Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records.

Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. Forty-four features including patient demographics, laboratory test results, medications, and vitals sign recordings were considered. The outcome of interest was the occurrence of a hypoglycemic event (blood glucose < 72 mg/dL) during a patient's ICU stay. Machine learning models used data prior to the second hour of the ICU stay to predict hypoglycemic outcome. Data from 61,575 patients who underwent 82,479 admissions at 199 hospitals were considered in the study. The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations.

A systematic review of federated learning applications for biomedical data.

OBJECTIVES: Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology. METHODS: We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool. RESULTS: 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data. CONCLUSION: Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code.

An interpretable RL framework for pre-deployment modeling in ICU hypotension management.

Computational methods from reinforcement learning have shown promise in inferring treatment strategies for hypotension management and other clinical decision-making challenges. Unfortunately, the resulting models are often difficult for clinicians to interpret, making clinical inspection and validation of these computationally derived strategies challenging in advance of deployment. In this work, we develop a general framework for identifying succinct sets of clinical contexts in which clinicians make very different treatment choices, tracing the effects of those choices, and inferring a set of recommendations for those specific contexts. By focusing on these few key decision points, our framework produces succinct, interpretable treatment strategies that can each be easily visualized and verified by clinical experts. This interrogation process allows clinicians to leverage the model's use of historical data in tandem with their own expertise to determine which recommendations are worth investigating further e.g. at the bedside. We demonstrate the value of this approach via application to hypotension management in the ICU, an area with critical implications for patient outcomes that lacks data-driven individualized treatment strategies; that said, our framework has broad implications on how to use computational methods to assist with decision-making challenges on a wide range of clinical domains.

Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding.

OBJECTIVE: Gastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU. METHODS: A machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates. RESULTS: The optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all. CONCLUSIONS: The potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.

Data-driven curation process for describing the blood glucose management in the intensive care unit.

Analysis of real-world glucose and insulin clinical data recorded in electronic medical records can provide insights into tailored approaches to clinical care, yet presents many analytic challenges. This work makes publicly available a dataset that contains the curated entries of blood glucose readings and administered insulin on a per-patient basis during ICU admissions in the Medical Information Mart for Intensive Care (MIMIC-III) database version 1.4. Also, the present study details the data curation process used to extract and match glucose values to insulin therapy. The curation process includes the creation of glucose-insulin pairing rules according to clinical expert-defined physiologic and pharmacologic parameters. Through this approach, it was possible to align nearly 76% of insulin events to a preceding blood glucose reading for nearly 9,600 critically ill patients. This work has the potential to reveal trends in real-world practice for the management of blood glucose. This data extraction and processing serve as a framework for future studies of glucose and insulin in the intensive care unit.

Real-world characterization of blood glucose control and insulin use in the intensive care unit.

The heterogeneity of critical illness complicates both clinical trial design and real-world management. This complexity has resulted in conflicting evidence and opinion regarding the optimal management in many intensive care scenarios. Understanding this heterogeneity is essential to tailoring management to individual patients. Hyperglycaemia is one such complication in the intensive care unit (ICU), accompanied by decades of conflicting evidence around management strategies. We hypothesized that analysis of highly-detailed electronic medical record (EMR) data would demonstrate that patients vary widely in their glycaemic response to critical illness and response to insulin therapy. Due to this variability, we believed that hyper- and hypoglycaemia would remain common in ICU care despite standardised approaches to management. We utilized the Medical Information Mart for Intensive Care III v1.4 (MIMIC) database. We identified 19,694 admissions between 2008 and 2012 with available glucose results and insulin administration data. We demonstrate that hyper- and hypoglycaemia are common at the time of admission and remain so 1 week into an ICU admission. Insulin treatment strategies vary significantly, irrespective of blood glucose level or diabetic status. We reveal a tremendous opportunity for EMR data to guide tailored management. Through this work, we have made available a highly-detailed data source for future investigation.

[Pulmonary vein ablation in atrial fibrillation. Initial experience]. / Ablación de las venas pulmonares en la fibrilación auricular. Experiencia inicial.

Despite the progress in the knowledge of the pathophysiology of the atrial fibrillation (AF), the pharmacologic and non pharmacologic approach to prevent and control this arrhythmia has been shown to be discouraging. In the past few years a new type of AF has been described, of which the focal mechanism -especially bound to the pulmonary veins- allows ablation treatment through the radiofrequency (RF) with a catheter. We present our initial experience with this type of method, in two young patients who suffered from multiples episodes of AF and resistance to the conventional treatment. In both patients the RF ablation was done in the left superior pulmonary vein. One of them received an ablation in only one focus, and the other needed a veno-atrial disconnection through the elimination of the pulmonary venous potential from this vein. After three month of follow-up, patients remain asymptomatic with no relapse.

Feocromocitoma cardíaco / Cardiac pheochromocytoma

Los feocromocitomas cardíacos primarios (FCP) son sumamente infrecuentes. Hasta el presente son menos de 50 los casos comunicados en el mundo. Presentamos el caso de un tumor intrapericárdico, que resultó ser un feocromocitoma primario, en una mujer de mediana edad, cuyo signo principal fue hipertensión arterial severa(HTAs). Los estudios diagnósticos por imágenes corroboraron la presencia de un tumor intrapericárdico como único hallazgo y los estudios bioquímicos de catecolaminas y sus metabolitos excretados por orina reafirmaron el diagnóstico etiológico. El tumor fue resecado quirúrgicamente sin complicaciones mediante cirugía cardíaca convencional con circulaciónextracorpórea (CEC) y paro cardíaco con cardioplejía. Siete meses después de la operación, la paciente se encuentra asintomática y normotensa.

Utilización completa de la arteria mamaria interna izquierda para bypass coronario / Maximal use of the left internal mammary artery for coronary by pass

Antecedentes: La máxima utilización de la arteria mamaria interna izquierda intenta optimizar el uso de este conducto para revasculizar el árbol coronario izquierdo. Objetivos: Presentar los resultados iniciales y posibles variantes técnicas. Diseño: Estudio observacional prospectivo. Población: 10 pacientes fueron revascularizados en forma completa con injertos arteriales. Métodos: Descripción de la técnica y análisis de morbimortalidad mediatos. Resultados: No hubo mortalidad operatoria. No se observó bajo débito cardíaco ni intra ni postoperatorio, tampoco hubo infarto perioperatorio. El promedio de alta domiciliaria fue de 6 días. Conclusión: La utilización completa de la arteria mamaria interna izquierda, es una alternativa válida que permite la revascularización de la arteria descendente, diagonal, o ramas intermedias con el injerto arterial que ha demostrado mayor permeabilidad a largo plazo