Centralized Multi-Patient Dashboards' Impact on ICU Clinician Performance and Satisfaction: A Systematic Review.

BACKGROUND: Intensive care unit (ICU) clinicians encounter frequent challenges with managing vast amounts of fragmented data while caring for multiple critically ill patients simultaneously. This may lead to increased provider cognitive load that may jeopardize patient safety. OBJECTIVES: This systematic review assesses the impact of centralized multi-patient dashboards on ICU clinician performance, perceptions regarding the use of these tools, and patient outcomes. METHODS: A literature search was conducted on February 9, 2023, using the EBSCO CINAHL, Cochrane Central Register of Controlled Trials, Embase, IEEE Xplore, MEDLINE, Scopus, and Web of Science Core Collection databases. Eligible studies that included ICU clinicians as participants and tested the effect of dashboards designed for use by multiple users to manage multiple patients on user performance and/or satisfaction compared to the standard practice. We narratively synthesized eligible studies following the SWiM guidelines. Studies were grouped based on dashboard type and outcomes assessed. RESULTS: The search yielded a total of 2407 studies. Five studies met inclusion criteria and were included. Among these, three studies evaluated interactive displays in the ICU, one study assessed two dashboards in the pediatric ICU (PICU), and one study examined centralized monitor in the PICU. Most studies reported several positive outcomes, including reductions in data gathering time before rounds, a decrease in misrepresentations during multidisciplinary rounds, improved daily documentation compliance, faster decision-making, and user satisfaction. One study did not report any significant association. CONCLUSIONS: The multi-patient dashboards were associated with improved ICU clinician performance and were positively perceived in most of the included studies. The risk of bias was high and the certainty of evidence was very low, due to inconsistencies, imprecision, indirectness in the outcome measure and methodological limitations. Designing and evaluating multi-patient tools using robust research methodologies is an important focus for future research.

Individualized prediction of critical illness in older adults: Validation of an elders risk assessment model.

BACKGROUND: The electronic health record (EHR) presents new opportunities for the timely identification of patients at high risk of critical illness and the implementation of preventive strategies. This study aims to externally validate an EHR-based Elders Risk Assessment (ERA) score to identify older patients at high risk of future critical illness during a primary care visit. METHODS: This historical cohort study included patients aged ≥65 years who had primary care visits at Mayo Clinic Rochester, MN, between July 2019 and December 2021. The ERA score at the time of the primary care visit was used to predict critical illness, defined as death or ICU admission within 1 year of the visit. RESULTS: A total of 12,885 patients were included in the analysis. The median age at the time of the primary care visit was 75 years, with 44.6% being male. 93.7% of participants were White, and 64.2% were married. The median (25th, 75th percentile) ERA score was 4 (0, 9). 11.3% of study participants were admitted to the ICU or died within 1 year of the visit. The ERA score predicted critical illness within 1 year of a primary care visit with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.83-0.85), which indicates good discrimination. An ERA score of 9 was identified as optimal for implementing and testing potential preventive strategies, with the odds ratio of having the primary outcome in patients with ERA score ≥9 being 11.33 (95%CI 9.98-12.87). CONCLUSIONS: This simple EHR-based risk assessment model can predict critical illness within 1 year of primary care visits in older patients. The findings of this study can serve as a basis for testing and implementation of preventive strategies to promote the well-being of older adults at risk of critical illness and its consequences.

Pulmonary function as a continuum of risk: critical care utilization and survival after allogeneic hematopoietic stem cell transplantation - a multicenter cohort study.

Abnormal pre-transplant pulmonary function tests (PFTs) are associated with reduced survival after allogeneic HCT. Existing scoring systems consider risk dichotomously, attributing risk only to those with abnormal lung function. In a multicenter cohort of 1717 allo-HCT recipients, we examined the association between pre-transplant PFT measures and need for ICU admission (120d), frequency of mechanical ventilation (120d) and overall survival (5 y). Predictive models were developed and validated using Cox proportional hazards, incorporating age, FEV1 (forced expiratory volume in 1-second) and diffusing capacity (DLCO). In univariate analysis, hazard ratios for each outcome (95% CI) were: mechanical ventilation (FEV1: 0.60 [0.52-0.69], DLCO: 0.69 [0.61-0.77], p < 0.001), ICU admission (FEV1: 0.74 [0.67-0.82], DLCO: 0.79 [0.72-0.86], p < 0.001) and overall survival (FEV1: HR 0.87 [0.81-0.94], DLCO: 0.83 [0.77-0.89], p < 0.001). A multivariable Cox model was developed and compared to the HCT-CI Pulmonary score in a validation cohort. This model was better at predicting need for ICU admission and mechanical ventilation, while both models predicted overall survival (p < 0.001). In conclusion, the risk conferred by pre-transplant pulmonary function should be considered in a continuous rather than dichotomous manner. A more granular prognostication system can better inform risk of critical care utilization in the early post-HCT period.

Early Prevention of Critical Illness in Older Adults: Adaptation and Pilot Testing of an Electronic Risk Score and Checklist.

OBJECTIVE: Given limited critical care resources and an aging population, early interventions to prevent critical illness are vital. In this work, we measured post-implementation outcomes after introducing a novel electronic scoring system (Elders Risk Assessment-ERA) and a risk-factor checklist, Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN), to detect older patients at high risk of critical illness in a primary care setting. METHODS: The study was conducted at a family medicine clinic in Kasson, MN. The ADAPT-ITT framework was used to modify the CERTAIN checklist for primary care during 2 co-design workshops involving interdisciplinary clinicians, held in April 2023. The ERA score and modified CERTAIN checklist were implemented between May and July 2023 and identify and assess all patients age ≥60 years at risk of critical illness during their primary care visits. Implementation outcomes were evaluated at the end of the study via an anonymous survey and EHR data extraction. RESULTS: Fourteen clinicians participated in 2 co-design workshops. A total of 19 clinicians participated in a post-pilot survey. All survey items were rated on a 5-point Likert type scale. Mean acceptability of the ERA score and checklist was rated 3.35 (SD = 0.75) and 3.09 (SD = 0.64), respectively. Appropriateness had a mean rating of 3.38 (SD = 0.82) for the ERA score and 3.19 (SD = 0.59) for the checklist. Mean feasibility was rated 3.38(SD = 0.85) and 2.92 (SD = 0.76) for the ERA score and checklist, respectively. The adoption rate was 50% (19/38) among clinicians, but the reach was low at 17% (49/289) of eligible patients. CONCLUSIONS: This pilot study evaluated the implementation of an intervention that introduced the ERA score and CERTAIN checklist into a primary care practice. Results indicate moderate acceptability, appropriateness, and feasibility of the ERA score, and similar ratings for the checklist, with slightly lower feasibility. While checklist adoption was moderate, reach was limited, indicating inconsistent use. RECOMMENDATIONS: We plan to use the open-ended resurvey responses to further modify the CERTAIN-FM checklist and implementation process. The ADAPT-ITT framework is a useful model for adapting the checklist to meet the primary care clinician needs.

Temporal trends in critical care utilization and outcomes in allogeneic hematopoietic stem cell transplant recipients.

Historically, the prognosis of allogeneic hematopoietic stem cell transplant (allo-HCT) recipients who require intensive care unit (ICU) admission has been poor. We aimed to describe the epidemiological trends of ICU utilization and outcomes in allo-HCT patients. We conducted a retrospective cohort study including adults (≥ 18) undergoing allo-HCT between 01/01/2005 and 31/12/2020 at Mayo Clinic, Rochester. Temporal trends in outcomes were assessed by robust linear regression modelling. Risk factors for hospital mortality were chosen a priori and assessed with multivariable logistic regression modelling. Of 1,249 subjects, there were 486 ICU admissions among 287 individuals. Although older patients underwent allo-HCT (1.64 [95% CI: 1.11 to 2.45] years per year; P = 0.025), there was no change in ICU utilization over time (P = 0.91). The ICU and hospital mortality rates were 19.2% (55/287) and 28.2% (81/287), respectively. There was a decline in ICU mortality (-0.38% [95% CI: -0.70 to -0.06%] per year; P = 0.035). The 1-year post-HCT mortality for those requiring ICU admission was 56.1% (161/287), with no significant difference over time, versus 15.8% (141/891, 71 missing) among those who did not. The frequency and duration of invasive mechanical ventilation (IMV) declined. In multivariable analyses, higher serum lactate, higher sequential organ failure assessment (SOFA) scores, acute respiratory distress (ARDS), and need for IMV were associated with greater odds of hospital mortality. Over time, rates of ICU utilization have remained stable, despite increasing patient age. Several trends suggest improvement in outcomes, notably lower ICU mortality and frequency of IMV. However, long-term survival remains unchanged. Further work is needed to improve long-term outcomes in this population.

Lung Injury Prediction Model in Bone Marrow Transplantation: A Multicenter Cohort Study.

Rationale: Pulmonary complications contribute significantly to nonrelapse mortality following hematopoietic stem cell transplantation (HCT). Identifying patients at high risk can help enroll such patients into clinical studies to better understand, prevent, and treat posttransplantation respiratory failure syndromes. Objectives: To develop and validate a prediction model to identify those at increased risk of acute respiratory failure after HCT. Methods: Patients underwent HCT between January 1, 2019, and December 31, 2021, at one of three institutions. Those treated in Rochester, MN, formed the derivation cohort, and those treated in Scottsdale, AZ, or Jacksonville, FL, formed the validation cohort. The primary outcome was the development of acute respiratory distress syndrome (ARDS), with secondary outcomes including the need for invasive mechanical ventilation (IMV) and/or noninvasive ventilation (NIV). Predictors were based on prior case-control studies. Measurements and Main Results: Of 2,450 patients undergoing stem cell transplantation, there were 1,718 hospitalizations (888 patients) in the training cohort and 1,005 hospitalizations (470 patients) in the test cohort. A 22-point model was developed, with 11 points from prehospital predictors and 11 points from posttransplantation or early (<24-h) in-hospital predictors. The model performed well in predicting ARDS (C-statistic, 0.905; 95% confidence interval [CI], 0.870-0.941) and the need for IMV and/or NIV (C-statistic, 0.863; 95% CI, 0.828-0.898). The test cohort differed markedly in demographic, medical, and hematologic characteristics. The model also performed well in this setting in predicting ARDS (C-statistic, 0.841; 95% CI, 0.782-0.900) and the need for IMV and/or NIV (C-statistic, 0.872; 95% CI, 0.831-0.914). Conclusions: A novel prediction model incorporating data elements from the pretransplantation, posttransplantation, and early in-hospital domains can reliably predict the development of post-HCT acute respiratory failure.

Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review.

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

Who needs clinician attention first? A qualitative study of critical care clinicians' needs that enable the prioritization of care for populations of acutely ill patients.

BACKGROUND: To adequately care for groups of acutely ill patients, clinicians maintain situational awareness to identify the most acute needs within the entire intensive care unit (ICU) population through constant reappraisal of patient data from electronic medical record and other information sources. Our objective was to understand the information and process requirements of clinicians caring for multiple ICU patients and how this information is used to support their prioritization of care among populations of acutely ill patients. Additionally, we wanted to gather insights on the organization of an Acute care multi-patient viewer (AMP) dashboard. METHODS: We conducted and audio-recorded semi-structured interviews of ICU clinicians who had worked with the AMP in three quaternary care hospitals. The transcripts were analyzed with open, axial, and selective coding. Data was managed using NVivo 12 software. RESULTS: We interviewed 20 clinicians and identified 5 main themes following data analysis: (1) strategies used to enable patient prioritization, (2) strategies used for optimizing task organization, (3) information and factors helpful for situational awareness within the ICU, (4) unrecognized or missed critical events and information, and (5) suggestions for AMP organization and content. Prioritization of critical care was largely determined by severity of illness and trajectory of patient clinical status. Important sources of information were communication with colleagues from the previous shift, bedside nurses, and patients, data from the electronic medical record and AMP, and physical presence and availability in the ICU. CONCLUSIONS: This qualitative study explored ICU clinicians' information and process requirements to enable the prioritization of care among populations of acutely ill patients. Timely recognition of patients who need priority attention and intervention provides opportunities for improvement of critical care and for preventing catastrophic events in the ICU.

Evaluation of Digital Health Strategy to Support Clinician-Led Critically Ill Patient Population Management: A Randomized Crossover Study.

To investigate whether a novel acute care multipatient viewer (AMP), created with an understanding of clinician information and process requirements, could reduce time to clinical decision-making among clinicians caring for populations of acutely ill patients compared with a widely used commercial electronic medical record (EMR). DESIGN: Single center randomized crossover study. SETTING: Quaternary care academic hospital. SUBJECTS: Attending and in-training critical care physicians, and advanced practice providers. INTERVENTIONS: AMP. MEASUREMENTS AND MAIN RESULTS: We compared ICU clinician performance in structured clinical task completion using two electronic environments-the standard commercial EMR (Epic) versus the novel AMP in addition to Epic. Twenty subjects (10 pairs of clinicians) participated in the study. During the study session, each participant completed the tasks on two ICUs (7-10 beds each) and eight individual patients. The adjusted time for assessment of the entire ICU and the adjusted total time to task completion were significantly lower using AMP versus standard commercial EMR (-6.11; 95% CI, -7.91 to -4.30 min and -5.38; 95% CI, -7.56 to -3.20 min, respectively; p < 0.001). The adjusted time for assessment of individual patients was similar using both the EMR and AMP (0.73; 95% CI, -0.09 to 1.54 min; p = 0.078). AMP was associated with a significantly lower adjusted task load (National Aeronautics and Space Administration-Task Load Index) among clinicians performing the task versus the standard EMR (22.6; 95% CI, -32.7 to -12.4 points; p < 0.001). There was no statistically significant difference in adjusted total errors when comparing the two environments (0.68; 95% CI, 0.36-1.30; p = 0.078). CONCLUSIONS: When compared with the standard EMR, AMP significantly reduced time to assessment of an entire ICU, total time to clinical task completion, and clinician task load. Additional research is needed to assess the clinicians' performance while using AMP in the live ICU setting.

Diagnostic error among vulnerable populations presenting to the emergency department with cardiovascular and cerebrovascular or neurological symptoms: a systematic review.

BACKGROUND: Diagnostic error (DE) is a common problem in clinical practice, particularly in the emergency department (ED) setting. Among ED patients presenting with cardiovascular or cerebrovascular/neurological symptoms, a delay in diagnosis or failure to hospitalise may be most impactful in terms of adverse outcomes. Minorities and other vulnerable populations may be at higher risk of DE. We aimed to systematically review studies reporting the frequency and causes of DE in under-resourced patients presenting to the ED with cardiovascular or cerebrovascular/neurological symptoms. METHODS: We searched EBM Reviews, Embase, Medline, Scopus and Web of Science from 2000 through 14 August 2022. Data were abstracted by two independent reviewers using a standardised form. The risk of bias (ROB) was assessed using the Newcastle-Ottawa Scale, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. RESULTS: Of the 7342 studies screened, we included 20 studies evaluating 7436,737 patients. Most studies were conducted in the USA, and one study was multicountry. 11 studies evaluated DE in patients with cerebrovascular/neurological symptoms, 8 studies with cardiovascular symptoms and 1 study examined both types of symptoms. 13 studies investigated missed diagnoses and 7 studies explored delayed diagnoses. There was significant clinical and methodological variability, including heterogeneity of DE definitions and predictor variable definitions as well as methods of DE assessment, study design and reporting.Among the studies evaluating cardiovascular symptoms, black race was significantly associated with higher odds of DE in 4/6 studies evaluating missed acute myocardial infarction (AMI)/acute coronary syndrome (ACS) diagnosis compared with white race (OR from 1.18 (1.12-1.24) to 4.5 (1.8-11.8)). The association between other analysed factors (ethnicity, insurance and limited English proficiency) and DE in this domain varied from study to study and was inconclusive.Among the studies evaluating DE in patients with cerebrovascular/neurological symptoms, no consistent association was found indicating higher or lower odds of DE. Although some studies showed significant differences, these were not consistently in the same direction.The overall ROB was low for most included studies; however, the certainty of evidence was very low, mostly due to serious inconsistency in definitions and measurement approaches across studies. CONCLUSIONS: This systematic review demonstrated consistent increased odds of missed AMI/ACS diagnosis among black patients presenting to the ED compared with white patients in most studies. No consistent associations between demographic groups and DE related to cerebrovascular/neurological diagnoses were identified. More standardised approaches to study design, measurement of DE and outcomes assessment are needed to understand this problem among vulnerable populations. TRIAL REGISTRATION NUMBER: The study protocol was registered in the International Prospective Register of Systematic Reviews PROSPERO 2020 CRD42020178885 and is available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178885.

Time to diagnostic certainty for saddle pulmonary embolism in hospitalized patients.

There is a lack of diagnostic performance measures associated with pulmonary embolism (PE). We aimed to explore the concept of the time to diagnostic certainty, which we defined as the time interval that elapses between first presentation of a patient to a confirmed PE diagnosis with computed tomography pulmonary angiogram (CT PA). This approach could be used to highlight variability in health system diagnostic performance, and to select patient outliers for structured chart review in order to identify underlying contributors to diagnostic error or delay. We performed a retrospective observational study at academic medical centers and associated community-based hospitals in one health system, examining randomly selected adult patients admitted to study sites with a diagnosis of acute saddle PE. One hundred patients were randomly selected from 340 patients discharged with saddle PE. Twenty-four patients were excluded. Among the 76 included patients, time to diagnostic certainty ranged from 1.5 to 310 hours. We found that 73/76 patients were considered to have PE present on admission (CT PA ≤ 48 hours). The proportion of patients with PE present on admission with time to diagnostic certainty of > 6 hours was 26% (19/73). The median (IQR) time to treatment (thrombolytics/anticoagulants) was 3.5 (2.5-5.1) hours among the 73 patients. The proportion of patients with PE present on admission with treatment delays of > 6 hours was 16% (12/73). Three patients acquired PE during hospitalization (CT PA > 48 hours). In this study, we developed and successfully tested the concept of time to diagnostic certainty for saddle PE.

Post-Transplant and In-Hospital Risk Factors for ARDS After Hematopoietic Stem Cell Transplantation.

BACKGROUND: ARDS is a serious complication of hematopoietic stem cell transplant (HSCT). Pre-transplant risk factors for developing ARDS after HSCT have been recently identified. The objective of this study was to better understand post-transplant risk factors for developing ARDS after HSCT. METHODS: This was a nested case-control study. ARDS cases were matched to hospitalized non-ARDS controls by age, type of transplantation (allogeneic vs autologous), and time from transplantation. In a conditional logistic regression model, any potential risk factors were adjusted a priori for risk factors known to be associated with ARDS development. RESULTS: One hundred and seventy ARDS cases were matched 1:1 to non-ARDS hospitalized controls. Pre-admission, cases were more likely to be on steroids (odds ratio [OR] 1.90 [1.13-3.19], P = .02). At time of admission, cases had lower platelet count (OR 0.95 [0.91-0.99], P = .02), lower bicarbonate (OR 0.94 [0.88-0.99], P = .035), and higher creatinine (OR 1.91 [1.23-2.94], P = .004). During the first 24 h after admission, cases were more likely to have received transfusion (OR 2.41 [1.48-3.94], P < .001), opioids (OR 2.94 [1.67-5.18], P < .001), and have greater fluid administration (OR 1.52 [1.30-1.78], P < .001). During the hospitalization, ARDS cases had higher temperature (OR 1.77 [1.34-2.33], P < .001) and higher breathing frequency (OR 1.52 [1.33-1.74], P < .001). ARDS cases were more likely to have had sepsis (OR 68.0 [15.2-301.7], P < .001), bloodstream infection (OR 4.59 [2.46-8.57], P < .001), and pneumonia (OR 9.76 [5.01-19.00], P < .001). CONCLUSIONS: Several post-transplant predictors of ARDS development specific to the HSCT population were identified in the pre-hospital and early in-hospital domains. These findings can provide insights into causal mechanisms of ARDS development and be used to develop HSCT-specific risk prediction models.

Contributors to Diagnostic Error or Delay in the Acute Care Setting: A Survey of Clinical Stakeholders.

Diagnostic error or delay (DEOD) is common in the acute care setting and results in poor patient outcomes. Many factors contribute to DEOD, but little is known about how contributors may differ across acute care areas and professional roles. As part of a sequential exploratory mixed methods research study, we surveyed acute care clinical stakeholders about the frequency with which different factors contribute to DEOD. Survey respondents could also propose solutions in open text fields. N = 220 clinical stakeholders completed the survey. Care Team Interactions, Systems and Process, Patient, Provider, and Cognitive factors were perceived to contribute to DEOD with similar frequency. Organization and Infrastructure factors were perceived to contribute to DEOD significantly less often. Responses did not vary across acute care setting. Physicians perceived Cognitive factors to contribute to DEOD more frequently compared to those in other roles. Commonly proposed solutions included: technological solutions, organization level fixes, ensuring staff know and are encouraged to work to the full scope of their role, and cultivating a culture of collaboration and respect. Multiple factors contribute to DEOD with similar frequency across acute care areas, suggesting the need for a multi-pronged approach that can be applied across acute care areas.

The Impact of Health Information Technology for Early Detection of Patient Deterioration on Mortality and Length of Stay in the Hospital Acute Care Setting: Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the impact of health information technology (HIT) for early detection of patient deterioration on patient mortality and length of stay (LOS) in acute care hospital settings. DATA SOURCES: We searched MEDLINE and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus from 1990 to January 19, 2021. STUDY SELECTION: We included studies that enrolled patients hospitalized on the floor, in the ICU, or admitted through the emergency department. Eligible studies compared HIT for early detection of patient deterioration with usual care and reported at least one end point of interest: hospital or ICU LOS or mortality at any time point. DATA EXTRACTION: Study data were abstracted by two independent reviewers using a standardized data extraction form. DATA SYNTHESIS: Random-effects meta-analysis was used to pool data. Among the 30 eligible studies, seven were randomized controlled trials (RCTs) and 23 were pre-post studies. Compared with usual care, HIT for early detection of patient deterioration was not associated with a reduction in hospital mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT interventions demonstrated a significant association with improved hospital mortality for the entire study cohort (odds ratio, 0.78 [95% CI, 0.70-0.87]) and reduced hospital LOS overall. CONCLUSIONS: HIT for early detection of patient deterioration in acute care settings was not significantly associated with improved mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT was associated with improved hospital mortality and LOS; however, these results should be interpreted with caution. The differences in patient outcomes between the findings of the RCTs and pre-post studies may be secondary to confounding caused by unmeasured improvements in practice and workflow over time.

What Contributes to Diagnostic Error or Delay? A Qualitative Exploration Across Diverse Acute Care Settings in the United States.

OBJECTIVES: Diagnostic error and delay is a prevalent and impactful problem. This study was part of a mixed-methods approach to understand the organizational, clinician, and patient factors contributing to diagnostic error and delay among acutely ill patients within a health system, as well as recommendations for the development of tailored, targeted, feasible, and effective interventions. METHODS: We did a multisite qualitative study using focus group methodology to explore the perspectives of key clinician stakeholders. We used a conceptual framework that characterized diagnostic error and delay as occurring within 1 of 3 stages of the patient's diagnostic journey-critical information gathering, synthesis of key information, and decision making and communication. We developed our moderator guide based on the sociotechnical frameworks previously described by Holden and Singh for understanding noncognitive factors that lead to diagnostic error and delay. Deidentified focus group transcripts were coded in triplicate and to consensus over a series of meetings. A final coded data set was then uploaded into NVivo software. The data were then analyzed to generate overarching themes and categories. RESULTS: We recruited a total of 64 participants across 4 sites from emergency departments, hospital floor, and intensive care unit settings into 11 focus groups. Clinicians perceive that diverse organizational, communication and coordination, individual clinician, and patient factors interact to impede the process of making timely and accurate diagnoses. CONCLUSIONS: This study highlights the complex sociotechnical system within which individual clinicians operate and the contributions of systems, processes, and institutional factors to diagnostic error and delay.

Clinical Characteristics, Treatment, and Outcomes of Critically Ill Patients With COVID-19: A Scoping Review.

A growing number of studies on coronavirus disease 2019 (COVID-19) are becoming available, but a synthesis of available data focusing on the critically ill population has not been conducted. We performed a scoping review to synthesize clinical characteristics, treatment, and clinical outcomes among critically ill patients with COVID-19. Between January 1, 2020, and May 15, 2020, we identified high-quality clinical studies describing critically ill patients with a sample size of greater than 20 patients by performing daily searches of the World Health Organization and LitCovid databases on COVID-19. Two reviewers independently reviewed all abstracts (2785 unique articles), full text (218 articles), and abstracted data (92 studies). The 92 studies included 61 from Asia, 16 from Europe, 10 from North and South America, and 5 multinational studies. Notable similarities among critically ill populations across all regions included a higher proportion of older males infected and with severe illness, high frequency of comorbidities (hypertension, diabetes, and cardiovascular disease), abnormal chest imaging findings, and death secondary to respiratory failure. Differences in regions included newly identified complications (eg, pulmonary embolism) and epidemiological risk factors (eg, obesity), less chest computed tomography performed, and increased use of invasive mechanical ventilation (70% to 100% vs 15% to 47% of intensive care unit patients) in Europe and the United States compared with Asia. Future research directions should include proof-of-mechanism studies to better understand organ injuries and large-scale collaborative clinical studies to evaluate the efficacy and safety of antivirals, antibiotics, interleukin 6 receptor blockers, and interferon. The current established predictive models require further verification in other regions outside China.

Pretransplant Risk Factors Can Predict Development of Acute Respiratory Distress Syndrome after Hematopoietic Stem Cell Transplantation.

Rationale: Acute respiratory distress syndrome (ARDS) is a common complication after hematopoietic stem cell transplantation (HCT) and is a major contributor to nonrelapse mortality. Objectives: To better understand pretransplant risk factors for developing ARDS after HCT. Methods: This is a single-center observational study comparing risk factors for ARDS development in 164 patients who went on to develop post-HCT ARDS compared with 492 patients who did not. The patients were matched 1 to 3 on age, sex, type of transplant (allogeneic vs. autologous), and underlying disease. Pertinent risk factors were analyzed separately in multivariable conditional logistic regression after adjustment for a priori variables known to be associated with ARDS development. Results: Patients with ARDS were more likely to have a lower pretransplant pulmonary function as measured by forced vital capacity (FVC) (odds ratio [OR], 0.54 [0.42-0.70] per liter increase in FVC; P < 0.001), forced expiratory volume in one second (FEV1) (OR, 0.52 [0.38-0.71] per liter increase in FEV1; P < 0.001) and diffusing capacity (OR, 0.92 [0.88-0.96] per ml/min/mm Hg increase in diffusing capacity; P < 0.001). Several laboratory indices were predictive of subsequent ARDS development including elevated AST (aspartate aminotransferase) (OR, 1.01 [1.00-1.01]; P < 0.008), lower serum albumin (OR, 0.44 [0.30-0.66]; P < 0.001), lower pretransplant hemoglobin (OR, 0.82 [0.73-0.92]; P = 0.001), and lower leukocyte count (OR, 0.88 [0.79-0.99]; P < 0.03). Patients who went on to develop ARDS were more likely to have been hospitalized in the year before the transplant (OR, 1.11 [1.04-1.20]; P = 0.003), and required invasive or noninvasive ventilation during that hospitalization. Lastly, patients with ARDS were significantly more likely to have received carboplatin, thalidomide, methotrexate, and cisplatin than the non-ARDS control subjects. Conclusions: Several risk factors for developing ARDS after HCT are identifiable at the time of transplantation, well before the development of critical illness and ARDS. The identification of risk factors long before ARDS develops is relatively unique to the HCT population. Further work is needed to develop usable risk prediction tools in this setting.

Biomarker-Concordant Steroid Use in Critically Ill Patients with Pneumonia.

OBJECTIVES: To evaluate the frequency and consequences of prescribing corticosteroids for pneumonia in a biomarker-concordant manner. PATIENTS AND METHODS: This was a single-center retrospective cohort study of adults with pneumonia admitted to the medical intensive care unit (ICU) at Mayo Clinic in Rochester, Minnesota, between January 1, 2009, and June 30, 2014. Steroid use was "biomarker concordant" if given when C-reactive protein (CRP) was ≥150 mg/L or withheld when CRP was <150 mg/L, and vice versa for biomarker discordant. RESULTS: Of 3481 ICU admissions with community-acquired pneumonia, 169 (4.9%) had CRPs measured within 48 hours of admission to the ICU. Steroid use in the ICU was biomarker concordant in 88 (52%) patients and biomarker discordant in 81 (48%) patients. Biomarker-concordant steroid use was associated with faster resolution of lung injury: median fraction of inspired oxygen on day 3 (0.4 [0.3, 0.5] vs 0.3 [0.21, 0.4], P=.005), day 4 (0.35 [0.3, 0.5] vs 0.28 [0.21, 0.38], P=<.001), and day 5 (0.30 [0.24, 0.45] vs 0.28 [0.21, 0.40], P=.03), and increased ICU (3.5; 95% CI, 0.5 to 6.4, P=.02), and hospital-free days (3.6; 95% CI, 0.4 to 6.8, P=.03) on multivariate analysis. CONCLUSIONS: In critically ill patients with community-acquired pneumonia, steroid use is rarely biomarker informed and often discordant with inflammatory biomarker levels. Biomarker-concordant steroid use was associated with a faster recovery of hypoxemia and increased ICU- and hospital-free days. Future well-designed prospective studies are justified to test the potential value of biomarker-concordant steroid therapy.

Hemodynamic profiles following digoxin use in patients with sepsis in the ICU.

PURPOSE: To explore the impact of digoxin on hemodynamic parameters in patients with sepsis and tachycardia admitted to the intensive care unit. MATERIALS AND METHODS: Retrospective review of adult patients admitted to the medical and mixed ICU at Mayo Clinic Rochester, Minnesota from March 2008 to February 2018, initiated on digoxin within 24 h of ICU stay. Hemodynamic parameters were reviewed before digoxin administration and at 6, 12 and 24 h after. Adverse events including new onset conduction abnormalities or arrhythmias during the first 48 h after digoxin administration were reviewed by a critical care cardiologist. RESULTS: Study included 180 patients. We observed significant decrease in heart rate from 124 (115-138) beats/min 1 h before digoxin to 101 (87-117) 6 h after digoxin and 94 (84-112) 12 h after (p < .01). Median systolic blood pressure increased from 100 (91-112) mm Hg 1 h before to 110 (100-122) (p < .01) and 111 (103-124) at 6 and 12 h respectively after digoxin. CONCLUSIONS: Early digoxin administration in patients with sepsis and tachycardia is uncommon but associated with improvements of hemodynamic parameters. These preliminary results will help formulate future hypotheses for focused trials on utility, efficacy and safety of digoxin in sepsis.