Diedrich, Daniel A.

Variability in novel inflammatory markers (HBP and SAA) post-surgery could indicate ongoing perioperative inflammation. This pilot cohort study collected blood from 99 patients undergoing elective cardiac surgery at baseline, 24 hours, 7 days, and 3 months post-surgery. Inflammatory markers (HBP, SAA, PCT, CRP, IL-6) were measured with a point-of-care device. Demographic and clinical data were extracted from health records. Serum SAA increased significantly in the acute postoperative phase (M e [IQR];SAA baseline =81.1[87] vs SAA 24hr= 232.65[88.02];U[72]=2343,p<0.0001), remained elevated at 7 days (Me 7d =252.6[88.56];U[57]=1484,p<0.0001) and were still more pronounced than baseline at t 3m (Me 3m =118.44[101.52];U[38]=448,p=0.0008). HBP showed a significant rise at seven days (HBP baseline =45[26;89] vs HBP 7d =121[69.75;224.5]; U[56]=805,p<0.0001) then returned to near baseline by t 3m (Me 3m =32.5[15.75;98.5]). Traditional inflammatory markers PCT, CRP, and IL-6 demonstrated a rapid increase at t 24h and plateaued at t 7d . None of the biomarkers showed variability at baseline or postoperatively with preoperative status or postoperative complications. Cluster analysis of the inflammatory markers at 24 hours (t 24h ) identified distinct subgroups. Cluster #1-patients with a modest increase in all markers; Cluster#2 characterized with a robust increase in IL-6; Cluster #3 showed increase in CRP and decline in HBP; Cluster #4 showed a modest increase in IL-6 and decline in HBP; Cluster #5 represented a decline in both CRP and SAA. Patients in these clusters differ with respect to age, pre-existing peripheral artery disease, and borderline differences in perioperative acetaminophen use. SAA and HBP remain elevated longer than IL-6, CRP, and PCT after surgery, suggesting that inflammation may persist beyond 3 months.

Abstract Background Lumbar puncture is a routinely performed procedure, yet it presents significant challenges in patients with abnormal spinal anatomy, such as severe scoliosis or obesity. Conventional ultrasound-assisted neuraxial techniques, while superior to landmark-based methods, are constrained by two fundamental limitations: inaccurate projection of the probe’s geometric center onto the skin when the probe is tilted or rocked, and the difficulty of consistently replicating the exact probe angle during needle insertion. These limitations contribute to multiple needle passes, prolonged procedure time, and increased patient discomfort and complication risk. To address these issues, we developed a novel angle-free ultrasound-assisted technique designed to eliminate the need for replicating probe angulation during needle insertion. Case presentation: This angle-free approach was utilized for serial lumbar punctures in two patients with spinal muscular atrophy requiring repeated intrathecal nusinersen administration. Both patients presented with extreme anatomical complexity, including high body mass index (30 and 35.1 kg/m²) and severe scoliosis (Cobb angles 90° and 83°). The technique follows a three-step protocol: (1) Preprocedural imaging to select the optimal interlaminar space; (2) Ultrasound-guided postural adjustment until the probe could be held perpendicular to the bed edge and parallel to the floor while maintaining target visualization; (3) Needle insertion along this orthogonal trajectory. All 22 procedures (11 per patient) were completed successfully without persistent neurological complications. Procedural outcomes demonstrated a first-attempt success rate of 81.8% (Case 1) and 100% (Case 2), with first-pass success rates of 45.5% and 54.5%, respectively. Longitudinal analysis across sequential procedures revealed a consistent reduction in both the number of needle passes and needling time, reflecting a favorable learning curve. Conclusions The angle-free ultrasound-assisted neuraxial technique offers a valuable alternative for patients with complex spinal anatomy. By using real-time postural adjustments to achieve a needle trajectory, it enhances procedural precision and eliminates the technical hurdle of replicating probe angulation. The structured "4P" framework (Planning, Positioning, Perpendicular, Parallel) offers a practical guide for clinical adoption. The observed learning curve suggests that clinicians can readily acquire and apply this technique to manage such challenging cases.

The insulin-like growth factor binding protein, acid-labile subunit (IGFALS), plays a crucial role in glucose metabolism and immune regulation, key processes in recovery from surgery. Here, we studied the perioperative serum IGFALS dynamics and explored potential clinical implications. A total of 79 patients undergoing elective cardiac surgery with implementation of cardiopulmonary bypass had their serum isolated at baseline, 24 h, seven days, and three months postoperatively to assess serum concentrations of IGFALS and insulin growth factor 1 (IGF-1). Markers of perioperative injury included troponin I (TnI), high-mobility group box 1 (HMGB-1), and heat shock protein 60 (Hsp-60). Inflammatory status was assessed via interleukin-6 (IL-6) and interleukin-8 (IL-8). Additionally, we measured in vitro cytokine production to viral stimulation of whole blood and monocytes. Surrogates of neuronal distress included neurofilament light chain (NF-L), total tau (τ), phosphorylated tau at threonine 181 (τp181), and amyloid β40 and β42. Renal impairment was defined by RIFLE criteria. Cardiac dysfunction was denoted by serum N-terminal pro-brain natriuretic peptide (NT-proBNP) levels. Serum IGFALS levels declined significantly after surgery and remained depressed even at 3 months. Administration of acetaminophen and acetylsalicylic acid differentiated IGFALS levels at the 24 h postoperatively. Serum IGFALS 24 h post-operatively correlated with production of cytokines by leukocytes after in vitro viral stimulation. Serum amyloid-β1-42 was significantly associated with IGFALS at baseline and 24 h post-surgery Patients discharged home had higher IGFALS levels at 28 days and 3 months than those discharged to healthcare facilities or who died. These findings suggest that IGFALS may serve as a prognostic biomarker for recovery trajectory and postoperative outcomes in cardiac surgery patients.

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.

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.

Abstract 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.

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.