Concise Versus Extended Lung Ultrasound Score to Monitor Critically Ill Patients With COVID-19.

BACKGROUND: Lung ultrasound (LUS) can be used to monitor critically ill patients with COVID-19, but the optimal number of examined lung zones is disputed. METHODS: This was a prospective observational study. The objective was to investigate whether concise (6 zones) and extended (12 zones) LUS scoring protocols are clinically equivalent in critically ill ICU subjects with COVID-19. The primary outcome of this study was (statistical) agreement between concise and extended LUS score index evaluated in both supine and prone position. Agreement was determined using correlation coefficients and Bland-Altman plots to detect systematic differences between protocols. Secondary outcomes were difference between LUS score index in supine and prone position using similar methods. RESULTS: We included 130 LUS examinations in 40 subjects (mean age 69.0 ± 8.5y, 75% male). Agreement between concise and extended LUS score index had no clinically relevant constant or proportional bias and limits of agreement were below the smallest detectable change. Across position changes, supine LUS score index was 8% higher than prone LUS score index and had limits above the smallest detectable change, indicating true LUS score index differences between protocols may occur due to the position change itself. Lastly, inter-rater and intra-rater agreement were very good. CONCLUSIONS: Concise LUS was equally informative as extended LUS for monitoring critically ill subjects with COVID-19 in supine or prone position. Clinicians can monitor patients undergoing position changes but must be wary that LUS score index alterations may result from the position change itself rather than disease progression or clinical improvement.

Holistic Ultrasound to Predict Extubation Failure in Clinical Practice.

BACKGROUND: A weaning trial can be considered a stress test of the cardiorespiratory system; it increases oxygen demand and thus warrants a higher cardiac index and elevated breathing effort. We hypothesized that the combination of easily performed ultrasound measurements of heart, lungs, and diaphragm would yield good diagnostic accuracy to predict extubation failure. METHODS: Adult subjects ventilated for > 72 h with a successful spontaneous breathing trial were included. Ultrasound measurements of heart (left ventricular function), lungs (number of B-lines), and diaphragm thickening fraction were performed during a spontaneous breathing trial. The primary outcomes were sensitivity, specificity, and area under the receiver operating characteristic curve of a holistic ultrasound approach for extubation failure. Re-intubation within 48 h was considered extubation failure. RESULTS: Eighty-three subjects were included, of whom 15 (18%) were re-intubated within 48 h. The sensitivity and specificity of a holistic approach were 100% (78.2-100%) and 7.7% (2.5-17.1%), respectively, with an area under the receiver operating characteristic curve of 0.54. The sensitivity and specificity of diaphragm thickening fraction, using a cutoff value of < 30% for extubation failure were 86.7% (59.5-98.3%) and 25.4% (15.5-37.5%), respectively, with an area under the receiver operating characteristic curve of 0.61. CONCLUSIONS: In subjects ventilated for > 72 h who had a successful spontaneous breathing trial, holistic ultrasound was a weak predictor for extubation failure. (ClinicalTrials.gov registration NCT04196361).

Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis.

INTRODUCTION: In recent years, reinforcement learning (RL) has gained traction in the healthcare domain. In particular, RL methods have been explored for haemodynamic optimization of septic patients in the Intensive Care Unit. Most hospitals however, lack the data and expertise for model development, necessitating transfer of models developed using external datasets. This approach assumes model generalizability across different patient populations, the validity of which has not previously been tested. In addition, there is limited knowledge on safety and reliability. These challenges need to be addressed to further facilitate implementation of RL models in clinical practice. METHOD: We developed and validated a new reinforcement learning model for hemodynamic optimization in sepsis on the MIMIC intensive care database from the USA using a dueling double deep Q network. We then transferred this model to the European AmsterdamUMCdb intensive care database. T-Distributed Stochastic Neighbor Embedding and Sequential Organ Failure Assessment scores were used to explore the differences between the patient populations. We apply off-policy policy evaluation methods to quantify model performance. In addition, we introduce and apply a novel deep policy inspection to analyse how the optimal policy relates to the different phases of sepsis and sepsis treatment to provide interpretable insight in order to assess model safety and reliability. RESULTS: The off-policy evaluation revealed that the optimal policy outperformed the physician policy on both datasets despite marked differences between the two patient populations and physician's policies. Our novel deep policy inspection method showed insightful results and unveiled that the model could initiate therapy adequately and adjust therapy intensity to illness severity and disease progression which indicated safe and reliable model behaviour. Compared to current physician behavior, the developed policy prefers a more liberal use of vasopressors with a more restrained use of fluid therapy in line with previous work. CONCLUSION: We created a reinforcement learning model for optimal bedside hemodynamic management and demonstrated model transferability between populations from the USA and Europe for the first time. We proposed new methods for deep policy inspection integrating expert domain knowledge. This is expected to facilitate progression to bedside clinical decision support for the treatment of critically ill patients.

Development of trigger-based semi-automated surveillance of ventilator-associated pneumonia and central line-associated bloodstream infections in a Dutch intensive care.

BACKGROUND: Availability of a patient data management system (PDMS) has created the opportunity to develop trigger-based electronic surveillance systems (ESSs). The aim was to evaluate a semi-automated trigger-based ESS for the detection of ventilator-associated pneumonia (VAP) and central line-associated blood stream infections (CLABSIs) in the intensive care. METHODS: Prospective comparison of surveillance was based on a semi-automated ESS with and without trigger. Components of the VAP/CLABSI definition served as triggers. These included the use of VAP/CLABSI-related antibiotics, the presence of mechanical ventilation or an intravenous central line, and the presence of specific clinical symptoms. Triggers were automatically fired by the PDMS. Chest X-rays and microbiology culture results were checked only on patient days with a positive trigger signal from the ESS. In traditional screening, no triggers were used; therefore, chest X-rays and culture results had to be screened for all patient days of all included patients. Patients with pneumonia at admission were excluded. RESULTS: A total of 553 patients were screened for VAP and CLABSI. The incidence of VAP was 3.3/1,000 ventilation days (13 VAP/3,927 mechanical ventilation days), and the incidence of CLABSI was 1.7/1,000 central line days (24 CLABSI/13.887 central line days). For VAP, the trigger-based screening had a sensitivity of 92.3%, a specificity of 100%, and a negative predictive value of 99.8% compared to traditional screening of all patients. For CLABSI, sensitivity was 91.3%, specificity 100%, and negative predictive value 99.6%. CONCLUSIONS: Pre-selection of patients to be checked for signs and symptoms of VAP and CLABSI by a computer-generated automated trigger system was time saving but slightly less accurate than conventional surveillance. However, this after-the-fact surveillance was mainly designed as a quality indicator over time rather than for precise determination of infection rates. Therefore, surveillance of VAP and CLABSI with a trigger-based ESS is feasible and effective.

Activated protein C in the treatment of acute lung injury and acute respiratory distress syndrome.

BACKGROUND: Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) frequently necessitate mechanical ventilation in the intensive care unit. The syndromes have a high mortality rate and there is at present no treatment specifically directed at the underlying pathogenesis. Central in the pathophysiology of ALI/ARDS is alveolocapillary inflammation leading to permeability edema. As a result of the crosstalk between inflammation and coagulation, activation of proinflammatory and procoagulant/antifibrinolytic pathways contributes to disruption of the endothelial barrier. Protein C (PC) plays a central role in maintaining the equilibrium between coagulation and inflammation. Additionally, natural anticoagulants, such as PC, are depleted, both in blood as well as in the lung. Therefore, the PC system is of interest as a therapeutic target in patients with ALI/ARDS. METHOD: This review is based on a Medline search of relevant basic and clinical studies. OBJECTIVE: It discusses the potential role of activated PC in modulating the proinflammatory/procoagulant state for enhancing endothelial barrier function in animal models and human ALI/ARDS.