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We compared the characteristics of hospitalized and nonhospitalized patients who had coronavirus disease in Atlanta, Georgia, USA. We found that risk for hospitalization increased with a patient's age and number of concurrent conditions. We also found a potential association between hospitalization and high hemoglobin A1c levels in persons with diabetes.

Background Coronavirus disease (COVID-19) can cause severe illness and death. Predictors of poor outcome collected on hospital admission may inform clinical and public health decisions. Methods We conducted a retrospective observational cohort investigation of 297 adults admitted to 8 academic and community hospitals in Georgia, United States, during March 2020. Using standardized medical record abstraction, we collected data on predictors including admission demographics, underlying medical conditions, outpatient antihypertensive medications, recorded symptoms, vital signs, radiographic findings, and laboratory values. We used random forest models to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for predictors of invasive mechanical ventilation (IMV) and death. Results Compared with age Conclusions After adjustment for patient and clinical characteristics, older age was the strongest predictor of death, exceeding comorbidities, abnormal vital signs, and laboratory test abnormalities. That coronary artery disease, but not chronic lung disease, was associated with death among hospitalized patients warrants further investigation, as do associations between certain antihypertensive medications and death.

SARS-CoV-2, the novel coronavirus that causes coronavirus disease 2019 (COVID-19), was first detected in the United States during January 2020 (1). Since then, >980,000 cases have been reported in the United States, including >55,000 associated deaths as of April 28, 2020 (2). Detailed data on demographic characteristics, underlying medical conditions, and clinical outcomes for persons hospitalized with COVID-19 are needed to inform prevention strategies and community-specific intervention messages. For this report, CDC, the Georgia Department of Public Health, and eight Georgia hospitals (seven in metropolitan Atlanta and one in southern Georgia) summarized medical record-abstracted data for hospitalized adult patients with laboratory-confirmed* COVID-19 who were admitted during March 2020. Among 305 hospitalized patients with COVID-19, 61.6% were aged

Purpose To determine if earlier initiation of renal replacement therapy (RRT) is associated with improved survival in patients with severe acute kidney injury. Methods We performed a retrospective case–control study of propensity-matched groups with multivariable logistic regression using Akaike Information Criteria to adjust for non-matched variables in a surgical ICU in a tertiary care hospital. Results We matched 169 of 205 (82%) patients with new initiation of RRT (EARLY group) to 169 similar patients who did not initiate RRT on that day (DEFERRED group). Eighteen (11%) of DEFERRED eventually received RRT before discharge. By univariate analysis, ICU mortality was higher in EARLY (n = 60 (36%) vs. n = 23 (14%), p Conclusions While ICU mortality was increased in the EARLY group, there was no difference in hospital mortality between EARLY and DEFERRED groups.

Objective: We sought to explore the technical and legal readiness of healthcare institutions for novel data-sharing methods that allow clinical information to be extracted from electronic health records (EHRs) and submitted securely to the Food and Drug Administration's (FDA's) blockchain through a secure data broker (SDB). Materials and Methods: This assessment was divided into four sections: an institutional EHR readiness assessment, legal consultation, institutional review board application submission, and a test of healthcare data transmission over a blockchain infrastructure. Results: All participating institutions reported the ability to electronically extract data from EHRs for research. Formal legal agreements were deemed unnecessary to the project but would be needed in future tests of real patient data exchange. Data transmission to the FDA blockchain met the success criteria of data connection from within the four institutions' firewalls, externally to the FDA blockchain via a SDB. Discussion: The readiness survey indicated advanced analytic capability in hospital institutions and highlighted inconsistency in Fast Healthcare Interoperability Resources format utilitzation across institutions, despite requirements of the 21st Century Cures Act. Further testing across more institutions and annual exercises leveraging the application of data exchange over a blockchain infrastructure are recommended actions for determining the feasibility of this approach during a public health emergency and broaden the understanding of technical requirements for multisite data extraction. Conclusion: The FDA's RAPID (Real-Time Application for Portable Interactive Devices) program, in collaboration with Discovery, the Critical Care Research Network's PREP (Program for Resilience and Emergency Preparedness), identified the technical and legal challenges and requirements for rapid data exchange to a government entity using the FDA blockchain infrastructure.

Background The epidemiological features and outcomes of hospitalized adults with coronavirus disease 2019 (COVID-19) have been described; however, the temporal progression and medical complications of disease among hospitalized patients require further study. Detailed descriptions of the natural history of COVID-19 among hospitalized patients are paramount to optimize health care resource utilization, and the detection of different clinical phenotypes may allow tailored clinical management strategies. Methods This was a retrospective cohort study of 305 adult patients hospitalized with COVID-19 in 8 academic and community hospitals. Patient characteristics included demographics, comorbidities, medication use, medical complications, intensive care utilization, and longitudinal vital sign and laboratory test values. We examined laboratory and vital sign trends by mortality status and length of stay. To identify clinical phenotypes, we calculated Gower’s dissimilarity matrix between each patient’s clinical characteristics and clustered similar patients using the partitioning around medoids algorithm. Results One phenotype of 6 identified was characterized by high mortality (49%), older age, male sex, elevated inflammatory markers, high prevalence of cardiovascular disease, and shock. Patients with this severe phenotype had significantly elevated peak C-reactive protein creatinine, D-dimer, and white blood cell count and lower minimum lymphocyte count compared with other phenotypes (P Conclusions Among a cohort of hospitalized adults, we identified a severe phenotype of COVID-19 based on the characteristics of its clinical course and poor prognosis. These findings need to be validated in other cohorts, as improved understanding of clinical phenotypes and risk factors for their development could help inform prognosis and tailored clinical management for COVID-19.

Supplemental Digital Content is available in the text., Objectives: Increasing time to mechanical ventilation and high-flow nasal cannula use may be associated with mortality in coronavirus disease 2019. We examined the impact of time to intubation and use of high-flow nasal cannula on clinical outcomes in patients with coronavirus disease 2019. Design: Retrospective cohort study. Setting: Six coronavirus disease 2019-specific ICUs across four university-affiliated hospitals in Atlanta, Georgia. Patients: Adults with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection who received high-flow nasal cannula or mechanical ventilation. Interventions: None. Measurements and Main Results: Among 231 patients admitted to the ICU, 109 (47.2%) were treated with high-flow nasal cannula and 97 (42.0%) were intubated without preceding high-flow nasal cannula use. Of those managed with high-flow nasal cannula, 78 (71.6%) ultimately received mechanical ventilation. In total, 175 patients received mechanical ventilation; 44.6% were female, 66.3% were Black, and the median age was 66 years (interquartile range, 56–75 yr). Seventy-six patients (43.4%) were intubated within 8 hours of ICU admission, 57 (32.6%) between 8 and 24 hours of admission, and 42 (24.0%) greater than or equal to 24 hours after admission. Patients intubated within 8 hours were more likely to have diabetes, chronic comorbidities, and higher admission Sequential Organ Failure Assessment scores. Mortality did not differ by time to intubation (≤ 8 hr: 38.2%; 8–24 hr: 31.6%; ≥ 24 hr: 38.1%; p = 0.7), and there was no association between time to intubation and mortality in adjusted analysis. Similarly, there was no difference in initial static compliance, duration of mechanical ventilation, or ICU length of stay by timing of intubation. High-flow nasal cannula use prior to intubation was not associated with mortality. Conclusions: In this cohort of critically ill patients with coronavirus disease 2019, neither time from ICU admission to intubation nor high-flow nasal cannula use were associated with increased mortality. This study provides evidence that coronavirus disease 2019 respiratory failure can be managed similarly to hypoxic respiratory failure of other etiologies.

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.

Objectives To determine mortality rates among adults with critical illness from coronavirus disease 2019. Design Observational cohort study of patients admitted from March 6, 2020, to April 17, 2020. Setting Six coronavirus disease 2019 designated ICUs at three hospitals within an academic health center network in Atlanta, Georgia, United States. Patients Adults greater than or equal to 18 years old with confirmed severe acute respiratory syndrome-CoV-2 disease who were admitted to an ICU during the study period. Interventions None. Measurements and main results Among 217 critically ill patients, mortality for those who required mechanical ventilation was 35.7% (59/165), with 4.8% of patients (8/165) still on the ventilator at the time of this report. Overall mortality to date in this critically ill cohort is 30.9% (67/217) and 60.4% (131/217) patients have survived to hospital discharge. Mortality was significantly associated with older age, lower body mass index, chronic renal disease, higher Sequential Organ Failure Assessment score, lower PaO2/FIO2 ratio, higher D-dimer, higher C-reactive protein, and receipt of mechanical ventilation, vasopressors, renal replacement therapy, or vasodilator therapy. Conclusions Despite multiple reports of mortality rates exceeding 50% among critically ill adults with coronavirus disease 2019, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.

SummaryWe report preliminary data from a cohort of adults admitted to COVID-designated intensive care units from March 6 through April 17, 2020 across an academic healthcare system. Among 217 critically ill patients, mortality for those who required mechanical ventilation was 29.7% (49/165), with 8.5% (14/165) of patients still on the ventilator at the time of this report. Overall mortality to date in this critically ill cohort is 25.8% (56/217), and 40.1% (87/217) patients have survived to hospital discharge. Despite multiple reports of mortality rates exceeding 50% among critically ill adults with COVID-19, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.

The American Society of Anesthesiologists Standards for Basic Monitoring recommends blood pressure (BP) measurement every 5 min. Research has shown distractions or technical factors can cause prolonged measurement intervals exceeding 5 min. We investigated the relationship between prolonged non-invasive BP (NIBP) measurement interval and the incidence of hypotension, detected postinterval. Our secondary outcome was to determine independent predictors of these prolonged NIBP measurement intervals. Retrospective data were analyzed from 139,509 general anesthesia cases from our institution’s Anesthesia Information Management System (AIMS). Absolute hypotension (AH) was defined a priori as a systolic BP < 80 mmHg and relative hypotension (RH) was defined as a 40% decrease in systolic BP from the preoperative baseline. Odds ratios (OR) with 95% confidence intervals and Pearson’s Chi square Test reported the association of prolonged NIBP measurement intervals on hypotension detected post-NIBP measurement interval. Logistic regression models were developed to determine independent predictors of NIBP measurement intervals. The analysis revealed that NIBP measurement intervals greater than 6 and 10 min are associated with an approximately four times higher incidence of a patient transitioning into hypotension (AH/RH > 6 min OR 4.0 / 3.6; AH/RH > 10 min OR 4.3 / 3.9; p< 0.001). A key finding was that the “> 10-minute AH model” indicated that age 41–80, increased co-morbidity profile, obesity and turning (repositioning) of the operative room table were significant predictors of prolonged NIBP measurement intervals (p < 0.001). While we do not suggest NIBP measurement intervals cause hypotension, intervals greater than 6 and 10 min are associated with a fourfold increase in the propensity of an undetected transition into both RH or AH. These data support current monitoring guidelines.

Background: Generating, reading, or interpreting data is a component of Telemedicine-Intensive Care Unit (Tele-ICU) utilization that has not been explored in the literature. Introduction: Using the idea of "coherence," a construct of Normalization Process Theory, we describe how intensive care unit (ICU) and Tele-ICU staff made sense of their shared work and how they made use of Tele-ICU together. Materials and Methods: We interviewed ICU and Tele-ICU staff involved in the implementation of Tele-ICU during site visits to a Tele-ICU hub and 3 ICUs, at preimplementation (43 interviews with 65 participants) and 6 months postimplementation (44 interviews with 67 participants). Data were analyzed using deductive coding techniques and lexical searches. Results: In the early implementation of Tele-ICU, ICU and Tele-ICU staff lacked consensus about how to share information and consequently how to make use of innovations in data tracking and interpretation offered by the Tele-ICU (e.g., acuity systems). Attempts to collaborate and create opportunities for utilization were supported by quality improvement (QI) initiatives. Discussion: Characterizing Tele-ICU utilization as an element of a QI process limited how ICU staff understood Tele-ICU as an innovation. It also did not promote an understanding of how the Tele-ICU used data and may therefore attenuate the larger promise of Tele-ICU as a potential tool for leveraging big data in critical care. Conclusions: Shared data practices lay the foundation for Tele-ICU program utilization but raise new questions about how the promise of big data can be operationalized for bedside ICU staff.

Raising public awareness of sepsis, a potentially life-threatening dysregulated host response to infection, to hasten its recognition has become a major focus of physicians, investigators, and both non-governmental and governmental agencies. While the internet is a common means by which to seek out healthcare information, little is understood about patterns and drivers of these behaviors. We sought to examine traffic to Wikipedia, a popular and publicly available online encyclopedia, to better understand how, when, and why users access information about sepsis. Utilizing pageview traffic data for all available language localizations of the sepsis and septic shock pages between July 1, 2015 and June 30, 2018, significantly outlying daily pageview totals were identified using a seasonal hybrid extreme studentized deviate approach. Consecutive outlying days were aggregated, and a qualitative analysis was undertaken of print and online news media coverage to identify potential correlates. Traffic patterns were further characterized using paired referrer to resource (i.e. clickstream) data, which were available for a temporal subset of the pageviews. Of the 20,557,055 pageviews across 65 linguistic localizations, 47 of the 1,096 total daily pageview counts were identified as upward outliers. After aggregating sequential outlying days, 25 epochs were examined. Qualitative analysis identified at least one major news media correlate for each, which were typically related to high-profile deaths from sepsis and, less commonly, awareness promotion efforts. Clickstream analysis suggests that most sepsis and septic shock Wikipedia pageviews originate from external referrals, namely search engines. Owing to its granular and publicly available traffic data, Wikipedia holds promise as a means by which to better understand global drivers of online sepsis information seeking. Further characterization of user engagement with this information may help to elucidate means by which to optimize the visibility, content, and delivery of awareness promotion efforts.

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.

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

The use of extracorporeal membrane oxygenation (ECMO) has grown rapidly in recent years. We sought to describe the rate of ECMO use in the United States, regional variation in ECMO use, the hospitals performing ECMO, and the primary payers for ECMO patients. Detailed data were obtained using the Healthcare Cost and Utilization Project (HCUPnet) summaries of State Inpatient Databases from 34 participating states for the years 2011-2014. The ECMO rates over time were modeled, overall and within subcategories of age group, bed size, hospital ownership, teaching status, and payer type. During the study period, the overall rate of ECMO use increased from 1.06 (1.01, 1.12) to 1.77 (1.72, 1.82) cases per 100,000 persons per year (p = 0.005). The rate of ECMO use varied significantly by region. Most ECMO patients are cared for at large hospitals, and at private, not-for-profit hospitals with teaching designation. The most common payer was private insurance; a minority of patient were uninsured. The use of ECMO increased significantly during the study period, but regional variation in the rate of ECMO use suggests that this technology is not being uniformly applied. Further research is warranted to determine why differences in ECMO use persist and what impact they have on patient outcomes.

Purpose: We sought to delineate highly visible publications related to sepsis. Within these subsets, elements of altmetrics performance, including mentions on Twitter, and the correlation between altmetrics and conventional citation counts were ascertained. Materials and Methods: Three subsets of sepsis publications from 2012 to 2017 were synthesized by the overall Altmetric.com attention score, number of mentions by unique Twitter users, and conventional citation counts. For these subsets, geolocated Twitter activity was plotted on a choropleth, the lag between publication date and altmetrics mentions was characterized, and correlations were examined between altmetrics performance and normalized conventional citation counts. Results: Of 57,152 PubMed query results, Altmetric.com data was available for 28,344 (49.6%). The top 50 publications by Altmetric.com attention score and Twitter attention represented a mix of original research and other types of work, garnering attention from Twitter users in 143 countries that was highly contemporaneous with publication. Altmetrics performance and conventional citation counts were poorly correlated. Conclusions: While unreliable to gauge impact or future citation potential, altmetrics may be valuable for parties who wish to detect and drive public awareness of research findings and may enable researchers to dynamically explore the reach of their work in novel dimensions.

OBJECTIVE: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes DESIGN: Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation SETTING: Tertiary teaching hospital system in Philadelphia, PA PATIENTS: All non-ICU admissions; algorithm derivation July 2011-June 2014 (n= 162,212); algorithm validation October-December 2015 (n=10,448); silent versus alert comparison January 2016-February 2017 (silent n= 22,280; alert n= 32,184). INTERVENTIONS: A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction. MEASUREMENT AND MAIN RESULT: Patients identified for training the algorithm were required to have ICD9 codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate > 2.2 mmol/L or a systolic blood pressure < 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and intravenous fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer. CONCLUSIONS: Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool, and optimizing algorithm design and delivery.

OBJECTIVES: In developed countries, public health systems have become adept at rapidly identifying the etiology and impact of public health emergencies. However, within the time course of clinical responses, shortfalls in readily analyzable patient-level data limit capabilities to understand clinical course, predict outcomes, ensure resource availability, and evaluate the effectiveness of diagnostic and therapeutic strategies for seriously ill and injured patients. To be useful in the timeline of a public health emergency, multi-institutional clinical investigation systems must be in place to rapidly collect, analyze, and disseminate detailed clinical information regarding patients across prehospital, emergency department, and acute care hospital settings, including ICUs. As an initial step to near real-time clinical learning during public health emergencies, we sought to develop an "all-hazards" core dataset to characterize serious illness and injuries and the resource requirements for acute medical response across the care continuum. SUBJECTS: A multidisciplinary panel of clinicians, public health professionals, and researchers with expertise in public health emergencies. DESIGN: Group consensus process. INTERVENTIONS: The consensus process included regularly scheduled conference calls, electronic communications, and an in-person meeting to generate candidate variables. Candidate variables were then reviewed by the group to meet the competing criteria of utility and feasibility resulting in the core dataset. MEASUREMENTS AND MAIN RESULTS: The 40-member panel generated 215 candidate variables for potential dataset inclusion. The final dataset includes 140 patient-level variables in the domains of demographics and anthropometrics (7), prehospital (11), emergency department (13), diagnosis (8), severity of illness (54), medications and interventions (38), and outcomes (9). CONCLUSIONS: The resulting all-hazard core dataset for seriously ill and injured persons provides a foundation to facilitate rapid collection, analyses, and dissemination of information necessary for clinicians, public health officials, and policymakers to optimize public health emergency response. Further work is needed to validate the effectiveness of the dataset in a variety of emergency settings. Language: en

The use of telemetry and invasive monitoring is exceptionally common in modern healthcare. To date the vast majority of this information is not stored for more than a brief duration on the local monitor. This prohibits extensive investigation into waveform data. We describe a system to collect such data in a quaternary care facility. Using standardized "packet sniffing" technology along with routine manual documentation, we reverse engineered the Unity network protocol used to transmit waveform data across the University of Michigan mission critical monitor network. Data was subsequently captured using a proprietary piece of software writing waveform data to local disks. Nightly, this data is post-processed using data from the admit-discharge-transfer system into individual patient waveforms for the day regardless of location. Over a 10 month period, over 2,785 individual patients had a total of 65,112 waveforms captured 15,978 from the operating rooms and 49,134 from the ICUs. The average OR case collected over 11 MB of data. The average single day data collection consisted of 8.6 GB of data. Entire hospital waveform data collection is possible using internally developed software enabling research on waveform data with minimal technical burden. Further research is required to determine the long-term storage and processing of such data.

Background: Acute respiratory distress syndrome (ARDS) remains a serious postoperative complication. Although ARDS prevention is a priority, the inability to identify patients at risk for ARDS remains a barrier to progress. The authors tested and refined the previously reported surgical lung injury prediction (SLIP) model in a multicenter cohort of at-risk surgical patients. Methods: This is a secondary analysis of a multicenter, prospective cohort investigation evaluating high-risk patients undergoing surgery. Preoperative ARDS risk factors and risk modifiers were evaluated for inclusion in a parsimonious risk-prediction model. Multiple imputation and domain analysis were used to facilitate development of a refined model, designated SLIP-2. Area under the receiver operating characteristic curve and the Hosmer–Lemeshow goodness-of-fit test were used to assess model performance. Results: Among 1,562 at-risk patients, ARDS developed in 117 (7.5%). Nine independent predictors of ARDS were identified: sepsis, high-risk aortic vascular surgery, high-risk cardiac surgery, emergency surgery, cirrhosis, admission location other than home, increased respiratory rate (20 to 29 and ≥30 breaths/min), Fio2 greater than 35%, and Spo2 less than 95%. The original SLIP score performed poorly in this heterogeneous cohort with baseline risk factors for ARDS (area under the receiver operating characteristic curve [95% CI], 0.56 [0.50 to 0.62]). In contrast, SLIP-2 score performed well (area under the receiver operating characteristic curve [95% CI], 0.84 [0.81 to 0.88]). Internal validation indicated similar discrimination, with an area under the receiver operating characteristic curve of 0.84. Conclusions: In this multicenter cohort of patients at risk for ARDS, the SLIP-2 score outperformed the original SLIP score. If validated in an independent sample, this tool may help identify surgical patients at high risk for ARDS.

Over the past two decades, the prescription of high dose opiate therapy has continued to accelerate in an attempt to treat patients with chronic pain. This presents a substantial challenge when patients on high dose opiate therapy require surgery, as opiate pain relief is a cornerstone of postoperative pain management. These patients have exceptionally challenging pain to control. This is likely due to downregulation of existing opiate receptors and the reluctance of clinicians to increase doses of opiates to exceptionally high levels to facilitate pain relief. We hypothesize that using the method of ultra-rapid opiate detoxification (UROD), it would be possible to rapidly increase the number of opiate receptors and return patients to a more naive state, which would be susceptible to exogenous opiate administration. Validation of this hypothesis is supported by two mechanisms, the first of which are reports of patients that underwent UROD for opiate addition that subsequently suffer respiratory arrests when beginning to rapidly abuse opiates shortly after treatment. Additionally there are data demonstrating the tapering of opiate therapy prior to elective surgery results in better pain control. In conclusion, we hypothesize that patients on chronic high dose opiates could obtain substantially better pain relief if they underwent UROD prior to surgery. This technique could be administered shortly before surgery and may dramatically improve the patients' recoveries.

Extubation failure is associated with worse outcomes. Physicians use respiratory parameters to help them to decide to extubate or not. The purpose of this study is to determine if novel measures of respiratory physiology such as complexity and variability can identify patients who will tolerate extubation.Eighty-three patients who required reinstitution of mechanical ventilation after extubation (Extub-Fail) were matched to 83 patients who successfully tolerated extubation (Extub-Success) and to 83 who failed a spontaneous breathing trial (SBT-Fail). Patients were compared using interbreath intervals and tidal volumes using means, SDs, measures of complexity, spikiness, and rapid shallow breathing index (RSBI).From SBT-Fail to Extub-Fail to Extub-Success, the interbreath intervals progressively lengthened (mean ± SD, 2.2 seconds ± 0.6 vs 2.5 ± 0.6 vs 2.7 ± 0.8; P.001), became more variable (mean ± SD, 0.57 ± 0.13 vs 0.62 ± 0.21 vs 0.66 ± 0.23; P = .012), and became more complex. Complexity as measured by approximate and sample entropy was greater in the Extub-Success group than in the SBT-Fail group. Patients who were in the SBT-Fail group had a higher RSBI than did either of the extubated groups, but there was no difference in RSBI between Extub-Success and Extub-Fail.Patients who successfully tolerate extubation have longer interbreath interval with greater complexity and variability of that series. Although the V(T) was similar between the Extub-Success and the SBT-Fail groups, the Extub-Success group had greater long-term complexity with no difference in short-term complexity. This implies that the respiratory controllers are differently impacted in patients tolerating or not tolerating extubation.

We present a patient on venovenous extracorporeal membrane oxygenation for respiratory failure, secondary to complete airway obstruction from pulmonary hemorrhage after open pulmonary embolectomy. Obstruction was refractory to conventional therapy, including mechanical debridement and saline lavage. By using perfluorodecalin, we were able to transiently clear the airways of thrombus and reopen distal airways and alveoli more completely than with conventional therapies. The use of perfluorodecalin (versus radiopaque perflubron) enabled us to visualize this reopening on chest X-ray.

INTRODUCTION childhood obesity is a risk factor for many perioperative complications. Hypotension is a well described complication of general anaesthesia in both adults and children. This observational study compared the incidence of preincision hypotension (PIH) between children with high BMI and lean controls. METHODS children aged 2-17 years undergoing noncardiac procedures were classified into high or normal BMI groups. The incidence of PIH was then compared between the two groups using SBP data abstracted from our electronic anaesthesia monitoring system. Binary logistic regression was used to examine factors associated with the likelihood of PIH. RESULTS the study population was 19 400 children (54% boys) with a mean (SD) age of 8.3 ± 4.7 years and BMI of 19.3 ± 5.7 kg m(-2). Most (94.7%) of the patients were elective American Society of Anesthesiology I-II (79.7%) procedures. Single episode of PIH occurred in 36.8% of patients, whereas 8.3% had at least three episodes of PIH. PIH was more frequent in children with high BMI than their lean peers (40.9 vs. 31.4%, P < 0.001). Independent predictors of PIH were high BMI, high American Society of Anesthesiology status, propofol coinduction, baseline hypotension, age and preincision duration. CONCLUSION these results imply that children with high BMI have a higher incidence of hypotension than their lean peers following induction of anaesthesia for noncardiac procedures.

Hypoxia is a common finding in the anesthetized patient. Although there are a variety of methods to address hypoxia, it is not well documented what strategies are used by anesthesiologists when faced with a hypoxic patient. Studies have identified that lung protective ventilation strategies have beneficial effects in both oxygenation and mortality in acute respiratory distress syndrome. We sought to describe the ventilation strategies in anesthetized patients with varying degrees of hypoxemia as defined by the Pao(2) to fraction of inspired oxygen (Fio(2)) (P/F) ratio.We conducted a review of all operations performed between January 1, 2005, and July 31, 2009, using a general anesthetic, excluding cardiac and thoracic procedures, to assess the ventilation settings that were used in patients with different P/F ratios. Patients older than 18 years who received a general anesthetic were included. Four cohorts of arterial blood gases (ABGs) were identified with P/F300, 300or = P/F200, 200or = P/F100, 100or = P/F. Using the standard predicted body weight (PBW) equation, we calculated the milliliters per kilogram (mL/kg PBW) with which the patient's lungs were being ventilated. Positive end-expiratory pressure (PEEP), peak inspiratory pressures (PIPs), Fio(2), oxygen saturation (Sao(2)), and tidal volume in mL/kg PBW were compared.A total of 28,706 ABGs from 11,445 operative cases met criteria for inclusion. There were 19,679 ABGs from the P/F300 group, 5364 ABGs from the 300or = P/F200 group, 3101 ABGs from the 200or = P/F100 group, and 562 ABGs from the 100or = P/F group identified. A comparison of ventilation strategies found statistical significance but clinically irrelevant differences. Tidal volumes ranged between 8.64 and 9.16 and the average PEEP varied from 2.5 to 5.5 cm H(2)O. There were substantial differences in the average Fio(2) and PIP among the groups, 59% to 91% and 22 to 29 cm H(2)O, respectively.Similar ventilation strategies in mL/kg PBW and PEEP were used among patients regardless of P/F ratio. The results of this study suggest that anesthesiologists, in general, are treating hypoxemia with higher Fio(2) and PIP. The average Fio(2) and PIP were significantly escalated depending on the P/F ratio.