Successfully transitioning an interruptive alert into a non-interruptive alert for central line dressing changes in the Neonatal Intensive Care Unit.

BACKGROUND: Interruptive alerts are known to be associated with clinician alert fatigue, and poorly performing alerts should be evaluated for alternative solutions. An interruptive alert to remind clinicians about a required peripherally inserted central catheter (PICC) dressing change within the first 48-hours after placement resulted in 617 firings in a 6-month period with only 11 (1.7%) actions taken from the alert. OBJECTIVE: To enhance a poorly functioning interruptive alert by converting it to a non-interruptive alert aiming to improve compliance with the institutional PICC dressing change protocol. The primary outcome was to measure the percentage of initial PICC dressing changes that occurred beyond the recommended 48-hour timeframe after PICC placement. Secondary outcomes included measuring the time to first dressing change and, qualitatively, if this solution could replace the manual process of maintaining a physical list of patients. METHODS: A clinical informatics team met with stakeholders to evaluate the clinical workflow and identified an additional need to track which patients qualified for dressing changes. A non-interruptive patient column clinical decision support (CDS) tool was created to replace an interruptive alert. A pre-post intervention mixed-methods cohort study was conducted between January 2022 - November 2022. RESULTS: The number of patients with overdue PICC dressing changes decreased from 21.9% (40/183) to 7.8% (10/128) of eligible patients (p <0.001), and mean time to first PICC dressing changes also significantly decreased from 40.8 hours to 30.7 hours (p = 0.02). There was universal adoption of the CDS tool, and clinicians no longer used the manual patient list. CONCLUSIONS: While previous studies have reported that non-interruptive CDS may not be as effective as interruptive CDS, this case report demonstrates that developing a population-based CDS in the patient list column that provides an additional desired functionality to clinicians may result in improved adoption of CDS.

Application of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults.

BACKGROUND: Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur. RESEARCH QUESTION: Do ML alerts, telemedicine system (TS)-generated alerts, or biomedical monitors (BMs) have superior performance for predicting episodes of intubation or administration of vasopressors? STUDY DESIGN AND METHODS: An ML algorithm was trained to predict intubation and vasopressor initiation events among critically ill adults. Its performance was compared with BM alarms and TS alerts. RESULTS: ML notifications were substantially more accurate and precise, with 50-fold lower alarm burden than TS alerts for predicting vasopressor initiation and intubation events. ML notifications of internal validation cohorts demonstrated similar performance for independent academic medical center external validation and COVID-19 cohorts. Characteristics were also measured for a control group of recent patients that validated event detection methods and compared TS alert and BM alarm performance. The TS test characteristics were substantially better, with 10-fold less alarm burden than BM alarms. The accuracy of ML alerts (0.87-0.94) was in the range of other clinically actionable tests; the accuracy of TS (0.28-0.53) and BM (0.019-0.028) alerts were not. Overall test performance (F scores) for ML notifications were more than fivefold higher than for TS alerts, which were higher than those of BM alarms. INTERPRETATION: ML-derived notifications for clinically actioned hemodynamic instability and respiratory failure events represent an advance because the magnitude of the differences of accuracy, precision, misclassification rate, and pre-event lead time is large enough to allow more proactive care and has markedly lower frequency and interruption of bedside physician work flows.

Impact of a switch to immediate release on the patient viewing of diagnostic test results in an online portal at an academic medical center.

Patient portals allow patients to access their personal health information. The 21st Century Cures Act in the United States sought to eliminate 'information blocking', requiring timely release upon request of electronic health information including diagnostic test results. Some health systems, including the one in the present study, chose a systematic switch to immediate release of all or nearly all diagnostic test results to patient portals as part of compliance with the Cures Act. Our primary objective was to study changes in the time to view test results by patients before and after implementation of Cures Act-related changes. This retrospective pre-post study included data from two 10-month time periods before and after implementation of Cures Act-related changes at an academic medical center. The study included all patients (adult and pediatric) with diagnostic testing (laboratory and imaging) performed in the outpatient, inpatient, or emergency department settings. Between February 9, 2020 and December 9, 2021, there was a total of 3 809 397 diagnostic tests from 204 605 unique patients (3 320 423 tests for adult patients; 488 974 for pediatric patients). Overall, 56.5% (115 627) of patients were female, 84.1% (172 048) white, and 96.5% (197 517) preferred English as primary language. The odds of viewing test results within 1 and 30 days after portal release increased monthly throughout both time periods before and after the Cures Act for all patients. The rate of increase was significantly higher after implementation only in the subgroup of tests belonging to adult patients with active MyChart accounts. Immediate release shifted a higher proportion of result/report release to weekends (3.2% pre-Cures vs 15.3% post-Cures), although patient viewing patterns by day of week and time of day were similar before and after immediate release changes. The switch to immediate release of diagnostic test results to the patient portal resulted in a higher fraction of results viewed within 1 day across outpatient, inpatient, and emergency department settings.

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.

We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88-0.90), sensitivity of 0.77 (95% CI = 0.75-0.78), specificity 0.85 (95% CI = 085-0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81-0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO2), standard deviation of the systolic blood pressure (SBP), O2 flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18-40) (AUROC = 0.93 [0.92-0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81-0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.

Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data.

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.

Characteristics and Risk Factors of Hospitalized and Nonhospitalized COVID-19 Patients, Atlanta, Georgia, USA, March-April 2020.

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.

The effect of timing of initiation of renal replacement therapy on mortality: A retrospective case-control study.

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 < 0.001) as was hospital mortality (n = 73 (43%) vs. n = 44 (26%), p = 0.001). Of the 18 RRT patients in DEFERRED, 12 (67%) died in ICU and 13 (72%) in hospital. After propensity matching and logistic regression, we found that EARLY initiation of RRT was associated with a more than doubling of ICU mortality (aOR = 2.310, 95% confidence interval = 1.254-4.257, p = 0.007). However, after similar adjustment, there was no difference in hospital mortality (aOR = 1.283, 95% CI = 0.753-2.186, p = 0.360). CONCLUSIONS: While ICU mortality was increased in the EARLY group, there was no difference in hospital mortality between EARLY and DEFERRED groups.

COVID-19 Clinical Phenotypes: Presentation and Temporal Progression of Disease in a Cohort of Hospitalized Adults in Georgia, United States.

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 < .01, all comparisons). 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.

Predictors at Admission of Mechanical Ventilation and Death in an Observational Cohort of Adults Hospitalized With Coronavirus Disease 2019.

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 <45 years, ages 65-74 years and ≥75 years were predictors of IMV (aORs, 3.12 [95% CI, 1.47-6.60] and 2.79 [95% CI, 1.23-6.33], respectively) and the strongest predictors for death (aORs, 12.92 [95% CI, 3.26-51.25] and 18.06 [95% CI, 4.43-73.63], respectively). Comorbidities associated with death (aORs, 2.4-3.8; P < .05) included end-stage renal disease, coronary artery disease, and neurologic disorders, but not pulmonary disease, immunocompromise, or hypertension. Prehospital use vs nonuse of angiotensin receptor blockers (aOR, 2.02 [95% CI, 1.03-3.96]) and dihydropyridine calcium channel blockers (aOR, 1.91 [95% CI, 1.03-3.55]) were associated with death. 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.

Timing of Intubation and Mortality Among Critically Ill Coronavirus Disease 2019 Patients: A Single-Center Cohort Study.

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.

ICU and ventilator mortality among critically ill adults with COVID-19.

We 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.

Characteristics Associated with Hospitalization Among Patients with COVID-19 - Metropolitan Atlanta, Georgia, March-April 2020.

The first reported U.S. case of coronavirus disease 2019 (COVID-19) was detected in January 2020 (1). As of June 15, 2020, approximately 2 million cases and 115,000 COVID-19-associated deaths have been reported in the United States.* Reports of U.S. patients hospitalized with SARS-CoV-2 infection (the virus that causes COVID-19) describe high proportions of older, male, and black persons (2-4). Similarly, when comparing hospitalized patients with catchment area populations or nonhospitalized COVID-19 patients, high proportions have underlying conditions, including diabetes mellitus, hypertension, obesity, cardiovascular disease, chronic kidney disease, or chronic respiratory disease (3,4). For this report, data were abstracted from the medical records of 220 hospitalized and 311 nonhospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 from six acute care hospitals and associated outpatient clinics in metropolitan Atlanta, Georgia. Multivariable analyses were performed to identify patient characteristics associated with hospitalization. The following characteristics were independently associated with hospitalization: age ≥65 years (adjusted odds ratio [aOR] = 3.4), black race (aOR = 3.2), having diabetes mellitus (aOR = 3.1), lack of insurance (aOR = 2.8), male sex (aOR = 2.4), smoking (aOR = 2.3), and obesity (aOR = 1.9). Infection with SARS-CoV-2 can lead to severe outcomes, including death, and measures to protect persons from infection, such as staying at home, social distancing (5), and awareness and management of underlying conditions should be emphasized for those at highest risk for hospitalization with COVID-19. Measures that prevent the spread of infection to others, such as wearing cloth face coverings (6), should be used whenever possible to protect groups at high risk. Potential barriers to the ability to adhere to these measures need to be addressed.

Characteristics and Clinical Outcomes of Adult Patients Hospitalized with COVID-19 - Georgia, March 2020.

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 <65 years, 50.5% were female, and 83.2% with known race/ethnicity were non-Hispanic black (black). Over a quarter of patients (26.2%) did not have conditions thought to put them at higher risk for severe disease, including being aged ≥65 years. The proportion of hospitalized patients who were black was higher than expected based on overall hospital admissions. In an adjusted time-to-event analysis, black patients were not more likely than were nonblack patients to receive invasive mechanical ventilation† (IMV) or to die during hospitalization (hazard ratio [HR] = 0.63; 95% confidence interval [CI] = 0.35-1.13). Given the overrepresentation of black patients within this hospitalized cohort, it is important for public health officials to ensure that prevention activities prioritize communities and racial/ethnic groups most affected by COVID-19. Clinicians and public officials should be aware that all adults, regardless of underlying conditions or age, are at risk for serious illness from COVID-19.

ICU and Ventilator Mortality Among Critically Ill Adults With Coronavirus Disease 2019.

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.

Utilized or Underutilized: A Qualitative Analysis of Building Coherence During Early Implementation of a Tele-Intensive Care Unit.

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.

Sepsis information-seeking behaviors via Wikipedia between 2015 and 2018: A mixed methods retrospective observational study.

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.

Trends in Extracorporeal Membrane Oxygenation Growth in the United States, 2011-2014.

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.