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BACKGROUND: The implementation of data science concepts, skills, and tools in critical care research and practice faces multiple, complex barriers. METHODS: We developed an implementation science-based framework and method for measuring the adoption, implementation, and sustainment of data science concepts, skills, and tools in critical care—the Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign (DSC) Implementation Research Logic Model (IRLM). Our IRLM specifies constructs for: 1) key determinants (i.e., barriers and facilitators) influencing the implementation of data science concepts, skills, and tools in critical care; 2) implementation strategies deployed by the SCCM Discovery DSC to address these determinants; 3) theorized mechanisms of action by which these strategies affect outcomes; and 4) upstream and downstream implementation outcomes influenced by implementation strategies. RESULTS AND CONCLUSIONS: We believe that our model can facilitate more rigorous measurement of theoretically grounded, empirically assessable factors driving implementation of data science concepts, skills, and tools in critical care.

Introduction: Life-support limits (i.e., code status), the first step in the continuum of palliation and end-of-life (EoL) care, stem from decisions made by patients and their providers to align care near the EoL with patients’ wishes. The pandemic created health care strain that may have influenced life-support limitation practices among patients with serious COVID-19. In this study, we examine variations in life-support limitations (do-not-resuscitate, DNR; do-not-intubate, DNI) depending on the hospital and period of hospitalization. Methods: We included all adults admitted to a hospital in the United States with COVID-19 from January 2020 to December 2021 using the VIRUS registry. Our outcome was any life-support limitation on admission (DNR, DNI, or both) using regression modeling. Main exposures were hospital and period of hospitalization (early vs. late pandemic). Covariates included age, sex, race/ethnicity, comorbidities, and hospitalization diagnoses. Results: There were 42,383 patients from 75 hospitals in the US. The median age was 63 (interquartile range: 50–75), 46.5% were female, and 37.5% were non-Hispanic White. The life-support limitation rate was 7.9%, and the palliative care consultation rate was 2.3%. The odds of life-support limitation were 1.86 (95% CI: 1.6–2.1) depending on the hospital, but the period of hospitalization was not associated with odds of life-support limitation. Older age was associated with the greatest increased odds (4.1 per standard deviation ∼ 17.9 years, 95% CI: 3.9–4.4), followed by comorbidities including paralysis (2.4, 95% CI: 1.8–3.0) and dementia (2.3, 95% CI: 2.0–2.6). Conclusions: After adjusting for patient-level factors, we report significant inter-hospital but no temporal variation in life-support limitation upon admission with COVID-19. Future studies should investigate specific practices that led to health care resilience to temporal practice variations in life-support limitation during the pandemic.

INTRODUCTION A retrospective cohort study on patients aged < 18 years included in the Society of Critical Care Medicine: Viral Infection and Respiratory Illness Universal Study registry from March 2020 to April 2024 with an objective of calculating the prevalence of sepsis as defined by the Phoenix Sepsis Score (PSS) and to validate the PSS with respect to outcomes in children with COVID-19. METHODS Linear mixed-effects regression was used to examine the relationship between the PSS and hospital length of stay after controlling for confounding factors. The performance of the PSS was assessed using the receiver operating characteristic (ROC) and the precision-recall curve (PRC). Cross-validation was performed using leave-one-out cross-validation. RESULTS Out of 1731 patients (58 hospitals), 326 (18.8%) met criteria for sepsis and 167 (9.7%) for septic shock. The overall mortality was 1.4% (25/1731), with significant differences between nonseptic (10/1405, 0.7%) and both sepsis (15/326, 4.6%) and septic shock (9/167, 5.4%) groups. After adjusting for confounders, the septic group was associated with a longer hospital length of stay than the nonseptic group. One unit increase in the numeric PSS led to a 70% increase in risk of mortality (odds ratio 1.70; P < .001). The area under the ROC curve was 0.80 and the area under the PRC curve was 0.13. The threshold of ≥ 2 for detection of mortality had a sensitivity of 0.63, specificity of 0.82, and positive predictive value of 0.05. CONCLUSION Phoenix Sepsis Criteria retain its validity in identifying sepsis in children with COVID-19 and can be used in further epidemiological studies in this population

OBJECTIVES: To develop the first core Critical Care Data Dictionary (C2D2) with common data elements (CDEs) to characterize critical illness and injuries. DESIGN: Group consensus process using modified Delphi approach. SETTING: Electronic surveys and in-person meetings. SUBJECTS: A multidisciplinary workgroup of clinicians and researchers with expertise in the care of the critically ill and injured. INTERVENTIONS: The Delphi process was divided into domain and CDE portions with each composed of two item generation rounds and one item reduction/refinement rounds. Two in-person meetings augmented this process to facilitate review and consideration of the domains and by panel members. The final set of domains and CDEs was then reviewed by the group to meet the competing criteria of utility and feasibility, resulting in the core dataset. MEASUREMENTS AND MAIN RESULTS: The 23-member Delphi panel was provided 1833 candidate variables for potential dataset inclusion. The final dataset includes 226 patient-level CDCs in nine domains, which include anthropometrics and demographics (8), chronic comorbid illnesses (18), advanced directives (1), ICU diagnoses (61), diagnostic tests (42), interventions (27), medications (38), objective assessments (26), and hospital course and outcomes (5). Upon final review, 91% of the panel endorsed the CDCs as meeting criteria for a minimum viable data dictionary. Data elements cross the lifespan of neonate through adult patients. CONCLUSIONS: The resulting C2D2 provides a foundation to facilitate rapid collection, analyses, and dissemination of information necessary for research, quality improvement, and clinical practice to optimize critical care outcomes. Further work is needed to validate the effectiveness of the dataset in a variety of critical care settings.

OBJECTIVES: There is a paucity of literature regarding administrative approvals required for clinical studies during a pandemic. We aimed to evaluate variation in duration of administrative approvals within the Viral Infection and Respiratory illness Universal Study (VIRUS): A Global COVID-19 Registry. DESIGN, SETTING, AND SUBJECTS: Survey analysis of 188 investigators who participated in the VIRUS: COVID-19 registry, a prospective, observational global registry database of 287 sites. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: For each study site approved through December 8, 2020, we assessed the duration in days: 1) from institutional review board (IRB) submission to IRB approval, 2) from IRB approval to Research Electronic Data Capture (REDCap) access, 3) from REDCap access to first patient data entry in REDCap, and 4) total duration from IRB submission to first patient data entry in REDCap. Analysis of variance and Wilcoxon rank-sum test were used to compare time durations. Of 287 sites, 188 sites (United States = 155, non-United States = 33) provided complete administrative data. There was considerable variability in duration from IRB submission to first patient data entry with median (interquartile range) of 28 days (16–50 d), with differences not significantly different by country (United States: 30 [17–50] vs non-United States: 23 d [8–46 d]; p = 0.08) or previous “multisite trial experience” (experienced: 27 [15–51] vs not experienced: 29 d [13–47 d]; p = 0.67). The U.S. sites had a higher proportion of female principal investigators (n = 77; 50%), compared with non-U.S. sites (n = 7; 21%; p = 0.002). Non-U.S. sites had a significantly shorter time to first patient data entry after REDCap access: 7 (1–28) versus 3 days (1–6 d) (p = 0.02). CONCLUSIONS: In this Society of Critical Care Medicine global VIRUS: COVID-19 Registry, we identified considerable variability in time from IRB submission to first patient data entry with no significant differences by country or prior multicenter trial experience. However, there was a significant difference between US and non-U.S. sites in the time from REDCap access to first data entry.

Background Although corticosteroids have become the standard of care for patients with coronavirus disease-2019 (COVID-19) on supplemental oxygen, there is growing evidence of differential treatment response. This study aimed to evaluate if there was an association between biomarker-concordant corticosteroid treatment and COVID-19 outcomes. Methods This registry-based cohort study included adult COVID-19 hospitalized patients between January 2020 and December 2021 from 109 institutions. Patients with available C-reactive protein (CRP) levels within 48 h of admission were evaluated. Those on steroids before admission, stayed in the hospital for <48 h, or were not on oxygen support were excluded. Corticosteroid treatment was biomarker-concordant if given with high baseline CRP ≥150 mg/L or withheld with low CRP (<150 mg/L) and vice-versa was considered discordant (low CRP with steroids, high CRP without steroids). Hospital mortality was the primary outcome. Sensitivity analyses were conducted using varying CRP level thresholds. The model interaction was tested to determine steroid effectiveness with increasing CRP levels. Results Corticosteroid treatment was biomarker-concordant in 1778 (49%) patients and discordant in 1835 (51%). The concordant group consisted of higher-risk patients than the discordant group. After adjusting for covariates, the odds of in-hospital mortality were significantly lower in the concordant group than the discordant (odds ratio [95% confidence interval (C.I.)] = 0.71 [0.51, 0.98]). Similarly, adjusted mortality difference was significant at the CRP thresholds of 100 and 200 mg/L (odds ratio [95% C.I.] = 0.70 [0.52, 0.95] and 0.57 [0.38, 0.85], respectively), and concordant steroid use was associated with lower need for invasive ventilation for 200 mg/L threshold (odds ratio [95% C.I.] = 0.52 [0.30, 0.91]). In contrast, no outcome benefit was observed at CRP threshold of 50. When the model interaction was tested, steroids were more effective at reducing mortality as CRP levels increased. Conclusion Biomarker-concordant corticosteroid treatment was associated with lower odds of in-hospital mortality in severe COVID-19.

OBJECTIVES: Assess clinical outcomes following PICU Liberation ABCDEF Bundle utilization. DESIGN: Prospective, multicenter, cohort study. SETTING: Eight academic PICUs. PATIENTS: Children greater than 2 months with expected PICU stay greater than 2 days and need for mechanical ventilation (MV). INTERVENTIONS: ABCDEF Bundle implementation. MEASUREMENT AND MAIN RESULTS: Over an 11-month period (3-mo baseline, 8-mo implementation), Bundle utilization was measured for 622 patients totaling 5,017 PICU days. Risk of mortality was quantified for 532 patients (4,275 PICU days) for correlation between Bundle utilization and MV duration, PICU length of stay (LOS), delirium incidence, and mortality. Utilization was analyzed as subject-specific (entire PICU stay) and day-specific (single PICU day). Median overall subject-specific utilization increased from 50% during the 3-month baseline to 63.9% during the last four implementation months ( p < 0.001). Subject-specific utilization for elements A and C did not change; utilization improved for B (0–12.5%; p = 0.007), D (22.2–61.1%; p < 0.001), E (17.7–50%; p = 0.003), and F (50–79.2%; p = 0.001). We observed no association between Bundle utilization and MV duration, PICU LOS, or delirium incidence. In contrast, on adjusted analysis, every 10% increase in subject-specific utilization correlated with mortality odds ratio (OR) reduction of 34%, p < 0.001; every 10% increase in day-specific utilization correlated with a mortality OR reduction of 1.4% ( p = 0.006). CONCLUSIONS: ABCDEF Bundle is applicable to children. Although enhanced Bundle utilization correlated with decreased mortality, increased utilization did not correlate with duration of MV, PICU LOS, or delirium incidence. Additional research in the domains of comparative effectiveness, implementation science, and human factors engineering is required to understand this clinical inconsistency and optimize PICU Liberation concept integration into clinical practice.