Literature DB >> 31862737

Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension.

Francesca Lucaroni1, Domenico Cicciarella Modica2, Mattia Macino2, Leonardo Palombi3, Alessio Abbondanzieri2, Giulia Agosti2, Giorgia Biondi2, Laura Morciano1, Antonio Vinci2.   

Abstract

OBJECTIVE: To provide an overview of the currently available risk prediction models (RPMs) for cardiovascular diseases (CVDs), diabetes and hypertension, and to compare their effectiveness in proper recognition of patients at risk of developing these diseases.
DESIGN: Umbrella systematic review. DATA SOURCES: PubMed, Scopus, Cochrane Library. ELIGIBILITY CRITERIA: Systematic reviews or meta-analysis examining and comparing performances of RPMs for CVDs, hypertension or diabetes in healthy adult (18-65 years old) population, published in English language. DATA EXTRACTION AND SYNTHESIS: Data were extracted according to the following parameters: number of studies included, intervention (RPMs applied/assessed), comparison, performance, validation and outcomes. A narrative synthesis was performed. Data were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. STUDY SELECTION: 3612 studies were identified. After title/abstract screening and removal of duplicate articles, 37 studies met the eligibility criteria. After reading the full text, 13 were deemed relevant for inclusion. Three further papers from the reference lists of these articles were then added. STUDY APPRAISAL: The methodological quality of the included studies was assessed using the AMSTAR tool. RISK OF BIAS IN INDIVIDUAL STUDIES: Risk of Bias evaluation was carried out using the ROBIS tool.
RESULTS: Sixteen studies met the inclusion criteria: six focused on diabetes, two on hypertension and eight on CVDs. Globally, prediction models for diabetes and hypertension showed no significant difference in effectiveness. Conversely, some promising differences among prediction tools were highlighted for CVDs. The Ankle-Brachial Index, in association with the Framingham tool, and QRISK scores provided some evidence of a certain superiority compared with Framingham alone. LIMITATIONS: Due to the significant heterogeneity of the studies, it was not possible to perform a meta-analysis. The electronic search was limited to studies in English and to three major international databases (MEDLINE/PubMed, Scopus and Cochrane Library), with additional works derived from the reference list of other studies; grey literature with unpublished documents was not included in the search. Furthermore, no assessment of potential adverse effects of RPMs was carried out.
CONCLUSIONS: Consistent evidence is available only for CVD prediction: the Framingham score, alone or in combination with the Ankle-Brachial Index, and the QRISK score can be confirmed as the gold standard. Further efforts should not be concentrated on creating new scores, but rather on performing external validation of the existing ones, in particular on high-risk groups. Benefits could be further improved by supplementing existing models with information on lifestyle, personal habits, family and employment history, social network relationships, income and education. PROSPERO REGISTRATION NUMBER: CRD42018088012. © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  cardiovascular diseases; diabetes; hypertension; risk prediction models

Mesh:

Year:  2019        PMID: 31862737      PMCID: PMC6937066          DOI: 10.1136/bmjopen-2019-030234

Source DB:  PubMed          Journal:  BMJ Open        ISSN: 2044-6055            Impact factor:   2.692


  33 in total

Review 1.  The combined effects of healthy lifestyle behaviors on all cause mortality: a systematic review and meta-analysis.

Authors:  Martin Loef; Harald Walach
Journal:  Prev Med       Date:  2012-06-24       Impact factor: 4.018

Review 2.  Assessment of claims of improved prediction beyond the Framingham risk score.

Authors:  Ioanna Tzoulaki; George Liberopoulos; John P A Ioannidis
Journal:  JAMA       Date:  2009-12-02       Impact factor: 56.272

Review 3.  Population risk prediction models for incident heart failure: a systematic review.

Authors:  Justin B Echouffo-Tcheugui; Stephen J Greene; Lampros Papadimitriou; Faiez Zannad; Clyde W Yancy; Mihai Gheorghiade; Javed Butler
Journal:  Circ Heart Fail       Date:  2015-03-03       Impact factor: 8.790

4.  Systematic reviews explained: AMSTAR-how to tell the good from the bad and the ugly.

Authors:  Mohammad O Sharif; Fyeza N Janjua-Sharif; Fyeza N Janjua Sharif; Hesham Ali; Farooq Ahmed
Journal:  Oral Health Dent Manag       Date:  2013-03

Review 5.  Risk assessment tools for detecting those with pre-diabetes: a systematic review.

Authors:  Shaun R Barber; Melanie J Davies; Kamlesh Khunti; Laura J Gray
Journal:  Diabetes Res Clin Pract       Date:  2014-03-18       Impact factor: 5.602

Review 6.  Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis.

Authors:  F G R Fowkes; G D Murray; I Butcher; C L Heald; R J Lee; L E Chambless; A R Folsom; A T Hirsch; M Dramaix; G deBacker; J-C Wautrecht; M Kornitzer; A B Newman; M Cushman; K Sutton-Tyrrell; F G R Fowkes; A J Lee; J F Price; R B d'Agostino; J M Murabito; P E Norman; K Jamrozik; J D Curb; K H Masaki; B L Rodríguez; J M Dekker; L M Bouter; R J Heine; G Nijpels; C D A Stehouwer; L Ferrucci; M M McDermott; H E Stoffers; J D Hooi; J A Knottnerus; M Ogren; B Hedblad; J C Witteman; M M B Breteler; M G M Hunink; A Hofman; M H Criqui; R D Langer; A Fronek; W R Hiatt; R Hamman; H E Resnick; J Guralnik; M M McDermott
Journal:  JAMA       Date:  2008-07-09       Impact factor: 56.272

7.  Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study.

Authors:  Ali Abbasi; Linda M Peelen; Eva Corpeleijn; Yvonne T van der Schouw; Ronald P Stolk; Annemieke M W Spijkerman; Daphne L van der A; Karel G M Moons; Gerjan Navis; Stephan J L Bakker; Joline W J Beulens
Journal:  BMJ       Date:  2012-09-18

Review 8.  Social relationships and mortality risk: a meta-analytic review.

Authors:  Julianne Holt-Lunstad; Timothy B Smith; J Bradley Layton
Journal:  PLoS Med       Date:  2010-07-27       Impact factor: 11.069

9.  25-Year trends in hypertension prevalence, awareness, treatment, and control in an Indian urban population: Jaipur Heart Watch.

Authors:  Rajeev Gupta; Vijay P Gupta; Hari Prakash; Aachu Agrawal; Krishna K Sharma; Prakash C Deedwania
Journal:  Indian Heart J       Date:  2017-11-13

10.  ROBIS: A new tool to assess risk of bias in systematic reviews was developed.

Authors:  Penny Whiting; Jelena Savović; Julian P T Higgins; Deborah M Caldwell; Barnaby C Reeves; Beverley Shea; Philippa Davies; Jos Kleijnen; Rachel Churchill
Journal:  J Clin Epidemiol       Date:  2015-06-16       Impact factor: 6.437

View more
  5 in total

Review 1.  Cardiovascular Disease Risk Modeling for Astronauts: Making the Leap From Earth to Space.

Authors:  Janice L Huff; Ianik Plante; Steve R Blattnig; Ryan B Norman; Mark P Little; Amit Khera; Lisa C Simonsen; Zarana S Patel
Journal:  Front Cardiovasc Med       Date:  2022-05-19

Review 2.  Methodological approaches for assessing certainty of the evidence in umbrella reviews: A scoping review.

Authors:  Saranrat Sadoyu; Kaniz Afroz Tanni; Nontaporn Punrum; Sobhon Paengtrai; Warittakorn Kategaew; Nattiwat Promchit; Nai Ming Lai; Ammarin Thakkinstian; Surachat Ngorsuraches; Mukdarut Bangpan; Sajesh Veettil; Nathorn Chaiyakunapruk
Journal:  PLoS One       Date:  2022-06-08       Impact factor: 3.752

3.  Genetically Informed Regression Analysis: Application to Aggression Prediction by Inattention and Hyperactivity in Children and Adults.

Authors:  Dorret I Boomsma; Toos C E M van Beijsterveldt; Veronika V Odintsova; Michael C Neale; Conor V Dolan
Journal:  Behav Genet       Date:  2020-12-01       Impact factor: 2.805

4.  Bipolar Disorder and Cardiovascular Risk in Rural versus Urban Populations in Colombia: A Comparative Clinical and Epidemiological Evaluation.

Authors:  Juan Pablo Forero; Alexander Ferrera; Jose Daniel Castaño; Sergio Ardila; Tanya Mesa; Dean Hosgood; Eugenio Ferro
Journal:  Ann Glob Health       Date:  2021-11-18       Impact factor: 2.462

5.  A Prediction Model Based on Noninvasive Indicators to Predict the 8-Year Incidence of Type 2 Diabetes in Patients with Nonalcoholic Fatty Liver Disease: A Population-Based Retrospective Cohort Study.

Authors:  Xintian Cai; Qing Zhu; Yuanyuan Cao; Shasha Liu; Mengru Wang; Ting Wu; Jing Hong; Ayguzal Ahmat; Xiayire Aierken; Nanfang Li
Journal:  Biomed Res Int       Date:  2021-05-14       Impact factor: 3.411

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.