baseline, as well as during treatment. In spite of therapy, malnutrition is associated with negative clinical and treatment outcomes which suggests that HAART may not be sufficient to address co-existing nutritional deficiencies.

BackgroundOver 420,000 people have initiated life-saving antiretroviral therapy (ART) in Ethiopia; however, lost-to-follow-up (LTFU) rates continues to be high. A clinical decision tool is needed to identify patients at higher risk for LTFU to provide individualized risk prediction to intervention. Therefore, this study aimed to develop and validate a statistical risk prediction tool that predicts the probability of LTFU among adult clients on ART.MethodsA retrospective follow-up study was conducted among 432 clients on ART in Gondar Town, northwest, Ethiopia. Prognostic determinates included in the analysis were determined by multivariable logistic regression. The area under the receiver operating characteristic (AUROC) and calibration plot were used to assess the model discriminative ability and predictive accuracy, respectively. Individual risk prediction for LTFU was determined using both regression formula and score chart rule. Youden index value was used to determine the cut-point for risk classification. The clinical utility of the model was evaluated using decision curve analysis (DCA).ResultsThe incidence of LTFU was 11.19 (95% CI 8.95–13.99) per 100-persons years of observation. Potential prognostic determinants for LTFU were rural residence, not using prophylaxis (either cotrimoxazole or Isoniazid or both), patient on appointment spacing model (ASM), poor drug adherence level, normal Body mass index (BMI), and high viral load (viral copies > 1000 copies/ml). The AUROC was 85.9% (95% CI 82.0–89.6) for the prediction model and the risk score was 81.0% (95% CI 76.7–85.3) which was a good discrimination probability. The maximum sensitivity and specificity of the probability of LTFU using the prediction model were 72.07% and 83.49%, respectively. The calibration plot of the model was good (p-value = 0.350). The DCA indicated that the model provides a higher net benefit following patients based on the risk prediction tool.ConclusionThe incidence of LTFU among clients on ART in Gondar town was high (> 3%). The risk prediction model presents an accurate and easily applicable prognostic prediction tool for clients on ART. A prospective follow-up study and external validation of the model is warranted before using the model.

The aim of this study was to evaluate HIV-1 drug resistance among patients failing first-line antiretroviral therapy in Ethiopia. A total of 699 adults infected with HIV (aged ≥15 years) who failed first-line Antiretroviral Therapy (ART) were recruited between 2017 and 2019 from 63 ART-providing sites in Ethiopia. Treatment failure was defined as patients with two consecutive viral loads (VLs) ≥1000 copies/mL within six months of follow-up. The pol gene region of HIV-1 was amplified and sequenced using an in-house assay of the Chinese Center for Disease Prevention and Control. The Stanford HIVDB v9.0 algorithm was used for identification of resistance mutations. Resistance mutations were characterized according to the 2019 International AIDS Society-USA mutation list. P values of <0.05 were considered statistically significant during multivariate analysis, which was done using SPSS v26.0 (SPSS Inc., Chicago, IL). Overall, HIV drug resistance (HIVDR) among patients failing first-line therapy in Ethiopia was 77.8%. Non-nucleoside/tide reverse transcriptase inhibitors (NNRTI) and NRTI resistance were 75.7% and 71.2%, respectively. Neverapine (NVP) and Efavirenz (EFV) accounted for 74.2% and 60.8% of HIVDR, respectively. About half (48.1%) of NRTI-associated mutations were responsible for Abacavir resistance, while 34% were responsible for multi-NRTI resistance. Mutations responsible for resistance to the commonly used EFV and NVP accounted for 62.9%, while resistance to Etravirine, Doravirine, and Rilivirine, which were not part of the country's ART program, were 37.1%, and can be explained by cross-resistance within the drug class. Protease Inhebitor(PI)associated resistance was detected in only 1.6% of the study's participants. The most common mutations identified were M184V (30.1%), K103N (18.7%), Y181C (13.6%), and K65R (12.1%). In a multivariate logistic regression analysis, predictors of HIVDR were prior ART exposure (adjusted odds ratio [AOR]=2.3; 95% confidence interval [CI]=1.8, 3.6), absence of HIV status disclosure (AOR=2.05; 95%CI=1.26, 3.35), CD4 count of ≤200 cells/mm3 (AOR=1.94; 95%CI=1.21, 3.12), and bedridden status (AOR=4.16; 95% CI=3.21, 5.16). The high-levels of HIVDR among patients with failure of first-line ART in Ethiopia calls for individualized HIVDR testing. Mutations associated with multi-NRTI and NNRTI cross-resistance may alert the program for considering drugs of higher genetic barrier targeting protease and other regions. Patients with low CD4 count and those who are bedridden should be given special attention for the potential development of HIVDR during clinical management.

BackgroundSince launching of antiretroviral (ART) treatment, the numbers of patients enrolled in to ART are increasing in many developing countries. But many studies done across Africa including Ethiopia on antiretroviral therapy programs have shown higher mortality at the first six months of treatment initiation. But the factors associated with this high mortality are poorly characterized. So this study aims to determine mortality and identify predictors of it among patients on ART.MethodsRetrospective cohort study was employed among a total of 520 records of patients who were enrolled on antiretroviral therapy in Aksum hospital from September 2006 to August 2011. Baseline patient records were extracted from electronic and paper based medical records database and analysed using Kaplan Meier survival and Cox proportional hazard model to identify the independent predictors of mortality of patients on ART.ResultsA total of 46 (8.85%) deaths was observed giving an overall mortality rate of 3.2 per 100 person-years. The independent predictor of mortality identified for this cohort were haemoglobin level <11 mg/dl (Hazard Ratio (HR) = 1.9, 95%-CI = 1.01, 3.52), CD4 cell counts lower than 50 cells/µl (HR = 2.1, 95%- CI = 1.13,3.89), Male gender (HR = 1.9, 95%-CI = 1.01,3.52), Weight <40 kg (HR = 2.3,95% CI = 1.24,4.55), primary level of education and lower (HR = 2.6, 95%- CI = 1.29,5.55).ConclusionsThe over all mortality of adults patients on ART was low but higher in the early months of ART initiation. low levels of haemoglobin <11 gm/dl, lower CD4 cell count, male gender, weight <40 Kg and individuals who have primary level of education and lower were indentified as the independent predictors of mortality. For this reason, early initiation of ART despite the CD4 count and method of HIV diagnosis, nutritional support and close monitoring of patients in the early periods of ART treatment initiation is very crucial to improve patient survival.

8.0 Antiretroviral therapy in specific populations

Background HIV is one of the most significant worldwide health concerns of the twenty-first century and a serious threat to human society. Hemoglobin level and CD4 cell count are two of the most important biomarkers of HIV progression and patient survival. The objective of this study was to identify common risk factors associated with CD4 cell count and hemoglobin level among adult female HIV-positive patients treated with ART at the University of Gondar Comprehensive Specialized Hospital, Ethiopia. Methods The source of data in this study was secondary data conducted in the University of Gondar Comprehensive Specialized Hospital from September 2015 to March 2022 . Data exploration in this study was normal histogram plot, box plot, and Q-Q plot considered to gain some visions of the data related to CD4 cell count and hemoglobin level. A Bayesian joint model was used in this longitudinal data set to get a wide range of information about adult female HIV-patients. Results The mean with a standard deviation of hematocrit (%), red blood cell (106/μl), lymphocyte (%), and weight (kg) of female patients were 37.2 (3.8), 4.0 (1.6), 43.6 (11.8), and 44.9 (9.4), respectively. In this study, the random intercept model for CD4 cell count and the random intercept and slope model for hemoglobin level were considered as the best selected model. Visit time, hematocrit, weight, RBC, lymphocyte count, educational status, marital status, disclosure, and substance use were common risk factors for CD4 cell count and hemoglobin level. Conclusion This study concluded that, the risk factors visit time, weight, secondary educators, tertiary educators, married patients, patients who disclosed their HIV status to family members were associated with high CD4 cell count and hemoglobin level. While, hematocrit, RBC, lymphocyte count, separated marital status, widowed marital status, and substance-addicted patients were associated with low CD4 cell count and hemoglobin level. The author recommended that FMOH or other health professionals, program planners, decision makers, project implementers, government, and nongovernmental organizations should be given special attention for adult female patinets to minimize the risk of HIV progression and improve their health status. The author also recommended that health staff should conduct health-related studies for patients to examine continuous checkups. Health professionals also should give more attention to types of ART medication to reduce the progression of disease when the patients come back again into the hospital. Finally, adult female HIV-positive patients should be given special attention based on these important factors to improve their CD4 cell count, hemoglobin level, and better health quality.

ObjectiveThis study aimed to pool the prevalence of virological failure and associated factors.DesignSystematic review and meta-analysis.Primary outcome measurePrevalence of virological failure.Secondary outcome measureFactors affecting virological failure.AnalysisThe extracted data were exported...

Given the lack of detailed studies on tuberculosis (TB) in patients on antiretroviral treatment (ART) in South-East Asia, we aimed to determine the incidence and risk factors for early (after ≤6 months of ART) and late (after >6 months of ART) incident TB in Cambodia. We conducted a retrospective analysis of all patients started on ART at a non-governmental hospital in Phnom Penh (March 2003-December 2010). TB diagnosis was performed according to WHO algorithms. Risk factor analysis was performed using multivariate Cox regression modeling. Overall, 2984 patients started ART. The median baseline CD4 count was 89 cells µl(-1) (IQR 25-209), median age 34 years (IQR 29-40). Fifty-three percent of the patients were female. Median follow-up time on ART was 2.4 years. In addition to 932 (31.2%) patients already on TB treatment at ART initiation, 313 (10.5%) developed TB, with an overall incidence rate of 3.9/100 patient-years. Of those developing TB, 179 (6.0%) patients were diagnosed with early TB and 134 (4.5%) with late TB, corresponding with a rate of 13.5 and 2.0 per 100 patient-years respectively. Risk factors for early TB included low body mass index, low baseline CD4 count and low hemoglobin levels. Low on-treatment CD4 counts and hemoglobin levels, being underweight while on ART and prevalent TB were identified as risk factors for late TB. The incidence of early TB was high, and predominantly associated with advanced HIV progression markers. Earlier ART initiation and enhanced TB screening prior to and after ART initiation is warranted. Late TB amounts to almost half of the total TB burden, meriting specific preventive and diagnostic approaches.

Immunological changes during treatment interruptions