Bariatric Health Index (BHI): quantification and classification of comorbidity in bariatric patients based on blood markers: integrated health/multidisciplinary care

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Abstract

Background
Comorbidities are common in bariatric patients. However, the degree of comorbidities is hard to quantify objectively as they develop gradually and do not independently reflect the continuum of metabolic syndrome.
Introduction
In the Catharina Hospital bariatric patients are monitored with extensive laboratory panels prior and after surgery. Besides detecting nutrient deficiencies, these parameters offer the opportunity to search for objective markers to describe the health of bariatric patients.
Objectives
The bariatric health index (BHI) is developed enabling quantification and classification of comorbidity in bariatric patients.
Methods
Machine learning is applied to comprehensive laboratory data, collected from 2367 patients containing both pre- and post-surgical data (6, 12 and 24 months). Since comorbidities of interest (diabetes, hypertension, and dyslipidemia), were correlated, an ordinal output variable was defined, stating presence as ‘none’, ‘one’, or ‘multiple’ comorbidities. Different ordinal logistic regression models were fit to the data and compared by AUC.
Results
Next to gender and age at surgery, blood marker levels of HbA1c, triglycerides, urea, potassium, and estimated GFR (CKD-EPI) appeared descriptive for the degree of comorbidity. For the classes ‘none’, ‘one’, or ‘multiple’ the best performing BHI model had an AUC (SE) of 0.82 (0.01), 0.66 (0.01), and 0.88 (0.01), respectively.
Conclusion
A model has been developed by mining bariatric laboratory data that enables quantification and classification of presence of comorbidity. The BHI provides the basis for a tool that predicts the evolution of bariatric health state and may be used to personalize the patient’s monitoring plan.
Original languageEnglish
Pages (from-to)652
Number of pages1
JournalObesity Surgery
Volume27
Publication statusPublished - Jul 2017
Event22nd World Congress of the International Federation for the Surgery of
Obesity and Metabolic Disorders (IFSO 2017)
- London, United Kingdom
Duration: 29 Aug 20172 Sep 2017

Cite this

@article{3cf203a70fbf40dcac192e389ff273ad,
title = "Bariatric Health Index (BHI): quantification and classification of comorbidity in bariatric patients based on blood markers: integrated health/multidisciplinary care",
abstract = "BackgroundComorbidities are common in bariatric patients. However, the degree of comorbidities is hard to quantify objectively as they develop gradually and do not independently reflect the continuum of metabolic syndrome.IntroductionIn the Catharina Hospital bariatric patients are monitored with extensive laboratory panels prior and after surgery. Besides detecting nutrient deficiencies, these parameters offer the opportunity to search for objective markers to describe the health of bariatric patients.ObjectivesThe bariatric health index (BHI) is developed enabling quantification and classification of comorbidity in bariatric patients.MethodsMachine learning is applied to comprehensive laboratory data, collected from 2367 patients containing both pre- and post-surgical data (6, 12 and 24 months). Since comorbidities of interest (diabetes, hypertension, and dyslipidemia), were correlated, an ordinal output variable was defined, stating presence as ‘none’, ‘one’, or ‘multiple’ comorbidities. Different ordinal logistic regression models were fit to the data and compared by AUC.Results Next to gender and age at surgery, blood marker levels of HbA1c, triglycerides, urea, potassium, and estimated GFR (CKD-EPI) appeared descriptive for the degree of comorbidity. For the classes ‘none’, ‘one’, or ‘multiple’ the best performing BHI model had an AUC (SE) of 0.82 (0.01), 0.66 (0.01), and 0.88 (0.01), respectively. ConclusionA model has been developed by mining bariatric laboratory data that enables quantification and classification of presence of comorbidity. The BHI provides the basis for a tool that predicts the evolution of bariatric health state and may be used to personalize the patient’s monitoring plan.",
author = "{van Loon}, S.L.M. and R. Deneer and S. Nienhuijs and {van Riel}, N.A.W. and {van den Heuvel}, E.R. and V. Scharnhorst and A. Boer",
year = "2017",
month = "7",
language = "English",
volume = "27",
pages = "652",
journal = "Obesity Surgery",
issn = "0960-8923",
publisher = "Springer",

}

TY - JOUR

T1 - Bariatric Health Index (BHI): quantification and classification of comorbidity in bariatric patients based on blood markers

T2 - integrated health/multidisciplinary care

AU - van Loon, S.L.M.

AU - Deneer, R.

AU - Nienhuijs, S.

AU - van Riel, N.A.W.

AU - van den Heuvel, E.R.

AU - Scharnhorst, V.

AU - Boer, A.

PY - 2017/7

Y1 - 2017/7

N2 - BackgroundComorbidities are common in bariatric patients. However, the degree of comorbidities is hard to quantify objectively as they develop gradually and do not independently reflect the continuum of metabolic syndrome.IntroductionIn the Catharina Hospital bariatric patients are monitored with extensive laboratory panels prior and after surgery. Besides detecting nutrient deficiencies, these parameters offer the opportunity to search for objective markers to describe the health of bariatric patients.ObjectivesThe bariatric health index (BHI) is developed enabling quantification and classification of comorbidity in bariatric patients.MethodsMachine learning is applied to comprehensive laboratory data, collected from 2367 patients containing both pre- and post-surgical data (6, 12 and 24 months). Since comorbidities of interest (diabetes, hypertension, and dyslipidemia), were correlated, an ordinal output variable was defined, stating presence as ‘none’, ‘one’, or ‘multiple’ comorbidities. Different ordinal logistic regression models were fit to the data and compared by AUC.Results Next to gender and age at surgery, blood marker levels of HbA1c, triglycerides, urea, potassium, and estimated GFR (CKD-EPI) appeared descriptive for the degree of comorbidity. For the classes ‘none’, ‘one’, or ‘multiple’ the best performing BHI model had an AUC (SE) of 0.82 (0.01), 0.66 (0.01), and 0.88 (0.01), respectively. ConclusionA model has been developed by mining bariatric laboratory data that enables quantification and classification of presence of comorbidity. The BHI provides the basis for a tool that predicts the evolution of bariatric health state and may be used to personalize the patient’s monitoring plan.

AB - BackgroundComorbidities are common in bariatric patients. However, the degree of comorbidities is hard to quantify objectively as they develop gradually and do not independently reflect the continuum of metabolic syndrome.IntroductionIn the Catharina Hospital bariatric patients are monitored with extensive laboratory panels prior and after surgery. Besides detecting nutrient deficiencies, these parameters offer the opportunity to search for objective markers to describe the health of bariatric patients.ObjectivesThe bariatric health index (BHI) is developed enabling quantification and classification of comorbidity in bariatric patients.MethodsMachine learning is applied to comprehensive laboratory data, collected from 2367 patients containing both pre- and post-surgical data (6, 12 and 24 months). Since comorbidities of interest (diabetes, hypertension, and dyslipidemia), were correlated, an ordinal output variable was defined, stating presence as ‘none’, ‘one’, or ‘multiple’ comorbidities. Different ordinal logistic regression models were fit to the data and compared by AUC.Results Next to gender and age at surgery, blood marker levels of HbA1c, triglycerides, urea, potassium, and estimated GFR (CKD-EPI) appeared descriptive for the degree of comorbidity. For the classes ‘none’, ‘one’, or ‘multiple’ the best performing BHI model had an AUC (SE) of 0.82 (0.01), 0.66 (0.01), and 0.88 (0.01), respectively. ConclusionA model has been developed by mining bariatric laboratory data that enables quantification and classification of presence of comorbidity. The BHI provides the basis for a tool that predicts the evolution of bariatric health state and may be used to personalize the patient’s monitoring plan.

M3 - Meeting Abstract

VL - 27

SP - 652

JO - Obesity Surgery

JF - Obesity Surgery

SN - 0960-8923

ER -