Search

OBJECTIVES:. Inadequate self-efficacy of resuscitation team members may impair team performance, but high self-efficacy does not guarantee competence. We evaluated the relationship between individual self-efficacy and resuscitation team competence. DESIGN:. Secondary analysis of a randomized controlled trial. SETTING:. High-fidelity in situ in-hospital cardiac arrest simulations at seven hospitals in Utah. SUBJECTS:. Multidisciplinary cardiac arrest resuscitation team members. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Resuscitation team members completed surveys evaluating resuscitation self-efficacy (confidence in resuscitation role, difficulty thinking clearly, and concerns about committing errors) after each simulation. The primary outcome was event-level chest compression hands-on fraction greater than 75%. Secondary outcomes included other measures of resuscitation quality, advanced cardiac life support protocol adherence, and nontechnical team performance. Analyses employed the Datta-Satten rank-sum method to account for response clustering within simulation events. Of 923 participants in 76 analyzable simulations, 612 (66%) submitted complete surveys and 33 (43%) resuscitation teams achieved hands-on fraction greater than 75%. Event-level chest compression hands-on fraction greater than 75% versus less than or equal to 75% was not associated with the percentage of resuscitation team members reporting confidence in their team role (n = 213 [74%] vs. n = 251 [77%], respectively, p = 0.18), lack of difficulty thinking clearly (n = 186 [65%] vs. n = 214 [66%], p = 0.92), or lack of worry about making errors (n = 155 [54%] vs. n = 180 [55%], p = 0.41). Team members’ confidence was also not associated with secondary outcomes, except that teams with confident members had better values for composite (3.55 [interquartile range, IQR 3.00–3.82] vs. 3.18 [IQR 2.57–3.64], p = 0.024) and global (8 [7–9] vs. 8 [6–8], p = 0.029) scales measuring nontechnical team performance. CONCLUSIONS:. Team members’ self-efficacy was not associated with most team-level competence metrics during simulated cardiac arrest resuscitation. These data suggest that self-efficacy should have a limited role for evaluation of resuscitation training programs and for initial certification and monitoring of individual resuscitation team members’ competence.

OBJECTIVES:. Sepsis survivors face increased risk for cardiovascular complications; however, the contribution of intrasepsis events to cardiovascular risk profiles is unclear. SETTING:. Kaiser Permanente Northern California (KPNC) and Intermountain Healthcare (IH) integrated healthcare delivery systems. SUBJECTS:. Sepsis survivors (2011–2017 [KPNC] and 2018–2020 [IH]) greater than or equal to 40 years old without prior cardiovascular disease. DESIGN:. Data across KPNC and IH were harmonized and grouped into presepsis (demographics, atherosclerotic cardiovascular disease scores, comorbidities) or intrasepsis factors (e.g., laboratory values, vital signs, organ support, infection source) with random split for training/internal validation datasets (75%/25%) within KPNC and IH. Models were bidirectionally, externally validated between healthcare systems. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Changes to predictive accuracy (C-statistic) of cause-specific proportional hazards models predicting 1-year cardiovascular outcomes (atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation events) were compared between models that did and did not contain intrasepsis factors. Among 39,590 KPNC and 16,388 IH sepsis survivors, 3,503 (8.8%) at Kaiser Permanente (KP) and 600 (3.7%) at IH experienced a cardiovascular event within 1-year after hospital discharge, including 996 (2.5%) at KP and 192 (1.2%) IH with an atherosclerotic event first, 564 (1.4%) at KP and 117 (0.7%) IH with a heart failure event, 2,310 (5.8%) at KP and 371 (2.3%) with an atrial fibrillation event. Death within 1 year after sepsis occurred for 7,948 (20%) KP and 2,085 (12.7%) IH patients. Combined models with presepsis and intrasepsis factors had better discrimination for cardiovascular events (KPNC C-statistic 0.783 [95% CI, 0.766–0.799]; IH 0.763 [0.726–0.801]) as compared with presepsis cardiovascular risk alone (KPNC: 0.666 [0.648–0.683], IH 0.660 [0.619–0.702]) during internal validation. External validation of models across healthcare systems showed similar performance (KPNC model within IH data C-statistic: 0.734 [0.725–0.744]; IH model within KPNC data: 0.787 [0.768–0.805]). CONCLUSIONS:. Across two large healthcare systems, intrasepsis factors improved postsepsis cardiovascular risk prediction as compared with presepsis cardiovascular risk profiles. Further exploration of sepsis factors that contribute to postsepsis cardiovascular events is warranted for improved mechanistic and predictive models.

While technological innovations are the invariable crux of speculation about the future of critical care, they cannot replace the clinician at the bedside. This article summarizes the work of the Society of Critical Care Medicine–appointed multiprofessional task for the Future of Critical Care. The Task Force notes that critical care practice will be transformed by novel technologies, integration of artificial intelligence decision support algorithms, and advances in seamless data operationalization across diverse healthcare systems and geographic regions and within federated datasets. Yet, new technologies will be relevant and meaningful only if they improve the very human endeavor of caring for someone who is critically ill.

IMPORTANCE:. Mechanical power and driving pressure have known associations with survival for patients with acute respiratory distress syndrome. OBJECTIVES:. To further understand the relative importance of mechanical power and driving pressure as clinical targets for ventilator management. DESIGN:. Secondary observational analysis of randomized clinical trial data. SETTING AND PARTICIPANTS:. Patients with the acute respiratory distress syndrome from three Acute Respiratory Distress Syndrome Network trials. MAIN OUTCOMES AND MEASURES:. After adjusting for patient severity in a multivariate Cox proportional hazards model, we examined the relative association of driving pressure and mechanical power with hospital mortality. Among 2,410 patients, the relationship between driving pressure and mechanical power with mortality was modified by respiratory rate, positive end-expiratory pressure, and flow. RESULTS:. Among patients with low respiratory rate (< 26), only power was significantly associated with mortality (power [hazard ratio, 1.82; 95% CI, 1.41–2.35; p < 0.001] vs driving pressure [hazard ratio, 1.01; 95% CI, 0.84–1.21; p = 0.95]), while among patients with high respiratory rate, neither was associated with mortality. Both power and driving pressure were associated with mortality at high airway flow (power [hazard ratio, 1.28; 95% CI, 1.15–1.43; p < 0.001] vs driving pressure [hazard ratio, 1.15; 95% CI, 1.01–1.30; p = 0.041]) and neither at low flow. At low positive end-expiratory pressure, neither was associated with mortality, whereas at high positive end-expiratory pressure (≥ 10 cm H2O), only power was significantly associated with mortality (power [hazard ratio, 1.22; 95% CI, 1.09–1.37; p < 0.001] vs driving pressure [hazard ratio, 1.16; 95% CI, 0.99–1.35; p = 0.059]). CONCLUSIONS AND RELEVANCE:. The relationship between mechanical power and driving pressure with mortality differed within severity subgroups defined by positive end-expiratory pressure, respiratory rate, and airway flow.

Objectives:. To understand how patients and family members experience dehumanizing or humanizing treatment when in the ICU. Design:. Qualitative study included web-based focus groups and open-ended surveys posted to ICU patient/family social media boards. Focus groups were audio recorded and transcribed. Social media responses were collected and organized by stakeholder group. Data underwent qualitative analysis. Setting:. Remote focus groups and online surveys. Patients:. ICU patient survivors, family members, and ICU teams. Interventions:. Not available. Measurements and Main Results:. Semi-structured questions and open-ended survey responses. We enrolled 40 patients/family members and 31 ICU team members. Focus groups and surveys revealed three primary themes orienting humanizing/dehumanizing ICU experiences: 1) communication, 2) outcomes, and 3) causes of dehumanization. Dehumanization occurred during “communication” exchanges when ICU team members talked “over” patients, made distressing remarks when patients were present, or failed to inform patients about ICU-related care. “Outcomes” of dehumanization were associated with patient loss of trust in the medical team, loss of motivation to participate in ICU recovery, feeling of distress, guilt, depression, and anxiety. Humanizing behaviors were associated with improved recovery, well-being, and trust. “Perceived causes” of dehumanizing behaviors were linked to patient, ICU team, and healthcare system factors. CONCLUSIONS:. Behaviors of ICU clinicians may cause patients and families to feel dehumanized when in the ICU. Negative behaviors are noticed by patients and families, possibly contributing to poor outcomes including mental health, recovery, and lack of trust in ICU teams. Supporting ICU clinicians may enable a more empathic environment and in turn more humanizing clinician-patient encounters.

Objectives:. Given finite ICU bed capacity, knowledge of ICU bed utilization during the coronavirus disease 2019 pandemic is critical to ensure future strategies for resource allocation and utilization. We sought to examine ICU census trends in relation to ICU bed capacity during the rapid increase in severe coronavirus disease 2019 cases early during the pandemic. Design:. Observational cohort study. Setting:. Thirteen geographically dispersed academic medical centers in the United States. Patients/Subjects:. We obtained daily ICU censuses from March 26 to June 30, 2020, as well as prepandemic ICU bed capacities. The primary outcome was daily census of ICU patients stratified by coronavirus disease 2019 and mechanical ventilation status in relation to ICU capacity. Interventions:. None. Measurements and Main Results:. Prepandemic overall ICU capacity ranged from 62 to 225 beds (median 109). During the study period, the median daily coronavirus disease 2019 ICU census per hospital ranged from 1 to 84 patients, and the daily ICU census exceeded overall ICU capacity for at least 1 day at five institutions. The number of critically ill patients exceeded ICU capacity for a median (interquartile range) of 17 (12–50) of 97 days at these five sites. All 13 institutions experienced decreases in their noncoronavirus disease ICU population, whereas local coronavirus disease 2019 cases increased. Coronavirus disease 2019 patients reached their greatest proportion of ICU capacity on April 12, 2020, when they accounted for 44% of ICU patients across all participating hospitals. Maximum ICU census ranged from 52% to 289% of overall ICU capacity, with three sites less than 80%, four sites 80–100%, five sites 100–128%, and one site 289%. Conclusions:. From March to June 2020, the coronavirus disease 2019 pandemic led to ICU censuses greater than ICU bed capacity at fives of 13 institutions evaluated. These findings demonstrate the short-term adaptability of U.S. healthcare institutions in redirecting limited resources to accommodate a public health emergency.