Abrahamsenwillumsen0439

Predicting the risk of in-hospital mortality on admission is challenging but essential for risk stratification of patient outcomes and designing an appropriate plan-of-care, especially among transferred patients.

Develop a model that uses administrative and clinical data within 24h of transfer to predict 30-day in-hospital mortality at an Academic Health Center (AHC).

Retrospective cohort study. We used 30 putative variables in a multiple logistic regression model in the full data set (n = 10,389) to identify 20 candidate variables obtained from the electronic medical record (EMR) within 24h of admission that were associated with 30-day in-hospital mortality (p < 0.05). These 20 variables were tested using multiple logistic regression and area under the curve (AUC)-receiver operating characteristics (ROC) analysis to identify an optimal risk threshold score in a randomly split derivation sample (n = 5194) which was then examined in the validation sample (n = 5195).

Ten thousand three hundred eighty accuracy among seriously ill transferred patients.

This model can use EMR and administrative data within 24 h of transfer to predict the risk of 30-day in-hospital mortality with reasonable accuracy among seriously ill transferred patients.

The impact of race and socioeconomic status on clinical outcomes has not been quantified in patients hospitalized with coronavirus disease 2019 (COVID-19).

To evaluate the association between patient sociodemographics and neighborhood disadvantage with frequencies of death, invasive mechanical ventilation (IMV), and intensive care unit (ICU) admission in patients hospitalized with COVID-19.

Retrospective cohort study.

Four hospitals in an integrated health system serving southeast Michigan.

Adult patients admitted to the hospital with a COVID-19 diagnosis confirmed by polymerase chain reaction.

Patient sociodemographics, comorbidities, and clinical outcomes were collected. Neighborhood socioeconomic variables were obtained at the census tract level from the 2018 American Community Survey. Relationships between neighborhood median income and clinical outcomes were evaluated using multivariate logistic regression models, controlling for patient age, sex, race, Charlson Comorbidity Index, obesity, smtage, which is closely associated with race, is a predictor of poor clinical outcomes in COVID-19. Measures of neighborhood disadvantage should be used to inform policies that aim to reduce COVID-19 disparities in the Black community.

Potentially inappropriately prescribed medications (PIPMs) among patients with chronic kidney disease (CKD) may vary among clinical settings. Rates of PIPM are unknown among Medicare-enrolled Medication Therapy Management (MTM) eligible patients.

Determine prevalence of PIPM among patients with CKD and evaluate characteristics of patients and providers associated with PIPM.

An observational cross-sectional investigation of a Medicare insurance plan for the year 2018.

Medicare-enrolled MTM eligible patients with stage 3-5 CKD.

PIPM was identified utilizing a tertiary database. find more Logistic regression assessed relationship between patient characteristics and PIPM.

Investigation included 3624 CKD patients 2856 (79%), 548 (15%), and 220 (6%) patients with stage 3, 4, and 5 CKD, respectively. Among patients with stage 3, stage 4, and stage 5 CKD, 618, 430, and 151 were with at least one PIPM, respectively. Logistic regression revealed patients with stage 4 or 5 CKD had 7-14 times the odds of having a PIPM tentially reduce PIPM among Medicare MTM-enrolled patients with CKD.

Over one-third of Medicare MTM eligible patients with CKD presented with at least one PIPM. Worsening renal function, length of MTM eligibility, female gender, and polypharmacy were associated with having PIPM. Majority of PIPMs were prescribed by PCPs. Clinical decision support tools may be considered to potentially reduce PIPM among Medicare MTM-enrolled patients with CKD.

Feedback improves trainee clinical performance, but the optimal way to provide it remains unclear. Peer feedback offers unique advantages but comes with significant challenges including a lack of rigorously studied methods. The SPIKES framework is a communication tool adapted from the oncology and palliative care literature for teaching trainees how to lead difficult conversations.

To determine if a brief educational intervention focused on the SPIKES framework improves peer feedback between internal medicine trainees on inpatient medicine services as compared to usual practice.

Randomized, controlled trial at an academic medical center during academic year 2017-2018.

Seventy-five PGY1 and 49 PGY2 internal medicine trainees were enrolled. PGY2s were randomized 11 to the intervention or control group.

The intervention entailed a 30-min, case-based didactic on the SPIKES framework followed by a refresher email on SPIKES sent to PGY2s before each inpatient medicine rotation. PGY1s were blinded as to wh notable limitations, a brief educational intervention focused on SPIKES increased PGY1 perception of the extent, specificity, and satisfaction with feedback from PGY2s.

Strategies are needed to better address the physical health needs of people with serious mental illness (SMI). Enhanced primary care for people with SMI has the potential to improve care of people with SMI, but evidence is lacking.

To examine the effect of a novel enhanced primary care model for people with SMI on service use and screening.

Using North Carolina Medicaid claims data, we performed a retrospective cohort analysis comparing healthcare use and screening receipt of people with SMI newly receiving enhanced primary care to people with SMI newly receiving usual primary care. We used inverse probability of treatment weighting to estimate average differences in outcomes between the treatment and comparison groups adjusting for observed baseline characteristics.

People with SMI newly receiving primary care in North Carolina.

Enhanced primary care that includes features tailored for individuals with SMI.

Outcome measures included outpatient visits, emergency department (ED) visits, inpatient stays and days, and recommended screenings 18 months after the initial primary care visit.