The Variation of Withdrawal of Life Sustaining Therapy in Older Adults With Traumatic Brain Injury

INTRODUCTION

The decision to withdraw life sustaining treatment (WDLST) in older adults with traumatic brain injury is subject to wide variability leading to nonbeneficial interventions and unnecessary use of hospital resources. We hypothesized that patient and hospital factors are associated with WDLST and WDLST timing.

METHODS

All traumatic brain injury patients ≥65 with Glasgow coma scores (GCS) of 4-11 from 2018 to 2019 at level I and II centers were selected from the National Trauma Data Bank. Patients with head abbreviated injury scores 5-6 or death within 24 h were excluded. Bayesian additive regression tree analysis was performed to identify the cumulative incidence function (CIF) and the relative risks (RR) over time for withdrawal of care, discharge to hospice (DH), and death. Death alone (no WDLST or DH) served as the comparator group for all analyses. A subanalysis of the composite outcome WDLST/DH (defined as end-of-life-care), with death (no WDLST or DH) as a comparator cohort was performed.

RESULTS

We included 2126 patients, of whom 1957 (57%) underwent WDLST, 402 (19%) died, and 469 (22%) were DH. 60% of patients were male, and the mean age was 80 y. The majority of patients were injured by fall (76%, n = 1644). Patients who were DH were more often female (51% DH versus 39% WDLST), had a past medical history of dementia (45% DH versus 18% WDLST), and had lower admission injury severity score (14 DH versus 18.6 WDLST) (P < 0.001). Compared to those who DH, those who underwent WDLST had a lower GCS (9.8 versus 8.4, P < 0.001). CIF of WDSLT and DH increased with age, stabilizing by day 3. At day 3, patients ≥90 y had an increased RR of DH compared to WDLST (RR 2.5 versus 1.4). As GCS increased, CIF and RR of WDLST decreased, while CIF and RR of DH increased (RR on day 3 for GCS 12: WDLST 0.42 versus DH 1.31).Patients at nonprofit institutions were more likely to undergo WDLST (RR 1.15) compared to DH (0.68). Compared to patients of White race, patients of Black race had a lower RR of WDLST at all timepoints.

CONCLUSIONS

Patient and hospital factors influence the practice of end-of-life-care (WDLST, DH, and death), highlighting the need to better understand variability to target palliative care interventions and standardize care across populations and trauma centers.

Implementation of an Active Screening Program for SARS-CoV2 - Experience at an Academic Medical Center

BACKGROUND

This study documents the experience of an academic medical center implementing SARS-CoV2 screening of asymptomatic research personnel to support the "return-to-work" initiative and donor cadavers to support in-person student education.

METHODS

Testing was performed on samples received June 1, 2020 (for the cadaver program) and July 20, 2020 (for the personnel screening program) through September 30, 2021. Data were evaluated to document the number of cases and the positivity rate.

RESULTS

Approximately 3000 specimens were tested across both programs, with an overall positivity rate of 2.5% and 3.6% in the personnel and cadaver screening programs, respectively.

DISCUSSION

This screening program serves as an example of institutional investment in the safety of its faculty, staff, and students alike to address specific needs of a global pandemic.

Variation among hospitals in the continuity of care for older hospitalized patients: a cross-sectional cohort study

BACKGROUND

Little is known about how continuity of care for hospitalized patients varies among hospitals. We describe the number of different general internal medicine physicians seeing hospitalized patients during a medical admission and how that varies by hospital.

METHODS

We conducted a retrospective study of a national 20% sample of Medicare inpatients from 01/01/16 to 12/31/18. In patients with routine medical admissions (length of stay of 3-6 days, no Intensive Care Unit stay, and seen by only one generalist per day), we assessed odds of receiving all generalist care from one generalist. We calculated rates for each hospital, adjusting for patient and hospital characteristics in a multi-level logistic regression model.

RESULTS

Among routine medical admissions with 3- to 6-day stays, only 43.1% received all their generalist care from the same physician. In those with a 3-day stay, 50.1% had one generalist providing care vs. 30.8% in those with a 6-day stay. In a two-level (admission and hospital) logistic regression model controlling for patient characteristics and length of stay, the odds of seeing just one generalist did not vary greatly by patient characteristics such as age, race/ethnicity, comorbidity or reason for admission. There were large variations in continuity of care among different hospitals and geographic areas. In the highest decile of hospitals, the adjusted mean percentage of patients receiving all generalist care from one physician was > 84.1%, vs. < 24.1% in the lowest decile. This large degree of variation persisted when hospitals were stratified by size, ownership, location or teaching status.

CONCLUSIONS

Continuity of care provided by generalist physicians to medical inpatients varies widely among hospitals. The impact of this variation on quality of care is unknown.

Transmission Dynamics of Clostridioides difficile in 2 High-Acuity Hospital Units

BACKGROUND

The key epidemiological drivers of Clostridioides difficile transmission are not well understood. We estimated epidemiological parameters to characterize variation in C. difficile transmission, while accounting for the imperfect nature of surveillance tests.

METHODS

We conducted a retrospective analysis of C. difficile surveillance tests for patients admitted to a bone marrow transplant (BMT) unit or a solid tumor unit (STU) in a 565-bed tertiary hospital. We constructed a transmission model for estimating key parameters, including admission prevalence, transmission rate, and duration of colonization to understand the potential variation in C. difficile dynamics between these 2 units.

RESULTS

A combined 2425 patients had 5491 admissions into 1 of the 2 units. A total of 3559 surveillance tests were collected from 1394 patients, with 11% of the surveillance tests being positive for C. difficile. We estimate that the transmission rate in the BMT unit was nearly 3-fold higher at 0.29 acquisitions per percentage colonized per 1000 days, compared to our estimate in the STU (0.10). Our model suggests that 20% of individuals admitted into either the STU or BMT unit were colonized with C. difficile at the time of admission. In contrast, the percentage of surveillance tests that were positive within 1 day of admission to either unit for C. difficile was 13.4%, with 15.4% in the STU and 11.6% in the BMT unit.

CONCLUSIONS

Although prevalence was similar between the units, there were important differences in the rates of transmission and clearance. Influential factors may include antimicrobial exposure or other patient-care factors.