Gordon Honerkamp-Smith

Low Rates of Malignancy and Mortality in Asymptomatic Patients With Suspected Neoplastic Pancreatic Cysts Beyond 5 Years of Surveillance

BACKGROUND & AIMS The 2015 American Gastroenterological Association guidelines recommend discontinuation of surveillance of pancreatic cysts after 5 years, although there are limited data to support this recommendation. We aimed to determine the rate of pancreatic cancer development from neoplastic pancreatic cysts after 5 years of surveillance. METHODS We performed a retrospective multicenter study, collecting data from 310 patients with asymptomatic suspected neoplastic pancreatic cysts, identified by endoscopic ultrasound from January 2002 to June 2010 at 4 medical centers in California. All patients were followed up for 5 years or more (median, 87 mo; range, 60-189 mo). Data were used to calculate the risk for pancreatic cancer and all-cause mortality. RESULTS Three patients (1%) developed invasive pancreatic adenocarcinoma. Based on American Gastroenterological Association high-risk features (cyst size > 3 cm, dilated pancreatic duct, mural nodule), risks for cancer were 0%, 1%, and 15% for patients with 0, 1, or 2 high-risk features, respectively. Mortality from nonpancreatic causes was 8-fold higher than mortality from pancreatic cancer after more than 5 years of surveillance. CONCLUSIONS There is a very low risk of malignant transformation of asymptomatic neoplastic pancreatic cysts after 5 years. Patients with pancreatic lesions and 0 or 1 high-risk feature have a less than 1% risk of developing pancreatic cancer, therefore discontinuation of surveillance can be considered for select patients. Patients with neoplastic pancreatic cysts with 2 high-risk features have a 15% risk of subsequent pancreatic cancer, therefore surgery or continued surveillance should be considered.

United Network for Organ Sharing regional variations in appeal denial rates with non-standard Model for End-Stage Liver Disease/Pediatric End-Stage Liver Disease exceptions: support for a national review board

Although it has been generally recognized that there are inconsistencies among Regional Review Boards in the assignment of points for model for end‐stage liver disease (MELD)/pediatric end‐stage liver disease (PELD) exception patients with resulting considerable variation in appeal denial rates, data to actually prove this have been limited. We reviewed 6533 MELD/PELD exception applications submitted between 2005 and 2008, calculated the variation in approval/denial rates, and followed these cases through mid‐2013 to assess the effects on patient outcomes. We found highly significant regional variations in denial rates for appeals by exception patients and in transplantation rates. The odds of transplant for patients whose appeals are approved is 2.45 times that of patients not approved; that this effect does not vary by region suggests that the variation in transplant rates is driven, at least in part, by the variation in appeal denial rates. Health deterioration or death accounts for more than two‐thirds of wait list removals among patients removed for reasons other than transplant. Our findings add to the weight of evidence that a national review board that uses current clinical expertise, peer review literature, and data to consistently assign priority could reduce regional inequities and move toward equitable allocation of organs and compliance with the United States Department of Health & Human Services Final Rule.

An educational intervention on drug interactions and contraceptive options for epilepsy patients: a pilot randomized controlled trial

OBJECTIVE This pilot study investigates whether an educational handout could increase short-term information retention about drug interactions between antiepileptic drugs (AEDs) and hormonal contraceptives among female epilepsy patients of reproductive age. STUDY DESIGN This is a pilot randomized controlled trial of an educational intervention among reproductive-age women with epilepsy in an academic neurology clinic. Investigators measured knowledge before and after participants received either usual care or the educational handout. The 10-question test assessed increased knowledge of which AEDs affected efficacy of certain hormonal contraceptives and was assessed by calculating the improvement in score between the pretest and posttest. The educational handout included the names of AEDs that have drug interactions with certain contraceptives and the efficacy of the contraceptives. RESULTS A total of 42 epilepsy patients participated in this study. Fourteen participants were taking AEDs that are enzyme p450 inducers and 13 participants were taking Lamotrigine. Twenty women were randomized to receive the educational handout and 22 women were randomized to usual care. We found no statistical difference in the groups with regard to age, ethnicity or level of education. We found a significantly higher improvement in quiz scores in the educational handout group (3.65 point increase) compared to the usual care group (0.68 point increase) as calculated by the Students two-sample t test (p<.001). CONCLUSIONS An educational handout on drug interactions and contraceptives resulted in increased short-term information retention on this topic among reproductive-age female epilepsy patients. IMPLICATIONS This pilot study highlights the need for further larger studies to evaluate the impact of educational interventions on improving patient knowledge about the drug interaction of AEDs and hormonal contraceptives.

Marijuana Use by Breastfeeding Mothers and Cannabinoid Concentrations in Breast Milk

With this study, we quantified levels of cannabinoids ∆9-THC, 11-OH-THC, cannabidiol, and cannabinol in 54 milk samples provided by breastfeeding mothers who reported recent marijuana use. BrightcoveDefaultPlayer10.1542/6138655441001PEDS-VA_2018-1076 Video Abstract BACKGROUND AND OBJECTIVE: Marijuana is the most commonly used recreational drug among breastfeeding women. With legalization of marijuana in several US states and a 1990 study in which authors documented psychomotor deficits in infants breastfed by mothers using marijuana, there is a need for information on potential exposure to the breastfed infant. Our objective with this study was to quantify cannabinoids in human milk after maternal marijuana use. METHODS: Between 2014 and 2017, 50 breastfeeding women who reported marijuana use provided 54 breast milk samples to a research repository, Mommy’s Milk. Concentrations of Δ-9-tetrahydrocannabinol (∆9-THC), 11-hydroxy-Δ-9-tetrahydrocannabinol, cannabidiol, and cannabinol were measured by using liquid chromatography mass spectrometry electrospray ionization. RESULTS: ∆9-THC was detectable in 34 (63%) of the 54 samples up to ∼6 days after last reported use; the median concentration of ∆9-THC was 9.47 ng/mL (range: 1.01–323.00). Five samples had detectable levels of 11-hydroxy-Δ-9-tetrahydrocannabinol (range: 1.33–12.80 ng/mL) or cannabidiol (range: 1.32–8.56 ng/mL). The sample with the highest concentration of cannabidiol (8.56 ng/mL) did not have measurable ∆9-THC. Cannabinol was not detected in any samples. The number of hours since last use was a significant predictor of log ∆9-THC concentrations (−0.03; 95% confidence interval [CI] −0.04 to −0.01; P = .005). Adjusted for time since last use, the number of daily uses and time from sample collection to analysis were also significant predictors of log ∆9-THC concentrations (0.51; 95% CI 0.03 to 0.99; P = .039; 0.08; 95% CI 0.00 to 0.15; P = .038, respectively). CONCLUSIONS: ∆9-THC was measurable in a majority of breast milk samples up to ∼6 days after maternal marijuana use.

Three measures of explained variation for correlated survival data under the proportional hazards mixed-effects model.

Measures of explained variation are useful in scientific research, as they quantify the amount of variation in an outcome variable of interest that is explained by one or more other variables. We develop such measures for correlated survival data, under the proportional hazards mixed-effects model. Because different approaches have been studied in the literature outside the classical linear regression model, we investigate three measures R(2) , Rres2, and ρ(2) that quantify three different population coefficients. We show that although the three population measures are not the same, they reflect similar amounts of variation explained by the predictors. Among the three measures, we show that R(2) , which is the simplest to compute, is also consistent for the first population measure under the usual asymptotic scenario when the number of clusters tends to infinity. The other two measures, on the other hand, all require that in addition the cluster sizes be large. We study the properties of the measures both analytically and through simulation studies. We illustrate their different usage on a multi-center clinical trial and a recurrent events data set. Copyright

Explained Variation for Correlated Survival Data Under the Proportional Hazards Mixed-Effects Model

Measures of explained variation are useful in scientific research, as they quantify the amount of variation in an outcome variable of interest that is explained by one or more other variables. We develop such measures for correlated survival data, under the proportional hazards mixed-effects model (PHMM). Since different approaches have been studied in the literature outside the classical linear regression model, we investigate four sample-based measures that estimate three different population coefficients. We show that although the three population measures are not the same, they reflect similar amounts of variation explained by the predictors. Among the four sample-based measures, we show that the first one (R2) which is the simplest to compute, is also consistent for the first population measure (\(\Omega ^{2}\)) under the usual asymptotic scenario when the number of clusters tends to infinity; the other three sample-based measures, on the other hand, all require that in addition the cluster sizes be large. We study the properties of the measures through simulation studies. We illustrate their usage on a multi-center clinical trial data set.