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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.

Both research and practice in critical care medicine have long been defined by syndromes. Though clinically recognizable entities, these are in fact loose amalgams of heterogeneous states, within which responses to therapy may vary. Mounting translational evidence suggests the current syndrome-based framework of critical illness should be reconsidered. Moreover, research done during the COVID-19 pandemic illustrates how the study of a more biologically homogeneous condition – respiratory failure due to SARS-CoV-2 infection – can increase the efficiency with which actionable results are generated. We discuss recent findings from basic science and clinical research in critical care, and explore how these might inform a new conceptual model of critical illness. De-emphasizing syndromes, we focus instead on the underlying biological changes that underpin critical illness states, and that may be amenable to treatment. We hypothesize that such an approach will accelerate translational critical care research, leading to a richer understanding of the pathobiology of critical illness, and of the proximate determinants of ICU outcomes. The specificity and granularity gained will support the design of more effective clinical trials, and inform a more precise, effective practice at the bedside.

Background Post-traumatic acute kidney injury has occurred in every major military conflict since its initial description during World War II. To ensure the proper treatment of combat casualties, early detection is critical. This study therefore aimed to investigate combat-related post-traumatic acute kidney injury in recent military conflicts, used machine learning algorithms to identify clinical and biomarker variables associated with the development of post-traumatic acute kidney injury, and evaluated the effects of post-traumatic acute kidney injury on wound healing and nosocomial infection. Methods We conducted a retrospective clinical cohort review of 73 critically injured US military service members who sustained major combat-related extremity wounds and had collected injury characteristics, assayed serum and tissue biopsy samples for the expression of protein and messenger ribonucleic acid biomarkers. Bivariate analyses and random forest recursive feature elimination classification algorithms were used to identify associated injury characteristics and biomarker variables. Results The incidence of post-traumatic acute kidney injury was 20.5%. Of that, 86% recovered baseline renal function and only 2 (15%) of the acute kidney injury group required renal replacement therapy. Random forest recursive feature elimination algorithms were able to estimate post-traumatic acute kidney injury with the area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.91. Post-traumatic acute kidney injury was associated with injury severity score, serum epidermal growth factor, and tissue activin A type receptor 1, matrix metallopeptidase 10, and X-C motif chemokine ligand 1 expression. Patients with post-traumatic acute kidney injury exhibited poor wound healing and increased incidence of nosocomial infections. Conclusion The occurrence of acute kidney injury in combat casualties may be estimated using injury characteristics and serum and tissue biomarkers. External validations of these models are necessary to generalize for all trauma patients.

IntroductionVenous thromboembolism (VTE) is a frequent complication of trauma associated with high mortality and morbidity. Clinicians lack appropriate tools for stratifying trauma patients for VTE, thus have yet to be able to predict when to intervene. We aimed to compare random forest (RF) and logistic regression (LR) predictive modelling for VTE using (1) clinical measures alone, (2) serum biomarkers alone and (3) clinical measures plus serum biomarkers.MethodsData were collected from 73 military casualties with at least one extremity wound and prospectively enrolled in an observational study between 2007 and 2012. Clinical and serum cytokine data were collected. Modelling was performed with RF and LR based on the presence or absence of deep vein thrombosis (DVT) and/or pulmonary embolism (PE). For comparison, LR was also performed on the final variables from the RF model. Sensitivity/specificity and area under the curve (AUC) were reported.ResultsOf the 73 patients (median Injury Severity Score=16), nine (12.3%) developed VTE, four (5.5%) with DVT, four (5.5%) with PE, and one (1.4%) with both DVT and PE. In all sets of predictive models, RF outperformed LR. The best RF model generated with clinical and serum biomarkers included five variables (interleukin-15, monokine induced by gamma, vascular endothelial growth factor, total blood products at resuscitation and presence of soft tissue injury) and had an AUC of 0.946, sensitivity of 0.992 and specificity of 0.838.ConclusionsVTE may be predicted by clinical and molecular biomarkers in trauma patients. This will allow the development of clinical decision support tools which can help inform the management of high-risk patients for VTE.

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.

IMPORTANCE: Discrepancies in oxygen saturation measured by pulse oximetry (SpO(2)), when compared with arterial oxygen saturation (SaO(2)) measured by arterial blood gas (ABG), may differentially affect patients according to race and ethnicity. However, the association of these disparities with health outcomes is unknown. OBJECTIVE: To examine racial and ethnic discrepancies between SaO(2) and SpO(2) measures and their associations with clinical outcomes. DESIGN, SETTING, AND PARTICIPANTS: This multicenter, retrospective, cross-sectional study included 3 publicly available electronic health record (EHR) databases (ie, the Electronic Intensive Care Unit–Clinical Research Database and Medical Information Mart for Intensive Care III and IV) as well as Emory Healthcare (2014–2021) and Grady Memorial (2014–2020) databases, spanning 215 hospitals and 382 ICUs. From 141 600 hospital encounters with recorded ABG measurements, 87 971 participants with first ABG measurements and an SpO(2) of at least 88% within 5 minutes before the ABG test were included. EXPOSURES: Patients with hidden hypoxemia (ie, SpO(2) ≥88% but SaO(2)

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.

Background Influenza causes substantial mortality, especially among older persons. Influenza vaccines are rarely more than 50% effective and rarely reach more than half of the US Medicare population, which is primarily an aged population. We wished to estimate the association between vaccination and mortality reduction. Method We used the US Center for Medicare and Medicaid Services (CMS) DataLink Project to determine vaccination status and timing during the 2017-2018 influenza season for more than 26 million Medicare enrollees. Patient-level demographic, health, co-morbidity, hospitalization, vaccination, and healthcare utilization claims data were supplied as covariates to general linear models in order to isolate and estimate the association between participation in the vaccination program and relative risk of death. Findings The 2017-2018 seasonal influenza vaccine reduced (Relative Risk Ratio [RRR] 0.936 [95% CI = 0.918-0.954]) the risk of all-cause death among beneficiaries following a hospitalization for sepsis and moreover the risk of death without a prior hospitalization during the 2.5-month outcome window (RRR 0.870 [95% CI = 0.853-0.887]). We estimate the number needed to vaccinate (NNV) to prevent a death in the ten-week outcome window is between 1,515 beneficiaries (95% CI = 1,351-1,754; derived from the average treatment effect of augmented inverse probability weighting) and 1,960 beneficiaries (95% CI = 1,695-2,381; derived from the average marginal effect of logistic regression). Among beneficiaries requiring hospitalization, the greatest death risk reduction accrued to those 85 + years of age who were hospitalized with sepsis, RRR 0.92 [95% CI = 0.89-0.95]. No apparent benefit was realized by beneficiaries who required custodial (nursing home) care. Interpretation Seasonal influenza immunization is associated with relative reduction of death risk among non-institutionalized Medicare beneficiaries. Funding All authors are full-time or contractual employees of the United States Federal Government, Department of Health and Human Services, the funding agency.

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.

Tools to assist clinicians in predicting pneumonia could lead to a significant decline in morbidity. Therefore, we sought to develop a model in combat trauma patients for identifying those at highest risk of pneumonia. This was a retrospective study of 73 primarily blast-injured casualties with combat extremity wounds. Binary classification models for pneumonia prediction were developed with measurements of injury severity from the Abbreviated Injury Scale (AIS), transfusion blood products received before arrival at Walter Reed National Military Medical Center (WRNMMC), and serum protein levels. Predictive models were generated with leave-one-out-cross-validation using the variable selection method of backward elimination (BE) and the machine learning algorithms of random forests (RF) and logistic regression (LR). BE was attempted with two predictor sets: (1) all variables and (2) serum proteins alone. Incidence of pneumonia was 12% (n = 9). Different variable sets were produced by BE when considering all variables and just serum proteins alone. BE selected the variables ISS, AIS chest, and cryoprecipitate within the first 24 h following injury for the first predictor set 1 and FGF-basic, IL-2R, and IL-6 for predictor set 2. Using both variable sets, a RF was generated with AUCs of 0.95 and 0.87—both higher than LR algorithms. Advanced modeling allowed for the identification of clinical and biomarker data predictive of pneumonia in a cohort of predominantly blast-injured combat trauma patients. The generalizability of the models developed here will require an external validation dataset.

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.

Importance Sepsis is a common syndrome with substantial morbidity and mortality. A combination of vitamin C, thiamine, and corticosteroids has been proposed as a potential treatment for patients with sepsis. Objective To determine whether a combination of vitamin C, thiamine, and hydrocortisone every 6 hours increases ventilator- and vasopressor-free days compared with placebo in patients with sepsis. Design, setting, and participants Multicenter, randomized, double-blind, adaptive-sample-size, placebo-controlled trial conducted in adult patients with sepsis-induced respiratory and/or cardiovascular dysfunction. Participants were enrolled in the emergency departments or intensive care units at 43 hospitals in the United States between August 2018 and July 2019. After enrollment of 501 participants, funding was withheld, leading to an administrative termination of the trial. All study-related follow-up was completed by January 2020. Interventions Participants were randomized to receive intravenous vitamin C (1.5 g), thiamine (100 mg), and hydrocortisone (50 mg) every 6 hours (n = 252) or matching placebo (n = 249) for 96 hours or until discharge from the intensive care unit or death. Participants could be treated with open-label corticosteroids by the clinical team, with study hydrocortisone or matching placebo withheld if the total daily dose was greater or equal to the equivalent of 200 mg of hydrocortisone. Main outcomes and measures The primary outcome was the number of consecutive ventilator- and vasopressor-free days in the first 30 days following the day of randomization. The key secondary outcome was 30-day mortality. Results Among 501 participants randomized (median age, 62 [interquartile range {IQR}, 50-70] years; 46% female; 30% Black; median Acute Physiology and Chronic Health Evaluation II score, 27 [IQR, 20.8-33.0]; median Sequential Organ Failure Assessment score, 9 [IQR, 7-12]), all completed the trial. Open-label corticosteroids were prescribed to 33% and 32% of the intervention and control groups, respectively. Ventilator- and vasopressor-free days were a median of 25 days (IQR, 0-29 days) in the intervention group and 26 days (IQR, 0-28 days) in the placebo group, with a median difference of -1 day (95% CI, -4 to 2 days; P = .85). Thirty-day mortality was 22% in the intervention group and 24% in the placebo group. Conclusions and relevance Among critically ill patients with sepsis, treatment with vitamin C, thiamine, and hydrocortisone, compared with placebo, did not significantly increase ventilator- and vasopressor-free days within 30 days. However, the trial was terminated early for administrative reasons and may have been underpowered to detect a clinically important difference. Trial registration ClinicalTrials.gov Identifier: NCT03509350.

Improper or delayed activation of a massive transfusion protocol may have consequences to individuals and institutions. We designed a complex predictive algorithm that was packaged within a smartphone application. We hypothesized it would accurately assess the need for massive transfusion protocol activation and assist clinicians in that decision.We prospectively enrolled patients at an urban, level I trauma center. The application recorded the surgeon's initial opinion for activation and then prompted inputs for the model. The application provided a prediction and recorded the surgeon's final decision on activation.Three hundred and twenty-one patients were enrolled (83% male; 59% penetrating; median Injury Severity Score 9; mean base deficit -4.11). Of 36 massive transfusion protocol activations, 26 had an app prediction of "high" or "moderate" probability. Of these, 4 (15%) patients received10 u blood as a result of early hemorrhage control. Two hundred and eighty-five patients did not have massive transfusion protocol activated by the surgeon with 27 (9%) patients having "moderate" or "high" likelihood predicted by the application. Twenty-four of these did not require massive transfusion, and all patients had acidosis that unrelated to hemorrhagic shock. For 13 (50%) of the patients with "high" probability, the surgeon correctly altered their initial decision based on this information. The algorithm demonstrated an adjusted accuracy of 0.96 (95% confidence interval [0.93-0.98); P ≤ .001]), sensitivity = 0.99, specificity 0.72, positive predictive value 0.96, negative predictive value 0.99, and area under the receiver operating curve = 0.86.A smartphone-based clinical decision tools can aid surgeons in the decision to active massive transfusion protocol in real time, although it does not completely replace clinician judgment.

ObjectiveTo measure heart rate variability metrics in critically ill COVID-19 patients with comparison to all-cause critically ill sepsis patients.Design and patientsRetrospective analysis of COVID-19 patients admitted to an ICU for at least 24h at any of Emory Healthcare ICUs between March and April 2020. The comparison group was a cohort of all-cause sepsis patients prior to COVID-19 pandemic.Interventionsnone.MeasurementsContinuous waveforms were captured from the patient monitor. The EKG was then analyzed for each patient over a 300 second (s) observational window, that was shifted by 30s in each iteration from admission till discharge. A total of 23 HRV metrics were extracted in each iteration. We use the Kruskal–Wallis and Steel–Dwass tests (p < 0.05) for statistical analysis and interpretations of HRV multiple measures.ResultsA total of 141 critically-ill COVID-19 patients met inclusion criteria, who were compared to 208 patients with all-cause sepsis. Demographic parameters were similar apart from a high proportion of African-Americans in the COVID-19 cohort. Three non-linear markers, including SD1:SD2, sample entropy, approximate entropy and four linear features mode of Beat-to-Beat interval (NN), Acceleration Capacity (AC), Deceleration Capacity (DC), and pNN50, were statistical significance between more than one binary combinations of the sub-groups (comparing survivors and non-survivors in both the COVID-19 and sepsis cohorts). The three nonlinear features and AC, DC, and NN (mode) were statistically significant across all four combinations. Temporal analysis of the main markers showed low variability across the 5 days of analysis, compared with sepsis patients.ConclusionsHeart rate variability is broadly implicated across patients infected with SARSCoV-2, and admitted to the ICU for critical illness. Comparing these metrics to patients with all-cause sepsis suggests a unique set of expressions that differentiate this viral phenotype. This finding could be investigated further as a potential biomarker to predict poor outcome in this patient population, and could also be a starting point to measure potential autonomic dysfunction in COVID-19.

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.

Objective To estimate the effect of implementing a tele-ICU and a critical care residency training program for advanced practice providers on service utilization and total Medicare episode spending. Data sources/study settings Medicare claims data for fee-for-service beneficiaries at 12 large, inpatient hospitals in the Atlanta Hospital Referral Region. Study design Difference-in-differences design where changes in spending and utilization for Medicare beneficiaries eligible for treatment in participating ICUs was compared to changes in a comparison group of clinically similar beneficiaries treated at similar hospitals' ICUs in the same hospital referral region. Extraction methods Using Medicare claims data from January 2010 through June 2015, we defined measures of Medicare episode spending during the ICU stay and subsequent 60 days after discharge, and utilization measures within 30 and 60 days after discharge. Principal findings Implementation of the advanced practice provider residency program and tele-ICU was associated with a significant reduction in average Medicare spending per episode, primarily driven by reduced readmissions within 60 days and substitution of home health care for institutional postacute care. Conclusions Innovations in workforce training and technology specific to the ICU may be useful in addressing the shortage of intensivist physicians, yielding benefits to patients and payers.

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.

Safe and effective care of critically ill patients requires a team of professionals including specialty physicians (intensivists) and critical care nurses experienced in providing care for patients in the intensive care unit (ICU). While critical illness can strike at any time and demands continuous attention, allocation of scarce staff follows a predictable pattern. The night shift is more likely to have disproportionately newer and thus less experienced nurses, and the experienced nurses on that shift are engaged in providing care to their own set of patients (Claffey 2006; Floyd 2003). At times this leaves them unable to sufficiently supervise the newer staff. There is also evidence of increased risks at night time with higher in-hospital mortality for admissions at night (Coiera et al. 2014).

The 21st century has brought with it a welcome call for increased rigor in observational research methods (1, 2). It is not that observational research methods are inherently flawed – they are not (3, 4). Observational studies can contribute valuable evidence supporting causal associations when designed and conducted using rigorous methods. The “flaws” are a result of reliance on outdated methodology, inadequate attention to threats to validity (such as confounding), opaque reporting of results, lack of replication, and a failure to interpret findings within the context of the limitations of observational research methodology. Aware of this situation and influenced by our experience as journal editors, we convened an ad hoc group of 47 editors of 35 respiratory, sleep, and critical care journals to offer guidance to authors, peer reviewers, and researchers on the design and reporting of observational causal inference studies. This guidance takes the form of a call for investigators to consider making major changes to their approach to such studies. This document represents our current best understanding of approaches to causal inference, an active area of research. We anticipate that best practice in this, as in any scientific endeavor, will continue to evolve, requiring this document to be updated every 5 to 10 years. We believe these changes will increase the rigor, validity, and value of the work we publish in our journals.

Supplemental Digital Content is available in the text., Background: Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes. Objectives: The objective of this study is to predict acute respiratory failure requiring any advanced respiratory support (including noninvasive ventilation). With the advent of the coronavirus disease pandemic, concern regarding acute respiratory failure has increased. Derivation Cohort: All admission encounters from January 2014 to June 2017 from three hospitals in the Emory Healthcare network (82,699). Validation Cohort: External validation cohort: all admission encounters from January 2014 to June 2017 from a fourth hospital in the Emory Healthcare network (40,143). Temporal validation cohort: all admission encounters from February to April 2020 from four hospitals in the Emory Healthcare network coronavirus disease tested (2,564) and coronavirus disease positive (389). Prediction Model: All admission encounters had vital signs, laboratory, and demographic data extracted. Exclusion criteria included invasive mechanical ventilation started within the operating room or advanced respiratory support within the first 8 hours of admission. Encounters were discretized into hour intervals from 8 hours after admission to discharge or advanced respiratory support initiation and binary labeled for advanced respiratory support. Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment, our eXtreme Gradient Boosting-based algorithm, was compared against Modified Early Warning Score. Results: Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment had significantly better discrimination than Modified Early Warning Score (area under the receiver operating characteristic curve 0.85 vs 0.57 [test], 0.84 vs 0.61 [external validation]). Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment maintained a positive predictive value (0.31–0.21) similar to that of Modified Early Warning Score greater than 4 (0.29–0.25) while identifying 6.62 (validation) to 9.58 (test) times more true positives. Furthermore, Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment performed more effectively in temporal validation (area under the receiver operating characteristic curve 0.86 [coronavirus disease tested], 0.93 [coronavirus disease positive]), while achieving identifying 4.25–4.51× more true positives. Conclusions: Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment is more effective than Modified Early Warning Score in predicting respiratory failure requiring advanced respiratory support at external validation and in coronavirus disease 2019 patients. Silent prospective validation necessary before local deployment.