942 resultados para cell count


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Current guidelines suggest that primary prophylaxis for Pneumocystis jiroveci pneumonia (PcP) can be safely stopped in human immunodeficiency virus (HIV)-infected patients who are receiving combined antiretroviral therapy (cART) and who have a CD4 cell count >200 cells/microL. There are few data regarding the incidence of PcP or safety of stopping prophylaxis in virologically suppressed patients with CD4 cell counts of 101-200 cells/microL.

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Background Although CD4 cell count monitoring is used to decide when to start antiretroviral therapy in patients with HIV-1 infection, there are no evidence-based recommendations regarding its optimal frequency. It is common practice to monitor every 3 to 6 months, often coupled with viral load monitoring. We developed rules to guide frequency of CD4 cell count monitoring in HIV infection before starting antiretroviral therapy, which we validated retrospectively in patients from the Swiss HIV Cohort Study. Methodology/Principal Findings We built up two prediction rules (“Snap-shot rule” for a single sample and “Track-shot rule” for multiple determinations) based on a systematic review of published longitudinal analyses of CD4 cell count trajectories. We applied the rules in 2608 untreated patients to classify their 18 061 CD4 counts as either justifiable or superfluous, according to their prior ≥5% or <5% chance of meeting predetermined thresholds for starting treatment. The percentage of measurements that both rules falsely deemed superfluous never exceeded 5%. Superfluous CD4 determinations represented 4%, 11%, and 39% of all actual determinations for treatment thresholds of 500, 350, and 200×106/L, respectively. The Track-shot rule was only marginally superior to the Snap-shot rule. Both rules lose usefulness for CD4 counts coming near to treatment threshold. Conclusions/Significance Frequent CD4 count monitoring of patients with CD4 counts well above the threshold for initiating therapy is unlikely to identify patients who require therapy. It appears sufficient to measure CD4 cell count 1 year after a count >650 for a threshold of 200, >900 for 350, or >1150 for 500×106/L, respectively. When CD4 counts fall below these limits, increased monitoring frequency becomes advisable. These rules offer guidance for efficient CD4 monitoring, particularly in resource-limited settings.

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Background Changes in CD4 cell counts are poorly documented in individuals with low or moderate-level viremia while on antiretroviral treatment (ART) in resource-limited settings. We assessed the impact of on-going HIV-RNA replication on CD4 cell count slopes in patients treated with a first-line combination ART. Method Naïve patients on a first-line ART regimen with at least two measures of HIV-RNA available after ART initiation were included in the study. The relationships between mean CD4 cell count change and HIV-RNA at 6 and 12 months after ART initiation (M6 and M12) were assessed by linear mixed models adjusted for gender, age, clinical stage and year of starting ART. Results 3,338 patients were included (14 cohorts, 64% female) and the group had the following characteristics: a median follow-up time of 1.6 years, a median age of 34 years, and a median CD4 cell count at ART initiation of 107 cells/μL. All patients with suppressed HIV-RNA at M12 had a continuous increase in CD4 cell count up to 18 months after treatment initiation. By contrast, any degree of HIV-RNA replication both at M6 and M12 was associated with a flat or a decreasing CD4 cell count slope. Multivariable analysis using HIV-RNA thresholds of 10,000 and 5,000 copies confirmed the significant effect of HIV-RNA on CD4 cell counts both at M6 and M12. Conclusion In routinely monitored patients on an NNRTI-based first-line ART, on-going low-level HIV-RNA replication was associated with a poor immune outcome in patients who had detectable levels of the virus after one year of ART.

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Background Most adults infected with HIV achieve viral suppression within a year of starting combination antiretroviral therapy (cART). It is important to understand the risk of AIDS events or death for patients with a suppressed viral load. Methods and Findings Using data from the Collaboration of Observational HIV Epidemiological Research Europe (2010 merger), we assessed the risk of a new AIDS-defining event or death in successfully treated patients. We accumulated episodes of viral suppression for each patient while on cART, each episode beginning with the second of two consecutive plasma viral load measurements <50 copies/µl and ending with either a measurement >500 copies/µl, the first of two consecutive measurements between 50–500 copies/µl, cART interruption or administrative censoring. We used stratified multivariate Cox models to estimate the association between time updated CD4 cell count and a new AIDS event or death or death alone. 75,336 patients contributed 104,265 suppression episodes and were suppressed while on cART for a median 2.7 years. The mortality rate was 4.8 per 1,000 years of viral suppression. A higher CD4 cell count was always associated with a reduced risk of a new AIDS event or death; with a hazard ratio per 100 cells/µl (95% CI) of: 0.35 (0.30–0.40) for counts <200 cells/µl, 0.81 (0.71–0.92) for counts 200 to <350 cells/µl, 0.74 (0.66–0.83) for counts 350 to <500 cells/µl, and 0.96 (0.92–0.99) for counts ≥500 cells/µl. A higher CD4 cell count became even more beneficial over time for patients with CD4 cell counts <200 cells/µl. Conclusions Despite the low mortality rate, the risk of a new AIDS event or death follows a CD4 cell count gradient in patients with viral suppression. A higher CD4 cell count was associated with the greatest benefit for patients with a CD4 cell count <200 cells/µl but still some slight benefit for those with a CD4 cell count ≥500 cells/µl.

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This study investigated the changes in somatic cell counts (SCC) in different fractions of milk, with special emphasis on the foremilk and cisternal milk fractions. Therefore, in Experiment 1, quarter milk samples were defined as strict foremilk (F), cisternal milk (C), first 400 g of alveolar milk (A1), and the remaining alveolar milk (A2). Experiment 2 included 6 foremilk fractions (F1 to F6), consisting of one hand-stripped milk jet each, and the remaining cisternal milk plus the entire alveolar milk (RM). In Experiment 1, changes during milking indicated the importance of the sampled milk fraction for measuring SCC because the decrease in the first 3 fractions (F, C, and A1) was enormous in milk with high total quarter SCC. The decline in SCC from F to C was 50% and was 80% from C to A1. Total quarter SCC presented a value of approximately 20% of SCC in F or 35% of SCC in C. Changes in milk with low or very low SCC were marginal during milking. Fractions F and C showed significant differences in SCC among different total SCC concentrations. These differences disappeared with the alveolar fractions A1 and A2. In Experiment 2, a more detailed investigation of foremilk fractions supported the findings of Experiment 1. A significant decline in the foremilk fractions even of F1 to F6 was observed in high-SCC milk at concentrations >350 x 10(3) cells/mL. Although one of these foremilk fractions presented only 0.1 to 0.2% of the total milk, the SCC was 2- to 3-fold greater than the total quarter milk SCC. Because the trait of interest (SCC) was measured directly by using the DeLaval cell counter (DCC), the quality of measurement was tested. Statistically interesting factors (repeatability, recovery rate, and potential matrix effects of milk) proved that the DCC is a useful tool for identifying the SCC of milk samples, and thus of grading udder health status. Generally, the DCC provides reliable results, but one must consider that SCC even in strict foremilk can differ dramatically from SCC in the total cisternal fraction, and thus also from SCC in the alveolar fraction.

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BACKGROUND: CD4+ T-cell recovery in patients with continuous suppression of plasma HIV-1 viral load (VL) is highly variable. This study aimed to identify predictive factors for long-term CD4+ T-cell increase in treatment-naive patients starting combination antiretroviral therapy (cART). METHODS: Treatment-naive patients in the Swiss HIV Cohort Study reaching two VL measurements <50 copies/ml >3 months apart during the 1st year of cART were included (n=1816 patients). We studied CD4+ T-cell dynamics until the end of suppression or up to 5 years, subdivided into three periods: 1st year, years 2-3 and years 4-5 of suppression. Multiple median regression adjusted for repeated CD4+ T-cell measurements was used to study the dependence of CD4+ T-cell slopes on clinical covariates and drug classes. RESULTS: Median CD4+ T-cell increases following VL suppression were 87, 52 and 19 cells/microl per year in the three periods. In the multiple regression model, median CD4+ T-cell increases over all three periods were significantly higher for female gender, lower age, higher VL at cART start, CD4+ T-cell <650 cells/microl at start of the period and low CD4+ T-cell increase in the previous period. Patients on tenofovir showed significantly lower CD4+ T-cell increases compared with stavudine. CONCLUSIONS: In our observational study, long-term CD4+ T-cell increase in drug-naive patients with suppressed VL was higher in regimens without tenofovir. The clinical relevance of these findings must be confirmed in, ideally, clinical trials or large, collaborative cohort projects but could influence treatment of older patients and those starting cART at low CD4+ T-cell levels.

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BACKGROUND: In recent years, treatment options for human immunodeficiency virus type 1 (HIV-1) infection have changed from nonboosted protease inhibitors (PIs) to nonnucleoside reverse-transcriptase inhibitors (NNRTIs) and boosted PI-based antiretroviral drug regimens, but the impact on immunological recovery remains uncertain. METHODS: During January 1996 through December 2004 [corrected] all patients in the Swiss HIV Cohort were included if they received the first combination antiretroviral therapy (cART) and had known baseline CD4(+) T cell counts and HIV-1 RNA values (n = 3293). For follow-up, we used the Swiss HIV Cohort Study database update of May 2007 [corrected] The mean (+/-SD) duration of follow-up was 26.8 +/- 20.5 months. The follow-up time was limited to the duration of the first cART. CD4(+) T cell recovery was analyzed in 3 different treatment groups: nonboosted PI, NNRTI, or boosted PI. The end point was the absolute increase of CD4(+) T cell count in the 3 treatment groups after the initiation of cART. RESULTS: Two thousand five hundred ninety individuals (78.7%) initiated a nonboosted-PI regimen, 452 (13.7%) initiated an NNRTI regimen, and 251 (7.6%) initiated a boosted-PI regimen. Absolute CD4(+) T cell count increases at 48 months were as follows: in the nonboosted-PI group, from 210 to 520 cells/muL; in the NNRTI group, from 220 to 475 cells/muL; and in the boosted-PI group, from 168 to 511 cells/muL. In a multivariate analysis, the treatment group did not affect the response of CD4(+) T cells; however, increased age, pretreatment with nucleoside reverse-transcriptase inhibitors, serological tests positive for hepatitis C virus, Centers for Disease Control and Prevention stage C infection, lower baseline CD4(+) T cell count, and lower baseline HIV-1 RNA level were risk factors for smaller increases in CD4(+) T cell count. CONCLUSION: CD4(+) T cell recovery was similar in patients receiving nonboosted PI-, NNRTI-, and boosted PI-based cART.

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BACKGROUND: Estimates of the decrease in CD4(+) cell counts in untreated patients with human immunodeficiency virus (HIV) infection are important for patient care and public health. We analyzed CD4(+) cell count decreases in the Cape Town AIDS Cohort and the Swiss HIV Cohort Study. METHODS: We used mixed-effects models and joint models that allowed for the correlation between CD4(+) cell count decreases and survival and stratified analyses by the initial cell count (50-199, 200-349, 350-499, and 500-750 cells/microL). Results are presented as the mean decrease in CD4(+) cell count with 95% confidence intervals (CIs) during the first year after the initial CD4(+) cell count. RESULTS: A total of 784 South African (629 nonwhite) and 2030 Swiss (218 nonwhite) patients with HIV infection contributed 13,388 CD4(+) cell counts. Decreases in CD4(+) cell count were steeper in white patients, patients with higher initial CD4(+) cell counts, and older patients. Decreases ranged from a mean of 38 cells/microL (95% CI, 24-54 cells/microL) in nonwhite patients from the Swiss HIV Cohort Study 15-39 years of age with an initial CD4(+) cell count of 200-349 cells/microL to a mean of 210 cells/microL (95% CI, 143-268 cells/microL) in white patients in the Cape Town AIDS Cohort > or =40 years of age with an initial CD4(+) cell count of 500-750 cells/microL. CONCLUSIONS: Among both patients from Switzerland and patients from South Africa, CD4(+) cell count decreases were greater in white patients with HIV infection than they were in nonwhite patients with HIV infection.

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OBJECTIVES: To examine the accuracy of the World Health Organization immunological criteria for virological failure of antiretroviral treatment. METHODS: Analysis of 10 treatment programmes in Africa and South America that monitor both CD4 cell counts and HIV-1 viral load. Adult patients with at least two CD4 counts and viral load measurements between month 6 and 18 after starting a non-nucleoside reverse transcriptase inhibitor-based regimen were included. WHO immunological criteria include CD4 counts persistently <100 cells/microl, a fall below the baseline CD4 count, or a fall of >50% from the peak value. Virological failure was defined as two measurements > or =10 0000 copies/ml (higher threshold) or > or =500 copies/ml (lower threshold). Measures of accuracy with exact binomial 95% confidence intervals (CI) were calculated. RESULTS: A total of 2009 patients were included. During 1856 person-years of follow up 63 patients met the immunological criteria and 35 patients (higher threshold) and 95 patients (lower threshold) met the virological criteria. Sensitivity [95% confidence interval (CI)] was 17.1% (6.6-33.6%) for the higher and 12.6% (6.7-21.0%) for the lower threshold. Corresponding results for specificity were 97.1% (96.3-97.8%) and 97.3% (96.5-98.0%), for positive predictive value 9.5% (3.6-19.6%) and 19.0% (10.2-30.9%) and for negative predictive value 98.5% (97.9-99.0%) and 95.7% (94.7-96.6%). CONCLUSIONS: The positive predictive value of the WHO immunological criteria for virological failure of antiretroviral treatment in resource-limited settings is poor, but the negative predictive value is high. Immunological criteria are more appropriate for ruling out than for ruling in virological failure in resource-limited settings.

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OBJECTIVES: CD4 cell count and plasma viral load are well known predictors of AIDS and mortality in HIV-1-infected patients treated with combination antiretroviral therapy (cART). This study investigated, in patients treated for at least 3 years, the respective prognostic importance of values measured at cART initiation, and 6 and 36 months later, for AIDS and death. METHODS: Patients from 15 HIV cohorts included in the ART Cohort Collaboration, aged at least 16 years, antiretroviral-naive when they started cART and followed for at least 36 months after start of cART were eligible. RESULTS: Among 14 208 patients, the median CD4 cell counts at 0, 6 and 36 months were 210, 320 and 450 cells/microl, respectively, and 78% of patients achieved viral load less than 500 copies/ml at 6 months. In models adjusted for characteristics at cART initiation and for values at all time points, values at 36 months were the strongest predictors of subsequent rates of AIDS and death. Although CD4 cell count and viral load at cART initiation were no longer prognostic of AIDS or of death after 36 months, viral load at 6 months and change in CD4 cell count from 6 to 36 months were prognostic for rates of AIDS from 36 months. CONCLUSIONS: Although current values of CD4 cell count and HIV-1 RNA are the most important prognostic factors for subsequent AIDS and death rates in HIV-1-infected patients treated with cART, changes in CD4 cell count from 6 to 36 months and the value of 6-month HIV-1 RNA are also prognostic for AIDS.

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Although associated with adverse outcomes in other cardiovascular diseases, the prognostic value of an elevated white blood cell (WBC) count, a marker of inflammation and hypercoagulability, is uncertain in patients with pulmonary embolism (PE). We therefore sought to assess the prognostic impact of the WBC in a large, state-wide retrospective cohort of patients with PE. We evaluated 14,228 patient discharges with a primary diagnosis of PE from 186 hospitals in Pennsylvania. We used random-intercept logistic regression to assess the independent association between WBC count levels at the time of presentation and mortality and hospital readmission within 30 days, adjusting for patient and hospital characteristics. Patients with an admission WBC count <5.0, 5.0-7.8, 7.9-9.8, 9.9-12.6, and >12.6 × 10(9) /L had a cumulative 30-day mortality of 10.9%, 6.2%, 5.4%, 8.3%, and 16.3% (P < 0.001), and a readmission rate of 17.6%, 11.9%, 10.9%, 11.5%, and 15.0%, respectively (P < 0.001). Compared with patients with a WBC count 7.9-9.8 × 10(9) /L, adjusted odds of 30-day mortality were significantly greater for patients with a WBC count <5.0 × 10(9) /L (odds ratio [OR] 1.52, 95% confidence interval [CI] 1.14-2.03), 9.9-12.6 × 10(9) /L (OR 1.55, 95% CI 1.26-1.91), or >12.6 × 10(9) /L (OR 2.22, 95% CI 1.83-2.69), respectively. The adjusted odds of readmission were also significantly increased for patients with a WBC count <5.0 × 10(9) /L (OR 1.34, 95% CI 1.07-1.68) or >12.6 × 10(9) /L (OR 1.29, 95% CI 1.10-1.51). In patients presenting with PE, WBC count is an independent predictor of short-term mortality and hospital readmission.

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Bovine mastitis is a frequent problem in Swiss dairy herds. One of the main pathogens causing significant economic loss is Staphylococcus aureus. Various Staph. aureus genotypes with different biological properties have been described. Genotype B (GTB) of Staph. aureus was identified as the most contagious and one of the most prevalent strains in Switzerland. The aim of this study was to identify risk factors associated with the herd-level presence of Staph. aureus GTB and Staph. aureus non-GTB in Swiss dairy herds with an elevated yield-corrected herd somatic cell count (YCHSCC). One hundred dairy herds with a mean YCHSCC between 200,000 and 300,000cells/mL in 2010 were recruited and each farm was visited once during milking. A standardized protocol investigating demography, mastitis management, cow husbandry, milking system, and milking routine was completed during the visit. A bulk tank milk (BTM) sample was analyzed by real-time PCR for the presence of Staph. aureus GTB to classify the herds into 2 groups: Staph. aureus GTB-positive and Staph. aureus GTB-negative. Moreover, quarter milk samples were aseptically collected for bacteriological culture from cows with a somatic cell count ≥150,000cells/mL on the last test-day before the visit. The culture results allowed us to allocate the Staph. aureus GTB-negative farms to Staph. aureus non-GTB and Staph. aureus-free groups. Multivariable multinomial logistic regression models were built to identify risk factors associated with the herd-level presence of Staph. aureus GTB and Staph. aureus non-GTB. The prevalence of Staph. aureus GTB herds was 16% (n=16), whereas that of Staph. aureus non-GTB herds was 38% (n=38). Herds that sent lactating cows to seasonal communal pastures had significantly higher odds of being infected with Staph. aureus GTB (odds ratio: 10.2, 95% CI: 1.9-56.6), compared with herds without communal pasturing. Herds that purchased heifers had significantly higher odds of being infected with Staph. aureus GTB (rather than Staph. aureus non-GTB) compared with herds without purchase of heifers. Furthermore, herds that did not use udder ointment as supportive therapy for acute mastitis had significantly higher odds of being infected with Staph. aureus GTB (odds ratio: 8.5, 95% CI: 1.6-58.4) or Staph. aureus non-GTB (odds ratio: 6.1, 95% CI: 1.3-27.8) than herds that used udder ointment occasionally or regularly. Herds in which the milker performed unrelated activities during milking had significantly higher odds of being infected with Staph. aureus GTB (rather than Staph. aureus non-GTB) compared with herds in which the milker did not perform unrelated activities at milking. Awareness of 4 potential risk factors identified in this study guides implementation of intervention strategies to improve udder health in both Staph. aureus GTB and Staph. aureus non-GTB herds.

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OBJECTIVE To illustrate an approach to compare CD4 cell count and HIV-RNA monitoring strategies in HIV-positive individuals on antiretroviral therapy (ART). DESIGN Prospective studies of HIV-positive individuals in Europe and the USA in the HIV-CAUSAL Collaboration and The Center for AIDS Research Network of Integrated Clinical Systems. METHODS Antiretroviral-naive individuals who initiated ART and became virologically suppressed within 12 months were followed from the date of suppression. We compared 3 CD4 cell count and HIV-RNA monitoring strategies: once every (1) 3 ± 1 months, (2) 6 ± 1 months, and (3) 9-12 ± 1 months. We used inverse-probability weighted models to compare these strategies with respect to clinical, immunologic, and virologic outcomes. RESULTS In 39,029 eligible individuals, there were 265 deaths and 690 AIDS-defining illnesses or deaths. Compared with the 3-month strategy, the mortality hazard ratios (95% CIs) were 0.86 (0.42 to 1.78) for the 6 months and 0.82 (0.46 to 1.47) for the 9-12 month strategy. The respective 18-month risk ratios (95% CIs) of virologic failure (RNA >200) were 0.74 (0.46 to 1.19) and 2.35 (1.56 to 3.54) and 18-month mean CD4 differences (95% CIs) were -5.3 (-18.6 to 7.9) and -31.7 (-52.0 to -11.3). The estimates for the 2-year risk of AIDS-defining illness or death were similar across strategies. CONCLUSIONS Our findings suggest that monitoring frequency of virologically suppressed individuals can be decreased from every 3 months to every 6, 9, or 12 months with respect to clinical outcomes. Because effects of different monitoring strategies could take years to materialize, longer follow-up is needed to fully evaluate this question.