Recent Publications by CFE Educators

BACKGROUND

The duration of an opioid prescribed at hospital discharge does not intrinsically account for opioid needs during a hospitalization. This discrepancy may lead to patients receiving much larger supplies of opioids on discharge than they truly require.

OBJECTIVE

Assess a novel discharge opioid supply metric that adjusts for opioid use during hospitalization, compared to the conventional discharge prescription signature.

DESIGN, SETTING, & PARTICIPANTS

Retrospective study using electronic health record data from June 2012 to November 2018 of adults who received opioids while hospitalized and after discharge from a single academic medical center.

MEASURES & ANALYSIS

We ascertained inpatient opioids received and milligrams of opioids supplied after discharge, then determined days of opioids supplied after discharge by the conventional prescription signature opioid-days ("conventional days") and novel hospital-adjusted opioid-days ("adjusted days") metrics. We calculated descriptive statistics, within-subject difference between measurements, and fold difference between measures. We used multiple linear regression to determine patient-level predictors associated with high difference in days prescribed between measures.

RESULTS

The adjusted days metric demonstrates a 2.4 day median increase in prescription duration as compared to the conventional days metric (9.4 vs. 7.0 days; P0.001). 95% of all adjusted days measurements fall within a 0.19 to 6.90-fold difference as compared to conventional days measurements, with a maximum absolute difference of 640 days. Receiving a liquid opioid prescription accounted for an increased prescription duration of 135.6% by the adjusted days metric (95% CI 39.1-299.0%; P = 0.001). Of patients who were not on opioids prior to admission and required opioids during hospitalization but not in the last 24 hours, 325 (8.6%) were discharged with an opioid prescription.

CONCLUSIONS

The adjusted days metric, based on inpatient opioid use, demonstrates that patients are often prescribed a supply lasting longer than the prescription signature suggests, though with marked variability for some patients that suggests potential under-prescribing as well. Adjusted days is more patient-centered, reflecting the reality of how patients will take their prescription rather than providers' intended prescription duration.

PURPOSE

Because of the COVID-19 pandemic, the Radiation Oncology Education Collaborative Study Group (ROECSG) hosted its annual international symposium using a virtual format in May 2020. This report details the experience of hosting a virtual meeting and presents attendee feedback on the platform.

METHODS AND MATERIALS

The ROECSG symposium was hosted virtually on May 15, 2020. A postsymposium survey was distributed electronically to assess attendee demographics, participation, and experience. Attendee preference and experience were queried using 3-point and 5-point Likert-type scales, respectively. Symplur LLC was used to generate analytics for the conference hashtag (#ROECSG).

RESULTS

The survey was distributed to all 286 registrants, with a response rate of 67% (191 responses). Seventeen nonattendee responses were omitted from this analysis, for a total of 174 included respondents. Eighty-two attendees (47%) were present for the entire symposium. A preference for a virtual symposium was expressed by 78 respondents (45%), whereas 44 (25%) had no preference and 52 (30%) preferred an in-person meeting. A total of 150 respondents (86%) rated the symposium as "extremely" well organized. Respondents who had not attended a prior in-person ROECSG symposium were more likely to prefer the virtual format (P = .03). Seventy-eight respondents (45%) reported a preference for the virtual platform for reviewing scholarly work, and 103 (59%) reported a preference for an in-person platform for networking. On the day of the symposium, #ROECSG had 408 tweets and 432,504 impressions.

CONCLUSIONS

The 2020 ROECSG symposium was well received and can serve as a framework for future virtual meetings. Although the virtual setting may facilitate sharing research, networking aspects are more limited. Effort is needed to develop hybrid virtual and in-person meetings that meet the needs of participants in both settings. Social media is a significant avenue for dissemination and discussion of information and may be valuable in the virtual setting.

OBJECTIVE

To determine surgical site infection and urinary tract infection (UTI) rates in the setting of urethroplasty. Given significant variation in the utilization of antibiotics, there is an opportunity to improve antibiotic stewardship. This study aims to elucidate the rate of both UTI and surgical site infection after urethroplasty on a standardized perioperative antibiotic regimen, and to obtain patient and operative characteristics that may predict infection.

METHODS

We prospectively treated 390 patients undergoing urethroplasty at 11 centers with a standardized perioperative antibiotic protocol. Patients had a urine culture or urine analysis within 3 weeks of surgery. After surgery, patients were discharged with an indwelling catheter, removed per usual surgeon practice. All were given nitrofurantoin from discharge until catheter removal. Logistic regression analyses were performed to determine the correlation between patient characteristics or operative categories with post-operative infection.

RESULTS

The rates of postoperative UTI and wound infection within 30 days were 6.7% and 4.1%, respectively. On multivariate analysis of demographics, comorbidities, and stricture characteristics and repair, only preoperative UTI (P = .012), history of cardiovascular disease (P = .015), and performing a membranous urethroplasty (0.018) were significant predictors of a UTI within 30 days postoperatively. Location of repair nor graft use increased the risk of UTI. There were no factors predictive of postoperative wound infection.

CONCLUSION

A standardized antibiotic protocol was created to narrow and limit excess antibiotic use. This protocol, with clear definitions of UTI and wound infection, allowed determination of accurate infection rates in urethroplasties. Preoperative UTI, even when properly treated, increases the risk of postoperative UTI.

Introduction

Interprofessional (IP) clinical care is ideally taught in authentic environments; however, training programs often lack authentic opportunities for health professions students to practice IP patient care. Skilled nursing facilities (SNFs) can offer such opportunities, particularly for geriatric patient care, but are underutilized as training sites. We present an IP nursing facility rotation (IP-SNF) in which medical, pharmacy, and physical therapy students provided collaborative geriatric patient care.

Methods

Our 10-day immersion rotation focused on four geriatric competencies common to all three professions: appropriate/hazardous medications, patient self-care capacity, evaluating and treating falls, and IP collaboration. Activities included conducting medication reviews, quarterly care planning, evaluating functional status/fall risk, and presenting team recommendations at SNF meetings. Facility faculty/staff provided preceptorship and assessed team presentations. Course evaluations included students' pre/post objective-based self-assessment, as well as facility faculty/staff evaluations of interactions with students.

Results

Thirty-two students (15 medical, 12 pharmacy, five physical therapy) participated in the first 2 years. Evaluations ( = 31) suggested IP-SNF filled gaps in students' geriatrics and IP education. Pre/post self-assessment showed significant improvement ( .001) in self-confidence related to course objectives. Faculty/staff indicated students added value to SNF patient care. Challenges included maximizing patient care experiences while allowing adequate team work time.

Discussion

IP-SNF showcases the feasibility of, and potential for, engaging learners in real-world IP geriatric patient care in a SNF. Activities and materials must be carefully designed and implemented to engage all levels/types of IP learners and ensure valuable learning experiences.

PURPOSE

Lung elastography aims at measuring the lung parenchymal tissue elasticity for applications ranging from diagnostic purposes to biomechanically guided deformations. Characterizing the lung tissue elasticity requires four-dimensional (4D) lung motion as an input, which is currently estimated by deformably registering 4D computed tomography (4DCT) datasets. Since 4DCT imaging is widely used only in a radiotherapy treatment setup, there is a need to predict the elasticity distribution in the absence of 4D imaging for applications within and outside of radiotherapy domain.

METHODS

In this paper, we present a machine learning-based method that predicts the three-dimensional (3D) lung tissue elasticity distribution for a given end-expiration 3DCT. The method to predict the lung tissue elasticity from an end-expiration 3DCT employed a deep neural network that predicts the tissue elasticity for the given CT dataset. For training and validation purposes, we employed five-dimensional CT (5DCT) datasets and a finite element biomechanical lung model. The 5DCT model was first used to generate end-expiration lung geometry, which was taken as the source lung geometry for biomechanical modeling. The deformation vector field pointing from end expiration to end inhalation was computed from the 5DCT model and taken as input in order to solve for the lung tissue elasticity. An inverse elasticity estimation process was employed, where we iteratively solved for the lung elasticity distribution until the model reproduced the ground-truth deformation vector field. The machine learning process uses a specific type of learning process, namely a constrained generalized adversarial neural network (cGAN) that learned the lung tissue elasticity in a supervised manner. The biomechanically estimated tissue elasticity together with the end-exhalation CT was the input for the supervised learning. The trained cGAN generated the elasticity from a given breath-hold CT image. The elasticity estimated was validated in two approaches. In the first approach, a L2-norm-based direct comparison was employed between the estimated elasticity and the ground-truth elasticity. In the second approach, we generated a synthetic four-dimensional CT (4DCT0 using a lung biomechanical model and the estimated elasticity and compared the deformations with the ground-truth 4D deformations using three image similarity metrics: mutual Information (MI), structured similarity index (SSIM), and normalized cross correlation (NCC).

RESULTS

The results show that a cGAN-based machine learning approach was effective in computing the lung tissue elasticity given the end-expiration CT datasets. For the training data set, we obtained a learning accuracy of 0.44 ± 0.2 KPa. For the validation dataset, consisting of 13 4D datasets, we were able to obtain an accuracy of 0.87 ± 0.4 KPa. These results show that the cGAN-generated elasticity correlates well with that of the underlying ground-truth elasticity. We then integrated the estimated elasticity with the biomechanical model and applied the same boundary conditions in order to generate the end inhalation CT. The cGAN-generated images were very similar to that of the original end inhalation CT. The average value of the MI is 1.77 indicating the high local symmetricity between the ground truth and the cGAN elasticity-generated end inhalation CT data. The average value of the structural similarity for the 13 patients was observed to be 0.89 indicating the high structural integrity of the cGAN elasticity-generated end inhalation CT. Finally, the average NCC value of 0.97 indicates that potential variations in the contrast and brightness of the cGAN elasticity-generated end inhalation CT and the ground-truth end inhalation CT.

CONCLUSION

The cGAN-generated lung tissue elasticity given an end-expiration CT image can be computed in near real time. Using the lung tissue elasticity along with a biomechanical model, 4D lung deformations can be generated from a given end-expiration CT image within clinically acceptable numerical accuracy.

INTRODUCTION

Clinical remission in patients with ankylosing spondylitis (AS) has been determined using composite indices such as the AS Disease Activity Score inactive disease (ASDAS-ID), Assessment of SpondyloArthritis international Society criteria partial remission (ASAS-PR), and low Bath AS Disease Activity Index (BASDAI) scores. The objective of this exploratory analysis was to evaluate the proportion of secukinumab-treated patients with AS achieving remission defined based on the ASDAS-ID (score 1.3), ASAS-PR or BASDAI score ≤  2.

METHODS

The analysis pooled data from the MEASURE 1 and 2 studies over 3 years. The proportion of patients who achieved ASDAS-ID, ASAS-PR, or BASDAI ≤ 2 with secukinumab was compared with placebo at week 16; results for secukinumab-treated patients were summarized through week 156. Sustainability of each criterion was assessed from week 16 to 156 using shift analysis. The association between each of these criteria and specific patient-reported outcomes (PROs), such as health-related quality of life, function, fatigue, and work impairment, was also explored.

RESULTS

At week 16, a higher proportion of secukinumab-treated patients versus placebo achieved ASDAS-ID (17.6 vs. 3.5%), ASAS-PR (15.4 vs. 4.1%), or BASDAI ≤ 2 (22.3 vs. 6.4%) criteria (all P  0.0001), which were sustained through 156 weeks. Shift analysis showed that the majority of secukinumab-treated patients achieving remission at week 16 maintained their status at week 156 (ASDAS-ID, 57.1%; ASAS-PR, 68.0% and BASDAI ≤ 2, 74.3%). Remission was also associated with improved PROs over 156 weeks.

CONCLUSIONS

Secukinumab-treated patients maintained ASDAS-ID, ASAS-PR, or BASDAI ≤ 2 from week 16 up to 3 years. Patients who achieved at least one of the three responses/states, reported improvement in PROs, which suggests an association of clinical remission/ID with PROs in patients with active AS.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT01358175, NCT01863732, and NCT01649375.