Real-world data on treatment patterns and survival among ALK+ NSCLC patients in Japan.

e20505 Background: Three ALK inhibitors (ALKi) are approved in Japan for treatment of patients with ALK-positive (ALK+) advanced or metastatic non-small cell lung cancer (NSCLC). However, the optimal sequence of therapy with ALKi is unclear. The objective of this study was to provide real-world data on the treatment patterns and survival among ALK+ NSCLC patients. Methods: ALK+ patients treated with ALKi in our institute were included in this retrospective analysis. Data on the treatment patterns and outcomes were collected from medical records. Results: In total, 60 patients were included. The median age at the diagnosis was 52.5 years, with 60% female and 65% non-smokers. The first treatment was chemotherapy in 67% and ALKi in 33%. The median overall survival (OS) was 186 weeks. We found differences in the OS for Crizotinib use at any line (118 weeks; presence vs. NR; absence) and first-line use of an ALKi (127 weeks; Crizotinib vs. 416 weeks; Alecitinib or Ceritinib, p = 0.0048). Conclusions: The role of Crizotinib in the treatment of ALK+ NSCLC is decreasing. Alectinib followed by Ceritinib seems to be promising. Treatment decision-making based on a re-biopsy is immature at present. The development of sequential therapy with ALKi based on resistance mechanisms is urgently needed. [Table: see text]

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

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e20002 ◽

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.

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Real-world data on treatment patterns and survival among ALK+ NSCLC patients in Japan. Treatment patterns and survival among ALK+ NSCLC patients in Japan: Single institute experience

Annals of Oncology ◽

10.1093/annonc/mdx671.024 ◽

2017 ◽

Vol 28 ◽

pp. x132-x133

Author(s):

T. Seto ◽

K. Nosaki ◽

R. Toyozawa ◽

K. Taguchi ◽

M. Edagawa ◽

...

Keyword(s):

Real World ◽

Treatment Patterns ◽

Real World Data ◽

World Data ◽

Nsclc Patients

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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 ◽

10.2337/db19-1043-p ◽

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

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

Japanese Journal of Ophthalmology ◽

10.1007/s10384-021-00850-8 ◽

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

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

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e19512 ◽

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]

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