The median follow-up time for the primary analysis was 587 days (IQR: 261-730 days). The association of baseline characteristics with time to treatment failure in univariate Cox proportional hazards modeling is shown in Table 1. There was a strong association of treatment failure with variables that could be surrogates for ART adherence, including ≥1 missed clinic visits in the prior year, prior virologic failure, and poor adherence to ART as documented in the medical record. A baseline CD4 cell count <200 cells/mm3, platelet count ≤200,000 cells/mm3, and absolute neutrophil count <1000 cells/mm3 were also associated with ART treatment failure. In contrast, a baseline regimen composed of nucleosides and nonnucleosides was the only characteristic associated with a lower risk of treatment failure.
Using stepwise selection, a final multivariate model adjusted for age, race, and gender was developed. Baseline characteristics significant in the multivariate model (Table 2) included poor adherence to antiretrovirals (HR = 3.44; P < 0.0001), absolute neutrophil count <1000 cells/mm3 (HR = 2.90; P = 0.013), not having virologic suppression on or before January 1, 2003 (HR = 2.69; P < 0.0001), virologic suppression for <12 months (HR = 1.64; P = 0.015), baseline CD4 count <200 cells/mm3 (HR = 1.90; P = 0.0007), a regimen composed only of nucleoside reverse transcriptase inhibitors (NRTIs; HR = 1.75; P = 0.022), prior virologic failure (HR = 1.70; P = 0.0019), and ≥1 prior missed clinic visits (HR = 1.56; P = 0.0072). A positive HCV antibody test was also selected in the final model but did not reach statistical significance (P = 0.059).
To compare this study with others, a secondary analysis using only virologic failure criteria was performed. A total of 160 patients experienced at least 1 episode of virologic failure (HIV RNA load >400 copies/mm3) during the 2-year study. Of these, 28 experienced transient viral “blips,” single HIV RNA measurements >400 copies/mL followed by a value under this threshold at the next measurement.8 Twenty-four patients stopped ART more than 2 weeks before losing virologic suppression and were not considered virologic failures. The remaining 108 patients (17.6% of the suppressed cohort) had a repeat HIV RNA measurement >400 copies/mL or were lost to follow-up after their initial failure; these patients were considered to have reached virologic failure. The median follow-up period for this analysis was 623 days (IQR: 292-730 days). With this criterion, patients who stopped ART or were transiently lost to clinic follow-up but later resumed care and then experienced confirmed virologic failure were included as endpoints. Baseline patient characteristics associated with the endpoint of virologic failure were similar to those in the primary analysis, except that <12 months of virologic suppression, an NRTI-only regimen, and ≥1 missed visit in the prior year were not significant in the final multivariate model, whereas a positive HCV antibody was (see Table 2). The multivariate models for treatment failure and virologic failure seemed to be robust, because different selection criteria resulted in similar results.
Fifty-eight patients, 9.4% of the suppressed cohort, were categorized as being poorly adherent to antiretrovirals before or at study entry. Forty of these patients (69%) developed treatment failure during the study follow-up, accounting for 24% of all treatment failures. Patients documented as having poor adherence were significantly more likely (P < 0.0001) to experience failure, as defined by either criteria, than patients with good adherence in the univariate and adjusted multivariate models (Fig. 2).
We analyzed data from the EHR for patients followed in an urban HIV clinic to understand predictors of ART failure. During a 2-year period, slightly more than a quarter of patients failed their ART, 17.6% by virologic criteria alone. The concurrent analysis, observing all suppressed clinic patients over the same time interval, identified 6 to 8 baseline patient characteristics associated with ART treatment failure. These results seemed to be robust, because the predictors were similar using the composite treatment failure endpoint or virologic failure alone as a criterion and included known risk factors. Other important risk factors were also identified, however, including an absolute neutrophil count <1000 cells/mm3, duration of virologic suppression, prior missed clinic visits, and documentation of prior adherence to ART. This analysis advances knowledge obtained from prior time-adjusted analyses,2,33,34 showing that in a concurrent analysis of a heterogeneous clinical cohort under usual care, a variety of potentially actionable clinical characteristics identify HIV-positive patients at increased risk of treatment failure. Identification of these predictors of treatment failure using the EHR offers the potential to use subsequent disease management interventions to improve outcomes of ART.35,36
Similar to other reports, prior virologic failure, a regimen consisting only of NRTIs, and a lower CD4 cell count were significant predictors of ART failure. In this study, however, a lower CD4 cell count at the beginning of the observation period rather than a low nadir CD4 cell count was associated with increased risk of failure over the following 2 years. The duration of suppression was also correlated with risk of failure. The risk was highest in patients who had only recently achieved virologic suppression but continued for the first 12 months of virologic suppression. The “most recent” CD4 cell count and duration of virologic suppression have both been reported to be independent predictors for loss of virologic suppression in the EuroSIDA cohort in a time-adjusted multivariate analysis.30 A novel predictor, absolute neutrophil count <1000 cells/mm3, was also highly significant for both endpoints and may reflect drug toxicity or concomitant illness.
Whether HCV adversely affects HIV treatment outcomes remains controversial. Analyses of large cohorts have failed to show significant responses to ART among HIV/HCV-coinfected patients,37,38 whereas others have reported poorer treatment outcomes.39-42 In this study, HCV seropositivity was associated with a higher risk for treatment failure, although in the final multivariate model, it was only marginally significant. HCV status was also strongly associated with virologic failure in univariate and multivariate models, suggesting that this should be considered a risk factor for poorer ART outcomes.
One of the notable findings in this study was the highly significant association with failure endpoints and documented patient adherence to ART in the EHR. Although adherence has been demonstrated to be important for clinical cohorts and trial participants, most studies have relied on pill counts, electronic measuring devices, or patient self-reported adherence questionnaires.2,13,18,43-45 Without these validated instruments, clinicians have generally been shown to be poor estimators of patient adherence.13,16,17 This study raises the possibility that HIV providers may have become more astute in their assessment of patient adherence over time. This speculation is further supported by the fact that adherence was documented in more than 90% of patient records in the visits just before or at study entry. Patients without a description of their adherence tended to be new patients to the clinic or individuals who had been doing well on the same regimen for an extended period.
Another important predictor of ART failure in these analyses was 1 or more missed clinic visits in the prior year; this could be another surrogate for medication adherence. Lucas et al2 previously demonstrated that missed clinic visits during the observation period correlated with worse treatment outcomes in a cohort of primarily nonwhite patients. The current study differed in that prior missed visits reflected baseline characteristics. In contrast, in the analysis by Lucas et al,2 missed visits during the observation period were included. Poor adherence and missed visits may reflect patient characteristics that are relatively stable over time, and therefore amenable to disease management interventions.46-49
Several NRTI-only regimens have been shown to have higher failure rates than currently preferred ART regimens,5,50 and in this analysis, patients on NRTI-only regimens had nearly twice the risk of treatment failure over the 2-year follow-up period. Rates of failure were also higher for virologic failure but were not significant in the adjusted multivariate proportional hazards model, possibly attributable, in part, to the smaller sample size. It may also be that NRTI-only regimens were selected for patients at higher risk of nonadherence to medications and/or clinic visits, which is included in the composite treatment failure endpoint.
Although demographic characteristics have been shown to be important predictors of treatment outcome in other analyses, they were not significant in this study, suggesting that other collinear factors may be responsible for lower treatment success associated with certain demographic groups of patients.
This analysis has several limitations. First, although more reliable than paper charts, EHR data are not as complete and may be less accurate than clinical trial or research databases. Although we did perform partial validation of the data, including confirmation of all treatment failures, it was not feasible to verify all clinical data included in this analysis. Despite this limitation, this study suggests that EHR data collected as part of routine care can be used to identify patients at increased risk of failing their antiretroviral regimens over a 2-year period. Second, HCV antibody positivity demonstrates prior exposure rather than active HCV infection; therefore, the observed association between HCV antibody and virologic failure could reflect collinear patient characteristics rather than increased failure as a result of HCV coinfection. Third, assessing and documenting patient adherence to ART was not standardized; however, this reflects usual clinical practice and was the strongest predictor for both failure analyses. Finally, because this study was limited to a single cohort, these findings should be confirmed in other clinical settings.
In summary, this analysis of a heterogeneous cohort of HIV-infected patients under usual clinical care showed that predictors of ART failure can be identified using data routinely captured in the EHR. Analyzing cohorts concurrently using EHR data rather than time-adjusted approaches seems to be robust to various definitions of treatment failure and lends itself more readily to population management, prospective monitoring, and targeted treatment interventions. Such interventions might include physician computer alerts and adherence interventions.35,36 Contrary to previous reports, providers' documentation of patients' adherence was a powerful predictor for both failure endpoints. The idea that HIV providers are unable to assess adherence accurately may no longer be true, although standardized adherence assessment may be even more useful. Other important baseline predictors of ART failure in this study included missed clinic visits in the prior year, a low absolute neutrophil count, and a low CD4 count. The approach we took in this study offers the potential for risk-stratifying entire clinical cohorts using routine care data in the EHR and then selecting high-risk patients for targeted disease management interventions to improve outcomes of ART.
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Definitions for patient baseline characteristics in a study of patients followed in an urban HIV clinic with virologic suppression on ART are provided in Table 3. Cited Here...