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Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches.

ABSTRACT: The link between glucose and HbA1c at steady state has previously been described using steady-state or longitudinal relationships. We evaluated five published methods for prediction of HbA1c after 26/28 weeks using data from four clinical trials. Methods (1) and (2): steady-state regression of HbA1c on fasting plasma glucose and mean plasma glucose, respectively, (3) an indirect response model of fasting plasma glucose effects on HbA1c, (4) model of glycosylation of red blood cells, and (5) coupled indirect response model for mean plasma glucose and HbA1c. Absolute mean prediction errors were 0.61, 0.38, 0.55, 0.37, and 0.15% points, respectively, for Methods 1 through 5. This indicates that predictions improved by using mean plasma glucose instead of fasting plasma glucose, by inclusion of longitudinal glucose data and further by inclusion of longitudinal HbA1c data until 12 weeks. For prediction of trial outcome, the longitudinal models based on mean plasma glucose (Methods 4 and 5) had substantially better performance compared with the other methods.

SUBMITTER: Moller JB

PROVIDER: S-EPMC4120014 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches.

Møller J B JB Kristensen N R NR Klim S S Karlsson M O MO Ingwersen S H SH Kjellsson M C MC

CPT: pharmacometrics & systems pharmacology 20140702

The link between glucose and HbA1c at steady state has previously been described using steady-state or longitudinal relationships. We evaluated five published methods for prediction of HbA1c after 26/28 weeks using data from four clinical trials. Methods (1) and (2): steady-state regression of HbA1c on fasting plasma glucose and mean plasma glucose, respectively, (3) an indirect response model of fasting plasma glucose effects on HbA1c, (4) model of glycosylation of red blood cells, and (5) coup ...[more]

PMID: 24988185

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Project description:Abstract Aims The development of acute heart failure (AHF) is a critical decision point in the natural history of the disease and carries a dismal prognosis. The lack of appropriate risk-stratification tools at hospital discharge of AHF patients significantly limits clinical ability to precisely tailor patient-specific therapeutic regimen at this pivotal juncture. Machine learning-based strategies may improve risk stratification by incorporating analysis of high-dimensional patient data with multiple covariates and novel prediction methodologies. In the current study, we aimed at evaluating the drivers for success in prediction models and establishing an institute-tailored artificial Intelligence-based prediction model for real-time decision support. Methods and results We used a cohort of all 10 868 patients AHF patients admitted to a tertiary hospital during a 12 years period. A total of 372 covariates were collected from admission to the end of the hospitalization. We assessed model performance across two axes: (i) type of prediction method and (ii) type and number of covariates. The primary outcome was 1-year survival from hospital discharge. For the model-type axis, we experimented with seven different methods: logistic regression (LR) with either L1 or L2 regularization, random forest (RF), Cox proportional hazards model (Cox), extreme gradient boosting (XGBoost), a deep neural-net (NeuralNet) and an ensemble classifier of all the above methods. We were able to achieve an area under receiver operator curve (AUROC) prediction accuracy of more than 80% with most prediction models including L1/L2-LR (80.4%/80.3%), Cox (80.2%), XGBoost (80.5%), NeuralNet (80.4%). RF was inferior to other methods (78.8%), and the ensemble model was slightly superior (81.2%). The number of covariates was a significant modifier (P < 0.001) of prediction success, the use of multiplex-covariates preformed significantly better (AUROC 80.4% for L1-LR) compared with a set of known clinical covariates (AUROC 77.8%). Demographics followed by lab-tests and administrative data resulted in the largest gain in model performance. Conclusions The choice of the predictive modelling method is secondary to the multiplicity and type of covariates for predicting AHF prognosis. The application of a structured data pre-processing combined with the use of multiple-covariates results in an accurate, institute-tailored risk prediction in AHF Graphical Abstract Graphical Abstract

Dataset Information

Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches.

Publications

Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches.

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