Relevant drug to drug interactions between itraconazole and non-nucleoside reverse transcriptase inhibitors in HIV-infected patients on maintenance therapy for disseminated histoplasmosis

Objective. Itraconazole is recommended life-long for preventing relapse of disseminated histoplasmosis in HIV-infected patients. I sought to determine if serum itraconazole levels are affected by the type of Highly Active Anti-Retroviral Therapy (NNRTI or PI) being taken concomitantly to treat HIV. ^ Design. Retrospective cohort. ^ Methods. De-identified data were used from an IRB-approved parent study which identified patients on HAART and maintenance itraconazole for confirmed disseminated histoplasmosis between January 2003 and December 2006. Available itraconazole blood levels were abstracted as well as medications taken by each patient at the time of the blood tests. Mean itraconazole levels were compared using the student's t-test. ^ Results. 11 patients met study criteria. Patient characteristics were: median age 36, 91% men, 18% white, 18% black, 55% Hispanic and 9% Asians, median CD4 cell count 120 cells/mm3. 14 blood levels were available for analysis—8 on PI, 4 on NNRTI and 2 on both. 8/8 itraconazole levels obtained while taking concomitant PI were therapeutic (>0.4 μg/mL) in contrast to 0/4 obtained while taking NNRTI. Two patients switched from NNRTI to PI and reached therapeutic levels. Mean levels on NNRTI (0.05 μg/mL, s.d. 0.0) and on PI (2.45 μg/mL, s.d. 0.21) for these two patients were compared via a paired t-test (t = 16.00, d.f. = 1, P = 0.04). Remaining patient levels were compared using an unpaired t-test. Mean itraconazole on concomitant PI (n = 6) was 1.37 μg/mL (s.d. 0.74), while the mean on concomitant NNRTI was 0.05 μg/mL (s.d. 0.0), t = 2.39, d.f. = 6, P = 0.05. ^ Conclusions. Co-administration of NNRTI and itraconazole results in significant decreases in itraconazole blood levels, likely by inducing the CYP3A4 enzyme system. Itraconazole drug levels should be monitored in patients on concomitant NNRTI. PI-based HAART may be preferred over NNRTI-based HAART when using itraconazole to treat HIV-infected patients with disseminated histoplasmosis. ^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

Information bias and lifetime mortality risks of radiation-induced cancer

Additive and multiplicative models of relative risk were used to measure the effect of cancer misclassification and DS86 random errors on lifetime risk projections in the Life Span Study (LSS) of Hiroshima and Nagasaki atomic bomb survivors. The true number of cancer deaths in each stratum of the cancer mortality cross-classification was estimated using sufficient statistics from the EM algorithm. Average survivor doses in the strata were corrected for DS86 random error ($\sigma$ = 0.45) by use of reduction factors. Poisson regression was used to model the corrected and uncorrected mortality rates with covariates for age at-time-of-bombing, age at-time-of-death and gender. Excess risks were in good agreement with risks in RERF Report 11 (Part 2) and the BEIR-V report. Bias due to DS86 random error typically ranged from $-$15% to $-$30% for both sexes, and all sites and models. The total bias, including diagnostic misclassification, of excess risk of nonleukemia for exposure to 1 Sv from age 18 to 65 under the non-constant relative projection model was $-$37.1% for males and $-$23.3% for females. Total excess risks of leukemia under the relative projection model were biased $-$27.1% for males and $-$43.4% for females. Thus, nonleukemia risks for 1 Sv from ages 18 to 85 (DRREF = 2) increased from 1.91%/Sv to 2.68%/Sv among males and from 3.23%/Sv to 4.02%/Sv among females. Leukemia excess risks increased from 0.87%/Sv to 1.10%/Sv among males and from 0.73%/Sv to 1.04%/Sv among females. Bias was dependent on the gender, site, correction method, exposure profile and projection model considered. Future studies that use LSS data for U.S. nuclear workers may be downwardly biased if lifetime risk projections are not adjusted for random and systematic errors. (Supported by U.S. NRC Grant NRC-04-091-02.) ^

POPULATION ASSESSMENTS OF INFECTION WITH SCHISTOSOMA MANSONI: EFFECTS OF BIAS CAUSED BY THE RELATIVE SENSITIVITY OF DIAGNOSTIC TECHNIQUES (PUERTO RICO)

This study establishes the extent and relevance of bias of population estimates of prevalence, incidence, and intensity of infection with Schistosoma mansoni caused by the relative sensitivity of stool examination techniques. The population studied was Parcelas de Boqueron in Las Piedras, Puerto Rico, where the Centers for Disease Control, had undertaken a prospective community-based study of infection with S. mansoni in 1972. During each January of the succeeding years stool specimens from this population were processed according to the modified Ritchie concentration (MRC) technique. During January 1979 additional stool specimens were collected from 30 individuals selected on the basis of their mean S. mansoni egg output during previous years. Each specimen was divided into ten 1-gm aliquots and three 42-mg aliquots. The relationship of egg counts obtained with the Kato-Katz (KK) thick smear technique as a function of the mean of ten counts obtained with the MRC technique was established by means of regression analysis. Additionally, the effect of fecal sample size and egg excretion level on technique sensitivity was evaluated during a blind assessment of single stool specimen samples, using both examination methods, from 125 residents with documented S. mansoni infections. The regression equation was: Ln KK = 2.3324 + 0.6319 Ln MRC, and the coefficient of determination (r('2)) was 0.73. The regression equation was then utilized to correct the term "m" for sample size in the expression P ((GREATERTHEQ) 1 egg) = 1 - e('-ms), which estimates the probability P of finding at least one egg as a function of the mean S. mansoni egg output "m" of the population and the effective stool sample size "s" utilized by the coprological technique. This algorithm closely approximated the observed sensitivity of the KK and MRC tests when these were utilized to blindly screen a population of known parasitologic status for infection with S. mansoni. In addition, the algorithm was utilized to adjust the apparent prevalence of infection for the degree of functional sensitivity exhibited by the diagnostic test. This permitted the estimation of true prevalence of infection and, hence, a means for correcting estimates of incidence of infection. ^

Chamber evaluation of a personal organic vapor dosimeter at ppb concentrations of VOCs, varying temperatures and humidities with 24 -hour exposures

An exposure system was constructed to evaluate the performance of a personal organic vapor dosimeter (3520 OVM) at ppb concentrations of nine selected target volatile organic compounds (VOCs). These concentration levels are generally encountered in community air environments, both indoor and outdoor. It was demonstrated that the chamber system could provide closely-controlled conditions of VOC concentrations, temperature and relative humidity (RH) required for the experiments. The target experimental conditions included combinations of three VOC concentrations (10, 20 and 200 $\rm\mu g/m\sp3),$ three temperatures (10, 25 and 40$\sp\circ$C) and three RHs (12, 50 and 90% RH), leading to a total of 27 exposure conditions. No backgrounds of target VOCs were found in the exposure chamber system. In the exposure chamber, the variation of the temperature was controlled within $\pm$1$\sp\circ$C, and the variation of RH was controlled within $\pm$1.5% at 12% RH, $\pm$2% at 50% RH and $\pm$3% at 90% RH. High-emission permeation tubes were utilized to generate the target VOCs. Various patterns of the permeation rates were observed over time. The lifetimes and permeation rates of the tubes differed by compound, length of the tube and manufacturer. By carefully selecting the source and length of the tubes, and closely monitoring tube weight loss over time, the permeation tubes can be used for delivering low and stable concentrations of VOCs during multiple days.^ The results of this study indicate that the performance of the 3520 OVM is compound-specific and depends on concentration, temperature and humidity. With the exception of 1,3-butadiene under most conditions, and styrene and methylene chloride at very high relative humidities, recoveries were generally within $\pm$25% of theory, indicating that the 3520 OVM can be effectively used over the range of concentrations and environmental conditions tested with a 24-hour sampling period. Increasing humidities resulted in increasing negative bias from full recovery. Reverse diffusion conducted at 200 $\rm\mu g/m\sp3$ and five temperature/humidity combinations indicated severe diffusion losses only for 1,3-butadiene, methylene chloride and styrene under increased humidity. Overall, the results of this study do not support the need to employ diffusion samplers with backup sections for the exposure conditions tested. ^

Left, right and interval bivariate censored data: Evaluating screening mammography in the presence of lead -time bias, length bias and over-detection

Of the large clinical trials evaluating screening mammography efficacy, none included women ages 75 and older. Recommendations on an upper age limit at which to discontinue screening are based on indirect evidence and are not consistent. Screening mammography is evaluated using observational data from the SEER-Medicare linked database. Measuring the benefit of screening mammography is difficult due to the impact of lead-time bias, length bias and over-detection. The underlying conceptual model divides the disease into two stages: pre-clinical (T0) and symptomatic (T1) breast cancer. Treating the time in these phases as a pair of dependent bivariate observations, (t0,t1), estimates are derived to describe the distribution of this random vector. To quantify the effect of screening mammography, statistical inference is made about the mammography parameters that correspond to the marginal distribution of the symptomatic phase duration (T1). This shows the hazard ratio of death from breast cancer comparing women with screen-detected tumors to those detected at their symptom onset is 0.36 (0.30, 0.42), indicating a benefit among the screen-detected cases. ^