Search

Pneumomediastinum is a rare complication among non-coronavirus patients but has been published with increased incidence in patients positive for SARS-CoV-2 infection. Most of these studies report patients on mechanical ventilation and an understanding of mechanisms causing this remains limited. We aim to use an increasing occurrence in patients not on mechanical ventilation to further explore mechanisms that predispose patients to pneumomediastinum and to assess characteristics potentially related to poor outcomes. We report a case series of 37 patients diagnosed with COVID-19 and pneumomediastinum at a 2-hospital institution between January 1, 2020 and April 30, 2021. At 28 days after diagnosis of pneumomediastinum, 19 (51.4%) were dead and mortality was significantly higher among those who were older (t = 2.147, P =.039), female (χ2 = 10.431, P =.015), body mass index ≥30 (χ2 = 6.0598, P =.01), intubated (χ2 = 4.937, P =.026), and had pre-existing lung disease (χ2 = 4.081, P =.043). Twenty-three patients (62.2%) were identified to have pneumomediastinum without receiving invasive mechanical ventilation, of which 11 (47.8%) were diagnosed without receiving noninvasive ventilation. The increased diagnosis of pneumomediastinum in patients with COVID-19 while not on mechanical ventilation, in this case series and in comparable studies, may attribute to mechanisms aside from positive pressure ventilation such as patient self-induced lung injury and pulmonary frailty. [ABSTRACT FROM AUTHOR]

Purpose: PaO2 to FiO2 ratio (P/F) is used to assess the degree of hypoxemia adjusted for oxygen requirements. The Berlin definition of Acute Respiratory Distress Syndrome (ARDS) includes P/F as a diagnostic criterion. P/F is invasive and cost-prohibitive for resource-limited settings. SaO2/FiO2 (S/F) ratio has the advantages of being easy to calculate, noninvasive, continuous, cost-effective, and reliable, as well as lower infection exposure potential for staff, and avoids iatrogenic anemia. Previous work suggests that the SaO2/FiO2 ratio (S/F) correlates with P/F and can be used as a surrogate in ARDS. Quantitative correlation between S/F and P/F has been verified, but the data for the relative predictive ability for ICU mortality remains in question. We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. Using a machine-learning approach, we hope to demonstrate the relative mortality predictive capacities of S/F and P/F.Methods: We extracted data from the eICU Collaborative Research Database. The features age, gender, SaO2, PaO2, FIO2, admission diagnosis, Apache IV, mechanical ventilation (MV), and ICU mortality were extracted. Mortality was the dependent variable for our prediction models. Exploratory data analysis was performed in Python. Missing data was imputed with Sklearn Iterative Imputer. Random assignment of all the encounters, 80% to the training (n = 26690) and 20% to testing (n = 6741), was stratified by positive and negative classes to ensure a balanced distribution. We scaled the data using the Sklearn Standard Scaler. Categorical values were encoded using Target Encoding. We used a gradient boosting decision tree algorithm variant called XGBoost as our model. Model hyperparameters were tuned using the Sklearn RandomizedSearchCV with tenfold cross-validation. We used AUC as our metric for model performance. Feature importance was assessed using SHAP, ELI5 (permutation importance), and a built-in XGBoost feature importance method. We constructed partial dependence plots to illustrate the relationship between mortality probability and S/F values.Results: The XGBoost hyperparameter optimized model had an AUC score of .85 on the test set. The hyperparameters selected to train the final models were as follows: colsample_bytree of 0.8, gamma of 1, max_depth of 3, subsample of 1, min_child_weight of 10, and scale_pos_weight of 3. The SHAP, ELI5, and XGBoost feature importance analysis demonstrates that the S/F ratio ranks as the strongest predictor for mortality amongst the physiologic variables. The partial dependence plots illustrate that mortality rises significantly above S/F values of 200.Conclusion: S/F was a stronger predictor of mortality than P/F based upon feature importance evaluation of our data. Our study is hypothesis-generating and a prospective evaluation is warranted. Take-Home Points. S/F ratio is a noninvasive continuous method of measuring hypoxemia as compared to P/F ratio. Our study shows that the S/F ratio is a better predictor of mortality than the more widely used P/F ratio to monitor and manage hypoxemia. [ABSTRACT FROM AUTHOR]

• We describe the role of a novel vascular access service in decreasing the rate of central line–associated bloodstream infections. • The reduction in central line–associated bloodstream infections was achieved by prioritizing insertion of the least invasive intravascular catheter and promoting vascular access device care and maintenance. • High-quality research is required to evaluate the impact of vascular access service in improving patient outcomes and safety. This study describes the role of a novel vascular access service in the reduction and prevention of central line–associated bloodstream infections (CLABSIs). We conducted a retrospective analysis of data obtained over a span of 24 months after implementation of our vascular access service. We identified a progressive decline in the CLABSI rate and standardized infection ratio (SIR) in 2017 (rate, 1.75; SIR, 1.25) and in 2018 (rate, 1.037; SIR, 0.91). The reduction in CLABSIs was attributed to appropriate triage, insertion, and maintenance of vascular access devices. [ABSTRACT FROM AUTHOR]

Objective: To determine the clinical and financial feasibility of implementing a poc-EEG system in a community hospital., Design: Data from a prospective cohort displaying abnormal mentation concerning for NCSE or rhythmic movements due to potential underlying seizure necessitating EEG was collected and compared to a control group containing patient data from 2020., Setting: A teaching community hospital with limited EEG support., Patients: The study group consisted of patients requiring emergent EEG during hours when conventional EEG was unavailable. Control group is made up of patients who were emergently transferred for EEG during the historical period., Interventions: Application and interpretation of Ceribell®, a poc-EEG system., Measurement and Main Results: 88 patients were eligible with indications for poc-EEG including hyperkinetic movements post-cardiac arrest (19%), abnormal mentation after possible seizure (46%), and unresponsive patients with concern for NCSE (35%). 21% had seizure burden on poc-EEG and 4.5% had seizure activity on follow-up EEG. A mean of 1.1 patients per month required transfer to a tertiary care center for continuous EEG. For the control period, a total of 22 patients or a mean of 2 patients per month were transferred for emergent EEG. Annually, we observed a decrease in the number of transferred patients in the post-implementation period by 10.8 (95% CI: -2.17-23.64, p  = 0.1). Financial analysis of the control found the hospital system incurred a loss of $3,463.11 per patient transferred for an annual loss of $83,114.64. In the study group, this would compute to an annual loss of $45,713.05 for an overall decrease in amount lost of $37,401.59. We compared amount lost per patient between historical controls and study patients. Implementation of poc-EEG resulted in an overall decrease in annual amount lost of $37,401.59 by avoidance of transfer fees. We calculated the amount gained per patient in the study group to be $13,936.44. To cover the cost of the poc-EEG system, 8.59 patients would need to avoid transfer annually., Conclusion: A poc-EEG system can be safely implemented in a community hospital leading to an absolute decrease in transfers to tertiary hospital. This decrease in patient transfers can cover the cost of implementing the poc-EEG system. The additional benefits from transfer avoidance include clinical benefits such as rapid appropriate treatment of seizures and avoidance of unnecessary treatment as well as negating transfer risk and keeping the patient at their local hospital., Competing Interests: The authors declare that AG and FR receive consulting fees from Ceribell® Inc.; however, Ceribell® Inc. did not have any oversight for this manuscript. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Ward, Green, Cole, Zarbiv, Dumond, Clough and Rincon.)

Purpose: PaO 2 to FiO 2 ratio (P/F) is used to assess the degree of hypoxemia adjusted for oxygen requirements. The Berlin definition of Acute Respiratory Distress Syndrome (ARDS) includes P/F as a diagnostic criterion. P/F is invasive and cost-prohibitive for resource-limited settings. SaO 2 /FiO 2 (S/F) ratio has the advantages of being easy to calculate, noninvasive, continuous, cost-effective, and reliable, as well as lower infection exposure potential for staff, and avoids iatrogenic anemia. Previous work suggests that the SaO 2 /FiO 2 ratio (S/F) correlates with P/F and can be used as a surrogate in ARDS. Quantitative correlation between S/F and P/F has been verified, but the data for the relative predictive ability for ICU mortality remains in question. We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. Using a machine-learning approach, we hope to demonstrate the relative mortality predictive capacities of S/F and P/F., Methods: We extracted data from the eICU Collaborative Research Database. The features age, gender, SaO 2 , PaO 2 , FIO 2 , admission diagnosis, Apache IV, mechanical ventilation (MV), and ICU mortality were extracted. Mortality was the dependent variable for our prediction models. Exploratory data analysis was performed in Python . Missing data was imputed with Sklearn Iterative Imputer. Random assignment of all the encounters, 80% to the training ( n  = 26690) and 20% to testing ( n  = 6741), was stratified by positive and negative classes to ensure a balanced distribution. We scaled the data using the Sklearn Standard Scaler. Categorical values were encoded using Target Encoding. We used a gradient boosting decision tree algorithm variant called XGBoost as our model. Model hyperparameters were tuned using the Sklearn RandomizedSearchCV with tenfold cross-validation. We used AUC as our metric for model performance. Feature importance was assessed using SHAP, ELI5 (permutation importance), and a built-in XGBoost feature importance method. We constructed partial dependence plots to illustrate the relationship between mortality probability and S/F values., Results: The XGBoost hyperparameter optimized model had an AUC score of .85 on the test set. The hyperparameters selected to train the final models were as follows: colsample_bytree of 0.8, gamma of 1, max_depth of 3, subsample of 1, min_child_weight of 10, and scale_pos_weight of 3. The SHAP, ELI5, and XGBoost feature importance analysis demonstrates that the S/F ratio ranks as the strongest predictor for mortality amongst the physiologic variables. The partial dependence plots illustrate that mortality rises significantly above S/F values of 200., Conclusion: S/F was a stronger predictor of mortality than P/F based upon feature importance evaluation of our data. Our study is hypothesis-generating and a prospective evaluation is warranted. Take-Home Points . S/F ratio is a noninvasive continuous method of measuring hypoxemia as compared to P/F ratio. Our study shows that the S/F ratio is a better predictor of mortality than the more widely used P/F ratio to monitor and manage hypoxemia., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Sharad Patel et al.)