Contributions
Abstract: EP1401
Type: ePoster
Abstract Category: Clinical aspects of MS - 8 Clinical assessment tools
Background: Diagnosis of secondary-progressive multiple sclerosis (SPMS) is challenging due to active and non-active progressive phases, following an initial relapsing course. To support timely diagnosis, a screening tool was previously developed based on qualitative interviews with patients and physicians, and data from a real-world observational study.
Objective: To create a scoring algorithm for the newly developed screening tool to support
physicians in the diagnosis of SPMS.
Methods: Multiple logistic regression analyses were performed on observational study data (n=2791) to identify predictive variables for SPMS. Qualitative interviews (n=8) were conducted to determine physician-perceived importance of variables for progression to SPMS. Additional variables were identified in qualitative interviews apart from those included in the observational study. Ranking
(1─26; 1=most important) and weighting (%) outputs were analysed by descriptive statistics; interview transcripts were qualitatively analysed. Variables of high, moderate or low importance were integrated in the algorithm. Concordance levels among physicians were assessed by Kendall's coefficient.
Results: Regression analyses identified mobility (odds ratio, 4.457, p< 0.0001) and self-care
(2.388; p< 0.0001) as the strongest patient-reported predictors. Expanded Disability Status Scale score (1.789; p< 0.0001), age (1.037; p< 0.0001) and MS disease activity (1.681; p< 0.05) were identified as the most significant physician-reported predictors. In physician interviews, the most important variables were stability/worsening of symptoms (average weighting [range]; average rank: 11% [1─33%]; 5), intermittent/persistent symptoms
(7% [0─17%]; 7) and presence of ambulatory symptoms (7% [0─15%]; 8). Moderately important variables included signs of new magnetic resonance imaging activity (6% [0─20%]; 14), recovery from last relapse (5% [0─12%]; 13) and impact on daily activities (5% [1─10%]; 13). Presence of fatigue, visual symptoms and impact on hobbies/leisure time were considered less important (2% [0─10%]; 16─19). Overall, concordance levels among physicians were significantly low to moderate (0.278; p=0.0004); greater agreement was observed within countries (US: 0.522, Germany: 0.385; p< 0.01).
Conclusions: Findings confirm the need for a prognostic tool to support early identification of SPMS. This is the first algorithm that integrates patient, physician and empirical assessments to be developed for clinical validation.
Disclosure: Funding source: This study was funded by Novartis Pharma AG, Basel, Switzerland.
Tjalf Ziemssen has received personal compensation for participating on advisory boards, trial steering committees and data and safety monitoring committees, as well as for scientific talks and project support from: Bayer HealthCare, Biogen, Elan, Genzyme, Merck Serono, Novartis, Roche, Sanofi-Aventis, Synthon and Teva.
Bryan Bennett, Chloe Tolley and Sarah Kilgariff are employees of Adelphi Values, Macclesfield, UK.
Davorka Tomic and Daniela Piani Meier are employees of Novartis.
The authors acknowledge Deniz Simsek, Raquel Lahoz and Elisabetta Verdun Di Cantogno for their participation during the initial phase of the development of this tool, and Heinke Schieb, Emer O'Hare for helpful discussions during the analysis.
Abstract: EP1401
Type: ePoster
Abstract Category: Clinical aspects of MS - 8 Clinical assessment tools
Background: Diagnosis of secondary-progressive multiple sclerosis (SPMS) is challenging due to active and non-active progressive phases, following an initial relapsing course. To support timely diagnosis, a screening tool was previously developed based on qualitative interviews with patients and physicians, and data from a real-world observational study.
Objective: To create a scoring algorithm for the newly developed screening tool to support
physicians in the diagnosis of SPMS.
Methods: Multiple logistic regression analyses were performed on observational study data (n=2791) to identify predictive variables for SPMS. Qualitative interviews (n=8) were conducted to determine physician-perceived importance of variables for progression to SPMS. Additional variables were identified in qualitative interviews apart from those included in the observational study. Ranking
(1─26; 1=most important) and weighting (%) outputs were analysed by descriptive statistics; interview transcripts were qualitatively analysed. Variables of high, moderate or low importance were integrated in the algorithm. Concordance levels among physicians were assessed by Kendall's coefficient.
Results: Regression analyses identified mobility (odds ratio, 4.457, p< 0.0001) and self-care
(2.388; p< 0.0001) as the strongest patient-reported predictors. Expanded Disability Status Scale score (1.789; p< 0.0001), age (1.037; p< 0.0001) and MS disease activity (1.681; p< 0.05) were identified as the most significant physician-reported predictors. In physician interviews, the most important variables were stability/worsening of symptoms (average weighting [range]; average rank: 11% [1─33%]; 5), intermittent/persistent symptoms
(7% [0─17%]; 7) and presence of ambulatory symptoms (7% [0─15%]; 8). Moderately important variables included signs of new magnetic resonance imaging activity (6% [0─20%]; 14), recovery from last relapse (5% [0─12%]; 13) and impact on daily activities (5% [1─10%]; 13). Presence of fatigue, visual symptoms and impact on hobbies/leisure time were considered less important (2% [0─10%]; 16─19). Overall, concordance levels among physicians were significantly low to moderate (0.278; p=0.0004); greater agreement was observed within countries (US: 0.522, Germany: 0.385; p< 0.01).
Conclusions: Findings confirm the need for a prognostic tool to support early identification of SPMS. This is the first algorithm that integrates patient, physician and empirical assessments to be developed for clinical validation.
Disclosure: Funding source: This study was funded by Novartis Pharma AG, Basel, Switzerland.
Tjalf Ziemssen has received personal compensation for participating on advisory boards, trial steering committees and data and safety monitoring committees, as well as for scientific talks and project support from: Bayer HealthCare, Biogen, Elan, Genzyme, Merck Serono, Novartis, Roche, Sanofi-Aventis, Synthon and Teva.
Bryan Bennett, Chloe Tolley and Sarah Kilgariff are employees of Adelphi Values, Macclesfield, UK.
Davorka Tomic and Daniela Piani Meier are employees of Novartis.
The authors acknowledge Deniz Simsek, Raquel Lahoz and Elisabetta Verdun Di Cantogno for their participation during the initial phase of the development of this tool, and Heinke Schieb, Emer O'Hare for helpful discussions during the analysis.