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Metabolic Health Index (MHI): Assessment of Comorbidity in Bariatric Patients Based on Biomarkers

  • Saskia L. M. van LoonEmail author
  • Ruben Deneer
  • Simon W. Nienhuijs
  • Anna Wilbik
  • Uzay Kaymak
  • Natal van Riel
  • Volkher Scharnhorst
  • Arjen-Kars Boer
Original Contributions

Abstract

Purpose

The focus of bariatric surgery is reduction of weight, reflected in body mass index (BMI). However, the resolution of comorbidity is a second important outcome indicator. The degree of comorbidity is hard to quantify objectively as comorbidities develop gradually and are interdependent. Multiple scoring systems quantifying comorbidity exist but they lack continuity and objectivity. In analogy with BMI as index for weight, the Metabolic Health Index (MHI) is developed as objective quantification of metabolic health status. Laboratory data were used as comorbidities affect biomarkers. Conversely, laboratory data can be used as objectively obtained variables to describe comorbidity.

Methods

Laboratory data were collected and crosschecked by national quality registry entries. Machine learning was applied to develop an ordinal logistic regression model, using 4 clinical and 32 laboratory input variables. The output was mathematically transformed into a continuous score for intuitive interpretation, ranging from 1 to 6 (MHI).

Results

In total, 4778 data records of 1595 patients were used. The degree of comorbidity is best described by age at phlebotomy, estimated Glomerular Filtration Rate (eGFR), and concentrations of glycated hemoglobin (HbA1c), triglycerides, and potassium. The model is independent of day of sampling and type of surgery. Mean MHI was significantly different between patient subgroups with increasing number of comorbidities.

Conclusion

The MHI reflects severity of comorbidity, enabling objective assessment of a bariatric patient’s metabolic health state, regardless day of sampling and surgery type. Next to weight-focused outcome measures like %TWL, the MHI can serve as outcome measure for metabolic health.

Keywords

Bariatric surgery METABOLIC SURGERY Hypertension Diabetes Dyslipidemia Metabolic syndrome Machine learning Biomarkers Value based health care Outcome measure 

Notes

Acknowledgements

We thank Edwin R. van den Heuvel (Prof) from the Eindhoven University of Technology for providing his insight and expertise that greatly assisted the research. In addition, we thank Carmen Gensen for her assistance in the data analysis process.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Ethical Approval

For this type of study formal consent is not required.

Supplementary material

11695_2019_4244_MOESM1_ESM.pptx (239 kb)
ESM 1. (PPTX 238 kb).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Clinical ChemistryCatharina HospitalEindhovenThe Netherlands
  2. 2.Department of Industrial Engineering and Innovation SciencesEindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.Expert Center Clinical ChemistryEindhovenThe Netherlands
  4. 4.Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  5. 5.Department of SurgeryCatharina HospitalEindhovenThe Netherlands

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