Familial hypercholesterolemia (FH) is a common genetic disorder with often delayed diagnosis. However, the risks for patients affected are enormous.
The condition is triggered by high levels of LDL-cholesterol in the blood (also known as “bad” cholesterol) and can cause serious cardiovascular events. Patients with FH are twice as likely to have a heart attack before the age of 50. Timely diagnosis is a challenge. With systematic screening, the early identification of FH would improve patients’ treatment and outcomes.
Sqilline has developed a special algorithm in Danny Platform to identify FH in patients with acute cardiovascular events during hospitalization.
Danny Decision Support for FH is programmed to calculate test results result following the Dutch Lipid Clinic Network (DLCN) score and inform physicians about a possible, probable, and definitive FH diagnosis.
Its main objective is to structure and filter information, so the physicians only analyze data relevant to the specific case.
Danny Decision Support Application for FH empowers physicians with a clinical decision support application to improve systematic screening during hospitalization.
Danny Decision Support for FH empowers physicians with evidence-based data technology to screen and identify FH in patients during hospitalization.
It provides structured visualization of clinical data to support the physician's workflow, assist in early diagnosis and timely treatment, reduce medical errors and overlooked items and contain costs. Screening for FH during hospitalization may allow for rapid and effective lipid management, prevention of recurrent events and reducing the burden on the healthcare system.
Danny Decision Support for FH enables pharmaceutical companies to better track and to uncover patient pathways on a statistical level of high risk for Familial hypercholesterolemia. It provides epidemiological analysis, patient profile for specific patient group and better RWE insights of patients’ pathway.
Danny Decision Support for FH accelerates the path to medical diagnosis for patients during hospitalization. Earlier diagnosis combined with ontime treatment can be crucial for easing the life of patients and their families. Identifying hard-to-diagnose diseases using AI and ML learning models are completely beneficial for patients.
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