Metabolic Health Index (MHI): assessment of comorbidity in bariatric patients based on biomarkers

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Uittreksel

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.

Originele taal-2Engels
Pagina's (van-tot)714-724
Aantal pagina's11
TijdschriftObesity Surgery
Volume30
Nummer van het tijdschrift2
DOI's
StatusGepubliceerd - 1 feb 2020

Vingerafdruk

Bariatrics
Comorbidity
Biomarkers
Health
Ambulatory Surgical Procedures
Body Mass Index
Logistic Models
Outcome Assessment (Health Care)
Weights and Measures
Phlebotomy
Bariatric Surgery
Glycosylated Hemoglobin A
Glomerular Filtration Rate
Health Status
Registries
Weight Loss
Potassium
Triglycerides

Citeer dit

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title = "Metabolic Health Index (MHI): assessment of comorbidity in bariatric patients based on biomarkers",
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, Biomarkers, Diabetes, Dyslipidemia, Hypertension, METABOLIC SURGERY, Machine learning, Metabolic syndrome, Outcome measure, Value based health care",
author = "{van Loon}, {Saskia L.M.} and Ruben Deneer and Nienhuijs, {Simon W.} and Anna Wilbik and Uzay Kaymak and {van Riel}, Natal and Volkher Scharnhorst and Arjen-Kars Boer",
year = "2020",
month = "2",
day = "1",
doi = "10.1007/s11695-019-04244-1",
language = "English",
volume = "30",
pages = "714--724",
journal = "Obesity Surgery",
issn = "0960-8923",
publisher = "Springer",
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}

TY - JOUR

T1 - Metabolic Health Index (MHI)

T2 - assessment of comorbidity in bariatric patients based on biomarkers

AU - van Loon, Saskia L.M.

AU - Deneer, Ruben

AU - Nienhuijs, Simon W.

AU - Wilbik, Anna

AU - Kaymak, Uzay

AU - van Riel, Natal

AU - Scharnhorst, Volkher

AU - Boer, Arjen-Kars

PY - 2020/2/1

Y1 - 2020/2/1

N2 - 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.

AB - 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.

KW - Bariatric surgery

KW - Biomarkers

KW - Diabetes

KW - Dyslipidemia

KW - Hypertension

KW - METABOLIC SURGERY

KW - Machine learning

KW - Metabolic syndrome

KW - Outcome measure

KW - Value based health care

UR - http://www.scopus.com/inward/record.url?scp=85075216741&partnerID=8YFLogxK

U2 - 10.1007/s11695-019-04244-1

DO - 10.1007/s11695-019-04244-1

M3 - Article

C2 - 31724117

VL - 30

SP - 714

EP - 724

JO - Obesity Surgery

JF - Obesity Surgery

SN - 0960-8923

IS - 2

ER -