Neural network models for predicting readmission among patients undergoing peripheral vascular intervention using electronic health record data and clinical registry data.

Objectives: To determine whether neural network models based on electronic health record (EHR) data can match and augment the performance of models based on clinical registry data in predicting readmission after peripheral vascular intervention (PVI).

Design: Observational cohort study.

Setting: Vascular Quality Initiative registry and INSIGHT Clinical Research Network EHR data from multiple academic institutions in New York City.

Gaps in the coordination of care for older adults with or at risk for cardiovascular disease.

Objectives: We sought to determine whether patients with cardiovascular disease (CVD) or CVD risk factors and fragmented care (ie, care spread across multiple providers) perceive any gaps in communication among their providers and whether any gaps are perceived as clinically significant (ie, leading to adverse events).

Study Design: We conducted a cross-sectional telephone survey of community-dwelling Medicare beneficiaries 65 years and older with CVD or at least 1 CVD risk factor and highly fragmented ambulatory care (reversed Bice-Boxerman Index score ≥ 0.85) who had been attributed to an accountable care organization in New York, New York.

Comparative Effectiveness of Two Methods for Assigning Care Coordinators to People Living With Dementia.

Background: We sought to determine the comparative effectiveness of two strategies for assigning care coordinators to people living with dementia (PLWD) and their caregivers.

Methods: We conducted a pragmatic randomized clinical trial embedded in a Medicare accountable care organization (ACO) in New York, NY in 2022-2024. We included community-dwelling PLWD ≥ 65 years who were attributed to the ACO and had highly fragmented ambulatory care in the previous year (reversed Bice-Boxerman Index ≥ 0.

A Co-Segmentation Algorithm to Predict Emotional Stress From Passively Sensed mHealth Data.

We develop a data-driven cosegmentation algorithm of passively sensed and self-reported active variables collected through smartphones to identify emotionally stressful states in middle-aged and older patients with mood disorders undergoing therapy, some of whom also have chronic pain. Our method leverages the association between the different types of time series. These data are typically nonstationary, with meaningful associations often occurring only over short time windows.

Prediction of remission of pharmacologically treated psychotic depression: A machine learning approach.

Background: The combination of antidepressant and antipsychotic medication is an effective treatment for major depressive disorder with psychotic features ('psychotic depression'). The present study aims to identify sociodemographic and clinical predictors of remission of psychotic depression treated with combination pharmacotherapy and determine the accuracy of prediction models.

Methods: Two hundred and sixty-nine participants aged 18 to 85 years with psychotic depression were acutely treated with protocolized sertraline plus olanzapine for up to 12 weeks.

Problem Adaptation Therapy for Older Adults with Chronic Pain and Negative Emotions in Primary Care (PATH-Pain): A Randomized Clinical Trial.

Objective: To test the efficacy of Problem Adaptation Therapy for Pain (PATH-Pain) versus Usual Care (UC) in reducing pain-related disability, pain intensity, and depression among older adults with chronic pain and negative emotions.

Design: RCT assessing the between-group differences during the acute (0-10 weeks) and follow-up (weeks 11-24) phase of treatment.

Setting: A geriatrics primary care site.

Examining Healthy Lifestyles as a Mediator of the Association Between Socially Determined Vulnerabilities and Incident Heart Failure.

Background: Increased burden of socially determined vulnerabilities (SDV), which include nonmedical conditions that contribute to patient health, is associated with incident heart failure (HF). Mediators of this association have not been examined. We aimed to determine if a healthy lifestyle mediates the association between SDV and HF.

The Cumulative Burden of Social Risk Factors and 10-Year Change in Quality of Life.

Background: Social risk factors are linked to adverse health outcomes, but their total impact on long-term quality of life is obscure. We hypothesized that a higher burden of social risk factors is associated with greater decline in quality of life over 10 years.

Methods: We examined associations between social risk factors count and decline >5 points in (i) physical component summary, and (ii) mental component summary scores from the Short Form-12 among Black and White participants in the Reasons for Geographic and Racial Differences in Stroke study (n = 14 401).

Diagnostic accuracy of phosphorylated tau217 in detecting Alzheimer's disease pathology among cognitively impaired and unimpaired: A systematic review and meta-analysis.

Our review summarizes the diagnostic accuracy of plasma and cerebrospinal fluid (CSF) phosphorylated tau 217 (p-tau217) in detecting amyloid and tau pathology on positron emission tomography (PET). We systematically reviewed studies that reported the diagnostic accuracy of plasma and CSF p-tau217, searching MEDLINE/PubMed, Scopus, and Web of Science through August 2024. The accuracy of p-tau217 in predicting amyloid and tau pathology on PET was evaluated in 30 studies.

Longitudinal trajectories of symptom change during antidepressant treatment among managed care patients with depression and anxiety.

Despite the high correlation between anxiety and depression, little remains known about the course of each condition when presenting concurrently. This study aimed to identify longitudinal patterns during antidepressant treatment in patients with depression and anxiety, and evaluate related factors associated with these patterns. By analyzing longitudinal self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores that tracked courses of depression and anxiety over a three-month window among the 577 adult participants, six depression and six anxiety trajectory subgroups were computationally derived using group-based trajectory modeling.

Increasing Completion of Daily Patient-Reported Outcomes in Psychotherapies for Late-Life Depression through User-Centered Design.

Background:  Treatment of depressive symptoms in older adults is a growing public health concern. Collecting patient-reported outcomes (PROs) may facilitate efficiently scaling psychotherapy for older adults but user-specific tailoring is needed to improve completion.

Objectives:  This study investigates (1) the effect of updating PRO collection tools for middle-aged and older adults with depressive symptoms through a user-centered design process on user completion of PRO questions, (2) what sociodemographic factors correspond with participant completion, and (3) how completion of PRO questions change during the course of a psychotherapy intervention.

2SpamH: A Two-Stage Pre-Processing Algorithm for Passively Sensed mHealth Data.

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices.

Gaps in the coordination of care for people living with dementia.

Background: One-third of people living with dementia (PLWD) have highly fragmented care (i.e., care spread across many ambulatory providers without a dominant provider).

Leveraging home health aides to improve outcomes in heart failure: A pilot study protocol.

Heart failure (HF) affects six million people in the U.S., is associated with high morbidity, mortality, and healthcare utilization.

Application of a data continuity prediction algorithm to an electronic health record-based pharmacoepidemiology study.

Background And Objectives: Use of algorithms to identify patients with high data-continuity in electronic health records (EHRs) may increase study validity. Practical experience with this approach remains limited.

Methods: We developed and validated four algorithms to identify patients with high data continuity in an EHR-based data source.

Association of Healthy Lifestyle and Incident Polypharmacy.

Background: Polypharmacy, commonly defined as taking ≥5 medications, is an undesirable state associated with lower quality of life. Strategies to prevent polypharmacy may be an important priority for patients. We sought to examine the association of healthy lifestyle, a modifiable risk factor, with incident polypharmacy.

Improved outcomes for depressed elder abuse victims with video-delivered psychotherapy during COVID-19.

Objective: There is a lack of evidence-based scalable therapies for elder abuse victims, with no current remotely delivered tailored psychotherapy. The purpose of this manuscript is to (a) examine the effectiveness of a brief therapy for depression for elder abuse victims, and (b) to compare remote intervention delivery via phone or video to the traditional in-person delivery.

Method: PROTECT, , is a brief therapy developed in collaboration with partners at the Department for the Aging (DFTA) of New York City.

Risk Prediction for Atherosclerotic Cardiovascular Disease With and Without Race Stratification.

Importance: Use of race-specific risk prediction in clinical medicine is being questioned. Yet, the most commonly used prediction tool for atherosclerotic cardiovascular disease (ASCVD)-pooled cohort risk equations (PCEs)-uses race stratification.

Objective: To quantify the incremental value of race-specific PCEs and determine whether adding social determinants of health (SDOH) instead of race improves model performance.