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Infants with hypoplastic left heart syndrome require an initial Norwood operation, followed some months later by a stage 2 palliation (S2P). The timing of S2P is critical for the operation's success and the infant's survival, but the optimal timing, if one exists, is unknown. We attempt to identify the optimal timing of S2P by analyzing data from the Single Ventricle Reconstruction Trial (SVRT), which randomized patients between two different types of Norwood procedure. In the SVRT, the timing of the S2P was chosen by the medical team; thus with respect to this exposure, the trial constitutes an observational study, and the analysis must adjust for potential confounding. To accomplish this, we propose an extended propensity score analysis that describes the time to surgery as a function of confounders in a discrete competing‐risk model. We then apply inverse probability weighting to estimate a spline hazard model for predicting survival from the time of S2P. Our analysis suggests that S2P conducted at 6 months after the Norwood gives the patient the best post‐S2P survival. Thus, we place the optimal time slightly later than a previous analysis in the medical literature that did not account for competing risks of death and heart transplantation. [ABSTRACT FROM AUTHOR]

Abstract Background Flaxseed (FS) is a dietary supplement known for its antioxidant and anti-inflammatory properties. Radiation exposure of lung tissues occurs either when given therapeutically to treat intrathoracic malignancies or incidentally, such as in the case of exposure from inhaled radioisotopes released after the detonation of a radiological dispersion devise (RDD). Such exposure is associated with pulmonary inflammation, oxidative tissue damage and irreversible lung fibrosis. We previously reported that dietary FS prevents pneumonopathy in a rodent model of thoracic X-ray radiation therapy (XRT). However, flaxseed's therapeutic usefulness in mitigating radiation effects post-exposure has never been evaluated. Methods We evaluated the effects of a 10%FS or isocaloric control diet given to mice (C57/BL6) in 2 separate experiments (n = 15-25 mice/group) on 0, 2, 4, 6 weeks post a single dose 13.5 Gy thoracic XRT and compared it to an established radiation-protective diet given preventively, starting at 3 weeks prior to XRT. Lungs were evaluated four months post-XRT for blood oxygenation levels, inflammation and fibrosis. Results Irradiated mice fed a 0%FS diet had a 4-month survival rate of 40% as compared to 70-88% survival in irradiated FS-fed mouse groups. Additionally, all irradiated FS-fed mice had decreased fibrosis compared to those fed 0%FS. Lung OH-Proline content ranged from 96.5 ± 7.1 to 110.2 ± 7.7 μg/ml (Mean ± SEM) in all irradiated FS-fed mouse groups, as compared to 138 ± 10.8 μg/ml for mice on 0%FS. Concomitantly, bronchoalveolar lavage (BAL) protein and weight loss associated with radiation cachexia was significantly decreased in all FS-fed groups. Inflammatory cell influx to lungs also decreased significantly except when FS diet was delayed by 4 and 6 weeks post XRT. All FS-fed mice (irradiated or not), maintained a higher blood oxygenation level as compared to mice on 0%FS. Similarly, multiplex cytokine analysis in the BAL fluid revealed a significant decrease of specific inflammatory cytokines in FS-fed mice. Conclusions Dietary FS given post-XRT mitigates radiation effects by decreasing pulmonary fibrosis, inflammation, cytokine secretion and lung damage while enhancing mouse survival. Dietary supplementation of FS may be a useful adjuvant treatment mitigating adverse effects of radiation in individuals exposed to inhaled radioisotopes or incidental radiation.

The Pennsylvania Cancer Registry was used to contact breast cancer patients and, through them, their adult sisters and daughters. The sisters and daughters were counseled concerning their higher than average risks for breast cancer and their need for mammography and breast self-examination. Results showed a 9 percent increase in mammography and a 10 percent increase in breast self-examination rates for the counseled over control group. Costs were $49 per counseled sister or daughter indicating a need to increase cost effectiveness before implementalion is practical. (Am J Public Health 1990; 81:386--388), To learn more about the effects of counseling first-degree relatives of breast cancer patients concerning their increased risk of having the disease, a cancer registry in Pennsylvania was used to identify breast cancer patients, and through them to reach female relatives. Sisters and daughters of patients who were diagnosed between June 1982 and June 1984 were contacted and counseled about their elevated risk and the importance of mammography and breast self-examination. The subjects (patients' sisters and daughters) were divided randomly into three groups to be counseled at different times during the study. Counseling sessions consisted of educational programs about breast cancer risks and the importance of mammography and breast self-examination. At the end of 21 months, subjects' knowledge and behavior concerning breast cancer were evaluated in telephone interviews. Those who had received counseling by this time (611 women) belonged to the experimental group; those who had not yet received it, to the control group (395). Results showed that the counseling program affected subjects' knowledge, attitudes and behavior, with the least impact on behavior. The experimental group had a 9 percent higher rate of undergoing mammography and a 10 percent higher rate of breast self-examination than the control group. It is noted that a significant proportion of physicians (29 percent) and patients (30 percent) refused to participate by providing names of patients or relatives, respectively. The cost of conducting the program was $49 per person counseled, which was not found to be cost-effective; improvements need to be made in this area. The use of a cancer registry for such purposes, however, seems feasible. (Consumer Summary produced by Reliance Medical Information, Inc.)

The analysis of observational data to determine the cost-effectiveness of medical treatments is complicated by the need to account for skewness, censoring, and the effects of measured and unmeasured confounders. We quantify cost-effectiveness as the Net Monetary Benefit (NMB), a linear combination of the treatment effects on cost and effectiveness that denominates utility in monetary terms. We propose a parametric estimation approach that describes cost with a Gamma generalized linear model and survival time (the canonical effectiveness variable) with a Weibull accelerated failure time model. To account for correlation between cost and survival, we propose a bootstrap procedure to compute confidence intervals for NMB. To examine sensitivity to unmeasured confounders, we derive simple approximate relationships between naïve parameters, assuming only measured confounders, and the values those parameters would take if there was further adjustment for a single unmeasured confounder with a specified distribution. A simulation study shows that the method returns accurate estimates for treatment effects on cost, survival, and NMB under the assumed model. We apply our method to compare two treatments for Stage II/III bladder cancer, concluding that the NMB is sensitive to hypothesized unmeasured confounders that represent smoking status and personal income. [ABSTRACT FROM AUTHOR]

Real-time prediction of clinical trial accrual can support logistical planning, ensuring that studies meet but do not exceed sample size targets. We describe a novel, simulation-based prediction method that is founded on a realistic model for the underlying processes of recruitment. The model reflects key features of enrollment such as the staggered initiation of new centers, heterogeneity in enrollment capacity, and declining accrual within centers. The model's first stage assumes that centers join the trial (ie, initiate accrual) according to an inhomogeneous Poisson process in discrete time. The second part assumes that each center's enrollment pattern reflects an early plateau followed by a slow decline, with a burst at the end of the trial following the announcement of an imminent closing date. By summing up achieved and projected enrollment, one can predict accrual as a function of time and, thereby, the time when the trial will achieve a planned enrollment target. We applied our method retrospectively to two real-world trials: NSABP B-38 and REMATCH (Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure). In both studies, the proposed method produced prediction intervals for time to completion that were more accurate than those from conventional predictions that assume a constant rate of enrollment, estimated either from the entire trial to date or over a recent time window. The advantage is substantial in the early stages of NSABP B-38. We conclude that a method based on a realistic accrual model offers improved accuracy in the prediction of enrollment landmarks, especially at the early stages of large trials that involve many centers. [ABSTRACT FROM AUTHOR]

Background: In event-based clinical trials, it is common to conduct interim analyses at planned landmark event counts. Accurate prediction of the timing of these events can support logistical planning and the efficient allocation of resources. As the trial progresses, one may wish to use the accumulating data to refine predictions. Purpose: Available methods to predict event times include parametric cure and non-cure models and a nonparametric approach involving Bayesian bootstrap simulation. The parametric methods work well when their underlying assumptions are met, and the nonparametric method gives calibrated but inefficient predictions across a range of true models. In the early stages of a trial, when predictions have high marginal value, it is difficult to infer the form of the underlying model. We seek to develop a method that will adaptively identify the best-fitting model and use it to create robust predictions. Methods: At each prediction time, we repeat the following steps: (1) resample the data; (2) identify, from among a set of candidate models, the one with the highest posterior probability; and (3) sample from the predictive posterior of the data under the selected model. Results: A Monte Carlo study demonstrates that the adaptive method produces prediction intervals whose coverage is robust within the family of selected models. The intervals are generally wider than those produced assuming the correct model, but narrower than nonparametric prediction intervals. We demonstrate our method with applications to two completed trials: The International Chronic Granulomatous Disease study and Radiation Therapy Oncology Group trial 0129. Limitations: Intervals produced under any method can be badly calibrated when the sample size is small and unhelpfully wide when predicting the remote future. Early predictions can be inaccurate if there are changes in enrollment practices or trends in survival. Conclusions: An adaptive event-time prediction method that selects the model given the available data can give improved robustness compared to methods based on less flexible parametric models. [ABSTRACT FROM AUTHOR]

Objective To develop a model that predicts the risk of 30-day, all-cause readmission in Medicaid patients hospitalized for heart failure. Design Retrospective study of a population cohort to create a predictive model. Setting and Participants We analyzed 2016-2019 Medicaid claims data from seven US states. We defined a heart failure admission as one in which either the admission diagnosis or the first or second clinical (discharge) diagnosis bore an ICD-10 code for heart failure. A readmission was an admission for any condition (not necessarily heart failure) that occurred within 30 days of a heart failure discharge. Methods We estimated a mixed-effects logistic model to predict 30-day readmission from patient demographic data, comorbidities, past healthcare utilization, and characteristics of the index hospitalization. We evaluated model fit graphically and measured predictive accuracy by the area under the receiver operating characteristics curve (AUC). Results 6,859 patients contributed 9,336 heart failure hospitalizations; 2,667 (28.6 percent) were 30-day readmissions. The final model included age, number of admissions and emergency room visits in the preceding year, length of stay, discharge status, index admission type, US state of admission, and past diagnoses. The observed vs. predicted plot showed good fit, and the estimated AUC of 0.745 was robust in sensitivity analyses. Conclusions and Implications Our model robustly and with moderate precision identifies Medicaid patients hospitalized for heart failure who are at a high risk of readmission. One can use the model to guide the development of post-discharge management interventions for reducing readmissions and for rigorously adjusting comparisons of 30-day readmission rates between sites/providers or over time. [ABSTRACT FROM AUTHOR]