A New Approach towards Minimizing the Risk of Misdosing Warfarin Initiation Doses.

It is a challenge to be able to prescribe the optimal initial dose of warfarin. There have been many studies focused on an efficient strategy to determine the optimal initial dose. Numerous clinical, genetic, and environmental factors affect the warfarin dose response. In practice, it is common that the initial warfarin dose is substantially different from the stable maintenance dose, which may increase the risk of bleeding or thrombosis prior to achieving the stable maintenance dose. In order to minimize the risk of misdosing, despite popular warfarin dose prediction models in the literature which create dose predictions solely based on patients' attributes, we have taken physicians' opinions towards the initial dose into consideration. The initial doses selected by clinicians, along with other standard clinical factors, are used to determine an estimate of the difference between the initial dose and estimated maintenance dose using shrinkage methods. The selected shrinkage method was LASSO (Least Absolute Shrinkage and Selection Operator). The estimated maintenance dose was more accurate than the original initial dose, the dose predicted by a linear model without involving the clinicians initial dose, and the values predicted by the most commonly used model in the literature, the Gage clinical model.

Learning optimal opioid prescribing and monitoring: a simulation study of medical residents.

OBJECTIVE: Hospitalized patients often receive opioids. There is a lack of consensus regarding evidence-based guidelines or training programs for effective management of pain in the hospital. We investigated the viability of using an Internet-based opioid dosing simulator to teach residents appropriate use of opioids to treat and manage acute pain. MATERIALS AND METHODS: We used a prospective, longitudinal design to evaluate the effects of simulator training. In face-to-face didactic sessions, we taught 120 (108 internal medicine and 12 family medicine) residents principles of pain management and how to use the simulator. Each trainee completed 10 training and, subsequently, 5 testing trials on the simulator. For each trial, we collected medications, doses, routes and times of administration, pain scores, and a summary score. We used mixed-effects regression models to assess the impact of simulation training on simulation performance scores, variability in pain score trajectories, appropriate use of short- and long-acting opioids, and use of naloxone. RESULTS: Trainees completed 1582 simulation trials (M = 13.2, SD = 6.8), with sustained improvements in their simulated pain management practices. Over time, trainees improved their overall simulated pain management scores (b = 0.05, P < .01), generated lower pain score trajectories with less variability (b = -0.02, P < .01), switched more rapidly from short-acting to long-acting agents (b = -0.50, P < .01), and used naloxone less often (b = -0.10, P < .01). DISCUSSION AND CONCLUSIONS: Trainees translated their understanding of didactically presented principles of pain management to their performance on simulated patient cases. Simulation-based training presents an opportunity for improving opioid-based inpatient acute pain management.

Revisiting Warfarin Dosing Using Machine Learning Techniques.

Determining the appropriate dosage of warfarin is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for patients. The models are either clinical models which contain clinical and demographic variables or pharmacogenetic models which additionally contain the genetic variables. In this paper, a new methodology for warfarin dosing is proposed. The patients are initially classified into two classes. The first class contains patients who require doses of >30 mg/wk and the second class contains patients who require doses of ≤30 mg/wk. This phase is performed using relevance vector machines. In the second phase, the optimal dose for each patient is predicted by two clinical regression models that are customized for each class of patients. The prediction accuracy of the model was 11.6 in terms of root mean squared error (RMSE) and 8.4 in terms of mean absolute error (MAE). This was 15% and 5% lower than IWPC and Gage models (which are the most widely used models in practice), respectively, in terms of RMSE. In addition, the proposed model was compared with fixed-dose approach of 35 mg/wk, and the model proposed by Sharabiani et al. and its outperformance were proved in terms of both MAE and RMSE.