An Analysis to Estimate Febrile Neutropenia (FN) in Patients Receiving Udenyca

Overview

This is a multicenter, prospective, observational cohort registry in subjects receiving myelosuppressive chemotherapy for a non-myeloid malignancy who are considered to be at high risk for developing febrile neutropenia (FN).

Little is known about the real-world risk of febrile neutropenia (FN) among subjects receiving Udenyca. Udenyca was approved due to the totality of evidence under the new Food and Drug Administration (FDA) pathway 351 (k), which met all the endpoints of pharmacokinetic (PK), pharmacodynamic (PD) and immunogenicity in healthy volunteers. Udenyca is the only pegfilgrastim that has not been studied in actual patient cohorts; it has only been studied in healthy volunteers. This prospective observational cohort study is designed to estimate the risk of FN among subjects who 1) are at high risk for FN while receiving myelosuppressive chemotherapy in a real-world outcomes setting and 2) receive Udenyca for FN prophylaxis with each cycle of highly myelotoxic chemotherapy. Study Plans: After signing the informed consent form, data collection will start at visit 1/cycle 1 (screening/enrollment \[day 1\]); subjects who received chemotherapy up to 1 week prior to enrollment are eligible to participate in the study. Visit 1/cycle 1 is to coincide with day 1 (± 7 days) of the subjects' chemotherapy treatment; data collection should start at day 1/visit 1/cycle 1 of chemotherapy. Entries will be recorded during a subject's medical chart review to obtain information on demographic factors, malignancy characteristics (e.g., malignancy histology, date of initial malignancy diagnosis, stage at diagnosis, current malignancy stage, any previous cancer therapy \[radiation, surgery, or chemotherapy\]), relevant comorbidities, laboratory data, and relevant concomitant therapies. Subjects' socio-demographic and clinical characteristics will be collected on the date of enrollment and include age, sex, geographic region, health plan information, socioeconomic status (employment status), body surface area, and tobacco use. Detailed clinical information and laboratory information will be collected, including subjects' medical and treatment history, comorbid conditions, and history of any other malignancies (as well as any treatment received for these other malignancies). Additionally, subject Eastern Cooperative Oncology Group (ECOG) Performance Status, relevant concomitant medications, and any adverse events that occur throughout the study will be recorded. Subjects will be followed from study enrollment (Cycle 1, Visit 1) throughout the end of their treatment cycle or until the earliest of loss to follow-up, withdrawal of consent, death, end of study (EOS), discontinuation of chemotherapy regimen prior to 4 cycles, or termination of study by the sponsor. The study data will be captured using the electronic case report form (eCRF), which will contain information collected directly about the subjects and their treatment/outcomes from the healthcare study research personnel. Data will be entered into the eCRF by research coordinators at the individual sites. The data to be entered into the eCRF will be obtained from paper or electronic medical records depending on the technology used by individual sites. The short form health survey SF-12 Health-related Quality of Life (HRQoL) instrument will be used for HRQoL outcomes. A data management plan will be created and will describe all functions, processes, and specifications for data collection, cleaning, and validation. The eCRFs will include programmable edits to obtain immediate feedback if data are missing, out of range, illogical, or potentially erroneous. Concurrent manual data review will be performed based on parameters dictated by the plan. Ad hoc queries will be generated within the electronic data capture (EDC) system and followed up until resolution. High data quality standards will be maintained, and processes and procedures will be utilized to repeatedly ensure that the data are as clean and accurate as possible when presented analysis. Data quality will be enhanced through a series of programmed data quality checks that automatically detect out-of-range or anomalous data.

Conditions

Interventions

Eligibility

Locations (3)

Outcomes

Central Contacts