Centralized Multi-Patient Dashboards' Impact on ICU Clinician Performance and Satisfaction: A Systematic Review.

BACKGROUND: Intensive care unit (ICU) clinicians encounter frequent challenges with managing vast amounts of fragmented data while caring for multiple critically ill patients simultaneously. This may lead to increased provider cognitive load that may jeopardize patient safety. OBJECTIVES: This systematic review assesses the impact of centralized multi-patient dashboards on ICU clinician performance, perceptions regarding the use of these tools, and patient outcomes. METHODS: A literature search was conducted on February 9, 2023, using the EBSCO CINAHL, Cochrane Central Register of Controlled Trials, Embase, IEEE Xplore, MEDLINE, Scopus, and Web of Science Core Collection databases. Eligible studies that included ICU clinicians as participants and tested the effect of dashboards designed for use by multiple users to manage multiple patients on user performance and/or satisfaction compared to the standard practice. We narratively synthesized eligible studies following the SWiM guidelines. Studies were grouped based on dashboard type and outcomes assessed. RESULTS: The search yielded a total of 2407 studies. Five studies met inclusion criteria and were included. Among these, three studies evaluated interactive displays in the ICU, one study assessed two dashboards in the pediatric ICU (PICU), and one study examined centralized monitor in the PICU. Most studies reported several positive outcomes, including reductions in data gathering time before rounds, a decrease in misrepresentations during multidisciplinary rounds, improved daily documentation compliance, faster decision-making, and user satisfaction. One study did not report any significant association. CONCLUSIONS: The multi-patient dashboards were associated with improved ICU clinician performance and were positively perceived in most of the included studies. The risk of bias was high and the certainty of evidence was very low, due to inconsistencies, imprecision, indirectness in the outcome measure and methodological limitations. Designing and evaluating multi-patient tools using robust research methodologies is an important focus for future research.

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.

OBJECTIVE: This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia, acute respiratory distress syndrome (ARDS) and normal lungs. MATERIALS AND METHODS: A diagnostic performance study was conducted using Chest X-Ray images from adult patients admitted to a medical intensive care unit between January 2003 and November 2014. X-ray images from 15,899 patients were assigned one of three prespecified categories: "ARDS", "Pneumonia", or "Normal". RESULTS: A two-step convolutional neural network (CNN) pipeline was developed and tested to distinguish between the three patterns with sensitivity ranging from 91.8% to 97.8% and specificity ranging from 96.6% to 98.8%. The CNN model was validated with a sensitivity of 96.3% and specificity of 96.6% using a previous dataset of patients with Acute Lung Injury (ALI)/ARDS. DISCUSSION: The results suggest that a deep learning model based on chest x-ray pattern recognition can be a useful tool in distinguishing patients with ARDS from patients with normal lungs, providing faster results than digital surveillance tools based on text reports. CONCLUSION: A CNN-based deep learning model showed clinically significant performance, providing potential for faster ARDS identification. Future research should prospectively evaluate these tools in a clinical setting.

Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives.

OBJECTIVES: Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters. MATERIALS AND METHODS: This qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software. RESULTS: We completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply-demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias. DISCUSSION: This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers. CONCLUSION: Artificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers.

Ultrasound-assisted lumbar puncture with a horizontal and perpendicular paramedian approach based on positioning in patients with abnormal spinal anatomy: a case report and technical description.

BACKGROUND: The use of ultrasound has been reported to be beneficial in challenging neuraxial procedures. The angled probe is responsible for the main limitations of previous ultrasound-assisted techniques. We developed a novel technique for challenging lumbar puncture, aiming to locate the needle entry point which allowed for a horizontal and perpendicular needle trajectory and thereby addressed the drawbacks of earlier ultrasound-assisted techniques. CASE PRESENTATION: Patient 1 was an adult patient with severe scoliosis who underwent a series of intrathecal injections of nusinersen. The preprocedural ultrasound scan revealed a cephalad probe's angulation (relative to the edge of the bed) in the paramedian sagittal oblique view, and then the probe was rotated 90° into a transverse plane and we noted that a rocking maneuver was required to obtain normalized views. Then the shoulders were moved forward to eliminate the need for cephalad angulation of the probe. The degree of rocking was translated to a lateral offset from the midline of the spine through an imaginary lumbar puncture's triangle model, and a needle entry point was marked. The spinal needle was advanced through this marking-point without craniocaudal and lateromedial angulation, and first-pass success was achieved in all eight lumbar punctures. Patient 2 was an elderly patient with ankylosing spondylitis who underwent spinal anesthesia for transurethral resection of the prostate. The patient was positioned anteriorly obliquely to create a vertebral rotation that eliminated medial angulation in the paramedian approach. The procedure succeeded on the first pass. CONCLUSIONS: This ultrasound-assisted paramedian approach with a horizontal and perpendicular needle trajectory may be a promising technique that can help circumvent challenging anatomy. Larger case series and prospective studies are warranted to define its superiority to alternative approaches of lumbar puncture for patients with difficulties.

Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review.

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

COVID-19: a modern trigger for Guillain-Barre syndrome, myasthenia gravis, and small fiber neuropathy.

COVID-19 infection has had a profound impact on society. During the initial phase of the pandemic, there were several suggestions that COVID-19 may lead to acute and protracted neurologic sequelae. For example, peripheral neuropathies exhibited distinctive features as compared to those observed in critical care illness. The peripheral nervous system, lacking the protection afforded by the blood-brain barrier, has been a particular site of sequelae and complications subsequent to COVID-19 infection, including Guillain-Barre syndrome, myasthenia gravis, and small fiber neuropathy. We will discuss these disorders in terms of their clinical manifestations, diagnosis, and treatment as well as the pathophysiology in relation to COVID-19.

Cortical Spreading Depolarization and Delayed Cerebral Ischemia; Rethinking Secondary Neurological Injury in Subarachnoid Hemorrhage.

Poor outcomes in Subarachnoid Hemorrhage (SAH) are in part due to a unique form of secondary neurological injury known as Delayed Cerebral Ischemia (DCI). DCI is characterized by new neurological insults that continue to occur beyond 72 h after the onset of the hemorrhage. Historically, it was thought to be a consequence of hypoperfusion in the setting of vasospasm. However, DCI was found to occur even in the absence of radiographic evidence of vasospasm. More recent evidence indicates that catastrophic ionic disruptions known as Cortical Spreading Depolarizations (CSD) may be the culprits of DCI. CSDs occur in otherwise healthy brain tissue even without demonstrable vasospasm. Furthermore, CSDs often trigger a complex interplay of neuroinflammation, microthrombi formation, and vasoconstriction. CSDs may therefore represent measurable and modifiable prognostic factors in the prevention and treatment of DCI. Although Ketamine and Nimodipine have shown promise in the treatment and prevention of CSDs in SAH, further research is needed to determine the therapeutic potential of these as well as other agents.

Information and Data Visualization Needs among Direct Care Nurses in the Intensive Care Unit.

OBJECTIVES: Intensive care unit (ICU) direct care nurses spend 22% of their shift completing tasks within the electronic health record (EHR). Miscommunications and inefficiencies occur, particularly during patient hand-off, placing patient safety at risk. Redesigning how direct care nurses visualize and interact with patient information during hand-off is one opportunity to improve EHR use. A web-based survey was deployed to better understand the information and visualization needs at patient hand-off to inform redesign. METHODS: A multicenter anonymous web-based survey of direct care ICU nurses was conducted (9-12/2021). Semi-structured interviews with stakeholders informed survey development. The primary outcome was identifying primary EHR data needs at patient hand-off for inclusion in future EHR visualization and interface development. Secondary outcomes included current use of the EHR at patient hand-off, EHR satisfaction, and visualization preferences. Frequencies, means, and medians were calculated for each data item then ranked in descending order to generate proportional quarters using SAS v9.4. RESULTS: In total, 107 direct care ICU nurses completed the survey. The majority (46%, n = 49/107) use the EHR at patient hand-off to verify exchanged verbal information. Sixty-four percent (n = 68/107) indicated that current EHR visualization was insufficient. At the start of an ICU shift, primary EHR data needs included hemodynamics (mean 4.89 ± 0.37, 98%, n = 105), continuous IV medications (4.55 ± 0.73, 93%, n = 99), laboratory results (4.60 ± 0.56, 96%, n = 103), mechanical circulatory support devices (4.62 ± 0.72, 90%, n = 97), code status (4.40 ± 0.85, 59%, n = 108), and ventilation status (4.35 + 0.79, 51%, n = 108). Secondary outcomes included mean EHR satisfaction of 65 (0-100 scale, standard deviation = ± 21) and preferred future EHR user-interfaces to be organized by organ system (53%, n = 57/107) and visualized by tasks/schedule (61%, n = 65/107). CONCLUSION: We identified information and visualization needs of direct care ICU nurses. The study findings could serve as a baseline toward redesigning an EHR interface.

Effects of Image Quality on the Accuracy Human Pose Estimation and Detection of Eye Lid Opening/Closing Using Openpose and DLib.

OBJECTIVE: The application of computer models in continuous patient activity monitoring using video cameras is complicated by the capture of images of varying qualities due to poor lighting conditions and lower image resolutions. Insufficient literature has assessed the effects of image resolution, color depth, noise level, and low light on the inference of eye opening and closing and body landmarks from digital images. METHOD: This study systematically assessed the effects of varying image resolutions (from 100 × 100 pixels to 20 × 20 pixels at an interval of 10 pixels), lighting conditions (from 42 to 2 lux with an interval of 2 lux), color-depths (from 16.7 M colors to 8 M, 1 M, 512 K, 216 K, 64 K, 8 K, 1 K, 729, 512, 343, 216, 125, 64, 27, and 8 colors), and noise levels on the accuracy and model performance in eye dimension estimation and body keypoint localization using the Dlib library and OpenPose with images from the Closed Eyes in the Wild and the COCO datasets, as well as photographs of the face captured at different light intensities. RESULTS: The model accuracy and rate of model failure remained acceptable at an image resolution of 60 × 60 pixels, a color depth of 343 colors, a light intensity of 14 lux, and a Gaussian noise level of 4% (i.e., 4% of pixels replaced by Gaussian noise). CONCLUSIONS: The Dlib and OpenPose models failed to detect eye dimensions and body keypoints only at low image resolutions, lighting conditions, and color depths. CLINICAL IMPACT: Our established baseline threshold values will be useful for future work in the application of computer vision in continuous patient monitoring.

Reintubation Summation Calculation: A Predictive Score for Extubation Failure in Critically Ill Patients.

OBJECTIVE: To derive and validate a multivariate risk score for the prediction of respiratory failure after extubation. PATIENTS AND METHODS: We performed a retrospective cohort study of adult patients admitted to the intensive care unit from January 1, 2006, to December 31, 2015, who received mechanical ventilation for ≥48 h. Extubation failure was defined as the need for reintubation within 72 h after extubation. Multivariate logistic regression model coefficient estimates generated the Re-Intubation Summation Calculation (RISC) score. RESULTS: The 6,161 included patients were randomly divided into 2 sets: derivation (n = 3,080) and validation (n = 3,081). Predictors of extubation failure in the derivation set included body mass index <18.5 kg/m2 [odds ratio (OR), 1.91; 95% CI, 1.12-3.26; P = 0.02], threshold of Glasgow Coma Scale of at least 10 (OR, 1.68; 95% CI, 1.31-2.16; P < 0.001), mean airway pressure at 1 min of spontaneous breathing trial <10 cmH2O (OR, 2.11; 95% CI, 1.68-2.66; P < 0.001), fluid balance ≥1,500 mL 24 h preceding extubation (OR, 2.36; 95% CI, 1.87-2.96; P < 0.001), and total mechanical ventilation days ≥5 (OR, 3.94; 95% CI 3.04-5.11; P < 0.001). The C-index for the derivation and validation sets were 0.72 (95% CI, 0.70-0.75) and 0.72 (95% CI, 0.69-0.75). Multivariate logistic regression demonstrated that an increase of 1 in RISC score increased odds of extubation failure 1.6-fold (OR, 1.58; 95% CI, 1.47-1.69; P < 0.001). CONCLUSION: RISC predicts extubation failure in mechanically ventilated patients in the intensive care unit using several clinically relevant variables available in the electronic medical record but requires a larger validation cohort before widespread clinical implementation.

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 <65 mmHg; 2) systolic blood pressure <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 <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.

Determinants of Endotracheal Intubation in Critically Ill Patients Undergoing Gastrointestinal Endoscopy Under Conscious Sedation.

OBJECTIVES: Our primary aim was to determine the factors leading to prophylactic endotracheal intubation in intensive care unit (ICU) patients undergoing gastrointestinal endoscopy. Secondary aims were to determine the rate of unplanned endotracheal intubations during endoscopy and to determine the rate of aspiration following endoscopy for patients admitted to the ICU. METHODS: Critically ill adult (≥18 years) patients who underwent upper and lower endoscopic procedures from January 2012 to July 2016 in a medical/surgical ICU were included. Determinants of prophylactic endotracheal intubation prior to endoscopy as well as other postprocedure outcomes were electronically captured by a validated data mart system. Given our focus on aspiration in those who were not endotracheally intubated prior to endoscopy, we used a validated definition a priori. RESULTS: A total of 320 patients were included in the final analysis: 76(24%) were intubated prior to endoscopy and 244 (76%) were not. The endotracheally intubated group had a significantly higher Acute Physiologic and Chronic Health Evaluation III (44.5 [16.2] vs 39.5 [15.5]; P = .02) and Sequential Organ Failure Assessment (6.9 [4.4] vs 3.8 [3]; P ≤ .01) scores, higher rate of hematemesis within 24 hours of endoscopy (28 [37%] vs 45 [18%]; P ≤ .01), and higher rate of upper endoscopy (72 [96%] vs 181 [74%]; P ≤ .01). We composed a composite outcome for multivariable analyses, which demonstrated the rate of any complication was significantly higher among those who were intubated prior to the procedure versus those who were not intubated previously (odds ratio: 2.80, 95% confidence interval (CI): 1.16-6.72, P = .02). CONCLUSION: Endoscopy performed in the ICU without endotracheal intubation is safe. However, patient selection for prophylactic intubation prior to endoscopy is of critical importance as illustrated in this study with higher illness severity, planned upper endoscopy, and hematemesis 24 hours prior being key factors on deciding to perform endotracheal intubation. Prophylactic intubation for endoscopy and preexisting cardiac disease were associated with a higher rate of adverse outcomes.

Implementation of a Goal-Directed Mechanical Ventilation Order Set Driven by Respiratory Therapists Improves Compliance With Best Practices for Mechanical Ventilation.

PURPOSE: Data regarding best practices for ventilator management strategies that improve outcomes in acute respiratory distress syndrome (ARDS) are readily available. However, little is known regarding processes to ensure compliance with these strategies. We developed a goal-directed mechanical ventilation order set that included physician-specified lung-protective ventilation and oxygenation goals to be implemented by respiratory therapists (RTs). We sought as a primary outcome to determine whether an RT-driven order set with predefined oxygenation and ventilation goals could be implemented and associated with improved adherence with best practice. METHODS: We evaluated 1302 patients undergoing invasive mechanical ventilation (1693 separate episodes of invasive mechanical ventilation) prior to and after institution of a standardized, goal-directed mechanical ventilation order set using a controlled before-and-after study design. Patient-specific goals for oxygenation partial pressure of oxygen in arterial blood (Pao 2), ARDS Network [Net] positive end-expiratory pressure [PEEP]/fraction of inspired oxygen [Fio 2] table use) and ventilation (pH, partial pressure of carbon dioxide) were selected by prescribers and implemented by RTs. RESULTS: Compliance with the new mechanical ventilation order set was high: 88.2% compliance versus 3.8% before implementation of the order set ( P < .001). Adherence to the PEEP/Fio 2 table after implementation of the order set was significantly greater (86.0% after vs 82.9% before, P = .02). There was no difference in duration of mechanical ventilation, intensive care unit (ICU) length of stay, and in-hospital or ICU mortality. CONCLUSIONS: A standardized best practice mechanical ventilation order set can be implemented by a multidisciplinary team and is associated with improved compliance to written orders and adherence to the ARDSNet PEEP/Fio 2 table.

The Incidence of and Risk Factors for Postintubation Hypotension in the Immunocompromised Critically Ill Adult.

OBJECTIVES: Our primary aim was to ascertain the frequency of postintubation hypotension in immunocompromised critically ill adults with secondary aims of arriving at potential risk factors for the development of postintubation hypotension and its impact on patient-related outcomes. METHODS: Critically ill adult patients (≥18 years) were included from January 1, 2010, to December 31, 2014. We defined immunocompromised as patients with any solid organ or nonsolid organ malignancy or transplant, whether solid organ or not, requiring current chemotherapy. Postintubation hypotension was defined as a decrease in systolic blood pressure to less than 90 mm Hg or a decrease in mean arterial pressure to less than 65 mm Hg or the initiation of any vasopressor medication. Patients were then stratified based on development of postintubation hypotension. Potential risk factors and intensive care unit (ICU) outcome metrics were electronically captured by a validated data mart system. RESULTS: The final cohort included 269 patients. Postintubation hypotension occurred in 141 (52%; 95% confidence interval: 46-58) patients. Several risk factors predicted postintubation hypotension on univariate analysis; however, only Acute Physiology and Chronic Health Evaluation III score in the first 24 hours, preintubation shock status, and preintubation hemodynamic instability remained significant on all 4 multivariate analyses. Patients developing postintubation hypotension had higher ICU and hospital mortality (54 [38%] vs 31 [24%], P = .01; 69 [49%] vs 47 [37%], P = .04). CONCLUSION: Based on previous literature, we found a higher frequency of postintubation hypotension in the immunocompromised than in the nonimmunocompromised critically ill adult patients. Acute Physiology and Chronic Health Evaluation III score in the first 24 hours, preintubation shock status, and preintubation hemodynamic instability were significant predictors on multivariate analyses. Postintubation hypotension led to higher ICU and hospital mortality in those experiencing this complication.

Endotracheal Intubation Among the Critically Ill: Protocol for a Multicenter, Observational, Prospective Study.

BACKGROUND: Endotracheal intubation can occur in up to 60% of critically ill patients. Despite the frequency with which endotracheal intubation occurs, the current practice is largely unknown. This is relevant, as advances in airway equipment (ie, video laryngoscopes) have become more prevalent, leading to possible improvement of care delivered during this process. In addition to new devices, a greater emphasis on airway plans and choices in sedation have evolved, although the influence on patient morbidity and mortality is largely unknown. OBJECTIVE: This study aims to derive and validate prediction models for immediate airway and hemodynamic complications of intensive care unit intubations. METHODS: A multicenter, observational, prospective study of adult critically ill patients admitted to both medical and surgical intensive care units (ICUs) was conducted. Participating ICU sites were located throughout eight health and human services regions of the United States for which endotracheal intubation was needed. A steering committee composed of both anesthesia and pulmonary critical care physicians proposed a core set of data variables. These variables were incorporated into a data collection form to be used within the multiple, participating ICUs across the United States during the time of intubation. The data collection form consisted of two basic components, focusing on airway management and hemodynamic management. The form was generated using RedCap and distributed to the participating centers. Quality checks on the dataset were performed several times with each center, such that they arrived at less than 10% missing values for each data variable; the checks were subsequently entered into a database. RESULTS: The study is currently undergoing data analysis. Results are expected in November 2018 with publication to follow thereafter. The study protocol has not yet undergone peer review by a funding body. CONCLUSIONS: The overall goal of this multicenter prospective study is to develop a scoring system for peri-intubation, hemodynamic, and airway-related complications so we can stratify those patients at greatest risk for decompensation as a result of these complications. This will allow critical care physicians to be better prepared in addressing these occurrences and will allow them to improve the quality of care delivered to the critically ill. TRIAL REGISTRATION: ClinicalTrials.gov NCT02508948; https://clinicaltrials.gov/ct2/show/NCT02508948 (Archived by WebCite at http://www.webcitation.org/73Oj6cTFu). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/11101.

Fiberoptic-Guided Use of an Airway Exchange Catheter During Exchange of a Kinked King LTS-D Laryngeal Tube for an Endotracheal Tube: A Case Report.

The use of supraglottic airway devices such as the King LTS-D laryngeal tube has increased in the prehospital setting because of their relative ease of successful insertion, even in the hands of inexperienced providers. However, these devices have their own associated complications. In patients with a known or suspected difficult airway, supraglottic airway device exchanges should occur under controlled conditions using an airway exchange catheter, preferably under direct visualization with a flexible fiberoptic bronchoscope. We report unanticipated difficulties with supraglottic airway exchange caused by a kinked King LTS-D laryngeal tube.

Survey on the Current State of Endotracheal Intubation Among the Critically Ill: HEMAIR Investigators.

OBJECTIVES: In the last decade, the practice of intubation in the intensive care unit (ICU) has evolved. To further examine the current intubation practice in the ICU, we administered a survey to critical care physicians. DESIGN: Cross-sectional survey study design. SETTING: Thirty-two academic/nonacademic centers nationally and internationally. MEASUREMENTS AND MAIN RESULTS: The survey was developed among a core group of physicians with the assistance of the Survey Research Center at Mayo Clinic, Rochester, Minnesota. The survey was pilot tested for functionality and reliability. The response rate was 82 (51%) of 160 among the 32 centers. Although propofol was the induction drug of choice, there was a significant difference with actual ketamine use and those who indicated a preference for it (ketamine: 52% vs 61%; P < .001). The most common airway device used for intubation was direct laryngoscopy (Miller laryngoscope blade) at 56 (68%) followed by video laryngoscopy at 26 (32%). Most (>90%) indicated that they have a difficult airway cart, but only 55 (67%) indicated they have a documented plan to handle a difficult airway with even lower results for documented review of adverse events (49%). CONCLUSION: Although propofol was the induction drug of choice, ketamine was a medication that many preferred to use, possibly relating to the fact that the most common complication postintubation is hypotension. Direct laryngoscopy remains the primary airway device for endotracheal intubation. Finally, although the majority stated they had a difficult airway cart available, most did not have a documented plan in place when encountering a difficult airway or a documented process to review adverse events surrounding intubation.