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Meta-Analysis
. 2018 Nov 20;18(1):145.
doi: 10.1186/s12874-018-0613-8.

Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis

Tiago Gregório  1   2 , Sara Pipa  3 , Pedro Cavaleiro  4 , Gabriel Atanásio  3 , Inês Albuquerque  5 , Paulo Castro Chaves  5   6   7 , Luís Azevedo  8
Affiliations
Meta-Analysis

Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis

Tiago Gregório et al. BMC Med Res Methodol. .

Abstract

Background: Prognostic 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.

Methods: PubMed, 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.

Results: Fifty 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.

Conclusions: Prognostic 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.

Keywords: Clinical prediction rules; Intracerebral hemorrhage; Morbidity; Mortality; Prognosis.

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The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Study selection flow chart
Fig. 2
Fig. 2
Predictor distribution according to mortality vs functional outcome prediction tool
Fig. 3
Fig. 3
Forrest plot of reported c statistics for mortality prediction tools
Fig. 4
Fig. 4
Forrest plot of reported c statistics for functional outcome prediction tools
See this image and copyright information in PMC

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