Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet. OBJECTIVES: To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS. SEARCH METHODS: We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies. SELECTION CRITERIA: We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome. DATA COLLECTION AND ANALYSIS: Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression. MAIN RESULTS: We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal. AUTHORS' CONCLUSIONS: The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.

Cognitive and Executive Function in Congenital Heart Disease: A Meta-analysis.

CONTEXT: Cognitive function and executive function (EF) impairments contribute to the long-term burden of congenital heart disease (CHD). However, the degree and profile of impairments are insufficiently described. OBJECTIVE: To systematically review and meta-analyze the evidence on cognitive function and EF outcomes in school-aged children operated for CHD and identify the risk factors for an unfavorable outcome. DATA SOURCES: Cochrane, Embase, Medline, and PsycINFO. STUDY SELECTION: Original peer-reviewed studies reporting cognitive or EF outcome in 5- to 17-year old children with CHD after cardiopulmonary bypass surgery. DATA EXTRACTION: Results of IQ and EF assessments were extracted, and estimates were transformed to means and SE. Standardized mean differences were calculated for comparison with healthy controls. RESULTS: Among 74 studies (3645 children with CHD) reporting total IQ, the summary estimate was 96.03 (95% confidence interval: 94.91 to 97.14). Hypoplastic left heart syndrome and univentricular CHD cohorts performed significantly worse than atrial and ventricular septum defect cohorts (P = .0003; P = .027). An older age at assessment was associated with lower IQ scores in cohorts with transposition of the great arteries (P = .014). Among 13 studies (774 children with CHD) reporting EF compared with controls, the standardized mean difference was -0.56 (95% confidence interval: -0.65 to -0.46) with no predilection for a specific EF domain or age effect. LIMITATIONS: Heterogeneity between studies was large. CONCLUSIONS: Intellectual impairments in CHD are frequent, with severity and trajectory depending on the CHD subtype. EF performance is poorer in children with CHD without a specific EF profile. The heterogeneity in studied populations and applied assessments is large. A uniform testing guideline is urgently needed.

Neurocognitive outcome of school-aged children with congenital heart disease who underwent cardiopulmonary bypass surgery: a systematic review protocol.

BACKGROUND: Over the past decades, survival rates of children born with congenital heart disease (CHD) have increased dramatically. Progress in prenatal diagnosis, less-invasive catheter techniques and perioperative intensive care as well as surgical techniques have led to an increased focus on extracardiac comorbidities, including potential neurodevelopmental sequelae associated with CHD. A growing body of literature reports impairments in early and school-age developmental outcome; however, there is a substantial variability in the spectrum of examined CHD types, assessment ages and applied test batteries. Furthermore, little information is available on executive function impairments in this population. Therefore, the aim of this systematic review is to determine the impact of CHD on intellectual outcome and executive functioning at school age and to determine risk factors for impaired outcomes by means of a systematic search. METHODS: A systematic review of literature that reports neurodevelopmental outcome in children with CHD undergoing cardiopulmonary bypass surgery. Intelligence quotient or executive function scores will be considered primary outcomes. Databases such as Cochrane, EMBASE, MEDLINE and PsycINFO will be searched. DISCUSSION: The results of this systematic review will summarize the current evidence on intellectual and executive function outcome after cardiopulmonary bypass surgery in school-age children with CHD. This review will thus be the basis for better patient and parental counselling and the establishment of tailored follow-up programmes and interventional trials. SYSTEMATIC REVIEW REGISTRATION: In accordance with the guidelines, our systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on January 9, 2019 (CRD42018086568). PROSPERO CRD42019118736 .