Prevalence and incidence rates and treatment patterns of non-infectious uveitis in Japan: real-world data using a claims database

2021 ◽  
Author(s):  
Akihiko Umazume ◽  
Nobuyuki Ohguro ◽  
Annabelle A. Okada ◽  
Kenichi Namba ◽  
Koh-Hei Sonoda ◽  
...  
Keyword(s):  
Real World ◽  
Incidence Rates ◽  
Treatment Patterns ◽  
Real World Data ◽  
Claims Database ◽  
World Data ◽  
Infectious Uveitis

1043-P: Glycemic Profiles and Treatment Patterns: Real-World Data from 8,020 Adults with Type 1 Diabetes Using the Omnipod® Insulin Management System with Cloud-Based Data Management

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1043-P
Author(s):  
JENNIFER E. LAYNE ◽  
JIALUN HE ◽  
JAY JANTZ ◽  
YIBIN ZHENG ◽  
ERIC BENJAMIN ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Data Management ◽  
Real World ◽  
Management System ◽  
Treatment Patterns ◽  
Real World Data ◽  
World Data

Analyzing treatment patterns and time to the next treatment in chronic lymphocytic leukemia real-world data using automated temporal phenotyping.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e19512-e19512
Author(s):  
Kyeryoung Lee ◽  
Zongzhi Liu ◽  
Meng Ma ◽  
Yun Mai ◽  
Christopher Gilman ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Real World ◽  
Clinical Decision Making ◽  
Single Agent ◽  
Treatment Patterns ◽  
Lymphocytic Leukemia ◽  
Time Interval ◽  
Real World Data ◽  
World Data ◽  
Therapeutic Class

e19512 Background: Targeted therapy is an important treatment for chronic lymphocytic leukemia (CLL). However, optimal strategies for deploying small molecule inhibitors or antibody therapies in the real world are not well understood, largely due to a lack of outcomes data. We implemented a novel temporal phenotyping algorithm pipeline to derive lines of therapy (LOT) and disease progression in CLL patients. Here, the CLL treatment pattern and time to the next treatment (TTNT) were analyzed in real-world data (RWD) using patient electronic health records. Methods: We identified a CLL cohort with LOT from the Mount Sinai Data Warehouse (2003-2020). Each LOT consisted of either a single agent or combinations defined by NCCN CLL guidelines. We developed a natural language processing (NLP)-based temporal phenotyping approach to automatically identify the number of lines and therapeutic regimens. The sequence of treatment and time interval for each patient were derived from the systematic treatment data. Time to event analysis and multivariate (i.e., age, gender, race, other treatment patterns) Cox proportional hazard (CoxPH) models were used to analyze the patterns and predictors of TTNT. Results: Four hundred eleven CLL patients received 1 to 7 LOTs. Ibrutinib was the predominant 1st LOT (40.8% of patients) followed by anti-CD20-based antibody therapies and chemotherapy in 30.6 and 19.2% of patients, respectively, followed by Acalabrutinib, Venetoclax, and Idelalisib in 3.4, 2.7, and 0.7% of patients, respectively (Table 1). The 2nd to 5th LOT showed the same or similar trends. We next analyzed the TTNT in the 1st line of each therapeutic class. Acalabrutinib resulted in a longer median TTNT than Ibrutinib. Both Acalabrutinib and Ibrutinib showed longer TTNT compared to Venetoclax (median TTNTs were 742 and 598 vs. 373 days: HR = 0.23, p=0.015 and HR = 0.48, p=0.03, respectively). In addition, patients with age equal to or older than 65 showed longer TNNT (HR=0.16, p=0.016). Conclusions: Our result shows the potential of RWD usage in clinical decision making as real-world evidence reported here is consistent with results derived from clinical trial data. Linking this study to genetic data and other covariates affecting treatment outcomes may provide additional insights into the optimal sequences of the targeted therapies in CLL. Table 1: Therapeutic class and patient numbers (%) in each line.[Table: see text]

scholarly journals INSIGHTS FOR CARE: USING REAL-WORLD DATA TO QUANTIFY DIABETES TREATMENT PATTERNS AND OUTCOMES IN AN URBAN HEALTH ECONOMY

2016 ◽  
Vol 19 (3) ◽  
pp. A211 ◽  
Author(s):  
D. Chapman ◽  
S. Song ◽  
R. Foxcroft ◽  
F. Bidgoli ◽  
H. Ronte ◽  
...  
Keyword(s):  
Urban Health ◽  
Real World ◽  
Treatment Patterns ◽  
Health Economy ◽  
Diabetes Treatment ◽  
Real World Data ◽  
World Data

Treatment patterns of EGFR mt+ NSCLC IV patients: Real world data of the NOWEL network

2020 ◽  
Author(s):  
J Roeper ◽  
K Wedeken ◽  
U Stropiep ◽  
M Falk ◽  
M Tiemann ◽  
...  
Keyword(s):  
Real World ◽  
Treatment Patterns ◽  
Real World Data ◽  
World Data

Treatment patterns and characteristics of acute promyelocytic leukemia (APL) patients receiving arsenic trioxide (ATO) therapy in a real-world setting.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18522-e18522
Author(s):  
Boxiong Tang ◽  
Susan Gabriel ◽  
Jifang Zhou ◽  
Ashutosh K. Pathak ◽  
Debra Irwin ◽  
...  
Keyword(s):  
Real World ◽  
Index Date ◽  
Claims Data ◽  
Treatment Patterns ◽  
Chronic Obstructive ◽  
Administrative Claims ◽  
First Line ◽  
Real World Data ◽  
World Data

e18522 Background: Clinical trials have shown that low-risk APL patients had significantly better outcomes when receiving first-line all-trans retinoic acid (ATRA) + ATO compared with standard ATRA + chemotherapy. Few published studies have used real-world data to describe patients using ATO and their current treatment patterns. This study used United States (US) administrative claims data to describe treatment patterns and characteristics of patients receiving first-line ATO. Methods: This retrospective, observational cohort study used claims data from the MarketScan databases. As there is no ICD-9-CM diagnosis code for APL, ATO treatment was used as a surrogate for the diagnosis of APL since ATO is typically used only in APL patients. Patients were selected if they had ≥1 claims for ATO between January 1, 2000, and June 30, 2015. Date of first use was designated the index date. To identify first-line ATO initiation, patients with ATRA or other APL-indicated chemotherapy claims any time before the index date were excluded. Variable baseline and follow-up periods consisting of ≥3 months of pre-index and ≥30 days of post-index continuous enrollment in medical and pharmacy benefit were used. Results: In total, 331 patients were identified with a subset (n = 265) having ≥2 claims for ATO. The analysis focused on these 265 patients, 54% of whom were male. Mean age was 60.6 years; 45% were covered by Medicare. The most common comorbid conditions measured were diabetes (6%), chronic obstructive pulmonary disease (5%), and congestive heart failure (4%). The most commonly selected APL treatments administered during follow-up were ATRA (17%) and daunorubicin (9%) with the use of idarubicin, cytarabine, and mitoxantrone at less than 3%. Maintenance therapy with methotrexate or 6-mercaptopurine was observed in 7% and 6% of patients, respectively. Conclusions: This is one of the first studies to examine patient characteristics and treatment patterns for first-line ATO using real-world data. Further research is needed to evaluate outcomes for patients receiving ATO as first-line therapy and to re-evaluate treatment guidelines in light of these outcomes.

Leveraging real world data and AIML to inform smart patient care in oncology.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e20002-e20002
Author(s):  
Li Zhou ◽  
Rob Steen ◽  
Lynn Lu
Keyword(s):  
Patient Care ◽  
Predictive Model ◽  
Real World ◽  
Model Performance ◽  
Treatment Decision ◽  
Treatment Patterns ◽  
Real World Data ◽  
World Data ◽  
Co Morbidity ◽  
Low Probability

e20002 Background: Identifying optimal therapy options can help maximize treatment outcomes. Finding ways to help improve treatment decision is of great value to achieve better patient care. With the availability of robust patient real world data and the application of state of the art Artificial Intelligence and Machine Learning (AIML) technology, new opportunities have emerged for a broad spectrum of research needs from oncology R&D to commercialization. To illustrate the above advancements, this study identified patients diagnosed with CLL who may progress to next line of treatment in the near future (e.g. future 3 months). More importantly, we can identify treatment patterns which are more effective in treating different types of CLL patients. Methods: This study includes multiple steps which have already been analyzed for feasibility: 1. Collect CLL patients. IQVIA's real world data contains ~60,000 active CLL treated patients. ~2,000 patients have progressed line of treatment in 3 month. 2. Define patients into positive and negative cohorts based on those who have/have not advanced to line L2+. 3. Determine patient profiles based on treatment regimens, symptoms, lab tests, doctor visits, hospital visits, and co-morbidity, etc. 4. Select patient and treatment features to fit an AIML predictive model. 5. Test different algorithms to achieve best model results and validate model performance. 6. Score and classify CLL patients into high and low probability based on the predictive model. 7. Match patients based on feature importance and compare regimens between positive and negative cohort. Results: Model accuracy is above 90%. Top clinical features are calculated for each patient. Optimum treatment patterns between high and low probability patients are identified, with controlling patient key features. Conclusions: Conclusions from this study is expected to yield deeper insight into more tailored treatments by patient type. CLL patients started with oral therapy(targeting) have better response than other treatments.

Cost as the Main Driver of T2D Treatment Patterns—Lessons from Real-World Data and Physician Focus Groups

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1254-P
Author(s):  
SZE-JUNG WU ◽  
AMIT D. RAVAL ◽  
JUDITH J. STEPHENSON ◽  
XIAOMEI PENG ◽  
HARRY WEISMAN ◽  
...  
Keyword(s):  
Focus Groups ◽  
Real World ◽  
Treatment Patterns ◽  
Real World Data ◽  
World Data ◽  
Main Driver
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