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Severe cases of COVID-19 pneumonia can lead to acute respiratory distress syndrome (ARDS). Release of interleukin (IL)-33, an epithelial-derived alarmin, and IL-33/ST2 pathway activation are linked with ARDS development in other viral infections. IL-22, a cytokine that modulates innate immunity through multiple regenerative and protective mechanisms in lung epithelial cells, is reduced in patients with ARDS. This study aimed to evaluate safety and efficacy of astegolimab, a human immunoglobulin G2 monoclonal antibody that selectively inhibits the IL-33 receptor, ST2, or efmarodocokin alfa, a human IL-22 fusion protein that activates IL-22 signaling, for treatment of severe COVID-19 pneumonia.Phase 2, double-blind, placebo-controlled study (COVID-astegolimab-IL).Hospitals.Hospitalized adults with severe COVID-19 pneumonia.Patients were randomized to receive IV astegolimab, efmarodocokin alfa, or placebo, plus standard of care. The primary endpoint was time to recovery, defined as time to a score of 1 or 2 on a 7-category ordinal scale by day 28.The study randomized 396 patients. Median time to recovery was 11 days (hazard ratio [HR], 1.01 d; p = 0.93) and 10 days (HR, 1.15 d; p = 0.38) for astegolimab and efmarodocokin alfa, respectively, versus 10 days for placebo. Key secondary endpoints (improved recovery, mortality, or prevention of worsening) showed no treatment benefits. No new safety signals were observed and adverse events were similar across treatment arms. Biomarkers demonstrated that both drugs were pharmacologically active.Treatment with astegolimab or efmarodocokin alfa did not improve time to recovery in patients with severe COVID-19 pneumonia.

Supplemental Digital Content is available in the text., Given the urgent need for coronavirus disease 2019 therapeutics, early in the pandemic the Accelerating Coronavirus Disease 2019 Therapeutic Interventions and Vaccines (ACTIV) public-private partnership rapidly designed a unique therapeutic agent intake and assessment process for candidate treatments of coronavirus disease 2019. These treatments included antivirals, immune modulators, severe acute respiratory syndrome coronavirus 2 neutralizing antibodies, and organ-supportive treatments at both the preclinical and clinical stages of development. The ACTIV Therapeutics-Clinical Working Group Agent Prioritization subgroup established a uniform data collection process required to perform an assessment of any agent type using review criteria that were identified and differentially weighted for each agent class. The ACTIV Therapeutics-Clinical Working Group evaluated over 750 therapeutic agents with potential application for coronavirus disease 2019 and prioritized promising candidates for testing within the master protocols conducted by ACTIV. In addition, promising agents among preclinical candidates were selected by ACTIV to be matched with laboratories that could assist in executing rigorous preclinical studies. Between April 14, 2020, and May 31, 2021, the Agent Prioritization subgroup advanced 20 agents into the Accelerating Coronavirus Disease 2019 Therapeutic Interventions and Vaccines master protocols and matched 25 agents with laboratories to assist with preclinical testing.

The pathophysiology of the inflammatory response after major trauma is complex, and the magnitude correlates with severity of tissue injury and outcomes. Study of infection-mediated immune pathways has demonstrated that cellular microRNAs may modulate the inflammatory response. The authors hypothesize that the expression of microRNAs would correlate to complicated recoveries in polytrauma patients (PtPs). METHODS: Polytrauma patients enrolled in the prospective observational Tissue and Data Acquisition Protocol with Injury Severity Score of >15 were selected for this study. Polytrauma patients were divided into complicated recoveries and uncomplicated recovery groups. Polytrauma patients' blood samples were obtained at the time of admission (T0). Established biomarkers of systemic inflammation, including cytokines and chemokines, were measured using multiplexed Luminex-based methods, and novel microRNAs were measured in plasma samples using multiplex RNA hybridization. RESULTS: Polytrauma patients (n = 180) had high Injury Severity Score (26 [20–34]) and complicated recovery rate of 33%. MicroRNAs were lower in PtPs at T0 compared with healthy controls, and bivariate analysis demonstrated that variations of microRNAs correlated with age, race, comorbidities, venous thromboembolism, pulmonary complications, complicated recovery, and mortality. Positive correlations were noted between microRNAs and interleukin 10, vascular endothelial growth factor, Acute Physiology and Chronic Health Evaluation, and Sequential Organ Failure Assessment scores. Multivariable Lasso regression analysis of predictors of complicated recovery based on microRNAs, cytokines, and chemokines revealed that miR-21-3p and monocyte chemoattractant protein-1 were predictive of complicated recovery with an area under the curve of 0.78. CONCLUSION: Systemic microRNAs were associated with poor outcomes in PtPs, and results are consistent with previously described trends in critically ill patients. These early biomarkers of inflammation might provide predictive utility in early complicated recovery diagnosis and prognosis. Because of their potential to regulate immune responses, microRNAs may provide therapeutic targets for immunomodulation. LEVEL OF EVIDENCE: Diagnostic Tests/Criteria; Level II.

BACKGROUND The timing of coverage of an open wound is based on heavily on clinical gestalt. DoD's Surgical Critical Care Initiative created a clinical decision support tool that predicts wound closure success using clinical and biomarker data. The military uses a regimented protocol consisting of serial washouts and debridements. While decisions around wound closure in civilian centers are subject to the same clinical parameters, preclosure wound management is, generally, much more variable. We hypothesized that the variability in management would affect local biomarker expression within these patients. METHODS We compared data from 116 wounds in 73 military patients (MP) to similar data from 88 wounds in 78 civilian patients (CP). We used Wilcoxon rank-sum tests to assess concentrations of 32 individual biomarkers taken from wound effluent. Along with differences in the debridement frequency, we focused on these local biomarkers in MP and CP at both the first washout and the washout performed just prior to attempted closure. RESULTS On average, CP waited longer from the time of injury to closure (21.9 days, vs. 11.6 days, p < 0.0001) but had a similar number of washouts (3.86 vs. 3.44, p = 0.52). When comparing the wound effluent between the two populations, they had marked biochemical differences both when comparing the results at the first washout and at the time of closure. However, in a subset of civilian patients whose average number of days between washouts was never more than 72 hours, these differences ceased to be significant for most variables. CONCLUSION There were significant differences in the baseline biochemical makeup of wounds in the CP and MP. These differences could be eliminated if both were treated under similar wound care paradigms. Variations in therapy affect not only outcomes but also the actual biochemical makeup of wounds. LEVEL OF EVIDENCE Therapeutic, level IV.

OBJECTIVES To train a model to predict vasopressor use in ICU patients with sepsis and optimize external performance across hospital systems using domain adaptation, a transfer learning approach. DESIGN Observational cohort study. SETTING Two academic medical centers from January 2014 to June 2017. PATIENTS Data were analyzed from 14,512 patients (9,423 at the development site and 5,089 at the validation site) who were admitted to an ICU and met Center for Medicare and Medicaid Services definition of severe sepsis either before or during the ICU stay. Patients were excluded if they never developed sepsis, if the ICU length of stay was less than 8 hours or more than 20 days or if they developed shock up to the first 4 hours of ICU admission. MEASUREMENTS AND MAIN RESULTS Forty retrospectively collected features from the electronic medical records of adult ICU patients at the development site (four hospitals) were used as inputs for a neural network Weibull-Cox survival model to derive a prediction tool for future need of vasopressors. Domain adaptation updated parameters to optimize model performance in the validation site (two hospitals), a different healthcare system over 2,000 miles away. The cohorts at both sites were randomly split into training and testing sets (80% and 20%, respectively). When applied to the test set in the development site, the model predicted vasopressor use 4-24 hours in advance with an area under the receiver operator characteristic curve, specificity, and positive predictive value ranging from 0.80 to 0.81, 56.2% to 61.8%, and 5.6% to 12.1%, respectively. Domain adaptation improved performance of the model to predict vasopressor use within 4 hours at the validation site (area under the receiver operator characteristic curve 0.81 [CI, 0.80-0.81] from 0.77 [CI, 0.76-0.77], p < 0.01; specificity 59.7% [CI, 58.9-62.5%] from 49.9% [CI, 49.5-50.7%], p < 0.01; positive predictive value 8.9% [CI, 8.5-9.4%] from 7.3 [7.1-7.4%], p < 0.01). CONCLUSIONS Domain adaptation improved performance of a model predicting sepsis-associated vasopressor use during external validation.

OBJECTIVES: To determine the impact of coronavirus disease 2019 on burnout syndrome in the multiprofessional ICU team and to identify factors associated with burnout syndrome. DESIGN: Longitudinal, cross-sectional survey. SETTING: All adult ICUs within an academic health system. SUBJECTS: Critical care nurses, advanced practice providers, physicians, respiratory therapists, pharmacists, social workers, and spiritual health workers were surveyed on burnout in 2017 and during the coronavirus disease 2019 pandemic in 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Burnout syndrome and contributing factors were measured using the Maslach Burnout Inventory of Health and Human Service and Areas of Worklife Survey. Response rates were 46.5% (572 respondents) in 2017 and 49.9% (710 respondents) in 2020. The prevalence of burnout increased from 59% to 69% (p < 0.001). Nurses were disproportionately impacted, with the highest increase during the pandemic (58-72%; p < 0.0001) with increases in emotional exhaustion and depersonalization, and personal achievement decreases. In contrast, although burnout was high before and during coronavirus disease 2019 in all specialties, most professions had similar or lower burnout in 2020 as they had in 2017. Physicians had the lowest rates of burnout, measured at 51% and 58%, respectively. There was no difference in burnout between clinicians working in ICUs who treated coronavirus disease 2019 than those who did not (71% vs 67%; p = 0.26). Burnout significantly increased in females (71% vs 60%; p = 0.001) and was higher than in males during the pandemic (71% vs 60%; p = 0.01). CONCLUSIONS: Burnout syndrome was common in all multiprofessional ICU team members prior to and increased substantially during the pandemic, independent of whether one treated coronavirus disease 2019 patients. Nurses had the highest prevalence of burnout during coronavirus disease 2019 and had the highest increase in burnout from the prepandemic baseline. Female clinicians were significantly more impacted by burnout than males. Different susceptibility to burnout syndrome may require profession-specific interventions as well as work system improvements.

Supplemental Digital Content is available in the text., OBJECTIVES: To provide updated information on the burdens of sepsis during acute inpatient admissions for Medicare beneficiaries. DESIGN: Analysis of paid Medicare claims via the Centers for Medicare and Medicaid Services DataLink Project. SETTING: All U.S. acute-care hospitals, excluding federally operated hospitals (Veterans Administration and Defense Health Agency). Patients: All Medicare beneficiaries, January 2012—February 2020, with an explicit sepsis diagnostic code assigned during an inpatient admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The count of Medicare Part A/B (fee-for-service) plus Medicare Advantage inpatient sepsis admissions rose from 981,027 (CY2012) to 1,700,433 (CY 2019). The proportion of total admissions with sepsis in the Medicare Advantage population rose from 21.43% to 35.39%, reflecting the increasing beneficiary proportion enrolled in Medicare Advantage. In CY2019, 6-month mortality rates in Medicare fee-for-service beneficiaries for sepsis continued to decline, but remained high: 59.9% for septic shock, 35.5% for severe sepsis, 30.8% for sepsis attributed to a specific organism, and 26.5% for unspecified sepsis. Total fee-for-service-only inpatient hospital costs rose from $17.79B (CY2012) to $22.98B (CY2019). We estimated that the aggregate cost of sepsis hospital care for the entire U.S. population was at least $57.47B in 2019. Inclusion of 14 months’ (January 2019—February 2020) newer data exposed new trends: the cost per patient, number of admissions, and fraction of patients with sepsis labeled as present on admission inflected around November 2015, coincident with the change to International Classification of Diseases, 10th Edition, and introduction of the Severe Sepsis and Septic Shock Management Bundle (SEP-1) metric. CONCLUSIONS: Sepsis among Medicare beneficiaries precoronavirus disease 2019 imposed immense burdens upon patients, their families, and the taxpayers.

Objectives To evaluate early activation of latent viruses in polytrauma patients and consider prognostic value of viral micro-RNAs in these patients. Design This was a subset analysis from a prospectively collected multicenter trauma database. Blood samples were obtained upon admission to the trauma bay (T0), and trauma metrics and recovery data were collected. Setting Two civilian Level 1 Trauma Centers and one Military Treatment Facility. Patients Adult polytrauma patients with Injury Severity Scores greater than or equal to 16 and available T0 plasma samples were included in this study. Patients with ICU admission greater than 14 days, mechanical ventilation greater than 7 days, or mortality within 28 days were considered to have a complicated recovery. Interventions None. Measurements and main results Polytrauma patients (n = 180) were identified, and complicated recovery was noted in 33%. Plasma samples from T0 underwent reverse transcriptase-quantitative polymerase chain reaction analysis for Kaposi's sarcoma-associated herpesvirus micro-RNAs (miR-K12_10b and miRK-12-12) and Epstein-Barr virus-associated micro-RNA (miR-BHRF-1), as well as Luminex multiplex array analysis for established mediators of inflammation. Ninety-eight percent of polytrauma patients were found to have detectable Kaposi's sarcoma-associated herpesvirus and Epstein-Barr virus micro-RNAs at T0, whereas healthy controls demonstrated 0% and 100% detection rate for Kaposi's sarcoma-associated herpesvirus and Epstein-Barr virus, respectively. Univariate analysis revealed associations between viral micro-RNAs and polytrauma patients' age, race, and postinjury complications. Multivariate least absolute shrinkage and selection operator analysis of clinical variables and systemic biomarkers at T0 revealed that interleukin-10 was the strongest predictor of all viral micro-RNAs. Multivariate least absolute shrinkage and selection operator analysis of systemic biomarkers as predictors of complicated recovery at T0 demonstrated that miR-BHRF-1, miR-K12-12, monocyte chemoattractant protein-1, and hepatocyte growth factor were independent predictors of complicated recovery with a model complicated recovery prediction area under the curve of 0.81. Conclusions Viral micro-RNAs were detected within hours of injury and correlated with poor outcomes in polytrauma patients. Our findings suggest that transcription of viral micro-RNAs occurs early in the response to trauma and may be associated with the biological processes involved in polytrauma-induced complicated recovery.

Intensive care unit (ICU) telemedicine is an established entity that has the ability to not only improve the effectiveness, efficiency, and safety of critical care, but to also serve as a tool to combat staffing shortages and resource-limited environments. Several areas for future innovation exist within the field, including the use of advanced practice providers, robust inclusion in medical education, and concurrent application of advanced machine learning. The globalization of critical care services will also likely be predominantly delivered by ICU telemedicine. Limitations faced by the field include technical issues, financial concerns, and organizational elements.

BACKGROUND Flow cytometry (FCM) is a rapid diagnostic tool for monitoring immune cell function. We sought to determine if assessment of cell phenotypes using standardized FCM could be used to identify nosocomial infection after trauma. METHODS Prospective study of trauma patients at a Level I center from 2014 to 2018. Clinical and FCM data were collected within 24 hours of admission. Random forest (RF) models were developed to estimate the risk of severe sepsis (SS), organ space infection (OSI), and ventilator-associated pneumonia (VAP). Variables were selected using backward elimination and models were validated with leave-one-out. RESULTS One hundred and thirty-eight patients were included (median age, 30 years [23-44 years]; median Injury Severity Score, 20 (14-29); 76% (105/138) Black; 60% (83/138) gunshots). The incidence of SS was 8.7% (12/138), OSI 16.7% (23/138), and VAP 18% (25/138). The final RF SS model resulted in five variables (RBCs transfused in first 24 hours; absolute counts of CD56- CD16+ lymphocytes, CD4+ T cells, and CD56 bright natural killer [NK] cells; percentage of CD16+ CD56+ NK cells) that identified SS with an AUC of 0.89, sensitivity of 0.98, and specificity of 0.78. The final RF OSI model resulted in four variables (RBC in first 24 hours, shock index, absolute CD16+ CD56+ NK cell counts, percentage of CD56 bright NK cells) that identified OSI with an AUC of 0.76, sensitivity of 0.68, and specificity of 0.82. The RF VAP model resulted in six variables (Sequential [Sepsis-related] Organ Failure Assessment score: Injury Severity Score; CD4- CD8- T cell counts; percentages of CD16- CD56- NK cells, CD16- CD56+ NK cells, and CD19+ B lymphocytes) that identified VAP with AUC of 0.86, sensitivity of 0.86, and specificity of 0.83. CONCLUSIONS Combined clinical and FCM data can assist with early identification of posttraumatic infections. The presence of NK cells supports the innate immune response that occurs during acute inflammation. Further research is needed to determine the functional role of these innate cell phenotypes and their value in predictive models immediately after injury. LEVEL OF EVIDENCE Prognostic, level III.

Supplemental Digital Content is available in the text., Objectives: To provide contemporary estimates of the burdens (costs and mortality) associated with acute inpatient Medicare beneficiary admissions for sepsis. Design: Analysis of paid Medicare claims via the Centers for Medicare & Medicaid Services DataLink Project. Setting: All U.S. acute care hospitals, excluding federally operated hospitals (Veterans Administration and Defense Health Agency). Patients: All Medicare beneficiaries, 2012–2018, with an inpatient admission including one or more explicit sepsis codes. Interventions: None. Measurements and Main Results: Total inpatient hospital and skilled nursing facility admission counts, costs, and mortality over time. From calendar year (CY)2012–CY2018, the total number of Medicare Part A/B (fee-for-service) beneficiaries with an inpatient hospital admission associated with an explicit sepsis code rose from 811,644 to 1,136,889. The total cost of inpatient hospital admission including an explicit sepsis code for those beneficiaries in those calendar years rose from $17,792,657,303 to $22,439,794,212. The total cost of skilled nursing facility care in the 90 days subsequent to an inpatient hospital discharge that included an explicit sepsis code for Medicare Part A/B rose from $3,931,616,160 to $5,623,862,486 over that same interval. Precise costs are not available for Medicare Part C (Medicare Advantage) patients. Using available federal data sources, we estimated the aggregate cost of inpatient admissions and skilled nursing facility admissions for Medicare Advantage patients to have risen from $6.0 to $13.4 billion over the CY2012–CY2018 interval. Combining data for fee-for-service beneficiaries and estimates for Medicare Advantage beneficiaries, we estimate the total inpatient admission sepsis cost and any subsequent skilled nursing facility admission for all (fee-for-service and Medicare Advantage) Medicare patients to have risen from $27.7 to $41.5 billion. Contemporary 6-month mortality rates for Medicare fee-for-service beneficiaries with a sepsis inpatient admission remain high: for septic shock, approximately 60%; for severe sepsis, approximately 36%; for sepsis attributed to a specific organism, approximately 31%; and for unspecified sepsis, approximately 27%. Conclusion: Sepsis remains common, costly to treat, and presages significant mortality for Medicare beneficiaries.

Objectives To distinguish characteristics of Medicare beneficiaries who will have an acute inpatient admission for sepsis from those who have an inpatient admission without sepsis, and to describe their further trajectories during and subsequent to those inpatient admissions. Design Analysis of paid Medicare claims via the Centers for Medicare and Medicaid Services DataLink Project. Setting All U.S. acute care hospitals, excepting federal hospitals (Veterans Administration and Defense Health Agency). Patients Medicare beneficiaries, 2012-2018, with an inpatient hospital admission including one or more explicit sepsis codes. Interventions None. Measurements and main results Prevalent diagnoses in the year prior to the inpatient admission; healthcare contacts in the week prior to the inpatient admission; discharges, transfers, readmissions, and deaths (trajectories) for 6 months following discharge from the inpatient admission. Beneficiaries with no sepsis inpatient hospital admission for a year prior to an index hospital admission for sepsis were nearly indistinguishable by accumulated diagnostic codes from beneficiaries who had an index hospital admission without sepsis. Although the timing of healthcare services in the week prior to inpatient hospital admission was similar among beneficiaries who would be admitted for sepsis versus those whose inpatient admission did not include a sepsis code, the setting differed: beneficiaries destined for a sepsis admission were more likely to have received skilled nursing or unskilled nursing (e.g., nursing aide for activities of daily living) care. In contrast, comparing beneficiaries who had been free of any inpatient admission for an entire year and then required an inpatient admission, acute inpatient stays that included a sepsis code led to more than three times as many deaths within 1 week of discharge, with more admissions to skilled nursing facilities and fewer discharges to home. Comparing all beneficiaries who were admitted to a skilled nursing facility after an inpatient hospital admission, those who had sepsis coded during the index admission were more likely to die in the skilled nursing facility; more likely to be readmitted to an acute inpatient hospital and subsequently die in that setting; or if they survive to discharge from the skilled nursing facility, they are more likely to go next to a custodial nursing home. Conclusions Although Medicare beneficiaries destined for an inpatient hospital admission with a sepsis code are nearly indistinguishable by other diagnostic codes from those whose admissions will not have a sepsis code, their healthcare trajectories following the admission are worse. This suggests that an inpatient stay that included a sepsis code not only identifies beneficiaries who were less resilient to infection but also signals increased risk for worsening health, for mortality, and for increased use of advanced healthcare services during and postdischarge along with an increased likelihood of an inpatient hospital readmission.

Supplemental Digital Content is available in the text., Objective: To evaluate the impact of sepsis, age, and comorbidities on death following an acute inpatient admission and to model and forecast inpatient and skilled nursing facility costs for Medicare beneficiaries during and subsequent to an acute inpatient sepsis admission. Design: Analysis of paid Medicare claims via the Centers for Medicare & Medicaid Services DataLink Project (CMS) and leveraging the CMS-Hierarchical Condition Category risk adjustment model. Setting: All U.S. acute care hospitals, excepting federal hospitals (Veterans Administration and Defense Health Agency). Patients: All Part A/B (fee-for-service) Medicare beneficiaries with an acute inpatient admission in 2017 and who had no inpatient sepsis admission in the prior year. Interventions: None. Measurements and Main Results: Logistic regression models to determine covariate risk contribution to death following an acute inpatient admission; conventional regression to predict Medicare beneficiary sepsis costs. Using the Hierarchical Condition Category risk adjustment model to illuminate influence of illness on outcome of inpatient admissions, representative odds ratios (with 95% CIs) for death within 6 months of an admission (referenced to beneficiaries admitted but without the characteristic) are as follows: septic shock, 7.27 (7.19–7.35); metastatic cancer and acute leukemia (Hierarchical Condition Category 8), 6.76 (6.71–6.82); all sepsis, 2.63 (2.62–2.65); respiratory arrest (Hierarchical Condition Category 83), 2.55 (2.35–2.77); end-stage liver disease (Hierarchical Condition Category 27), 2.53 (2.49–2.56); and severe sepsis without shock, 2.48 (2.45–2.51). Models of the cost of sepsis care for Medicare beneficiaries forecast arise approximately 13% over 2 years owing the rising enrollments in Medicare offset by the cost of care per admission. Conclusions: A sepsis inpatient admission is associated with marked increase in risk of death that is comparable to the risks associated with inpatient admissions for other common and serious chronic illnesses. The aggregate costs of sepsis care for Medicare beneficiaries will continue to increase.

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Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis.Observational cohort study.Academic medical center from January 2013 to December 2015.Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively.None.High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable.Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed sepsis prediction model.

To identify circumstances in which repeated measures of organ failure would improve mortality prediction in ICU patients.Retrospective cohort study, with external validation in a deidentified ICU database.Eleven ICUs in three university hospitals within an academic healthcare system in 2014.Adults (18 yr old or older) who satisfied the following criteria: 1) two of four systemic inflammatory response syndrome criteria plus an ordered blood culture, all within 24 hours of hospital admission; and 2) ICU admission for at least 2 calendar days, within 72 hours of emergency department presentation.NoneMEASUREMENTS AND MAIN RESULTS:: Data were collected until death, ICU discharge, or the seventh ICU day, whichever came first. The highest Sequential Organ Failure Assessment score from the ICU admission day (ICU day 1) was included in a multivariable model controlling for other covariates. The worst Sequential Organ Failure Assessment scores from the first 7 days after ICU admission were incrementally added and retained if they obtained statistical significance (p0.05). The cohort was divided into seven subcohorts to facilitate statistical comparison using the integrated discriminatory index. Of the 1,290 derivation cohort patients, 83 patients (6.4%) died in the ICU, compared with 949 of the 8,441 patients (11.2%) in the validation cohort. Incremental addition of Sequential Organ Failure Assessment data up to ICU day 5 improved the integrated discriminatory index in the validation cohort. Adding ICU day 6 or 7 Sequential Organ Failure Assessment data did not further improve model performance.Serial organ failure data improve prediction of ICU mortality, but a point exists after which further data no longer improve ICU mortality prediction of early sepsis.

INTRODUCTION: The novel coronavirus (SARS-CoV-2) has led to a large cascade of transmissions resulting in high numbers of individuals hospitalized for the coronavirus disease 2019 (COVID-19), an impact still being accounted across the globe In this study, we seek to evaluate whether novel heart rate variability (HRV) measures can predict mortality within 24 hours of admission to the ICU among critically ill COVID-19 patients METHODS: All medical, surgical, neurocritical, transplant, and cardiac ICU admissions with COVID-19 between March 1 through April 31st, 2020 within Emory Healthcare system were screened Patients were selected to be included in the analysis if they were in the ICU for greater than 24 hours, had at least one positive qRT-PCR test for SARS-CoV-2 earlier in their hospitalization EKG (250 Hz) was then analyzed for each patient over a 300 second (s) observational window, that was shifted by 30s in each iteration for the first 24 hrs after admission MATLAB® was used to analyze the continuous EKG and extract relevant HRV features We use the Kruskal-Wallis and Steel-Dwass tests (P < 0 05) for statistical analysis and interpretations of HRV multiple measures RESULTS: A total of 312 COVID-19 patients were identified in a clinical chart review for SARS-CoV-2 by use of quantitative RT-PCR (qRT-PCR), of which 85 patients were admitted to the ICU and had sufficient data quality The median HRV aggregates, including AC, DC, LFHF, VLF, SD1, SD2, SD1SD2, RMSSD, and pNN50 were all statistically significant (FDR p

Precision medicine aims to identify, select, and schedule therapies that are most likely to yield a favorable outcome for an individual patient (1). Conventional and precision approaches may differ according to the choices of drug, device or operation; to the intensities of the chosen treatments; an

Literature generally finds no advantages in mortality risk for albumin over cheaper alternatives in many settings. Few studies have combined financial and nonfinancial strategies to reduce albumin overuse. We evaluated the effect of a sequential multifaceted intervention on decreasing albumin use in ICU and explore the effects of different strategies.Prospective prepost cohort study.Eight ICUs at two hospitals in an academic healthcare system.Adult patients admitted to study ICUs from September 2011 to August 2014 (n = 22,004).Over 2 years, providers in study ICUs participated in an intervention to reduce albumin use involving monthly feedback and explicit financial incentives in the first year and internal guidelines and order process changes in the second year.Outcomes measured were albumin orders per ICU admission, direct albumin costs, and mortality. Mean (SD) utilization decreased 37% from 2.7 orders (6.8) per admission during the baseline to 1.7 orders (4.6) during the intervention (p0.001). Regression analysis revealed that the intervention was independently associated with 0.9 fewer orders per admission, a 42% relative decrease. This adjusted effect consisted of an 18% reduction in the probability of using any albumin (p0.001) and a 29% reduction in the number of orders per admission among patients receiving any (p0.001). Secondary analysis revealed that probability reductions were concurrent with internal guidelines and order process modification while reductions in quantity occurred largely during the financial incentives and feedback period. Estimated cost savings totaled $2.5M during the 2-year intervention. There was no significant difference in ICU or hospital mortality between baseline and intervention.A sequential intervention achieved significant reductions in ICU albumin use and cost savings without changes in patient outcomes, supporting the combination of financial and nonfinancial strategies to align providers with evidence-based practices.

Purpose: To explore potential design for pharmacogenomics trials in sepsis, we investigate the interaction between pharmacogenomic biomarkers and response to drotrecogin alfa (activated) (DrotAA). This trial was designed to validate whether previously identified improved response polymorphisms (IRPs A and B) were associated with an improved response to DrotAA in severe sepsis. Methods: Patients with severe sepsis at high risk of death, who received DrotAA or not, with DNA available were included and matched to controls adjusting for age, APACHE II or SAPS II, organ dysfunction, ventilation, medical/surgical status, infection site, and propensity score (probability that a patient would have received DrotAA given their baseline characteristics). Independent genotyping and two-phase data transfer mitigated bias. The primary analysis compared the effect of DrotAA in IRP+ and IRP− groups on in-hospital 28-day mortality. Secondary endpoints included time to death in hospital; intensive care unit (ICU)-, hospital-, and ventilator-free days; and overall DrotAA treatment effect on mortality. Results: Six hundred and ninety-two patients treated with DrotAA were successfully matched to 1935 patients not treated with DrotAA. Genotyping was successful for 639 (DrotAA) and 1684 (nonDrotAA) matched patients. The primary hypothesis of a genotype-by-treatment interaction (assessed by conditional logistic regression analysis) was not significant (P = 0.30 IRP A; P = 0.78 IRP B), and there was no significant genotype by treatment interaction for any secondary endpoint. Conclusions: Neither IRP A nor IRP B predicted differential response to DrotAA on in-hospital 28-day mortality. ClinicalTrials.gov registration NCT01486524

The biomarker profile of trauma patients may allow for the creation of models to assist bedside decision making and prediction of complications. We sought to determine the utility of modeling in the prediction of bacteremia and pneumonia in combat casualties.This is a prospective, observational trial of patients with complex wounds treated at Walter Reed National Military Medical Center (2007-2012). Tissue, serum, and wound effluent samples were collected during operative interventions until wound closure. Clinical, biomarker, and outcome data were used in machine learning algorithms to develop models predicting bacteremia or pneumonia. Modeling was performed on the first operative washout to maximize predictive benefit. Variable selection of dataset variables was performed and the best-fitting Bayesian belief network (BBN), using Bayesian information criterion (BIC), was selected for predictive modeling. Random forest was performed using variables from BBN step. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) analysis.Seventy-three patients (mean age 23, mean Injury Severity Score 25) were enrolled. Patients required a median of 3 (2-13) operations. The incidence of bacteremia and pneumonia was 22% and 12%, respectively. Best-fitting variable selected BBNs were maximum-minimum parents and children (MMPC) for both bacteremia (BIC-24948) and pneumonia (BIC-17886). Full variable and MMPC random forest models AUC were 0.721 and 0.834, respectively, for bacteremia and 0.809 and 0.856, respectively, for pneumonia.We identified a profile predictive of bacteremia and pneumonia in combat casualties. This has important clinical implications and should be validated in the civilian trauma population. This and similar tools will allow for increasing precision in the management of critically ill and injured patients.Prognostic, level III.

Purpose Using administrative codes and minimal physiologic and laboratory data, we sought a high-specificity identification strategy for patients whose sepsis initially appeared during their ICU stay. Materials and methods We studied all patients discharged from an academic hospital between September 1, 2013 and October 31, 2014. Administrative codes and minimal physiologic and laboratory criteria were used to identify patients at high risk of developing the onset of sepsis in the ICU. Two clinicians then independently reviewed the patient record to verify that the screened-in patients appeared to become septic during their ICU admission. Results Clinical chart review verified sepsis in 437/466 ICU stays (93.8%). Of these 437 encounters, only 151 (34.6%) were admitted to the ICU with neither SIRS nor evidence of infection and therefore appeared to become septic during their ICU stay. Conclusions Selected administrative codes coupled to SIRS criteria and applied to patients admitted to ICU can yield up to 94% authentic sepsis patients. However, only 1/3 of patients thus identified appeared to become septic during their ICU stay. Studies that depend on high-intensity monitoring for description of the time course of sepsis require clinician review and verification that sepsis initially appeared during the monitoring period.

Healthcare systems strive to provide quality care at lower cost. Arterial blood gas testing, chest radiographs, and RBC transfusions provide an important example of opportunities to reduce excess resource utilization within the ICU. We describe the effect of a multifaceted quality improvement program designed to decrease the avoidable arterial blood gases, chest radiographs, and RBC utilization on utilization of these resources and patient outcomes.Prospective pre-post cohort study.Seven ICUs in an academic healthcare system.All adult ICU patients admitted to study ICUs during consecutive baseline (n = 7,357), intervention (n = 7,553), and follow-up (n = 7,657) years between September 2010 and August 2013.A multifaceted quality improvement program including provider education, audit and feedback, and unit-based provider financial incentives targeting arterial blood gas, chest radiograph, and RBC utilization.The primary outcome was the number of orders for arterial blood gases, chest radiographs, and RBCs per patient. Compared with the baseline period, unadjusted arterial blood gas, chest radiograph, and RBC utilization in the intervention period was reduced by 42%, 26%, and 17%, respectively (p0.01). After adjusting for potentially relevant patient factors, the intervention was associated with 128 fewer arterial blood gases, 73 fewer chest radiographs, and 16 fewer RBCs per 100 patients (p0.01). This effect was durable during the follow-up year. This reduction yielded an approximate net savings of $1.5 M in direct costs over the intervention and follow-up years after accounting for the direct costs of the program. Unadjusted hospital mortality decreased from 7% in the baseline period to 5.2% in the intervention period (p0.01). This reduction remained significant after adjusting for patient factors (odds ratio = 0.43; p0.01).Implementation of a multifaceted quality improvement program including financial incentives was associated with significant improvements in resource utilization. Our findings provide evidence supporting the safety, effectiveness, and sustainability of incentive-based quality improvement interventions.

Objective Representatives of five international critical care societies convened topic specialists and a nonexpert jury to review, assess, and report on studies of targeted temperature management and to provide clinical recommendations. Data sources Questions were allocated to experts who reviewed their areas, made formal presentations, and responded to questions. Jurors also performed independent searches. Sources used for consensus derived exclusively from peer-reviewed reports of human and animal studies. Study selection Question-specific studies were selected from literature searches; jurors independently determined the relevance of each study included in the synthesis. Conclusions and recommendations 1) The jury opines that the term "targeted temperature management" replace "therapeutic hypothermia." 2) The jury opines that descriptors (e.g., "mild") be replaced with explicit targeted temperature management profiles. 3) The jury opines that each report of a targeted temperature management trial enumerate the physiologic effects anticipated by the investigators and actually observed and/or measured in subjects in each arm of the trial as a strategy for increasing knowledge of the dose/duration/response characteristics of temperature management. This enumeration should be kept separate from the body of the report, be organized by body systems, and be made without assertions about the impact of any specific effect on the clinical outcome. 4) The jury STRONGLY RECOMMENDS targeted temperature management to a target of 32°C-34°C as the preferred treatment (vs. unstructured temperature management) of out-of-hospital adult cardiac arrest victims with a first registered electrocardiography rhythm of ventricular fibrillation or pulseless ventricular tachycardia and still unconscious after restoration of spontaneous circulation (strong recommendation, moderate quality of evidence). 5) The jury WEAKLY RECOMMENDS the use of targeted temperature management to 33°C-35.5°C (vs. less structured management) in the treatment of term newborns who sustained asphyxia and exhibit acidosis and/or encephalopathy (weak recommendation, moderate quality of evidence).

Objective Pathogens that cause pneumonia may be treated in a targeted fashion by antibiotics, but if this therapy fails, then treatment involves only nonspecific supportive measures, independent of the inciting infection. The purpose of this study was to determine whether host response is similar after disparate infections with similar mortalities. Design Prospective, randomized controlled study. Setting Animal laboratory in a university medical center. Interventions Pneumonia was induced in FVB/N mice by either Streptococcus pneumoniae or two different concentrations of Pseudomonas aeruginosa. Plasma and bronchoalveolar lavage fluid from septic animals was assayed by a microarray immunoassay measuring 18 inflammatory mediators at multiple time points. Measurements and main results The host response was dependent on the causative organism as well as kinetics of mortality, but the pro-inflammatory and anti-inflammatory responses were independent of inoculum concentration or degree of bacteremia. Pneumonia caused by different concentrations of the same bacteria, Pseudomonas aeruginosa, also yielded distinct inflammatory responses; however, inflammatory mediator expression did not directly track the severity of infection. For all infections, the host response was compartmentalized, with markedly different concentrations of inflammatory mediators in the systemic circulation and the lungs. Hierarchical clustering analysis resulted in the identification of five distinct clusters of the host response to bacterial infection. Principal components analysis correlated pulmonary macrophage inflammatory peptide-2 and interleukin-10 with progression of infection, whereas elevated plasma tumor necrosis factor sr2 and macrophage chemotactic peptide-1 were indicative of fulminant disease with >90% mortality within 48 hrs. Conclusions Septic mice have distinct local and systemic responses to Streptococcus pneumoniae and Pseudomonas aeruginosa pneumonia. Targeting specific host inflammatory responses induced by distinct bacterial infections could represent a potential therapeutic approach in the treatment of sepsis.

To investigate the significance of functional polymorphisms of inflammatory response genes by analysis of a large population of patients, both with and without severe sepsis, and representative of the diverse populations (geographic diversity, physician diversity, clinical treatment diversity) that would be encountered in critical care clinical practice.: Collaborative case-control study conducted from July 2001 to December 2005.A heterogeneous population of patients from 12 U.S. intensive care units represented by the Genetic Predisposition to Severe Sepsis archive.A total of 854 patients with severe sepsis and an equal number of mortality, age, gender, and race-matched patients also admitted to the intensive care unit without evidence of any infection (matched nonseptic controls).We developed assays for six functional single nucleotide polymorphisms present before the first codon of tumor necrosis factor at -308, IL1B at -511, IL6 at -174, IL10 at -819, and CD14 at -159, and in the first intron of LTA (also known as tumor necrosis factor-B) at +252 (LTA[+252]). The Project IMPACT critical care clinical database information management system developed by the Society of Critical Care Medicine and managed by Tri-Analytics and Cerner Corporation was utilized. Template-directed dye-terminator incorporation assay with fluorescence polarization detection was used as a high-throughput genotyping strategy. Fifty-three percent of the patients were male with 87.3% and 6.4% of Caucasian and African American racial types, respectively. Overall mortality was 35.1% in both severe sepsis and matched nonseptic control patients group. Average ages (standard deviation) of the severe sepsis and matched nonseptic control patients were 63.0 (16.05) and 65.0 (15.58) yrs old, respectively. Among the six single nucleotide polymorphisms, LTA (+252) was most overrepresented in the septic patient group (% severe sepsis; AA 45.6: AG 51.1: GG 56.7, p = .005). Furthermore, the genetic risk effect was most pronounced in males, age60 yrs (p = .005).LTA(+252) may influence predisposition to severe sepsis, a predisposition that is modulated by gender and age. Although the genetic influences can be overwhelmed by both comorbid factors and acute illness in individual cases, population studies suggest that this is an influential biological pathway modulating risk of critical illnesses.

To determine a) if a checklist covering a diverse group of intensive care unit protocols and objectives would improve clinician consideration of these domains and b) if improved consideration would change practice patterns.Pre- and post observational study.A 24-bed surgical/burn/trauma intensive care unit in a teaching hospital.A total of 1399 patients admitted between June 2006 and May 2007.The first component of the study evaluated whether mandating verbal review of a checklist covering 14 intensive care unit best practices altered verbal consideration of these domains. Evaluation was performed using real-time bedside audits on morning rounds. The second component evaluated whether the checklist altered implementation of these domains by changing practice patterns. Evaluation was performed by analyzing data from the Project IMPACT database after patients left the intensive care unit.Verbal consideration of evaluable domains improved from 90.9% (530/583) to 99.7% (669/671, p.0001) after verbal review of the checklist was mandated. Bedside consideration improved on the use of deep venous thrombosis prophylaxis (p.05), stress ulcer prophylaxis (p.01), oral care for ventilated patients (p0.01), electrolyte repletion (p.01), initiation of physical therapy (p.05), and documentation of restraint orders (p.0001). Mandatory verbal review of the checklist resulted in a greater than two-fold increase in transferring patients out of the intensive care unit on telemetry (16% vs. 35%, p.0001) and initiation of physical therapy (28% vs. 42%, p.0001) compared with baseline practice.A mandatory verbal review of a checklist covering a wide range of objectives and goals at each patient's bedside is an effective method to improve both consideration and implementation of intensive care unit best practices. A bedside checklist is a simple, cost-effective method to prevent errors of omission in basic domains of intensive care unit management that might otherwise be forgotten in the setting of more urgent care requirements.

Objective: The purpose of this study was to determine the effects of a simple low-cost oral care protocol on ventilator-associated pneumonia rates in a surgical intensive care unit. Design: Preintervention and postintervention observational study. Setting: Twenty-four bed surgical/trauma/burn intensive care units in an urban university hospital. Patients: All mechanically ventilated patients that were admitted to the intensive care unit between June 1, 2004 and May 31, 2005. Interventions: An oral care protocol to assist in prevention of bacterial growth of plaque by cleaning the patients' teeth with sodium monoflurophosphate 0.7% paste and brush, rinsing with tap water, and subsequent application of a 0.12% chlorhexidine gluconate chemical solution done twice daily at 12-hour intervals. Measurements and main results: During the preintervention period from June 1, 2003 to May 31, 2004, there were 24 infections in 4606 ventilator days (rate = 5.2 infections per 1000 ventilator days). After the institution of the oral care protocol, there were 10 infections in 4158 ventilator days, resulting in a lower rate of 2.4 infections per 1000 ventilator days. This 46% reduction in ventilator-associated pneumonia was statistically significant (P = .04). Staff compliance with the oral care protocol during the 12-month period was also monitored biweekly and averaged 81%. The total cost of the oral care protocol was US$2187.49. There were 14 fewer cases of ventilator-associated pneumonia, which led to a decrease in cost of US$140 000 to US$560 000 based on the estimated cost per ventilator-associated pneumonia infection of US$10 000 to US$40 000. There was an overall reduction in ventilator-associated pneumonia without a change to the gram-negative or gram-positive microorganism profile. Conclusions: The implementation of a simple, low-cost oral care protocol in the surgical intensive care unit led to a significantly decreased risk of acquiring ventilator-associated pneumonia.

Objective: Evidence-based practice recommendations abound, but implementation is often unstructured and poorly audited. We assessed the ability of a peer network to implement an evidence-based best practice protocol and to measure patient outcomes. Design: Consensus definition of spontaneous breathing trial followed by implementation in eight academic medical centers. Setting: Six medical, two surgical, and two combined medical/ surgical adult intensive care units among eight academic medical centers. Study Population: Patients initiating mechanical ventilation through an endotracheal tube during a 12-wk interval formed the study population. Interventions: Adoption and implementation of a common spontaneous breathing trial protocol across multiple intensive care units. Measurements and Main Results: Seven hundred five patients had 3,486 safety screens for conducting a spontaneous breathing trial; 2072 (59%) patients failed the safety screen. Another 379 (11%) patients failed a 2-min tolerance screen and 1,122 (34%) patients had a full 30‐120 min spontaneous breathing trial performed. Seventy percent of eligible patients were enrolled. Only 55% of passing spontaneous breathing trials resulted in liberation from mechanical ventilatory support before another spontaneous breathing trial was performed. Conclusions: Peer networks can be effective in promoting and implementing evidence-based best practices. Implementation of a best practice (spontaneous breathing trial) may be necessary for, but by itself insufficient to achieve, consistent and timely liberation from ventilator support. (Crit Care Med 2008; 36:2753‐2762)

BACKGROUND Age influences outcome of sepsis and septic shock. The mechanism of this age-dependent vulnerability to sepsis remains largely unknown. Because much of the mortality and morbidity associated with sepsis and septic shock is the result of severe derangements in the cardiovascular system, it is possible that the myocardium responds to injury in a developmentally influenced manner. We hypothesized that analysis of cardiac RNA expression profiles may differentiate between the myocardial response to sepsis in young and old mice. METHODS AND RESULTS Sixteen FVB/N male mice were stratified based on age. Young animals were 6 wks old, correlating to 4 to 6 human years, and aged animals were 20 months old correlating to 70 to 80 human years. Animals underwent either cecal ligation and puncture to produce polymicrobial sepsis or a sham operation. Both ventricles were excised after kill at 24 hrs. There were 53 genes that differed in RNA abundance between the four groups (false discovery rate of 0.005, p < 0.00001). Additionally, four genes were associated with an age-dependent response to sepsis: CYP2B2 (cytochrome P450, family 2, subfamily B, polypeptide 6), VGLL2 (vestigial like 2), and PAH (phenylalanine hydroxylase). The fourth gene is an expressed sequence tag, the function of which is related to the cytochrome P450 family. These genes play roles in phenylalanine, tyrosine, tryptophan, and fatty acid metabolism. CONCLUSIONS This report describes the transcriptional response of the heart to sepsis. In addition, our findings suggest that these differences are in part age-dependent and serve as hypothesis generation.

Purpose Complex autonomic communication can be assessed by autonomic information flow (AIF) functions. The objective was to evaluate new complexity measures involving physiologically relevant time scales to predict the length of stay (LOS) in the hospital after abdominal aortic surgery (AAS). Our hypothesis was that AAS disrupts AIF, that restoration of AIF is necessary for recovery from major surgery, and that measures of AIF are superior to conventional heart rate variability (HRV) measures and equivalent to APACHE IV score in predicting LOS. Materials and Methods Twenty-four–hour Holter recordings were analyzed in 94 patients after AAS for standard time, frequency domain, and several complexity measures of different time scales derived from AIF functions. The risk of staying in the hospital for longer than 7 days as a function of HRV measures and APACHE IV score was modeled by logistic regression. The area under the curve (AUC) of receiver operating characteristic with 95% confidence interval as measure of predictive accuracy was calculated and internally cross-validated. Results The long-term dacay of AIF over 100s (LD100) with cross-validated AUC = 0.67 (0.56-0.79) nearly reached the predictive accuracy of the APACHE IV score with AUC = 0.69 (0.58-0.79). None of the traditional time and frequency domain HRV measures remained in the multivariate models. The LD100 adjusted for ventilatory support with AUC = 0.70 (0.59-0.81) was equivalent to the APACHE IV score in this patient group. Although the strongest correlation between AIF measures and the APACHE IV score was found for LD100, r was only −0.37. Conclusions Results confirm the hypothesis that AIF measures characterize pathophysiologic autonomic communication better than traditional HRV measures and may have similar predictive value to the APACHE IV score for LOS. The relative independence of the APACHE IV score and AIF measures suggests that AIF measures could add to clinical risk prediction.