Ericksonmacias6102

97) but found more events of severe bleeding with aspirin (OR aspirin vs. no aspirin 6.9, 1.5-31.2). An excess in intracranial haemorrhage in the aspirin group was judged plausible based on two non-randomised studies.

The review findings are limited because studies include only people with Alzheimer's-type dementia and lack confirmatory studies, although an increased risk of bleeding events is recognised. Further research that addresses the benefits and risks of aspirin in more representative groups of people with dementia is needed to guide prescribing decisions.

The review findings are limited because studies include only people with Alzheimer's-type dementia and lack confirmatory studies, although an increased risk of bleeding events is recognised. Further research that addresses the benefits and risks of aspirin in more representative groups of people with dementia is needed to guide prescribing decisions.

In several settings, a shorter time to diagnosis has been shown to lead to improved clinical outcomes. The implementation of a rapid laboratory testing allows for a pre-visit testing in the outpatient clinic, meaning that test results are available during the first outpatient visit.

To determine whether the pre-visit laboratory testing leads to a shorter time to diagnosis in the general internal medicine outpatient clinic.

An "on-off" trial, allocating subjects to one of two treatment arms in consecutive alternating blocks.

All new referrals to the internal medicine outpatient clinic of a university hospital were included, excluding second opinions. A total of 595 patients were eligible; one person declined to participate, leaving data from 594 patients for analysis.

In the intervention group, patients had a standardized pre-visit laboratory testing before the first visit.

The primary outcome was the time to diagnosis. Secondary outcomes were the correctness of the preliminary diagnosis on the first day, health care utilization, and patient and physician satisfaction.

There was no difference in time to diagnosis between the two groups (median 35 days vs 35 days; hazard ratio 1.03 [0.87-1.22]; p = .71). The pre-visit testing group had higher proportions of both correct preliminary diagnoses on day 1 (24% vs 14%; p = .003) and diagnostic workups being completed on day 1 (10% vs 3%; p < .001). The intervention group had more laboratory tests done (50.0 [interquartile range (IQR) 39.0-69.0] vs 43.0 [IQR 31.0-68.5]; p < .001). Otherwise, there were no differences between the groups.

Pre-visit testing did not lead to a shorter overall time to diagnosis. However, a greater proportion of patients had a correct diagnosis on the first day. Further studies should focus on customizing pre-visit laboratory panels, to improve their efficacy.

NL5009.

NL5009.Our multidisciplinary research team is composed of 6 faculty with expertise in internal medicine, nephrology, maternal/fetal medicine, health services research, statistics, and community-based research, and 36 program staff including biostatisticians, nurses, program coordinators, program assistants, and medical assistants/phlebotomists. With the emergence of the COVID-19 pandemic and the impact it was having on our community, especially the ethnic minority population in inner-city Milwaukee, we felt it was critical to stay engaged and figure out how to ask meaningful research questions that are important to the community, are relevant to the times, and will lead to lasting change. While navigating this unprecedented challenge, our research team made difficult decisions but were able to engage our staff and respond to community needs. We organized our lessons learned to serve as a perspective on how to effectively remain committed to vision and serve our communities, while collecting evidence that can inform policy in difficult times.

Identifying which patients receive referrals to and which ones attend weight management programs can provide insights into how physicians manage obesity.

To describe patient factors associated with referrals, which primarily reflect physician priorities, and attendance, which reflects patient priorities. We also examine the influence of the individual physician by comparing adjusted rates of referral and attendance across physicians.

Retrospective cohort study.

Adults with a body mass index (BMI) ≥ 30 kg/m

who had a primary care visit between 2015 and 2018 at a large integrated health system MAIN MEASURES Referrals and visits to programs were collected from the EHR in 2019 and analyzed in 2019-2020. Multilevel logistic regression models were used to identify the association between patient characteristics and (1) receiving a referral, and (2) attending a visit after a referral. We compared physicians' adjusted probabilities of referring patients and of their patients attending a visit.

Our study included 160,163 adults, with a median BMI of 35 kg/m

. Seventeen percent of patients received ≥ 1 referral and 29% of those attended a visit. The adjusted odds of referral increased 57% for patients with a BMI 35-39 (versus 30-34) and 32% for each comorbidity (p < 0.01). Attending a visit was less strongly associated with BMI (aOR 1.18 for 35-39 versus 30-34, 95% CI 1.09-1.27) and not at all with comorbidity. For the physician-level analysis, the adjusted probability of referral had a much wider range (0 to 83%; mean = 19%) than did the adjusted probability of attendance (range 27 to 34%).

Few patients attended a weight management program. Physicians vary greatly in their probability of referring patients to programs but not in their patients' probability of attending.

Few patients attended a weight management program. Physicians vary greatly in their probability of referring patients to programs but not in their patients' probability of attending.

Although many predictive models have been developed to risk assess medical intensive care unit (MICU) readmissions, they tend to be cumbersome with complex calculations that are not efficient for a clinician planning a MICU discharge.

To develop a simple scoring tool that comprehensively takes into account not only patient factors but also system and process factors in a single model to predict MICU readmissions.

Retrospective chart review.

We included all patients admitted to the MICU of Robert Wood Johnson University Hospital, a tertiary care center, between June 2016 and May 2017 except those who were < 18 years of age, pregnant, or planned for hospice care at discharge.

Logistic regression models and a scoring tool for MICU readmissions were developed on a training set of 409 patients, and validated in an independent set of 474 patients.

Readmission rate in the training and validation sets were 8.8% and 9.1% respectively. check details The scoring tool derived from the training dataset included the following variables MICU admission diagnosis of sepsis, intubation during MICU stay, duration of mechanical ventilation, tracheostomy during MICU stay, non-emergency department admission source to MICU, weekend MICU discharge, and length of stay in the MICU.