Objective: Decades-old, common ICU practices including deep sedation, immobilization, and limited family access are being challenged. We endeavoured to evaluate the relationship between ABCDEF bundle performance and patient-centered outcomes in critical care. Design: Prospective, multicenter, cohort study from a national quality improvement collaborative. Setting: 68 academic, community, and federal ICUs collected data during a 20-month period. Patients: 15,226 adults with at least one ICU day. Interventions: We defined ABCDEF bundle performance (our main exposure) in two ways: 1) complete performance (patient received every eligible bundle element on any given day) and 2) proportional performance (percentage of eligible bundle elements performed on any given day). We explored the association between complete and proportional ABCDEF bundle performance and three sets of outcomes: patient-related (mortality, ICU and hospital discharge), symptom-related (mechanical ventilation, coma, delirium, pain, restraint use), and system-related (ICU readmission, discharge destination). All models were adjusted for a minimum of 18 a priori determined potential confounders. Measurements and Results: Complete ABCDEF bundle performance was associated with lower likelihood of seven outcomes: hospital death within 7 days (adjusted hazard ratio, 0.32; CI, 0.17–0.62), next-day mechanical ventilation (adjusted odds ratio [AOR], 0.28; CI, 0.22–0.36), coma (AOR, 0.35; CI, 0.22–0.56), delirium (AOR, 0.60; CI, 0.49–0.72), physical restraint use (AOR, 0.37; CI, 0.30–0.46), ICU readmission (AOR, 0.54; CI, 0.37–0.79), and discharge to a facility other than home (AOR, 0.64; CI, 0.51–0.80). There was a consistent dose-response relationship between higher proportional bundle performance and improvements in each of the above-mentioned clinical outcomes (all p < 0.002). Significant pain was more frequently reported as bundle performance proportionally increased (p = 0.0001). Conclusions: ABCDEF bundle performance showed significant and clinically meaningful improvements in outcomes including survival, mechanical ventilation use, coma, delirium, restraint-free care, ICU readmissions, and post-ICU discharge disposition.

Objectives: The objective was to determine whether patients with low health literacy have higher emergency department (ED) utilization and higher ED recidivism than patients with adequate health literacy.Methods: The study was conducted at an urban academic ED with more than 95,000 annual visits that is part of a 13‐hospital health system, using electronic records that are captured in a central data repository. As part of a larger, cross‐sectional, convenience sample study, health literacy testing was performed using the short test of functional health literacy in adults (S‐TOFHLA) and standard test thresholds identifying those with inadequate, marginal, and adequate health literacy. The authors collected patients' demographic and clinical data, including items known to affect recidivism. This was a structured electronic record review directed at determining 1) the median number of total ED visits in this health system within a 2‐year period and 2) the proportion of patients with each level of health literacy who had return visits within 3, 7, and 14 days of index visits. Descriptive data for demographics and ED returns are reported, stratified by health literacy level. The Mantel‐Haenszel chi‐square was used to test whether there is an association between health literacy and ED recidivism. A negative binomial multivariable model was performed to examine whether health literacy affects ED use, including variables significant at the 0.1 alpha level on bivariate analysis and retaining those significant at an alpha of 0.05 in the final model.Results: Among 431 patients evaluated, 13.2% had inadequate, 10% had marginal, and 76.3% had adequate health literacy as identified by S‐TOFHLA. Patients with inadequate health literacy had higher ED utilization compared to those with adequate health literacy (p = 0.03). Variables retained in the final model included S‐TOFHLA score, number of medications, having a personal doctor, being a property owner, race, insurance, age, and simple comorbidity score. During the study period, 118 unique patients each made at least one return ED visit within a 14‐day period. The proportion of patients with inadequate health literacy making at least one return visit was higher than that of patients with adequate health literacy at 14 days, but was not significantly higher within 3 or 7 days.Conclusions: In this single‐center study, higher utilization of the ED by patients with inadequate health literacy when compared to those with adequate health literacy was observed. Patients with inadequate health literacy made a higher number of return visits at 14 days but not at 3 or 7 days.

Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value—a second-generation p-value (p_δ)–that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null. The second-generation p-value is the proportion of data-supported hypotheses that are also null hypotheses. As such, second-generation p-values indicate when the data are compatible with null hypotheses (p_δ = 1), or with alternative hypotheses (p_δ = 0), or when the data are inconclusive (0 < p_δ < 1). Moreover, second-generation p-values provide a proper scientific adjustment for multiple comparisons and reduce false discovery rates. This is an advance for environments rich in data, where traditional p-value adjustments are needlessly punitive. Second-generation p-values promote transparency, rigor and reproducibility of scientific results by a priori specifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with alternative or null hypotheses.

Background: Medications that impact insulin sensitivity or cause weight gain may increase heart failure risk. Our aim was to compare heart failure and cardiovascular death outcomes among patients initiating sulfonylureas for diabetes mellitus treatment versus metformin. Methods and Results: National Veterans Health Administration databases were linked to Medicare, Medicaid, and National Death Index data. Veterans aged ≥18 years who initiated metformin or sulfonylureas between 2001 and 2011 and whose creatinine was <1.4 (females) or 1.5 mg/dL (males) were included. Each metformin patient was propensity score‐matched to a sulfonylurea initiator. The outcome was hospitalization for acute decompensated heart failure as the primary reason for admission or a cardiovascular death. There were 126 867 and 79 192 new users of metformin and sulfonylurea, respectively. Propensity score matching yielded 65 986 per group. Median age was 66 years, and 97% of patients were male; hemoglobin A _1c 6.9% (6.3, 7.7); body mass index 30.7 kg/m ² (27.4, 34.6); and 6% had heart failure history. There were 1236 events (1184 heart failure hospitalizations and 52 cardiovascular deaths) among sulfonylurea initiators and 1078 events (1043 heart failure hospitalizations and 35 cardiovascular deaths) among metformin initiators. There were 12.4 versus 8.9 events per 1000 person‐years of use (adjusted hazard ratio 1.32, 95% CI 1.21, 1.43). The rate difference was 4 heart failure hospitalizations or cardiovascular deaths per 1000 users of sulfonylureas versus metformin annually. Conclusions: Predominantly male patients initiating treatment for diabetes mellitus with sulfonylurea had a higher risk of heart failure and cardiovascular death compared to similar patients initiating metformin.

Background: Test-trace-isolate programs are an essential part of coronavirus disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. Methods and findings: We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R₀: 2.5, generation time: 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses. Conclusions: Effective test-trace-isolate programs first need to be strong in the “test” component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures.