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Prognostic models for complete recovery in ischemic stroke: a systematic review and meta-analysis.

ABSTRACT: BACKGROUND:Prognostic models have been increasingly developed to predict complete recovery in ischemic stroke. However, questions arise about the performance characteristics of these models. The aim of this study was to systematically review and synthesize performance of existing prognostic models for complete recovery in ischemic stroke. METHODS:We searched journal publications indexed in PUBMED, SCOPUS, CENTRAL, ISI Web of Science and OVID MEDLINE from inception until 4 December, 2017, for studies designed to develop and/or validate prognostic models for predicting complete recovery in ischemic stroke patients. Two reviewers independently examined titles and abstracts, and assessed whether each study met the pre-defined inclusion criteria and also independently extracted information about model development and performance. We evaluated validation of the models by medians of the area under the receiver operating characteristic curve (AUC) or c-statistic and calibration performance. We used a random-effects meta-analysis to pool AUC values. RESULTS:We included 10 studies with 23 models developed from elderly patients with a moderately severe ischemic stroke, mainly in three high income countries. Sample sizes for each study ranged from 75 to 4441. Logistic regression was the only analytical strategy used to develop the models. The number of various predictors varied from one to 11. Internal validation was performed in 12 models with a median AUC of 0.80 (95% CI 0.73 to 0.84). One model reported good calibration. Nine models reported external validation with a median AUC of 0.80 (95% CI 0.76 to 0.82). Four models showed good discrimination and calibration on external validation. The pooled AUC of the two validation models of the same developed model was 0.78 (95% CI 0.71 to 0.85). CONCLUSIONS:The performance of the 23 models found in the systematic review varied from fair to good in terms of internal and external validation. Further models should be developed with internal and external validation in low and middle income countries.

SUBMITTER: Jampathong N

PROVIDER: S-EPMC5845155 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Project description:BackgroundPrognostic tools for intracerebral hemorrhage (ICH) patients are potentially useful for ascertaining prognosis and recommended in guidelines to facilitate streamline assessment and communication between providers. In this systematic review with meta-analysis we identified and characterized all existing prognostic tools for this population, performed a methodological evaluation of the conducting and reporting of such studies and compared different methods of prognostic tool derivation in terms of discrimination for mortality and functional outcome prediction.MethodsPubMed, ISI, Scopus and CENTRAL were searched up to 15th September 2016, with additional studies identified using reference check. Two reviewers independently extracted data regarding the population studied, process of tool derivation, included predictors and discrimination (c statistic) using a predesignated spreadsheet based in the CHARMS checklist. Disagreements were solved by consensus. C statistics were pooled using robust variance estimation and meta-regression was applied for group comparisons using random effect models.ResultsFifty nine studies were retrieved, including 48,133 patients and reporting on the derivation of 72 prognostic tools. Data on discrimination (c statistic) was available for 53 tools, 38 focusing on mortality and 15 focusing on functional outcome. Discrimination was high for both outcomes, with a pooled c statistic of 0.88 for mortality and 0.87 for functional outcome. Forty three tools were regression based and nine tools were derived using machine learning algorithms, with no differences found between the two methods in terms of discrimination (p?=?0.490). Several methodological issues however were identified, relating to handling of missing data, low number of events per variable, insufficient length of follow-up, absence of blinding, infrequent use of internal validation, and underreporting of important model performance measures.ConclusionsPrognostic tools for ICH discriminated well for mortality and functional outcome in derivation studies but methodological issues require confirmation of these findings in validation studies. Logistic regression based risk scores are particularly promising given their good performance and ease of application.

Project description:Background Coronavirus disease 2019 (COVID-19), a contagious infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread across the world. Apart from respiratory complications, an increasing number of patients with ischemic stroke have been reporting. Objective This systematic review and meta-analysis aims to explore the characteristics of ischemic stroke after SARS-CoV-2 infection, and provides valuable reference materials for subsequent clinical treatment. Materials and methods PubMed, Web of Science, and Ovid-Embase databases were searched up to 24th March 2021. We utilized the search strategy of medical subject headings combined with entry terms to search all related literatures. All studies identified with the electronic and manual searches were listed by citation, title, authors, and abstract. Only studies involving patients with COVID-19-related stroke were eligible. The references of included studies were also manually screened. Results The meta-analysis was conducted following the PRISMA and MOOSE reporting guidelines. Bias risk was assessed using the Newcastle–Ottawa Scale (NOS). Ten articles, including 26,691 participants and 280 patients with ischemic stroke and COVID-19, were selected. The pooled prevalence of ischemic stroke in COVID-19 was 2% (95% CI 1–2%; p < 0.01). The pooled proportions of hypertension, hyperlipidemia and diabetes in COVID-19-related ischemic stroke was 66% (95% CI 51–81%; p < 0.01), 48% (95% CI 19–76%; p < 0.01) and 40% (95% CI 29–51%; p < 0.01), respectively. Notably, the pooled proportions of female was 36% (95% CI 21–50%; p < 0.01) in patients with COVID-19 and stroke. In addition, in TOAST classification, cryptogenic stroke subtype was associated with a high trend, and its pooled proportion was 35% (95% CI 12–59%; p < 0.01). Conclusion Ischemic stroke caused by COVID-19 has its own unique clinical features. Although common high-risk factors can also be observed, its importance may have changed. The major inflammatory storm of COVID-19 is more likely to occur in male patients. The increase in the proportion of cryptogenic stroke has also made stroke related to COVID-19 complicated. Supplementary Information The online version contains supplementary material available at 10.1007/s00415-021-10837-7.

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Prognostic models for complete recovery in ischemic stroke: a systematic review and meta-analysis.

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