Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach.

BACKGROUND: Various surrogate markers of insulin resistance have been developed, capable of predicting coronary artery disease (CAD) without the need to detect serum insulin. For accurate prediction, they depend only on glucose and lipid profiles, as well as anthropometric features. However, there is still no agreement on the most suitable one for predicting CAD. METHODS: We followed a cohort of 2,000 individuals, ranging in age from 20 to 74, for a duration of 9.9 years. We utilized multivariate Cox proportional hazard models to investigate the association between TyG-index, TyG-BMI, TyG-WC, TG/HDL, plus METS-IR and the occurrence of CAD. The receiver operating curve (ROC) was employed to compare the predictive efficacy of these indices and their corresponding cutoff values for predicting CAD. We also used three distinct embedded feature selection methods: LASSO, Random Forest feature selection, and the Boruta algorithm, to evaluate and compare surrogate markers of insulin resistance in predicting CAD. In addition, we utilized the ceteris paribus profile on the Random Forest model to illustrate how the model's predictive performance is affected by variations in individual surrogate markers, while keeping all other factors consistent in a diagram. RESULTS: The TyG-index was the only surrogate marker of insulin resistance that demonstrated an association with CAD in fully adjusted model (HR: 2.54, CI: 1.34-4.81). The association was more prominent in females. Moreover, it demonstrated the highest area under the ROC curve (0.67 [0.63-0.7]) in comparison to other surrogate indices for insulin resistance. All feature selection approaches concur that the TyG-index is the most reliable surrogate insulin resistance marker for predicting CAD. Based on the Ceteris paribus profile of Random Forest the predictive ability of the TyG-index increased steadily after 9 with a positive slope, without any decline or leveling off. CONCLUSION: Due to the simplicity of assessing the TyG-index with routine biochemical assays and given that the TyG-index was the most effective surrogate insulin resistance index for predicting CAD based on our results, it seems suitable for inclusion in future CAD prevention strategies.

A multimodal framework for extraction and fusion of satellite images and public health data.

In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health.

DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era.

In the big data era, integrating diverse data modalities poses significant challenges, particularly in complex fields like healthcare. This paper introduces a new process model for multimodal Data Fusion for Data Mining, integrating embeddings and the Cross-Industry Standard Process for Data Mining with the existing Data Fusion Information Group model. Our model aims to decrease computational costs, complexity, and bias while improving efficiency and reliability. We also propose "disentangled dense fusion," a novel embedding fusion method designed to optimize mutual information and facilitate dense inter-modality feature interaction, thereby minimizing redundant information. We demonstrate the model's efficacy through three use cases: predicting diabetic retinopathy using retinal images and patient metadata, domestic violence prediction employing satellite imagery, internet, and census data, and identifying clinical and demographic features from radiography images and clinical notes. The model achieved a Macro F1 score of 0.92 in diabetic retinopathy prediction, an R-squared of 0.854 and sMAPE of 24.868 in domestic violence prediction, and a macro AUC of 0.92 and 0.99 for disease prediction and sex classification, respectively, in radiological analysis. These results underscore the Data Fusion for Data Mining model's potential to significantly impact multimodal data processing, promoting its adoption in diverse, resource-constrained settings.

Critical appraisal of machine learning prognostic models for acute pancreatitis: protocol for a systematic review.

Acute pancreatitis (AP) is an acute inflammatory disorder that is common, costly, and is increasing in incidence worldwide with over 300,000 hospitalizations occurring yearly in the United States alone. As its course and outcomes vary widely, a critical knowledge gap in the field has been a lack of accurate prognostic tools to forecast AP patients' outcomes. Despite several published studies in the last three decades, the predictive performance of published prognostic models has been found to be suboptimal. Recently, non-regression machine learning models (ML) have garnered intense interest in medicine for their potential for better predictive performance. Each year, an increasing number of AP models are being published. However, their methodologic quality relating to transparent reporting and risk of bias in study design has never been systematically appraised. Therefore, through collaboration between a group of clinicians and data scientists with appropriate content expertise, we will perform a systematic review of papers published between January 2021 and December 2023 containing artificial intelligence prognostic models in AP. To systematically assess these studies, the authors will leverage the CHARMS checklist, PROBAST tool for risk of bias assessment, and the most current version of the TRIPOD-AI. (Research Registry ( http://www.reviewregistry1727 .).

Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review.

BACKGROUND: Imaging plays a pivotal role in eye assessment. With the introduction of advanced machine learning and artificial intelligence (AI), the focus has shifted to imaging datasets in ophthalmology. While disparities and health inequalities hidden within data are well-documented, the ophthalmology field faces specific challenges to the creation and maintenance of datasets. Optical Coherence Tomography (OCT) is useful for the diagnosis and monitoring of retinal pathologies, making it valuable for AI applications. This review aims to identify and compare the landscape of publicly available optical coherence tomography databases for AI applications. METHODS: We conducted a literature review on OCT and AI articles with publicly accessible datasets, using PubMed, Scopus, and Web of Science databases. The review retrieved 183 articles, and after full-text analysis, 50 articles were included. From the included articles were identified 8 publicly available OCT datasets, focusing on patient demographics and clinical details for thorough assessment and comparison. RESULTS: The resulting datasets encompass 154,313 images collected from Spectralis, Cirrus HD, Topcon 3D, and Bioptigen devices. These datasets included normal exams, age-related macular degeneration, and diabetic maculopathy, among others. Comprehensive demographic information is available in one dataset and the USA is the most represented population. DISCUSSION: Current publicly available OCT databases for AI applications exhibit limitations, stemming from their non-representative nature and the lack of comprehensive demographic information. Limited datasets hamper research and equitable AI development. To promote equitable AI algorithmic development in ophthalmology, there is a need for the creation and dissemination of more representative datasets.

BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos.

Introduction: The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups. Methods: Data from three São Paulo outpatient centers yielded demographic and medical information from electronic records, including nationality, age, sex, clinical history, insulin use, and duration of diabetes diagnosis. A retinal specialist labeled images for anatomical features (optic disc, blood vessels, macula), quality control (focus, illumination, image field, artifacts), and pathologies (e.g., diabetic retinopathy). Diabetic retinopathy was graded using International Clinic Diabetic Retinopathy and Scottish Diabetic Retinopathy Grading. Validation used Dino V2 Base for feature extraction, with 70% training and 30% testing subsets. Support Vector Machines (SVM) and Logistic Regression (LR) were employed with weighted training. Performance metrics included area under the receiver operating curve (AUC) and Macro F1-score. Results: BRSET comprises 65.1% Canon CR2 and 34.9% Nikon NF5050 images. 61.8% of the patients are female, and the average age is 57.6 years. Diabetic retinopathy affected 15.8% of patients, across a spectrum of disease severity. Anatomically, 20.2% showed abnormal optic discs, 4.9% abnormal blood vessels, and 28.8% abnormal macula. Models were trained on BRSET in three prediction tasks: "diabetes diagnosis"; "sex classification"; and "diabetic retinopathy diagnosis". Discussion: BRSET is the first multilabel ophthalmological dataset in Brazil and Latin America. It provides an opportunity for investigating model biases by evaluating performance across demographic groups. The model performance of three prediction tasks demonstrates the value of the dataset for external validation and for teaching medical computer vision to learners in Latin America using locally relevant data sources.

Silk fibroin-based dressings with antibacterial and anti-inflammatory properties.

Silk fibroin is a fibrillar protein obtained from arthropods such as mulberry and non-mulberry silkworms. Silk fibroin has been used as a dressing in wound treatment for its physical, chemical, mechanical, and biological properties. This systematic review analyzed studies from PubMed, Web of Science, and Scopus databases to identify the molecules preferred for functionalizing silk fibroin-based dressings and to describe their mechanisms of exhibiting anti-inflammatory and antibacterial properties. The analysis of the selected articles allowed us to classify the dressings into different conformations, such as membranes, films, hydrogels, sponges, and bioadhesives. The incorporation of various molecules, including antibiotics, natural products, peptides, nanocomposites, nanoparticles, secondary metabolites, growth factors, and cytokines, has allowed the development of dressings that promote wound healing with antibacterial and immunomodulatory properties. In addition, silk fibroin-based dressings have been established to have the potential to regenerate wounds such as venous ulcers, arterial ulcers, diabetic foot, third-degree burns, and neoplastic ulcers. Evaluation of the efficacy of silk fibroin-based dressings in tissue engineering is an area of great activity that has shown significant advances in recent years.

A scoping review of the landscape of health-related open datasets in Latin America.

Artificial intelligence (AI) algorithms have the potential to revolutionize healthcare, but their successful translation into clinical practice has been limited. One crucial factor is the data used to train these algorithms, which must be representative of the population. However, most healthcare databases are derived from high-income countries, leading to non-representative models and potentially exacerbating health inequities. This review focuses on the landscape of health-related open datasets in Latin America, aiming to identify existing datasets, examine data-sharing frameworks, techniques, platforms, and formats, and identify best practices in Latin America. The review found 61 datasets from 23 countries, with the DATASUS dataset from Brazil contributing to the majority of articles. The analysis revealed a dearth of datasets created by the authors themselves, indicating a reliance on existing open datasets. The findings underscore the importance of promoting open data in Latin America. We provide recommendations for enhancing data sharing in the region.

Breast cancer metastasizing to the upper gastrointestinal tract (the esophagus and the stomach): A comprehensive review of the literature.

Breast cancer can infrequently metastasize to the upper gastrointestinal (GI) tract but the exact incidence is not well established-there is considerable variation between incidence reported from clinical studies and incidence noted in autopsy series. Clinical presentation can be very non-specific and often mimics primary gastrointestinal conditions. Endoscopy alone may not be sufficient to make a diagnosis and misdiagnosis is also common. A high degree of awareness and clinical suspicion is required to establish metastases to the upper GI tract. We undertook a comprehensive review of the available literature on breast cancer metastases to the esophagus and stomach including the clinical symptoms and presentation, endoscopic features, additional diagnostic imaging modalities, treatment and outcomes.

Multidimensional Machine Learning Model to Calculate a COVID-19 Vulnerability Index.

In Colombia, the first case of COVID-19 was confirmed on 6 March 2020. On 13 March 2023, Colombia registered 6,360,780 confirmed positive cases of COVID-19, representing 12.18% of the total population. The National Administrative Department of Statistics (DANE) in Colombia published in 2020 a COVID-19 vulnerability index, which estimates the vulnerability (per city block) of being infected with COVID-19. Unfortunately, DANE did not consider multiple factors that could increase the risk of COVID-19 (in addition to demographic and health), such as environmental and mobility data (found in the related literature). The proposed multidimensional index considers variables of different types (unemployment rate, gross domestic product, citizens' mobility, vaccination data, and climatological and spatial information) in which the incidence of COVID-19 is calculated and compared with the incidence of the COVID-19 vulnerability index provided by DANE. The collection, data preparation, modeling, and evaluation phases of the Cross-Industry Standard Process for Data Mining methodology (CRISP-DM) were considered for constructing the index. The multidimensional index was evaluated using multiple machine learning models to calculate the incidence of COVID-19 cases in the main cities of Colombia. The results showed that the best-performing model to predict the incidence of COVID-19 in Colombia is the Extra Trees Regressor algorithm, obtaining an R-squared of 0.829. This work is the first step toward a multidimensional analysis of COVID-19 risk factors, which has the potential to support decision making in public health programs. The results are also relevant for calculating vulnerability indexes for other viral diseases, such as dengue.

Risk Factors for COVID-19: A Systematic Mapping Study.

INTRODUCTION: COVID-19 has affected people in several countries around the world. They experience respiratory symptoms that can be mild, moderate, or severe. Several reviews that characterize the risk factors of COVID-19 have been performed, but most address only risk factors associated with medical conditions, ignoring environmental and sociodemographic-socioeconomic factors. OBJECTIVE: This study aims at characterizing different risk factors in the published literature that influence contagion by COVID-19. METHODS: The review consists of three stages, including a systematic mapping with studies found in the Scopus database, an analysis of results, and finally the identification of relevant COVID-19 risk factors. RESULTS: A map of studies id provided considering two main groups: the type of research and context. Most studies consider risk factors associated with medical conditions, while research on other factors is scarce. CONCLUSIONS: Medical conditions such as diabetes, obesity, cardiovascular disease, hypertension, and factors such as age and sex, appear to be the ones that increase the risk of contracting COVID-19. Further research is needed on environmental, sociodemographic, and socioeconomic risk factors.

Improving Diagnostic Yield in Indeterminate Biliary Strictures.

Indeterminate biliary strictures are defined as a narrowing of the bile duct that cannot be differentiated as malignant or benign after performing cross-sectional imaging and an ERCP. Identifying the etiology of a bile duct stricture is the single most important step in determining whether a complex and potentially morbid surgical resection is warranted. Due to this diagnostic and therapeutic dilemma, new technologies, laboratory tests, and procedures are emerging to solve this problem.

Comprehensive Decision Support for Prehospital Combat Casualty Care: The Airway Clearance Model.

Airway management is a foremost priority for combat medics treating battlefield casualties, as a compromised airway is the second leading cause of potentially survivable death on the battlefield, accounting for 1 in 10 preventable combat deaths. Effective suction is a critical component of airway clearance. However, currently available commercial devices are too heavy and bulky for combat medics to carry, and/or lack sufficient power to be useful. Clinical decision support systems (DSS) can close the gap between existing commercial devices and their clinical use and enhance combat medic clinical performance by providing the right "tooth-to-tail" tools to accomplish the task of clearing the airway. Our DSS approach will provide a focused, real-time set of guidelines and recommendations that are tailored to the combat medic. Our proposal will create a knowledge-based algorithm and clinical guideline regarding the use of suction, delivering to the combat medic the "right information, to the right person, in the right format, through the right channel at the right time."

Innovative Solution for Airway Securement in Combat and Trauma Scenarios.

Airway management is one of the most challenging problems in prehospital combat casualty care. Airway assessment and intervention are second only to hemorrhage control in priority in the initial treatment of trauma patients, and airway compromise continues to account for approximately 1 in 10 preventable battlefield deaths. Combat medics often provide care in no- or low-light conditions, surrounded by the chaos of combat, and with the limited dexterity that accompanies bulky body armor, gloves, and heavy equipment. Far-forward medical care is also limited by available resources, which are often only what a combat medic can fit in the aid bag. Therefore, a procedure such as airway management that currently requires a high degree of skill becomes substantially more complex. Improved airway devices are listed among the top five in a comprehensive list of battlefield research and development priorities by the Defense Health Board, yet the challenge of airway management has received little investment compared to other causes of preventable battlefield death such as exsanguinating hemorrhage and traumatic brain injury.

Histoplasmosis of the Upper Extremities: Clinical Case, Treatment Algorithm, and Systematic Review.

Background: Histoplasmosis of the upper extremities is extremely rare, and there is no consensus in its management. The aim of this study was to present a case report and literature review of histoplasmosis in the upper extremities to provide the best treatment recommendation and to increase awareness of this disease. Methods: A systematic review of the literature was performed in PubMed, MEDLINE, and EMBASE databases with the search terms "(Tenosynovitis AND histoplasmosis)" OR "(Upper limbs AND histoplasmosis)" OR "(Upper extremities AND histoplasmosis)" OR "((finger OR arm OR hand OR forearm OR wrist) AND histoplasmosis)." Manuscripts were included in the study if they described diagnosis of histoplasmosis in the bone, joint, synovia, or tendons of the upper extremities. Results: In all, 14 manuscripts and 15 cases of histoplasmosis of the upper extremities from 1992 to 2015 met the inclusion criteria. Eight (53.3%) patients were diagnosed with tenosynovitis, 5 (33.3%) with osteomyelitis, 1 (6.7%) with carpal tunnel syndrome, and 1 (6.7%) with joint wrist abscess. Immunosuppression, travel history, previous diagnosis with histoplasmosis, and persistent symptoms may suggest infectious causes. Surgery and medical treatment together are recommended to optimize outcomes compared with single treatment alone. Conclusions: Histoplasmosis of the upper extremities may have various clinical presentations, including tenosynovitis, osteomyelitis, carpal tunnel syndrome, and joint wrist abscess. The type of surgical approach may depend on the etiology of disease. Increased awareness and knowledge of the clinical signs, symptoms, and effective treatment modalities will help decrease possible complications, such as tendon rupture and functional impairment of the upper extremities.

Clinical Case of Acute Non-Traumatic Hand Compartment Syndrome and Systematic Review for the Upper Extremity.

Background: While trauma to the upper extremity is known to cause acute compartment syndrome (ACS), nontraumatic causes of ACS of the upper extremity are rare. Nontraumatic ACS of the upper extremity can lead to adverse outcomes if not recognized early. There are limited reports of spontaneous ACS published in the literature. The aim of this comprehensive systematic review is to increase awareness among plastic surgeons and hand surgeons of this acute event and provide an algorithmic approach to management in the acute setting through an illustrative case example. Methods: A comprehensive systematic review of published literature was conducted in the Medline/PubMed database with the search terms, "compartment syndrome," "extremity," "spontaneous," "nontraumatic," and "atraumatic" without timeframe limitations. Articles were identified and included in this review based on ACS localization in the upper extremity and etiology of nontraumatic, spontaneous origin. Results: Sixteen publications and 19 total cases of nontraumatic ACS of the upper extremity from 1993 to 2016 met our search criteria. A bleeding disorder was the etiology in three cases, systemic anticoagulation in three cases, infection in six cases, and unknown in three cases. The remaining four cases included systemic sclerosis, Ehlers-Danlos syndrome, rhabdomyolysis, and McArdle disease. Conclusions: Nontraumatic causes of ACS of the upper extremity include infection, anticoagulation therapy, and bleeding disorders. Even though trauma is the most common cause of ACS, clinicians should be aware of these other potential causes of ACS in the nontraumatic setting. Appropriate medical and surgical intervention should be done to avoid potential adverse outcomes.

Multi-Dimensional Dataset of Open Data and Satellite Images for Characterization of Food Security and Nutrition.

BACKGROUND: Nutrition is one of the main factors affecting the development and quality of life of a person. From a public health perspective, food security is an essential social determinant for promoting healthy nutrition. Food security embraces four dimensions: physical availability of food, economic and physical access to food, food utilization, and the sustainability of the dimensions above. Integrally addressing the four dimensions is vital. Surprisingly most of the works focused on a single dimension of food security: the physical availability of food. OBJECTIVE: The paper proposes a multi-dimensional dataset of open data and satellite images to characterize food security in the department of Cauca, Colombia. METHODS: The food security dataset integrates multiple open data sources; therefore, the Cross-Industry Standard Process for Data Mining methodology was used to guide the construction of the dataset. It includes sources such as population and agricultural census, nutrition surveys, and satellite images. RESULTS: An open multidimensional dataset for the Department of Cauca with 926 attributes and 9 rows (each row representing a Municipality) from multiple sources in Colombia, is configured. Then, machine learning models were used to characterize food security and nutrition in the Cauca Department. As a result, The Food security index calculated for Cauca using a linear regression model (Mean Absolute Error of 0.391) is 57.444 in a range between 0 and 100, with 100 the best score. Also, an approach for extracting four features (Agriculture, Habitation, Road, Water) of satellite images were tested with the ResNet50 model trained from scratch, having the best performance with a macro-accuracy, macro-precision, macro-recall, and macro-F1-score of 91.7, 86.2, 66.91, and 74.92%, respectively. CONCLUSION: It shows how the CRISP-DM methodology can be used to create an open public health data repository. Furthermore, this methodology could be generalized to other types of problems requiring the creation of a dataset. In addition, the use of satellite images presents an alternative for places where data collection is challenging. The model and methodology proposed based on open data become a low-cost and effective solution that could be used by decision-makers, especially in developing countries, to support food security planning.

Novel expandable architected breathing tube for improving airway securement in emergency care.

Life-saving interventions utilize endotracheal intubation to secure a patient's airway, but performance of the clinical standard of care endotracheal tube (ETT) is inadequate. For instance, in the current COVID-19 crisis, patients can expect prolonged intubation. This protracted intubation may produce health complications such as tracheal stenosis, pneumonia, and necrosis of tracheal tissue, as current ETTs are not designed for extended use. In this work, we propose an improved ETT design that seeks to overcome these limitations by utilizing unique geometries which enable a novel expanding cylinder. The mechanism provides a better distribution of the contact forces between the ETT and the trachea, which should enhance patient tolerability. Results show that at full expansion, our new ETT exerts pressures in a silicone tracheal phantom well within the recommended standard of care. Also, preliminary manikin tests demonstrated that the new ETT can deliver similar performance in terms of air pressure and air volume when compared with the current gold standard ETT. The potential benefits of this new architected ETT are threefold, by limiting exposure of healthcare providers to patient pathogens through streamlining the intubation process, reducing downstream complications, and eliminating the need of multiple size ETT as one architected ETT fits all.

Umbilical Reconstruction Techniques: A Literature Review.

BACKGROUND: There are many instances in which sacrificing the umbilicus is unavoidable. Umbilical reconstruction (umbiliconeoplasty) is an important surgical procedure to complete the abdomen's reconstruction and to give again a pleasant cosmetic appearance. OBJECTIVES: To provide a complete overview of all surgical techniques for umbiliconeoplasty described in the literature. METHODS: PubMed database was queried using 'umbilical and reconstruction', 'umbilicus and reconstruction', 'navel and reconstruction', 'umbiliconeoplasty', 'neo-omphaloplasty' or 'umbilicaneoplasty' to select the papers dealing with the reconstruction of the umbilicus. RESULTS: Sixty different techniques for the reconstruction of the missing umbilicus were described in 77 papers. Local skin flaps and the purse-string suture technique were the most frequently described techniques. The Three flaps technique, the Four flaps technique and the 2 Lateral rectangular pedicle lateral flaps technique were the most popular local flap techniques. Indications ranged from congenital pediatric defects to reconstruction during abdominoplasty. CONCLUSIONS: Several surgical techniques were described for umbilicus reconstruction. While there is not a universal algorithm for the choice of the technique, the surgeon may decide which technique to use based on other surgeons' experiences reports. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .