HIV in the tropics: staging in the resource-limited setting

Evaluating the utility of provider-recorded clinical status in the medical records of HIV-positive adults in a limited-resource setting

Provider-reported summaries of clinical status may assist with clinical management of HIV in resource poor settings if they reflect underlying biological processes associated with HIV disease progression. However, their ability to do so is rarely evaluated. Therefore, we aimed to assess the relationship between a provider-recorded summary of clinical status and indicators of HIV progression. Data were abstracted from 201 randomly selected medical records at a large HIV clinic in the Dominican Republic. Multivariable logistic regressions were used to examine the relationship between provider-assigned clinical status and demographic (gender, age, nationality, education) and clinical factors (reported medication adherence, CD4 cell count, viral load). The mean age of patients was 41.2 (SD = ±10.9) years and most were female (n = 115, 57%). None of the examined characteristics were significantly associated with provider-recorded clinical status. Higher CD4 cell counts were more likely for females (OR = 2.2 CI: 1.12-4.31) and less likely for those with higher viral loads (OR = 0.33 CI: 0.15-0.72). Poorer adherence and lower CD4 cell counts were significantly associated with higher viral loads (OR = 4.46 CI: 1.11-20.29 and 6.84 CI: 1.47-37.23, respectively). Clinics using provider-reported summaries of clinical status should evaluate the performance of these assessments to ensure they are associated with biologic indicators of disease progression.

Head to head comparisons in performance of CD4 point-of-care assays: a Bayesian meta-analysis (2000–2013)

Abstract Timely detection, staging, and treatment initiation are pertinent to controlling HIV infection. CD4+ cell-based point-of-care (POC) devices offer the potential to rapidly stage patients, and decide on initiating treatment, but a comparative evaluation of their performance has not yet been performed. With this in mind, we conducted a systematic review and meta-analyses. For the period January 2000 to April 2014, 19 databases were systematically searched, 6619 citations retrieved, and 25 articles selected. Diagnostic performance was compared across devices (i.e., PIMA, CyFlow, miniPOC, MBioCD4 System) and across specimens (i.e., capillary blood vs. venous blood). A Bayesian approach was used to meta-analyze the data. The primary outcome, the Bland–Altman (BA) mean bias (which represents agreement between cell counts from POC device and flow cytometry), was analyzed with a Bayesian hierarchical normal model. We performed a head-to-head comparison of two POC devices including the PIMA and PointCareNOW CD4. PIMA appears to perform better vs. PointCareNOW with venous samples (BA mean bias: –9.5 cells/μL; 95% CrI: –37.71 to 18.27, vs. 139.3 cells/μL; 95% CrI: –0.85 to 267.4, mean difference = 148.8, 95% CrI: 11.8, 285.8); importantly, PIMA performed well when used with capillary samples (BA mean bias: 2.2 cells/μL; 95% CrI: –19.32 to 23.6). Sufficient data were available to allow pooling of sensitivity and specificity data only at the 350 cells/μL cutoff. For PIMA device sensitivity 91.6 (84.7–95.5) and specificity was 94.8 (90.1–97.3), respectively. There were not sufficient data to allow comparisons between any other devices. PIMA device was comparable to flow cytometry. The estimated differences between the CD4+ cell counts of the device and the reference was small and best estimated in capillary blood specimens. As the evidence stands, the PointCareNOW device will need to improve prior to widespread use and more data on MBio and MiniPOC are needed. Findings inform implementation of PIMA and improvements in other CD4 POC device prior to recommending widespread use.

Near patient CD4 count in a hospitalized HIV patient population

BACKGROUND

Point-of-care (POC) CD4 T-cell counting is increasingly recognized as providing improved linkage-to-care during management of HIV infection, particularly in resource-limited settings where disease burden is highest. This study evaluated prototype POC CD4 T-cell counters from MBio Diagnostics in the context of low CD4 count, hospitalized patients in Mozambique. This study measured system performance when presented with challenging, low count samples from HIV/AIDS patients with acute illnesses resulting in hospitalization.

METHODS

Forty whole blood samples were collected from donors on the medical service at Maputo Central Hospital and absolute CD4 counts were generated on the MBio CD4 system and a reference laboratory using flow cytometry.

RESULTS

The mean and median CD4 counts by the flow cytometry reference were 173 and 80 cells/µL, respectively. Correlation between the MBio CD4 System and the reference was good. Bland-Altman analysis showed a mean bias of +15 cells/µL (+9 to +21 cells/µL, 95% CI), and limits of agreement of -47 to 77 cells/µL. For samples with counts >100 cells/µL (N = 14), the mean coefficient of variation was 7.3%. For samples with counts <50 cells/µL, mean absolute bias of replicate samples was 4.8 cells/µL. When two MBio readers were compared, Bland-Altman bias was -4 cells/µL (-13 to +6 cells/µL, 95% CI), and limits of agreement of -63 and +55 cells/µL.

CONCLUSIONS

The MBio System holds promise as a POC system for quantitation of CD4 T cells in resource-limited settings given system throughput (80-100 cartridges/day), design simplicity, and ease-of-use. © 2015 International Clinical Cytometry Society.

Electronic medical record systems are associated with appropriate placement of HIV patients on antiretroviral therapy in rural health facilities in Kenya: a retrospective pre-post study

BACKGROUND AND OBJECTIVE

There is little evidence that electronic medical record (EMR) use is associated with better compliance with clinical guidelines on initiation of antiretroviral therapy (ART) among ART-eligible HIV patients. We assessed the effect of transitioning from paper-based to an EMR-based system on appropriate placement on ART among eligible patients.

METHODS

We conducted a retrospective, pre-post EMR study among patients enrolled in HIV care and eligible for ART at 17 rural Kenyan clinics and compared the: (1) proportion of patients eligible for ART based on CD4 count or WHO staging who initiate therapy; (2) time from eligibility for ART to ART initiation; (3) time from ART initiation to first CD4 test.

RESULTS

7298 patients were eligible for ART; 54.8% (n=3998) were enrolled in HIV care using a paper-based system while 45.2% (n=3300) were enrolled after the implementation of the EMR. EMR was independently associated with a 22% increase in the odds of initiating ART among eligible patients (adjusted OR (aOR) 1.22, 95% CI 1.12 to 1.33). The proportion of ART-eligible patients not receiving ART was 20.3% and 15.1% for paper and EMR, respectively (χ(2)=33.5, p<0.01). Median time from ART eligibility to ART initiation was 29.1 days (IQR: 14.1-62.1) for paper compared to 27 days (IQR: 12.9-50.1) for EMR.

CONCLUSIONS

EMRs can improve quality of HIV care through appropriate placement of ART-eligible patients on treatment in resource limited settings. However, other non-EMR factors influence timely initiation of ART.

Discordance between clinical and immunological ART eligibility criteria for children in Malawi

Background

Since May 2014, all HIV positive children aged less than five years in Malawi are eligible for ART. For children older than five years they are eligible if they are in WHO stage III/IV, if stage I/II, if their CD4 < 500 cells/mm³. Our goal was to compare the WHO clinical classification criteria (WHO stage + CD4/age) to CD4 count (CD4/age) on all children. Prior to 2014, children aged 2–5 years in stage I and II were eligible for ART if their CD4 was < 750 cells/mm³. We were interested in the increase in numbers of children in this age group who now meet the eligibility criteria and their average CD4 count.

Methods

Data including age, stage and CD4 count were used. We examined the effect of using two different criteria; WHO staging and checking CD4 count if stage I or II versus CD4 count on all, on the numbers of children eligibility for ART in a cohort of 969 children aged 0 to 14 years in Blantyre, Malawi.

Results

Using WHO stage + CD4/age, 786 patients out of 969 would have been treated and 183 would not. Using CD4/age, 745 patients out of 969 would have been treated and 224 would not. Within the 224 patients not treated by CD4 classification, 41 were clinical stage III or IV. The most common staging condition in these 41 children was low weight for age (i.e. underweight). 41% of children age2-5 years have a CD4 count >750.

Conclusion

Most children are correctly started on treatment using recent guidelines. 41% more children <5 years will be started on ART.

Head to head comparisons in performance of CD4 point–of-care assays: A Bayesian meta-analysis (2000-2013)

Background: Timely detection, staging, treatment initiation are pertinent to controlling HIV Infection. CD4+ cell-based point-of-care (POC) devices offer the potential to rapidly stage patients, and decide on initiating treatment, but a comparative evaluation of their performance has not yet been performed. With this in mind, we conducted a systematic review and meta-analyses. Methods: For the period Jan 2000 to April 2015, 19 databases were systematically searched, 6619 citations retrieved, and 25 articles selected. Diagnostic performance was compared across devices (i.e., PIMA, CyFlow, miniPOC, MBioCD4 System) and across specimens (i.e., capillary blood vs. venous blood). A Bayesian approach was used to meta-analyze the data. The primary outcome, the Bland-Altman (BA) mean bias (which represents agreement between cell counts from POC device and flow cytometry), was analyzed with a Bayesian hierarchical normal model. Findings: We performed a head-to-head comparison of two point-of-care devices, PIMA and PointCareNOW CD4. PIMA appears to perform better vs. PointCareNOW with venous samples (BA mean bias: -9.5 cells/μL; 95% CrI:-37.71 to 18.27 vs. 139.3 cells/μL; 95% CrI:-0.85 to 267.4, mean difference = 148.8, 95% CrI: 11.8, 285.8); however, PIMA’s best performed when used with capillary samples (BA mean bias: 2.2 cells/μL; 95% CrI:-19.32 to 23.6). Sufficient data was available to allow pooling of sensitivity and specificity data only at the 350 cells/μL cutoff. For PIMA device sensitivity 91.6 (84.7 to 95.5) and specificity was 94.8 (90.1 to 97.3) respectively. There was not sufficient data to allow comparisons between any other devices. Conclusions: PIMA device was comparable to flow cytometry. The estimated differences between the CD4+ cell counts of the device and the reference was small and best estimated in capillary blood specimens. As the evidence stands, the PointCareNOW device will need to improve prior to widespread use and more data on MBio and MiniPOC are needed. Findings inform implementation of PIMA and improvements in other CD4 POC device prior to recommending widespread use.