Systematic review of prediction models in relapsing remitting multiple sclerosis

Brown, Fraser S; Glasmacher, Stella A.; Kearns, Patrick K. A.; MacDougall, Niall; Hunt, David; Connick, Peter; Chandran, Siddharthan

doi:10.1371/journal.pone.0233575

Cited by 38 publications

(45 citation statements)

References 67 publications

(198 reference statements)

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“…As clinical outcomes, we selected the EDSS score, the most widely validated MS disability measure, which was used in all prognostic studies, 6 , 7 , 9 - 12 , 14 , 15 and evolution to SPMS, that is associated with a poor prognosis, mostly because of the limited DMT effect in these patients. During the 6.4-year follow-up, 45% of patients had a worsening of disability and 16% of patients with RRMS evolved to SPMS.…”

Section: Discussionmentioning

confidence: 99%

“…In line with previous studies, older age, longer disease duration, and higher baseline EDSS score were associated with poorer clinical outcomes. 7 , 37 In particular, a higher baseline EDSS score was retained by multivariable analysis as a predictor of both worsening of disability and evolution to SPMS. This is in line with a recent meta-analysis, indicating baseline EDSS score as the most frequent predictor of future disease course.…”

Section: Discussionmentioning

confidence: 99%

“… 4 However, the predictive value of conventional MRI on subsequent disease course is still debated, with only some studies detecting a significant influence of white matter (WM) lesion burden on disease evolution. 5 - 7 …”

mentioning

confidence: 99%

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Network Damage Predicts Clinical Worsening in Multiple Sclerosis

Rocca

Valsasina

Meani

et al. 2021

Neurol Neuroimmunol Neuroinflamm

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ObjectiveIn multiple sclerosis (MS), clinical impairment is likely due to both structural damage and abnormal brain function. We assessed the added value of integrating structural and functional network MRI measures to predict 6.4-year MS clinical disability deterioration.MethodsBaseline 3D T1-weighted and resting-state functional MRI scans were obtained from 233 patients with MS and 77 healthy controls. Patients underwent a neurologic evaluation at baseline and at 6.4-year median follow-up (interquartile range = 5.06–7.51 years). At follow-up, patients were classified as clinically stable/worsened according to disability changes. In relapsing-remitting (RR) MS, secondary progressive (SP) MS conversion was evaluated. Global brain volumetry was obtained. Furthermore, independent component analysis identified the main functional connectivity (FC) and gray matter (GM) network patterns.ResultsAt follow-up, 105/233 (45%) patients were clinically worsened; 26/157 (16%) patients with RRMS evolved to SPMS. The treatment-adjusted random forest model identified normalized GM and brain volumes, decreased FC between default-mode networks, increased FC of the left precentral gyrus in the sensorimotor network (SMN), and GM atrophy in the fronto-parietal network (false discovery rate [FDR]-corrected p = range 0.01–0.09) as predictors of clinical worsening (out-of-bag [OOB] accuracy = 0.74). An expected contribution of baseline disability was also present (FDR-p = 0.01). Baseline disability, normalized GM volume, and GM atrophy in the SMN (FDR-p = range 0.01–0.09) were independently associated with SPMS conversion (OOB accuracy = 0.84). At receiver operating characteristic analysis, including network MRI variables improved disability worsening (p = 0.05) and SPMS conversion (p = 0.02) prediction.ConclusionsIntegration of MRI network measures helped determining the relative contributions of global/local GM damage and functional reorganization to clinical deterioration in MS.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Network Damage Predicts Clinical Worsening in Multiple Sclerosis

Rocca

Valsasina

Meani

et al. 2021

Neurol Neuroimmunol Neuroinflamm

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“…The highest discrimination in the included studies was 0.96 and the lowest discrimination was 0.64. These studies showed a good discrimination (0.7-0.8 is regarded as acceptable and 0.8-0.9 is excellent [49]).…”

Section: Discussionmentioning

confidence: 84%

Risk Prediction Models for Liver Cancer in Adults: A Systematic Review and Meta-Analysis

Cao

Sun

et al. 2021

Preprint

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Background Prediction of liver cancer risk is beneficial to define high-risk population of liver cancer and guide clinical decisions. We aimed to review and critically appraise the quality of existing risk-prediction models for liver cancer. Methods This systematic review followed the guidelines of CHARMS (Checklist for Critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and Preferred Reporting Items for Systematic Reviews and Meta (PRISMA). We searched for PubMed, Embase, Web of Science, and the Cochrane Library from inception to July 2020. Prediction model Risk Of Bias Assessment Tool was used to assess the risk of bias of all potential articles. A narrative description and meta-analysis were conducted. Results After removal irrespective and duplicated citations, 20 risk prediction publications were finally included. Within the 20 studies, 15 studies performed model derivation and validation process, three publications only conducted developed procedure without validation and two articles were used to validate existing models. Discrimination was expressed as area under curve or C statistic, which was acceptable for most models, ranging from 0.64 to 0.96. Calibration of the predictions model were rarely assessed. All models were graded at high risk of bias. The risk bias of applicability in 13 studies was considered low. Conclusions This systematic review gives an overall review of the prediction risk models for liver cancer, pointing out several methodological issues in their development. No prediction risk models were recommended due to the high risk of bias.Systematic review registration: This systematic has been registered in PROSPERO (International Prospective Register of Systemic Review: CRD42020203244).

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“…Most CPMs in our study were rated high ROB, in line with previous systematic reviews using PROBAST. [16][17][18][19][20] This is inherent to the structure of PROBAST: one incorrectly performed item in one domain determines an overall judgment of high ROB. While the high percentage of models with high ROB might be interpreted as a reflection of the low overall quality of the literature, it might also reflect limitations of the tool.…”

Section: Discussionmentioning

confidence: 99%

Large-scale validation of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

Vemena

Wessler

Paulus

et al. 2021

Preprint

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ObjectiveTo assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation.Study Design and SettingWe evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation and assessed the change in discrimination (dAUC) between the derivation and the validation cohorts (n=1,147).ResultsPROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 529/556 CPMs (95%) were classified as high ROB, 20 (4%) low ROB, and 7 (1%) unclear ROB. Median change in discrimination was significantly smaller in low ROB models (dAUC −0.9%, IQR −6.2%–4.2%) compared to high ROB models (dAUC −11.7%, IQR −33.3%–2.6%; p<0.001).ConclusionHigh ROB is pervasive among published CPMs. It is associated with poor performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews.What is newHigh risk of bias is pervasive among published clinical prediction modelsHigh risk of bias identified with PROBAST is associated with poorer model performance at validationA subset of questions can distinguish between models with high and low risk of bias

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Systematic review of prediction models in relapsing remitting multiple sclerosis

Cited by 38 publications

References 67 publications

Network Damage Predicts Clinical Worsening in Multiple Sclerosis

Network Damage Predicts Clinical Worsening in Multiple Sclerosis

Risk Prediction Models for Liver Cancer in Adults: A Systematic Review and Meta-Analysis

Large-scale validation of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

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