The risk of uveitis due to prostaglandin analogs in pediatric glaucoma

PURPOSE

To examine the incidence of uveitis in children prescribed prostaglandin analogs (PGAs) for glaucoma.

METHODS

In this dual-center cohort study, the medical records of consecutive patients <18 years old treated with a PGA between January 1, 2012, and December 31, 2018, were reviewed retrospectively. Patients with all forms of glaucoma, including those with a prior history of uveitis, were included. Patients who had been on a PGA prior to their first recorded visit were excluded. Patient charts were reviewed for new or recurrent uveitis during the first year of PGA therapy.

RESULTS

A total of 103 children (147 eyes) were included, with a total PGA exposure of 1,352 child-months. Ninety-eight children (142 eyes) tolerated the PGA without an episode of uveitis. Five patients with a documented prior history of uveitis experienced a unilateral episode of uveitis. A review of their medical records identified prescribed or unscheduled decrease in topical steroids or immunosuppressive medication as the most likely cause of uveitis recurrence.

CONCLUSIONS

This study provides further evidence that PGAs are unlikely to induce uveitis in children being treated for glaucoma and suggests that this may also be true in those with a history of uveitis. We are unable to evaluate whether PGAs make recurrence more likely or the tapering of steroids more difficult.

Accurate model predicting sustained response at week 4 of therapy with pegylated interferon with ribavirin in patients with chronic hepatitis C

Current models used to predict response to peginterferon plus ribavirin treatment, based on viral decline during the first 12 weeks of therapy, have focused on creating an early stopping rule to avoid unnecessary prolongation of therapy. We developed a multivariate model that predicted sustained virological response and nonresponse at baseline and during the first 12 weeks of therapy using collected data from 186 unselected patients with chronic hepatitis C treated with peginterferon plus ribavirin. This model employed ordinal regression with similarity least squares technology to assign the probability of a given outcome. Model variables include sex, age, prior treatment status, genotype, baseline serum alanine aminotransferase levels, histologic necroinflammation and fibrosis scores and serum hepatitis C virus RNA concentration at baseline and weeks, 4, 8, and 12. A multivariate model demonstrated high performance values at all time points. At baseline, the model demonstrated a negative predictive value (NPV) and a positive predictive value (PPV) of 91% and 95%, respectively. At week 4, these values improved to 97% and 100%, respectively, with 95% sensitivity, 89% specificity and 93% accuracy. At week 4, the model was equally efficient for naïve or previously treated patients. Internal validation demonstrated 90% PPV, 94% NPV, 95% sensitivity, 88% specificity and 92% accuracy. A week 4 stopping rule for patients with chronic hepatitis C treated with peginterferon with ribavirin might be proposed by using the model developed in our study.

Assessment of hepatitis C virus RNA stability in serum by the Quantiplex branched DNA assay

OBJECTIVES

Quantification of hepatitis C virus (HCV) RNA in serum is used to assess the probability of treatment response and to monitor antiviral therapy. Since serum specimens often are shipped to central sites for HCV RNA testing, it is important to define conditions that preserve HCV RNA integrity.

METHODS

We evaluated the stability of HCV RNA in 25 previously frozen (PF) and 11 fresh, never previously frozen (NPF) specimens subjected to handling and short-term storage conditions that mimic those encountered during interlaboratory shipping. All sera were separated within 4 h of collection. PF samples covering a approximately 3 log10 HCV RNA dynamic range were thawed, divided into aliquots, incubated at 4, 23, and 37 degrees C (+/- 1.5 degrees C) for 24, 48, 72 and 96 h (+/- 2 h), and then refrozen at -70 degrees C prior to testing with the Quantiplex HCV RNA 2.0 assay. Eleven NPF samples were stored at -70, -20, and 4 degrees C (+/- 1.5 degrees C) for up to 1 month prior to testing.

RESULTS

Linear regression analysis showed no HCV RNA degradation in PF specimens kept at 4 degrees C over 4 days. However, HCV RNA levels in PF specimens decreased over 4 days by 20 and 105% at 23 and 37 degrees C, respectively. Three independent statistical methods showed that the probability of specimen failure in PF specimens, defined as a loss of 20% or more of HCV RNA, was lowest at 4 degrees C and increased with increasing temperature. The HCV RNA quantification of the 11 NPF specimens stored at 4 degrees C was similar to their frozen controls.

CONCLUSION

HCV RNA in separated serum specimens is stable for at least 4 days at 4 degrees C.

Effect of multiple freeze-thaw cycles on hepatitis B virus DNA and hepatitis C virus RNA quantification as measured with branched-DNA technology

Quantification of hepatitis B virus (HBV) DNA and hepatitis C virus (HCV) RNA often is performed in specimens that have been frozen and thawed more than once. To ensure optimal therapeutic and prognostic value, it is important to establish whether viral load measurements are affected by repeated freeze-thaw (FT) cycles. We therefore evaluated the effect of multiple FT cycles on HBV DNA and HCV RNA quantification by testing serum specimens subjected to one (baseline), two, four, and eight FT cycles with the appropriate Chiron Quantiplex assay. Linear regression analysis showed minor increases of 1.7% per FT cycle for both HBV DNA and HCV RNA. The rise in HCV RNA levels was more pronounced among low-concentration samples, since further analysis revealed an increase of 3.2% per FT cycle among samples with 0.2 to 3.86 Meq of HCV RNA per ml. Given that the coefficient of variation for the Quantiplex assays is generally 10 to 15%, the minor increases in HBV DNA and HCV RNA levels with progressive FT cycles for the specimens tested were recognized only because analysis of variance revealed a statistically significant trend (P < 0.05). Due to the minor statistical trend, the clinical impact for individual patient specimens is likely to be limited, but it may deserve further study. In conclusion, the concentration of HBV DNA and HCV RNA in serum specimens subjected to up to eight short-term FT cycles was stable.

Statistical models for predicting a beneficial response to interferon-alpha in patients with chronic hepatitis B

Therapy with interferon-alpha has been reported to induce remissions in 35% of patients with chronic hepatitis B. The ability to identify patients likely to respond would be helpful in making recommendations for treatment. In this statistical analysis we included 82 patients with chronic hepatitis B who received interferon-alpha in clinical trials at the National Institutes of Health between 1984 and 1991. A response was defined as the loss of hepatitis B virus (HBV) DNA and hepatitis B e antigen (HBeAg) within 1 year of therapy. Multiple clinical parameters measured at pretreatment (month 0) and after the first month (month 1) of therapy were selected by stepwise regression to support the development of the prognostic models: the two-stage logistic regression model and a neural network that utilized higher-order non-linear interactions between variables. Among the 82 patients, 24 (29%) were responders. The two-stage logistic model using pretreatment variables: sex, hepatic fibrosis and alanine aminotransferase (ALT) levels correctly identified 61% of responders and 76% of non-responders. When HBV DNA at month 1 along with sex, initial ALT and fibrosis was included, the resultant model correctly identified 69% of responders and 77% of non-responders. The neural network, by incorporating interactions between variables, correctly identified 77% and 86% of responders, and 87% and 92% of non-responders, using pretreatment factors alone and the combination of pretreatment and month 1 factors respectively. Hence, the neural network was more accurate than the simple logistic regression model in predicting a response to interferon-alpha in chronic hepatitis B. The universality of these models needs to be further verified.

Assessment of hepatitis B virus DNA stability in serum by the Chiron Quantiplex branched-DNA assay

Quantification of hepatitis B virus (HBV) DNA in serum is used to establish eligibility for treatment and to monitor therapeutic response. With the trend toward centralized testing, defining the conditions that preserve sample integrity is of paramount importance. We therefore evaluated the stability of HBV DNA in 26 previously frozen (PF) and 5 fresh, never previously frozen serum specimens. PF specimens, covering a 3-log10 HBV DNA dynamic range, were thawed and stored at -70, 4, 23, 37, and 45 degrees C (+/-1.5 degrees C) for 0, 24, 72, and 120 h (+/-2 h) and were refrozen at -70 degrees C prior to testing. Five fresh specimens were split into two groups. Both group FG1 and group FG2 specimens were handled as described above; however, group FG1 specimens were subsequently maintained at 4 degrees C and were never frozen prior to testing. Linear regression analysis of PF specimens demonstrated no significant HBV DNA degradation at < or =4 degrees C over 5 days; however, HBV DNA levels decreased by 1.8, 3.4, and 20% per day at 23, 37, and 45 degrees C, respectively. Three independent statistical methods confirmed that the probability of specimen failure, defined as a loss of 20% or more of HBV DNA and/or coagulation of serum, was lowest at < or =4 degrees C and increased with temperature. Because only 10 to 20% of individual patient specimens demonstrated losses of HBV DNA of > or =20% at 23 or 37 degrees C, sufficient numbers of serum specimens must be evaluated to determine overall statistical trends. In conclusion, HBV DNA integrity in separated serum specimens is preserved for at least 5 days when the specimens are stored at -70 or 4 degrees C.

Analysis of clinical data using neural nets

Clinical studies investigate the interdependence of dosing regimen, efficacy, and side effects. These relationships often involve complex dynamical functions. The study of phenomena intermediate between dosing and efficacy, e.g., pharmacokinetics (PK), helps one identify and understand these interdependences. However, efficacy still tends to be a complicated function of PK parameters, and indeed these parameters are becoming more complex as a function of dosing regimen, viz., studies involving immunosuppressants, biotechnology drugs, sophisticated delivery systems, and dosing strategies. Stationary and time-dependent neural nets can help one identify and model such unknown complex dynamical functions with few assumptions and limited data (1-7). Neural nets can relate dosing directly to efficacy, dosing to PK, PK to efficacy, or any component in the complex associations among treatments, pharmacodynamics, efficacy, and side effects. Neural nets can also assist one in the design of clinical trials involving complex and sophisticated procedures, e.g., randomized controlled clinical trials.

Generalized ridge analysis with application to population pharmacokinetics/dynamics

Given a set of measurements on a patient in clinical studies or on a drug response in bioassays and immunoassays, the data series for the individual can be incomplete, noisy, and haphazard. Hence, meaningful analysis on such limited data is at best difficult given the typical assumptions on the nonlinear structure of systematic and random components involving both individual and population effects. This paper describes a simple direct semiparametric procedure to incorporate population-wide information from as many individuals as required to support analysis of data on any specific individual. This notion is motivated by the pattern-processing capabilities of experts as they evaluate data on an individual based on their reservoir of accumulated knowledge of the given application on many individuals.