Corrigendum to Longitudinal Machine Learning Modeling of Ms Patient Trajectories Improves Predictions of Disability Progression: (Computer Methods and Programs in Biomedicine (2021) 208, (S0169260721002546), (10.1016/J.Cmpb.2021.106180))

Corrigendum to Longitudinal Machine Learning Modeling of Ms Patient Trajectories Improves Predictions of Disability Progression: (Computer Methods and Programs in Biomedicine (2021) 208, (S0169260721002546), (10.1016/J.Cmpb.2021.106180)) Publisher Pubmed

De Brouwer E¹ ; Becker T² ; Moreau Y¹ ; Havrdova EK⁴ ; Trojano M⁵ ; Eichau S⁶ ; Ozakbas S⁷ ; Onofrj M⁸ ; Grammond P⁹ ; Kuhle J¹⁰ ; Kappos L¹⁰ ; Sola P¹¹ ; Cartechini E¹² ; Lechnerscott J¹³ Show All Authors

De Brouwer E¹
Becker T²
Moreau Y¹
Havrdova EK⁴
Trojano M⁵
Eichau S⁶
Ozakbas S⁷
Onofrj M⁸
Grammond P⁹
Kuhle J¹⁰
Kappos L¹⁰
Sola P¹¹
Cartechini E¹²
Lechnerscott J¹³
Alroughani R¹⁴
Gerlach O¹⁵
Kalincik T^{16, 17}
Granella F¹⁸
Grandmaison F¹⁹
Bergamaschi R²⁰
Sa MJ²¹
Van Wijmeersch B²²
Soysal A²³
Sanchezmenoyo JL²⁴
Solaro C²⁵
Boz C²⁶
Iuliano G²⁷
Buzzard K²⁸
Agueramorales E²⁹
Terzi M³⁰
Trivio TC³¹
Spitaleri D³²
Van Pesch V³³
Shaygannejad V³⁴
Moore F³⁵
Orejaguevara C³⁶
Maimone D³⁷
Gouider R³⁸
Csepany T³⁹
Ramotello C⁴⁰
Peeters L^{2, 3}

Show Affiliations

1. ESAT-STADIUS, KU Leuven, Leuven, 3001, Belgium
2. I-Biostat, Data Science Institute, Hasselt University, Diepenbeek, Belgium
3. Department of Immunology, Biomedical Research Institute, Hasselt University, Diepenbeek, 3590, Belgium
4. Charles University in Prague General University Hospital, Prague, Czech Republic
5. Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari, Bari, Italy
6. Hospital Universitario Virgen Macarena, Sevilla, Spain
7. Dokuz Eylul University, Konak/Izmir, Turkey
8. University G. d'Annunzio, Chieti, Italy
9. CISSS Chaudire-Appalache, Levis, Canada
10. Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
11. Azienda Ospedaliera Universitaria, Modena, Italy
12. Azienda Sanitaria Unica Regionale Marche - AV3, Macerata, Italy
13. University Newcastle, Newcastle, Australia
14. Amiri Hospital, Sharq, Kuwait
15. Zuyderland Ziekenhuis, Sittard, Netherlands
16. Melbourne MS Centre, Department of Neurology, Royal Melbourne Hospital, Melbourne, Australia
17. CORe, Department of Medicine, University of Melbourne, Melbourne, Australia
18. University of Parma, Parma, Italy
19. Neuro Rive-Sud, Quebec, Canada
20. IRCCS Mondino Foundation, Pavia, Italy
21. Department of Neurology, Centro Hospitalar Universitario de So Joo and University Fernando Pessoa, Porto, Portugal
22. Rehabilitation and MS-Centre Overpelt Hasselt University, Hasselt, Belgium
23. Bakirkoy Education and Research Hospital for Psychiatric and Neurological Diseases, Istanbul, Turkey
24. Hospital de Galdakao-Usansolo, Galdakao, Spain
25. Dept of Rehabilitation mons L Novarese Hospital, Moncrivello, Italy
26. KTU Medical Faculty Farabi Hospital, Trabzon, Turkey
27. previously at Ospedali Riuniti di Salerno, Salerno, Italy
28. Box Hill Hospital, Melbourne, Australia
29. University Hospital Reina Sofia, Cordoba, Spain
30. 19 Mayis University, Samsun, Turkey
31. Hospital Universitario Donostia, San Sebastain, Spain
32. Azienda Ospedaliera di Rilievo Nazionale San Giuseppe Moscati Avellino, Avellino, Italy
33. Cliniques Universitaires Saint-Luc, Brussels, Belgium
34. Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
35. Jewish General Hospital, Montreal, Canada
36. Hospital Clinico San Carlos, Madrid, Spain
37. Garibaldi Hospital, Catania, Italy
38. Razi Hospital, Manouba, Tunisia
39. University of Debrecen, Debrecen, Hungary
40. Hospital Germans Trias i Pujol, Badalona, Spain

Source: Computer Methods and Programs in Biomedicine Published:2022

Abstract

The authors regret an error in the pre-processing of the dataset that went unnoticed in the extensive code used for pre-processing of the study data. As a result of this error, the last EDSS overall for each patient was included as the last EDSS in the observation period. We corrected the error and repeated all analyses. Reassuringly, the main message of the paper remains unchanged: incorporating longitudinal data is beneficial for the prediction of disability progression. Yet, the following changes are to be noted. • The absolute performance metrics of all compared methods have dropped by about 20%. That is: • AUC of the static setup is 0.63• AUC of the dynamic setup is now 0.67• AUC of the longitudinal is 0.68• The difference between the compared methods is also less pronounced but importantly, the improvement between the static and the dynamic/longitudinal methods is still substantial.• The feature importance list has changed, with the full EDSS trajectory now becoming the most important factor, therefore strengthening one of the main findings of the study.The AUCs and AUC-PR originally reported in Table 3 have now been updated as presented below. [Table Presented] (The best results are in bold. If several values are in bold, the results are not significantly different.) The feature importance list (Table 4) has also been updated, with the full EDSS trajectory now becoming the most important factor. [Table Presented] In the appendix, the following changes are to be noted for the comparison of MS types (table G1): [Table Presented] In the discussion, setting the sensitivity at 70% for a cohort of 1000 patients results in 421 false positives in the static case versus 354 false positives in the dynamic case. The authors apologize for any inconvenience caused. © 2021 Elsevier B.V.

2. External Validation of a Clinical Prediction Model in Multiple Sclerosis, Multiple Sclerosis Journal (2023)

3. The Kurtzke Edss Rank Stability Increases 4 Years After the Onset of Multiple Sclerosis: Results From the Msbase Registry, Journal of Neurology, Neurosurgery and Psychiatry (2012)

4. Development of the International Cardiac Rehabilitation Registry Including Variable Selection and Definition Process, Global Heart (2022)

Experts (# of related papers)

Vahid Shaygannejad (3)

Masoumeh Sadeghi (1)

Style	Citing Format
MLA	De Brouwer E, et al.. "Corrigendum to Longitudinal Machine Learning Modeling of Ms Patient Trajectories Improves Predictions of Disability Progression: (Computer Methods and Programs in Biomedicine (2021) 208, (S0169260721002546), (10.1016/J.Cmpb.2021.106180))." Computer Methods and Programs in Biomedicine, vol. 213, no. , 2022, pp. -.
APA	De Brouwer E, Becker T, Moreau Y, Havrdova EK, Trojano M, Eichau S, Ozakbas S, Onofrj M, Grammond P, Kuhle J, Kappos L, Sola P, Cartechini E, Lechnerscott J, Alroughani R, Gerlach O, Kalincik T, Granella F, Grandmaison F, ... Peeters L (2022). Corrigendum to Longitudinal Machine Learning Modeling of Ms Patient Trajectories Improves Predictions of Disability Progression: (Computer Methods and Programs in Biomedicine (2021) 208, (S0169260721002546), (10.1016/J.Cmpb.2021.106180)). Computer Methods and Programs in Biomedicine, 213(), -.
Chicago	De Brouwer E, Becker T, Moreau Y, Havrdova EK, Trojano M, Eichau S, Ozakbas S, et al.. "Corrigendum to Longitudinal Machine Learning Modeling of Ms Patient Trajectories Improves Predictions of Disability Progression: (Computer Methods and Programs in Biomedicine (2021) 208, (S0169260721002546), (10.1016/J.Cmpb.2021.106180))." Computer Methods and Programs in Biomedicine 213, no. (2022): -.
Harvard	De Brouwer E et al. (2022) 'Corrigendum to Longitudinal Machine Learning Modeling of Ms Patient Trajectories Improves Predictions of Disability Progression: (Computer Methods and Programs in Biomedicine (2021) 208, (S0169260721002546), (10.1016/J.Cmpb.2021.106180))', Computer Methods and Programs in Biomedicine, 213(), pp. -.
Vancouver	De Brouwer E, Becker T, Moreau Y, Havrdova EK, Trojano M, Eichau S, et al.. Corrigendum to Longitudinal Machine Learning Modeling of Ms Patient Trajectories Improves Predictions of Disability Progression: (Computer Methods and Programs in Biomedicine (2021) 208, (S0169260721002546), (10.1016/J.Cmpb.2021.106180)). Computer Methods and Programs in Biomedicine. 2022;213():-.
BibTex	@article{ author = {De Brouwer E and Becker T and Moreau Y and Havrdova EK and Trojano M and Eichau S and Ozakbas S and Onofrj M and Grammond P and Kuhle J and Kappos L and Sola P and Cartechini E and Lechnerscott J and Alroughani R and Gerlach O and Kalincik T and Granella F and Grandmaison F and Bergamaschi R and Sa MJ and Van Wijmeersch B and Soysal A and Sanchezmenoyo JL and Solaro C and Boz C and Iuliano G and Buzzard K and Agueramorales E and Terzi M and Trivio TC and Spitaleri D and Van Pesch V and Shaygannejad V and Moore F and Orejaguevara C and Maimone D and Gouider R and Csepany T and Ramotello C and Peeters L}, title = {Corrigendum to Longitudinal Machine Learning Modeling of Ms Patient Trajectories Improves Predictions of Disability Progression: (Computer Methods and Programs in Biomedicine (2021) 208, (S0169260721002546), (10.1016/J.Cmpb.2021.106180))}, journal = {Computer Methods and Programs in Biomedicine}, volume = {213}, number = {}, pages = {-}, year = {2022} }
RIS	TY - JOUR AU - De Brouwer E AU - Becker T AU - Moreau Y AU - Havrdova EK AU - Trojano M AU - Eichau S AU - Ozakbas S AU - Onofrj M AU - Grammond P AU - Kuhle J AU - Kappos L AU - Sola P AU - Cartechini E AU - Lechnerscott J AU - Alroughani R AU - Gerlach O AU - Kalincik T AU - Granella F AU - Grandmaison F AU - Bergamaschi R AU - Sa MJ AU - Van Wijmeersch B AU - Soysal A AU - Sanchezmenoyo JL AU - Solaro C AU - Boz C AU - Iuliano G AU - Buzzard K AU - Agueramorales E AU - Terzi M AU - Trivio TC AU - Spitaleri D AU - Van Pesch V AU - Shaygannejad V AU - Moore F AU - Orejaguevara C AU - Maimone D AU - Gouider R AU - Csepany T AU - Ramotello C AU - Peeters L TI - Corrigendum to Longitudinal Machine Learning Modeling of Ms Patient Trajectories Improves Predictions of Disability Progression: (Computer Methods and Programs in Biomedicine (2021) 208, (S0169260721002546), (10.1016/J.Cmpb.2021.106180)) JO - Computer Methods and Programs in Biomedicine VL - 213 IS - SP - EP - PY - 2022 ER -

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