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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Predicting diabetes clinical outcomes using longitudinal risk factor trajectories.

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Analyzing electronic health records (EHR) reveals that risk factor trajectories, like blood glucose levels, can predict type 2 diabetes onset. Cumulative exposure to hyperglycemia increases risk, even if temporarily resolved.

Keywords:
DiabetesDiabetes trajectoriesPrediabetesRisk assessment

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Area of Science:

  • Biomedical Informatics
  • Epidemiology
  • Endocrinology

Background:

  • Electronic health records (EHR) provide longitudinal data for disease prediction.
  • Observing risk factor trajectories in EHR can enhance prediction of incident type 2 diabetes.

Purpose of the Study:

  • To assess the predictive value of risk factor trajectories from EHR for incident type 2 diabetes.
  • To develop and evaluate a novel Cumulative Exposure (CE) method for diabetes risk prediction.

Main Methods:

  • Retrospective cohort study of 71,545 non-diabetic adults over 13 years.
  • Computed trajectories of fasting plasma glucose, lipids, BMI, and blood pressure.
  • Applied Cox proportional hazards regression with a novel CE method, comparing against the Framingham Diabetes Risk Scoring (FDRS) Model.

Main Results:

  • The novel CE model significantly outperformed the FDRS Model (0.802 vs. 0.660).
  • Even short periods of hyperglycemia were associated with increased diabetes risk.
  • Returning to normoglycemia reduced risk but did not eliminate it; sustained glycemic control lowered future risk.

Conclusions:

  • Risk factor trajectories from EHR substantially improve type 2 diabetes prediction models.
  • The CE method offers insights into how cumulative exposure to risk factors influences diabetes onset over time.