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Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study.

ABSTRACT: BACKGROUND:Primary prevention of overweight is to be preferred above secondary prevention, which has shown moderate effectiveness. OBJECTIVE:To develop and internally validate a dynamic prediction model to identify young children in the general population, applicable at every age between birth and age 6, at high risk of future overweight (age 8). METHODS:Data were used from the Prevention and Incidence of Asthma and Mite Allergy birth cohort, born in 1996 to 1997, in the Netherlands. Participants for whom data on the outcome overweight at age 8 and at least three body mass index SD scores (BMI SDS) at the age of ?3?months and ?6?years were available, were included (N = 2265). The outcome of the prediction model is overweight (yes/no) at age 8 (range 7.4-10.5?years), defined according to the sex- and age-specific BMI cut-offs of the International Obesity Task Force. RESULTS:After backward selection in a Generalized Estimating Equations analysis, the prediction model included the baseline predictors maternal BMI, paternal BMI, paternal education, birthweight, sex, ethnicity and indoor smoke exposure; and the longitudinal predictors BMI SDS, and the linear and quadratic terms of the growth curve describing a child's BMI SDS development over time, as well as the longitudinal predictors' interactions with age. The area under the curve of the model after internal validation was 0.845 and Nagelkerke R2 was 0.351. CONCLUSIONS:A dynamic prediction model for overweight was developed with a good predictive ability using easily obtainable predictor information. External validation is needed to confirm that the model has potential for use in practice.

SUBMITTER: Welten M

PROVIDER: S-EPMC7507129 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study.

Welten Marieke M Wijga Alet H AH Hamoen Marleen M Gehring Ulrike U Koppelman Gerard H GH Twisk Jos W R JWR Raat Hein H Heymans Martijn W MW de Kroon Marlou L A MLA

Pediatric obesity 20200513 9

<h4>Background</h4>Primary prevention of overweight is to be preferred above secondary prevention, which has shown moderate effectiveness.<h4>Objective</h4>To develop and internally validate a dynamic prediction model to identify young children in the general population, applicable at every age between birth and age 6, at high risk of future overweight (age 8).<h4>Methods</h4>Data were used from the Prevention and Incidence of Asthma and Mite Allergy birth cohort, born in 1996 to 1997, in the Ne ...[more]

PMID: 32400070

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Project description:BackgroundInactive behaviour is common in older adults during hospitalisation and associated with poor health outcomes. If patients at high risk of spending little time standing/walking could be identified early after admission, they could be given interventions aimed at increasing their time spent standing/walking. This study aims to identify older adults at high risk of low physical activity (PA) levels during hospitalisation.MethodsProspective cohort study of 165 older adults (≥ 70 years) admitted to the department of Internal Medicine of Maastricht University Medical Centre for acute medical illness. Two prediction models were developed to predict the probability of low PA levels during hospitalisation. Time spent standing/walking per day was measured with an accelerometer until discharge (≤ 12 days). The average time standing/walking per day between inclusion and discharge was dichotomized into low/high PA levels by dividing the cohort at the median (50.0%) in model 1, and lowest tertile (33.3%) in model 2. Potential predictors-Short Physical Performance Battery (SPPB), Activity Measure for Post-Acute Care (AM-PAC), age, sex, walking aid use, and disabilities in activities of daily living-were selected based on literature and analysed using logistic regression analysis. Models were internally validated using bootstrapping. Model performance was quantified using measures of discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer and Lemeshow (H-L) goodness-of-fit test and calibration plots).ResultsModel 1 predicts a probability of spending ≤ 64.4 min standing/walking and holds the predictors SPPB, AM-PAC and sex. Model 2 predicts a probability of spending ≤ 47.2 min standing/walking and holds the predictors SPPB, AM-PAC, age and walking aid use. AUCs of models 1 and 2 were .80 (95% confidence interval (CI) = .73-.87) and .86 (95%CI = .79-.92), respectively, indicating good discriminative ability. Both models demonstrate near perfect calibration of the predicted probabilities and good overall performance, with model 2 performing slightly better.ConclusionsThe developed and internally validated prediction models may enable clinicians to identify older adults at high risk of low PA levels during hospitalisation. External validation and determining the clinical impact are needed before applying the models in clinical practise.

Project description:Background: Septic shock heterogeneity has important implications for the conduct of clinical trials and individual patient management. We previously addressed this heterogeneity by indentifying 3 putative subclasses of children with septic shock based on a 100-gene expression signature corresponding to adaptive immunity and glucocorticoid receptor signaling. Herein we attempted to prospectively validate the existence of these gene expression-based subclasses in a validation cohort. Methods: Gene expression mosaics were generated from the 100 class-defining genes for 82 individual patients in the validation cohort. Patients were classified into 1 of 3 subclasses (“A”, “B”, or “C”) based on color and pattern similarity relative to reference mosaics generated from the original derivation cohort. Separate classifications were conducted by 21 individual clinicians and a computer-based algorithm. After subclassification the clinical database was mined for clinical phenotyping. Results: In the final consensus subclassification generated by clinicians, subclass A patients had a higher illness severity, as measured by illness severity scores and maximal organ failure, relative to subclasses B and C. The k coefficient across all possible inter-evaluator comparisons was 0.633. Similar observations were made based on the computer-generated subclassification. Patients in subclass A were also characterized by repression of a large number of genes having functional annotations related to zinc biology. Conclusions: We have validated the existence of subclasses of children with septic shock based on a biologically relevant, 100-gene expression signature. The subclasses can be indentified by clinicians without formal bioinformatics training, at a clinically relevant time point, and have clinically relevant phenotypic differences. Expression data from 82 children with septic shock and 21 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data from 100 class defining genes to validate the existence of pediatric septic shock subclasses having phenotypic differences.

Project description:Objectives To develop and validate updated QRISK3 prediction algorithms to estimate the 10 year risk of cardiovascular disease in women and men accounting for potential new risk factors.Design Prospective open cohort study.Setting General practices in England providing data for the QResearch database.Participants 1309 QResearch general practices in England: 981 practices were used to develop the scores and a separate set of 328 practices were used to validate the scores. 7.89 million patients aged 25-84 years were in the derivation cohort and 2.67 million patients in the validation cohort. Patients were free of cardiovascular disease and not prescribed statins at baseline.Methods Cox proportional hazards models in the derivation cohort to derive separate risk equations in men and women for evaluation at 10 years. Risk factors considered included those already in QRISK2 (age, ethnicity, deprivation, systolic blood pressure, body mass index, total cholesterol: high density lipoprotein cholesterol ratio, smoking, family history of coronary heart disease in a first degree relative aged less than 60 years, type 1 diabetes, type 2 diabetes, treated hypertension, rheumatoid arthritis, atrial fibrillation, chronic kidney disease (stage 4 or 5)) and new risk factors (chronic kidney disease (stage 3, 4, or 5), a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, systemic lupus erythematosus (SLE), atypical antipsychotics, severe mental illness, and HIV/AIDs). We also considered erectile dysfunction diagnosis or treatment in men. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for individual subgroups by age group, ethnicity, and baseline disease status.Main outcome measures Incident cardiovascular disease recorded on any of the following three linked data sources: general practice, mortality, or hospital admission records.Results 363?565 incident cases of cardiovascular disease were identified in the derivation cohort during follow-up arising from 50.8 million person years of observation. All new risk factors considered met the model inclusion criteria except for HIV/AIDS, which was not statistically significant. The models had good calibration and high levels of explained variation and discrimination. In women, the algorithm explained 59.6% of the variation in time to diagnosis of cardiovascular disease (R2, with higher values indicating more variation), and the D statistic was 2.48 and Harrell's C statistic was 0.88 (both measures of discrimination, with higher values indicating better discrimination). The corresponding values for men were 54.8%, 2.26, and 0.86. Overall performance of the updated QRISK3 algorithms was similar to the QRISK2 algorithms.Conclusion Updated QRISK3 risk prediction models were developed and validated. The inclusion of additional clinical variables in QRISK3 (chronic kidney disease, a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, SLE, atypical antipsychotics, severe mental illness, and erectile dysfunction) can help enable doctors to identify those at most risk of heart disease and stroke.

Project description:BackgroundPhysical inactivity is associated with numerous health risks, including cancer, cardiovascular disease, type 2 diabetes, increased health care expenditure, and preventable, premature deaths. The majority of Americans fall short of clinical guideline goals (ie, 8000-10,000 steps per day). Behavior prediction algorithms could enable efficacious interventions to promote physical activity by facilitating delivery of nudges at appropriate times.ObjectiveThe aim of this paper is to develop and validate algorithms that predict walking (ie, >5 min) within the next 3 hours, predicted from the participants' previous 5 weeks' steps-per-minute data.MethodsWe conducted a retrospective, closed cohort, secondary analysis of a 6-week microrandomized trial of the HeartSteps mobile health physical-activity intervention conducted in 2015. The prediction performance of 6 algorithms was evaluated, as follows: logistic regression, radial-basis function support vector machine, eXtreme Gradient Boosting (XGBoost), multilayered perceptron (MLP), decision tree, and random forest. For the MLP, 90 random layer architectures were tested for optimization. Prior 5-week hourly walking data, including missingness, were used for predictors. Whether the participant walked during the next 3 hours was used as the outcome. K-fold cross-validation (K=10) was used for the internal validation. The primary outcome measures are classification accuracy, the Mathew correlation coefficient, sensitivity, and specificity.ResultsThe total sample size included 6 weeks of data among 44 participants. Of the 44 participants, 31 (71%) were female, 26 (59%) were White, 36 (82%) had a college degree or more, and 15 (34%) were married. The mean age was 35.9 (SD 14.7) years. Participants (n=3, 7%) who did not have enough data (number of days <10) were excluded, resulting in 41 (93%) participants. MLP with optimized layer architecture showed the best performance in accuracy (82.0%, SD 1.1), whereas XGBoost (76.3%, SD 1.5), random forest (69.5%, SD 1.0), support vector machine (69.3%, SD 1.0), and decision tree (63.6%, SD 1.5) algorithms showed lower performance than logistic regression (77.2%, SD 1.2). MLP also showed superior overall performance to all other tried algorithms in Mathew correlation coefficient (0.643, SD 0.021), sensitivity (86.1%, SD 3.0), and specificity (77.8%, SD 3.3).ConclusionsWalking behavior prediction models were developed and validated. MLP showed the highest overall performance of all attempted algorithms. A random search for optimal layer structure is a promising approach for prediction engine development. Future studies can test the real-world application of this algorithm in a "smart" intervention for promoting physical activity.

Dataset Information

Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study.

Publications

Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study.

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