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Probabilistic broken-stick model: A regression algorithm for irregularly sampled data with application to eGFR.

Norman Poh1, Santosh Tirunagari2, Nicholas Cole3

  • 1Department of Computer Science, University of Surrey, UK; QuintilesIMS, London, UK.

Journal of Biomedical Informatics
|October 19, 2017
PubMed
Summary
This summary is machine-generated.

A new probabilistic broken-stick model accurately identifies short-term and long-term disease trends in irregular patient data. This approach improves clinical decision-making for chronic kidney disease and other conditions.

Keywords:
Broken-sticksChronic kidney diseaseClinical time seriesElectronic medical recordsEstimated glomerular filtration rateRegressioneGFR

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Time Series Analysis

Background:

  • Clinical time series data are often irregularly sampled and influenced by various factors, complicating trend analysis.
  • Existing regression models struggle to simultaneously capture both acute events and chronic disease progression in biomedical measurements.
  • Accurate identification of disease trends is crucial for effective clinical management, including drug dosage and treatment scheduling.

Purpose of the Study:

  • To extend broken-stick regression models for improved modeling of clinical time series.
  • To develop a method that robustly estimates both short-term and long-term trends in irregularly sampled biomedical data.
  • To provide a more reliable non-linear estimate of the annual rate of change in measurements.

Main Methods:

  • Proposed a probabilistic extension to broken-stick regression models.
  • Developed a parametric and completely generative model.
  • Utilized the model's first derivative for estimating the annual rate of change.
  • Applied the model to estimated glomerular filtration rate (eGFR) data for chronic kidney disease (CKD) patients.

Main Results:

  • The probabilistic broken-stick model robustly estimates short-term and long-term trends simultaneously.
  • The model effectively accommodates unequal length and irregularly sampled clinical time series.
  • The model's derivative provides a more reliable non-linear estimate of annual change compared to linear regression.
  • Demonstrated utility using eGFR data for CKD management.

Conclusions:

  • The proposed probabilistic broken-stick model offers a flexible and interpretable approach for analyzing clinical time series.
  • This method enhances the ability of clinicians to monitor disease progression and make informed treatment decisions.
  • The model shows significant promise for managing chronic conditions like chronic kidney disease.