Savan Research S.L
Madrid, 28013, Spain
NCT Number: NCT05117775
This will be an international, multicenter, retrospective, observational, and data-driven study using secondary data captured in EHRs. The extraction of the data captured in the EHRs will be performed with SAVANA's EHRead®, an innovative data-driven system based on Natural Language Processing (NLP) and machine learning. For all patients, the Index Date is defined as the timepoint within the study period when they fulfill ALL inclusion criteria and no exclusion criteria. Follow-up comprises the period between Index Date and the last EHR available within the study period. Additional variable-specific time windows may be considered to optimize data collection.
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Notify Me18 year and older
All sexes
Observational
Madrid, 28013, Spain
The present study aims to describe the clinical characteristics of patients with HNSCC in a real-world setting by analyzing readily available information in the Electronic Health Records (EHRs). This study will gain a deep insight of the clinical characteristics and real-world outcomes of patients with all stages (early, locally advanced, and metastatic) of HNSCC. It will focus on developing two predictive models to apply in the clinical setting, one for electing patients with high-risk of recurrence after radical treatment, and the second one for selecting recurrent or metastatic patients who could benefit from immunotherapy.
To achieve the proposed study objectives we will use SAVANA´s EHRead® (11-15), a technology that applies Natural Language Processing (NLP) (16) and machine learning to extract, organize, and analyze the unstructured clinical information jotted down by health professionals in patients' EHRs.
Primary objectives
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
All the groups will be descriptive, there is not intervention, as it is an Observational study applying artificial Intelligence (RWE).
Time frame: From 1st Jan 2021
To develop a predictive model based on dynamic risk stratification (DRS) for the risk of recurrence or disease progression following a primary curative treatment in HNSCC patients with early and locally advanced disease.
Time frame: From 1st Jan 2021
To develop a predictive model based on dynamic risk stratification (DRS) aimed at identifying patients' features that predict long-term survival after immunotherapy in recurrent and metastatic HNSCC patients
Time frame: From 1st Jan 2021
To describe median OS by primary tumor location (oral cavity, oropharynx, larynx, and hypopharynx) in HNSCC patients after stratification for prognostic factors, including tumor stage and treatment.
Time frame: From 1st Jan 2021
Time frame: From 1st Jan 2021
HNSCC:
Time frame: From 1st Jan 2021
To describe the demographics, clinical characteristics, and treatment of patients with nasopharynx, paranasal sinus, and salivary gland tumors.
Savana Research
Network
Towards A Better Paradigm for Head and Neck Cancer Treatment Applying Artificial Intelligence: an International Cohort Study of Electronic Health Records. HNC-TACTIC.
Acronym: HNC-TACTIC
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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