Derivation and external validation of a simple prediction model for the diagnosis of type 2 Diabetes Mellitus in the Brazilian urban population

Sousa, Anésio Mendes de; Pereira, Alexandre C.; Marquezine, Guilherme Figueiredo; Nascimento-Neto, Raimundo Marques do; Freitas, Sílvia Nascimento de; Nicolato, Roney Luiz de Carvalho; Machado-Coelho, George Luiz Lins; Rodrigues, Sérgio Lamêgo; Mill, José Geraldo; Krieger, José Eduardo

doi:10.1007/s10654-009-9314-2

Cited by 41 publications

(48 citation statements)

References 21 publications

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“…Eight articles (21%) were from the USA [31,34,36,39,43,56,57,59], thirteen articles (33%) were from Europe [23-25,32,33,35,40,42,46,52,54,55], thirteen articles (33%) were from Asia [26,27,29,37,41,44,45,47-49,51,60], two were from Africa [30,53], one was from Australia [28] and one was from Brazil [50]. …”

Section: Resultsmentioning

confidence: 99%

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

Collins

Mallett

Omar

et al. 2011

BMC Med

420

419

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BackgroundThe World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.MethodsWe conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.ResultsThirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).ConclusionsWe found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.

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Section: Resultsmentioning

confidence: 99%

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

Collins

Mallett

Omar

et al. 2011

BMC Med

420

419

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“…The sample used can be considered to be representative of the adult population in Vitória based in other studies [26][27][28] . The initial loss (n = 66) happened because the 24-hour urine collection was not conducted properly.…”

Section: Discussionmentioning

confidence: 99%

Estudo de validação das equações de Tanaka e de Kawasaki para estimar a excreção diária de sódio através da coleta da urina casual

Mill

Rodrigues

Baldo

et al. 2015

Rev. bras. epidemiol.

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ABSTRACT:Objective: To validate Tanaka and Kawasaki's formulas to calculate the salt intake by the sodium/creatinine ratio in spot of urine. Methods: Two hundred and seventy two adults (20 -69 years old; 52.6% women) with 24 h urine collection and two urinary spots collected on the same day (while fastingspot 1 -or not fasting -spot 2). Anthropometry, blood pressure and fasting blood were measured on the same day. The analysis of agreement between salt consumption measured in the 24 h urine test and urinary spots were determined by the Pearson's correlation (r) and the Bland & Altman method. Results: The mean salt consumption measured by the 24 h sodium excretion was 10.4 ± 5.3 g/day. The correlation between the measured 24 h sodium excretion and the estimation based on spots 1 and 2, respectively, was only moderated according to Tanaka (r = 0.51 and r = 0.55; p < 0.001) and to Kawasaki (r = 0.52 and r = 0.54; p < 0.001). We observed an increasing underestimation of salt consumption by Tanaka to increasing salt consumption and conversely, an overestimation of consumption by the Kawasaki formula. The estimation of salt consumption (difference between measured and calculated salt consumption lower than 1 g/day) was adequate only when the consumption was between 9 -12 g/day (Tanaka) and 12 -18 g/day (Kawasaki). Conclusion: Spot urine sampling is adequate to estimate salt consumption only among individuals with an actual consumption near the population mean.

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“…Finally, validated questionnaires evaluating nutrient intake (e.g., calcium and vitamins) and activities of daily living, allow to evaluate the role of environmental factors in endocrine conditions and locomotor diseases of the elderly. For recent references in EJE see [144–172]. …”

Section: Endocrine Diseasesmentioning

confidence: 99%

The Rotterdam Study: 2012 objectives and design update

et al. 2011

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The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, oncological, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over a 1,000 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

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Derivation and external validation of a simple prediction model for the diagnosis of type 2 Diabetes Mellitus in the Brazilian urban population

Cited by 41 publications

References 21 publications

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

Estudo de validação das equações de Tanaka e de Kawasaki para estimar a excreção diária de sódio através da coleta da urina casual

The Rotterdam Study: 2012 objectives and design update

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