PHREND®-A Real-World Data-Driven Tool Supporting Clinical Decisions to Optimize Treatment in Relapsing-Remitting Multiple Sclerosis

BackgroundWith increasing availability of disease-modifying therapies (DMTs), treatment decisions in relapsing-remitting multiple sclerosis (RRMS) have become complex. Data-driven algorithms based on real-world outcomes may help clinicians optimize control of disease activity in routine praxis.Objec...

Ful tanımlama

Kaydedildi:
Detaylı Bibliyografya
Asıl Yazarlar: Stefan Braune (Yazar), Elisabeth Stuehler (Yazar), Yanic Heer (Yazar), Philip van Hoevell (Yazar), Arnfin Bergmann (Yazar), NeuroTransData Study Group (Yazar)
Materyal Türü: Kitap
Baskı/Yayın Bilgisi: Frontiers Media S.A., 2022-03-01T00:00:00Z.
Konular:
Online Erişim:Connect to this object online.
Etiketler: Etiketle
Etiket eklenmemiş, İlk siz ekleyin!

MARC

LEADER 00000 am a22000003u 4500
001 doaj_72e6cc9cd07c4748a70f8e8f76e9a276
042 |a dc 
100 1 0 |a Stefan Braune  |e author 
700 1 0 |a Elisabeth Stuehler  |e author 
700 1 0 |a Yanic Heer  |e author 
700 1 0 |a Philip van Hoevell  |e author 
700 1 0 |a Arnfin Bergmann  |e author 
700 1 0 |a NeuroTransData Study Group  |e author 
245 0 0 |a PHREND®-A Real-World Data-Driven Tool Supporting Clinical Decisions to Optimize Treatment in Relapsing-Remitting Multiple Sclerosis 
260 |b Frontiers Media S.A.,   |c 2022-03-01T00:00:00Z. 
500 |a 2673-253X 
500 |a 10.3389/fdgth.2022.856829 
520 |a BackgroundWith increasing availability of disease-modifying therapies (DMTs), treatment decisions in relapsing-remitting multiple sclerosis (RRMS) have become complex. Data-driven algorithms based on real-world outcomes may help clinicians optimize control of disease activity in routine praxis.ObjectivesWe previously introduced the PHREND® (Predictive-Healthcare-with-Real-World-Evidence-for-Neurological-Disorders) algorithm based on data from 2018 and now follow up on its robustness and utility to predict freedom of relapse and 3-months confirmed disability progression (3mCDP) during 1.5 years of clinical practice.MethodsThe impact of quarterly data updates on model robustness was investigated based on the model's C-index and credible intervals for coefficients. Model predictions were compared with results from randomized clinical trials (RCTs). Clinical relevance was evaluated by comparing outcomes of patients for whom model recommendations were followed with those choosing other treatments.ResultsModel robustness improved with the addition of 1.5 years of data. Comparison with RCTs revealed differences <10% of the model-based predictions in almost all trials. Treatment with the highest-ranked (by PHREND®) or the first-or-second-highest ranked DMT led to significantly fewer relapses (p < 0.001 and p < 0.001, respectively) and 3mCDP events (p = 0.007 and p = 0.035, respectively) compared to non-recommended DMTs.ConclusionThese results further support usefulness of PHREND® in a shared treatment-decision process between physicians and patients. 
546 |a EN 
690 |a multiple sclerosis (MS) 
690 |a personalized medicine 
690 |a disease modifying agent 
690 |a real word data 
690 |a treatment 
690 |a effectiveness 
690 |a Medicine 
690 |a R 
690 |a Public aspects of medicine 
690 |a RA1-1270 
690 |a Electronic computers. Computer science 
690 |a QA75.5-76.95 
655 7 |a article  |2 local 
786 0 |n Frontiers in Digital Health, Vol 4 (2022) 
787 0 |n https://www.frontiersin.org/articles/10.3389/fdgth.2022.856829/full 
787 0 |n https://doaj.org/toc/2673-253X 
856 4 1 |u https://doaj.org/article/72e6cc9cd07c4748a70f8e8f76e9a276  |z Connect to this object online.