The importance of prediction model validation and assessment in obesity and nutrition research

Ivanescu, Andrada E.; Li, Peng; George, Brandon; Brown, Andrew W.; Keith, Scott W.; Raju, Dheeraj; Allison, David B.

doi:10.1038/ijo.2015.214

Cited by 74 publications

(56 citation statements)

References 54 publications

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“…Because of the uniqueness of the data collection, external validation of the full model was not feasible but validity was strongly supported by internal validation, using two complementary methods. [30, 31] We recognise that the study population may not be representative of a ‘normal’ obstetric population as they were participants in an RCT, an important reason for pursuing external validation of the predictive models. Future evalution studies could also include validation at earlier gestations.…”

Section: Discussionmentioning

confidence: 99%

Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention

et al. 2016

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All obese women are categorised as being of equally high risk of gestational diabetes (GDM) whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0–18+6 weeks’ gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR) metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337) developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria). A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios) provided an area under the curve of 0.71 (95%CI 0.68–0.74). This increased to 0.77 (95%CI 0.73–0.80) with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c), fructosamine, adiponectin, sex hormone binding globulin, triglycerides), but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74–0.81). Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value) later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described which could enable targeted interventions for GDM prevention in women who will benefit the most.

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Section: Discussionmentioning

confidence: 99%

Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention

et al. 2016

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“…The ground truth collection, type of regression model used and the parameter optimization for the regression model are also described in steps two, three and four of Table 1. Separate 3-fold cross validation models were used to evaluate the performance of the learning algorithm for the prediction of body fat in children and adults [26,27]. Both datasets were then shuffled randomly and divided into three equal subsets.…”

Section: Methodsmentioning

confidence: 99%

A method for measuring human body composition using digital images

et al. 2018

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Background/ObjectivesBody mass index (BMI) is a proxy for obesity that is commonly used in spite of its limitation in estimating body fatness. Trained observers with repeated exposure to different body types can estimate body fat (BF) of individuals compared to criterion methods with reasonable accuracy. The purpose of this study was to develop and validate a computer algorithm to provide a valid estimate %BF using digital photographs.Subjects/MethodsOur sample included 97 children and 226 adults (age in years: 11.3±3.3; 38.1±11.6, respectively). Measured height and weight were used (BMI in kg/m2: 20.4±4.4; 28.7±6.6 for children and adults, respectively). Dual x-ray absorptiometry (DXA) was the criterion method. Body volume (BVPHOTO) and body shape (BSPHOTO) were derived from two digital images. Final support vector regression (SVR) models were trained using age, sex, race, BMI for % BFNOPHOTO, plus BVPHOTO and BSPHOTO for %BFPHOTO. Separate validation models were used to evaluate the learning algorithm in children and adults. The differences in correlations between %BFDXA, %BFNOPHOTO and %BFPHOTO were tested using the Fisher’s Z-score transformation.ResultsMean BFDXA and BFPHOTO were 27.0%±9.2 vs. 26.7%± 7.4 in children and 32.9± 10.4% vs. 32.8%±9.3 in adults. SVR models produced %BFPHOTO values strongly correlated with %BFDXA. Our final model produced correlations of rDP = 0.80 and rDP = 0.87 in children and adults, respectively for %BFPHOTO vs. %BFDXA. The correlation between %BFNOPHOTO and %BFDXA was moderate, yet statistically significant in both children rDB = 0.70; p <0.0001 and adults rDB = 0.86; p<0.0001. However, the correlations for rDP were statistically higher than rDB (%BFDXA vs. %BFNOPHOTO) in both children and adults (children: Z = 5.95, p<0.001; adults: Z = 3.27, p<0.0001).ConclusionsOur photographic method produced valid estimates of BF in both children and adults. Further research is needed to create norms for subgroups by sex, race/ethnicity, and mobility status.

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“…Model performance was assessed by two metrics commonly used to assess the performance of regression models (Ivanescu et al, 2016;Fernandes et al, 2017): Pearsons's correlation coefficient (r) and the mean squared error (MSE). The correlation coefficient characterizes the linear relationship between observed and predicted labels; the MSE is calculated as the average of the squared differences between the observed and predicted labels.…”

Section: Model Evaluation and Interpretationmentioning

confidence: 99%

Pain-related fear – Dissociable neural sources of different fear constructs

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Vrana

et al. 2018

Preprint

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The ability to infer emotional states through self-reports is often limited. Their measurement becomes even more challenging when considering emotional phenomena such as pain-related fear where different associated fear constructs have been proposed. Demonstrating significant predictive value regarding disability in patients with persistent musculoskeletal pain, pain-related fear is often assessed by questionnaires focusing on either fear of movement/(re)injury/kinesiophobia, fear avoidance beliefs or pain anxiety. Furthermore, the relationship of general anxiety measures such as trait anxiety to pain-related fear remains ambiguous. Advances in neuroimaging might help to support potential commonalities or differences across psychological constructs using appropriate machine learning techniques with the ability to reveal predictive relationships between neural information and questionnaire scores.Here, we applied a pattern regression approach using functional magnetic resonance imaging data of 20 non-specific chronic low back pain (LBP) patients. More specifically, we applied a novel approach using Multiple Kernel Learning that allows investigating the contribution of experimental conditions and regional neural information to a prediction model. We hypothesized to find evidence for or against a common fear construct by computing and comparing the prediction model of each questionnaire according to the contribution of fear-related neural information and conditions. The current results underpin the diversity of fear constructs among self-report measures of pain-related fear by demonstrating evidence of non-overlapping and differentially contributing neural sources within fear processing regions. Thus, the current approach might ultimately help to further understand and dissect the fear constructs captured by the various pain-related fear questionnaires. Significance. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/251751 doi: bioRxiv preprint first posted online Jan. 25, 2018; Pain-related fear, often assessed through self-reports such as questionnaires, has shown prognostic value and clinical utility for a variety of musculoskeletal pain disorders. However, it remains difficult to determine a common underlying fear construct of pain-related fear due to several proposed constructs among questionnaires. The current study describes a novel neuroscientific approach using machine learning of neural patterns within the fear network of chronic LBP patients that might have the potential to identify neural commonalities or differences among the various fear constructs. Ultimately, this approach might afford a deeper understanding of the suggested fear constructs of pain-related fear and might be also applied to other domains where ambiguity exists between emotional phenomena and underlying psychological constructs.

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The importance of prediction model validation and assessment in obesity and nutrition research

Cited by 74 publications

References 54 publications

Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention

Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention

A method for measuring human body composition using digital images

Pain-related fear – Dissociable neural sources of different fear constructs

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