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Review

Reporting and Methods in Developing Prognostic Prediction Models for Metabolic Syndrome: A Systematic Review and Critical Appraisal

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Pages 4981-4992 | Published online: 15 Dec 2020

References

  • Wilson PWF, D’Agostino RB, Parise H, Sullivan L, Meigs JB. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation. 2005;112(20):3066–3072. doi:10.1161/CIRCULATIONAHA.105.539528
  • Semnani-Azad Z, Khan TA, Blanco Mejia S, et al. Association of major food sources of fructose-containing sugars with incident metabolic syndrome: a systematic review and meta-analysis. JAMA Netw Open. 2020;3(7):e209993. doi:10.1001/jamanetworkopen.2020.9993
  • Moore JX, Chaudhary N, Akinyemiju TA. Metabolic syndrome prevalence by race/ethnicity and sex in the United States, National Health and Nutrition Examination Survey, 1988–2012. Prev Chronic Dis. 2017;14:160287. doi:10.5888/pcd14.160287
  • Lu JL, Wang LM, Li M, et al. Metabolic syndrome among adults in China: the 2010 China Noncommunicable Disease Surveillance. J Clin Endocr Metab. 2017;102(2):507–515.
  • Sergi G, Dianin M, Bertocco A, et al. Gender differences in the impact of metabolic syndrome components on mortality in older people: a systematic review and meta-analysis. Nutr Metab Cardiovasc Dis. 2020.
  • Zheng X, Yu H, Qiu X, Ying CS. The effects of a nurse-led lifestyle intervention program on cardiovascular risk, self-efficacy and health promoting behaviours among patients with metabolic syndrome: randomized controlled trial. Int J Nurs Stud. 2020;2020:103638.
  • Moons KG, Royston P, Vergouwe Y, Grobbee DE. Prognosis and prognostic research: what, why, and how? BMJ. 2009;338:b375.
  • Steyerberg EW, Moons KG, van der Windt DA, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381.
  • Damen J, Hooft L. The increasing need for systematic reviews of prognosis studies: strategies to facilitate review production and improve quality of primary research. Diagn Progn Res. 2019;3:2.
  • Ibrahim MS, Pang D, Pappas Y, Randhawa G. Metabolic syndrome risk models and scores: a systematic review. Biomed J Sci Tech Res. 2020;26(1):19695–19707.
  • Ibrahim MS, Pang D, Randhawa G, Pappas Y. Risk models and scores for metabolic syndrome: systematic review protocol. BMJ Open. 2019;9(9):e027326. doi:10.1136/bmjopen-2018-027326
  • Cochrane handbook for systematic reviews of interventions: the Cochrane Collaboration; 2011. Available from: http://handbook-5-1.cochrane.org/. Accessed November 23, 2020.
  • Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ. 2020;369:m1328.
  • Ensor J, Riley RD, Moore D, Snell KI, Bayliss S, Fitzmaurice D. Systematic review of prognostic models for recurrent venous thromboembolism (VTE) post-treatment of first unprovoked VTE. BMJ Open. 2016;6(6):e011190. doi:10.1136/bmjopen-2016-011190
  • Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11(10):e1001744. doi:10.1371/journal.pmed.1001744
  • Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med. 2019;170(1):W1–W33. doi:10.7326/M18-1377
  • Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br J Surg. 2015;102(3):148–158. doi:10.1002/bjs.9736
  • Moher D, Liberati A, Tetzlaff J, Altman DG. The PRISMA Group (2009) Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 2009;6(7):e1000097. doi:10.1371/journal.pmed.1000097
  • Gao YS, Bd L. Bayesian model averaging method for predicting 5 years of metabolic syndrome risk in men and women. Shandong Med J. 2016;56(39):91–94.
  • Hirose H, Takayama T, Hozawa S, Hibi T, Saito I. Prediction of metabolic syndrome using artificial neural network system based on clinical data including insulin resistance index and serum adiponectin. Comput Biol Med. 2011;41(11):1051–1056. doi:10.1016/j.compbiomed.2011.09.005
  • Hsiao F-C, Wu C-Z, Hsieh C-H, He C-T, Hung Y-J, Pei D. Chinese metabolic syndrome risk score. South Med J. 2009;102(2):159–164. doi:10.1097/SMJ.0b013e3181836b19
  • Obokata M, Negishi K, Ohyama Y, Okada H, Imai K, Kurabayashi M. A risk score with additional four independent factors to predict the incidence and recovery from metabolic syndrome: development and validation in large Japanese cohorts. PLoS One. 2015;10(7):7. doi:10.1371/journal.pone.0133884
  • Ohyama Y. Risk prediction model of metabolic syndrome in health management Population. J Shandong Univ. 2017;6(55):87–92.
  • Yang X, Tao Q, Sun F, Cao C, Zhan S. Setting up a risk prediction model on metabolic syndrome among 35–74 year-olds based on the Taiwan MJ Health-checkup Database. Zhonghua Liuxingbingxue Zazhi. 2013;34(9):874–878.
  • Zhang W, Chen Q, Yuan Z, et al. A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population. BMC Public Health. 2015;15(1):64. doi:10.1186/s12889-015-1424-z
  • Zou -T-T, Zhou Y-J, Zhou X-D, et al. MetS risk score: a clear scoring model to predict a 3-year risk for metabolic syndrome. Hormone Metab Res. 2018;50(9):683–689. doi:10.1055/a-0677-2720
  • Karimi-Alavijeh F, Jalili S, Sadeghi M. Predicting metabolic syndrome using decision tree and support vector machine methods. ARYA Atheroscler. 2016;12(3):146–152.
  • Pujos-Guillot E, Brandolini M, Petera M, et al. Systems metabolomics for prediction of metabolic syndrome. J Proteome Res. 2017;16(6):2262–2272. doi:10.1021/acs.jproteome.7b00116
  • Efstathiou SP, Skeva II, Zorbala E, Georgiou E, Mountokalakis TD. Metabolic syndrome in adolescence can it be predicted from natal and parental profile? The Prediction of Metabolic Syndrome in Adolescence (PREMA) study. Circulation. 2012;125(7):902–910. doi:10.1161/CIRCULATIONAHA.111.034546
  • Ganna A, Reilly M, de Faire U, Pedersen N, Magnusson P, Ingelsson E. Risk prediction measures for case-cohort and nested case-control designs: an application to cardiovascular disease. Am J Epidemiol. 2012;175(7):715–724. doi:10.1093/aje/kwr374
  • Ogundimu EO, Altman DG, Collins GS. DG, GS C. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol. 2016;76:175–182. doi:10.1016/j.jclinepi.2016.02.031
  • van der Ploeg T, Austin PC, Steyerberg EW. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol. 2014;14(1):137. doi:10.1186/1471-2288-14-137
  • Courvoisier DS, Combescure C, Agoritsas T, Gayet-Ageron A, Perneger TV. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. J Clin Epidemiol. 2011;64(9):993–1000. doi:10.1016/j.jclinepi.2010.11.012
  • Vergouwe Y, Steyerberg EW, Eijkemans MJC, Habbema JDF. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol. 2005;58(5):475–483. doi:10.1016/j.jclinepi.2004.06.017
  • Vergouwe Y, Royston P, Moons KGM, Altman DG. Development and validation of a prediction model with missing predictor data: a practical approach. J Clin Epidemiol. 2010;63(2):205–214. doi:10.1016/j.jclinepi.2009.03.017
  • Marshall A, Altman DG, Royston P, Holder RL. Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study. BMC Med Res Methodol. 2010;10.
  • Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;25(1):127–141. doi:10.1002/sim.2331
  • Collins GS, Ogundimu EO, Cook JA, Manach YL, Altman DG. Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model. Stat Med. 2016;35(23):4124–4135. doi:10.1002/sim.6986
  • Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer; 2019.
  • Royston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. BMJ. 2009;338(mar31 1):b604. doi:10.1136/bmj.b604
  • Pavlou M, Ambler G, Seaman SR, Guttmann O, Elliott PKM. How to develop a more accurate risk prediction model when there are few events. BMJ. 2015;351:h3868.
  • Steyerberg EW, Vickers AJ, Cook N, et al.Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology. 2010;21(1):128. doi:10.1097/EDE.0b013e3181c30fb2
  • Karimi-Alavijeh F, Jalili S, Sadeghi M. Predicting metabolic syndrome using decision tree and support vector machine methods. ARYA Atheroscler. 2016;12:3.
  • Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012;98(9):683–690.
  • Zhang W, Chen Q, Yuan Z, et al. A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population. BMC Public Health. 2015;15:64.
  • Pujos-Guillot E, Brandolini M, Pétéra M, et al. Systems metabolomics for prediction of metabolic syndrome. J Proteome Res. 2017;16(6):2262–2272.