Pharmaceutical interventions in the management of tuberculosis in a pneumophtisiology department, Ivory Coast.

OBJECTIVES: This study aims to analyze the profile and relevance of pharmaceutical interventions (PIs) in the management of tuberculosis (TB) at inpatient settings. PATIENTS AND METHODS: Cross-sectional descriptive study conducted from March to December 2014 within the inpatient unit of pneumophtisiology department, Ivory Coast. Information collected was based on the classification of drug-related problems (DRPs) and PIs outlined by the French Society of Clinical Pharmacy. A score was assigned to each PI according to the importance of the potential clinical impact. This score was correlated with the severity of clinical consequences avoided by the intervention. The listing of interventions was made by pneumophtisiology specialists. The score assigned to each intervention ranged from 0 (without clinical impact) to 3 (vital clinical impact). The acceptance rate of interventions by physicians was evaluated. RESULTS: Of 130 patients, 28.5% received PIs. The main reasons for interventions were drug-drug interactions (26.4%), noncompliance with recommendations (24.5%), and adverse effects (24.5%). Antituberculosis drugs were involved in 40.3% of DRPs. Interventions were predominantly proposals for monitoring treatment effectiveness and safety parameters (52.7%) followed by proposals of therapeutic choice (28.1%). All interventions were accepted by the physicians. Most interventions (59.6%) were listed as interventions with significant clinical impact. CONCLUSION: The presence of a pharmacist at inpatient setting has contributed to the prevention and resolution of problems related to the pharmacotherapeutic management of TB. Pharmacists can position themselves as major players in the therapeutic management of TB inpatient in resource-limited setting.

Assessment of the impact of adherence and other predictors during HAART on various CD4 cell responses in resource-limited settings.

OBJECTIVE: The aim of this study was to quantify, by modeling, the impact of significant predictors on CD4 cell response during antiretroviral therapy in a resource-limited setting. METHODS: Modeling was used to determine which antiretroviral therapy response predictors (baseline CD4 cell count, clinical state, age, and adherence) significantly influence immunological response in terms of CD4 cell gain compared to a reference value at different periods of monitoring. RESULTS: At 6 months, CD4 cell response was significantly influenced by baseline CD4 count alone. The probability of no increase in CD4 cells was 2.6 higher in patients with a baseline CD4 cell count of ≥200/mm(3). At 12 months, CD4 cell response was significantly influenced by both baseline CD4 cell count and adherence. The probability of no increase in CD4 cells was three times higher in patients with a baseline CD4 cell count of ≥200/mm(3) and 0.15 times lower with adherent patients. At 18 months, CD4 cell response was also significantly influenced by both baseline CD4 cell count and adherence. The probability of no increase in CD4 cells was 5.1 times higher in patients with a baseline CD4 cell count of ≥200/mm(3) and 0.28 times lower with adherent patients. At 24 months, optimal CD4 cell response was significantly influenced by adherence alone. Adherence increased the probability (by 5.8) of an optimal increase in CD4 cells. Age and baseline clinical state had no significant influence on immunological response. CONCLUSION: The relationship between adherence and CD4 cell response was the most significant compared to that of baseline CD4 cell count. Counseling before initiation of treatment and educational therapy during follow-up must always help to strengthen adherence and optimize the efficiency of antiretroviral therapy in a resource-limited setting.