Health care costs associated with unrecognized progression to late-stage kidney disease.

Objectives: Many individuals with chronic kidney disease (CKD) are undiagnosed or unaware of the disease and at risk of not receiving services to manage their condition and of "crashing" into dialysis. Past studies report higher health care costs among patients with delayed nephrology care and suboptimal dialysis initiation, but they are limited because they focused on patients undergoing dialysis and did not evaluate costs associated with unrecognized disease for patients "upstream," or patients with late-stage CKD. We compared costs for patients with unrecognized progression to late-stage (stages G4 and G5) CKD and end-stage kidney disease (ESKD) with costs for individuals with prior CKD recognition.

Procedure for Organizing a Post-FDA-approval Evaluation of Antidepressants.

Purpose: The study reports the construction of a cohort used to study the effectiveness of antidepressants.

Methods: The cohort includes experiences of 3,678,082 patients with depression in the United States on antidepressants between January 1, 2001, and December 31, 2018. A total of 10,221,145 antidepressant treatment episodes were analyzed.

Designing Equitable Health Care Outreach Programs From Machine Learning Patient Risk Scores.

There is growing interest in ensuring equity and guarding against bias in the use of risk scores produced by machine learning and artificial intelligence models. Risk scores are used to select patients who will receive outreach and support. Inappropriate use of risk scores, however, can perpetuate disparities.

Effectiveness of common antidepressants: a post market release study.

Background: This study summarizes the experiences of patients, who have multiple comorbidities, with 15 mono-treated antidepressants.

Methods: This is a retrospective, observational, matched case control study. The cohort was organized using claims data available through OptumLabs for depressed patients treated with antidepressants between January 1, 2001 and December 31, 2018.

Deep neural network models for identifying incident dementia using claims and EHR datasets.

This study investigates the use of deep learning methods to improve the accuracy of a predictive model for dementia, and compares the performance to a traditional machine learning model. With sufficient accuracy the model can be deployed as a first round screening tool for clinical follow-up including neurological examination, neuropsychological testing, imaging and recruitment to clinical trials. Seven cohorts with two years of data, three to eight years prior to index date, and an incident cohort were created.

Predicting Onset of Dementia Using Clinical Notes and Machine Learning: Case-Control Study.

Background: Clinical trials need efficient tools to assist in recruiting patients at risk of Alzheimer disease and related dementias (ADRD). Early detection can also assist patients with financial planning for long-term care. Clinical notes are an important, underutilized source of information in machine learning models because of the cost of collection and complexity of analysis.

Assessment of Potentially Inappropriate Prescribing of Opioid Analgesics Requiring Prior Opioid Tolerance.

Importance: Opioid-tolerant only (OTO) medications, such as transmucosal immediate-release fentanyl products and certain extended-release opioid analgesics, require prior opioid tolerance for safe use, as patients without tolerance may be at increased risk of overdose. Studies using insurance claims have found that many patients initiating these medications do not appear to be opioid tolerant.

Objectives: To measure prevalence of opioid tolerance in patients initiating OTO medications and to determine whether linked electronic health record (EHR) data contribute evidence of opioid tolerance not found in insurance claims data.

Machine learning models to predict onset of dementia: A label learning approach.

Introduction: The study objective was to build a machine learning model to predict incident mild cognitive impairment, Alzheimer's Disease, and related dementias from structured data using administrative and electronic health record sources.

Methods: A cohort of patients (n = 121,907) and controls (n = 5,307,045) was created for modeling using data within 2 years of patient's incident diagnosis date. Additional cohorts 3-8 years removed from index data are used for prediction.

Identifying incident dementia by applying machine learning to a very large administrative claims dataset.

Alzheimer's disease and related dementias (ADRD) are highly prevalent conditions, and prior efforts to develop predictive models have relied on demographic and clinical risk factors using traditional logistical regression methods. We hypothesized that machine-learning algorithms using administrative claims data may represent a novel approach to predicting ADRD. Using a national de-identified dataset of more than 125 million patients including over 10,000 clinical, pharmaceutical, and demographic variables, we developed a cohort to train a machine learning model to predict ADRD 4-5 years in advance.

Development and Validation of HealthImpact: An Incident Diabetes Prediction Model Based on Administrative Data.

Objective: To develop and validate a model of incident type 2 diabetes based solely on administrative data.

Data Sources/study Setting: Optum Labs Data Warehouse (OLDW), a national commercial administrative dataset.

Study Design: HealthImpact model was developed and internally validated using nested case-control study design; n = 473,049 in training cohort and n = 303,025 in internal validation cohort.

All-cause and incremental per patient per year cost associated with chronic hepatitis C virus and associated liver complications in the United States: a managed care perspective.

Background: Approximately 3.2-3.9 million U.