Diedrich, Daniel

Stellate ganglion blockade (SGB) is an uncommon but increasingly relevant therapy used to modulate the autonomic nervous system and reduce the risk of atrial fibrillation (AF). This narrative review focuses specifically on the potential therapeutic role of SGB in new-onset perioperative atrial fibrillation (POAF), a complication associated with adverse long-term outcomes that may diminish the overall benefits of surgery. SGB may reduce AF through several inflammation-mediated mechanisms, including attenuation of direct sympathetic activation and modulation of perioperative inflammatory responses. Because the clinical effects of SGB may persist beyond the expected duration of local anesthetic nerve blockade, we emphasize immunomodulation as a potential mechanism sustaining its beneficial effects and influencing longer-term outcomes. In this review, we examine the immunological effects of SGB in the context of postoperative AF, with particular emphasis on human studies most relevant to clinical practice. Available evidence suggests that, beyond nonspecific inflammation, several less commonly studied cellular components, including mast cells and T cells, may contribute to the initiation and persistence of POAF. These findings support the hypothesis that SGB may reduce POAF by limiting stellate ganglionitis driven by perioperative localized leukocyte activation. Future studies should define the extent, timing, and frequency of this inflammatory process and determine whether specific inflammatory or autonomic phenotypes can identify patients most likely to benefit from SGB.

Background Medical providers often face challenges in accurately predicting the survival of critically sick patients. Optimistic forecasts can lead to the overuse of resources, while overly cautious predictions might restrict treatments. This study examines the role of specific psychological factors, analyzed realistically and holistically, in predicting survival outcomes for intensive care unit patients. Methods This single-center cohort study evaluated health care providers (e.g., physicians, residents and fellows, and advanced practice practitioners) using two 7-d clinical vignettes. Providers assessed the need for mechanical ventilation (MV), renal replacement therapy (RRT), a percutaneous endoscopic gastrostomy (PEG) tube, or palliative care. Psychological factors were measured using scales that assessed ambiguity tolerance, rationality versus emotional defensiveness, anxiety related to uncertainty, decision-making style, and risk taking. These psychological traits were analyzed using a more realistic and holistic approach, employing cluster techniques. Providers also determined whether they had enough information to evaluate the patient’s condition and compared their survival estimates to APACHE II scores. Results In general, engagement in MV and RRT was common by day 2, although physicians were significantly less likely to recommend RRT. Providers generally suggested starting a palliative care consultation by day 6, with a noticeable shift on day 4. Three distinct composite psychological groups emerged: optimistic denial individuals (ODI), optimistic providers (OP), and resilient providers (RP). While these composite psychological groups did not significantly influence engagement in mechanical therapies, they did affect palliative care decisions: RP were more likely to request palliative care, whereas ODI were much less likely to do so. In contrast, individual psychological traits had nonsignificant correlations with the decision to use therapies. Providers initially overestimated survival probabilities during the first 3 d compared with APACHE II survival estimates. However, after day 4, this trend reversed, with providers becoming significantly more pessimistic versus the predictive score and increasingly requesting palliative care involvement. Conclusions Providers’ psychological profiles, rather than their clinical experience, significantly influenced decisions about organ-support therapies and palliative care. Survival estimates showed a biphasic pattern: initially, providers overestimated survival compared with APACHE II predictions, then became more pessimistic and more likely to consult palliative care after day 4. Highlights Intensive care unit survival predictions by providers followed a biphasic pattern: optimistic early on, then increasingly pessimistic after day 4. Psychological traits such as denial and ambiguity tolerance influenced palliative care decisions more than clinical experience did. Resilient providers were more likely to initiate timely palliative care, while denial-prone providers delayed it. Clinicians and critical care teams should be aware of how their psychological makeup can affect patient care decisions and outcomes.

The early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and response. This study aimed to develop a computer vision model to quantify the number and type (clinician vs. visitor) of people in an intensive care unit (ICU) room, study the trajectory of their movement, and preliminarily explore its relationship with delirium as a marker of illness severity. To quantify the number of people present, we implemented a counting-by-detection supervised strategy using images from ICU rooms. This was accomplished through developing three methods: single-frame, multi-frame, and tracking-to-count. We then explored how the type of person and distribution in the room corresponded to the presence of delirium. Our designed pipeline was tested with a different set of detection models. We report model performance statistics and preliminary insights into the relationship between the number and type of persons in the ICU room and delirium. We evaluated our method and compared it with other approaches, including density estimation, counting by detection, regression methods, and their adaptability to ICU environments.

Abstract 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 multipatient 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 with the standard practice. We narratively synthesized eligible studies following the SWiM (Synthesis Without Meta-analysis) guidelines. Studies were grouped based on dashboard type and outcomes assessed. Results The search yielded a total of 2,407 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. Conclusion The multipatient 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 multipatient tools using robust research methodologies is an important focus for future research.

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.