Does the Combination of Phone, Email and Text-Based Reminders Improve No-show Rates for Patients in Breast Imaging?

The issue of no-shows in radiology is complicated and challenging. Mammography and ultrasound have the highest rate of no-shows among radiologic exams. Screening mammography is one of the most cost-effective ways to reduce breast cancer related deaths. However, the benefit of screening is heavily dependent on patient compliance to routine exams. Enhancing patients' commitments to their scheduled appointments, thereby improving early detection and decreasing breast cancer related mortality. Retrospective analysis of no-show visits scheduled from August 2017 to December 2017 (before the implementation of combined phone, email and text-based reminders) and from August 2019 to December 2019 (after the implementation of reminder and follow-up phone calls after missed appointments by the coordinator) in an urban academic breast imaging center was conducted. There were 368 no-show patients in 2017 and 238 no-show patients in 2019. Percentage of no-shows, and delay time to the rescheduled missed appointment were calculated. Subgroup analysis of the type of studies that were missed and those who did not reschedule the missed appointment was conducted. Mann Whitney U test was used to analyze differences between group means. No-show visits decreased by 50% in 2019 when compared to 2017. The average wait time between the missed appointment and the rescheduled appointment decreased significantly from 30.7 weeks in 2017 to 12.1 weeks in 2019 (P = 0.047). The percentage of no-show visits was highest among the unemployed, patients scheduled for screening mammograms and patients with a high average of no-show visits. No-show visits adversely impact patient outcome and contribute to increased cost of healthcare. Through a deeper understanding of the factors contributing to no-shows, we can strive to make appropriate interventions to alleviate the consequences of no-shows.

In vitro and in vivo Antibacterial effect of Commiphora gileadensis Methanolic Extract against Methicillin-Resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa.

BACKGROUND AND OBJECTIVE: Commiphora gileadensis is a plant in the Burseraceae family that grows in the western area of Saudi Arabia. Traditionally, it is used in the treatment of some superficial infections. MATERIALS AND METHODS: The methanolic extract of Commiphora gileadensis isolated from its leaves and branches. The in vitro study was conducted to determine the effect of this extract on Methicillin-Resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa using an agar diffusion and Minimum inhibitory concentration (MIC) methods. The in vivo study was conducted through two different methods. The first method, 20 male Balb c-1 mice were used for the determination of Commiphora gileadensis methanolic extract toxicity (LD50). In the second method, 40 male mice were used and were put into four groups. The first and second groups were injected subcutaneously with 108 CFU of MRSA 1 mL-1, while the third and fourth groups were injected with 108 CFU of Pseudomonas aeruginosa 1 mL-1. The comparison between groups was done by using a t-test (p<0.05). RESULTS: The methanolic extract of Commiphora gileadensis had a greater sensitivity zone on MRSA and Pseudomonas aeruginosa, 7 and 3 mm respectively. The MIC of the extract was 1/8 and 1/2 for MRSA and Pseudomonas aeruginosa respectively. The in vivo study showed that the extract was non-toxic, it also showed that the extract decreased the mortality of mice induced by MRSA injection significantly (p<0.05) While insignificantly with Pseudomonas aeruginosa. CONCLUSION: The total Commiphora gileadensis methanolic extract had an antibacterial effect on MRSA and Pseudomonas aeruginosa. This extract was non-toxic for the mice.

Blood Flow and Glucose Metabolism Dissociation in the Putamen Is Predictive of Levodopa Induced Dyskinesia in Parkinson's Disease Patients.

Background: The forefront treatment of Parkinson's disease (PD) is Levodopa. When patients are treated with Levodopa cerebral blood flow is increased while cerebral metabolic rate is decreased in key subcortical regions including the putamen. This phenomenon is especially pronounced in patients with Levodopa-induced dyskinesia (LID). Method: To study the effect of clinically-determined anti-parkinsonian medications, 10 PD patients (5 with LID and 5 without LID) have been scanned with FDG-PET (a probe for glucose metabolism) and perfusion MRI (a probe for cerebral blood flow) both when they are ON and OFF medications. Patients additionally underwent resting state fMRI to detect changes in dopamine-mediated cortico-striatal connectivity. The degree of blood flow-glucose metabolism dissociation was quantified by comparing the FDG-PET and perfusion MRI data. Results: A significant interaction effect (imaging modality × medication; blood flow-glucose metabolism dissociation) has been found in the putamen (p = 0.023). Post-hoc analysis revealed that anti-parkinsonian medication consistently normalized the pathologically hyper-metabolic state of the putamen while mixed effects were observed in cerebral blood flow changes. This dissociation was especially predominant in patients with LID compared to those without. Unlike the prior study, this differentiation was not observed when cortico-striatal functional connectivity was assessed. Conclusion: We confirmed striatal neurovascular dissociation between FDG-PET and perfusion MRI in response to clinically determined anti-parkinsonian medication. We further proposed a novel analytical method to quantify the degree of dissociation in the putamen using only the ON condition scans, Putamen-to-thalamus Hyper-perfusion/hypo-metabolism Index (PHI), which may have the potential to be used as a biomarker for LID (correctly classifying 8 out 10 patients). For wider use of PHI, a larger validation study is warranted.

Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia.

Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson's Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.

Distinct brain metabolic patterns separately associated with cognition, motor function, and aging in Parkinson's disease dementia.

We explored whether patients with Parkinson's disease dementia (PDD) show a distinct spatial metabolic pattern that characterizes cognitive deficits in addition to motor dysfunction. Eighteen patients with PDD underwent 3 separate positron emission tomography sessions with [18F]fluorodeoxyglucose (for glucose metabolism), fluorinated N-3-fluoropropyl-2-beta-carboxymethoxy-3-beta-(4-iodophenyl) nortropane (for dopamine transporter density) and Pittsburgh compound-B (for beta-amyloid load). We confirmed in PDD versus normal controls, overall hypometabolism in the posterior and prefrontal brain regions accompanied with hypermetabolism in subcortical structures and the cerebellar vermis. A multivariate network analysis then revealed 3 metabolic patterns that are separately associated with cognitive performance (p = 0.042), age (p = 0.042), and motor symptom severity (p = 0.039). The age-related pattern's association with aging was replicated in healthy controls (p = 0.047) and patients with Alzheimer's disease (p = 0.002). The cognition-related pattern's association with cognitive performance was observed, with a trend-level of correlation, in patients with dementia with Lewy bodies (p = 0.084) but not in patients with Alzheimer's disease (p = 0.974). We found no association with fluorinated N-3-fluoropropyl-2-beta-carboxymethoxy-3-beta-(4-iodophenyl) nortropane and Pittsburgh compound-B positron emission tomography with patients' cognitive performance.