Examining the Nurse Manager Practice Environment Effects on Burnout: Results of a Cross-sectional Multisite Study.

OBJECTIVE: This study examined the relationships between the dimensions of the nurse manager (NM) practice environment (PE) and burnout. BACKGROUND: NMs are critical to the success of their unit(s). Understanding the degree to which their PE characteristics impact their level of burnout is important to NM retention. METHOD: A cross-sectional research design using a 71-item questionnaire was used to measure NM characteristics, hospital characteristics, NM PE, and burnout. There were 110 completed respondents across 22 hospitals in the United States. RESULTS: Moderate inverse relationships exist between the NM PE and 2 of the 3 (work and personal) dimensions of burnout. NM age and tenure also showed an inverse relationship with burnout. CONCLUSIONS: This study added evidence showing a statistically significant relationship between NM PE and the work and personal dimensions of burnout. These results also suggest the relationships NMs have with the patients on their unit(s) do not add to their level of burnout.

A Comprehensive Analysis of Risk Factors Associated With Inpatient Falls.

OBJECTIVES: The primary objective of this study was to identify the relationship between rates of falls among hospitalized patients and the use of inpatient medications associated with falls. METHODS: This is a retrospective study on patients older than 60 years, hospitalized between January 1, 2021, and December 31, 2021. Ventilated patients and patients with a length of stay or fall less than 48 hours after admission were excluded. Falls were determined by assessing documented post fall assessments in the medical record. Patients who fell were matched 3:1 with control patients based on demographic data (age, sex, length of stay up to the fall time, and Elixhauser Comorbidity score). For controls, a pseudo time to fall was assigned based on matching. Medication information was gathered from barcode administration data. Statistical analysis was conducted using R and RStudio. RESULTS: A total of 6363 fall patients and 19,089 controls met the inclusion and exclusion criteria. Seven drug classes were identified as statistically significant ( P < 0.001) in increasing an inpatient's rate of falling: angiotensin-converting enzyme inhibitors (unadjusted odds ratio [OR], 1.22), antipsychotics (OR, 1.93), benzodiazepines (OR, 1.57), serotonin modulators (OR, 1.2), selective serotonin-reuptake inhibitors (OR, 1.26), tricyclics and norepinephrine reuptake inhibitors (OR, 1.45), and miscellaneous antidepressants (OR, 1.54). CONCLUSIONS: Hospitalized patients older than 60 years are more likely to fall while taking angiotensin-converting enzyme inhibitors, antipsychotics, benzodiazepines, serotonin modulators, selective serotonin-reuptake inhibitors, tricyclics, norepinephrine reuptake inhibitors, or miscellaneous antidepressants. Patients on opiates and diuretics had a significant decrease in rate of falls.

A preliminary diagnostic accuracy study of quantitative MRI biomarkers for differentiating parotid tumor types.

Background: Differentiating among the different types of parotid tumors on imaging is useful for guiding clinical disposition, which ultimately may lead to surgical management. The goal of this study was to determine whether quantitative T2 signal characteristics and morphologic features on magnetic resonance imaging (MRI) can serve as predictive biomarkers for distinguishing between tumor types. Methods: A retrospective review of T2-weighted MRIs in patients with pathology-proven parotid tumors was performed. Quantitative T2 maps and surface regularity measurements of the tumors were obtained via semi-automated regions of interest (ROI). Linear Discriminant Analysis was used to populate the receiver operating characteristics (ROCs) curves for these variables. A P value of <0.05 was considered to be significant. Results: A total of 35 tumors (21 benign and 14 malignant neoplasms) were included in this analysis. For differentiating the benign versus malignant classes of parotid tumors, T2 signal and surface regularity combined yielded an area under the curve of 0.62 (P value: 0.2) through the ROC analysis. However, for the pleomorphic adenomas versus other types of parotid tumors, using both T2 signal and surface regularity yielded an area under the curve of 0.81 (P value: 0.007) through the ROC analysis. Conclusions: T2 signal and surface regularity combined can significantly differentiate pleomorphic adenomas from other types of parotid tumors and can potentially be used as a predictive imaging biomarker.

Application of a novel T1 retrospective quantification using internal references (T1-REQUIRE) algorithm to derive quantitative T1 relaxation maps of the brain.

Most MRI sequences used clinically are qualitative or weighted. While such images provide useful information for clinicians to diagnose and monitor disease progression, they lack the ability to quantify tissue damage for more objective assessment. In this study, an algorithm referred to as the T1-REQUIRE is presented as a proof-of-concept which uses nonlinear transformations to retrospectively estimate T1 relaxation times in the brain using T1-weighted MRIs, the appropriate signal equation, and internal, healthy tissues as references. T1-REQUIRE was applied to two T1-weighted MR sequences, a spin-echo and a MPRAGE, and validated with a reference standard T1 mapping algorithm in vivo. In addition, a multiscanner study was run using MPRAGE images to determine the effectiveness of T1-REQUIRE in conforming the data from different scanners into a more uniform way of analyzing T1-relaxation maps. The T1-REQUIRE algorithm shows good agreement with the reference standard (Lin's concordance correlation coefficients of 0.884 for the spin-echo and 0.838 for the MPRAGE) and with each other (Lin's concordance correlation coefficient of 0.887). The interscanner studies showed improved alignment of cumulative distribution functions after T1-REQUIRE was performed. T1-REQUIRE was validated with a reference standard and shown to be an effective estimate of T1 over a clinically relevant range of T1 values. In addition, T1-REQUIRE showed excellent data conformity across different scanners, providing evidence that T1-REQUIRE could be a useful addition to big data pipelines.

Differentiating low and high grade mucoepidermoid carcinoma of the salivary glands using CT radiomics.

BACKGROUND: The purpose of this study is to determine if Haralick texture analysis on CT imaging of mucoepidermoid carcinomas (MEC) can differentiate low-grade and high-grade tumors. METHODS: A retrospective review of 18 patients with MEC of the salivary glands, corresponding CT imaging and pathology report was performed. Tumors were manually segmented and image analysis was performed to calculate radiomic features. Radiomic features were compared between low-grade and high-grade MEC. A multivariable logistic regression model and receiver operating characteristic analysis was performed. RESULTS: A total of 18 patients (mean age, 51, range 9-83 years, 8 men and 10 women) were included. Nine patients had low-grade pathology and nine patients had high-grade pathology. Of the 18 cases, 7 (39%) occurred in the parotid gland and 11 (61%) occurred in minor salivary glands. No individual feature was significantly different between low-grade and high-grade MEC. A logistic regression model including surface regularity, energy and information measure II of correlation was performed and was able to predict high-grade MEC accurately (sensitivity 89%, specificity 68%). The area under the receiver operating characteristic curve was 0.802. CONCLUSIONS: High-grade MEC tend to have a low energy, high correlation texture as well as surface irregularity. Together, these three features may comprise a tumor phenotype that is able to predict high-grade pathology in MECs.

Correlation of post-contrast T1-weighted MRI surface regularity, tumor bulk, and necrotic volume with Ki67 and p53 in glioblastomas.

PURPOSE: p53 and Ki67 status can be relevant to the management of glioblastoma. The goal of this study is to determine whether tumor morphology and bulk depicted on MRI correlate with p53 and Ki67 in glioblastoma. METHODS: A retrospective review of 223 patients with glioblastoma and corresponding p53 or Ki67 status, along with T1-weighted post-contrast MR images was performed. Enhancing tumors were outlined for determining surface regularity, tumor bulk, and necrotic volume. The median value of 0.1 was chosen for p53 and 0.2 for Ki67 to separate each data set into two classes. T tests and receiver operating characteristic analysis were performed to determine the separation of the classes and the predicting power of each feature. RESULTS: There were significant differences between tumor surface regularity (p = 0.01) and necrotic volume (p = 0.0429) according to Ki67 levels, although neither had statistically significant predictive power (AUC = 0.697, p = 0.0506 and AUC = 0.577, p = 0.164, respectively). There were also significant differences between tumor bulk (p = 0.0239) and necrotic volume (p = 0.0200) according to p53 levels, but again no significant predictive power was found using ROC analysis (AUC = 0.5882, p = 0.0894 and AUC = 0.567, p = 0.155, respectively). CONCLUSION: Quantitative morphological tumor characteristics on post-contrast T1-weighted MRI can to a certain degree provide insights regarding Ki67 and p53 status in patients with glioblastoma.