Use of a continuous single lead electrocardiogram analytic to predict patient deterioration requiring rapid response team activation.

Identifying the onset of patient deterioration is challenging despite the potential to respond to patients earlier with better vital sign monitoring and rapid response team (RRT) activation. In this study an ECG based software as a medical device, the Analytic for Hemodynamic Instability Predictive Index (AHI-PI), was compared to the vital signs of heart rate, blood pressure, and respiratory rate, evaluating how early it indicated risk before an RRT activation. A higher proportion of the events had risk indication by AHI-PI (92.

Are Hospital Admissions (Costs) and Mortality Rate Impacted by Guideline-driven Treatment of Heart Failure?: A Comparison between Participants in the "CorBene" CMP and Standard-care Patients on the Basis of Propensity Score Matching.

Heart failure (HF) is one of the most common diagnoses on admission to hospital in Germany, and one which incurs high costs. Integrated care in case management programs (CMPs) aims to improve treatment quality in the sense of guideline-driven treatment, while reducing hospital admissions, hospital costs, and mortality. A total of 1,844 patient data records from 11 German statutory health insurance companies enrolled in the CMP (intervention group [IG]) were compared with 1,844 standard-care patients (control group) using propensity score matching.

Changes of pulse wave transit time after haemodynamic manoeuvres in healthy adults: a prospective randomised observational trial (PWTT volunteer study).

Background: Pulse wave transit time (PWTT) shows promise for monitoring intravascular fluid status intraoperatively. Presently, it is unknown how PWTT mirrors haemodynamic variables representing preload, inotropy, or afterload.

Methods: PWTT was measured continuously in 24 adult volunteers.

Patient knowledge about risk factors, achievement of target values, and guideline-adherent secondary prevention therapies 12 months after acute myocardial infarction.

Aims: The prospective GULLIVE-R study aimed to evaluate adherence to guideline-recommended secondary prevention, physicians' and patients' estimation of cardiac risk, and patients' knowledge about target values of risk factors after acute myocardial infarction (AMI).

Methods And Results: We performed a prospective study enrolling patients 9-12 months after AMI. Guideline-recommended secondary prevention therapies and physicians as well as patients' estimation about their risk and patients' knowledge about target values were prospectively collected.

Detection of atrial fibrillation using an implantable loop recorder in patients with previous cryptogenic stroke: the SPIDER-AF registry (Stroke Prevention by Increasing DEtection Rates of Atrial Fibrillation).

Background: The use of implantable loop recorder (ILR) to detect atrial fibrillation (AF) in patients with a history of cryptogenic stroke (CS) has seldom been investigated in "real-world" settings.

Objective: This study aimed to present the results of the Stroke Prevention by Increasing DEtection Rates of Atrial Fibrillation (SPIDER-AF) registry.

Method: SPIDER is a multicentric, observational registry, including 35 facilities all over Germany.

The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States.

Background: Diphenhydramine (DPH) is an antihistamine medication that in overdose can result in anticholinergic symptoms and serious complications, including arrhythmia and coma. We aimed to compare the value of various machine learning (ML) models, including light gradient boosting machine (LGBM), logistic regression (LR), and random forest (RF), in the outcome prediction of DPH poisoning.

Materials And Methods: We used the National Poison Data System database and included all of the human exposures of DPH from January 01, 2017 to December 31, 2017, and excluded those cases with missing information, duplicated cases, and those who reported co-ingestion.

Deep learning neural network derivation and testing to distinguish acute poisonings.

Introduction: Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs.

Research Design & Methods: Data were queried from the National Poison Data System (NPDS) from 2014 through 2018 for eight single-agent poisonings (acetaminophen, diphenhydramine, aspirin, calcium channel blockers, sulfonylureas, benzodiazepines, bupropion, and lithium).

Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures.

Background: This study aimed to compare clinical and laboratory characteristics of supra-therapeutic (RSTI) and acute acetaminophen exposures using a predictive decision tree (DT) algorithm.

Methods: We conducted a retrospective cohort study using the National Poison Data System (NPDS). All patients with RSTI acetaminophen exposure (n = 4,522) between January 2012 and December 2017 were included.

The role of decision tree and machine learning models for outcome prediction of bupropion exposure: A nationwide analysis of more than 14 000 patients in the United States.

Bupropion is widely used for the treatment of major depressive disorder and for smoking cessation assistance. Unfortunately, there are no practical systems to assist clinicians or poison centres in predicting outcomes based on clinical features. Hence, the purpose of this study was to use a decision tree approach to inform early diagnosis of outcomes secondary to bupropion overdose.

Dynamic reaction picklist for improving allergy reaction documentation: A usability study.

Objective: To assess novel dynamic reaction picklists for improving allergy reaction documentation compared to a static reaction picklist.

Materials And Methods: We developed three web-based user interfaces (UIs) mimicking the Mass General Brigham's EHR allergy module: the first and second UIs (i.e.

Classification of acute poisoning exposures with machine learning models derived from the National Poison Data System.

The primary aim of this pilot study was to develop a machine learning algorithm to predict and distinguish eight poisoning agents based on clinical symptoms. Data were used from the National Poison Data System from 2014 to 2018, for patients 0-89 years old with single-agent exposure to eight drugs or drug classes (acetaminophen, aspirin, benzodiazepines, bupropion, calcium channel blockers, diphenhydramine, lithium and sulfonylureas). Four classifier prediction models were applied to the data: logistic regression, LightGBM, XGBoost, and CatBoost.

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System.

Background: With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin's toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study the efficacy of machine learning algorithms in predicting the outcome of metformin poisoning using two well-known classification methods, including support vector machine (SVM) and decision tree (DT).

Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing.

Background: Drug challenge tests serve to evaluate whether a patient is allergic to a medication. However, the allergy list in the electronic health record (EHR) is not consistently updated to reflect the results of the challenge, affecting clinicians' prescription decisions and contributing to inaccurate allergy labels, inappropriate drug-allergy alerts, and potentially ineffective, more toxic, and/or costly care. In this study, we used natural language processing (NLP) to automatically detect discrepancies between the EHR allergy list and drug challenge test results and to inform the clinical recommendations provided in a real-time allergy reconciliation module.

Identifying and Reconciling Patients' Allergy Information Within the Electronic Health Record.

Allergy information is often documented in diverse sections of the electronic health record (EHR). Systematically reconciling allergy information across the EHR is critical to improve the accuracy and completeness of patients' allergy lists and ensure patient safety. In this retrospective cohort study, we examined the prevalence of incompleteness, inaccuracy, and redundancy of allergy information for patients with a clinical encounter at any Mass General Brigham facility between January 1, 2018 and December 31, 2018.

Decision tree algorithm can determine the outcome of repeated supratherapeutic ingestion (RSTI) exposure to acetaminophen: review of 4500 national poison data system cases.

This study is aimed at establishing the outcome of RSTI exposure to acetaminophen based on a decision tree algorithm for the first time. This study used the National Poison Data System (NPDS) to conduct a six-year retrospective cohort analysis, which included 4522 individuals. The patients had a mean age of 26.

Heterogeneity of Drug Allergies and Reaction Lists in Two U.S. Health Care Systems' Electronic Health Records.

Background: Health care institutions have their own "picklist" for clinicians to document adverse drug reactions (ADRs) into the electronic health record (EHR) allergy list. Whether the lack of a nationally standardized picklist impacts clinician data entries is unknown.

Objectives: The objective of this study was to assess the impact of defined reaction picklists on clinical documentation and, therefore, downstream analytics and clinical research using these data at two institutions.

Unsupervised clinical relevancy ranking of structured medical records to retrieve condition-specific information in the emergency department.

Background: Decision making in the Emergency Department (ED) requires timely identification of clinical information relevant to the complaints. Existing information retrieval solutions for the electronic health record (EHR) focus on patient cohort identification and lack clinical relevancy ranking. We aimed to compare knowledge-based (KB) and unsupervised statistical methods for ranking EHR information by relevancy to a chief complaint of chest or back pain among ED patients.

Reduced admission rates and resource utilization for chest pain patients using an electronic health record-embedded clinical pathway in the emergency department.

Objectives: Assess the impact of an electronic health record (EHR)-embedded clinical pathway (ePATH) as compared to a paper-based clinical decision support tool on outcomes for patients presenting to the emergency department (ED) with suspected acute coronary syndrome (ACS).

Methods: A retrospective, quasi-experimental study using difference-in-differences and interrupted time series specifications to evaluate the impact of an EHR-embedded clinical pathway between April 2013 and July 2017. The intervention was implemented in February 2016 at a large academic tertiary hospital and compared to a local community hospital without the intervention.

Cardiac Rehabilitation Knowledge and Attitudes of Cardiology Fellows.

Purpose: Cardiac rehabilitation (CR) is underutilized with only 8-31% of eligible patients participating. Lack of referral and lack of physician endorsement are well-known barriers to participation. Physicians who lack insights regarding CR are less likely to refer patients and recommend it.

Improved antibiotic prescribing using indication-based clinical decision support in the emergency department.

Background: Evaluate an indication-based clinical decision support tool to improve antibiotic prescribing in the emergency department.

Methods: Encounters where an antibiotic was prescribed between January 2015 and October 2017 were analyzed before and after the introduction of a clinical decision support tool to improve clinicians' selection of a guideline-approved antibiotic based on clinical indication. Evaluation was conducted on a pre-defined subset of conditions that included skin and soft tissue infections, respiratory infections, and urinary infections.