A machine learning framework to adjust for learning effects in medical device safety evaluation.

Objectives: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects.

Limitations in the use of automated mental status detection for clinical decision support.

Background: Clinical decision support (CDS) tools improve adherence to evidence-based practices but are dependent upon data quality in the electronic health record (EHR). Mental status is an integral component of many risk stratification scores, but it is not known whether EHR-measures of altered mental status are reliable. The Glasgow Coma Scale (GCS) is a measure of altered mentation that is widely adopted and entered in the EHR in structured format.

Medicare's Hospital Readmission Reduction Program reduced fall-related health care use: An unexpected benefit?

Objective: To assess whether Medicare's Hospital Readmissions Reduction Program (HRRP) was associated with a reduction in severe fall-related injuries (FRIs).

Data Sources And Study Setting: Secondary data from Medicare were used.

Study Design: Using an event study design, among older (≥65) Medicare fee-for-service beneficiaries, we assessed changes in 30- and 90-day FRI readmissions before and after HRRP's announcement (April 2010) and implementation (October 2012) for conditions targeted by the HRRP (acute myocardial infarction [AMI], congestive heart failure [CHF], and pneumonia) versus "non-targeted" (gastrointestinal) conditions.

Novel integration of governmental data sources using machine learning to identify super-utilization among U.S. counties.

Background: Super-utilizers consume the greatest share of resource intensive healthcare (RIHC) and reducing their utilization remains a crucial challenge to healthcare systems in the United States (U.S.).

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

Background: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships.

Team-Based Coaching Intervention to Improve Contrast-Associated Acute Kidney Injury: A Cluster-Randomized Trial.

Background: Up to 14% of patients in the United States undergoing cardiac catheterization each year experience AKI. Consistent use of risk minimization preventive strategies may improve outcomes. We hypothesized that team-based coaching in a Virtual Learning Collaborative (Collaborative) would reduce postprocedural AKI compared with Technical Assistance (Assistance), both with and without Automated Surveillance Reporting (Surveillance).

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.

Association of Frailty and the Expanded Operative Stress Score with Preoperative Acute Serious Conditions, Complications, and Mortality in Males Compared to Females: A Retrospective Observational Study.

Objective: The aim of this study was to expand Operative Stress Score (OSS) increasing procedural coverage and assessing OSS and frailty association with Preoperative Acute Serious Conditions (PASC), complications and mortality in females versus males.

Summary Background Data: Veterans Affairs male-dominated study showed high mortality in frail veterans even after very low stress surgeries (OSS1).

Methods: Retrospective cohort using NSQIP data (2013-2019) merged with 180-day postoperative mortality from multiple hospitals to evaluate PASC, 30-day complications and 30-, 90-, and 180-day mortality.

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.

Enhancing trust in AI through industry self-governance.

Artificial intelligence (AI) is critical to harnessing value from exponentially growing health and healthcare data. Expectations are high for AI solutions to effectively address current health challenges. However, there have been prior periods of enthusiasm for AI followed by periods of disillusionment, reduced investments, and progress, known as "AI Winters.

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.

Renin-angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis.

Background: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension.

Selection for depression-specific dementia cases with replication in two cohorts.

The latent variable "δ" (for "dementia") provides an etiologically "agnostic" omnibus dementia severity metric capable of recognizing the dementing potential of any condition. Depressive symptoms are independent predictors of δ and are thereby implicated as "dementing". Serum resistin levels partially mediate the association between depressive symptoms and δ.

Posthospital Fall Injuries and 30-Day Readmissions in Adults 65 Years and Older.

Importance: Falls are common among older adults, particularly those with previous falls and cognitive impairment and in the postdischarge period. Hospitals have financial incentives to reduce both inpatient falls and hospital readmissions, yet little is known about whether fall-related injuries (FRIs) are common diagnoses for 30-day hospital readmissions.

Objective: To compare fall-related readmissions with other leading rehospitalization diagnoses, including for patients at greatest risk of readmission.

Underreporting of Fall Injuries of Older Adults: Implications for Wellness Visit Fall Risk Screening.

Objectives: To compare the accuracy of and factors affecting the accuracy of self-reported fall-related injuries (SFRIs) with those of administratively obtained FRIs (AFRIs).

Design: Retrospective observational study SETTING: United States PARTICIPANTS: Fee-for-service Medicare beneficiaries aged 65 and older (N=47,215).

Measurements: We used 24-month self-report recall data from 2000-2012 Health and Retirement Study data to identify SFRIs and linked inpatient, outpatient, and ambulatory Medicare data to identify AFRIs.

Serum proteins mediate depression's association with dementia.

The latent variable "δ" (for "dementia") uniquely explains dementia severity. Depressive symptoms are independent predictors of δ. We explored 115 serum proteins as potential causal mediators of the effect of depressive symptoms on δ in a large, ethnically diverse, longitudinal cohort.

Few serum proteins mediate APOE's association with dementia.

The latent variable "δ" (for "dementia") appears to be uniquely responsible for the dementing aspects of cognitive impairment. Age, depression, gender and the apolipoprotein E (APOE) e4 allele are independently associated with δ. In this analysis, we explore serum proteins as potential mediators of APOE's specific association with δ in a large, ethnically diverse longitudinal cohort, the Texas Alzheimer's Research and Care Consortium (TARCC).

Interprofessional Perspectives on ABCDE Bundle Implementation: A Focus Group Study.

Background: The ABCDE bundle is a multifaceted, interprofessional intervention that is associated with reduced ventilator and delirium days as well as increased likelihood of mobility in intensive care.

Objectives: The aim of this study is to describe organizational domains that contribute to variation in ABCDE bundle implementation as reported by intensive care unit providers and to examine the capability of a conceptual framework for identifying variation in ABCDE bundle implementation.

Methods: We conducted 2 separate focus groups that included nurses, respiratory therapists, occupational and physical therapists (N = 16) from the surgical and medical intensive care units at 1 academic medical center.

Hereditary spastic paraplegia.

The hereditary spastic paraplegias (HSPs) comprise a large group of inherited neurologic disorders. HSP is classified according to the mode of inheritance, the HSP locus when known, and whether the spastic paraplegia syndrome occurs alone or is accompanied by additional neurologic or systemic abnormalities. Analysis of 11 recently discovered HSP genes provides insight into HSP pathogenesis.