Several evidence-informed consent practices (ECPs) have been shown to improve informed consent in clinical trials but are not routinely used. These include optimizing consent formatting, using plain language, using validated instruments to assess understanding, and involving legally authorized representatives when appropriate. We hypothesized that participants receiving an implementation science toolkit and a social media push would have increased adoption of ECPs and other outcomes.

We conducted a 1-year trial with clinical research professionals in the USA (n = 1284) who have trials open to older adults or focus on Alzheimer’s disease. We randomized participants to receive information on ECPs via receiving a toolkit with a social media push (intervention) or receiving an online learning module (active control). Participants completed a baseline survey and a follow-up survey after 1 year. A subset of participants was interviewed (n = 43).

Participants who engaged more with the toolkit were more likely to have tried to implement an ECP during the trial than participants less engaged with the toolkit or the active control group. However, there were no significant differences in the adoption of ECPs, intention to adopt, or positive attitudes. Participants reported the toolkit and social media push were satisfactory, and participating increased their awareness of ECPs. However, they reported lacking the time needed to engage with the toolkit more fully.

Using an implementation science approach to increase the use of ECPs was only modestly successful. Data suggest that having institutional review boards recommend or require ECPs may be an effective way to increase their use.

Hospital readmission is unsettling to patients and caregivers, costly to the healthcare system, and may leave patients at additional risk for hospital-acquired infections and other complications. We evaluated the association between comorbidities present during index coronavirus disease 2019 (COVID-19) hospitalization and the risk of 30-day readmission.

We used the Premier Healthcare database to perform a retrospective cohort study of COVID-19 hospitalized patients discharged between April 2020 and March 2021 who were followed for 30 days after discharge to capture readmission to the same hospital.

Among the 331,136 unique patients in the index cohort, 36,827 (11.1%) had at least 1 all-cause readmission within 30 days. Of the readmitted patients, 11,382 (3.4%) were readmitted with COVID-19 as the primary diagnosis. In the multivariable model adjusted for demographics, hospital characteristics, coexisting comorbidities, and COVID-19 severity, each additional comorbidity category was associated with an 18% increase in the odds of all-cause readmission (adjusted odds ratio [aOR], 1.18; 95% confidence interval [CI], 1.17–1.19) and a 10% increase in the odds of readmission with COVID-19 as the primary readmission diagnosis (aOR, 1.10; 95% CI, 1.09–1.11). Lymphoma (aOR, 1.86; 95% CI, 1.58–2.19), renal failure (aOR, 1.32; 95% CI, 1.25–1.40), and chronic lung disease (aOR, 1.29; 95% CI, 1.24–1.34) were most associated with readmission for COVID-19.

Readmission within 30 days was common among COVID-19 survivors. A better understanding of comorbidities associated with readmission will aid hospital care teams in improving postdischarge care. Additionally, it will assist hospital epidemiologists and quality administrators in planning resources, allocating staff, and managing bed-flow issues to improve patient care and safety.

Participants and research professionals often overestimate how well participants understand and appreciate consent information for clinical trials, and experts often vary in their determinations of participant’s capacity to consent to research. Past research has developed and validated instruments designed to assess participant understanding and appreciation, but the frequency with which they are utilized is unknown.

We administered a survey to clinical researchers working with older adults or those at risk of cognitive impairment (N = 1284), supplemented by qualitative interviews (N = 60).

We found that using a validated assessment of consent is relatively uncommon, being used by only 44% of researchers who had an opportunity. Factors that predicted adoption of validated assessments included not seeing the study sponsor as a barrier, positive attitudes toward assessments, and being confident that they had the resources needed to implement an assessment. The perceived barriers to adopting validated assessments of consent included lack of awareness, lack of knowledge, being unsure of how to administer such an assessment, and the burden associated with implementing this practice.

Increasing the use of validated assessments of consent will require educating researchers on the practice and emphasizing very practical assessments, and may require Institutional Review Boards (IRBs) or study sponsors to champion the use of assessments.

To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection (HO-CDI) across multiple institutions and whether they could be used to improve risk adjustment.

All patients at least 18 years of age admitted to 3 hospitals in Maryland between January 1, 2016, and January 1, 2018.

Comorbid conditions were assigned using the Elixhauser comorbidity index. Multivariable log-binomial regression was conducted for each hospital using significant covariates (P < .10) in a bivariate analysis. Standardized infection ratios (SIRs) were computed using current Centers for Disease Control and Prevention (CDC) risk adjustment methodology and with the addition of Elixhauser score and individual comorbidities.

At hospital 1, 314 of 48,057 patient admissions (0.65%) had a HO-CDI; 41 of 8,791 patient admissions (0.47%) at community hospital 2 had a HO-CDI; and 75 of 29,211 patient admissions (0.26%) at community hospital 3 had a HO-CDI. In multivariable regression, Elixhauser score was a significant risk factor for HO-CDI at all hospitals when controlling for age, antibiotic use, and antacid use. Abnormal leukocyte level at hospital admission was a significant risk factor at hospital 1 and hospital 2. When Elixhauser score was included in the risk adjustment model, it was statistically significant (P < .01). Compared with the current CDC SIR methodology, the SIR of hospital 1 decreased by 2%, whereas the SIRs of hospitals 2 and 3 increased by 2% and 6%, respectively, but the rankings did not change.

Electronically available patient comorbidities are important risk factors for HO-CDI and may improve risk-adjustment methodology.

To evaluate 123-I mZIENT (2(S)-[(S)-2b-carbomethoxy-3b-[3′-((Z)-2-iodoethenyl)phenyl]nortropane), a novel radiopharmaceutical for imaging SERT. The bio-distribution of the radiopharmaceutical in humans was investigated and dosimetry performed.

The study includes three healthy volunteers and three patients receiving SSRIs. Whole body images obtained on a gamma camera at 10 minutes, 1, 2, 3, 6, 24 and 48 hours post administration. Dosimetry was performed. ROIs were drawn over the brain, heart, kidneys, liver, lungs, salivary glands, spleen, thyroid and intestines. Blood was sampled at 5, 15, & 30 minutes and 1, 2, 3, 6, 24 and 48 hours post administration. Urine was collected at 1, 2, 3, 4, 6, 24 and 48 hours. Brain SPECT images were obtained using a neuroSPECT scanner at 4 hours, evaluated visually and analysed using ROI analysis.

High quality SPECT images can be obtained after 100–150 MBq 123-ImZEINT. Regional brain uptake was observed in midbrain and basal ganglia in healthy volunteers, consistent with the known distribution of SERT. Biodistribution images demonstrated highest uptake in the lungs, brain, liver and intestines. The effective dose was within range of other commonly used ligands and is acceptable for clinical imaging.

123-ImZIENT is a promising agent for imaging SERT in humans with acceptable dosimetry.

Targeted screening for carbapenem-resistant organisms (CROs), including carbapenem-resistant Enterobacteriaceae (CRE) and carbapenemase-producing organisms (CPOs), remains limited; recent data suggest that existing policies miss many carriers.

Our objective was to measure the prevalence of CRO and CPO perirectal colonization at hospital unit admission and to use machine learning methods to predict probability of CRO and/or CPO carriage.

We performed an observational cohort study of all patients admitted to the medical intensive care unit (MICU) or solid organ transplant (SOT) unit at The Johns Hopkins Hospital between July 1, 2016 and July 1, 2017. Admission perirectal swabs were screened for CROs and CPOs. More than 125 variables capturing preadmission clinical and demographic characteristics were collected from the electronic medical record (EMR) system. We developed models to predict colonization probabilities using decision tree learning.

Evaluating 2,878 admission swabs from 2,165 patients, we found that 7.5% and 1.3% of swabs were CRO and CPO positive, respectively. Organism and carbapenemase diversity among CPO isolates was high. Despite including many characteristics commonly associated with CRO/CPO carriage or infection, overall, decision tree models poorly predicted CRO and CPO colonization (C statistics, 0.57 and 0.58, respectively). In subgroup analyses, however, models did accurately identify patients with recent CRO-positive cultures who use proton-pump inhibitors as having a high likelihood of CRO colonization.

In this inpatient population, CRO carriage was infrequent but was higher than previously published estimates. Despite including many variables associated with CRO/CPO carriage, models poorly predicted colonization status, likely due to significant host and organism heterogeneity.

Timely identification of multidrug-resistant gram-negative infections remains an epidemiological challenge. Statistical models for predicting drug resistance can offer utility where rapid diagnostics are unavailable or resource-impractical. Logistic regression–derived risk scores are common in the healthcare epidemiology literature. Machine learning–derived decision trees are an alternative approach for developing decision support tools. Our group previously reported on a decision tree for predicting ESBL bloodstream infections. Our objective in the current study was to develop a risk score from the same ESBL dataset to compare these 2 methods and to offer general guiding principles for using each approach.

Using a dataset of 1,288 patients with Escherichia coli or Klebsiella spp bacteremia, we generated a risk score to predict the likelihood that a bacteremic patient was infected with an ESBL-producer. We evaluated discrimination (original and cross-validated models) using receiver operating characteristic curves and C statistics. We compared risk score and decision tree performance, and we reviewed their practical and methodological attributes.

In total, 194 patients (15%) were infected with ESBL-producing bacteremia. The clinical risk score included 14 variables, compared to the 5 decision-tree variables. The positive and negative predictive values of the risk score and decision tree were similar (>90%), but the C statistic of the risk score (0.87) was 10% higher.

A decision tree and risk score performed similarly for predicting ESBL infection. The decision tree was more user-friendly, with fewer variables for the end user, whereas the risk score offered higher discrimination and greater flexibility for adjusting sensitivity and specificity.

Suicide is a devastating public health problem and very few biological treatments have been found to be effective for quickly reducing the intensity of suicidal ideation (SI). We have previously shown that a single dose of ketamine, a glutamate N-methyl-d-aspartate (NMDA) receptor antagonist, is associated with a rapid reduction in depressive symptom severity and SI in patients with treatment-resistant depression.

We conducted a randomized, controlled trial of ketamine in patients with mood and anxiety spectrum disorders who presented with clinically significant SI (n = 24). Patients received a single infusion of ketamine or midazolam (as an active placebo) in addition to standard of care. SI measured using the Beck Scale for Suicidal Ideation (BSI) 24 h post-treatment represented the primary outcome. Secondary outcomes included the Montgomery–Asberg Depression Rating Scale – Suicidal Ideation (MADRS-SI) score at 24 h and additional measures beyond the 24-h time-point.

The intervention was well tolerated and no dropouts occurred during the primary 7-day assessment period. BSI score was not different between the treatment groups at 24 h (p = 0.32); however, a significant difference emerged at 48 h (p = 0.047). MADRS-SI score was lower in the ketamine group compared to midazolam group at 24 h (p = 0.05). The treatment effect was no longer significant at the end of the 7-day assessment period.

The current findings provide initial support for the safety and tolerability of ketamine as an intervention for SI in patients who are at elevated risk for suicidal behavior. Larger, well-powered studies are warranted.