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Journal of Surgery and Research 2024Damage to the peripheral and central nervous systems is frequently irreversible. Surgically induced neurological damage and anesthesia may result in catastrophic...
Damage to the peripheral and central nervous systems is frequently irreversible. Surgically induced neurological damage and anesthesia may result in catastrophic situations for patients and their families. The incidence of significant neurological complications during the perioperative period is examined in this article. In contrast to other organs like the kidney, heart, liver, lungs, and skeletal system, native neurological function cannot be replaced with artificial parts or devices soon. Ignoring brain function during the perioperative period has been a systemic problem in anesthesiology, even though the central and peripheral nervous systems are crucial. This bold claim is intended to draw attention to the fact that, unlike the circulatory and respiratory systems, which have been routinely monitored for decades, the brain and other neural structures do not have a standard monitoring during surgery and anesthesia. Given that the brain and spinal cord are the principal therapeutic targets of analgesics and anesthetics, this deficiency in clinical care is even more alarming. Organs that are notoriously hard to repair or replace after damage have, up until now, received comparatively little attention. In this article, a succinct overview of five neurological complications associated with surgery and anesthesia is presented. After critically reviewing the literature on the subject, the article is focused to common (delirium), controversial (postoperative cognitive decline), and potentially catastrophic (stroke, spinal cord ischemia, or postoperative visual loss) adverse events in the neurological surgery setting. The findings will increase awareness of major neurological complications to the involved surgical and anesthesia team and enhance preventive and treatment strategies during the perioperative period.
PubMed: 38947250
DOI: No ID Found -
Research Square Jun 2024Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only....
AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis.
Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths. We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score. During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342). In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.
PubMed: 38947043
DOI: 10.21203/rs.3.rs-4433105/v1 -
Nucleic Acids Research Jul 2024Fluorogenic RNA aptamer tags with high affinity enable RNA purification and imaging. The G-quadruplex (G4) based Mango (M) series of aptamers were selected to bind a...
Fluorogenic RNA aptamer tags with high affinity enable RNA purification and imaging. The G-quadruplex (G4) based Mango (M) series of aptamers were selected to bind a thiazole orange based (TO1-Biotin) ligand. Using a chemical biology and reselection approach, we have produced a MII.2 aptamer-ligand complex with a remarkable set of properties: Its unprecedented KD of 45 pM, formaldehyde resistance (8% v/v), temperature stability and ligand photo-recycling properties are all unusual to find simultaneously within a small RNA tag. Crystal structures demonstrate how MII.2, which differs from MII by a single A23U mutation, and modification of the TO1-Biotin ligand to TO1-6A-Biotin achieves these results. MII binds TO1-Biotin heterogeneously via a G4 surface that is surrounded by a stadium of five adenosines. Breaking this pseudo-rotational symmetry results in a highly cooperative and homogeneous ligand binding pocket: A22 of the G4 stadium stacks on the G4 binding surface while the TO1-6A-Biotin ligand completely fills the remaining three quadrants of the G4 ligand binding face. Similar optimization attempts with MIII.1, which already binds TO1-Biotin in a homogeneous manner, did not produce such marked improvements. We use the novel features of the MII.2 complex to demonstrate a powerful optically-based RNA purification system.
PubMed: 38945550
DOI: 10.1093/nar/gkae493 -
American Journal of Preventive Medicine Jun 2024
PubMed: 38945180
DOI: 10.1016/j.amepre.2024.06.021 -
Nature Medicine Jun 2024As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare...
As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous research established AI's capacity to infer demographic data from chest X-rays, leading to a key concern: do models using demographic shortcuts have unfair predictions across subpopulations? In this study, we conducted a thorough investigation into the extent to which medical AI uses demographic encodings, focusing on potential fairness discrepancies within both in-distribution training sets and external test sets. Our analysis covers three key medical imaging disciplines-radiology, dermatology and ophthalmology-and incorporates data from six global chest X-ray datasets. We confirm that medical imaging AI leverages demographic shortcuts in disease classification. Although correcting shortcuts algorithmically effectively addresses fairness gaps to create 'locally optimal' models within the original data distribution, this optimality is not true in new test settings. Surprisingly, we found that models with less encoding of demographic attributes are often most 'globally optimal', exhibiting better fairness during model evaluation in new test environments. Our work establishes best practices for medical imaging models that maintain their performance and fairness in deployments beyond their initial training contexts, underscoring critical considerations for AI clinical deployments across populations and sites.
PubMed: 38942996
DOI: 10.1038/s41591-024-03113-4 -
Clinical Radiology May 2024In the rapidly evolving field of artificial intelligence (AI) for radiology, with a plethora of vendor options and use-cases and evidence claims to sift through, the... (Review)
Review
In the rapidly evolving field of artificial intelligence (AI) for radiology, with a plethora of vendor options and use-cases and evidence claims to sift through, the pressing question is how to effectively implement the right tool for enhanced patient care? This article presents a structured approach to AI deployment, drawing from a comprehensive case study in South West London. We underscore the necessity of forming a dedicated AI team with a clear vision and assertive leadership to navigate such complexities. Central to our discussion is the significance of crafting an AI implementation plan, with an overarching aim to augment patient care, promote operational efficiency, and lay down standardized protocols for seamless AI adoption. By presenting a blueprint for AI implementation within the National Health Service (NHS), we intend to demystify the process for radiology departments across the UK, enabling them to make informed decisions and empowering their staff to embrace and leverage AI responsibly ensuring that patient welfare remains at the heart of innovation. Thus, having a framework to follow when implementing an AI solution that addresses a vision for scalable adoption, core team members with diversity of skillset, staff engagement and education, plan for vendor selection, and change management is crucial for success.
PubMed: 38942706
DOI: 10.1016/j.crad.2024.05.018 -
Cardiology Journal 2024
Topics: Humans; Artificial Intelligence; Writing; Periodicals as Topic; Cardiology; Biomedical Research
PubMed: 38940258
DOI: 10.5603/cj.94335 -
JACC. Advances Sep 2023
PubMed: 38939488
DOI: 10.1016/j.jacadv.2023.100578 -
JACC. Advances Sep 2023Most risk prediction models are confined to specific medical conditions, thus limiting their application to general medical populations.
BACKGROUND
Most risk prediction models are confined to specific medical conditions, thus limiting their application to general medical populations.
OBJECTIVES
The MARKER-HF (Machine learning Assessment of RisK and EaRly mortality in Heart Failure) risk model was developed in heart failure (HF) patients. We assessed the ability of MARKER-HF to predict 1-year mortality in a large community-based hospital registry database including patients with and without HF.
METHODS
This study included 41,749 consecutive patients who underwent echocardiography in a tertiary referral hospital (4,640 patients with and 37,109 without HF). Patients without HF were further subdivided into those with (n = 22,946) and without cardiovascular disease (n = 14,163) and also into cohorts based on recent acute coronary syndrome or history of atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, diabetes mellitus, hypertension, or malignancy.
RESULTS
The median age of the 41,749 patients was 65 years, and 56.2% were male. The receiver operated area under the curves for MARKER-HF prediction of 1-year mortality of patients with HF was 0.729 (95% CI: 0.706-0.752) and for patients without HF was 0.770 (95% CI: 0.760-0.780). MARKER-HF prediction of mortality was consistent across subgroups with and without cardiovascular disease and in patients diagnosed with acute coronary syndrome, atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, diabetes mellitus, or hypertension. Patients with malignancy demonstrated higher mortality at a given MARKER-HF score than did patients in the other groups.
CONCLUSIONS
MARKER-HF predicts mortality for patients with HF as well as for patients suffering from a variety of diseases.
PubMed: 38939487
DOI: 10.1016/j.jacadv.2023.100554 -
JACC. Advances Aug 2023Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or...
BACKGROUND
Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate.
OBJECTIVES
The authors applied artificial intelligence (AI) to analyze a single apical 4-chamber transthoracic echocardiogram video clip to detect HFpEF.
METHODS
A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber video clips to classify patients with HFpEF (diagnosis of heart failure, ejection fraction ≥50%, and echocardiographic evidence of increased filling pressure; cases) vs without HFpEF (ejection fraction ≥50%, no diagnosis of heart failure, normal filling pressure; controls). Model outputs were classified as HFpEF, no HFpEF, or nondiagnostic (high uncertainty). Performance was assessed in an independent multisite data set and compared to previously validated clinical scores.
RESULTS
Training and validation included 2,971 cases and 3,785 controls (validation holdout, 16.8% patients), and demonstrated excellent discrimination (area under receiver-operating characteristic curve: 0.97 [95% CI: 0.96-0.97] and 0.95 [95% CI: 0.93-0.96] in training and validation, respectively). In independent testing (646 cases, 638 controls), 94 (7.3%) were nondiagnostic; sensitivity (87.8%; 95% CI: 84.5%-90.9%) and specificity (81.9%; 95% CI: 78.2%-85.6%) were maintained in clinically relevant subgroups, with high repeatability and reproducibility. Of 701 and 776 indeterminate outputs from the Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing (HFA-PEFF), and Final Etiology and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder, and Filling Pressure (H2FPEF) scores, the AI HFpEF model correctly reclassified 73.5% and 73.6%, respectively. During follow-up (median: 2.3 [IQR: 0.5-5.6] years), 444 (34.6%) patients died; mortality was higher in patients classified as HFpEF by AI (HR: 1.9 [95% CI: 1.5-2.4]).
CONCLUSIONS
An AI HFpEF model based on a single, routinely acquired echocardiographic video demonstrated excellent discrimination of patients with vs without HFpEF, more often than clinical scores, and identified patients with higher mortality.
PubMed: 38939447
DOI: 10.1016/j.jacadv.2023.100452