Calibrating Longitudinal eGFR in Patience Records Stored in
ClinicalPractices Using a Mixture of
Linear Regressions
[email protected] 2012
Norman Poh Simon de Lusignan
Dept of ComputingFaculty of Electronics and Physical Sciences
Clinical Informatics Research Group
Faculty of Economics, Business and Law
University of SurreyUnited Kingdom
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MotivationBiomedical signal is under- and irregularly
sampled and very noisy due to circardian rythme, assay methods, and confounding factors, among others.
Challenge: Finding signal from noiseOur objective: correct for structural noise
due to assay methodsInnovation: blindly distinguish the assay
methods using mixture of regression.Result: Successfully applied to calibrating
estimated Glomerular Filatration Rate (eGFR)
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Problem statement
Serum creatinine
eGFR lab reporting(original)
eGFR lab reporting
(Gold standard)
Since 1990’s 2006 20102006-2009Report
ing m
easu
res
eGFR not used
Serum creatinine
eGFR calibrated to the Gold standardeGFR lab reporting
(Gold standard)
Since 1990’s 2006 20102006-2009Report
ing m
easu
res
Not distinguishable from each other
Example of data across patients (without calibration)
4
eG
FR=
est
imate
d G
lom
eru
lar
filt
rati
on r
ate
Eff
ect
iveness
of
kidney f
unct
ions
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Sample results for one patient (original data)
Phase 0 Phase 1Phase 2
Phases 0, 1 and 2 are found via mixture of
linear regression
Both time-series are
indistinguishable
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Calibrated to Phase 1
Phase 0 Phase 1Phase 2
Non calibrated eGFR obtained from Serum
Creatinine
calibrated eGFR to Phase 1 (green
points)
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Calibrated to Phase II
Phase 0 Phase 1Phase 2
calibrated eGFR to
Phase 1 (red points)
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Results (across all patients)Non-calibrated eGFR Calibrated eGFR
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A functional representation
𝑐
X𝑔𝑆 X𝑔
𝐿
𝑔𝐿eGFR
Serum Creatinin
e(SCr)
Self-calculated Lab-calculated
X𝑐❑
𝑀𝑀𝐷𝑅𝐷
𝑀𝑢𝑔𝑆
Obtained from health record
Calculated variable
𝑀 𝑙𝑎𝑏
CF = confounding factorsGiven
We can deduce:
𝑈𝑛𝑘𝑛𝑜𝑤𝑛
Input data : a time-series of and .
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