Biomathematical Modeling Predicts Fatigue Risk in General Surgery Residents.

OBJECTIVE: To assess resident fatigue risk using objective and predicted sleep data in a biomathematical model of fatigue. DESIGN: 8-weeks of sleep data and shift schedules from 2019 for 24 surgical residents were assessed with a biomathematical model to predict performance ("effectiveness"). SETTING: Greater Washington, DC area hospitals RESULTS: As shift lengths increased, effectiveness scores decreased and the time spent below criterion increased. Additionally, 11.13% of time on shift was below the effectiveness criterion and 42.7% of shifts carried excess sleep debt. Sleep prediction was similar to actual sleep, and both predicted similar performance (p ≤ 0.001). CONCLUSIONS: Surgical resident sleep and shift patterns may create fatigue risk. Biomathematical modeling can aid the prediction of resident sleep patterns and performance. This approach provides an important tool to help educators in creating work-schedules that minimize fatigue risk.

Trends in Strategic Napping in Surgical Residents by Gender, Postgraduate Year, Work Schedule, and Clinical Rotation.

OBJECTIVE: To identify surgical resident and clinical rotation attributes which predict on-shift napping through objectively measured sleep patterns and work schedules over a 2-month period. DESIGN: In a cross-sectional study, participants provided schedules, completed the Epworth Sleepiness Scale (ESS), and wore sleep-tracking devices (Zulu watch) continuously for 8 weeks. Multiple linear regression predicted percent days with on-shift napping from resident and rotation characteristics. SETTING: Greater Washington, DC area hospitals. PARTICIPANTS: Twenty-two (n = 22) surgical residents rotating in at least 1 of 5 different clinical rotation categories. RESULTS: Residents slept 6 hours within a 24-hour period (370 ± 129 minutes) with normal sleep efficiency (sleep efficiency (SE): 87.13% ± 7.55%). Resident ESS scores indicated excessive daytime sleepiness (11.64 ± 4.03). Ninety-five percent (n = 21) of residents napped on-shift. Residents napped on-shift approximately 32% of their working days and were most likely to nap when working between 23:00 and 05:00 hours. Earlier shift start times predicted less on-shift napping (B = -0.08, SE = 0.04, ß = -2.40, t = -2.09, p = 0.05) while working more night shifts (B = 1.55, SE = 0.44, ß = 4.12, t = 3.52, p = 0.003) and shifts over 24 hours (B = 1.45, SE = 0.55, ß = 1.96, t = 2.63, p = 0.01) predicted more frequent on-shift napping. CONCLUSIONS: Residents are taking advantage of opportunities to nap on-shift. Working at night seems to drive on-shift napping. However, residents still exhibit insufficient sleep and daytime sleepiness which could reduce competency and represent a safety risk to themselves and/or patients. These findings will help inform intervention strategies which are tailored to surgical residents using a biomathematical model of fatigue.

A feasibility study to reduce misclassification error in occupational dose estimates for epidemiological studies using body size-dependent computational phantoms.

In the epidemiological study on the health effects of participants in the United States Radiologic Technologists (USRT) study, organ dosimetry was performed based on surveys and literature reviews. To convert dosimeter readings to organ doses, organ dose coefficients were adopted. However, the existing dose coefficients were derived from computational human phantoms with ICRP reference height and weight not accounting for the variation in body size. We first calculated preliminary body size-dependent organ dose coefficients using selected body size-dependent phantoms combined with Monte Carlo radiation transport method. We then tested the accuracy of these body-size dependent coefficients against the ICRP 74 reference size coefficients in comparison with five individual-specific organ dose coefficients computed from computed tomography (CT) image-based anatomical models of five adult males with different body sizes also using Monte Carlo methods. The reference size dose coefficients overall underestimate the patient-specific dose coefficients by up to 51%. Body size-dependent phantoms overall provided more accurate organ dose coefficients for the five patients. In case of the esophagus, the dose underestimation of 51% in the comparison with the reference phantom was reduced to 7%. The results confirm that potential dosimetric misclassification caused by using reference size phantom-based dose coefficients can be resolved by using the body size-dependent dose coefficients.

Automatic segmentation of cardiac structures for breast cancer radiotherapy.

BACKGROUND AND PURPOSE: We developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy. MATERIAL AND METHODS: We used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparing cardiac doses for simulated breast radiotherapy between manual and automatic contours. We analyzed the impact of the number of cardiac atlas in the library and the use of manual guide points on the performance of our method. RESULTS: The Dice Similarity Coefficients from the cross-validation reached up to 97% (whole heart) and 80% (chambers). The Average Surface Distance for the coronary arteries was less than 10.3 mm on average, with the best agreement (7.3 mm) in the left anterior descending artery (LAD). The dose comparison for simulated breast radiotherapy showed differences less than 0.06 Gy for the whole heart and atria, and 0.3 Gy for the ventricles. For the coronary arteries, the dose differences were 2.3 Gy (LAD) and 0.3 Gy (other arteries). The sensitivity analysis showed no notable improvement beyond ten atlases and the manual guide points does not significantly improve performance. CONCLUSION: We developed an automated method to contour cardiac substructures for radiotherapy CTs. When combined with accurate dose calculation techniques, our method should be useful for cardiac dose reconstruction of a large number of patients in epidemiological studies or clinical trials.

Automatic Mapping of CT Scan Locations on Computational Human Phantoms for Organ Dose Estimation.

To develop an algorithm to automatically map CT scan locations of patients onto computational human phantoms to provide with patient-specific organ doses. We developed an algorithm that compares a two-dimensional skeletal mask generated from patient CTs with that of a whole body computational human phantom. The algorithm selected the scan locations showing the highest Dice Similarity Coefficient (DSC) calculated between the skeletal masks of a patient and a phantom. To test the performance of the algorithm, we randomly selected five sets of neck, chest, and abdominal CT images from the National Institutes of Health Clinical Center. We first automatically mapped scan locations of the CT images on a computational human phantom using our algorithm. We had several radiologists to manually map the same CT images on the phantom and compared the results with the automated mapping. Finally, organ doses for automated and manual mapping locations were calculated by an in-house CT dose calculator and compared to each other. The visual comparison showed excellent agreement between manual and automatic mapping locations for neck, chest, and abdomen-pelvis CTs. The difference in mapping locations averaged over the start and end in the five patients was less than 1 cm for all neck, chest, and AP scans: 0.9, 0.7, and 0.9 cm for neck, chest, and AP scans, respectively. Five cases out of ten in the neck scans show zero difference between the average manual and automatic mappings. Average of absolute dose differences between manual and automatic mappings was 2.3, 2.7, and 4.0% for neck, chest, and AP scans, respectively. The automatic mapping algorithm provided accurate scan locations and organ doses compared to manual mapping. The algorithm will be useful in cases requiring patient-specific organ dose for a large number of patients such as patient dose monitoring, clinical trials, and epidemiologic studies.

Lens Dose Reduction by Patient Posture Modification During Neck CT.

OBJECTIVE: Radiation exposure of the lens during neck CT may increase a patient's risk of developing cataracts. Radiologists at the National Institutes of Health worked with technicians to modify the neck CT scanning procedure to include a reduction in the scanning range, a reduction in the tube potential (kilovoltage), and a change in neck positioning using a head tilt. We objectively quantified the organ dose changes after this procedure modification using a computer simulation. MATERIALS AND METHODS: We retrospectively analyzed CT images of 40 patients (20 men and 20 women) scanned before and after the procedure change. Radiation dose to the lens delivered before and after the procedure change was calculated using an in-house CT dose calculator combined with computational human phantoms deformed to match head tilt angles. We also calculated the doses to other radiosensitive organs including the brain, pituitary gland, eye globes, and salivary glands before and after the procedure change. RESULTS: Our dose calculations showed that modifying the neck position, shortening the scanning range, and reducing the tube potential reduced the dose to the lens by 89% (p < 0.0001). The median brain, pituitary gland, globes, and salivary gland doses also decreased by 59%, 52%, 66%, and 29%, respectively. We found that overranging significantly affects the lens dose. CONCLUSION: Combining head tilt and scanning range reduction is an easy and effective method that significantly reduces radiation dose to the lens and other radiosensitive head and neck organs.