Ultrasound feedback and motivational interviewing targeting smoking cessation in the second and third trimesters of pregnancy.

INTRODUCTION: Cigarette smoking during pregnancy is associated with poor maternal and child health outcomes. Effective interventions to increase smoking cessation rates are needed particularly for pregnant women unable to quit in their first trimester. Real-time ultrasound feedback focused on potential effects of smoking on the fetus may be an effective treatment adjunct, improving smoking outcomes. METHODS: A prospective randomized trial was conducted to evaluate the efficacy of a smoking cessation intervention consisting of personalized feedback during ultrasound plus motivational interviewing-based counseling sessions. Pregnant smokers (N = 360) between 16 and 26 weeks of gestation were randomly assigned to one of three groups: Best Practice (BP) only, Best Practice plus ultrasound feedback (BP+US), or Motivational Interviewing-based counseling plus ultrasound feedback (MI+US). Assessments were conducted at baseline and end of pregnancy (EOP). RESULTS: Analyses of cotinine-verified self-reported smoking status at EOP indicated that 10.8% of the BP group was not smoking at EOP; 14.2% in the BP+US condition and 18.3% who received MI+US were abstinent, but differences were not statistically significant. Intervention effects were found conditional upon level of baseline smoking, however. Nearly 34% of light smokers (< or =10 cigarettes/day) in the MI+US condition were abstinent at EOP, followed by 25.8% and 15.6% in the BP+US and BP conditions, respectively. Heavy smokers (>10 cigarettes/day) were notably unaffected by the intervention. DISCUSSION: Future research should confirm benefit of motivational interviewing plus ultrasound feedback for pregnant light smokers and explore mechanisms of action. Innovative interventions for pregnant women smoking at high levels are sorely needed.

The Role of Cell Sterilization in Population Based Studies of Radiogenic Second Cancers Following Radiation Therapy

Advances in radiotherapy have generated increased interest in comparative studies of treatment techniques and their effectiveness. In this respect, pediatric patients are of specific interest because of their sensitivity to radiation induced second cancers. However, due to the rarity of childhood cancers and the long latency of second cancers, large sample sizes are unavailable for the epidemiological study of contemporary radiotherapy treatments. Additionally, when specific treatments are considered, such as proton therapy, sample sizes are further reduced due to the rareness of such treatments. We propose a method to improve statistical power in micro clinical trials. Specifically, we use a more biologically relevant quantity, cancer equivalent dose (DCE), to estimate risk instead of mean absorbed dose (DMA). Our objective was to demonstrate that when DCE is used fewer subjects are needed for clinical trials. Thus, we compared the impact of DCE vs. DMA on sample size in a virtual clinical trial that estimated risk for second cancer (SC) in the thyroid following craniospinal irradiation (CSI) of pediatric patients using protons vs. photons. Dose reconstruction, risk models, and statistical analysis were used to evaluate SC risk from therapeutic and stray radiation from CSI for 18 patients. Absorbed dose was calculated in two ways: with (1) traditional DMA and (2) with DCE. DCE and DMA values were used to estimate relative risk of SC incidence (RRCE and RRMA, respectively) after proton vs. photon CSI. Ratios of RR for proton vs. photon CSI (RRRCE and RRRMA) were then used in comparative estimations of sample size to determine the minimal number of patients needed to maintain 80% statistical power when using DCE vs. DMA. For all patients, we found that protons substantially reduced the risk of developing a second thyroid cancer when compared to photon therapy. Mean RRR values were 0.052±0.014 and 0.087±0.021 for RRRMA and RRRCE, respectively. However, we did not find that use of DCE reduced the number of patents needed for acceptable statistical power (i.e, 80%). In fact, when considerations were made for RRR values that met equipoise requirements and the need for descriptive statistics, the minimum number of patients needed for a micro-clinical trial increased from 17 using DMA to 37 using DCE. Subsequent analyses revealed that for our sample, the most influential factor in determining variations in sample size was the experimental standard deviation of estimates for RRR across the patient sample. Additionally, because the relative uncertainty in dose from proton CSI was so much larger (on the order of 2000 times larger) than the other uncertainty terms, it dominated the uncertainty in RRR. Thus, we found that use of corrections for cell sterilization, in the form of DCE, may be an important and underappreciated consideration in the design of clinical trials and radio-epidemiological studies. In addition, the accurate application of cell sterilization to thyroid dose was sensitive to variations in absorbed dose, especially for proton CSI, which may stem from errors in patient positioning, range calculation, and other aspects of treatment planning and delivery.