Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts.

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.

Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort.

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

Importance: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models.

Objective: To compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model.

The Association between Cytokines and 365-Day Readmission or Mortality in Adult Cardiac Surgery.

Cardiac surgery results in a multifactorial systemic inflammatory response with inflammatory cytokines, such as interleukin-10 and 6 (IL-10 and IL-6), shown to have potential in the prediction of adverse outcomes including readmission or mortality. This study sought to measure the association between IL-6 and IL-10 levels and 1-year hospital readmission or mortality following cardiac surgery. Plasma biomarkers IL-6 and IL-10 were measured in 1,047 patients discharged alive after isolated coronary artery bypass graft surgery from eight medical centers participating in the Northern New England Cardiovascular Disease Study Group between 2004 and 2007.

End-of-Life Spending and Healthcare Utilization Among Older Adults with Chronic Obstructive Pulmonary Disease.

Background: End-of-life spending and healthcare utilization among older adults with COPD have not been previously described.

Methods: We examined data on Medicare beneficiaries aged 65 years or older with chronic obstructive pulmonary disease (COPD) who died during the period of 2013-2014. End-of-life measures were retrospectively reviewed for 2 years prior to death.

Are Urinary Biomarkers Better Than Acute Kidney Injury Duration for Predicting Readmission?

Background: Acute kidney injury (AKI) is a common complication of cardiac surgery. Postprocedural AKI is a risk factor for 30-day readmission. We sought to examine the association of AKI and kidney injury biomarkers with readmission after cardiac surgery.

Long-term dataset on aquatic responses to concurrent climate change and recovery from acidification.

Concurrent regional and global environmental changes are affecting freshwater ecosystems. Decadal-scale data on lake ecosystems that can describe processes affected by these changes are important as multiple stressors often interact to alter the trajectory of key ecological phenomena in complex ways. Due to the practical challenges associated with long-term data collections, the majority of existing long-term data sets focus on only a small number of lakes or few response variables.

Aluminum toxicity risk reduction as a result of reduced acid deposition in Adirondack lakes and ponds.

In 1990, the US Congress amended the Clean Air Act (CAA) to reduce regional-scale ecosystem degradation from SO and NO emissions which have been responsible for acid deposition in regions such as the Adirondack Mountains of New York State. An ecosystem assessment project was conducted from 1994 to 2012 by the Darrin Fresh Water Institute to determine the effect of these emission reduction policies on aquatic systems. The project investigated water chemistry and biota in 30 Adirondack lakes and ponded waters.