Lindroth, Heidi

AbstractBACKGROUNDDelirium, a common syndrome with heterogeneous etiologies and clinical presentations, is associated with poor long‐term outcomes. Recording and analyzing all delirium equally could be hindering the field's understanding of pathophysiology and identification of targeted treatments. Current delirium subtyping methods reflect clinically evident features but likely do not account for underlying biology.METHODSThe Delirium Subtyping Initiative (DSI) held three sessions with an international panel of 25 experts.RESULTSMeeting participants suggest further characterization of delirium features to complement the existing Diagnostic and Statistical Manual of Mental Disorders Fifth Edition Text Revision diagnostic criteria. These should span the range of delirium‐spectrum syndromes and be measured consistently across studies. Clinical features should be recorded in conjunction with biospecimen collection, where feasible, in a standardized way, to determine temporal associations of biology coincident with clinical fluctuations.DISCUSSIONThe DSI made recommendations spanning the breadth of delirium research including clinical features, study planning, data collection, and data analysis for characterization of candidate delirium subtypes.Highlights Delirium features must be clearly defined, standardized, and operationalized. Large datasets incorporating both clinical and biomarker variables should be analyzed together. Delirium screening should incorporate communication and reasoning.

Abstract Effective mentorship relationships increase mentee academic success and satisfaction. However, existing mentorship models are limited by miscommunication, undefined roles, and mismatched goals. The agile mentorship process aims to address these limitations by leveraging insights from agile science and the existing evidence on effective mentorship models to support effective mentoring relationships in healthcare environments. To illustrate the agile mentorship process and the growth of a mentored clinician–scientist (H.L., first author), we describe the model and share qualitative findings generated from the independent analysis of 18 months of mentee reflections. In two iterative cycles, reflections (n = 56) were analyzed using exploratory content and relational analysis. Coauthors C.S. and B.T. employed inductive and deductive coding approaches to explore the data using an ontological lens. We discuss and share quotes representing the identified four main themes. Identification of shortcomings, adaptive perspective, managing relationships, and personal growth. In addition, personal growth had three subthemes: Awareness, continual reflection, and toolkit development. In summary, the reflections of one mentee within the agile mentorship process illustrated the growth process which occurred within an effective mentorship relationship. The agile mentorship process is a scalable and sustainable framework that is adaptable to various career development processes. Further evaluation is needed to understand the longitudinal impact of the model on mentee performance and satisfaction.

OBJECTIVES: To investigate whether a novel acute care multipatient viewer (AMP), created with an understanding of clinician information and process requirements, could reduce time to clinical decision-making among clinicians caring for populations of acutely ill patients compared with a widely used commercial electronic medical record (EMR). DESIGN: Single center randomized crossover study. SETTING: Quaternary care academic hospital. SUBJECTS: Attending and in-training critical care physicians, and advanced practice providers. INTERVENTIONS: AMP. MEASUREMENTS AND MAIN RESULTS: We compared ICU clinician performance in structured clinical task completion using two electronic environments—the standard commercial EMR (Epic) versus the novel AMP in addition to Epic. Twenty subjects (10 pairs of clinicians) participated in the study. During the study session, each participant completed the tasks on two ICUs (7–10 beds each) and eight individual patients. The adjusted time for assessment of the entire ICU and the adjusted total time to task completion were significantly lower using AMP versus standard commercial EMR (–6.11; 95% CI, –7.91 to –4.30 min and –5.38; 95% CI, –7.56 to –3.20 min, respectively; p < 0.001). The adjusted time for assessment of individual patients was similar using both the EMR and AMP (0.73; 95% CI, –0.09 to 1.54 min; p = 0.078). AMP was associated with a significantly lower adjusted task load (National Aeronautics and Space Administration-Task Load Index) among clinicians performing the task versus the standard EMR (22.6; 95% CI, –32.7 to –12.4 points; p < 0.001). There was no statistically significant difference in adjusted total errors when comparing the two environments (0.68; 95% CI, 0.36–1.30; p = 0.078). CONCLUSIONS: When compared with the standard EMR, AMP significantly reduced time to assessment of an entire ICU, total time to clinical task completion, and clinician task load. Additional research is needed to assess the clinicians’ performance while using AMP in the live ICU setting.

Despite many studies of potential delirium biomarkers, delirium pathophysiology remains unclear. Evidence shows that the quality of reporting delirium biomarker studies is sub-optimal. Better reporting of delirium biomarker studies is needed to understand delirium pathophysiology better. To improve robustness, transparency and uniformity of delirium biomarker study reports, the REDEEMS (Reporting Essentials for DElirium bioMarker Studies) guideline was developed by an international group of delirium researchers through a three-stage process, including a systematic review, a three-round Delphi study, and a follow-up consensus meeting. This process resulted in a 9-item guideline to inform delirium fluid biomarker studies. To enhance implementation of the REDEEMS guideline, this Explanation and Elaboration paper provides a detailed explanation of each item. We anticipate that the REDEEMS guideline will help to accelerate our understanding of delirium pathophysiology by improving the reporting of delirium biomarker research and, consequently the capacity to synthesise results across studies.

OBJECTIVES: Critically ill patients with COVID-19 experience high rates of delirium and coma. Whether delirium occurs through novel mechanisms in COVID-19 is not known. We analyzed the relationship among biomarkers of inflammation (C-reactive protein [CRP]), hypercoagulability (d-dimer), and lung macrophage activation (ferritin), and the primary composite outcome of delirium/coma next day. We also measured associations between biomarkers and next day delirium and coma independently, and delirium severity. DESIGN: Retrospective, observational cohort study. SETTING: ICUs at two large, urban, academic referral hospitals. PATIENTS: All consecutive adult patients admitted to the ICU from March 1, 2020, to June 7, 2020, with COVID-19 with clinical biomarkers and delirium assessments performed. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Daily concentrations of CRP, d-dimer, and ferritin were obtained. Coma (assessed by Richmond Agitation-Sedation Scale) and delirium (assessed by Confusion Assessment Method for the ICU/Confusion Assessment Method for the ICU-7) were measured bid. A cohort of 197 ICU patients with COVID-19 were included. Higher d-dimer (odds ratio [OR], 1.57; 95% CI, 1.17–2.12; p < 0.01) and ferritin quartiles (OR, 1.36; 95% CI, 1.02–1.81; p < 0.01) were associated with greater odds of the composite outcome of delirium/coma next day. d-dimer was associated with greater odds of next day delirium (OR, 1.49; 95% CI, 1.14–1.94; p < 0.01) and coma independently (OR, 1.52; 95% CI, 1.08–2.14; p = 0.017). Higher ferritin quartiles were associated with greater odds of next day delirium (OR, 1.33; 95% CI, 1.04–1.70; p = 0.026) and coma independently (OR, 1.59; 95% CI, 1.14–2.23; p < 0.01). Higher CRP quartiles were associated with coma (OR, 1.36; 95% CI, 1.03–1.79; p = 0.030) and delirium severity the next day (β = 0.30; se, 0.07; p ≤ 0.01). CONCLUSIONS: Our hypothesis-generating study found d-dimer and ferritin were associated with delirium/coma the following day, as well as delirium and coma independently. CRP was associated with next day coma and delirium severity. Larger studies to validate these results are needed.

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.