Biostatistical Methods Poster Presentation

P0014 - Personalized and dynamic prognostic model from the Barcelona CIS cohot (ID 1607)

  • M. Tintore
  • M. Tintore
  • P. Carbonell-Mirabent
  • S. Otero-Romero
  • A. Cobo Calvo
  • G. Arrambide
  • J. Río
  • C. Tur
  • M. Comabella
  • C. Nos
  • M. Arévalo-Navinés
  • L. Midaglia
  • Í. Galán
  • A. Vidal-Jordana
  • J. Castilló
  • B. Rodríguez-Acevedo
  • A. Zabalza
  • M. Rodríguez
  • A. Sao-Avilés
  • A. Barros
  • C. Auger
  • J. Sastre-Garriga
  • A. Rovira
  • X. Montalban
Presentation Number
Presentation Topic
Biostatistical Methods



In the constantly evolving field of MS, personalized medicine is still one of the most important unmet need that requires further attention


We aimed to develop a dynamic risk calculator to predict the long-term prognosis of MS in the context of a large MS Centre in Catalonia


This is an observational study based on data prospectively acquired from a deeply phenotyped CIS cohort from Barcelona. We first built a natural history baseline risk score (BRS) for predicting moderate disability, integrating baseline prognostic factors: Sex, age at CIS, CIS topography, number of T2 lesions, contrast-enhancing lesions (CEL) and oligoclonal bands. This BRS was designed as follows: For untreated patients, we built a Weibull model to estimate the median time to confirmed EDSS 3.0 and with these estimates we identified risk groups based on the median of the cut-offs of 2000 survival trees. Then we obtained the BRS of the full cohort. In patients with more than ten years of follow-up, we performed an inverse probability weighting to balance patients during their follow up for the propensity of being treated or lost to follow-up. The weights were estimated via a proportional hazards (PH) Cox model considering both baseline information (CIS year, BRS) and time-dependent (diagnosis status, new T2 lesions, CEL and cumulative number of relapses). Finally, a weighted PH Cox model was built to estimate the time to confirmed EDSS 3.0 considering the BRS and time-dependent events (new T2 lesions, cumulative number of relapses and first or second-line treatment use). Sensitivity analyses using other disability outcomes and different follow-ups were conducted.


Of 956 patients, 577 (60.4%) were untreated before confirmed EDSS 3.0. Two BRS were obtained: low and high-BRS. Of 400 patients followed for more than ten years, 226 (56.5%) were low-BRS and 174 (43.5%) were high-BRS. High-BRS showed a HR=2.16 95%CI (1.16,4.02). Each new T2 lesion presented HR=1.04 95%CI (1.00,1.08) and each new relapse HR=1.46 95%CI (1.23,1.74). Being on second-line treatment showed a protective effect (HR=0.23 95%CI (0.06,0.94)) but no association was found for first-line treatments (HR=1.32 95%CI (0.67,2.60). Sensitivity analyses confirmed the association between BRS, new T2 lesions and the accumulation of relapses with the prognosis. However, treatment results were inconclusive.


Presenting a high-BRS doubles the risk of reaching moderate disability. Each new lesion and new relapse increses the risk by 4% and 46%, respectively; and second-line treatments seem to be protective. If validated, this risk calculator could be a crucial step to personalized medicine.