Citations

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.

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.

Abstract 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.

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.

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.

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.

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.

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.