Publications by authors named "Patricia Dorling"
Article Synopsis
- Fabry disease (FD) is a rare metabolic disorder with varying symptoms, making it essential to identify patient characteristics that affect treatment outcomes due to a lack of direct treatment comparisons.
- A systematic literature review (SLR) was performed to analyze real-world evidence from 119 studies, revealing potential treatment effect modifiers (TEMs) such as age, sex, timing of treatment, and renal function.
- Key findings included that males tend to have poorer renal outcomes, younger patients benefit more from early treatment, and factors like left ventricular hypertrophy and baseline kidney function significantly influence disease progression and treatment efficacy.
Article Synopsis
- Project HERCULES created a detailed natural history model for Duchenne muscular dystrophy (DMD) that outlines eight health states, including varying levels of ambulatory and non-ambulatory conditions.
- The study analyzed data from 1,173 DMD patients, revealing that older age correlates with worse motor, pulmonary, and cardiac functionalities as patients progress through the health states.
- Key metrics like the North Star Ambulatory Assessment (NSAA) score and forced vital capacity (FVC) indicate significant declines in function from the early ambulatory state to advanced stages, emphasizing the need for improved economic modeling and decision-making in DMD treatment.
Article Synopsis
- Muscular dystrophies (MDs) are a diverse group of genetic disorders that lead to progressive muscle weakness and disabilities, with Duchenne MD (DMD) and Becker MD being two of the most common forms.
- The study aimed to create and validate algorithms to identify DMD patients specifically, including their nonambulatory and ventilation-dependent statuses, using administrative claims data linked with electronic health records (EHRs).
- A retrospective cohort of 1,300 male patients diagnosed with Duchenne/Becker MD was analyzed to validate the algorithms, with their performance assessed through positive predictive value and the comparison of algorithm outputs against documented clinical EHR information.
Background: No algorithms exist to identify important osteoarthritis (OA) patient subgroups (i.e., moderate-to-severe disease, inadequate response to pain treatments) in electronic healthcare data, possibly due to the complexity in defining these characteristics as well as the lack of relevant measures in these data sources.
View Article and Find Full Text PDFObjectives: As understanding of the pathogenesis and treatment strategies for osteoarthritis (OA) evolves, it is important to understand how patient factors are also changing. Our goal was to examine demographics and known risk factors of patients with OA over time.
Design: Open-cohort retrospective study using electronic health records.
Objectives: Opioids are commonly used to manage pain, despite an increased risk of adverse events and complications when used against recommendations. This register study uses data of osteoarthritis (OA) patients with joint replacement surgery to identify and characterize problematic opioid use (POU) prescription patterns.
Methods: The study population included adult patients diagnosed with OA in specialty care undergoing joint replacement surgery in Denmark, Finland, Norway, and Sweden during 1 January 2011 to 31 December 2014.
Introduction: Osteoarthritis (OA) is a complex disease, and prior studies have documented the health and economic burdens of patients with OA compared to those without OA. Our goal was to use two strategies to further stratify OA patients based on both pain and treatment intensity to examine healthcare utilization and costs using electronic records from 2001 to 2018 at a large integrated health system.
Methods: Adult patients with ≥1 pain numerical rating scale (NRS) and diagnosis of OA were included.