Perforated appendicitis due to fishbone.

Appendicitis is a common condition in daily clinical practice. Appendicitis due to foreign bodies is uncommon and may result from obstruction or perforation mechanism. We present a rare case of a 43-year-old male patient who was diagnosed with perforated appendicitis due to a fish bone by imaging studies and confirmed postoperatively. Confirming the fish bone causing the perforation on images is sometimes difficult, requiring the radiologist to actively search and determine the source. In addition to appendectomy, the surgeon also needs to pay attention to removing all foreign objects and treating perforations of surrounding organs.

Parametric Dose Standardization for Optimizing Two-Agent Combinations in a Phase I-II Trial with Ordinal Outcomes.

A Bayesian model and design are described for a phase I-II trial to jointly optimise the doses of a targeted agent and a chemotherapy agent for solid tumors. A challenge in designing the trial was that both the efficacy and toxicity outcomes were defined as four-level ordinal variables. To reflect possibly complex joint effects of the two doses on each of the two outcomes, for each marginal distribution a generalised continuation ratio model was assumed, with each agent's dose parametrically standardised in the linear term. A copula was assumed to obtain a bivariate distribution. Elicited outcome probabilities were used to construct a prior, with variances calibrated to obtain small prior effective sample size. Elicited numerical utilities of the 16 elementary outcomes were used to compute posterior mean utilities as criteria for selecting dose pairs, with adaptive randomisation to reduce the risk of getting stuck at a suboptimal pair. A simulation study showed that parametric dose standardisation with additive dose effects provides a robust, reliable model for dose pair optimisation in this setting, and it compares favourably with designs based on alternative models that include dose-dose interaction terms. The proposed model and method are applicable generally to other clinical trial settings with similar dose and outcome structures.

Phase I/II study of the hypoxia-activated prodrug PR104 in refractory/relapsed acute myeloid leukemia and acute lymphoblastic leukemia.

We previously demonstrated vast expansion of hypoxic areas in the leukemic microenvironment and provided a rationale for using hypoxia-activated prodrugs. PR104 is a phosphate ester that is rapidly hydrolyzed in vivo to the corresponding alcohol PR-104A and further reduced to the amine and hydroxyl-amine nitrogen mustards that induce DNA cross-linking in hypoxic cells under low oxygen concentrations. In this phase I/II study, patients with relapsed/refractory acute myeloid leukemia (n=40) after 1 or 2 prior treatments or acute lymphoblastic leukemia (n=10) after any number of prior treatments received PR104; dose ranged from 1.1 to 4 g/m(2). The most common treatment-related grade 3/4 adverse events were myelosuppression (anemia 62%, neutropenia 50%, thrombocytopenia 46%), febrile neutropenia (40%), infection (24%), and enterocolitis (14%). Ten of 31 patients with acute myeloid leukemia (32%) and 2 of 10 patients with acute lymphoblastic leukemia (20%) who received 3 g/m(2) or 4 g/m(2) had a response (complete response, n=1; complete response without platelet recovery, n=5; morphological leukemia-free state, n=6). The extent of hypoxia was evaluated by the hypoxia tracer pimonidazole administered prior to a bone marrow biopsy and by immunohistochemical assessments of hypoxia-inducible factor alpha and carbonic anhydrase IX. A high fraction of leukemic cells expressed these markers, and PR104 administration resulted in measurable decrease of the proportions of hypoxic cells. These findings indicate that hypoxia is a prevalent feature of the leukemic microenvironment and that targeting hypoxia with hypoxia-activated prodrugs warrants further evaluation in acute leukemia. The trial is registered at clinicaltrials.gov identifier: 01037556.

Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome.

The Intubation-Surfactant-Extubation (INSURE) procedure is used worldwide to treat pre-term newborn infants suffering from respiratory distress syndrome, which is caused by an insufficient amount of the chemical surfactant in the lungs. With INSURE, the infant is intubated, surfactant is administered via the tube to the trachea, and at completion the infant is extubated. This improves the infant's ability to breathe and thus decreases the risk of long term neurological or motor disabilities. To perform the intubation safely, the newborn infant first must be sedated. Despite extensive experience with INSURE, there is no consensus on what sedative dose is best. This paper describes a Bayesian sequentially adaptive design for a multi-institution clinical trial to optimize the sedative dose given to pre-term infants undergoing the INSURE procedure. The design is based on three clinical outcomes, two efficacy and one adverse, using elicited numerical utilities of the eight possible elementary outcomes. A flexible Bayesian parametric trivariate dose-outcome model is assumed, with the prior derived from elicited mean outcome probabilities. Doses are chosen adaptively for successive cohorts of infants using posterior mean utilities, subject to safety and efficacy constraints. A computer simulation study of the design is presented.

Effective sample size for computing prior hyperparameters in Bayesian phase I-II dose-finding.

BACKGROUND: The efficacy-toxicity trade-off based design is a practical Bayesian phase I-II dose-finding methodology. Because the design's performance is very sensitive to prior hyperparameters and the shape of the target trade-off contour, specifying these two design elements properly is essential. PURPOSE: The goals are to provide a method that uses elicited mean outcome probabilities to derive a prior that is neither overly informative nor overly disperse, and practical guidelines for specifying the target trade-off contour. METHODS: A general algorithm is presented that determines prior hyperparameters using least squares penalized by effective sample size. Guidelines for specifying the trade-off contour are provided. These methods are illustrated by a clinical trial in advanced prostate cancer. A new version of the efficacy-toxicity program is provided for implementation. RESULTS: Together, the algorithm and guidelines provide substantive improvements in the design's operating characteristics. LIMITATIONS: The method requires a substantial number of elicited values and design parameters, and computer simulations are required to obtain an acceptable design. CONCLUSION: The two key improvements greatly enhance the efficacy-toxicity design's practical usefulness and are straightforward to implement using the updated computer program. The algorithm for determining prior hyperparameters to ensure a specified level of informativeness is general, and may be applied to models other than that underlying the efficacy-toxicity method.

Using joint utilities of the times to response and toxicity to adaptively optimize schedule-dose regimes.

A Bayesian two-stage phase I-II design is proposed for optimizing administration schedule and dose of an experimental agent based on the times to response and toxicity in the case where schedules are non-nested and qualitatively different. Sequentially adaptive decisions are based on the joint utility of the two event times. A utility function is constructed by partitioning the two-dimensional positive real quadrant of possible event time pairs into rectangles, eliciting a numerical utility for each rectangle, and fitting a smooth parametric function to the elicited values. We assume that each event time follows a gamma distribution with shape and scale parameters both modeled as functions of schedule and dose. A copula is assumed to obtain a bivariate distribution. To ensure an ethical trial, adaptive safety and efficacy acceptability conditions are imposed on the (schedule, dose) regimes. In stage 1 of the design, patients are randomized fairly among schedules and, within each schedule, a dose is chosen using a hybrid algorithm that either maximizes posterior mean utility or randomizes among acceptable doses. In stage 2, fair randomization among schedules is replaced by the hybrid algorithm. A modified version of this algorithm is used for nested schedules. Extensions of the model and utility function to accommodate death or discontinuation of follow up are described. The method is illustrated by an autologous stem cell transplantation trial in multiple myeloma, including a simulation study.

Adaptive randomization to improve utility-based dose-finding with bivariate ordinal outcomes.

A sequentially outcome-adaptive Bayesian design is proposed for choosing the dose of an experimental therapy based on elicited utilities of a bivariate ordinal (toxicity, efficacy) outcome. Subject to posterior acceptability criteria to control the risk of severe toxicity and exclude unpromising doses, patients are randomized adaptively among the doses having posterior mean utilities near the maximum. The utility increment used to define near-optimality is nonincreasing with sample size. The adaptive randomization uses each dose's posterior probability of a set of good outcomes, defined by a lower utility cutoff. Saturated parametric models are assumed for the marginal dose-toxicity and dose-efficacy distributions, allowing the possible requirement of monotonicity in dose, and a copula is used to obtain a joint distribution. Prior means are computed by simulation using elicited outcome probabilities, and prior variances are calibrated to control prior effective sample size and obtain a design with good operating characteristics. The method is illustrated by a Phase I/II trial of radiation therapy for children with brainstem gliomas.

A Hybrid Geometric Phase II/III Clinical Trial Design based on Treatment Failure Time and Toxicity.

The problem of comparing several experimental treatments to a standard arises frequently in medical research. Various multi-stage randomized phase II/III designs have been proposed that select one or more promising experimental treatments and compare them to the standard while controlling overall Type I and Type II error rates. This paper addresses phase II/III settings where the joint goals are to increase the average time to treatment failure and control the probability of toxicity while accounting for patient heterogeneity. We are motivated by the desire to construct a feasible design for a trial of four chemotherapy combinations for treating a family of rare pediatric brain tumors. We present a hybrid two-stage design based on two-dimensional treatment effect parameters. A targeted parameter set is constructed from elicited parameter pairs considered to be equally desirable. Bayesian regression models for failure time and the probability of toxicity as functions of treatment and prognostic covariates are used to define two-dimensional covariate-adjusted treatment effect parameter sets. Decisions at each stage of the trial are based on the ratio of posterior probabilities of the alternative and null covariate-adjusted parameter sets. Design parameters are chosen to minimize expected sample size subject to frequentist error constraints. The design is illustrated by application to the brain tumor trial design.

Optimizing the concentration and bolus of a drug delivered by continuous infusion.

We consider treatment regimes in which an agent is administered continuously at a specified concentration until either a response is achieved or a predetermined maximum infusion time is reached. Response is an event defined to characterize therapeutic efficacy. A portion of the maximum planned total amount administered is given as an initial bolus. For such regimes, the amount of the agent received by the patient depends on the time to response. An additional complication when response is evaluated periodically rather than continuously is that the response time is interval censored. We address the problem of designing a clinical trial in which such response time data and a binary indicator of toxicity are used together to jointly optimize the concentration and the size of the bolus. We propose a sequentially adaptive Bayesian design that chooses the optimal treatment for successive patients by maximizing the posterior mean utility of the joint efficacy-toxicity outcome. The methodology is illustrated by a trial in which tissue plasminogen activator is infused intraarterially as rapid treatment for acute ischemic stroke.

Maintenance therapy with low-dose azacitidine after allogeneic hematopoietic stem cell transplantation for recurrent acute myelogenous leukemia or myelodysplastic syndrome: a dose and schedule finding study.

BACKGROUND: Recurrence is a major cause of treatment failure after allogeneic transplantation for acute myelogenous leukemia (AML) and myelodysplastic syndrome (MDS), and treatment options are very limited. Azacitidine is a DNA methyltransferase inhibitor with activity in myeloid disease. The authors hypothesized that low-dose azacitidine administered after transplant would reduce recurrence rates, and conducted a study to determine a safe dose/schedule combination. METHODS: Forty-five high-risk patients were treated. Median age was 60 years; median number of comorbidities was 3; 67% were not in remission. By using a Bayesian adaptive method to determine the best dose/schedule combination based on time to toxicity, the authors investigated combinations of 5 daily azacitidine doses, 8, 16, 24, 32, and 40 mg/m2, and 4 schedules: 1, 2, 3, or 4 cycles, each with 5 days of drug and 25 days of rest. Cycle 1 started on Day +40. RESULTS: Reversible thrombocytopenia was the dose-limiting toxicity. The optimal combination was 32 mg/m2 given for 4 cycles. Median follow-up was 20.5 months. One-year event-free and overall survival were 58% and 77%, justifying further studies to estimate long-term clinical benefit. No dose significantly affected DNA global methylation. CONCLUSIONS: Azacitidine at 32 mg/m2 given for 5 days is safe and can be administered after allogeneic transplant for at least 4 cycles to heavily pretreated AML/MDS patients. The trial also suggested that this treatment may prolong event-free and overall survival, and that more cycles may be associated with greater benefit.

Patient-specific dose finding based on bivariate outcomes and covariates.

SUMMARY: A Bayesian sequential dose-finding procedure based on bivariate (efficacy, toxicity) outcomes that accounts for patient covariates and dose-covariate interactions is presented. Historical data are used to obtain an informative prior on covariate main effects, with uninformative priors assumed for all dose effect parameters. Elicited limits on the probabilities of efficacy and toxicity for each of a representative set of covariate vectors are used to construct bounding functions that determine the acceptability of each dose for each patient. Elicited outcome probability pairs that are equally desirable for a reference patient are used to define two different posterior criteria, either of which may be used to select an optimal covariate-specific dose for each patient. Because the dose selection criteria are covariate specific, different patients may receive different doses at the same point in the trial, and the set of eligible patients may change adaptively during the trial. The method is illustrated by a dose-finding trial in acute leukemia, including a simulation study.