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Register a new userEvaluating the Effect of Longitudinal Dose and INR Data on Maintenance Warfarin Dose Predictions
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Warfarin, a commonly prescribed drug to prevent blood clots, has a highly variable individual response. Determining a maintenance warfarin dose that achieves a therapeutic blood clotting time, as measured by the international normalized ratio (INR), is crucial in preventing complications. Machine learning algorithms are increasingly being used for warfarin dosing; usually, an initial dose is predicted with clinical and genotype factors, and this dose is revised after a few days based on previous doses and current INR. Since a sequence of prior doses and INR better capture the variability in individual warfarin response, we hypothesized that longitudinal dose response data will improve maintenance dose predictions. To test this hypothesis, we analyzed a dataset from the COAG warfarin dosing study, which includes clinical data, warfarin doses and INR measurements over the study period, and maintenance dose when therapeutic INR was achieved. Various machine learning regression models to predict maintenance warfarin dose were trained with clinical factors and dosing history and INR data as features. Overall, dose revision algorithms with a single dose and INR achieved comparable performance as the baseline dose revision algorithm. In contrast, dose revision algorithms with longitudinal dose and INR data provided maintenance dose predictions that were statistically significantly much closer to the true maintenance dose. Focusing on the best performing model, gradient boosting (GB), the proportion of ideal estimated dose, i.e., defined as within $pm$20% of the true dose, increased from the baseline (54.92%) to the GB model with the single (63.11%) and longitudinal (75.41%) INR. More accurate maintenance dose predictions with longitudinal dose response data can potentially achieve therapeutic INR faster, reduce drug-related complications and improve patient outcomes with warfarin therapy.
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Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite this, most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodology, called Safe Efficacy Exploration Dose Allocation (SEEDA), that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability. We evaluate performance objectives that have operational meanings in practical clinical trials, including cumulative efficacy, recommendation/allocation success probabilities, toxicity violation probability, and sample efficiency. An extended SEEDA-Plateau algorithm that is tailored for the increase-then-plateau efficacy behavior of molecularly targeted agents (MTA) is also presented. Through numerical experiments using both synthetic and real-world datasets, we show that SEEDA outperforms state-of-the-art clinical trial designs by finding the optimal dose with higher success rate and fewer patients.
Comets are small bodies thought to contain the most pristine material in the solar system. However, since their formation 4.5 Gy ago, they have been altered by different processes. While not exposed to much electromagnetic radiation, they experience intense particle radiation. Galactic cosmic rays and solar energetic particles have a broad spectrum of energies and interact with the cometary surface and subsurface; they are the main source of space weathering for a comet in the Kuiper Belt or in the Oort cloud; and also affect the ice prior to the comet agglomeration. While low energy particles interact only with the cometary surface, the most energetic ones deposit a significant amount of energy down to tens of meters. This interaction can modify the isotopic ratios in cometary ices and create secondary compounds through radiolysis, such as O2 and H2O2 (paper II: Maggiolo et al., 2020). In this paper, we model the energy deposition of energetic particles as a function of depth using a Geant4 application modified to account for the isotope creation process. We quantify the energy deposited in cometary nucleus by galactic cosmic rays and solar energetic particles. The consequences of the energy deposition on the isotopic and chemical composition of cometary ices and their implication on the interpretation of cometary observations, notably of 67P/Churyumov Gerasimenko by the ESA/Rosetta spacecraft, will be discussed in Paper II.
We propose BaySize, a sample size calculator for phase I clinical trials using Bayesian models. BaySize applies the concept of effect size in dose finding, assuming the MTD is defined based on an equivalence interval. Leveraging a decision framework that involves composite hypotheses, BaySize utilizes two prior distributions, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under desirable statistical power. Look-up tables are generated to facilitate practical applications. To our knowledge, BaySize is the first sample size tool that can be applied to a broad range of phase I trial designs.
This paper focuses on some dosimetry aspects of proton therapy and pencil beam scanning based on the experience accumulated at Paul Scherrer Institute(PSI). The basic formalism for absolute dosimetry in proton therapy is outlined and the two main techniques and equipment to perform the primary beam monitor chamber calibration are presented. Depth-dose curve and lateral beam width measurements are exposed and discussed in detail, with particular attention to the size of the ionization chamber and the characteristic of scintillating-CCD dosimetry systems, respectively. It is also explained how the angular-spatial distribution of individual pencil beams can be determined in practice. The equipment and the techniques for performing regularmachine-specific quality checks are focused on (i)output constancy checks, (ii)pencil beam position and size checks and (iii)beam energy checks. Finally, patient-specific verification is addressed.
While compressed sensing (CS) based reconstructions have been developed for low-dose CBCT, a clear understanding on the relationship between the image quality and imaging dose at low dose levels is needed. In this paper, we qualitatively investigate this subject in a comprehensive manner with extensive experimental and simulation studies. The basic idea is to plot image quality and imaging dose together as functions of number of projections and mAs per projection over the whole clinically relevant range. A clear understanding on the tradeoff between image quality and dose can be achieved and optimal low-dose CBCT scan protocols can be developed for various imaging tasks in IGRT. Main findings of this work include: 1) Under the CS framework, image quality has little degradation over a large dose range, and the degradation becomes evident when the dose < 100 total mAs. A dose < 40 total mAs leads to a dramatic image degradation. Optimal low-dose CBCT scan protocols likely fall in the dose range of 40-100 total mAs, depending on the specific IGRT applications. 2) Among different scan protocols at a constant low-dose level, the super sparse-view reconstruction with projection number less than 50 is the most challenging case, even with strong regularization. Better image quality can be acquired with other low mAs protocols. 3) The optimal scan protocol is the combination of a medium number of projections and a medium level of mAs/view. This is more evident when the dose is around 72.8 total mAs or below and when the ROI is a low-contrast or high-resolution object. Based on our results, the optimal number of projections is around 90 to 120. 4) The clinically acceptable lowest dose level is task dependent. In our study, 72.8mAs is a safe dose level for visualizing low-contrast objects, while 12.2 total mAs is sufficient for detecting high-contrast objects of diameter greater than 3 mm.
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