Four-dimensional computed tomography-based ventilation imaging in intensity-modulated radiation therapy treatment planning for pulmonary functional avoidance.

PURPOSE: To incorporate four-dimensional computed tomography (4DCT)-based ventilation imaging into intensity-modulated radiation therapy (IMRT) treatment planning for pulmonary functional avoidance. METHODS AND MATERIALS: Nineteen locally advanced lung cancer patients are retrospectively studied. 4DCT images are employed to create ventilation maps for each patient via a density-change-based algorithm with mass correction. The regional ventilation is directly incorporated into the mathematical formulation of a direct aperture optimization model in IMRT treatment planning to achieve functional avoidance and a voxel-based treatment plan. The proposed functional avoidance planning and voxel-based planning are compared to the conventional treatment planning approach purely based on the anatomy of patients. Paired sample t-tests are conducted to see whether dosimetric differences among the three approaches are significant. RESULTS: Similar planning target volume (PTV) coverage is achieved by anatomical, functional avoidance, and voxel-based approaches. The voxel-based treatment planning performs better than both functional avoidance and anatomical planning to the lung. For a total lung, the average volume reductions in a functional avoidance plan from an anatomical plan, a voxel-based plan from an anatomical plan, and a voxel-based plan from a functional avoidance plan are 7.0%, 16.8%, and 10.6%, respectively for V40 ; and 0.4%, 6.4%, and 6.0%, respectively for mean Lung Dose (MLD). For a functional lung, the reductions are 8.8%, 17.2%, and 9.2%, respectively, for fV40 ; and 1.1%, 6.2%, and 5.2%, respectively, for functional mean lung dose (fMLD). These reductions are obtained without significantly increasing doses to other organs-at-risk. All the pairwise treatment planning comparisons for both total lung and functional lung are statistically significant (p-value < α = 0.05 $< \alpha =0.05$ ) except for the functional avoidance plan with the anatomical plan pair in which the p-value > α = 0.05 $> \alpha =0.05$ . From these results, we can conclude that voxel-based treatment planning outperforms both anatomical and functional-avoidance planning. CONCLUSIONS: We propose a treatment planning framework that directly utilizes functional images and compares voxel-based treatment planning with functional avoidance and anatomical treatment planning.

Optimizing insecticide allocation strategies based on houses and livestock shelters for visceral leishmaniasis control in Bihar, India.

Visceral leishmaniasis (VL) is the most deadly form of the leishmaniasis family of diseases, which affects numerous developing countries. The Indian state of Bihar has the highest prevalence and mortality rate of VL in the world. Insecticide spraying is believed to be an effective vector control program for controlling the spread of VL in Bihar; however, it is expensive and less effective if not implemented systematically. This study develops and analyzes a novel optimization model for VL control in Bihar that identifies an optimal (best possible) allocation of chosen insecticide (dichlorodiphenyltrichloroethane [DDT] or deltamethrin) based on the sizes of human and cattle populations in the region. The model maximizes the insecticide-induced sandfly death rate in human and cattle dwellings while staying within the current state budget for VL vector control efforts. The model results suggest that deltamethrin might not be a good replacement for DDT because the insecticide-induced sandfly deaths are 3.72 times more in case of DDT even after 90 days post spray. Different insecticide allocation strategies between the two types of sites (houses and cattle sheds) are suggested based on the state VL-control budget and have a direct implication on VL elimination efforts in a resource-limited region.

A data-integrated simulation-based optimization for assigning nurses to patient admissions.

The health care system in the United States has a shortage of nurses. A careful planning of nurse resources is needed to ease the health care system from the burden of the nurse shortage and standardize nurse workload. An earlier research study developed a data-integrated simulation to evaluate nurse-patient assignments (SIMNA) at the beginning of a shift based on a real data set provided by a northeast Texas hospital. In this research, with the aid of the same SIMNA model, two policies are developed to make nurse-to-patient assignments when new patients are admitted during a shift. A heuristic (HEU) policy assigns a newly-admitted patient to the nurse who has performed the least assigned direct care among all the nurses. A partially-optimized (OPT) policy seeks to minimize the difference in workload among nurses for the entire shift by estimating the assigned direct care from SIMNA. Results comparing HEU and OPT policies are presented.

Phase I: creating an electronic prototype to generate equitable hospital nurse-to-patient assignments.

This pilot program is a software-based prototype providing a nurse-to-patient assignment presented to two groups of RNs enrolled in a nursing research course in a North Texas university. The goal of the pilot program was to obtain input regarding the assessment, functionality, and practicality of a nurse-to-patient electronic prototype. Registered nurse students were given a presurvey, instructions, and details on the use of the prototype, followed by a postsurvey. Prototype speed and lack of bias were reported as most favorable. Registered nurse students requested additions of multiple diagnoses, patient acuity, and experience level of the nurse to enhance the prototype. Seventy-three percent (n = 24) of the participants said that they would use the prototype, and 15% (n = 5) said that they would not.

A data-integrated simulation model to evaluate nurse-patient assignments.

This research develops a novel data-integrated simulation to evaluate nurse-patient assignments (SIMNA) based on a real data set provided by a northeast Texas hospital. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree models, data mining tools for prediction and classification, were used to develop five tree structures: (a) four classification trees from which transition probabilities for nurse movements are determined, and (b) a regression tree from which the amount of time a nurse spends in a location is predicted based on factors such as the primary diagnosis of a patient and the type of nurse. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Results obtained from SIMNA to evaluate nurse-patient assignments in Medical/Surgical unit I of the northeast Texas hospital are discussed.