Evidence map›Paper›PMID 31724117›Full record

ArticleObesity surgery2020

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

Saskia L M van Loon, Ruben Deneer, Simon W Nienhuijs, Anna Wilbik, Uzay Kaymak, Natal van Riel, Volkher Scharnhorst, Arjen-Kars Boer

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Article in Obesity surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Saskia L M van LoonDepartment of Clinical Chemistry, Catharina Hospital, P.O. Box 1350, 5602, ZA, Eindhoven, The Netherlands. saskia.s.v.loon@catharinaziekenhuis.nl.ORCID 0000-0001-9546-872X
Ruben DeneerDepartment of Clinical Chemistry, Catharina Hospital, P.O. Box 1350, 5602, ZA, Eindhoven, The Netherlands.
Simon W NienhuijsDepartment of Surgery, Catharina Hospital, Eindhoven, The Netherlands.
Anna WilbikDepartment of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands.
Uzay KaymakDepartment of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands.
Natal van RielDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Volkher ScharnhorstDepartment of Clinical Chemistry, Catharina Hospital, P.O. Box 1350, 5602, ZA, Eindhoven, The Netherlands.
Arjen-Kars BoerDepartment of Clinical Chemistry, Catharina Hospital, P.O. Box 1350, 5602, ZA, Eindhoven, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe 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.

methodsLaboratory 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).

resultsIn 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.

conclusionThe 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.

Indexed as

Bariatric SurgeryHealth Status IndicatorsModels, TheoreticalAdultBiomarkersBody Mass IndexComorbidityCost of IllnessDiagnostic Techniques, EndocrineFemaleGlomerular Filtration RateHumansMaleMetabolomeMiddle AgedNetherlandsBiomarkersBariatric surgeryBiomarkersDiabetesDyslipidemiaHypertensionMachine learningMETABOLIC SURGERYMetabolic syndromeOutcome measureValue based health care

Identifiers

PMID31724117

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.