Risk Factors for and Prediction of Post-Intubation Hypotension in Critically Ill Adults: A Multicenter Prospective Cohort Study

OBJECTIVE: Hypotension following endotracheal intubation in the ICU is associated with poor outcomes. There is no formal prediction tool to help estimate the onset of this hemodynamic compromise. Our objective was to derive and validate a prediction model for immediate hypotension following endotracheal intubation. METHODS: A multicenter, prospective, cohort study enrolling 934 adults who underwent endotracheal intubation across 16 medical/surgical ICUs in the United States from July 2015-January 2017 was conducted to derive and validate a prediction model for immediate hypotension following endotracheal intubation. We defined hypotension as: 1) mean arterial pressure \u3c 65 mmHg; 2) systolic blood pressure \u3c 80 mmHg and/or decrease in systolic blood pressure of 40% from baseline; 3) or the initiation or increase in any vasopressor in the 30 minutes following endotracheal intubation. RESULTS: Post-intubation hypotension developed in 344 (36.8%) patients. In the full cohort, 11 variables were independently associated with hypotension: increasing illness severity; increasing age; sepsis diagnosis; endotracheal intubation in the setting of cardiac arrest, mean arterial pressure \u3c 65 mmHg, and acute respiratory failure; diuretic use 24 hours preceding endotracheal intubation; decreasing systolic blood pressure from 130 mmHg; catecholamine and phenylephrine use immediately prior to endotracheal intubation; and use of etomidate during endotracheal intubation. A model excluding unstable patients’ pre-intubation (those receiving catecholamine vasopressors and/or who were intubated in the setting of cardiac arrest) was also developed and included the above variables with the exception of sepsis and etomidate. In the full cohort, the 11 variable model had a C-statistic of 0.75 (95% CI 0.72, 0.78). In the stable cohort, the 7 variable model C-statistic was 0.71 (95% CI 0.67, 0.75). In both cohorts, a clinical risk score was developed stratifying patients’ risk of hypotension. CONCLUSIONS: A novel multivariable risk score predicted post-intubation hypotension with accuracy in both unstable and stable critically ill patients. STUDY REGISTRATION: Clinicaltrials.gov identifier: NCT02508948 and Registered Report Identifier: RR2-10.2196/11101

Towards the prevention of acute lung injury: a population based cohort study protocol

<p>Abstract</p> <p>Background</p> <p>Acute lung injury (ALI) is an example of a critical care syndrome with limited treatment options once the condition is fully established. Despite improved understanding of pathophysiology of ALI, the clinical impact has been limited to improvements in supportive treatment. On the other hand, little has been done on the prevention of ALI. Olmsted County, MN, geographically isolated from other urban areas offers the opportunity to study clinical pathogenesis of ALI in a search for potential prevention targets.</p> <p>Methods/Design</p> <p>In this population-based observational cohort study, the investigators identify patients at high risk of ALI using the prediction model applied within the first six hours of hospital admission. Using a validated system-wide electronic surveillance, Olmsted County patients at risk are followed until ALI, death or hospital discharge. Detailed in-hospital (second hit) exposures and meaningful short and long term outcomes (quality-adjusted survival) are compared between ALI cases and high risk controls matched by age, gender and probability of developing ALI. Time sensitive biospecimens are collected for collaborative research studies. Nested case control comparison of 500 patients who developed ALI with 500 matched controls will provide an adequate power to determine significant differences in common hospital exposures and outcomes between the two groups.</p> <p>Discussion</p> <p>This population-based observational cohort study will identify patients at high risk early in the course of disease, the burden of ALI in the community, and the potential targets for future prevention trials.</p

Validation of Automated Data Abstraction for SCCM Discovery VIRUS COVID-19 Registry: Practical EHR Export Pathways (VIRUS-PEEP)

BACKGROUND: The gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities. OBJECTIVE: This study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients. MATERIALS AND METHODS: This observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen\u27s kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson\u27s correlation coefficient and Bland-Altman plots. The strength of agreement was defined as almost perfect (0.81-1.00), substantial (0.61-0.80), and moderate (0.41-0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00-0.30), low (0.30-0.50), moderate (0.50-0.70), high (0.70-0.90), and extremely high (0.90-1.00). MEASUREMENTS AND MAIN RESULTS: The cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%. CONCLUSION AND RELEVANCE: Our study confirms the feasibility and validity of an automated process to gather data from the EHR

Validation of automated data abstraction for SCCM discovery VIRUS COVID-19 registry: practical EHR export pathways (VIRUS-PEEP)

BackgroundThe gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities.ObjectiveThis study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients.Materials and methodsThis observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen’s kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson’s correlation coefficient and Bland–Altman plots. The strength of agreement was defined as almost perfect (0.81–1.00), substantial (0.61–0.80), and moderate (0.41–0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00–0.30), low (0.30–0.50), moderate (0.50–0.70), high (0.70–0.90), and extremely high (0.90–1.00).Measurements and main resultsThe cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%.Conclusion and relevanceOur study confirms the feasibility and validity of an automated process to gather data from the EHR

Metabolic Syndrome and Acute Respiratory Distress Syndrome in Hospitalized Patients With COVID-19

Importance: Obesity, diabetes, and hypertension are common comorbidities in patients with severe COVID-19, yet little is known about the risk of acute respiratory distress syndrome (ARDS) or death in patients with COVID-19 and metabolic syndrome. Objective: To determine whether metabolic syndrome is associated with an increased risk of ARDS and death from COVID-19. Design, setting, and participants: This multicenter cohort study used data from the Society of Critical Care Medicine Discovery Viral Respiratory Illness Universal Study collected from 181 hospitals across 26 countries from February 15, 2020, to February 18, 2021. Outcomes were compared between patients with metabolic syndrome (defined as ≥3 of the following criteria: obesity, prediabetes or diabetes, hypertension, and dyslipidemia) and a control population without metabolic syndrome. Participants included adult patients hospitalized for COVID-19 during the study period who had a completed discharge status. Data were analyzed from February 22 to October 5, 2021. Exposures: Exposures were SARS-CoV-2 infection, metabolic syndrome, obesity, prediabetes or diabetes, hypertension, and/or dyslipidemia. Main outcomes and measures: The primary outcome was in-hospital mortality. Secondary outcomes included ARDS, intensive care unit (ICU) admission, need for invasive mechanical ventilation, and length of stay (LOS). Results: Among 46 441 patients hospitalized with COVID-19, 29 040 patients (mean [SD] age, 61.2 [17.8] years; 13 059 [45.0%] women and 15713 [54.1%] men; 6797 Black patients [23.4%], 5325 Hispanic patients [18.3%], and 16 507 White patients [57.8%]) met inclusion criteria. A total of 5069 patients (17.5%) with metabolic syndrome were compared with 23 971 control patients (82.5%) without metabolic syndrome. In adjusted analyses, metabolic syndrome was associated with increased risk of ICU admission (adjusted odds ratio [aOR], 1.32 [95% CI, 1.14-1.53]), invasive mechanical ventilation (aOR, 1.45 [95% CI, 1.28-1.65]), ARDS (aOR, 1.36 [95% CI, 1.12-1.66]), and mortality (aOR, 1.19 [95% CI, 1.08-1.31]) and prolonged hospital LOS (median [IQR], 8.0 [4.2-15.8] days vs 6.8 [3.4-13.0] days; P \u3c .001) and ICU LOS (median [IQR], 7.0 [2.8-15.0] days vs 6.4 [2.7-13.0] days; P \u3c .001). Each additional metabolic syndrome criterion was associated with increased risk of ARDS in an additive fashion (1 criterion: 1147 patients with ARDS [10.4%]; P = .83; 2 criteria: 1191 patients with ARDS [15.3%]; P \u3c .001; 3 criteria: 817 patients with ARDS [19.3%]; P \u3c .001; 4 criteria: 203 patients with ARDS [24.3%]; P \u3c .001). Conclusions and relevance: These findings suggest that metabolic syndrome was associated with increased risks of ARDS and death in patients hospitalized with COVID-19. The association with ARDS was cumulative for each metabolic syndrome criteria present

GW190412: Observation of a Binary-Black-Hole Coalescence with Asymmetric Masses

We report the observation of gravitational waves from a binary-black-hole coalescence during the first two weeks of LIGO’s and Virgo’s third observing run. The signal was recorded on April 12, 2019 at 05∶30∶44 UTC with a network signal-to-noise ratio of 19. The binary is different from observations during the first two observing runs most notably due to its asymmetric masses: a ∼30 M_⊙ black hole merged with a ∼8 M_⊙ black hole companion. The more massive black hole rotated with a dimensionless spin magnitude between 0.22 and 0.60 (90% probability). Asymmetric systems are predicted to emit gravitational waves with stronger contributions from higher multipoles, and indeed we find strong evidence for gravitational radiation beyond the leading quadrupolar order in the observed signal. A suite of tests performed on GW190412 indicates consistency with Einstein’s general theory of relativity. While the mass ratio of this system differs from all previous detections, we show that it is consistent with the population model of stellar binary black holes inferred from the first two observing runs

Erratum: “Searches for Gravitational Waves from Known Pulsars at Two Harmonics in 2015–2017 LIGO Data” (2019, ApJ, 879, 10)

Due to an error at the publisher, in the published article the number of pulsars presented in the paper is incorrect in multiple places throughout the text. Specifically, "222" pulsars should be "221." Additionally, the number of pulsars for which we have EM observations that fully overlap with O1 and O2 changes from "168" to "167." Elsewhere, in the machine-readable table of Table 1 and in Table 2, the row corresponding to pulsar J0952-0607 should be excised as well. Finally, in the caption for Table 2 the number of pulsars changes from "188" to "187.