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Despite etiologic and severity heterogeneity in neutropenic sepsis, management is often uniform. Understanding host response clinical subphenotypes might inform treatment strategies for neutropenic sepsis.This retrospective two-hospital study analyzed whether temperature trajectory modeling could identify distinct, clinically relevant subphenotypes among neutropenic oncology patients with suspected infection.Among adult oncologic admissions with neutropenia and blood cultures within 24 hours, a previously validated model classified patients' initial 72-hour temperature trajectories into one of four subphenotypes. We analyzed subphenotypes' independent relationships with hospital mortality and bloodstream infection using multivariable models.Patients (primary cohort n=1145, validation cohort n=6564) fit into one of four temperature subphenotypes. "Hyperthermic slow resolvers" (pooled n=1140 [14.8%], mortality n=104 [9.1%]) and "hypothermic" encounters (n=1612 [20.9%], mortality n=138 [8.6%]) had higher mortality than "hyperthermic fast resolvers" (n=1314 [17.0%], mortality n=47 [3.6%]) and "normothermic" (n=3643 [47.3%], mortality n=196 [5.4%]) encounters (p0.001). Bloodstream infections were more common among hyperthermic slow resolvers (n=248 [21.8%]) and hyperthermic fast resolvers (n=240 [18.3%]) than among hypothermic (n=188 [11.7%]) or normothermic encounters (n=418 [11.5%]), p0.001). Adjusted for confounders, hyperthermic slow resolvers had increased adjusted odds for mortality (primary cohort odds ratio 1.91 [p=0.03]; validation cohort odds ratio 2.19 [p0.001]) and bloodstream infection (primary odds ratio 1.54 [p=0.04]; validation cohort odds ratio 2.15 [p0.001]).Temperature trajectory subphenotypes were independently associated with important outcomes among hospitalized neutropenic patients in two independent cohorts.

The objective of this study was to develop and externally validate a model to predict adjunctive vasopressin response in patients with septic shock being treated with norepinephrine for bedside use in the intensive care unit.This was a retrospective analysis of two adult tertiary intensive care unit septic shock populations.Barnes-Jewish Hospital (BJH) from 2010 to 2017 and Beth Israel Deaconess Medical Center (BIDMC) from 2001 to 2012.Two septic shock populations (548 BJH patients and 464 BIDMC patients) that received vasopressin as second-line vasopressor.Patients who were vasopressin responsive were compared with those who were nonresponsive. Vasopressin response was defined as survival with at least a 20% decrease in maximum daily norepinephrine requirements by one calendar day after vasopressin initiation, without a third-line vasopressor.Two supervised machine learning models (gradient-boosting machine [XGBoost] and elastic net penalized logistic regression [EN]) were trained in 1000 bootstrap replications of the BJH data and externally validated in the BIDMC data to predict vasopressin responsiveness.Vasopressin responsiveness was similar among each cohort (BJH 45% and BIDMC 39%). Mortality was lower for vasopressin responders compared with nonresponders in the BJH (51% vs. 73%) and BIDMC (45% vs. 83%) cohorts, respectively. Both models demonstrated modest discrimination in the training (XGBoost area under receiver operator curve [AUROC] 0.61 [95% confidence interval (CI) 0.61-0.61], EN 0.59 [95% CI 0.58-0.59]) and external validation (XGBoost 0.68 [95% CI 0.63-0.73], EN 0.64 [95% CI 0.59-0.69]) datasets.Vasopressin nonresponsiveness is common and associated with increased mortality. The models' modest performances highlight the complexity of septic shock and indicate that more research will be required before clinical decision support tools can aid in anticipating patient-specific responsiveness to vasopressin.

This cohort study uses data from electronic health records to assess variability in a sepsis prediction model across 9 hospitals.

In three recent and related publications, researchers from Johns Hopkins University and Bayesian Health report results from implementing and prospectively evaluating the Targeted Real-time Early Warning System (TREWS) for sepsis at five hospitals.

PURPOSE To elicit end-user and stakeholder perceptions regarding design and implementation of an inpatient clinical deterioration early warning system (EWS) for oncology patients to better fit routine clinical practices and enhance clinical impact. METHODS In an explanatory-sequential mixed-methods study, we evaluated a stakeholder-informed oncology early warning system (OncEWS) using surveys and semistructured interviews. Stakeholders were physicians, advanced practice providers (APPs), and nurses. For qualitative data, we used grounded theory and thematic content analysis via the constant comparative method to identify determinants of OncEWS implementation. RESULTS Survey respondents generally agreed that an oncology-focused EWS could add value beyond clinical judgment, with nurses endorsing this notion significantly more strongly than other clinicians (nurse: median 5 on a 6-point scale [6 = strongly agree], interquartile range 4-5; doctors/advanced practice providers: 4 [4-5]; P = .005). However, some respondents would not trust an EWS to identify risk accurately (n = 36 [42%] somewhat or very concerned), while others were concerned that institutional culture would not embrace such an EWS (n = 17 [28%]). Interviews highlighted important aspects of the EWS and the local context that might facilitate implementation, including (1) a model tailored to the subtleties of oncology patients, (2) transparent model information, and (3) nursing-centric workflows. Interviewees raised the importance of sepsis as a common and high-risk deterioration syndrome. CONCLUSION Stakeholders prioritized maximizing the degree to which the OncEWS is understandable, informative, actionable, and workflow-complementary, and perceived these factors to be key for translation into clinical benefit.

Artificial intelligence (AI) has been rapidly adopted in various health care domains. Molecular imaging, accordingly, has demonstrated growing academic and commercial interest in AI. Unprepared and inequitable implementation and scale-up of AI in health care may pose challenges. Implementation of AI, as a complex intervention, may face various barriers, at individual, interindividual, organizational, health system, and community levels. To address these barriers, recommendations have been developed to consider health equity as a critical lens to sensitize implementation, engage stakeholders in implementation and evaluation, recognize and incorporate the iterative nature of implementation, and integrate equity and implementation in early-stage AI research.

Intensive care unit (ICU)-ward patient transfers are inherently high risk, and clinician miscommunication has been linked to adverse events and negative outcomes. Despite these risks, few educational tools exist to improve resident handoff communication at ICU-ward transfer.We used human-centered design (HCD) methods to cocreate a novel electronic health record ICU-ward transfer tool alongside Internal Medicine residents at three academic hospitals.We conducted HCD workshops at each hospital, performing process mapping, brainstorming, and rapid prototyping. We performed thematic analysis on verbatim-transcribed workshop audio recordings to inform development and adaptation of the final resident prototype into the ICU-PAUSE tool.ICU-PAUSE focuses on reasons for ICU admission and problem-based ICU course (I); Code status, goals of care, and family contacts (C); a diagnostic pause acknowledging Uncertainty (U); Pending tests (P); Active consultants (A); high-risk medications, including medications to be Unprescribed (U); Summary of problems and to-dos (S); and a current physical Exam (E).We used HCD to cocreate a novel, more user-friendly electronic ICU-ward transfer tool, ICU-PAUSE, alongside Internal Medicine trainees. Future steps will involve formal usability testing, evidence-driven implementation, and clinical evaluation of ICU-PAUSE across multiple hospitals.

Background Patients with COVID-19-related acute respiratory distress syndrome (ARDS) have been postulated to present with distinct respiratory subphenotypes. However, most phenotyping schema have been limited by sample size, disregard for temporal dynamics, and insufficient validation. We aimed to identify respiratory subphenotypes of COVID-19-related ARDS using unbiased data-driven approaches.Methods PRoVENT-COVID was an investigator-initiated, national, multicentre, prospective, observational cohort study at 22 intensive care units (ICUs) in the Netherlands. Consecutive patients who had received invasive mechanical ventilation for COVID-19 (aged 18 years or older) served as the derivation cohort, and similar patients from two ICUs in the USA served as the replication cohorts. COVID-19 was confirmed by positive RT-PCR. We used latent class analysis to identify subphenotypes using clinically available respiratory data cross-sectionally at baseline, and longitudinally using 8-hourly data from the first 4 days of invasive ventilation. We used group-based trajectory modelling to evaluate trajectories of individual variables and to facilitate potential clinical translation. The PRoVENT-COVID study is registered with ClinicalTrials.gov, NCT04346342.Findings Between March 1, 2020, and May 15, 2020, 1007 patients were admitted to participating ICUs in the Netherlands, and included in the derivation cohort. Data for 288 patients were included in replication cohort 1 and 326 in replication cohort 2. Cross-sectional latent class analysis did not identify any underlying subphenotypes. Longitudinal latent dass analysis identified two distinct subphenotypes. Subphenotype 2 was characterised by higher mechanical power, minute ventilation, and ventilatory ratio over the first 4 days of invasive mechanical ventilation than subphenotype 1, but PaO2/FiO(2), pH, and compliance of the respiratory system did not differ between the two subphenotypes. 185 (28%) of 671 patients with subphenotype 1 and 109 (32%) of 336 patients with subphenotype 2 had died at day 28 (p=0.10). However, patients with subphenotype 2 had fewer ventilator-free days at day 28 (median 0, IQR 0-15 vs 5, 0-17; p=0.016) and more frequent venous thrombotic events (109[32%] of 336 patients vs 176 [26%] of 671 patients; p=0.048) compared with subphenotype 1. Group-based trajectory modelling revealed trajectories of ventilatory ratio and mechanical power with similar dynamics to those observed in latent class analysis-derived trajectory subphenotypes. The two trajectories were: a stable value for ventilatory ratio or mechanical power over the first 4 days of invasive mechanical ventilation (trajectory A) or an upward trajectory (trajectory B). However, upward trajectories were better independent prognosticators for 28-day mortality (OR 1.64, 95% CI 1.17-2.29 for ventilatory ratio; 1.82, 1.24-2.66 for mechanical power). The association between upward ventilatory ratio trajectories (trajectory B) and 28-day mortality was confirmed in the replication cohorts (OR 4.65, 95% CI 1. 87-11 6 for ventilatory ratio in replication cohort 1; 1-89, 1.05-3-37 for ventilatory ratio in replication cohort 2).Interpretation At baseline, COVID-19-related ARDS has no consistent respiratory subphenotype. Patients diverged from a fairly homogenous to a more heterogeneous population, with trajectories of ventilatory ratio and mechanical power being the most discriminatory. Modelling these parameters alone provided prognostic value for duration of mechanical ventilation and mortality. Copyright (C) 2021 Published by Elsevier Ltd. All rights reserved.

Background The intensive care unit (ICU)–ward transfer poses a particularly high-risk period for patients. The period after transfer has been associated with adverse events and additional work for care teams related to miscommunication or omission of information. Standardized handoff processes have been found to reduce communication errors and adverse patient events in other clinical environments but are understudied at the ICU-ward interface. We previously developed an electronic ICU-ward transfer tool, ICU-PAUSE, which embeds the key elements and diagnostic reasoning to facilitate a safe transfer of care at ICU discharge. Objective The aim of this study is to evaluate the implementation process of the ICU-PAUSE handoff tool across 10 academic medical centers, including the rate of adoption and acceptability, as perceived by clinical care teams. Methods ICU-PAUSE will be implemented in the medical ICU across 10 academic hospitals, with each site customizing the tool to their institution’s needs. Our mixed methods study will include a combination of a chart review, quantitative surveys, and qualitative interviews. After a 90-day implementation period, we will conduct a retrospective chart review to evaluate the rate of uptake of ICU-PAUSE. We will also conduct postimplementation surveys of providers to assess perceptions of the tool and its impact on the frequency of communication errors and adverse events during ICU-ward transfers. Lastly, we will conduct semistructured interviews of faculty stakeholders with subsequent thematic analysis with the goal of identifying benefits and barriers in implementing and using ICU-PAUSE. Results ICU-PAUSE was piloted in the medical ICU at Barnes-Jewish Hospital, the teaching hospital of Washington University School of Medicine in St. Louis, in 2019. As of July 2022, implementation of ICU-PAUSE is ongoing at 6 of 10 participating sites. Our results will be published in 2023. Conclusions Our process of ICU-PAUSE implementation embeds each step of template design, uptake, and customization in the needs of users and key stakeholders. Here, we introduce our approach to evaluate its acceptability, usability, and impact on communication errors according to the tenets of sociotechnical theory. We anticipate that ICU-PAUSE will offer an effective handoff tool for the ICU-ward transition that can be generalized to other institutions. International Registered Report Identifier (IRRID) DERR1-10.2196/40918

This descriptive study evaluates the association between nursing reports of sepsis overalerting and alert volume by quantifying the number of alerts generated by the Epic Sepsis Model at 24 US hospitals before and during the COVID-19 pandemic.

ObjectivesTo implement a unified non-emergency medical transportation (NEMT) service across a large integrated healthcare delivery network.MethodsWe assessed needs among key organisational stakeholders, then reviewed proposals. We selected a single NEMT vendor best aligned with organisational priorities and implemented this solution system-wide.ResultsOur vendor’s hybrid approach combined rideshares with contracted vehicles able to serve patients with equipment and other needs. After 6195 rides in the first year, we observed shorter wait times and lower costs compared with our prior state.DiscussionEssential lessons included (1) understanding user and patient needs, (2) obtaining complete, accurate and comprehensive baseline data and (3) adapting existing workflows—rather than designing de novo—whenever possible.ConclusionsOur implementation of a single-vendor NEMT solution validates the need for NEMT at large healthcare organisations, geographical challenges to establishing NEMT organisation-wide, and the importance of baseline data and stakeholder engagement.

Objectives To characterize the rapid response team activations, and the patients receiving them, in the American Heart Association-sponsored Get With The Guidelines Resuscitation-Medical Emergency Team cohort between 2005 and 2015. Design Retrospective multicenter cohort study. Setting Three hundred sixty U.S. hospitals. Patients Consecutive adult patients experiencing rapid response team activation. Interventions Rapid response team activation. Measurements and main results The cohort included 402,023 rapid response team activations from 347,401 unique healthcare encounters. Respiratory triggers (38.0%) and cardiac triggers (37.4%) were most common. The most frequent interventions-pulse oximetry (66.5%), other monitoring (59.6%), and supplemental oxygen (62.0%)-were noninvasive. Fluids were the most common medication ordered (19.3%), but new antibiotic orders were rare (1.2%). More than 10% of rapid response teams resulted in code status changes. Hospital mortality was over 14% and increased with subsequent rapid response activations. Conclusions Although patients requiring rapid response team activation have high inpatient mortality, most rapid response team activations involve relatively few interventions, which may limit these teams' ability to improve patient outcomes.

PURPOSE: Patients hospitalized outside the intensive care unit (ICU) frequently experience clinical deterioration. Little has been done to describe the landscape of clinical deterioration among inpatients with cancer. We aimed to describe the frequency of clinical deterioration among patients with cancer hospitalized on the wards at a major academic hospital and to identify independent risk factors for clinical deterioration among these patients. METHODS: This was a retrospective cohort study at a 1,300-bed urban academic hospital with a 138-bed inpatient cancer center. We included consecutive admissions to the oncology wards between January 1, 2014, and June 30, 2017. We defined clinical deterioration as the composite of ward death and transfer to the ICU. RESULTS: We evaluated 21,219 admissions from 9,058 patients. The composite outcome occurred during 1,945 admissions (9.2%): 1,365 (6.4%) had at least one ICU transfer, and 580 (2.7%) involved ward death. Logistic regression identified several independent risk factors for clinical deterioration, including the following: age (odds ratio [OR], 1.33 per decade; 95% CI, 1.07 to 1.67), male sex (OR, 1.15; 95% CI, 1.05 to 1.33), comorbidities, illness severity (OR, 1.11; 95% CI, 1.10 to 1.13), emergency admission (OR, 1.45; 95% CI, 1.26 to 1.67), hospitalization on particular wards (OR, 1.525; 95% CI, 1.326 to 1.67), bacteremia (OR, 1.24; 95% CI, 1.01 to 1.52), fungemia (OR, 3.76; 95% CI, 1.90 to 7.41), tumor lysis syndrome (OR, 3.01; 95% CI, 2.41 to 3.76), and receipt of antimicrobials (OR, 2.04; 95% CI, 1.72 to 2.42) and transfusions (OR, 1.65; 95% CI, 1.42 to 1.92). CONCLUSION: Clinical deterioration was common; it occurred in more than 9% of admissions. Factors independently associated with deterioration included comorbidities, admission source, infections, and blood product transfusion.

Supplemental Digital Content is available in the text., Objectives: Multicenter data on the characteristics and outcomes of children hospitalized with coronavirus disease 2019 are limited. Our objective was to describe the characteristics, ICU admissions, and outcomes among children hospitalized with coronavirus disease 2019 using Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study: Coronavirus Disease 2019 registry. Design: Retrospective study. Setting: Society of Critical Care Medicine Viral Infection and Respiratory Illness Universal Study (Coronavirus Disease 2019) registry. Patients: Children (< 18 yr) hospitalized with coronavirus disease 2019 at participating hospitals from February 2020 to January 2021. Interventions: None. Measurements and Main Results: The primary outcome was ICU admission. Secondary outcomes included hospital and ICU duration of stay and ICU, hospital, and 28-day mortality. A total of 874 children with coronavirus disease 2019 were reported to Viral Infection and Respiratory Illness Universal Study registry from 51 participating centers, majority in the United States. Median age was 8 years (interquartile range, 1.25–14 yr) with a male:female ratio of 1:2. A majority were non-Hispanic (492/874; 62.9%). Median body mass index (n = 817) was 19.4 kg/m2 (16–25.8 kg/m2), with 110 (13.4%) overweight and 300 (36.6%) obese. A majority (67%) presented with fever, and 43.2% had comorbidities. A total of 238 of 838 (28.2%) met the Centers for Disease Control and Prevention criteria for multisystem inflammatory syndrome in children, and 404 of 874 (46.2%) were admitted to the ICU. In multivariate logistic regression, age, fever, multisystem inflammatory syndrome in children, and pre-existing seizure disorder were independently associated with a greater odds of ICU admission. Hospital mortality was 16 of 874 (1.8%). Median (interquartile range) duration of ICU (n = 379) and hospital (n = 857) stay were 3.9 days (2–7.7 d) and 4 days (1.9–7.5 d), respectively. For patients with 28-day data, survival was 679 of 787, 86.3% with 13.4% lost to follow-up, and 0.3% deceased. Conclusions: In this observational, multicenter registry of children with coronavirus disease 2019, ICU admission was common. Older age, fever, multisystem inflammatory syndrome in children, and seizure disorder were independently associated with ICU admission, and mortality was lower among children than mortality reported in adults.

RATIONALE: Patient transfers from the intensive care unit (ICU) to the hospital ward are a vulnerable time for patients recovering from critical illness. Our prior work showed that ICU-ward transfers vary widely across sites. Previously, we used Human-Centered Design-an iterative, collaborative process for user-focused solutions-to develop a prototype structured ICU-ward transfer communication tool for clinicians at three academic Internal Medicine residency programs. We describe here an iterative approach to rapid prototyping and updating of this novel tool for integration into the electronic health record (EHR), based on structured focus groups using Design Thinking methodology. METHODS: In spring 2020, we conducted voluntary focus groups of PGY-2 and PGY-3 Internal Medicine Residents to co-create new iterations of an ICUward transfer tool created through prior focus groups. Each site conducted 2 focus groups with 4-10 participants per session. All focus groups were conducted via videoconference due to the COVID-19 pandemic. We used participant feedback to iteratively modify the ICU-ward transfer tool towards an ideal state. With participants' consent, we recorded and anonymously transcribed the focus group discussions conducted over the Zoom platform. We performed qualitative inquiry on transcripts for coding and thematic content analysis. Coding was performed independently by two coders (LS, BG) using both a theory-driven (deductive) and data-driven (inductive) approach;disparate coding was reconciled via in-depth discussion between coders. RESULTS: Focus groups identified several main themes around the role, content, and workflows related to an ideal ICU-wards handoff tool: (1) how the tool can best serve the needs of ICU and wards teams;(2) that the ICU-ward handoff process must balance thoroughness and usability;and (3) that design and implementation must consider user experience and training in best practices. Under these themes, participants identified specific attributes of an ideal handoff tool (Table 1), which coalesced around 3 main goals: (1) to synthesize key details and communicate the ICU team's thought process (summarized in Table 1A);(2) to integrate the new tool into the EHR within a customizable note template that minimizes click burden and redundancy (1B);and (3) to serve as a standardized outline to reduce errors of omission in written and verbal handoffs processes (1C). CONCLUSIONS: Participants valued organization of handoff information, EHR integration and user experience, and standardization and adaptability of workflows when modifying a framework based on prior resident user input to create a more user-friendly ICU-ward transfer tool using a Design Thinking Methodology.

Background: Interpersonal and communication skills are essential for physicians practicing in critical care settings. Accordingly, demonstration of these skills has been a core competency of the Accreditation Council for Graduate Medical Education since 2014. However, current practices regarding communication skills training in adult and pediatric critical care fellowships are not well described. Objective: To describe the current state of communication curricula and training methods in adult and pediatric critical care training programs as demonstrated by the published literature. Methods: We performed a systematic review of the published literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Three authors reviewed a comprehensive set of databases and independently selected articles on the basis of a predefined set of inclusion and exclusion criteria. Data were independently extracted from the selected articles. Results: The 23 publications meeting inclusion criteria fell into the following study classifications: intervention (n = 15), cross-sectional survey (n = 5), and instrument validation (n = 3). Most interventional studies assessed short-term and self-reported outcomes (e.g., learner attitudes and perspectives) only. Fifteen of 22 publications represented pediatric subspecialty programs. Conclusion: Opportunities exist to evaluate the influence of communication training programs on important outcomes, including measured learner behavior and patient and family outcomes, and the durability of skill retention.

Objectives Assess the impact of heterogeneity among established sepsis criteria (Sepsis-1, Sepsis-3, Centers for Disease Control and Prevention Adult Sepsis Event, and Centers for Medicare and Medicaid severe sepsis core measure 1) through the comparison of corresponding sepsis cohorts. Design Retrospective analysis of data extracted from electronic health record. Setting Single, tertiary-care center in St. Louis, MO. Patients Adult, nonsurgical inpatients admitted between January 1, 2012, and January 6, 2018. Interventions None. Measurements and main results In the electronic health record data, 286,759 encounters met inclusion criteria across the study period. Application of established sepsis criteria yielded cohorts varying in prevalence: Centers for Disease Control and Prevention Adult Sepsis Event (4.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (4.8%), International Classification of Disease code (7.2%), Sepsis-3 (7.5%), and Sepsis-1 (11.3%). Between the two modern established criteria, Sepsis-3 (n = 21,550) and Centers for Disease Control and Prevention Adult Sepsis Event (n = 12,494), the size of the overlap was 7,763. The sepsis cohorts also varied in time from admission to sepsis onset (hr): Sepsis-1 (2.9), Sepsis-3 (4.1), Centers for Disease Control and Prevention Adult Sepsis Event (4.6), and Centers for Medicare and Medicaid severe sepsis core measure 1 (7.6); sepsis discharge International Classification of Disease code rate: Sepsis-1 (37.4%), Sepsis-3 (40.1%), Centers for Medicare and Medicaid severe sepsis core measure 1 (48.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (54.5%); and inhospital mortality rate: Sepsis-1 (13.6%), Sepsis-3 (18.8%), International Classification of Disease code (20.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (22.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (24.1%). Conclusions The application of commonly used sepsis definitions on a single population produced sepsis cohorts with low agreement, significantly different baseline demographics, and clinical outcomes.

OBJECTIVES To assess whether Black race is associated with a higher rate of all-cause readmission compared with White race following community-onset sepsis. DESIGN Retrospective cohort study. SETTING One-thousand three-hundred bed urban academic medical centers. PATIENTS Three-thousand three-hundred ninety patients hospitalized with community-onset sepsis between January 1, 2010, and December 31, 2017. INTERVENTIONS Community-onset sepsis was defined as patients admitted through the emergency department with an International Classification of Disease, ninth revision, Clinical Modification code for either severe sepsis (995.92) or septic shock (785.52). Beginning in 2015, we used International Classification of Disease, Tenth Revision, Clinical Modification codes R65.20 (severe sepsis) and R65.21 (septic shock). We excluded those individuals hospitalized at another acute care facility that were transferred to our facility. Race was abstracted electronically, and patients who expired or self-identified as a race other than Black or White race were excluded. Patients who experienced a subsequent hospitalization at our facility were considered to be readmitted. MEASUREMENTS AND MAIN RESULTS Compared with White race, Black race demonstrated a significantly higher rate of all-cause readmission (60.8% vs 71.1%; p < 0.001), including a higher rate of readmission for sepsis (14.0% vs 19.8%; p < 0.001). Black patients also resided in zip codes with a lower median household income and were more likely to use public insurance compared with White race. Similar rates of comorbid diseases and disease burden were observed between the two groups, but vasopressors were less likely to be administered to Black patients. Multivariable analysis showed that Black race was associated with a 50% increased odds (odds ratio, 1.52, 99% CI, 1.25-1.84) in all-cause readmission risk compared with White race. CONCLUSIONS Black race was associated with a higher rate of all-cause and sepsis readmission, possibly as a result of unaddressed health disparities, compared with White race. Programs addressing healthcare disparities should use readmission as another marker of equity.

Objectives:. To describe the infectious complications and interleukin-6 trajectories in mechanically ventilated patients with coronavirus disease 2019. Design:. Retrospective cohort study. Setting:. ICUs at Washington University-Barnes Jewish Hospital in St. Louis, MO. Participants:. All consecutive patients admitted to the medical ICU and requiring mechanical ventilation from March 12, 2020, to April 21, 2020, were included. Interventions:. Tocilizumab, an interleukin-6 receptor blocker, was prescribed at the discretion of the treating physicians to patients with a clinical picture compatible with cytokine release syndrome. Measurements:. All the patients were followed to death or hospital discharge. Demographic and laboratory data were collected retrospectively from the electronic medical record. Interleukin-6 levels were measured at days 0, 3, 7, 14, and 21. Infections were divided into culture positive and culture negative (clinically suspected and treated). The main outcomes were infectious complications and interleukin-6 levels at different points in time. Results:. Forty-three patients with respiratory failure secondary to coronavirus disease 2019 were on mechanical ventilation during the study period. Twenty-seven (68%) were male, and 31 (72.1%) were African-American. Median Charlson score was 2 (interquartile range, 0–4). Median Pao2/Fio2 was 171.5 (122–221) on the day of mechanical ventilation initiation, and 13 patients (30.2%) required vasopressors. C-reactive protein was 142.7 (97.7–213.7), d-dimer 1,621 (559–13,434), and Acute Physiology and Chronic Health Evaluation-II 11 (9–15). Interleukin-6 levels at admission were 61 pg/mL (interquartile range, 28.6–439 pg/mL). Patients treated with tocilizumab had higher levels of interleukin-6 at each measurement (days 0, 3, 7, 14, and 21) compared with patients receiving standard of care. Both groups reached peak interleukin-6 levels at day 7. Administration of tocilizumab was associated with a trend toward increased risk of infection. Conclusions:. Interleukin-6 levels peak at day 7 in patients with severe coronavirus disease 2019 pneumonia requiring mechanical ventilation and follows a similar trajectory in patients with coronavirus disease 2019 pneumonia requiring mechanical ventilation irrespective of treatment with interleukin-6R blockers. Interleukin-6 levels continued to rise in nonsurvivors, in comparison with survivors, where the rise in interleukin-6 levels was followed by a decline.

There are limited data on longitudinal outcomes for coronavirus disease 2019 (COVID-19) hospitalizations that account for transitions between clinical states over time. Using electronic health record data from a hospital network in the St. Louis, Missouri, region, we performed multistate analyses to examine longitudinal transitions and outcomes among hospitalized adults with laboratory-confirmed COVID-19 with respect to 15 mutually exclusive clinical states. Between March 15 and July 25, 2020, a total of 1,577 patients in the network were hospitalized with COVID-19 (49.9% male; median age, 63 years (interquartile range, 50–75); 58.8% Black). Overall, 34.1% (95% confidence interval (CI): 26.4, 41.8) had an intensive care unit admission and 12.3% (95% CI: 8.5, 16.1) received invasive mechanical ventilation (IMV). The risk of decompensation peaked immediately after admission; discharges peaked around days 3–5, and deaths plateaued between days 7 and 16. At 28 days, 12.6% (95% CI: 9.6, 15.6) of patients had died (4.2% (95% CI: 3.2, 5.2) had received IMV) and 80.8% (95% CI: 75.4, 86.1) had been discharged. Among those receiving IMV, 35.1% (95% CI: 28.2, 42.0) remained intubated after 14 days; after 28 days, 37.6% (95% CI: 30.4, 44.7) had died and only 37.7% (95% CI: 30.6, 44.7) had been discharged. Multistate methods offer granular characterizations of the clinical course of COVID-19 and provide essential information for guiding both clinical decision-making and public health planning.

A previously healthy 35-year-old male presented to our hospital in mid-March 2020 with an acute subarachnoid hemorrhage (SAH). The patient had been in the shower when he experienced a sudden, pounding frontal headache associated with nausea and vomiting, blurred vision, and weakness. Initial imaging

Rationale: Determining when an intensive care unit (ICU) patient is ready for discharge to the ward is a complex daily challenge for any ICU care team. Patients who experience unplanned readmissions to the ICU have increased mortality, length of stay, and cost compared with those not readmitted during their hospital stay. The accuracy of clinician prediction for ICU readmission is unknown. Objectives: To determine the accuracy of ICU physicians and nurses for predicting ICU readmissions Methods: We conducted a prospective study in the medical ICU of an academic hospital from October 2015 to September 2017. After daily rounding for patients being transferred to the ward, ICU clinicians (nurses, residents, fellows, and attendings) were asked to report the likelihood of readmission within 48 hours (using a 1–10 scale, with 10 being “extremely likely”). The accuracy of the clinician prediction score (1–10) was assessed for all clinicians and by clinician type using sensitivity, specificity, and area under the curve (AUC) for the receiver operating characteristic curve for predicting the primary outcome, which was ICU readmission within 48 hours of ICU discharge. Results: A total of 2,833 surveys was collected for 938 ICU-to-ward transfers, of which 40 (4%) were readmitted to the ICU within 48 hours of transfer. The median clinician likelihood of readmission score was 3 (interquartile range, 2–4). When physician and nurse likelihood scores were combined, the median clinician likelihood score had an AUC of 0.70 (95% confidence interval [CI], 0.62–0.78) for predicting ICU readmission within 48 hours. Nurses were significantly more accurate than interns at predicting 48-hour ICU readmission (AUC, 0.73 [95% CI, 0.64–0.82] vs. AUC, 0.60 [95% CI, 0.49–0.71]; P = 0.03). All other pairwise comparisons were not significantly different for predicting ICU readmission within 48 hours (P > 0.05 for all comparisons). Conclusions: We found that all clinicians surveyed in our ICU, regardless of the level of experience or clinician type, had only fair accuracy for predicting ICU readmission. Further research is needed to determine if clinical decision support tools would provide prognostic value above and beyond clinical judgment for determining who is ready for ICU discharge.

BackgroundThere is limited literature about physician handoffs between the intensive care unit (ICU) and the ward, and best practices have not been described. These patients are uniquely vulnerable given their medical complexity, diagnostic uncertainty and reduced monitoring intensity. We aimed to characterise the structure, perceptions and processes of ICU–ward handoffs across three teaching hospitals using multimodal methods: by identifying the handoff components involved in communication failures and describing common processes of patient transfer.MethodsWe conducted a study at three academic medical centres using two methods to characterise the structure, perceptions and processes of ICU–ward transfers: (1) an anonymous resident survey characterising handoff communication during ICU–ward transfer, and (2) comparison of process maps to identify similarities and differences between ICU–ward transfer processes across the three hospitals.ResultsOf the 295 internal medicine residents approached, 175 (59%) completed the survey. 87% of the respondents recalled at least one adverse event related to communication failure during ICU–ward transfer. 95% agreed that a well-structured handoff template would improve ICU–ward transfer. Rehabilitation needs, intravenous access/hardware and risk assessments for readmission to the ICU were the most frequently omitted or incorrectly communicated components of handoff notes. More than 60% of the respondents reported that notes omitted or miscommunicated pending results, active subspecialty consultants, nutrition and intravenous fluids, antibiotics, and healthcare decision-maker information at least twice per month. Despite variable process across the three sites, all process maps demonstrated flaws and potential for harm in critical steps of the ICU–ward transition.ConclusionIn this multisite study, despite significant process variation across sites, almost all resident physicians recalled an adverse event related to the ICU–ward handoff. Future work is needed to determine best practices for ICU–ward handoffs at academic medical centres.

Hospital-acquired pneumonia (HAP) is the leading cause of death from hospital-acquired infection. Little work has been done on strategies for prevention of HAP. This review aims to describe potential HAP prevention strategies and the evidence supporting them. Oral care and aspiration precautions may attenuate some risk for HAP. Oral and digestive decontamination with antibiotics may be effective but could increase risk for resistant organisms. Other preventive measures, including isolation practices, remain theoretical or experimental.Hospital-acquired pneumonia occurs because of pharyngeal colonization with pathogenic organisms and subsequent aspiration of these pathogens.Most potential HAP prevention strategies remain unproven.

Introduction Rapid response systems are commonly employed by hospitals to identify and respond to deteriorating patients outside of the intensive care unit. Controversy exists about the benefits of rapid response systems. Aims We aimed to review the current state of the rapid response literature, including evolving aspects of afferent (risk detection) and efferent (intervention) arms, outcome measurement, process improvement, and implementation. Data sources Articles written in English and published in PubMed. Results Rapid response systems are heterogeneous, with important differences among afferent and efferent arms. Clinically meaningful outcomes may include unexpected mortality, in-hospital cardiac arrest, length of stay, cost, and processes of care at end of life. Both positive and negative interventional studies have been published, although the two largest randomized trials involving rapid response systems – the Medical Early Response and Intervention Trial (MERIT) and the Effect of a Pediatric Early Warning System on All-Cause Mortality in Hospitalized Pediatric Patients (EPOCH) trial – did not find a mortality benefit with these systems, albeit with important limitations. Advances in monitoring technologies, risk assessment strategies, and behavioral ergonomics may offer opportunities for improvement. Conclusions Rapid responses may improve some meaningful outcomes, although these findings remain controversial. These systems may also improve care for patients at the end of life. Rapid response systems are expected to continue evolving with novel developments in monitoring technologies, risk prediction informatics, and work in human factors.

BACKGROUND: Bloodstream infections (BSIs) are common after hematopoietic stem cell transplantation (HSCT) and are associated with increased long-term morbidity and mortality. However, short-term outcomes related to BSI in this population remain unknown. More specifically, it is unclear whether choices related to empiric antimicrobials for potentially infected patients are associated with patient outcomes. RESEARCH QUESTION: Are potential delays in appropriate antibiotics associated with hospital outcomes among HSCT recipients with BSI? STUDY DESIGN AND METHODS: We conducted a retrospective cohort study at a large comprehensive inpatient academic cancer center between January 2014 and June 2017. We identified all admissions for HSCT and prior recipients of HSCT. We defined potential delay in appropriate antibiotics as > 24 h between positive blood culture results and the initial dose of an antimicrobial with activity against the pathogen. RESULTS: We evaluated 2,751 hospital admissions from 1,086 patients. Of these admissions, 395 (14.4%) involved one or more BSIs. Of these 395 hospitalizations, 44 (11.1%) involved potential delays in appropriate antibiotics. The incidence of mortality was higher in BSI hospitalizations than in those without BSI (23% vs 4.5%; P < .001). In multivariable analysis, BSI was an independent predictor of mortality (OR, 8.14; 95% CI, 5.06-13.1; P < .001). Mortality was higher for admissions with potentially delayed appropriate antibiotics than for those with appropriate antibiotics (48% vs 20%; P < .001). Potential delay in antibiotics was also an independent predictor of mortality in multivariable analysis (OR, 13.8; 95% CI, 5.27-35.9; P < .001). INTERPRETATION: BSIs were common and independently associated with increased morbidity and mortality. Delays in administration of appropriate antimicrobials were identified as an important factor in hospital morbidity and mortality. These findings may have important implications for our current practice of empiric antibiotic treatment in HSCT patients.