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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 highgrade 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 highgrade pathology in MECs.

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.

Objectives The anti-inflammatory properties of selective serotonin reuptake inhibitors (SSRI)s, particularly fluvoxamine, have been hypothesized to reduce clinical deterioration in patients with COVID-19 when administered early in the disease course. The objective of this analysis was to examine the effect of maintenance SSRI administration, including variation among different medications, on the outcomes of hospitalized patients with COVID-19. Methods Retrospective analysis of disease progression and mortality risk of over 230,000 patients hospitalized with COVID-19 at facilities associated with a large healthcare system in the United States. Key findings Receipt of SSRIs during the hospital encounter occurred in approximately 10.6% (n = 24,690) of COVID-19 patients. When matched for patient characteristics, disease severity and other treatments, receipt of any SSRI was associated with a 30% reduction in the relative risk of mortality (RR: 0.70, 95% confidence interval [CI]: 0.67–0.73; adjusted P-value &lt;0.001). Similar reductions in the relative risk of mortality were seen with nearly every individual SSRI; for sertraline-treated patients, the most commonly used SSRI in the data set, there was a 29% reduction in the relative risk of mortality (RR: 0.71, 95% CI: 0.66–0.77; adjusted P-value &lt;0.001). Conclusions In total, this retrospective analysis suggests that there is a significant association between SSRI antidepressants and reduced morality among patients hospitalized with COVID-19.

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 proofof-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 spinecho 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.