Causal Machine Learning for Left Atrial Appendage Occlusion in Patients With Atrial Fibrillation.

Background: Transcatheter left atrial appendage occlusion (LAAO) is an alternative to lifelong anticoagulation, but optimal patient selection remains challenging.

Objectives: This study sought to apply a novel causal machine learning framework to identify patients who would benefit from LAAO vs a direct oral anticoagulant (DOAC).

Methods: We identified 744,190 adult patients with atrial fibrillation treated with either LAAO or DOAC between March 13, 2015, and December 31, 2019, using data from OptumLabs Data Warehouse.

Emergency department risk model: timely identification of patients for outpatient care coordination.

Objective: Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patients for CoCM to prevent frequent ED utilization remains unclear. This study aimed to develop and validate a risk identification model to proactively detect patients with MDD in CoCM at high risk of frequent (≥ 3) ED visits.

Longitudinal trajectories of glycemic control among U.S. Adults with newly diagnosed diabetes.

Aims: To identify longitudinal trajectories of glycemic control among adults with newly diagnosed diabetes, overall and by diabetes type.

Methods: We analyzed claims data from OptumLabs® Data Warehouse for 119,952 adults newly diagnosed diabetes between 2005 and 2018. We applied a novel Mixed Effects Machine Learning model to identify longitudinal trajectories of hemoglobin A (HbA) over 3 years of follow-up and used multinomial regression to characterize factors associated with each trajectory.

Clinical Phenotyping with an Outcomes-driven Mixture of Experts for Patient Matching and Risk Estimation.

Observational medical data present unique opportunities for analysis of medical outcomes and treatment decision making. However, because these datasets do not contain the strict pairing of randomized control trials, matching techniques are to draw comparisons among patients. A key limitation to such techniques is verification that the variables used to model treatment decision making are also relevant in identifying the risk of major adverse events.

Changes in Kidney Function After Diagnosis of Advanced Heart Failure.

Background: Kidney function and its association with outcomes in patients with advanced heart failure (HF) has not been well-defined.

Methods And Results: We conducted a retrospective cohort study comprising all adult residents of Olmsted County, Minnesota, with HF who developed advanced HF from 2007 to 2017. Patients were grouped by estimated glomerular filtration rate (eGFR) at advanced HF diagnosis using the 2021 Chronic Kidney Disease Epidemiology Collaboration equation.

Identifying treatment heterogeneity in atrial fibrillation using a novel causal machine learning method.

Background: Lifelong oral anticoagulation is recommended in patients with atrial fibrillation (AF) to prevent stroke. Over the last decade, multiple new oral anticoagulants (OACs) have expanded the number of treatment options for these patients. While population-level effectiveness of OACs has been compared, it is unclear if there is variability in benefit and risk across patient subgroups.

A prediction model for asthma exacerbations after stopping asthma biologics.

Background: Little is known regarding the prediction of the risks of asthma exacerbation after stopping asthma biologics.

Objective: To develop and validate a predictive model for the risk of asthma exacerbations after stopping asthma biologics using machine learning models.

Methods: We identified 3057 people with asthma who stopped asthma biologics in the OptumLabs Database Warehouse and considered a wide range of demographic and clinical risk factors to predict subsequent outcomes.

Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records.

Idiopathic pulmonary fibrosis (IPF) is a lethal fibrosing interstitial lung disease with a mean survival time of less than 5 years. Nonspecific presentation, a lack of effective early screening tools, unclear pathobiology of early-stage IPF and the need for invasive and expensive procedures for diagnostic confirmation hinder early diagnosis. In this study, we introduce a new screening tool for IPF in primary care settings that requires no new laboratory tests and does not require recognition of early symptoms.

Impact of labor characteristics on maternal and neonatal outcomes of labor: A machine-learning model.

Introduction: Since Friedman's seminal publication on laboring women, numerous publications have sought to define normal labor progress. However, there is paucity of data on contemporary labor cervicometry incorporating both maternal and neonatal outcomes. The objective of this study is to establish intrapartum prediction models of unfavorable labor outcomes using machine-learning algorithms.

Identifying Patients With Advanced Heart Failure Using Administrative Data.

Objective: To develop algorithms to identify patients with advanced heart failure (HF) that can be applied to administrative data.

Patients And Methods: In a population-based cohort of all residents of Olmsted County, Minnesota, with greater than or equal to 1 HF billing code 2007-2017 (n=8657), we identified all patients with advanced HF (n=847) by applying the gold standard European Society of Cardiology advanced HF criteria via manual medical review by an HF cardiologist. The advanced HF index date was the date the patient first met all European Society of Cardiology criteria.

Introduction to Machine Learning in Obstetrics and Gynecology.

In the digital age of the 21st century, we have witnessed an explosion in data matched by remarkable progress in the field of computer science and engineering, with the development of powerful and portable artificial intelligence-powered technologies. At the same time, global connectivity powered by mobile technology has led to an increasing number of connected users and connected devices. In just the past 5 years, the convergence of these technologies in obstetrics and gynecology has resulted in the development of innovative artificial intelligence-powered digital health devices that allow easy and accurate patient risk stratification for an array of conditions spanning early pregnancy, labor and delivery, and care of the newborn.

Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization.

Introduction: The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders.

Methods: We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017.

Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population.

Purpose: The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challenging due to complexity and quality of the data. Therefore, we developed large electronic population-based cohorts with comprehensive harmonised and processed EHR data.

Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.

Importance: Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models. Machine learning algorithms may result in superior predictive models to aid in clinical decision-making.

Objective: To compare the performance of 3 machine learning approaches with the commonly used HAS-BLED (hypertension, abnormal kidney and liver function, stroke, bleeding, labile international normalized ratio, older age, and drug or alcohol use) risk score in predicting antithrombotic-related GIB.

Differences in Physical Activity and Sedentary Behavior Patterns of Postmenopausal Women With Normal vs. Low Total Hip Bone Mineral Density.

Recent evidence suggests that sedentary behavior (SB) may be associated with bone health. This study compares free-living physical activity (PA) and SB distribution patterns of postmenopausal women with normal vs. low total hip bone mineral density (BMD).

A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression.

Background: Patient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research.

Comparative Effectiveness and Safety of Oral Anticoagulants Across Kidney Function in Patients With Atrial Fibrillation.

Background: Patients with atrial fibrillation and severely decreased kidney function were excluded from the pivotal non-vitamin K antagonist oral anticoagulants (NOAC) trials, thereby raising questions about comparative safety and effectiveness in patients with reduced kidney function. The study aimed to compare oral anticoagulants across the range of kidney function in patients with atrial fibrillation.

Methods And Results: Using a US administrative claims database with linked laboratory data, 34 569 new users of oral anticoagulants with atrial fibrillation and estimated glomerular filtration rate ≥15 mL/(min·1.

Development and Validation of a Risk Stratification Model Using Disease Severity Hierarchy for Mortality or Major Cardiovascular Event.

Importance: Clinical domain knowledge about diseases and their comorbidities, severity, treatment pathways, and outcomes can facilitate diagnosis, enhance preventive strategies, and help create smart evidence-based practice guidelines.

Objective: To introduce a new representation of patient data called disease severity hierarchy that leverages domain knowledge in a nested fashion to create subpopulations that share increasing amounts of clinical details suitable for risk prediction.

Design, Setting, And Participants: This retrospective cohort study included 51 969 patients aged 45 to 85 years, with 10 674 patients who received primary care at the Mayo Clinic between January 2004 and December 2015 in the training cohort and 41 295 patients who received primary care at Fairview Health Services from January 2010 to December 2017 in the validation cohort.

Fewer gastrointestinal bleeds with ticagrelor and prasugrel compared with clopidogrel in patients with acute coronary syndrome following percutaneous coronary intervention.

Background: Gastrointestinal bleeding (GIB) frequently occurs following percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS) with the prescription of P2Y inhibiting antiplatelet agents. Compared with clopidogrel, the newer P2Y inhibitors lower major adverse cardiac events with similar or possibly higher major bleeding events. The comparative GIB rates of these medications remain poorly understood.

Quality over quantity: skeletal loading intensity plays a key role in understanding the relationship between physical activity and bone density in postmenopausal women.

Objective: Increasing physical activity (PA) is regularly cited as a modifiable target to improve health outcomes and quality of life in the aging population, especially postmenopausal women who exhibit low bone mineral density (BMD) and high fracture risk. In this cross-sectional study, we aimed to quantify real-world PA and its association with BMD in postmenopausal women.

Methods: Seventy postmenopausal women, aged 46 to 79 years, received a dual-energy X-ray absorptiometry scan measuring total hip BMD and wore bilateral triaxial accelerometers on the ankles for 7 days to measure PA in their free-living environment.

Estimation of manual wheelchair-based activities in the free-living environment using a neural network model with inertial body-worn sensors.

Shoulder pain is common in manual wheelchair (MWC) users. Overuse is thought to be a major cause, but little is known about exposure to activities of daily living (ADLs). The study goal was to develop a method to estimate three conditions in the field: (1) non-propulsion activity, (2) MWC propulsion, and (3) static time using an inertial measurement unit (IMU).

Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry.

Objective: Nonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion.

Methods: The authors queried the 2012-2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion.