ISPOR EU: Prescription Pattern & Physician Propensity for Congestive Heart Failure Treatments in United States & Europe
The role of general physicians in managing Congestive Heart Failure (CHF) has been important to reduce mortality and hospitalizations, particularly in stable patients, as these patients drive the majority of CHF-related morbidity and mortality. CHF treatment is often initiated by cardiologists with routine follow-up done by general physicians, indicating a possible gap in disease management between physician specialties. This research explores prescribing pattern between cardiologists and general physicians in the United States (US) and European CHF populations.
ISPOR EU: Evaluation of Missing Data Imputation Strategies in Clinical Trial and EMR Data Using Standardized Data Models
Missing data is a major challenge in clinical trials and observational real-world healthcare research. Studies often exclude records which contain incomplete data, but this strategy can introduce bias. Therefore, we evaluated the utility of different techniques for imputing missing data. Because biases tend to differ between randomized controlled trial and observational datasets, we conducted this assessment in both clinical trial and real-world datasets for Acute Myeloid Leukemia (AML). Evaluations of this sort are challenged by data presented in variable formats so in this research we used a standard data model to facilitate meaningful comparison across data types.
ISPOR EU: Health Resource Utilization in Advanced Ovarian and Endometrial Cancer in a United States Insurance Claims Database
Two of the most prevalent female genitourinary cancers are Ovarian and Endometrial Cancer. Despite recent innovations in immuno-oncology, cytotoxic platinum and taxane therapies remain a mainstay of standard treatment, and these diseases, once advanced, continue to represent significant unmet need. This study assesses recent changes in treatment patterns and health resource utilization in these two cancers.
ISPOR EU: Using Clinical Data Standards to Facilitate Comparisons of Acute Myeloid Leukemia Treatment Patterns and Outcomes in Real-World Clinical Care and Pooled Clinical Trial Populations
While randomized controlled clinical trials are the gold standard for demonstrating efficacy, there is a need to facilitate comparison of trial findings with real world populations. Shis poster shows how we can leverage common data modeling and vocabularies to compare Acute Myeloid Leukemia in real-world clinical practice to a pooled synthetic cohort of clinical trial subjects.
ISPOR EU: Patterns and Prediction for Cognitive Decline in Alzheimer’s Patients as Assessed by the Mini-Mental Status Exam in an Ambulatory Electronic Medical Record
Patient-reported outcomes (PRO) measures, when used in routine clinical care, have been shown to benefit physician satisfaction and patient outcomes. Implementation into clinical work flows, however, can be challenging. This poster examines the Mini-Mental Status Exam (MMSE) usage in an ambulatory Electronic Medical Record (EMR) in Alzheimer’s patients, including evaluating predictors of cognitive decline.
ISPOR EU: 3C-HF Prognostic Score to Predict Worsening Cardiac Function Among Congestive Heart Failure Patients in United States and Europe
The use of prognostic score is important to guide Congestive Heart Failure (CHF) management and treatment decisions. Despite a myriad of scoring approaches in practice, not all variables to calculate these scores are readily available. The Cardiac and Comorbid Conditions HF (3C-HF) score has an advantage of incorporating commonly recorded data from routine visits (e.g., comorbidities and treatment history) to predict 1-year mortality in HF patients. This research examines how 3C-HF score can be extended to predict worsening of cardiac function in the US and European CHF patients as well as identify high-risk subgroups within the populations.
Patient safety concerns, both related and unrelated to treatment, are a critical concern to managing patient care in oncology, and can limit treatment adherence and achieving optimal clinical outcomes. This research outlines a scalable approach leveraging common data standards for predicting patient clinical events across multiple real-world electronic medical records systems.
Prior research in Multiple Sclerosis has shown strong persistence to oral Disease-Modifying Therapies (DMT’s) compared with injectables, with medication possession ratios >85%. Given the increasing availability of oral DMT options, this study explores changes in real-world DMT persistence over time, as well as utilization in relapsing-remitting versus progressive disease.
ISPOR US: PMU8: A Systematic Approach For Synthetic Replication Of Clinical Trial Cohorts Using Retrospective Real-world And Clinical Trial Data
Replication of clinical trials through retrospective data has potential applications ranging from in silico modeling and synthetic control arm creation to extrapolation of clinical trial findings to real world practice. We outline here a systematic approach leveraging common data standards for data pooling and study replication.
ISPOR US: PCN246: A Systematic Approach For Quantifying Heterogeneity Within Clinical Trial Populations
Heterogeneity in clinical trial populations can contribute to variability in observed treatment effect. According to the Cochrane Handbook, sources can be either clinical or methodological diversity. Two proven quantitative approaches to addressing heterogeneity include use of clustering algorithms and of propensity score modeling techniques. We propose here a quantitative, systematic, scalable approach to assessing clinical trial population heterogeneity.
ISPOR US: PCN219: Predicting Chemotherapy-associated Thrombocytopenia In Real World Clinical Settings
Chemotherapy-induced thrombocytopenia is a frequent challenge in the management of cancer patients and can limit the ability to maintain effective dosing and treatment duration. In this study, we assess real-world rates of chemotherapy-associated thrombocytopenia, measure the impact on patient dosing, and explore the potential of machine learning methods, using commonly available clinical variables, to predict development of this common, potentially treatment-limiting side effect.
While randomized controlled clinical trials are the gold standard for demonstrating efficacy, there is a need to facilitate comparison of trial findings with real world populations. In this study we replicate the study cohort from an Alzheimer’s trial in a real-world data source, leveraging common data modeling and vocabularies.