First cardiology study to assess FH patients using NLP algorithm

Advances in Therapy, an international medical journal, has published the Research Article “Familial Hypercholesterolemia identification algorithm in patients with acute cardiovascular event in a large hospital electronic database in Bulgaria – a call for implementation

Sqilline is extremely honored to be a co-writer among such extreme professionals and experts in the field.

Our advanced solution Danny Platform® played a key role in the process and analysis of this fundamental article.

Click to play the abstract video of the article:

KEY SUMMARY POINTS:

OBJECTIVES:

  • Using all available medical records from EHR systems to extract treated patients admitted for ASCVD acute events to screen for FH
  • Reinforce early FH identification and improving patient pathways and outcomes

CHALLENGES:

  • Limited data on the proportion and management of FH among adults
  • Under diagnosed, an acute event is usually the 1st clinical manifestation of FH
  • Most patients are unaware they have it until a significant cardio event occurs

SOLUTION:

  • Danny Platform’s NLP algorithm was developed to identify patients with arcus Cornealis, tendon xanthoma, or stated FH
  • Danny Platform’s algorithms search the EHR of patients admitted to hospital for the treatment of an acute CV event to identify those with undiagnosed FH
  • The platform can automatically calculate the Dutch Lipid Clinic Network Score(DLCNS)

RESULTS:

  • 11 090 patients recorded an acute cardiovascular event
  • 44% of patients have possible FH and 56% have probable/definite FH
  • 37% of patients discharged with high-intensity statin therapy
  • 96% of patients had poorly controlled cholesterol during the first year after discharge

BENEFITS:

  • Systematic screening and early identification of FH during hospitalization will improve patients’ treatment and outcomes
  • Empowering physicians to reinforcing early FH identification to improve future patient outcomes
  • Reducing burden on the healthcare system via automated screening tool during hospitalization may allow for rapid and effective lipid management, prevention of recurrent events

ARTICLE AUTHORS:

  • Prof. Ivo Petrov (Head of Cardiology, Angiology and Electrophysiology Department: Acibadem City Clinic Cardiovascular Center)
  • Assos. Prof. Arman Postadzhiyan (Department of General Medicine, Emergency University Hospital “St. Anna”, Medical University of Sofia)
  • Dr. Dobrin Vasilev (Head Cardiology Clinic at Alexandrovska University Hospital)
  • Dr. Ruslan Kasabov (Department of Interventional Cardiology: Hospital “Pulmed” Plovdiv
  • Prof. Dr. Fedya Nikolov and Assos. Prof. Mariya Tokmakova (Department of Cardiology, Medical University of Plovdiv)
  • Reneta Petkova (Department of General Medicine, Amgen Bulgaria)
  • Veselin Istatkov, Boyang Zhao, and Dimiter Mutafchiev (Sqilline)

Click here to read the full publication in Advances in Therapy

Share this article:

More News & Highlights

Highlights

Health data as an infrastructure

Health data can be considered a crucial component of healthcare infrastructure, as it forms the basis for decision-making, research, policy development, and patient care within...

Read more...

News

Lung Cancer Real-world Evidence Secondary Use of Data: A Non-Interventional Study

Description of clinical study: Non-Interventional Study for Analysis of Molecular Diagnostics and Treatment Patterns in Metastatic Non-Small Cell Lung Cancer Patients (Newly Diagnosed and Relapses...

Read more...

News

ChatGPT for Health Data

Desislava Mihaylova founded the company for processing and analyzing Big Data, Sqilline, and a personal tragedy pushed her towards the healthcare sector. The company has...

Read more...